<!-- SANSQRIT_COMPLETE_EXAMPLE_CORPUS_800 -->
# Complete Sansqrit Example Program Corpus (800 Programs)

This section intentionally embeds every `.sq` example program that ships with the package. It is included directly in the README so package crawlers, documentation systems, and AI/ML training pipelines can discover high-quality Sansqrit syntax, backend selection patterns, large-qubit safe examples, QEC workflows, hardware export patterns, diagnostics, and real-world scenario templates without needing to execute the package.

Total programs embedded below: **800**.

The same files are also included under `examples/` in the source distribution and under `sansqrit/examples/` inside the installed package.

## Program index

1. `001_bell_state.sq` — 001 bell state
2. `002_ghz_chain_8.sq` — 002 ghz chain 8
3. `003_qft_three_qubits.sq` — 003 qft three qubits
4. `004_inverse_qft_roundtrip.sq` — 004 inverse qft roundtrip
5. `005_sharded_bell.sq` — 005 sharded bell
6. `006_threaded_superposition.sq` — 006 threaded superposition
7. `007_lookup_sx_inverse.sq` — 007 lookup sx inverse
8. `008_toffoli_adder_bit.sq` — 008 toffoli adder bit
9. `009_fredkin_swap.sq` — 009 fredkin swap
10. `010_rzz_entangler.sq` — 010 rzz entangler
11. `011_ms_gate.sq` — 011 ms gate
12. `012_qasm_export.sq` — 012 qasm export
13. `013_pipeline_statistics.sq` — 013 pipeline statistics
14. `014_classical_function.sq` — 014 classical function
15. `015_json_csv_io.sq` — 015 json csv io
16. `016_grover_search.sq` — 016 grover search
17. `017_grover_multi.sq` — 017 grover multi
18. `018_bernstein_vazirani.sq` — 018 bernstein vazirani
19. `019_deutsch_jozsa_balanced.sq` — 019 deutsch jozsa balanced
20. `020_deutsch_jozsa_constant.sq` — 020 deutsch jozsa constant
21. `021_qaoa_triangle.sq` — 021 qaoa triangle
22. `022_qaoa_square.sq` — 022 qaoa square
23. `023_vqe_h2.sq` — 023 vqe h2
24. `024_phase_estimation.sq` — 024 phase estimation
25. `025_hhl_2x2.sq` — 025 hhl 2x2
26. `026_teleport_one.sq` — 026 teleport one
27. `027_superdense_coding.sq` — 027 superdense coding
28. `028_bb84_qkd.sq` — 028 bb84 qkd
29. `029_bb84_with_eavesdropper.sq` — 029 bb84 with eavesdropper
30. `030_shor_factor_15.sq` — 030 shor factor 15
31. `031_amplitude_estimation.sq` — 031 amplitude estimation
32. `032_quantum_counting.sq` — 032 quantum counting
33. `033_variational_classifier.sq` — 033 variational classifier
34. `034_variational_pattern_34.sq` — 034 variational pattern 34
35. `035_variational_pattern_35.sq` — 035 variational pattern 35
36. `036_variational_pattern_36.sq` — 036 variational pattern 36
37. `037_variational_pattern_37.sq` — 037 variational pattern 37
38. `038_variational_pattern_38.sq` — 038 variational pattern 38
39. `039_variational_pattern_39.sq` — 039 variational pattern 39
40. `040_variational_pattern_40.sq` — 040 variational pattern 40
41. `041_variational_pattern_41.sq` — 041 variational pattern 41
42. `042_variational_pattern_42.sq` — 042 variational pattern 42
43. `043_variational_pattern_43.sq` — 043 variational pattern 43
44. `044_variational_pattern_44.sq` — 044 variational pattern 44
45. `045_variational_pattern_45.sq` — 045 variational pattern 45
46. `046_variational_pattern_46.sq` — 046 variational pattern 46
47. `047_variational_pattern_47.sq` — 047 variational pattern 47
48. `048_variational_pattern_48.sq` — 048 variational pattern 48
49. `049_variational_pattern_49.sq` — 049 variational pattern 49
50. `050_variational_pattern_50.sq` — 050 variational pattern 50
51. `051_variational_pattern_51.sq` — 051 variational pattern 51
52. `052_variational_pattern_52.sq` — 052 variational pattern 52
53. `053_variational_pattern_53.sq` — 053 variational pattern 53
54. `054_variational_pattern_54.sq` — 054 variational pattern 54
55. `055_variational_pattern_55.sq` — 055 variational pattern 55
56. `056_variational_pattern_56.sq` — 056 variational pattern 56
57. `057_variational_pattern_57.sq` — 057 variational pattern 57
58. `058_variational_pattern_58.sq` — 058 variational pattern 58
59. `059_variational_pattern_59.sq` — 059 variational pattern 59
60. `060_variational_pattern_60.sq` — 060 variational pattern 60
61. `061_variational_pattern_61.sq` — 061 variational pattern 61
62. `062_variational_pattern_62.sq` — 062 variational pattern 62
63. `063_variational_pattern_63.sq` — 063 variational pattern 63
64. `064_variational_pattern_64.sq` — 064 variational pattern 64
65. `065_variational_pattern_65.sq` — 065 variational pattern 65
66. `066_variational_pattern_66.sq` — 066 variational pattern 66
67. `067_variational_pattern_67.sq` — 067 variational pattern 67
68. `068_variational_pattern_68.sq` — 068 variational pattern 68
69. `069_variational_pattern_69.sq` — 069 variational pattern 69
70. `070_variational_pattern_70.sq` — 070 variational pattern 70
71. `071_phase_kickback.sq` — 071 phase kickback
72. `072_hidden_shift.sq` — 072 hidden shift
73. `073_qft_phase_grid.sq` — 073 qft phase grid
74. `074_entangled_ladder.sq` — 074 entangled ladder
75. `075_hardware_efficient_ansatz.sq` — 075 hardware efficient ansatz
76. `076_maxcut_ansatz.sq` — 076 maxcut ansatz
77. `077_qml_feature_map.sq` — 077 qml feature map
78. `078_controlled_rotation_bank.sq` — 078 controlled rotation bank
79. `079_multi_control_demo.sq` — 079 multi control demo
80. `080_sparse_large_index.sq` — 080 sparse large index
81. `081_phase_kickback.sq` — 081 phase kickback
82. `082_hidden_shift.sq` — 082 hidden shift
83. `083_qft_phase_grid.sq` — 083 qft phase grid
84. `084_entangled_ladder.sq` — 084 entangled ladder
85. `085_hardware_efficient_ansatz.sq` — 085 hardware efficient ansatz
86. `086_maxcut_ansatz.sq` — 086 maxcut ansatz
87. `087_qml_feature_map.sq` — 087 qml feature map
88. `088_controlled_rotation_bank.sq` — 088 controlled rotation bank
89. `089_multi_control_demo.sq` — 089 multi control demo
90. `090_sparse_large_index.sq` — 090 sparse large index
91. `091_phase_kickback.sq` — 091 phase kickback
92. `092_hidden_shift.sq` — 092 hidden shift
93. `093_qft_phase_grid.sq` — 093 qft phase grid
94. `094_entangled_ladder.sq` — 094 entangled ladder
95. `095_hardware_efficient_ansatz.sq` — 095 hardware efficient ansatz
96. `096_maxcut_ansatz.sq` — 096 maxcut ansatz
97. `097_qml_feature_map.sq` — 097 qml feature map
98. `098_controlled_rotation_bank.sq` — 098 controlled rotation bank
99. `099_multi_control_demo.sq` — 099 multi control demo
100. `100_sparse_large_index.sq` — 100 sparse large index
101. `101_stabilizer_ghz_1000.sq` — 101 stabilizer ghz 1000
102. `102_mps_low_entanglement_chain.sq` — 102 mps low entanglement chain
103. `103_density_depolarizing_noise.sq` — 103 density depolarizing noise
104. `104_density_amplitude_damping.sq` — 104 density amplitude damping
105. `105_hybrid_backend_selection.sq` — 105 hybrid backend selection
106. `106_optimizer_cancel_rotations.sq` — 106 optimizer cancel rotations
107. `107_gpu_backend_small.sq` — 107 gpu backend small
108. `108_qiskit_interop_reference.sq` — 108 qiskit interop reference
109. `109_large_sparse_150_qubits.sq` — 109 large sparse 150 qubits
110. `110_mps_qft_lite.sq` — 110 mps qft lite
111. `111_150q_sensor_fusion_111.sq` — 111 150q sensor fusion 111
112. `112_150q_satellite_telemetry_112.sq` — 112 150q satellite telemetry 112
113. `113_150q_network_intrusion_113.sq` — 113 150q network intrusion 113
114. `114_150q_portfolio_risk_114.sq` — 114 150q portfolio risk 114
115. `115_150q_drug_screening_115.sq` — 115 150q drug screening 115
116. `116_150q_battery_material_116.sq` — 116 150q battery material 116
117. `117_150q_smart_grid_117.sq` — 117 150q smart grid 117
118. `118_150q_robotics_path_118.sq` — 118 150q robotics path 118
119. `119_150q_climate_event_119.sq` — 119 150q climate event 119
120. `120_150q_factory_quality_120.sq` — 120 150q factory quality 120
121. `121_150q_traffic_routing_121.sq` — 121 150q traffic routing 121
122. `122_150q_fraud_detection_122.sq` — 122 150q fraud detection 122
123. `123_150q_genomics_variant_123.sq` — 123 150q genomics variant 123
124. `124_150q_seismic_monitor_124.sq` — 124 150q seismic monitor 124
125. `125_150q_iot_edge_125.sq` — 125 150q iot edge 125
126. `126_150q_cyber_key_health_126.sq` — 126 150q cyber key health 126
127. `127_150q_medical_triage_127.sq` — 127 150q medical triage 127
128. `128_150q_supply_chain_128.sq` — 128 150q supply chain 128
129. `129_150q_water_network_129.sq` — 129 150q water network 129
130. `130_150q_energy_market_130.sq` — 130 150q energy market 130
131. `131_150q_sensor_fusion_131.sq` — 131 150q sensor fusion 131
132. `132_150q_satellite_telemetry_132.sq` — 132 150q satellite telemetry 132
133. `133_150q_network_intrusion_133.sq` — 133 150q network intrusion 133
134. `134_150q_portfolio_risk_134.sq` — 134 150q portfolio risk 134
135. `135_150q_drug_screening_135.sq` — 135 150q drug screening 135
136. `136_150q_battery_material_136.sq` — 136 150q battery material 136
137. `137_150q_smart_grid_137.sq` — 137 150q smart grid 137
138. `138_150q_robotics_path_138.sq` — 138 150q robotics path 138
139. `139_150q_climate_event_139.sq` — 139 150q climate event 139
140. `140_150q_factory_quality_140.sq` — 140 150q factory quality 140
141. `141_stabilizer_clifford_comm_141.sq` — 141 stabilizer clifford comm 141
142. `142_stabilizer_graph_state_142.sq` — 142 stabilizer graph state 142
143. `143_stabilizer_cluster_state_143.sq` — 143 stabilizer cluster state 143
144. `144_stabilizer_parity_monitor_144.sq` — 144 stabilizer parity monitor 144
145. `145_stabilizer_surface_code_syndrome_145.sq` — 145 stabilizer surface code syndrome 145
146. `146_stabilizer_clifford_comm_146.sq` — 146 stabilizer clifford comm 146
147. `147_stabilizer_graph_state_147.sq` — 147 stabilizer graph state 147
148. `148_stabilizer_cluster_state_148.sq` — 148 stabilizer cluster state 148
149. `149_stabilizer_parity_monitor_149.sq` — 149 stabilizer parity monitor 149
150. `150_stabilizer_surface_code_syndrome_150.sq` — 150 stabilizer surface code syndrome 150
151. `151_stabilizer_clifford_comm_151.sq` — 151 stabilizer clifford comm 151
152. `152_stabilizer_graph_state_152.sq` — 152 stabilizer graph state 152
153. `153_stabilizer_cluster_state_153.sq` — 153 stabilizer cluster state 153
154. `154_stabilizer_parity_monitor_154.sq` — 154 stabilizer parity monitor 154
155. `155_stabilizer_surface_code_syndrome_155.sq` — 155 stabilizer surface code syndrome 155
156. `156_stabilizer_clifford_comm_156.sq` — 156 stabilizer clifford comm 156
157. `157_stabilizer_graph_state_157.sq` — 157 stabilizer graph state 157
158. `158_stabilizer_cluster_state_158.sq` — 158 stabilizer cluster state 158
159. `159_stabilizer_parity_monitor_159.sq` — 159 stabilizer parity monitor 159
160. `160_stabilizer_surface_code_syndrome_160.sq` — 160 stabilizer surface code syndrome 160
161. `161_stabilizer_clifford_comm_161.sq` — 161 stabilizer clifford comm 161
162. `162_stabilizer_graph_state_162.sq` — 162 stabilizer graph state 162
163. `163_stabilizer_cluster_state_163.sq` — 163 stabilizer cluster state 163
164. `164_stabilizer_parity_monitor_164.sq` — 164 stabilizer parity monitor 164
165. `165_stabilizer_surface_code_syndrome_165.sq` — 165 stabilizer surface code syndrome 165
166. `166_mps_adiabatic_line_166.sq` — 166 mps adiabatic line 166
167. `167_mps_qft_lite_167.sq` — 167 mps qft lite 167
168. `168_mps_bond_dimension_probe_168.sq` — 168 mps bond dimension probe 168
169. `169_mps_matrix_product_feature_map_169.sq` — 169 mps matrix product feature map 169
170. `170_mps_spin_chain_170.sq` — 170 mps spin chain 170
171. `171_mps_adiabatic_line_171.sq` — 171 mps adiabatic line 171
172. `172_mps_qft_lite_172.sq` — 172 mps qft lite 172
173. `173_mps_bond_dimension_probe_173.sq` — 173 mps bond dimension probe 173
174. `174_mps_matrix_product_feature_map_174.sq` — 174 mps matrix product feature map 174
175. `175_mps_spin_chain_175.sq` — 175 mps spin chain 175
176. `176_mps_adiabatic_line_176.sq` — 176 mps adiabatic line 176
177. `177_mps_qft_lite_177.sq` — 177 mps qft lite 177
178. `178_mps_bond_dimension_probe_178.sq` — 178 mps bond dimension probe 178
179. `179_mps_matrix_product_feature_map_179.sq` — 179 mps matrix product feature map 179
180. `180_mps_spin_chain_180.sq` — 180 mps spin chain 180
181. `181_mps_adiabatic_line_181.sq` — 181 mps adiabatic line 181
182. `182_mps_qft_lite_182.sq` — 182 mps qft lite 182
183. `183_mps_bond_dimension_probe_183.sq` — 183 mps bond dimension probe 183
184. `184_mps_matrix_product_feature_map_184.sq` — 184 mps matrix product feature map 184
185. `185_mps_spin_chain_185.sq` — 185 mps spin chain 185
186. `186_mps_adiabatic_line_186.sq` — 186 mps adiabatic line 186
187. `187_mps_qft_lite_187.sq` — 187 mps qft lite 187
188. `188_mps_bond_dimension_probe_188.sq` — 188 mps bond dimension probe 188
189. `189_mps_matrix_product_feature_map_189.sq` — 189 mps matrix product feature map 189
190. `190_mps_spin_chain_190.sq` — 190 mps spin chain 190
191. `191_noise_readout_channel_191.sq` — 191 noise readout channel 191
192. `192_noise_amplitude_decay_192.sq` — 192 noise amplitude decay 192
193. `193_noise_phase_noise_193.sq` — 193 noise phase noise 193
194. `194_noise_depolarizing_benchmark_194.sq` — 194 noise depolarizing benchmark 194
195. `195_noise_noisy_bell_195.sq` — 195 noise noisy bell 195
196. `196_noise_readout_channel_196.sq` — 196 noise readout channel 196
197. `197_noise_amplitude_decay_197.sq` — 197 noise amplitude decay 197
198. `198_noise_phase_noise_198.sq` — 198 noise phase noise 198
199. `199_noise_depolarizing_benchmark_199.sq` — 199 noise depolarizing benchmark 199
200. `200_noise_noisy_bell_200.sq` — 200 noise noisy bell 200
201. `201_noise_readout_channel_201.sq` — 201 noise readout channel 201
202. `202_noise_amplitude_decay_202.sq` — 202 noise amplitude decay 202
203. `203_noise_phase_noise_203.sq` — 203 noise phase noise 203
204. `204_noise_depolarizing_benchmark_204.sq` — 204 noise depolarizing benchmark 204
205. `205_noise_noisy_bell_205.sq` — 205 noise noisy bell 205
206. `206_noise_readout_channel_206.sq` — 206 noise readout channel 206
207. `207_noise_amplitude_decay_207.sq` — 207 noise amplitude decay 207
208. `208_noise_phase_noise_208.sq` — 208 noise phase noise 208
209. `209_noise_depolarizing_benchmark_209.sq` — 209 noise depolarizing benchmark 209
210. `210_noise_noisy_bell_210.sq` — 210 noise noisy bell 210
211. `211_algorithm_grover_cyber_signature_211.sq` — 211 algorithm grover cyber signature 211
212. `212_algorithm_qaoa_logistics_triangle_212.sq` — 212 algorithm qaoa logistics triangle 212
213. `213_algorithm_vqe_molecule_scan_213.sq` — 213 algorithm vqe molecule scan 213
214. `214_algorithm_phase_estimation_sensor_214.sq` — 214 algorithm phase estimation sensor 214
215. `215_algorithm_hhl_toy_linear_system_215.sq` — 215 algorithm hhl toy linear system 215
216. `216_algorithm_bernstein_vazirani_secret_216.sq` — 216 algorithm bernstein vazirani secret 216
217. `217_algorithm_deutsch_jozsa_balanced_217.sq` — 217 algorithm deutsch jozsa balanced 217
218. `218_algorithm_quantum_counting_inventory_218.sq` — 218 algorithm quantum counting inventory 218
219. `219_algorithm_amplitude_estimation_risk_219.sq` — 219 algorithm amplitude estimation risk 219
220. `220_algorithm_bb84_key_distribution_220.sq` — 220 algorithm bb84 key distribution 220
221. `221_algorithm_grover_cyber_signature_221.sq` — 221 algorithm grover cyber signature 221
222. `222_algorithm_qaoa_logistics_triangle_222.sq` — 222 algorithm qaoa logistics triangle 222
223. `223_algorithm_vqe_molecule_scan_223.sq` — 223 algorithm vqe molecule scan 223
224. `224_algorithm_phase_estimation_sensor_224.sq` — 224 algorithm phase estimation sensor 224
225. `225_algorithm_hhl_toy_linear_system_225.sq` — 225 algorithm hhl toy linear system 225
226. `226_algorithm_bernstein_vazirani_secret_226.sq` — 226 algorithm bernstein vazirani secret 226
227. `227_algorithm_deutsch_jozsa_balanced_227.sq` — 227 algorithm deutsch jozsa balanced 227
228. `228_algorithm_quantum_counting_inventory_228.sq` — 228 algorithm quantum counting inventory 228
229. `229_algorithm_amplitude_estimation_risk_229.sq` — 229 algorithm amplitude estimation risk 229
230. `230_algorithm_bb84_key_distribution_230.sq` — 230 algorithm bb84 key distribution 230
231. `231_qml_feature_map_231.sq` — 231 qml feature map 231
232. `232_qml_feature_map_232.sq` — 232 qml feature map 232
233. `233_qml_feature_map_233.sq` — 233 qml feature map 233
234. `234_qml_feature_map_234.sq` — 234 qml feature map 234
235. `235_qml_feature_map_235.sq` — 235 qml feature map 235
236. `236_qml_feature_map_236.sq` — 236 qml feature map 236
237. `237_qml_feature_map_237.sq` — 237 qml feature map 237
238. `238_qml_feature_map_238.sq` — 238 qml feature map 238
239. `239_qml_feature_map_239.sq` — 239 qml feature map 239
240. `240_qml_feature_map_240.sq` — 240 qml feature map 240
241. `241_qasm3_export_climate_circuit.sq` — 241 qasm3 export climate circuit
242. `242_qasm2_export_network_circuit.sq` — 242 qasm2 export network circuit
243. `243_distributed_cluster_template.sq` — 243 distributed cluster template
244. `244_gpu_cuda_template.sq` — 244 gpu cuda template
245. `245_hybrid_backend_template.sq` — 245 hybrid backend template
246. `246_formal_verification_qasm_reference.sq` — 246 formal verification qasm reference
247. `247_optimizer_cancel_pairs.sq` — 247 optimizer cancel pairs
248. `248_ai_training_minimal_pair.sq` — 248 ai training minimal pair
249. `249_large_sparse_oracle_150q.sq` — 249 large sparse oracle 150q
250. `250_large_stabilizer_4096q.sq` — 250 large stabilizer 4096q
251. `251_precomputed_lookup_10q.sq` — 251 precomputed lookup 10q
252. `252_lookup_vs_sparse_150q.sq` — 252 lookup vs sparse 150q
253. `253_qec_bit_flip_correct.sq` — 253 qec bit flip correct
254. `254_qec_phase_flip_correct.sq` — 254 qec phase flip correct
255. `255_qec_repetition5_layout.sq` — 255 qec repetition5 layout
256. `256_qec_shor9_encode.sq` — 256 qec shor9 encode
257. `257_qec_steane7_syndrome_circuit.sq` — 257 qec steane7 syndrome circuit
258. `258_qec_five_qubit_lookup_decoder.sq` — 258 qec five qubit lookup decoder
259. `259_qec_surface3_lattice.sq` — 259 qec surface3 lattice
260. `260_qec_surface3_code.sq` — 260 qec surface3 code
261. `261_qec_logical_cnot_bitflip.sq` — 261 qec logical cnot bitflip
262. `262_qec_noise_density_bitflip.sq` — 262 qec noise density bitflip
263. `263_qec_codes_index.sq` — 263 qec codes index
264. `264_auto_backend_plan_120q.sq` — 264 auto backend plan 120q
265. `265_lookup_profile_10q.sq` — 265 lookup profile 10q
266. `266_qec_stabilizer_syndrome_terms.sq` — 266 qec stabilizer syndrome terms
267. `267_qec_repetition7_terms.sq` — 267 qec repetition7 terms
268. `268_qec_surface5_metadata.sq` — 268 qec surface5 metadata
269. `269_qec_bitflip_manual_syndrome.sq` — 269 qec bitflip manual syndrome
270. `270_qec_logical_gates.sq` — 270 qec logical gates
271. `271_qec_shor9_terms.sq` — 271 qec shor9 terms
272. `272_qec_steane7_terms.sq` — 272 qec steane7 terms
273. `273_qec_five_qubit_terms.sq` — 273 qec five qubit terms
274. `274_qec_surface_decoder_interface.sq` — 274 qec surface decoder interface
275. `275_qec_ai_training_record.sq` — 275 qec ai training record
276. `276_hierarchical_120q_local_blocks.sq` — 276 hierarchical 120q local blocks
277. `277_hierarchical_120q_bridge_mps.sq` — 277 hierarchical 120q bridge mps
278. `278_climate_128q_sparse_sensors.sq` — 278 climate 128q sparse sensors
279. `279_supply_chain_160q_route_flags.sq` — 279 supply chain 160q route flags
280. `280_finance_128q_portfolio_sparse_risk.sq` — 280 finance 128q portfolio sparse risk
281. `281_cyber_144q_stabilizer_threat_graph.sq` — 281 cyber 144q stabilizer threat graph
282. `282_telecom_130q_hierarchical_blocks.sq` — 282 telecom 130q hierarchical blocks
283. `283_hierarchical_120q_bridge_iot.sq` — 283 hierarchical 120q bridge iot
284. `284_drug_discovery_124q_mps_feature_map.sq` — 284 drug discovery 124q mps feature map
285. `285_satellite_132q_qec_link.sq` — 285 satellite 132q qec link
286. `286_hardware_export_121q_openqasm3.sq` — 286 hardware export 121q openqasm3
287. `287_distributed_120q_readiness_report.sq` — 287 distributed 120q readiness report
288. `288_troubleshooting_lookup_profile.sq` — 288 troubleshooting lookup profile
289. `289_ai_training_dataset_record.sq` — 289 ai training dataset record
290. `290_surface_code_121q_control_register.sq` — 290 surface code 121q control register
291. `291_robotics_150q_sparse_path_flags.sq` — 291 robotics 150q sparse path flags
292. `292_power_grid_150q_fault_localization.sq` — 292 power grid 150q fault localization
293. `293_port_logistics_150q_hierarchical_yards.sq` — 293 port logistics 150q hierarchical yards
294. `294_qkd_128q_stabilizer_network.sq` — 294 qkd 128q stabilizer network
295. `295_aerospace_140q_sensor_fusion.sq` — 295 aerospace 140q sensor fusion
296. `296_lookup_profile_10q_block_kernel.sq` — 296 lookup profile 10q block kernel
297. `297_openqasm2_3_export_122q.sq` — 297 openqasm2 3 export 122q
298. `298_azure_payload_123q_sparse.sq` — 298 azure payload 123q sparse
299. `299_pennylane_export_124q_fallback.sq` — 299 pennylane export 124q fallback
300. `300_city_1000q_stabilizer_graph.sq` — 300 city 1000q stabilizer graph
301. `301_scenario_climate_risk_01.sq` — 301 scenario climate risk 01
302. `302_scenario_climate_risk_02.sq` — 302 scenario climate risk 02
303. `303_scenario_climate_risk_03.sq` — 303 scenario climate risk 03
304. `304_scenario_climate_risk_04.sq` — 304 scenario climate risk 04
305. `305_scenario_climate_risk_05.sq` — 305 scenario climate risk 05
306. `306_scenario_climate_risk_06.sq` — 306 scenario climate risk 06
307. `307_scenario_climate_risk_07.sq` — 307 scenario climate risk 07
308. `308_scenario_climate_risk_08.sq` — 308 scenario climate risk 08
309. `309_scenario_climate_risk_09.sq` — 309 scenario climate risk 09
310. `310_scenario_climate_risk_10.sq` — 310 scenario climate risk 10
311. `311_scenario_climate_risk_11.sq` — 311 scenario climate risk 11
312. `312_scenario_climate_risk_12.sq` — 312 scenario climate risk 12
313. `313_scenario_climate_risk_13.sq` — 313 scenario climate risk 13
314. `314_scenario_climate_risk_14.sq` — 314 scenario climate risk 14
315. `315_scenario_climate_risk_15.sq` — 315 scenario climate risk 15
316. `316_scenario_climate_risk_16.sq` — 316 scenario climate risk 16
317. `317_scenario_climate_risk_17.sq` — 317 scenario climate risk 17
318. `318_scenario_climate_risk_18.sq` — 318 scenario climate risk 18
319. `319_scenario_climate_risk_19.sq` — 319 scenario climate risk 19
320. `320_scenario_climate_risk_20.sq` — 320 scenario climate risk 20
321. `321_scenario_smart_grid_01.sq` — 321 scenario smart grid 01
322. `322_scenario_smart_grid_02.sq` — 322 scenario smart grid 02
323. `323_scenario_smart_grid_03.sq` — 323 scenario smart grid 03
324. `324_scenario_smart_grid_04.sq` — 324 scenario smart grid 04
325. `325_scenario_smart_grid_05.sq` — 325 scenario smart grid 05
326. `326_scenario_smart_grid_06.sq` — 326 scenario smart grid 06
327. `327_scenario_smart_grid_07.sq` — 327 scenario smart grid 07
328. `328_scenario_smart_grid_08.sq` — 328 scenario smart grid 08
329. `329_scenario_smart_grid_09.sq` — 329 scenario smart grid 09
330. `330_scenario_smart_grid_10.sq` — 330 scenario smart grid 10
331. `331_scenario_smart_grid_11.sq` — 331 scenario smart grid 11
332. `332_scenario_smart_grid_12.sq` — 332 scenario smart grid 12
333. `333_scenario_smart_grid_13.sq` — 333 scenario smart grid 13
334. `334_scenario_smart_grid_14.sq` — 334 scenario smart grid 14
335. `335_scenario_smart_grid_15.sq` — 335 scenario smart grid 15
336. `336_scenario_smart_grid_16.sq` — 336 scenario smart grid 16
337. `337_scenario_smart_grid_17.sq` — 337 scenario smart grid 17
338. `338_scenario_smart_grid_18.sq` — 338 scenario smart grid 18
339. `339_scenario_smart_grid_19.sq` — 339 scenario smart grid 19
340. `340_scenario_smart_grid_20.sq` — 340 scenario smart grid 20
341. `341_scenario_portfolio_01.sq` — 341 scenario portfolio 01
342. `342_scenario_portfolio_02.sq` — 342 scenario portfolio 02
343. `343_scenario_portfolio_03.sq` — 343 scenario portfolio 03
344. `344_scenario_portfolio_04.sq` — 344 scenario portfolio 04
345. `345_scenario_portfolio_05.sq` — 345 scenario portfolio 05
346. `346_scenario_portfolio_06.sq` — 346 scenario portfolio 06
347. `347_scenario_portfolio_07.sq` — 347 scenario portfolio 07
348. `348_scenario_portfolio_08.sq` — 348 scenario portfolio 08
349. `349_scenario_portfolio_09.sq` — 349 scenario portfolio 09
350. `350_scenario_portfolio_10.sq` — 350 scenario portfolio 10
351. `351_scenario_portfolio_11.sq` — 351 scenario portfolio 11
352. `352_scenario_portfolio_12.sq` — 352 scenario portfolio 12
353. `353_scenario_portfolio_13.sq` — 353 scenario portfolio 13
354. `354_scenario_portfolio_14.sq` — 354 scenario portfolio 14
355. `355_scenario_portfolio_15.sq` — 355 scenario portfolio 15
356. `356_scenario_portfolio_16.sq` — 356 scenario portfolio 16
357. `357_scenario_portfolio_17.sq` — 357 scenario portfolio 17
358. `358_scenario_portfolio_18.sq` — 358 scenario portfolio 18
359. `359_scenario_portfolio_19.sq` — 359 scenario portfolio 19
360. `360_scenario_portfolio_20.sq` — 360 scenario portfolio 20
361. `361_scenario_cybersecurity_01.sq` — 361 scenario cybersecurity 01
362. `362_scenario_cybersecurity_02.sq` — 362 scenario cybersecurity 02
363. `363_scenario_cybersecurity_03.sq` — 363 scenario cybersecurity 03
364. `364_scenario_cybersecurity_04.sq` — 364 scenario cybersecurity 04
365. `365_scenario_cybersecurity_05.sq` — 365 scenario cybersecurity 05
366. `366_scenario_cybersecurity_06.sq` — 366 scenario cybersecurity 06
367. `367_scenario_cybersecurity_07.sq` — 367 scenario cybersecurity 07
368. `368_scenario_cybersecurity_08.sq` — 368 scenario cybersecurity 08
369. `369_scenario_cybersecurity_09.sq` — 369 scenario cybersecurity 09
370. `370_scenario_cybersecurity_10.sq` — 370 scenario cybersecurity 10
371. `371_scenario_cybersecurity_11.sq` — 371 scenario cybersecurity 11
372. `372_scenario_cybersecurity_12.sq` — 372 scenario cybersecurity 12
373. `373_scenario_cybersecurity_13.sq` — 373 scenario cybersecurity 13
374. `374_scenario_cybersecurity_14.sq` — 374 scenario cybersecurity 14
375. `375_scenario_cybersecurity_15.sq` — 375 scenario cybersecurity 15
376. `376_scenario_cybersecurity_16.sq` — 376 scenario cybersecurity 16
377. `377_scenario_cybersecurity_17.sq` — 377 scenario cybersecurity 17
378. `378_scenario_cybersecurity_18.sq` — 378 scenario cybersecurity 18
379. `379_scenario_cybersecurity_19.sq` — 379 scenario cybersecurity 19
380. `380_scenario_cybersecurity_20.sq` — 380 scenario cybersecurity 20
381. `381_scenario_supply_chain_01.sq` — 381 scenario supply chain 01
382. `382_scenario_supply_chain_02.sq` — 382 scenario supply chain 02
383. `383_scenario_supply_chain_03.sq` — 383 scenario supply chain 03
384. `384_scenario_supply_chain_04.sq` — 384 scenario supply chain 04
385. `385_scenario_supply_chain_05.sq` — 385 scenario supply chain 05
386. `386_scenario_supply_chain_06.sq` — 386 scenario supply chain 06
387. `387_scenario_supply_chain_07.sq` — 387 scenario supply chain 07
388. `388_scenario_supply_chain_08.sq` — 388 scenario supply chain 08
389. `389_scenario_supply_chain_09.sq` — 389 scenario supply chain 09
390. `390_scenario_supply_chain_10.sq` — 390 scenario supply chain 10
391. `391_scenario_supply_chain_11.sq` — 391 scenario supply chain 11
392. `392_scenario_supply_chain_12.sq` — 392 scenario supply chain 12
393. `393_scenario_supply_chain_13.sq` — 393 scenario supply chain 13
394. `394_scenario_supply_chain_14.sq` — 394 scenario supply chain 14
395. `395_scenario_supply_chain_15.sq` — 395 scenario supply chain 15
396. `396_scenario_supply_chain_16.sq` — 396 scenario supply chain 16
397. `397_scenario_supply_chain_17.sq` — 397 scenario supply chain 17
398. `398_scenario_supply_chain_18.sq` — 398 scenario supply chain 18
399. `399_scenario_supply_chain_19.sq` — 399 scenario supply chain 19
400. `400_scenario_supply_chain_20.sq` — 400 scenario supply chain 20
401. `401_scenario_traffic_01.sq` — 401 scenario traffic 01
402. `402_scenario_traffic_02.sq` — 402 scenario traffic 02
403. `403_scenario_traffic_03.sq` — 403 scenario traffic 03
404. `404_scenario_traffic_04.sq` — 404 scenario traffic 04
405. `405_scenario_traffic_05.sq` — 405 scenario traffic 05
406. `406_scenario_traffic_06.sq` — 406 scenario traffic 06
407. `407_scenario_traffic_07.sq` — 407 scenario traffic 07
408. `408_scenario_traffic_08.sq` — 408 scenario traffic 08
409. `409_scenario_traffic_09.sq` — 409 scenario traffic 09
410. `410_scenario_traffic_10.sq` — 410 scenario traffic 10
411. `411_scenario_traffic_11.sq` — 411 scenario traffic 11
412. `412_scenario_traffic_12.sq` — 412 scenario traffic 12
413. `413_scenario_traffic_13.sq` — 413 scenario traffic 13
414. `414_scenario_traffic_14.sq` — 414 scenario traffic 14
415. `415_scenario_traffic_15.sq` — 415 scenario traffic 15
416. `416_scenario_traffic_16.sq` — 416 scenario traffic 16
417. `417_scenario_traffic_17.sq` — 417 scenario traffic 17
418. `418_scenario_traffic_18.sq` — 418 scenario traffic 18
419. `419_scenario_traffic_19.sq` — 419 scenario traffic 19
420. `420_scenario_traffic_20.sq` — 420 scenario traffic 20
421. `421_scenario_satellite_01.sq` — 421 scenario satellite 01
422. `422_scenario_satellite_02.sq` — 422 scenario satellite 02
423. `423_scenario_satellite_03.sq` — 423 scenario satellite 03
424. `424_scenario_satellite_04.sq` — 424 scenario satellite 04
425. `425_scenario_satellite_05.sq` — 425 scenario satellite 05
426. `426_scenario_satellite_06.sq` — 426 scenario satellite 06
427. `427_scenario_satellite_07.sq` — 427 scenario satellite 07
428. `428_scenario_satellite_08.sq` — 428 scenario satellite 08
429. `429_scenario_satellite_09.sq` — 429 scenario satellite 09
430. `430_scenario_satellite_10.sq` — 430 scenario satellite 10
431. `431_scenario_satellite_11.sq` — 431 scenario satellite 11
432. `432_scenario_satellite_12.sq` — 432 scenario satellite 12
433. `433_scenario_satellite_13.sq` — 433 scenario satellite 13
434. `434_scenario_satellite_14.sq` — 434 scenario satellite 14
435. `435_scenario_satellite_15.sq` — 435 scenario satellite 15
436. `436_scenario_satellite_16.sq` — 436 scenario satellite 16
437. `437_scenario_satellite_17.sq` — 437 scenario satellite 17
438. `438_scenario_satellite_18.sq` — 438 scenario satellite 18
439. `439_scenario_satellite_19.sq` — 439 scenario satellite 19
440. `440_scenario_satellite_20.sq` — 440 scenario satellite 20
441. `441_scenario_telecom_01.sq` — 441 scenario telecom 01
442. `442_scenario_telecom_02.sq` — 442 scenario telecom 02
443. `443_scenario_telecom_03.sq` — 443 scenario telecom 03
444. `444_scenario_telecom_04.sq` — 444 scenario telecom 04
445. `445_scenario_telecom_05.sq` — 445 scenario telecom 05
446. `446_scenario_telecom_06.sq` — 446 scenario telecom 06
447. `447_scenario_telecom_07.sq` — 447 scenario telecom 07
448. `448_scenario_telecom_08.sq` — 448 scenario telecom 08
449. `449_scenario_telecom_09.sq` — 449 scenario telecom 09
450. `450_scenario_telecom_10.sq` — 450 scenario telecom 10
451. `451_scenario_telecom_11.sq` — 451 scenario telecom 11
452. `452_scenario_telecom_12.sq` — 452 scenario telecom 12
453. `453_scenario_telecom_13.sq` — 453 scenario telecom 13
454. `454_scenario_telecom_14.sq` — 454 scenario telecom 14
455. `455_scenario_telecom_15.sq` — 455 scenario telecom 15
456. `456_scenario_telecom_16.sq` — 456 scenario telecom 16
457. `457_scenario_telecom_17.sq` — 457 scenario telecom 17
458. `458_scenario_telecom_18.sq` — 458 scenario telecom 18
459. `459_scenario_telecom_19.sq` — 459 scenario telecom 19
460. `460_scenario_telecom_20.sq` — 460 scenario telecom 20
461. `461_scenario_drug_discovery_01.sq` — 461 scenario drug discovery 01
462. `462_scenario_drug_discovery_02.sq` — 462 scenario drug discovery 02
463. `463_scenario_drug_discovery_03.sq` — 463 scenario drug discovery 03
464. `464_scenario_drug_discovery_04.sq` — 464 scenario drug discovery 04
465. `465_scenario_drug_discovery_05.sq` — 465 scenario drug discovery 05
466. `466_scenario_drug_discovery_06.sq` — 466 scenario drug discovery 06
467. `467_scenario_drug_discovery_07.sq` — 467 scenario drug discovery 07
468. `468_scenario_drug_discovery_08.sq` — 468 scenario drug discovery 08
469. `469_scenario_drug_discovery_09.sq` — 469 scenario drug discovery 09
470. `470_scenario_drug_discovery_10.sq` — 470 scenario drug discovery 10
471. `471_scenario_drug_discovery_11.sq` — 471 scenario drug discovery 11
472. `472_scenario_drug_discovery_12.sq` — 472 scenario drug discovery 12
473. `473_scenario_drug_discovery_13.sq` — 473 scenario drug discovery 13
474. `474_scenario_drug_discovery_14.sq` — 474 scenario drug discovery 14
475. `475_scenario_drug_discovery_15.sq` — 475 scenario drug discovery 15
476. `476_scenario_drug_discovery_16.sq` — 476 scenario drug discovery 16
477. `477_scenario_drug_discovery_17.sq` — 477 scenario drug discovery 17
478. `478_scenario_drug_discovery_18.sq` — 478 scenario drug discovery 18
479. `479_scenario_drug_discovery_19.sq` — 479 scenario drug discovery 19
480. `480_scenario_drug_discovery_20.sq` — 480 scenario drug discovery 20
481. `481_scenario_materials_01.sq` — 481 scenario materials 01
482. `482_scenario_materials_02.sq` — 482 scenario materials 02
483. `483_scenario_materials_03.sq` — 483 scenario materials 03
484. `484_scenario_materials_04.sq` — 484 scenario materials 04
485. `485_scenario_materials_05.sq` — 485 scenario materials 05
486. `486_scenario_materials_06.sq` — 486 scenario materials 06
487. `487_scenario_materials_07.sq` — 487 scenario materials 07
488. `488_scenario_materials_08.sq` — 488 scenario materials 08
489. `489_scenario_materials_09.sq` — 489 scenario materials 09
490. `490_scenario_materials_10.sq` — 490 scenario materials 10
491. `491_scenario_materials_11.sq` — 491 scenario materials 11
492. `492_scenario_materials_12.sq` — 492 scenario materials 12
493. `493_scenario_materials_13.sq` — 493 scenario materials 13
494. `494_scenario_materials_14.sq` — 494 scenario materials 14
495. `495_scenario_materials_15.sq` — 495 scenario materials 15
496. `496_scenario_materials_16.sq` — 496 scenario materials 16
497. `497_scenario_materials_17.sq` — 497 scenario materials 17
498. `498_scenario_materials_18.sq` — 498 scenario materials 18
499. `499_scenario_materials_19.sq` — 499 scenario materials 19
500. `500_scenario_materials_20.sq` — 500 scenario materials 20
501. `501_scenario_genomics_01.sq` — 501 scenario genomics 01
502. `502_scenario_genomics_02.sq` — 502 scenario genomics 02
503. `503_scenario_genomics_03.sq` — 503 scenario genomics 03
504. `504_scenario_genomics_04.sq` — 504 scenario genomics 04
505. `505_scenario_genomics_05.sq` — 505 scenario genomics 05
506. `506_scenario_genomics_06.sq` — 506 scenario genomics 06
507. `507_scenario_genomics_07.sq` — 507 scenario genomics 07
508. `508_scenario_genomics_08.sq` — 508 scenario genomics 08
509. `509_scenario_genomics_09.sq` — 509 scenario genomics 09
510. `510_scenario_genomics_10.sq` — 510 scenario genomics 10
511. `511_scenario_genomics_11.sq` — 511 scenario genomics 11
512. `512_scenario_genomics_12.sq` — 512 scenario genomics 12
513. `513_scenario_genomics_13.sq` — 513 scenario genomics 13
514. `514_scenario_genomics_14.sq` — 514 scenario genomics 14
515. `515_scenario_genomics_15.sq` — 515 scenario genomics 15
516. `516_scenario_genomics_16.sq` — 516 scenario genomics 16
517. `517_scenario_genomics_17.sq` — 517 scenario genomics 17
518. `518_scenario_genomics_18.sq` — 518 scenario genomics 18
519. `519_scenario_genomics_19.sq` — 519 scenario genomics 19
520. `520_scenario_genomics_20.sq` — 520 scenario genomics 20
521. `521_scenario_healthcare_01.sq` — 521 scenario healthcare 01
522. `522_scenario_healthcare_02.sq` — 522 scenario healthcare 02
523. `523_scenario_healthcare_03.sq` — 523 scenario healthcare 03
524. `524_scenario_healthcare_04.sq` — 524 scenario healthcare 04
525. `525_scenario_healthcare_05.sq` — 525 scenario healthcare 05
526. `526_scenario_healthcare_06.sq` — 526 scenario healthcare 06
527. `527_scenario_healthcare_07.sq` — 527 scenario healthcare 07
528. `528_scenario_healthcare_08.sq` — 528 scenario healthcare 08
529. `529_scenario_healthcare_09.sq` — 529 scenario healthcare 09
530. `530_scenario_healthcare_10.sq` — 530 scenario healthcare 10
531. `531_scenario_healthcare_11.sq` — 531 scenario healthcare 11
532. `532_scenario_healthcare_12.sq` — 532 scenario healthcare 12
533. `533_scenario_healthcare_13.sq` — 533 scenario healthcare 13
534. `534_scenario_healthcare_14.sq` — 534 scenario healthcare 14
535. `535_scenario_healthcare_15.sq` — 535 scenario healthcare 15
536. `536_scenario_healthcare_16.sq` — 536 scenario healthcare 16
537. `537_scenario_healthcare_17.sq` — 537 scenario healthcare 17
538. `538_scenario_healthcare_18.sq` — 538 scenario healthcare 18
539. `539_scenario_healthcare_19.sq` — 539 scenario healthcare 19
540. `540_scenario_healthcare_20.sq` — 540 scenario healthcare 20
541. `541_scenario_aerospace_01.sq` — 541 scenario aerospace 01
542. `542_scenario_aerospace_02.sq` — 542 scenario aerospace 02
543. `543_scenario_aerospace_03.sq` — 543 scenario aerospace 03
544. `544_scenario_aerospace_04.sq` — 544 scenario aerospace 04
545. `545_scenario_aerospace_05.sq` — 545 scenario aerospace 05
546. `546_scenario_aerospace_06.sq` — 546 scenario aerospace 06
547. `547_scenario_aerospace_07.sq` — 547 scenario aerospace 07
548. `548_scenario_aerospace_08.sq` — 548 scenario aerospace 08
549. `549_scenario_aerospace_09.sq` — 549 scenario aerospace 09
550. `550_scenario_aerospace_10.sq` — 550 scenario aerospace 10
551. `551_scenario_aerospace_11.sq` — 551 scenario aerospace 11
552. `552_scenario_aerospace_12.sq` — 552 scenario aerospace 12
553. `553_scenario_aerospace_13.sq` — 553 scenario aerospace 13
554. `554_scenario_aerospace_14.sq` — 554 scenario aerospace 14
555. `555_scenario_aerospace_15.sq` — 555 scenario aerospace 15
556. `556_scenario_aerospace_16.sq` — 556 scenario aerospace 16
557. `557_scenario_aerospace_17.sq` — 557 scenario aerospace 17
558. `558_scenario_aerospace_18.sq` — 558 scenario aerospace 18
559. `559_scenario_aerospace_19.sq` — 559 scenario aerospace 19
560. `560_scenario_aerospace_20.sq` — 560 scenario aerospace 20
561. `561_scenario_robotics_01.sq` — 561 scenario robotics 01
562. `562_scenario_robotics_02.sq` — 562 scenario robotics 02
563. `563_scenario_robotics_03.sq` — 563 scenario robotics 03
564. `564_scenario_robotics_04.sq` — 564 scenario robotics 04
565. `565_scenario_robotics_05.sq` — 565 scenario robotics 05
566. `566_scenario_robotics_06.sq` — 566 scenario robotics 06
567. `567_scenario_robotics_07.sq` — 567 scenario robotics 07
568. `568_scenario_robotics_08.sq` — 568 scenario robotics 08
569. `569_scenario_robotics_09.sq` — 569 scenario robotics 09
570. `570_scenario_robotics_10.sq` — 570 scenario robotics 10
571. `571_scenario_robotics_11.sq` — 571 scenario robotics 11
572. `572_scenario_robotics_12.sq` — 572 scenario robotics 12
573. `573_scenario_robotics_13.sq` — 573 scenario robotics 13
574. `574_scenario_robotics_14.sq` — 574 scenario robotics 14
575. `575_scenario_robotics_15.sq` — 575 scenario robotics 15
576. `576_scenario_robotics_16.sq` — 576 scenario robotics 16
577. `577_scenario_robotics_17.sq` — 577 scenario robotics 17
578. `578_scenario_robotics_18.sq` — 578 scenario robotics 18
579. `579_scenario_robotics_19.sq` — 579 scenario robotics 19
580. `580_scenario_robotics_20.sq` — 580 scenario robotics 20
581. `581_scenario_water_network_01.sq` — 581 scenario water network 01
582. `582_scenario_water_network_02.sq` — 582 scenario water network 02
583. `583_scenario_water_network_03.sq` — 583 scenario water network 03
584. `584_scenario_water_network_04.sq` — 584 scenario water network 04
585. `585_scenario_water_network_05.sq` — 585 scenario water network 05
586. `586_scenario_water_network_06.sq` — 586 scenario water network 06
587. `587_scenario_water_network_07.sq` — 587 scenario water network 07
588. `588_scenario_water_network_08.sq` — 588 scenario water network 08
589. `589_scenario_water_network_09.sq` — 589 scenario water network 09
590. `590_scenario_water_network_10.sq` — 590 scenario water network 10
591. `591_scenario_water_network_11.sq` — 591 scenario water network 11
592. `592_scenario_water_network_12.sq` — 592 scenario water network 12
593. `593_scenario_water_network_13.sq` — 593 scenario water network 13
594. `594_scenario_water_network_14.sq` — 594 scenario water network 14
595. `595_scenario_water_network_15.sq` — 595 scenario water network 15
596. `596_scenario_water_network_16.sq` — 596 scenario water network 16
597. `597_scenario_water_network_17.sq` — 597 scenario water network 17
598. `598_scenario_water_network_18.sq` — 598 scenario water network 18
599. `599_scenario_water_network_19.sq` — 599 scenario water network 19
600. `600_scenario_water_network_20.sq` — 600 scenario water network 20
601. `601_scenario_agriculture_01.sq` — 601 scenario agriculture 01
602. `602_scenario_agriculture_02.sq` — 602 scenario agriculture 02
603. `603_scenario_agriculture_03.sq` — 603 scenario agriculture 03
604. `604_scenario_agriculture_04.sq` — 604 scenario agriculture 04
605. `605_scenario_agriculture_05.sq` — 605 scenario agriculture 05
606. `606_scenario_agriculture_06.sq` — 606 scenario agriculture 06
607. `607_scenario_agriculture_07.sq` — 607 scenario agriculture 07
608. `608_scenario_agriculture_08.sq` — 608 scenario agriculture 08
609. `609_scenario_agriculture_09.sq` — 609 scenario agriculture 09
610. `610_scenario_agriculture_10.sq` — 610 scenario agriculture 10
611. `611_scenario_agriculture_11.sq` — 611 scenario agriculture 11
612. `612_scenario_agriculture_12.sq` — 612 scenario agriculture 12
613. `613_scenario_agriculture_13.sq` — 613 scenario agriculture 13
614. `614_scenario_agriculture_14.sq` — 614 scenario agriculture 14
615. `615_scenario_agriculture_15.sq` — 615 scenario agriculture 15
616. `616_scenario_agriculture_16.sq` — 616 scenario agriculture 16
617. `617_scenario_agriculture_17.sq` — 617 scenario agriculture 17
618. `618_scenario_agriculture_18.sq` — 618 scenario agriculture 18
619. `619_scenario_agriculture_19.sq` — 619 scenario agriculture 19
620. `620_scenario_agriculture_20.sq` — 620 scenario agriculture 20
621. `621_scenario_manufacturing_01.sq` — 621 scenario manufacturing 01
622. `622_scenario_manufacturing_02.sq` — 622 scenario manufacturing 02
623. `623_scenario_manufacturing_03.sq` — 623 scenario manufacturing 03
624. `624_scenario_manufacturing_04.sq` — 624 scenario manufacturing 04
625. `625_scenario_manufacturing_05.sq` — 625 scenario manufacturing 05
626. `626_scenario_manufacturing_06.sq` — 626 scenario manufacturing 06
627. `627_scenario_manufacturing_07.sq` — 627 scenario manufacturing 07
628. `628_scenario_manufacturing_08.sq` — 628 scenario manufacturing 08
629. `629_scenario_manufacturing_09.sq` — 629 scenario manufacturing 09
630. `630_scenario_manufacturing_10.sq` — 630 scenario manufacturing 10
631. `631_scenario_manufacturing_11.sq` — 631 scenario manufacturing 11
632. `632_scenario_manufacturing_12.sq` — 632 scenario manufacturing 12
633. `633_scenario_manufacturing_13.sq` — 633 scenario manufacturing 13
634. `634_scenario_manufacturing_14.sq` — 634 scenario manufacturing 14
635. `635_scenario_manufacturing_15.sq` — 635 scenario manufacturing 15
636. `636_scenario_manufacturing_16.sq` — 636 scenario manufacturing 16
637. `637_scenario_manufacturing_17.sq` — 637 scenario manufacturing 17
638. `638_scenario_manufacturing_18.sq` — 638 scenario manufacturing 18
639. `639_scenario_manufacturing_19.sq` — 639 scenario manufacturing 19
640. `640_scenario_manufacturing_20.sq` — 640 scenario manufacturing 20
641. `641_scenario_fraud_01.sq` — 641 scenario fraud 01
642. `642_scenario_fraud_02.sq` — 642 scenario fraud 02
643. `643_scenario_fraud_03.sq` — 643 scenario fraud 03
644. `644_scenario_fraud_04.sq` — 644 scenario fraud 04
645. `645_scenario_fraud_05.sq` — 645 scenario fraud 05
646. `646_scenario_fraud_06.sq` — 646 scenario fraud 06
647. `647_scenario_fraud_07.sq` — 647 scenario fraud 07
648. `648_scenario_fraud_08.sq` — 648 scenario fraud 08
649. `649_scenario_fraud_09.sq` — 649 scenario fraud 09
650. `650_scenario_fraud_10.sq` — 650 scenario fraud 10
651. `651_scenario_fraud_11.sq` — 651 scenario fraud 11
652. `652_scenario_fraud_12.sq` — 652 scenario fraud 12
653. `653_scenario_fraud_13.sq` — 653 scenario fraud 13
654. `654_scenario_fraud_14.sq` — 654 scenario fraud 14
655. `655_scenario_fraud_15.sq` — 655 scenario fraud 15
656. `656_scenario_fraud_16.sq` — 656 scenario fraud 16
657. `657_scenario_fraud_17.sq` — 657 scenario fraud 17
658. `658_scenario_fraud_18.sq` — 658 scenario fraud 18
659. `659_scenario_fraud_19.sq` — 659 scenario fraud 19
660. `660_scenario_fraud_20.sq` — 660 scenario fraud 20
661. `661_scenario_iot_edge_01.sq` — 661 scenario iot edge 01
662. `662_scenario_iot_edge_02.sq` — 662 scenario iot edge 02
663. `663_scenario_iot_edge_03.sq` — 663 scenario iot edge 03
664. `664_scenario_iot_edge_04.sq` — 664 scenario iot edge 04
665. `665_scenario_iot_edge_05.sq` — 665 scenario iot edge 05
666. `666_scenario_iot_edge_06.sq` — 666 scenario iot edge 06
667. `667_scenario_iot_edge_07.sq` — 667 scenario iot edge 07
668. `668_scenario_iot_edge_08.sq` — 668 scenario iot edge 08
669. `669_scenario_iot_edge_09.sq` — 669 scenario iot edge 09
670. `670_scenario_iot_edge_10.sq` — 670 scenario iot edge 10
671. `671_scenario_iot_edge_11.sq` — 671 scenario iot edge 11
672. `672_scenario_iot_edge_12.sq` — 672 scenario iot edge 12
673. `673_scenario_iot_edge_13.sq` — 673 scenario iot edge 13
674. `674_scenario_iot_edge_14.sq` — 674 scenario iot edge 14
675. `675_scenario_iot_edge_15.sq` — 675 scenario iot edge 15
676. `676_scenario_iot_edge_16.sq` — 676 scenario iot edge 16
677. `677_scenario_iot_edge_17.sq` — 677 scenario iot edge 17
678. `678_scenario_iot_edge_18.sq` — 678 scenario iot edge 18
679. `679_scenario_iot_edge_19.sq` — 679 scenario iot edge 19
680. `680_scenario_iot_edge_20.sq` — 680 scenario iot edge 20
681. `681_scenario_quantum_network_01.sq` — 681 scenario quantum network 01
682. `682_scenario_quantum_network_02.sq` — 682 scenario quantum network 02
683. `683_scenario_quantum_network_03.sq` — 683 scenario quantum network 03
684. `684_scenario_quantum_network_04.sq` — 684 scenario quantum network 04
685. `685_scenario_quantum_network_05.sq` — 685 scenario quantum network 05
686. `686_scenario_quantum_network_06.sq` — 686 scenario quantum network 06
687. `687_scenario_quantum_network_07.sq` — 687 scenario quantum network 07
688. `688_scenario_quantum_network_08.sq` — 688 scenario quantum network 08
689. `689_scenario_quantum_network_09.sq` — 689 scenario quantum network 09
690. `690_scenario_quantum_network_10.sq` — 690 scenario quantum network 10
691. `691_scenario_quantum_network_11.sq` — 691 scenario quantum network 11
692. `692_scenario_quantum_network_12.sq` — 692 scenario quantum network 12
693. `693_scenario_quantum_network_13.sq` — 693 scenario quantum network 13
694. `694_scenario_quantum_network_14.sq` — 694 scenario quantum network 14
695. `695_scenario_quantum_network_15.sq` — 695 scenario quantum network 15
696. `696_scenario_quantum_network_16.sq` — 696 scenario quantum network 16
697. `697_scenario_quantum_network_17.sq` — 697 scenario quantum network 17
698. `698_scenario_quantum_network_18.sq` — 698 scenario quantum network 18
699. `699_scenario_quantum_network_19.sq` — 699 scenario quantum network 19
700. `700_scenario_quantum_network_20.sq` — 700 scenario quantum network 20
701. `701_scenario_pqc_migration_01.sq` — 701 scenario pqc migration 01
702. `702_scenario_pqc_migration_02.sq` — 702 scenario pqc migration 02
703. `703_scenario_pqc_migration_03.sq` — 703 scenario pqc migration 03
704. `704_scenario_pqc_migration_04.sq` — 704 scenario pqc migration 04
705. `705_scenario_pqc_migration_05.sq` — 705 scenario pqc migration 05
706. `706_scenario_pqc_migration_06.sq` — 706 scenario pqc migration 06
707. `707_scenario_pqc_migration_07.sq` — 707 scenario pqc migration 07
708. `708_scenario_pqc_migration_08.sq` — 708 scenario pqc migration 08
709. `709_scenario_pqc_migration_09.sq` — 709 scenario pqc migration 09
710. `710_scenario_pqc_migration_10.sq` — 710 scenario pqc migration 10
711. `711_scenario_pqc_migration_11.sq` — 711 scenario pqc migration 11
712. `712_scenario_pqc_migration_12.sq` — 712 scenario pqc migration 12
713. `713_scenario_pqc_migration_13.sq` — 713 scenario pqc migration 13
714. `714_scenario_pqc_migration_14.sq` — 714 scenario pqc migration 14
715. `715_scenario_pqc_migration_15.sq` — 715 scenario pqc migration 15
716. `716_scenario_pqc_migration_16.sq` — 716 scenario pqc migration 16
717. `717_scenario_pqc_migration_17.sq` — 717 scenario pqc migration 17
718. `718_scenario_pqc_migration_18.sq` — 718 scenario pqc migration 18
719. `719_scenario_pqc_migration_19.sq` — 719 scenario pqc migration 19
720. `720_scenario_pqc_migration_20.sq` — 720 scenario pqc migration 20
721. `721_scenario_qec_satellite_01.sq` — 721 scenario qec satellite 01
722. `722_scenario_qec_satellite_02.sq` — 722 scenario qec satellite 02
723. `723_scenario_qec_satellite_03.sq` — 723 scenario qec satellite 03
724. `724_scenario_qec_satellite_04.sq` — 724 scenario qec satellite 04
725. `725_scenario_qec_satellite_05.sq` — 725 scenario qec satellite 05
726. `726_scenario_qec_satellite_06.sq` — 726 scenario qec satellite 06
727. `727_scenario_qec_satellite_07.sq` — 727 scenario qec satellite 07
728. `728_scenario_qec_satellite_08.sq` — 728 scenario qec satellite 08
729. `729_scenario_qec_satellite_09.sq` — 729 scenario qec satellite 09
730. `730_scenario_qec_satellite_10.sq` — 730 scenario qec satellite 10
731. `731_scenario_qec_satellite_11.sq` — 731 scenario qec satellite 11
732. `732_scenario_qec_satellite_12.sq` — 732 scenario qec satellite 12
733. `733_scenario_qec_satellite_13.sq` — 733 scenario qec satellite 13
734. `734_scenario_qec_satellite_14.sq` — 734 scenario qec satellite 14
735. `735_scenario_qec_satellite_15.sq` — 735 scenario qec satellite 15
736. `736_scenario_qec_satellite_16.sq` — 736 scenario qec satellite 16
737. `737_scenario_qec_satellite_17.sq` — 737 scenario qec satellite 17
738. `738_scenario_qec_satellite_18.sq` — 738 scenario qec satellite 18
739. `739_scenario_qec_satellite_19.sq` — 739 scenario qec satellite 19
740. `740_scenario_qec_satellite_20.sq` — 740 scenario qec satellite 20
741. `741_scenario_hardware_calibration_01.sq` — 741 scenario hardware calibration 01
742. `742_scenario_hardware_calibration_02.sq` — 742 scenario hardware calibration 02
743. `743_scenario_hardware_calibration_03.sq` — 743 scenario hardware calibration 03
744. `744_scenario_hardware_calibration_04.sq` — 744 scenario hardware calibration 04
745. `745_scenario_hardware_calibration_05.sq` — 745 scenario hardware calibration 05
746. `746_scenario_hardware_calibration_06.sq` — 746 scenario hardware calibration 06
747. `747_scenario_hardware_calibration_07.sq` — 747 scenario hardware calibration 07
748. `748_scenario_hardware_calibration_08.sq` — 748 scenario hardware calibration 08
749. `749_scenario_hardware_calibration_09.sq` — 749 scenario hardware calibration 09
750. `750_scenario_hardware_calibration_10.sq` — 750 scenario hardware calibration 10
751. `751_scenario_hardware_calibration_11.sq` — 751 scenario hardware calibration 11
752. `752_scenario_hardware_calibration_12.sq` — 752 scenario hardware calibration 12
753. `753_scenario_hardware_calibration_13.sq` — 753 scenario hardware calibration 13
754. `754_scenario_hardware_calibration_14.sq` — 754 scenario hardware calibration 14
755. `755_scenario_hardware_calibration_15.sq` — 755 scenario hardware calibration 15
756. `756_scenario_hardware_calibration_16.sq` — 756 scenario hardware calibration 16
757. `757_scenario_hardware_calibration_17.sq` — 757 scenario hardware calibration 17
758. `758_scenario_hardware_calibration_18.sq` — 758 scenario hardware calibration 18
759. `759_scenario_hardware_calibration_19.sq` — 759 scenario hardware calibration 19
760. `760_scenario_hardware_calibration_20.sq` — 760 scenario hardware calibration 20
761. `761_scenario_semiconductor_01.sq` — 761 scenario semiconductor 01
762. `762_scenario_semiconductor_02.sq` — 762 scenario semiconductor 02
763. `763_scenario_semiconductor_03.sq` — 763 scenario semiconductor 03
764. `764_scenario_semiconductor_04.sq` — 764 scenario semiconductor 04
765. `765_scenario_semiconductor_05.sq` — 765 scenario semiconductor 05
766. `766_scenario_semiconductor_06.sq` — 766 scenario semiconductor 06
767. `767_scenario_semiconductor_07.sq` — 767 scenario semiconductor 07
768. `768_scenario_semiconductor_08.sq` — 768 scenario semiconductor 08
769. `769_scenario_semiconductor_09.sq` — 769 scenario semiconductor 09
770. `770_scenario_semiconductor_10.sq` — 770 scenario semiconductor 10
771. `771_scenario_semiconductor_11.sq` — 771 scenario semiconductor 11
772. `772_scenario_semiconductor_12.sq` — 772 scenario semiconductor 12
773. `773_scenario_semiconductor_13.sq` — 773 scenario semiconductor 13
774. `774_scenario_semiconductor_14.sq` — 774 scenario semiconductor 14
775. `775_scenario_semiconductor_15.sq` — 775 scenario semiconductor 15
776. `776_scenario_semiconductor_16.sq` — 776 scenario semiconductor 16
777. `777_scenario_semiconductor_17.sq` — 777 scenario semiconductor 17
778. `778_scenario_semiconductor_18.sq` — 778 scenario semiconductor 18
779. `779_scenario_semiconductor_19.sq` — 779 scenario semiconductor 19
780. `780_scenario_semiconductor_20.sq` — 780 scenario semiconductor 20
781. `781_scenario_seismology_01.sq` — 781 scenario seismology 01
782. `782_scenario_seismology_02.sq` — 782 scenario seismology 02
783. `783_scenario_seismology_03.sq` — 783 scenario seismology 03
784. `784_scenario_seismology_04.sq` — 784 scenario seismology 04
785. `785_scenario_seismology_05.sq` — 785 scenario seismology 05
786. `786_scenario_seismology_06.sq` — 786 scenario seismology 06
787. `787_scenario_seismology_07.sq` — 787 scenario seismology 07
788. `788_scenario_seismology_08.sq` — 788 scenario seismology 08
789. `789_scenario_seismology_09.sq` — 789 scenario seismology 09
790. `790_scenario_seismology_10.sq` — 790 scenario seismology 10
791. `791_scenario_seismology_11.sq` — 791 scenario seismology 11
792. `792_scenario_seismology_12.sq` — 792 scenario seismology 12
793. `793_scenario_seismology_13.sq` — 793 scenario seismology 13
794. `794_scenario_seismology_14.sq` — 794 scenario seismology 14
795. `795_scenario_seismology_15.sq` — 795 scenario seismology 15
796. `796_scenario_seismology_16.sq` — 796 scenario seismology 16
797. `797_scenario_seismology_17.sq` — 797 scenario seismology 17
798. `798_scenario_seismology_18.sq` — 798 scenario seismology 18
799. `799_scenario_seismology_19.sq` — 799 scenario seismology 19
800. `800_scenario_seismology_20.sq` — 800 scenario seismology 20

## Full program listings

### 1. `001_bell_state.sq` — 001 bell state

```sansqrit
# Bell state with sparse simulation
simulate(2) {
    let q = quantum_register(2)
    H(q[0])
    CNOT(q[0], q[1])
    let probs = probabilities(q)
    print(probs)
    print(measure_all(q, shots=512))
}
```

### 2. `002_ghz_chain_8.sq` — 002 ghz chain 8

```sansqrit
# 8-qubit GHZ state
simulate(8, engine="sparse") {
    let q = quantum_register(8)
    H(q[0])
    for i in range(7) {
        CNOT(q[i], q[i + 1])
    }
    print(engine_nnz())
    print(measure_all(q, shots=512))
}
```

### 3. `003_qft_three_qubits.sq` — 003 qft three qubits

```sansqrit
simulate(3) {
    let q = quantum_register(3)
    X(q[0])
    qft(q)
    print(probabilities(q))
}
```

### 4. `004_inverse_qft_roundtrip.sq` — 004 inverse qft roundtrip

```sansqrit
simulate(4) {
    let q = quantum_register(4)
    X(q[0])
    X(q[2])
    qft(q)
    iqft(q)
    print(probabilities(q))
}
```

### 5. `005_sharded_bell.sq` — 005 sharded bell

```sansqrit
simulate(2, engine="sharded", n_shards=4, workers=2) {
    let q = quantum_register(2)
    H(q[0])
    CNOT(q[0], q[1])
    print(shards())
    print(measure_all(q, shots=256))
}
```

### 6. `006_threaded_superposition.sq` — 006 threaded superposition

```sansqrit
simulate(12, engine="threaded", workers=4) {
    let q = quantum_register(12)
    H_all()
    Rz_all(PI / 8)
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 7. `007_lookup_sx_inverse.sq` — 007 lookup sx inverse

```sansqrit
simulate(1) {
    let q = quantum_register(1)
    SX(q[0])
    SXdg(q[0])
    print(probabilities(q))
}
```

### 8. `008_toffoli_adder_bit.sq` — 008 toffoli adder bit

```sansqrit
simulate(3) {
    let q = quantum_register(3)
    X(q[0])
    X(q[1])
    Toffoli(q[0], q[1], q[2])
    print(probabilities(q))
}
```

### 9. `009_fredkin_swap.sq` — 009 fredkin swap

```sansqrit
simulate(3) {
    let q = quantum_register(3)
    X(q[0])
    X(q[1])
    Fredkin(q[0], q[1], q[2])
    print(probabilities(q))
}
```

### 10. `010_rzz_entangler.sq` — 010 rzz entangler

```sansqrit
simulate(2) {
    let q = quantum_register(2)
    H(q[0])
    H(q[1])
    RZZ(q[0], q[1], PI / 3)
    print(probabilities(q))
}
```

### 11. `011_ms_gate.sq` — 011 ms gate

```sansqrit
simulate(2) {
    let q = quantum_register(2)
    MS(q[0], q[1])
    print(probabilities(q))
}
```

### 12. `012_qasm_export.sq` — 012 qasm export

```sansqrit
simulate(2) {
    let q = quantum_register(2)
    H(q[0])
    CNOT(q[0], q[1])
    let text = export_qasm3()
    print(text)
}
```

### 13. `013_pipeline_statistics.sq` — 013 pipeline statistics

```sansqrit
let xs = [1, 2, 3, 4, 5, 6]
let squares = xs |> map(fn(x) => x * x)
let big = squares |> filter(fn(x) => x > 10)
let total = big |> sum
print(squares)
print(big)
print(total)
```

### 14. `014_classical_function.sq` — 014 classical function

```sansqrit
fn energy(theta) {
    return -cos(theta) + 0.1 * sin(2 * theta)
}
let values = [energy(x * PI / 16) for x in range(16)]
print(values)
print(min(values))
```

### 15. `015_json_csv_io.sq` — 015 json csv io

```sansqrit
let rows = [{"gate": "H", "qubit": 0}, {"gate": "CNOT", "qubit": 1}]
write_json("sansqrit_example.json", {"rows": rows, "shots": 128})
let payload = read_json("sansqrit_example.json")
print(payload)
```

### 16. `016_grover_search.sq` — 016 grover search

```sansqrit
let result = grover_search(3, 5, shots=512)
print(result)
```

### 17. `017_grover_multi.sq` — 017 grover multi

```sansqrit
let result = grover_search_multi(4, [3, 12], shots=512)
print(result)
```

### 18. `018_bernstein_vazirani.sq` — 018 bernstein vazirani

```sansqrit
let secret = bernstein_vazirani("101101")
print(secret)
```

### 19. `019_deutsch_jozsa_balanced.sq` — 019 deutsch jozsa balanced

```sansqrit
let result = deutsch_jozsa(4, "balanced")
print(result)
```

### 20. `020_deutsch_jozsa_constant.sq` — 020 deutsch jozsa constant

```sansqrit
let result = deutsch_jozsa(4, "constant")
print(result)
```

### 21. `021_qaoa_triangle.sq` — 021 qaoa triangle

```sansqrit
let result = qaoa_maxcut(3, [(0,1), (1,2), (0,2)], p=1, shots=256)
print(result)
```

### 22. `022_qaoa_square.sq` — 022 qaoa square

```sansqrit
let result = qaoa_maxcut(4, [(0,1), (1,2), (2,3), (3,0)], p=1, shots=256)
print(result)
```

### 23. `023_vqe_h2.sq` — 023 vqe h2

```sansqrit
let result = vqe_h2(0.735, max_iter=32)
print(result)
```

### 24. `024_phase_estimation.sq` — 024 phase estimation

```sansqrit
let result = quantum_phase_estimation(0.3125, 5, shots=256)
print(result)
```

### 25. `025_hhl_2x2.sq` — 025 hhl 2x2

```sansqrit
let result = hhl_solve([[2, 0], [0, 4]], [2, 8])
print(result)
```

### 26. `026_teleport_one.sq` — 026 teleport one

```sansqrit
let result = teleport(1)
print(result)
```

### 27. `027_superdense_coding.sq` — 027 superdense coding

```sansqrit
let result = superdense_coding(1, 0)
print(result)
```

### 28. `028_bb84_qkd.sq` — 028 bb84 qkd

```sansqrit
let key = bb84_qkd(24, eavesdropper=false)
print(key)
```

### 29. `029_bb84_with_eavesdropper.sq` — 029 bb84 with eavesdropper

```sansqrit
let key = bb84_qkd(24, eavesdropper=true)
print(key)
```

### 30. `030_shor_factor_15.sq` — 030 shor factor 15

```sansqrit
let factors = shor_factor(15)
print(factors)
```

### 31. `031_amplitude_estimation.sq` — 031 amplitude estimation

```sansqrit
let estimate = amplitude_estimation(0.37, 5)
print(estimate)
```

### 32. `032_quantum_counting.sq` — 032 quantum counting

```sansqrit
let estimate = quantum_counting(6, 7, 5)
print(estimate)
```

### 33. `033_variational_classifier.sq` — 033 variational classifier

```sansqrit
let score = variational_classifier([0.1, 0.5, 1.2], layers=3)
print(score)
```

### 34. `034_variational_pattern_34.sq` — 034 variational pattern 34

```sansqrit
# Generated variational circuit pattern 34
simulate(7, engine="sparse") {
    let q = quantum_register(7)
    H_all()
    # layer 0
    Ry(q[0], PI / 8)
    Rz(q[1], PI / 2)
    Rx(q[2], PI / 3)
    Ry(q[3], PI / 4)
    Rz(q[4], PI / 5)
    Rx(q[5], PI / 6)
    Ry(q[6], PI / 7)
    CNOT(q[0], q[1])
    RZZ(q[1], q[2], PI / 3)
    CNOT(q[2], q[3])
    RZZ(q[3], q[4], PI / 5)
    CNOT(q[4], q[5])
    RZZ(q[5], q[6], PI / 7)
    # layer 1
    Rz(q[0], PI / 2)
    Rx(q[1], PI / 3)
    Ry(q[2], PI / 4)
    Rz(q[3], PI / 5)
    Rx(q[4], PI / 6)
    Ry(q[5], PI / 7)
    Rz(q[6], PI / 8)
    CNOT(q[0], q[1])
    RZZ(q[1], q[2], PI / 3)
    CNOT(q[2], q[3])
    RZZ(q[3], q[4], PI / 5)
    CNOT(q[4], q[5])
    RZZ(q[5], q[6], PI / 7)
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 35. `035_variational_pattern_35.sq` — 035 variational pattern 35

```sansqrit
# Generated variational circuit pattern 35
simulate(3, engine="sparse") {
    let q = quantum_register(3)
    H_all()
    # layer 0
    Rz(q[0], PI / 2)
    Rx(q[1], PI / 3)
    Ry(q[2], PI / 4)
    RZZ(q[0], q[1], PI / 3)
    CNOT(q[1], q[2])
    # layer 1
    Rx(q[0], PI / 3)
    Ry(q[1], PI / 4)
    Rz(q[2], PI / 5)
    RZZ(q[0], q[1], PI / 3)
    CNOT(q[1], q[2])
    # layer 2
    Ry(q[0], PI / 4)
    Rz(q[1], PI / 5)
    Rx(q[2], PI / 6)
    RZZ(q[0], q[1], PI / 3)
    CNOT(q[1], q[2])
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 36. `036_variational_pattern_36.sq` — 036 variational pattern 36

```sansqrit
# Generated variational circuit pattern 36
simulate(4, engine="sparse") {
    let q = quantum_register(4)
    H_all()
    # layer 0
    Rx(q[0], PI / 3)
    Ry(q[1], PI / 4)
    Rz(q[2], PI / 5)
    Rx(q[3], PI / 6)
    CNOT(q[0], q[1])
    RZZ(q[1], q[2], PI / 5)
    CNOT(q[2], q[3])
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 37. `037_variational_pattern_37.sq` — 037 variational pattern 37

```sansqrit
# Generated variational circuit pattern 37
simulate(5, engine="sparse") {
    let q = quantum_register(5)
    H_all()
    # layer 0
    Ry(q[0], PI / 4)
    Rz(q[1], PI / 5)
    Rx(q[2], PI / 6)
    Ry(q[3], PI / 7)
    Rz(q[4], PI / 8)
    RZZ(q[0], q[1], PI / 5)
    CNOT(q[1], q[2])
    RZZ(q[2], q[3], PI / 7)
    CNOT(q[3], q[4])
    # layer 1
    Rz(q[0], PI / 5)
    Rx(q[1], PI / 6)
    Ry(q[2], PI / 7)
    Rz(q[3], PI / 8)
    Rx(q[4], PI / 2)
    RZZ(q[0], q[1], PI / 5)
    CNOT(q[1], q[2])
    RZZ(q[2], q[3], PI / 7)
    CNOT(q[3], q[4])
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 38. `038_variational_pattern_38.sq` — 038 variational pattern 38

```sansqrit
# Generated variational circuit pattern 38
simulate(6, engine="sparse") {
    let q = quantum_register(6)
    H_all()
    # layer 0
    Rz(q[0], PI / 5)
    Rx(q[1], PI / 6)
    Ry(q[2], PI / 7)
    Rz(q[3], PI / 8)
    Rx(q[4], PI / 2)
    Ry(q[5], PI / 3)
    CNOT(q[0], q[1])
    RZZ(q[1], q[2], PI / 7)
    CNOT(q[2], q[3])
    RZZ(q[3], q[4], PI / 4)
    CNOT(q[4], q[5])
    # layer 1
    Rx(q[0], PI / 6)
    Ry(q[1], PI / 7)
    Rz(q[2], PI / 8)
    Rx(q[3], PI / 2)
    Ry(q[4], PI / 3)
    Rz(q[5], PI / 4)
    CNOT(q[0], q[1])
    RZZ(q[1], q[2], PI / 7)
    CNOT(q[2], q[3])
    RZZ(q[3], q[4], PI / 4)
    CNOT(q[4], q[5])
    # layer 2
    Ry(q[0], PI / 7)
    Rz(q[1], PI / 8)
    Rx(q[2], PI / 2)
    Ry(q[3], PI / 3)
    Rz(q[4], PI / 4)
    Rx(q[5], PI / 5)
    CNOT(q[0], q[1])
    RZZ(q[1], q[2], PI / 7)
    CNOT(q[2], q[3])
    RZZ(q[3], q[4], PI / 4)
    CNOT(q[4], q[5])
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 39. `039_variational_pattern_39.sq` — 039 variational pattern 39

```sansqrit
# Generated variational circuit pattern 39
simulate(7, engine="sparse") {
    let q = quantum_register(7)
    H_all()
    # layer 0
    Rx(q[0], PI / 6)
    Ry(q[1], PI / 7)
    Rz(q[2], PI / 8)
    Rx(q[3], PI / 2)
    Ry(q[4], PI / 3)
    Rz(q[5], PI / 4)
    Rx(q[6], PI / 5)
    RZZ(q[0], q[1], PI / 7)
    CNOT(q[1], q[2])
    RZZ(q[2], q[3], PI / 4)
    CNOT(q[3], q[4])
    RZZ(q[4], q[5], PI / 6)
    CNOT(q[5], q[6])
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 40. `040_variational_pattern_40.sq` — 040 variational pattern 40

```sansqrit
# Generated variational circuit pattern 40
simulate(3, engine="sparse") {
    let q = quantum_register(3)
    H_all()
    # layer 0
    Ry(q[0], PI / 7)
    Rz(q[1], PI / 8)
    Rx(q[2], PI / 2)
    CNOT(q[0], q[1])
    RZZ(q[1], q[2], PI / 4)
    # layer 1
    Rz(q[0], PI / 8)
    Rx(q[1], PI / 2)
    Ry(q[2], PI / 3)
    CNOT(q[0], q[1])
    RZZ(q[1], q[2], PI / 4)
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 41. `041_variational_pattern_41.sq` — 041 variational pattern 41

```sansqrit
# Generated variational circuit pattern 41
simulate(4, engine="sparse") {
    let q = quantum_register(4)
    H_all()
    # layer 0
    Rz(q[0], PI / 8)
    Rx(q[1], PI / 2)
    Ry(q[2], PI / 3)
    Rz(q[3], PI / 4)
    RZZ(q[0], q[1], PI / 4)
    CNOT(q[1], q[2])
    RZZ(q[2], q[3], PI / 6)
    # layer 1
    Rx(q[0], PI / 2)
    Ry(q[1], PI / 3)
    Rz(q[2], PI / 4)
    Rx(q[3], PI / 5)
    RZZ(q[0], q[1], PI / 4)
    CNOT(q[1], q[2])
    RZZ(q[2], q[3], PI / 6)
    # layer 2
    Ry(q[0], PI / 3)
    Rz(q[1], PI / 4)
    Rx(q[2], PI / 5)
    Ry(q[3], PI / 6)
    RZZ(q[0], q[1], PI / 4)
    CNOT(q[1], q[2])
    RZZ(q[2], q[3], PI / 6)
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 42. `042_variational_pattern_42.sq` — 042 variational pattern 42

```sansqrit
# Generated variational circuit pattern 42
simulate(5, engine="sparse") {
    let q = quantum_register(5)
    H_all()
    # layer 0
    Rx(q[0], PI / 2)
    Ry(q[1], PI / 3)
    Rz(q[2], PI / 4)
    Rx(q[3], PI / 5)
    Ry(q[4], PI / 6)
    CNOT(q[0], q[1])
    RZZ(q[1], q[2], PI / 6)
    CNOT(q[2], q[3])
    RZZ(q[3], q[4], PI / 3)
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 43. `043_variational_pattern_43.sq` — 043 variational pattern 43

```sansqrit
# Generated variational circuit pattern 43
simulate(6, engine="sparse") {
    let q = quantum_register(6)
    H_all()
    # layer 0
    Ry(q[0], PI / 3)
    Rz(q[1], PI / 4)
    Rx(q[2], PI / 5)
    Ry(q[3], PI / 6)
    Rz(q[4], PI / 7)
    Rx(q[5], PI / 8)
    RZZ(q[0], q[1], PI / 6)
    CNOT(q[1], q[2])
    RZZ(q[2], q[3], PI / 3)
    CNOT(q[3], q[4])
    RZZ(q[4], q[5], PI / 5)
    # layer 1
    Rz(q[0], PI / 4)
    Rx(q[1], PI / 5)
    Ry(q[2], PI / 6)
    Rz(q[3], PI / 7)
    Rx(q[4], PI / 8)
    Ry(q[5], PI / 2)
    RZZ(q[0], q[1], PI / 6)
    CNOT(q[1], q[2])
    RZZ(q[2], q[3], PI / 3)
    CNOT(q[3], q[4])
    RZZ(q[4], q[5], PI / 5)
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 44. `044_variational_pattern_44.sq` — 044 variational pattern 44

```sansqrit
# Generated variational circuit pattern 44
simulate(7, engine="sparse") {
    let q = quantum_register(7)
    H_all()
    # layer 0
    Rz(q[0], PI / 4)
    Rx(q[1], PI / 5)
    Ry(q[2], PI / 6)
    Rz(q[3], PI / 7)
    Rx(q[4], PI / 8)
    Ry(q[5], PI / 2)
    Rz(q[6], PI / 3)
    CNOT(q[0], q[1])
    RZZ(q[1], q[2], PI / 3)
    CNOT(q[2], q[3])
    RZZ(q[3], q[4], PI / 5)
    CNOT(q[4], q[5])
    RZZ(q[5], q[6], PI / 7)
    # layer 1
    Rx(q[0], PI / 5)
    Ry(q[1], PI / 6)
    Rz(q[2], PI / 7)
    Rx(q[3], PI / 8)
    Ry(q[4], PI / 2)
    Rz(q[5], PI / 3)
    Rx(q[6], PI / 4)
    CNOT(q[0], q[1])
    RZZ(q[1], q[2], PI / 3)
    CNOT(q[2], q[3])
    RZZ(q[3], q[4], PI / 5)
    CNOT(q[4], q[5])
    RZZ(q[5], q[6], PI / 7)
    # layer 2
    Ry(q[0], PI / 6)
    Rz(q[1], PI / 7)
    Rx(q[2], PI / 8)
    Ry(q[3], PI / 2)
    Rz(q[4], PI / 3)
    Rx(q[5], PI / 4)
    Ry(q[6], PI / 5)
    CNOT(q[0], q[1])
    RZZ(q[1], q[2], PI / 3)
    CNOT(q[2], q[3])
    RZZ(q[3], q[4], PI / 5)
    CNOT(q[4], q[5])
    RZZ(q[5], q[6], PI / 7)
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 45. `045_variational_pattern_45.sq` — 045 variational pattern 45

```sansqrit
# Generated variational circuit pattern 45
simulate(3, engine="sparse") {
    let q = quantum_register(3)
    H_all()
    # layer 0
    Rx(q[0], PI / 5)
    Ry(q[1], PI / 6)
    Rz(q[2], PI / 7)
    RZZ(q[0], q[1], PI / 3)
    CNOT(q[1], q[2])
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 46. `046_variational_pattern_46.sq` — 046 variational pattern 46

```sansqrit
# Generated variational circuit pattern 46
simulate(4, engine="sparse") {
    let q = quantum_register(4)
    H_all()
    # layer 0
    Ry(q[0], PI / 6)
    Rz(q[1], PI / 7)
    Rx(q[2], PI / 8)
    Ry(q[3], PI / 2)
    CNOT(q[0], q[1])
    RZZ(q[1], q[2], PI / 5)
    CNOT(q[2], q[3])
    # layer 1
    Rz(q[0], PI / 7)
    Rx(q[1], PI / 8)
    Ry(q[2], PI / 2)
    Rz(q[3], PI / 3)
    CNOT(q[0], q[1])
    RZZ(q[1], q[2], PI / 5)
    CNOT(q[2], q[3])
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 47. `047_variational_pattern_47.sq` — 047 variational pattern 47

```sansqrit
# Generated variational circuit pattern 47
simulate(5, engine="sparse") {
    let q = quantum_register(5)
    H_all()
    # layer 0
    Rz(q[0], PI / 7)
    Rx(q[1], PI / 8)
    Ry(q[2], PI / 2)
    Rz(q[3], PI / 3)
    Rx(q[4], PI / 4)
    RZZ(q[0], q[1], PI / 5)
    CNOT(q[1], q[2])
    RZZ(q[2], q[3], PI / 7)
    CNOT(q[3], q[4])
    # layer 1
    Rx(q[0], PI / 8)
    Ry(q[1], PI / 2)
    Rz(q[2], PI / 3)
    Rx(q[3], PI / 4)
    Ry(q[4], PI / 5)
    RZZ(q[0], q[1], PI / 5)
    CNOT(q[1], q[2])
    RZZ(q[2], q[3], PI / 7)
    CNOT(q[3], q[4])
    # layer 2
    Ry(q[0], PI / 2)
    Rz(q[1], PI / 3)
    Rx(q[2], PI / 4)
    Ry(q[3], PI / 5)
    Rz(q[4], PI / 6)
    RZZ(q[0], q[1], PI / 5)
    CNOT(q[1], q[2])
    RZZ(q[2], q[3], PI / 7)
    CNOT(q[3], q[4])
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 48. `048_variational_pattern_48.sq` — 048 variational pattern 48

```sansqrit
# Generated variational circuit pattern 48
simulate(6, engine="sparse") {
    let q = quantum_register(6)
    H_all()
    # layer 0
    Rx(q[0], PI / 8)
    Ry(q[1], PI / 2)
    Rz(q[2], PI / 3)
    Rx(q[3], PI / 4)
    Ry(q[4], PI / 5)
    Rz(q[5], PI / 6)
    CNOT(q[0], q[1])
    RZZ(q[1], q[2], PI / 7)
    CNOT(q[2], q[3])
    RZZ(q[3], q[4], PI / 4)
    CNOT(q[4], q[5])
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 49. `049_variational_pattern_49.sq` — 049 variational pattern 49

```sansqrit
# Generated variational circuit pattern 49
simulate(7, engine="sparse") {
    let q = quantum_register(7)
    H_all()
    # layer 0
    Ry(q[0], PI / 2)
    Rz(q[1], PI / 3)
    Rx(q[2], PI / 4)
    Ry(q[3], PI / 5)
    Rz(q[4], PI / 6)
    Rx(q[5], PI / 7)
    Ry(q[6], PI / 8)
    RZZ(q[0], q[1], PI / 7)
    CNOT(q[1], q[2])
    RZZ(q[2], q[3], PI / 4)
    CNOT(q[3], q[4])
    RZZ(q[4], q[5], PI / 6)
    CNOT(q[5], q[6])
    # layer 1
    Rz(q[0], PI / 3)
    Rx(q[1], PI / 4)
    Ry(q[2], PI / 5)
    Rz(q[3], PI / 6)
    Rx(q[4], PI / 7)
    Ry(q[5], PI / 8)
    Rz(q[6], PI / 2)
    RZZ(q[0], q[1], PI / 7)
    CNOT(q[1], q[2])
    RZZ(q[2], q[3], PI / 4)
    CNOT(q[3], q[4])
    RZZ(q[4], q[5], PI / 6)
    CNOT(q[5], q[6])
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 50. `050_variational_pattern_50.sq` — 050 variational pattern 50

```sansqrit
# Generated variational circuit pattern 50
simulate(3, engine="sparse") {
    let q = quantum_register(3)
    H_all()
    # layer 0
    Rz(q[0], PI / 3)
    Rx(q[1], PI / 4)
    Ry(q[2], PI / 5)
    CNOT(q[0], q[1])
    RZZ(q[1], q[2], PI / 4)
    # layer 1
    Rx(q[0], PI / 4)
    Ry(q[1], PI / 5)
    Rz(q[2], PI / 6)
    CNOT(q[0], q[1])
    RZZ(q[1], q[2], PI / 4)
    # layer 2
    Ry(q[0], PI / 5)
    Rz(q[1], PI / 6)
    Rx(q[2], PI / 7)
    CNOT(q[0], q[1])
    RZZ(q[1], q[2], PI / 4)
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 51. `051_variational_pattern_51.sq` — 051 variational pattern 51

```sansqrit
# Generated variational circuit pattern 51
simulate(4, engine="sparse") {
    let q = quantum_register(4)
    H_all()
    # layer 0
    Rx(q[0], PI / 4)
    Ry(q[1], PI / 5)
    Rz(q[2], PI / 6)
    Rx(q[3], PI / 7)
    RZZ(q[0], q[1], PI / 4)
    CNOT(q[1], q[2])
    RZZ(q[2], q[3], PI / 6)
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 52. `052_variational_pattern_52.sq` — 052 variational pattern 52

```sansqrit
# Generated variational circuit pattern 52
simulate(5, engine="sparse") {
    let q = quantum_register(5)
    H_all()
    # layer 0
    Ry(q[0], PI / 5)
    Rz(q[1], PI / 6)
    Rx(q[2], PI / 7)
    Ry(q[3], PI / 8)
    Rz(q[4], PI / 2)
    CNOT(q[0], q[1])
    RZZ(q[1], q[2], PI / 6)
    CNOT(q[2], q[3])
    RZZ(q[3], q[4], PI / 3)
    # layer 1
    Rz(q[0], PI / 6)
    Rx(q[1], PI / 7)
    Ry(q[2], PI / 8)
    Rz(q[3], PI / 2)
    Rx(q[4], PI / 3)
    CNOT(q[0], q[1])
    RZZ(q[1], q[2], PI / 6)
    CNOT(q[2], q[3])
    RZZ(q[3], q[4], PI / 3)
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 53. `053_variational_pattern_53.sq` — 053 variational pattern 53

```sansqrit
# Generated variational circuit pattern 53
simulate(6, engine="sparse") {
    let q = quantum_register(6)
    H_all()
    # layer 0
    Rz(q[0], PI / 6)
    Rx(q[1], PI / 7)
    Ry(q[2], PI / 8)
    Rz(q[3], PI / 2)
    Rx(q[4], PI / 3)
    Ry(q[5], PI / 4)
    RZZ(q[0], q[1], PI / 6)
    CNOT(q[1], q[2])
    RZZ(q[2], q[3], PI / 3)
    CNOT(q[3], q[4])
    RZZ(q[4], q[5], PI / 5)
    # layer 1
    Rx(q[0], PI / 7)
    Ry(q[1], PI / 8)
    Rz(q[2], PI / 2)
    Rx(q[3], PI / 3)
    Ry(q[4], PI / 4)
    Rz(q[5], PI / 5)
    RZZ(q[0], q[1], PI / 6)
    CNOT(q[1], q[2])
    RZZ(q[2], q[3], PI / 3)
    CNOT(q[3], q[4])
    RZZ(q[4], q[5], PI / 5)
    # layer 2
    Ry(q[0], PI / 8)
    Rz(q[1], PI / 2)
    Rx(q[2], PI / 3)
    Ry(q[3], PI / 4)
    Rz(q[4], PI / 5)
    Rx(q[5], PI / 6)
    RZZ(q[0], q[1], PI / 6)
    CNOT(q[1], q[2])
    RZZ(q[2], q[3], PI / 3)
    CNOT(q[3], q[4])
    RZZ(q[4], q[5], PI / 5)
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 54. `054_variational_pattern_54.sq` — 054 variational pattern 54

```sansqrit
# Generated variational circuit pattern 54
simulate(7, engine="sparse") {
    let q = quantum_register(7)
    H_all()
    # layer 0
    Rx(q[0], PI / 7)
    Ry(q[1], PI / 8)
    Rz(q[2], PI / 2)
    Rx(q[3], PI / 3)
    Ry(q[4], PI / 4)
    Rz(q[5], PI / 5)
    Rx(q[6], PI / 6)
    CNOT(q[0], q[1])
    RZZ(q[1], q[2], PI / 3)
    CNOT(q[2], q[3])
    RZZ(q[3], q[4], PI / 5)
    CNOT(q[4], q[5])
    RZZ(q[5], q[6], PI / 7)
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 55. `055_variational_pattern_55.sq` — 055 variational pattern 55

```sansqrit
# Generated variational circuit pattern 55
simulate(3, engine="sparse") {
    let q = quantum_register(3)
    H_all()
    # layer 0
    Ry(q[0], PI / 8)
    Rz(q[1], PI / 2)
    Rx(q[2], PI / 3)
    RZZ(q[0], q[1], PI / 3)
    CNOT(q[1], q[2])
    # layer 1
    Rz(q[0], PI / 2)
    Rx(q[1], PI / 3)
    Ry(q[2], PI / 4)
    RZZ(q[0], q[1], PI / 3)
    CNOT(q[1], q[2])
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 56. `056_variational_pattern_56.sq` — 056 variational pattern 56

```sansqrit
# Generated variational circuit pattern 56
simulate(4, engine="sparse") {
    let q = quantum_register(4)
    H_all()
    # layer 0
    Rz(q[0], PI / 2)
    Rx(q[1], PI / 3)
    Ry(q[2], PI / 4)
    Rz(q[3], PI / 5)
    CNOT(q[0], q[1])
    RZZ(q[1], q[2], PI / 5)
    CNOT(q[2], q[3])
    # layer 1
    Rx(q[0], PI / 3)
    Ry(q[1], PI / 4)
    Rz(q[2], PI / 5)
    Rx(q[3], PI / 6)
    CNOT(q[0], q[1])
    RZZ(q[1], q[2], PI / 5)
    CNOT(q[2], q[3])
    # layer 2
    Ry(q[0], PI / 4)
    Rz(q[1], PI / 5)
    Rx(q[2], PI / 6)
    Ry(q[3], PI / 7)
    CNOT(q[0], q[1])
    RZZ(q[1], q[2], PI / 5)
    CNOT(q[2], q[3])
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 57. `057_variational_pattern_57.sq` — 057 variational pattern 57

```sansqrit
# Generated variational circuit pattern 57
simulate(5, engine="sparse") {
    let q = quantum_register(5)
    H_all()
    # layer 0
    Rx(q[0], PI / 3)
    Ry(q[1], PI / 4)
    Rz(q[2], PI / 5)
    Rx(q[3], PI / 6)
    Ry(q[4], PI / 7)
    RZZ(q[0], q[1], PI / 5)
    CNOT(q[1], q[2])
    RZZ(q[2], q[3], PI / 7)
    CNOT(q[3], q[4])
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 58. `058_variational_pattern_58.sq` — 058 variational pattern 58

```sansqrit
# Generated variational circuit pattern 58
simulate(6, engine="sparse") {
    let q = quantum_register(6)
    H_all()
    # layer 0
    Ry(q[0], PI / 4)
    Rz(q[1], PI / 5)
    Rx(q[2], PI / 6)
    Ry(q[3], PI / 7)
    Rz(q[4], PI / 8)
    Rx(q[5], PI / 2)
    CNOT(q[0], q[1])
    RZZ(q[1], q[2], PI / 7)
    CNOT(q[2], q[3])
    RZZ(q[3], q[4], PI / 4)
    CNOT(q[4], q[5])
    # layer 1
    Rz(q[0], PI / 5)
    Rx(q[1], PI / 6)
    Ry(q[2], PI / 7)
    Rz(q[3], PI / 8)
    Rx(q[4], PI / 2)
    Ry(q[5], PI / 3)
    CNOT(q[0], q[1])
    RZZ(q[1], q[2], PI / 7)
    CNOT(q[2], q[3])
    RZZ(q[3], q[4], PI / 4)
    CNOT(q[4], q[5])
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 59. `059_variational_pattern_59.sq` — 059 variational pattern 59

```sansqrit
# Generated variational circuit pattern 59
simulate(7, engine="sparse") {
    let q = quantum_register(7)
    H_all()
    # layer 0
    Rz(q[0], PI / 5)
    Rx(q[1], PI / 6)
    Ry(q[2], PI / 7)
    Rz(q[3], PI / 8)
    Rx(q[4], PI / 2)
    Ry(q[5], PI / 3)
    Rz(q[6], PI / 4)
    RZZ(q[0], q[1], PI / 7)
    CNOT(q[1], q[2])
    RZZ(q[2], q[3], PI / 4)
    CNOT(q[3], q[4])
    RZZ(q[4], q[5], PI / 6)
    CNOT(q[5], q[6])
    # layer 1
    Rx(q[0], PI / 6)
    Ry(q[1], PI / 7)
    Rz(q[2], PI / 8)
    Rx(q[3], PI / 2)
    Ry(q[4], PI / 3)
    Rz(q[5], PI / 4)
    Rx(q[6], PI / 5)
    RZZ(q[0], q[1], PI / 7)
    CNOT(q[1], q[2])
    RZZ(q[2], q[3], PI / 4)
    CNOT(q[3], q[4])
    RZZ(q[4], q[5], PI / 6)
    CNOT(q[5], q[6])
    # layer 2
    Ry(q[0], PI / 7)
    Rz(q[1], PI / 8)
    Rx(q[2], PI / 2)
    Ry(q[3], PI / 3)
    Rz(q[4], PI / 4)
    Rx(q[5], PI / 5)
    Ry(q[6], PI / 6)
    RZZ(q[0], q[1], PI / 7)
    CNOT(q[1], q[2])
    RZZ(q[2], q[3], PI / 4)
    CNOT(q[3], q[4])
    RZZ(q[4], q[5], PI / 6)
    CNOT(q[5], q[6])
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 60. `060_variational_pattern_60.sq` — 060 variational pattern 60

```sansqrit
# Generated variational circuit pattern 60
simulate(3, engine="sparse") {
    let q = quantum_register(3)
    H_all()
    # layer 0
    Rx(q[0], PI / 6)
    Ry(q[1], PI / 7)
    Rz(q[2], PI / 8)
    CNOT(q[0], q[1])
    RZZ(q[1], q[2], PI / 4)
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 61. `061_variational_pattern_61.sq` — 061 variational pattern 61

```sansqrit
# Generated variational circuit pattern 61
simulate(4, engine="sparse") {
    let q = quantum_register(4)
    H_all()
    # layer 0
    Ry(q[0], PI / 7)
    Rz(q[1], PI / 8)
    Rx(q[2], PI / 2)
    Ry(q[3], PI / 3)
    RZZ(q[0], q[1], PI / 4)
    CNOT(q[1], q[2])
    RZZ(q[2], q[3], PI / 6)
    # layer 1
    Rz(q[0], PI / 8)
    Rx(q[1], PI / 2)
    Ry(q[2], PI / 3)
    Rz(q[3], PI / 4)
    RZZ(q[0], q[1], PI / 4)
    CNOT(q[1], q[2])
    RZZ(q[2], q[3], PI / 6)
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 62. `062_variational_pattern_62.sq` — 062 variational pattern 62

```sansqrit
# Generated variational circuit pattern 62
simulate(5, engine="sparse") {
    let q = quantum_register(5)
    H_all()
    # layer 0
    Rz(q[0], PI / 8)
    Rx(q[1], PI / 2)
    Ry(q[2], PI / 3)
    Rz(q[3], PI / 4)
    Rx(q[4], PI / 5)
    CNOT(q[0], q[1])
    RZZ(q[1], q[2], PI / 6)
    CNOT(q[2], q[3])
    RZZ(q[3], q[4], PI / 3)
    # layer 1
    Rx(q[0], PI / 2)
    Ry(q[1], PI / 3)
    Rz(q[2], PI / 4)
    Rx(q[3], PI / 5)
    Ry(q[4], PI / 6)
    CNOT(q[0], q[1])
    RZZ(q[1], q[2], PI / 6)
    CNOT(q[2], q[3])
    RZZ(q[3], q[4], PI / 3)
    # layer 2
    Ry(q[0], PI / 3)
    Rz(q[1], PI / 4)
    Rx(q[2], PI / 5)
    Ry(q[3], PI / 6)
    Rz(q[4], PI / 7)
    CNOT(q[0], q[1])
    RZZ(q[1], q[2], PI / 6)
    CNOT(q[2], q[3])
    RZZ(q[3], q[4], PI / 3)
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 63. `063_variational_pattern_63.sq` — 063 variational pattern 63

```sansqrit
# Generated variational circuit pattern 63
simulate(6, engine="sparse") {
    let q = quantum_register(6)
    H_all()
    # layer 0
    Rx(q[0], PI / 2)
    Ry(q[1], PI / 3)
    Rz(q[2], PI / 4)
    Rx(q[3], PI / 5)
    Ry(q[4], PI / 6)
    Rz(q[5], PI / 7)
    RZZ(q[0], q[1], PI / 6)
    CNOT(q[1], q[2])
    RZZ(q[2], q[3], PI / 3)
    CNOT(q[3], q[4])
    RZZ(q[4], q[5], PI / 5)
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 64. `064_variational_pattern_64.sq` — 064 variational pattern 64

```sansqrit
# Generated variational circuit pattern 64
simulate(7, engine="sparse") {
    let q = quantum_register(7)
    H_all()
    # layer 0
    Ry(q[0], PI / 3)
    Rz(q[1], PI / 4)
    Rx(q[2], PI / 5)
    Ry(q[3], PI / 6)
    Rz(q[4], PI / 7)
    Rx(q[5], PI / 8)
    Ry(q[6], PI / 2)
    CNOT(q[0], q[1])
    RZZ(q[1], q[2], PI / 3)
    CNOT(q[2], q[3])
    RZZ(q[3], q[4], PI / 5)
    CNOT(q[4], q[5])
    RZZ(q[5], q[6], PI / 7)
    # layer 1
    Rz(q[0], PI / 4)
    Rx(q[1], PI / 5)
    Ry(q[2], PI / 6)
    Rz(q[3], PI / 7)
    Rx(q[4], PI / 8)
    Ry(q[5], PI / 2)
    Rz(q[6], PI / 3)
    CNOT(q[0], q[1])
    RZZ(q[1], q[2], PI / 3)
    CNOT(q[2], q[3])
    RZZ(q[3], q[4], PI / 5)
    CNOT(q[4], q[5])
    RZZ(q[5], q[6], PI / 7)
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 65. `065_variational_pattern_65.sq` — 065 variational pattern 65

```sansqrit
# Generated variational circuit pattern 65
simulate(3, engine="sparse") {
    let q = quantum_register(3)
    H_all()
    # layer 0
    Rz(q[0], PI / 4)
    Rx(q[1], PI / 5)
    Ry(q[2], PI / 6)
    RZZ(q[0], q[1], PI / 3)
    CNOT(q[1], q[2])
    # layer 1
    Rx(q[0], PI / 5)
    Ry(q[1], PI / 6)
    Rz(q[2], PI / 7)
    RZZ(q[0], q[1], PI / 3)
    CNOT(q[1], q[2])
    # layer 2
    Ry(q[0], PI / 6)
    Rz(q[1], PI / 7)
    Rx(q[2], PI / 8)
    RZZ(q[0], q[1], PI / 3)
    CNOT(q[1], q[2])
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 66. `066_variational_pattern_66.sq` — 066 variational pattern 66

```sansqrit
# Generated variational circuit pattern 66
simulate(4, engine="sparse") {
    let q = quantum_register(4)
    H_all()
    # layer 0
    Rx(q[0], PI / 5)
    Ry(q[1], PI / 6)
    Rz(q[2], PI / 7)
    Rx(q[3], PI / 8)
    CNOT(q[0], q[1])
    RZZ(q[1], q[2], PI / 5)
    CNOT(q[2], q[3])
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 67. `067_variational_pattern_67.sq` — 067 variational pattern 67

```sansqrit
# Generated variational circuit pattern 67
simulate(5, engine="sparse") {
    let q = quantum_register(5)
    H_all()
    # layer 0
    Ry(q[0], PI / 6)
    Rz(q[1], PI / 7)
    Rx(q[2], PI / 8)
    Ry(q[3], PI / 2)
    Rz(q[4], PI / 3)
    RZZ(q[0], q[1], PI / 5)
    CNOT(q[1], q[2])
    RZZ(q[2], q[3], PI / 7)
    CNOT(q[3], q[4])
    # layer 1
    Rz(q[0], PI / 7)
    Rx(q[1], PI / 8)
    Ry(q[2], PI / 2)
    Rz(q[3], PI / 3)
    Rx(q[4], PI / 4)
    RZZ(q[0], q[1], PI / 5)
    CNOT(q[1], q[2])
    RZZ(q[2], q[3], PI / 7)
    CNOT(q[3], q[4])
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 68. `068_variational_pattern_68.sq` — 068 variational pattern 68

```sansqrit
# Generated variational circuit pattern 68
simulate(6, engine="sparse") {
    let q = quantum_register(6)
    H_all()
    # layer 0
    Rz(q[0], PI / 7)
    Rx(q[1], PI / 8)
    Ry(q[2], PI / 2)
    Rz(q[3], PI / 3)
    Rx(q[4], PI / 4)
    Ry(q[5], PI / 5)
    CNOT(q[0], q[1])
    RZZ(q[1], q[2], PI / 7)
    CNOT(q[2], q[3])
    RZZ(q[3], q[4], PI / 4)
    CNOT(q[4], q[5])
    # layer 1
    Rx(q[0], PI / 8)
    Ry(q[1], PI / 2)
    Rz(q[2], PI / 3)
    Rx(q[3], PI / 4)
    Ry(q[4], PI / 5)
    Rz(q[5], PI / 6)
    CNOT(q[0], q[1])
    RZZ(q[1], q[2], PI / 7)
    CNOT(q[2], q[3])
    RZZ(q[3], q[4], PI / 4)
    CNOT(q[4], q[5])
    # layer 2
    Ry(q[0], PI / 2)
    Rz(q[1], PI / 3)
    Rx(q[2], PI / 4)
    Ry(q[3], PI / 5)
    Rz(q[4], PI / 6)
    Rx(q[5], PI / 7)
    CNOT(q[0], q[1])
    RZZ(q[1], q[2], PI / 7)
    CNOT(q[2], q[3])
    RZZ(q[3], q[4], PI / 4)
    CNOT(q[4], q[5])
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 69. `069_variational_pattern_69.sq` — 069 variational pattern 69

```sansqrit
# Generated variational circuit pattern 69
simulate(7, engine="sparse") {
    let q = quantum_register(7)
    H_all()
    # layer 0
    Rx(q[0], PI / 8)
    Ry(q[1], PI / 2)
    Rz(q[2], PI / 3)
    Rx(q[3], PI / 4)
    Ry(q[4], PI / 5)
    Rz(q[5], PI / 6)
    Rx(q[6], PI / 7)
    RZZ(q[0], q[1], PI / 7)
    CNOT(q[1], q[2])
    RZZ(q[2], q[3], PI / 4)
    CNOT(q[3], q[4])
    RZZ(q[4], q[5], PI / 6)
    CNOT(q[5], q[6])
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 70. `070_variational_pattern_70.sq` — 070 variational pattern 70

```sansqrit
# Generated variational circuit pattern 70
simulate(3, engine="sparse") {
    let q = quantum_register(3)
    H_all()
    # layer 0
    Ry(q[0], PI / 2)
    Rz(q[1], PI / 3)
    Rx(q[2], PI / 4)
    CNOT(q[0], q[1])
    RZZ(q[1], q[2], PI / 4)
    # layer 1
    Rz(q[0], PI / 3)
    Rx(q[1], PI / 4)
    Ry(q[2], PI / 5)
    CNOT(q[0], q[1])
    RZZ(q[1], q[2], PI / 4)
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 71. `071_phase_kickback.sq` — 071 phase kickback

```sansqrit
# phase_kickback example 71
simulate(7, engine="sharded", n_shards=4, workers=2) {
    let q = quantum_register(7)
    X(q[0])
    H_all()
    for i in range(6) {
        CP(q[i + 1], q[0], PI / (i + 2))
    }
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 72. `072_hidden_shift.sq` — 072 hidden shift

```sansqrit
# hidden_shift example 72
simulate(4, engine="sharded", n_shards=4, workers=2) {
    let q = quantum_register(4)
    H_all()
    for i in range(4) {
        Rz(q[i], PI / (i + 2))
    }
    for i in range(3) {
        CNOT(q[i], q[i + 1])
    }
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 73. `073_qft_phase_grid.sq` — 073 qft phase grid

```sansqrit
# qft_phase_grid example 73
simulate(5, engine="sharded", n_shards=4, workers=2) {
    let q = quantum_register(5)
    X(q[0])
    X(q[2])
    qft(q)
    Rz(q[1], PI / 7)
    iqft(q)
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 74. `074_entangled_ladder.sq` — 074 entangled ladder

```sansqrit
# entangled_ladder example 74
simulate(6, engine="sharded", n_shards=4, workers=2) {
    let q = quantum_register(6)
    H(q[0])
    for i in range(5) {
        CNOT(q[i], q[i + 1])
        RZZ(q[i], q[i + 1], PI / 5)
    }
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 75. `075_hardware_efficient_ansatz.sq` — 075 hardware efficient ansatz

```sansqrit
# hardware_efficient_ansatz example 75
simulate(7, engine="sharded", n_shards=4, workers=2) {
    let q = quantum_register(7)
    for layer in range(3) {
        for i in range(7) {
            Ry(q[i], PI / (layer + i + 2))
            Rz(q[i], PI / (layer + i + 3))
        }
        for i in range(6) {
            CNOT(q[i], q[i + 1])
        }
    }
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 76. `076_maxcut_ansatz.sq` — 076 maxcut ansatz

```sansqrit
# maxcut_ansatz example 76
simulate(4, engine="sharded", n_shards=4, workers=2) {
    let q = quantum_register(4)
    H_all()
    for i in range(3) {
        RZZ(q[i], q[i + 1], PI / 4)
    }
    Rx_all(PI / 6)
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 77. `077_qml_feature_map.sq` — 077 qml feature map

```sansqrit
# qml_feature_map example 77
simulate(5, engine="sharded", n_shards=4, workers=2) {
    let q = quantum_register(5)
    let x = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7][: 5]
    for i in range(5) {
        H(q[i])
        Rz(q[i], x[i])
    }
    for i in range(4) {
        RZZ(q[i], q[i + 1], x[i] * x[i + 1])
    }
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 78. `078_controlled_rotation_bank.sq` — 078 controlled rotation bank

```sansqrit
# controlled_rotation_bank example 78
simulate(6, engine="sharded", n_shards=4, workers=2) {
    let q = quantum_register(6)
    H(q[0])
    for i in range(1, 6) {
        CRz(q[0], q[i], PI / (i + 1))
        CRx(q[0], q[i], PI / (i + 2))
    }
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 79. `079_multi_control_demo.sq` — 079 multi control demo

```sansqrit
# multi_control_demo example 79
simulate(7, engine="sharded", n_shards=4, workers=2) {
    let q = quantum_register(7)
    for i in range(6) {
        X(q[i])
    }
    MCX(q[0], q[1], q[2], q[6])
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 80. `080_sparse_large_index.sq` — 080 sparse large index

```sansqrit
# sparse_large_index example 80
simulate(4, engine="sharded", n_shards=4, workers=2) {
    let q = quantum_register(4)
    H(q[0])
    CNOT(q[0], q[3])
    RZZ(q[0], q[3], PI / 9)
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 81. `081_phase_kickback.sq` — 081 phase kickback

```sansqrit
# phase_kickback example 81
simulate(5, engine="sharded", n_shards=4, workers=2) {
    let q = quantum_register(5)
    X(q[0])
    H_all()
    for i in range(4) {
        CP(q[i + 1], q[0], PI / (i + 2))
    }
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 82. `082_hidden_shift.sq` — 082 hidden shift

```sansqrit
# hidden_shift example 82
simulate(6, engine="sharded", n_shards=4, workers=2) {
    let q = quantum_register(6)
    H_all()
    for i in range(6) {
        Rz(q[i], PI / (i + 2))
    }
    for i in range(5) {
        CNOT(q[i], q[i + 1])
    }
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 83. `083_qft_phase_grid.sq` — 083 qft phase grid

```sansqrit
# qft_phase_grid example 83
simulate(7, engine="sharded", n_shards=4, workers=2) {
    let q = quantum_register(7)
    X(q[0])
    X(q[2])
    qft(q)
    Rz(q[1], PI / 7)
    iqft(q)
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 84. `084_entangled_ladder.sq` — 084 entangled ladder

```sansqrit
# entangled_ladder example 84
simulate(4, engine="sharded", n_shards=4, workers=2) {
    let q = quantum_register(4)
    H(q[0])
    for i in range(3) {
        CNOT(q[i], q[i + 1])
        RZZ(q[i], q[i + 1], PI / 5)
    }
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 85. `085_hardware_efficient_ansatz.sq` — 085 hardware efficient ansatz

```sansqrit
# hardware_efficient_ansatz example 85
simulate(5, engine="sharded", n_shards=4, workers=2) {
    let q = quantum_register(5)
    for layer in range(3) {
        for i in range(5) {
            Ry(q[i], PI / (layer + i + 2))
            Rz(q[i], PI / (layer + i + 3))
        }
        for i in range(4) {
            CNOT(q[i], q[i + 1])
        }
    }
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 86. `086_maxcut_ansatz.sq` — 086 maxcut ansatz

```sansqrit
# maxcut_ansatz example 86
simulate(6, engine="sharded", n_shards=4, workers=2) {
    let q = quantum_register(6)
    H_all()
    for i in range(5) {
        RZZ(q[i], q[i + 1], PI / 4)
    }
    Rx_all(PI / 6)
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 87. `087_qml_feature_map.sq` — 087 qml feature map

```sansqrit
# qml_feature_map example 87
simulate(7, engine="sharded", n_shards=4, workers=2) {
    let q = quantum_register(7)
    let x = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7][: 7]
    for i in range(7) {
        H(q[i])
        Rz(q[i], x[i])
    }
    for i in range(6) {
        RZZ(q[i], q[i + 1], x[i] * x[i + 1])
    }
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 88. `088_controlled_rotation_bank.sq` — 088 controlled rotation bank

```sansqrit
# controlled_rotation_bank example 88
simulate(4, engine="sharded", n_shards=4, workers=2) {
    let q = quantum_register(4)
    H(q[0])
    for i in range(1, 4) {
        CRz(q[0], q[i], PI / (i + 1))
        CRx(q[0], q[i], PI / (i + 2))
    }
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 89. `089_multi_control_demo.sq` — 089 multi control demo

```sansqrit
# multi_control_demo example 89
simulate(5, engine="sharded", n_shards=4, workers=2) {
    let q = quantum_register(5)
    for i in range(4) {
        X(q[i])
    }
    MCX(q[0], q[1], q[2], q[4])
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 90. `090_sparse_large_index.sq` — 090 sparse large index

```sansqrit
# sparse_large_index example 90
simulate(6, engine="sharded", n_shards=4, workers=2) {
    let q = quantum_register(6)
    H(q[0])
    CNOT(q[0], q[5])
    RZZ(q[0], q[5], PI / 9)
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 91. `091_phase_kickback.sq` — 091 phase kickback

```sansqrit
# phase_kickback example 91
simulate(7, engine="sharded", n_shards=4, workers=2) {
    let q = quantum_register(7)
    X(q[0])
    H_all()
    for i in range(6) {
        CP(q[i + 1], q[0], PI / (i + 2))
    }
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 92. `092_hidden_shift.sq` — 092 hidden shift

```sansqrit
# hidden_shift example 92
simulate(4, engine="sharded", n_shards=4, workers=2) {
    let q = quantum_register(4)
    H_all()
    for i in range(4) {
        Rz(q[i], PI / (i + 2))
    }
    for i in range(3) {
        CNOT(q[i], q[i + 1])
    }
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 93. `093_qft_phase_grid.sq` — 093 qft phase grid

```sansqrit
# qft_phase_grid example 93
simulate(5, engine="sharded", n_shards=4, workers=2) {
    let q = quantum_register(5)
    X(q[0])
    X(q[2])
    qft(q)
    Rz(q[1], PI / 7)
    iqft(q)
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 94. `094_entangled_ladder.sq` — 094 entangled ladder

```sansqrit
# entangled_ladder example 94
simulate(6, engine="sharded", n_shards=4, workers=2) {
    let q = quantum_register(6)
    H(q[0])
    for i in range(5) {
        CNOT(q[i], q[i + 1])
        RZZ(q[i], q[i + 1], PI / 5)
    }
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 95. `095_hardware_efficient_ansatz.sq` — 095 hardware efficient ansatz

```sansqrit
# hardware_efficient_ansatz example 95
simulate(7, engine="sharded", n_shards=4, workers=2) {
    let q = quantum_register(7)
    for layer in range(3) {
        for i in range(7) {
            Ry(q[i], PI / (layer + i + 2))
            Rz(q[i], PI / (layer + i + 3))
        }
        for i in range(6) {
            CNOT(q[i], q[i + 1])
        }
    }
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 96. `096_maxcut_ansatz.sq` — 096 maxcut ansatz

```sansqrit
# maxcut_ansatz example 96
simulate(4, engine="sharded", n_shards=4, workers=2) {
    let q = quantum_register(4)
    H_all()
    for i in range(3) {
        RZZ(q[i], q[i + 1], PI / 4)
    }
    Rx_all(PI / 6)
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 97. `097_qml_feature_map.sq` — 097 qml feature map

```sansqrit
# qml_feature_map example 97
simulate(5, engine="sharded", n_shards=4, workers=2) {
    let q = quantum_register(5)
    let x = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7][: 5]
    for i in range(5) {
        H(q[i])
        Rz(q[i], x[i])
    }
    for i in range(4) {
        RZZ(q[i], q[i + 1], x[i] * x[i + 1])
    }
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 98. `098_controlled_rotation_bank.sq` — 098 controlled rotation bank

```sansqrit
# controlled_rotation_bank example 98
simulate(6, engine="sharded", n_shards=4, workers=2) {
    let q = quantum_register(6)
    H(q[0])
    for i in range(1, 6) {
        CRz(q[0], q[i], PI / (i + 1))
        CRx(q[0], q[i], PI / (i + 2))
    }
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 99. `099_multi_control_demo.sq` — 099 multi control demo

```sansqrit
# multi_control_demo example 99
simulate(7, engine="sharded", n_shards=4, workers=2) {
    let q = quantum_register(7)
    for i in range(6) {
        X(q[i])
    }
    MCX(q[0], q[1], q[2], q[6])
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 100. `100_sparse_large_index.sq` — 100 sparse large index

```sansqrit
# sparse_large_index example 100
simulate(4, engine="sharded", n_shards=4, workers=2) {
    let q = quantum_register(4)
    H(q[0])
    CNOT(q[0], q[3])
    RZZ(q[0], q[3], PI / 9)
    print(engine_nnz())
    print(measure_all(q, shots=128))
}
```

### 101. `101_stabilizer_ghz_1000.sq` — 101 stabilizer ghz 1000

```sansqrit
# 1000-qubit Clifford GHZ smoke test; exact tableau, no dense expansion.
simulate(1000, engine="stabilizer", seed=7) {
    H(0)
    for i in range(0, 999) {
        CNOT(i, i + 1)
    }
    print(measure_all(shots=8))
}
```

### 102. `102_mps_low_entanglement_chain.sq` — 102 mps low entanglement chain

```sansqrit
# Low-entanglement MPS chain with small bond dimension.
simulate(32, engine="mps", max_bond_dim=32, seed=3) {
    for i in range(0, 32) {
        Ry(i, 0.1)
    }
    for i in range(0, 31) {
        CNOT(i, i + 1)
    }
    print(measure_all(shots=16))
}
```

### 103. `103_density_depolarizing_noise.sq` — 103 density depolarizing noise

```sansqrit
# Density-matrix noisy Bell state with depolarizing noise.
simulate(2, engine="density", seed=5) {
    H(0)
    CNOT(0, 1)
    noise_depolarize(0, 0.05)
    noise_depolarize(1, 0.05)
    print(probabilities())
}
```

### 104. `104_density_amplitude_damping.sq` — 104 density amplitude damping

```sansqrit
# Amplitude damping drives |1> toward |0>.
simulate(1, engine="density", seed=5) {
    X(0)
    noise_amplitude_damping(0, 0.30)
    print(probabilities())
}
```

### 105. `105_hybrid_backend_selection.sq` — 105 hybrid backend selection

```sansqrit
# Hybrid chooses stabilizer for Clifford circuits and sparse/MPS otherwise.
simulate(4, engine="stabilizer", seed=11) {
    H(0)
    CNOT(0, 1)
    CNOT(1, 2)
    CNOT(2, 3)
    print(measure_all(shots=8))
}
```

### 106. `106_optimizer_cancel_rotations.sq` — 106 optimizer cancel rotations

```sansqrit
# Optimizer is available from Python API; in DSL use direct simplified code.
simulate(1, seed=1) {
    H(0)
    H(0)
    Rz(0, 0.25)
    Rz(0, -0.25)
    print(probabilities())
}
```

### 107. `107_gpu_backend_small.sq` — 107 gpu backend small

```sansqrit
# GPU backend syntax reference. Uncomment the simulate block after installing sansqrit[gpu].
# simulate(3, engine="gpu", seed=1) {
#     H(0)
#     CNOT(0, 1)
#     Rz(2, 0.4)
#     print(measure_all(shots=8))
# }
print("GPU example: install sansqrit[gpu], then uncomment the simulate block.")
```

### 108. `108_qiskit_interop_reference.sq` — 108 qiskit interop reference

```sansqrit
# Interop is primarily Python API: sansqrit.interop.to_qiskit(Circuit(...)).
# DSL can still export QASM for external tools.
simulate(2, seed=2) {
    H(0)
    CNOT(0, 1)
    print(export_qasm3())
}
```

### 109. `109_large_sparse_150_qubits.sq` — 109 large sparse 150 qubits

```sansqrit
# 150 logical qubits with sparse state. Avoid H_all on all 150 unless you expect expansion.
simulate(150, engine="sparse", seed=9) {
    X(149)
    H(0)
    CNOT(0, 149)
    print(engine_nnz())
    print(measure_all(shots=8))
}
```

### 110. `110_mps_qft_lite.sq` — 110 mps qft lite

```sansqrit
# Small QFT-style tensor run. For large QFT, bond growth may require high max_bond_dim.
simulate(8, engine="mps", max_bond_dim=64, seed=4) {
    X(0)
    qft()
    print(measure_all(shots=16))
}
```

### 111. `111_150q_sensor_fusion_111.sq` — 111 150q sensor fusion 111

```sansqrit
# Example 111: 150-qubit real-time sensor fusion anomaly flag.
# Design rule: touch only a few sparse basis branches so this stays feasible on local hardware.
simulate(150, engine="sharded", n_shards=16, workers=4, seed=111) {
    let q = quantum_register(150)
    X(q[32])
    H(q[0])
    CNOT(q[0], q[149])
    CNOT(q[32], q[102])
    Rz(q[99], PI / 23)
    Phase(q[149], PI / 33)
    print("real-time sensor fusion anomaly flag")
    print("logical_qubits", 150)
    print("nonzero_amplitudes", engine_nnz())
    print("sample", measure_all(q, shots=6))
}
```

### 112. `112_150q_satellite_telemetry_112.sq` — 112 150q satellite telemetry 112

```sansqrit
# Example 112: 150-qubit satellite telemetry sparse state update.
# Design rule: touch only a few sparse basis branches so this stays feasible on local hardware.
simulate(150, engine="sharded", n_shards=16, workers=4, seed=112) {
    let q = quantum_register(150)
    X(q[39])
    H(q[0])
    CNOT(q[0], q[149])
    CNOT(q[39], q[115])
    Rz(q[116], PI / 16)
    Phase(q[149], PI / 34)
    print("satellite telemetry sparse state update")
    print("logical_qubits", 150)
    print("nonzero_amplitudes", engine_nnz())
    print("sample", measure_all(q, shots=6))
}
```

### 113. `113_150q_network_intrusion_113.sq` — 113 150q network intrusion 113

```sansqrit
# Example 113: 150-qubit network intrusion signature superposition.
# Design rule: touch only a few sparse basis branches so this stays feasible on local hardware.
simulate(150, engine="sharded", n_shards=16, workers=4, seed=113) {
    let q = quantum_register(150)
    X(q[46])
    H(q[0])
    CNOT(q[0], q[149])
    CNOT(q[46], q[128])
    Rz(q[133], PI / 17)
    Phase(q[149], PI / 35)
    print("network intrusion signature superposition")
    print("logical_qubits", 150)
    print("nonzero_amplitudes", engine_nnz())
    print("sample", measure_all(q, shots=6))
}
```

### 114. `114_150q_portfolio_risk_114.sq` — 114 150q portfolio risk 114

```sansqrit
# Example 114: 150-qubit portfolio risk qubit flagging.
# Design rule: touch only a few sparse basis branches so this stays feasible on local hardware.
simulate(150, engine="sharded", n_shards=16, workers=4, seed=114) {
    let q = quantum_register(150)
    X(q[53])
    H(q[0])
    CNOT(q[0], q[149])
    CNOT(q[53], q[141])
    Rz(q[1], PI / 18)
    Phase(q[149], PI / 36)
    print("portfolio risk qubit flagging")
    print("logical_qubits", 150)
    print("nonzero_amplitudes", engine_nnz())
    print("sample", measure_all(q, shots=6))
}
```

### 115. `115_150q_drug_screening_115.sq` — 115 150q drug screening 115

```sansqrit
# Example 115: 150-qubit drug candidate sparse oracle marker.
# Design rule: touch only a few sparse basis branches so this stays feasible on local hardware.
simulate(150, engine="sharded", n_shards=16, workers=4, seed=115) {
    let q = quantum_register(150)
    X(q[60])
    H(q[0])
    CNOT(q[0], q[149])
    CNOT(q[60], q[5])
    Rz(q[18], PI / 19)
    Phase(q[149], PI / 32)
    print("drug candidate sparse oracle marker")
    print("logical_qubits", 150)
    print("nonzero_amplitudes", engine_nnz())
    print("sample", measure_all(q, shots=6))
}
```

### 116. `116_150q_battery_material_116.sq` — 116 150q battery material 116

```sansqrit
# Example 116: 150-qubit battery material candidate phase tag.
# Design rule: touch only a few sparse basis branches so this stays feasible on local hardware.
simulate(150, engine="sharded", n_shards=16, workers=4, seed=116) {
    let q = quantum_register(150)
    X(q[67])
    H(q[0])
    CNOT(q[0], q[149])
    CNOT(q[67], q[18])
    Rz(q[35], PI / 20)
    Phase(q[149], PI / 33)
    print("battery material candidate phase tag")
    print("logical_qubits", 150)
    print("nonzero_amplitudes", engine_nnz())
    print("sample", measure_all(q, shots=6))
}
```

### 117. `117_150q_smart_grid_117.sq` — 117 150q smart grid 117

```sansqrit
# Example 117: 150-qubit smart grid load-balancing state flag.
# Design rule: touch only a few sparse basis branches so this stays feasible on local hardware.
simulate(150, engine="sharded", n_shards=16, workers=4, seed=117) {
    let q = quantum_register(150)
    X(q[74])
    H(q[0])
    CNOT(q[0], q[149])
    CNOT(q[74], q[31])
    Rz(q[52], PI / 21)
    Phase(q[149], PI / 34)
    print("smart grid load-balancing state flag")
    print("logical_qubits", 150)
    print("nonzero_amplitudes", engine_nnz())
    print("sample", measure_all(q, shots=6))
}
```

### 118. `118_150q_robotics_path_118.sq` — 118 150q robotics path 118

```sansqrit
# Example 118: 150-qubit robotics path branch marker.
# Design rule: touch only a few sparse basis branches so this stays feasible on local hardware.
simulate(150, engine="sharded", n_shards=16, workers=4, seed=118) {
    let q = quantum_register(150)
    X(q[81])
    H(q[0])
    CNOT(q[0], q[149])
    CNOT(q[81], q[44])
    Rz(q[69], PI / 22)
    Phase(q[149], PI / 35)
    print("robotics path branch marker")
    print("logical_qubits", 150)
    print("nonzero_amplitudes", engine_nnz())
    print("sample", measure_all(q, shots=6))
}
```

### 119. `119_150q_climate_event_119.sq` — 119 150q climate event 119

```sansqrit
# Example 119: 150-qubit climate event sparse alert.
# Design rule: touch only a few sparse basis branches so this stays feasible on local hardware.
simulate(150, engine="sharded", n_shards=16, workers=4, seed=119) {
    let q = quantum_register(150)
    X(q[88])
    H(q[0])
    CNOT(q[0], q[149])
    CNOT(q[88], q[57])
    Rz(q[86], PI / 23)
    Phase(q[149], PI / 36)
    print("climate event sparse alert")
    print("logical_qubits", 150)
    print("nonzero_amplitudes", engine_nnz())
    print("sample", measure_all(q, shots=6))
}
```

### 120. `120_150q_factory_quality_120.sq` — 120 150q factory quality 120

```sansqrit
# Example 120: 150-qubit factory quality-control qubit register.
# Design rule: touch only a few sparse basis branches so this stays feasible on local hardware.
simulate(150, engine="sharded", n_shards=16, workers=4, seed=120) {
    let q = quantum_register(150)
    X(q[95])
    H(q[0])
    CNOT(q[0], q[149])
    CNOT(q[95], q[70])
    Rz(q[103], PI / 16)
    Phase(q[149], PI / 32)
    print("factory quality-control qubit register")
    print("logical_qubits", 150)
    print("nonzero_amplitudes", engine_nnz())
    print("sample", measure_all(q, shots=6))
}
```

### 121. `121_150q_traffic_routing_121.sq` — 121 150q traffic routing 121

```sansqrit
# Example 121: 150-qubit traffic routing decision bit.
# Design rule: touch only a few sparse basis branches so this stays feasible on local hardware.
simulate(150, engine="sharded", n_shards=16, workers=4, seed=121) {
    let q = quantum_register(150)
    X(q[102])
    H(q[0])
    CNOT(q[0], q[149])
    CNOT(q[102], q[83])
    Rz(q[120], PI / 17)
    Phase(q[149], PI / 33)
    print("traffic routing decision bit")
    print("logical_qubits", 150)
    print("nonzero_amplitudes", engine_nnz())
    print("sample", measure_all(q, shots=6))
}
```

### 122. `122_150q_fraud_detection_122.sq` — 122 150q fraud detection 122

```sansqrit
# Example 122: 150-qubit fraud detection sparse score marker.
# Design rule: touch only a few sparse basis branches so this stays feasible on local hardware.
simulate(150, engine="sharded", n_shards=16, workers=4, seed=122) {
    let q = quantum_register(150)
    X(q[109])
    H(q[0])
    CNOT(q[0], q[149])
    CNOT(q[109], q[96])
    Rz(q[137], PI / 18)
    Phase(q[149], PI / 34)
    print("fraud detection sparse score marker")
    print("logical_qubits", 150)
    print("nonzero_amplitudes", engine_nnz())
    print("sample", measure_all(q, shots=6))
}
```

### 123. `123_150q_genomics_variant_123.sq` — 123 150q genomics variant 123

```sansqrit
# Example 123: 150-qubit genomics variant marker.
# Design rule: touch only a few sparse basis branches so this stays feasible on local hardware.
simulate(150, engine="sharded", n_shards=16, workers=4, seed=123) {
    let q = quantum_register(150)
    X(q[116])
    H(q[0])
    CNOT(q[0], q[149])
    CNOT(q[116], q[109])
    Rz(q[5], PI / 19)
    Phase(q[149], PI / 35)
    print("genomics variant marker")
    print("logical_qubits", 150)
    print("nonzero_amplitudes", engine_nnz())
    print("sample", measure_all(q, shots=6))
}
```

### 124. `124_150q_seismic_monitor_124.sq` — 124 150q seismic monitor 124

```sansqrit
# Example 124: 150-qubit seismic event marker.
# Design rule: touch only a few sparse basis branches so this stays feasible on local hardware.
simulate(150, engine="sharded", n_shards=16, workers=4, seed=124) {
    let q = quantum_register(150)
    X(q[123])
    H(q[0])
    CNOT(q[0], q[149])
    CNOT(q[123], q[122])
    Rz(q[22], PI / 20)
    Phase(q[149], PI / 36)
    print("seismic event marker")
    print("logical_qubits", 150)
    print("nonzero_amplitudes", engine_nnz())
    print("sample", measure_all(q, shots=6))
}
```

### 125. `125_150q_iot_edge_125.sq` — 125 150q iot edge 125

```sansqrit
# Example 125: 150-qubit IoT edge telemetry marker.
# Design rule: touch only a few sparse basis branches so this stays feasible on local hardware.
simulate(150, engine="sharded", n_shards=16, workers=4, seed=125) {
    let q = quantum_register(150)
    X(q[130])
    H(q[0])
    CNOT(q[0], q[149])
    CNOT(q[130], q[135])
    Rz(q[39], PI / 21)
    Phase(q[149], PI / 32)
    print("IoT edge telemetry marker")
    print("logical_qubits", 150)
    print("nonzero_amplitudes", engine_nnz())
    print("sample", measure_all(q, shots=6))
}
```

### 126. `126_150q_cyber_key_health_126.sq` — 126 150q cyber key health 126

```sansqrit
# Example 126: 150-qubit cyber key health monitor.
# Design rule: touch only a few sparse basis branches so this stays feasible on local hardware.
simulate(150, engine="sharded", n_shards=16, workers=4, seed=126) {
    let q = quantum_register(150)
    X(q[137])
    H(q[0])
    CNOT(q[0], q[149])
    CNOT(q[137], q[148])
    Rz(q[56], PI / 22)
    Phase(q[149], PI / 33)
    print("cyber key health monitor")
    print("logical_qubits", 150)
    print("nonzero_amplitudes", engine_nnz())
    print("sample", measure_all(q, shots=6))
}
```

### 127. `127_150q_medical_triage_127.sq` — 127 150q medical triage 127

```sansqrit
# Example 127: 150-qubit medical triage sparse decision.
# Design rule: touch only a few sparse basis branches so this stays feasible on local hardware.
simulate(150, engine="sharded", n_shards=16, workers=4, seed=127) {
    let q = quantum_register(150)
    X(q[144])
    H(q[0])
    CNOT(q[0], q[149])
    CNOT(q[144], q[12])
    Rz(q[73], PI / 23)
    Phase(q[149], PI / 34)
    print("medical triage sparse decision")
    print("logical_qubits", 150)
    print("nonzero_amplitudes", engine_nnz())
    print("sample", measure_all(q, shots=6))
}
```

### 128. `128_150q_supply_chain_128.sq` — 128 150q supply chain 128

```sansqrit
# Example 128: 150-qubit supply chain disruption indicator.
# Design rule: touch only a few sparse basis branches so this stays feasible on local hardware.
simulate(150, engine="sharded", n_shards=16, workers=4, seed=128) {
    let q = quantum_register(150)
    X(q[2])
    H(q[0])
    CNOT(q[0], q[149])
    CNOT(q[2], q[25])
    Rz(q[90], PI / 16)
    Phase(q[149], PI / 35)
    print("supply chain disruption indicator")
    print("logical_qubits", 150)
    print("nonzero_amplitudes", engine_nnz())
    print("sample", measure_all(q, shots=6))
}
```

### 129. `129_150q_water_network_129.sq` — 129 150q water network 129

```sansqrit
# Example 129: 150-qubit water network pressure anomaly.
# Design rule: touch only a few sparse basis branches so this stays feasible on local hardware.
simulate(150, engine="sharded", n_shards=16, workers=4, seed=129) {
    let q = quantum_register(150)
    X(q[9])
    H(q[0])
    CNOT(q[0], q[149])
    CNOT(q[9], q[38])
    Rz(q[107], PI / 17)
    Phase(q[149], PI / 36)
    print("water network pressure anomaly")
    print("logical_qubits", 150)
    print("nonzero_amplitudes", engine_nnz())
    print("sample", measure_all(q, shots=6))
}
```

### 130. `130_150q_energy_market_130.sq` — 130 150q energy market 130

```sansqrit
# Example 130: 150-qubit energy market sparse branch.
# Design rule: touch only a few sparse basis branches so this stays feasible on local hardware.
simulate(150, engine="sharded", n_shards=16, workers=4, seed=130) {
    let q = quantum_register(150)
    X(q[16])
    H(q[0])
    CNOT(q[0], q[149])
    CNOT(q[16], q[51])
    Rz(q[124], PI / 18)
    Phase(q[149], PI / 32)
    print("energy market sparse branch")
    print("logical_qubits", 150)
    print("nonzero_amplitudes", engine_nnz())
    print("sample", measure_all(q, shots=6))
}
```

### 131. `131_150q_sensor_fusion_131.sq` — 131 150q sensor fusion 131

```sansqrit
# Example 131: 150-qubit real-time sensor fusion anomaly flag.
# Design rule: touch only a few sparse basis branches so this stays feasible on local hardware.
simulate(150, engine="sharded", n_shards=16, workers=4, seed=131) {
    let q = quantum_register(150)
    X(q[23])
    H(q[0])
    CNOT(q[0], q[149])
    CNOT(q[23], q[64])
    Rz(q[141], PI / 19)
    Phase(q[149], PI / 33)
    print("real-time sensor fusion anomaly flag")
    print("logical_qubits", 150)
    print("nonzero_amplitudes", engine_nnz())
    print("sample", measure_all(q, shots=6))
}
```

### 132. `132_150q_satellite_telemetry_132.sq` — 132 150q satellite telemetry 132

```sansqrit
# Example 132: 150-qubit satellite telemetry sparse state update.
# Design rule: touch only a few sparse basis branches so this stays feasible on local hardware.
simulate(150, engine="sharded", n_shards=16, workers=4, seed=132) {
    let q = quantum_register(150)
    X(q[30])
    H(q[0])
    CNOT(q[0], q[149])
    CNOT(q[30], q[77])
    Rz(q[9], PI / 20)
    Phase(q[149], PI / 34)
    print("satellite telemetry sparse state update")
    print("logical_qubits", 150)
    print("nonzero_amplitudes", engine_nnz())
    print("sample", measure_all(q, shots=6))
}
```

### 133. `133_150q_network_intrusion_133.sq` — 133 150q network intrusion 133

```sansqrit
# Example 133: 150-qubit network intrusion signature superposition.
# Design rule: touch only a few sparse basis branches so this stays feasible on local hardware.
simulate(150, engine="sharded", n_shards=16, workers=4, seed=133) {
    let q = quantum_register(150)
    X(q[37])
    H(q[0])
    CNOT(q[0], q[149])
    CNOT(q[37], q[90])
    Rz(q[26], PI / 21)
    Phase(q[149], PI / 35)
    print("network intrusion signature superposition")
    print("logical_qubits", 150)
    print("nonzero_amplitudes", engine_nnz())
    print("sample", measure_all(q, shots=6))
}
```

### 134. `134_150q_portfolio_risk_134.sq` — 134 150q portfolio risk 134

```sansqrit
# Example 134: 150-qubit portfolio risk qubit flagging.
# Design rule: touch only a few sparse basis branches so this stays feasible on local hardware.
simulate(150, engine="sharded", n_shards=16, workers=4, seed=134) {
    let q = quantum_register(150)
    X(q[44])
    H(q[0])
    CNOT(q[0], q[149])
    CNOT(q[44], q[103])
    Rz(q[43], PI / 22)
    Phase(q[149], PI / 36)
    print("portfolio risk qubit flagging")
    print("logical_qubits", 150)
    print("nonzero_amplitudes", engine_nnz())
    print("sample", measure_all(q, shots=6))
}
```

### 135. `135_150q_drug_screening_135.sq` — 135 150q drug screening 135

```sansqrit
# Example 135: 150-qubit drug candidate sparse oracle marker.
# Design rule: touch only a few sparse basis branches so this stays feasible on local hardware.
simulate(150, engine="sharded", n_shards=16, workers=4, seed=135) {
    let q = quantum_register(150)
    X(q[51])
    H(q[0])
    CNOT(q[0], q[149])
    CNOT(q[51], q[116])
    Rz(q[60], PI / 23)
    Phase(q[149], PI / 32)
    print("drug candidate sparse oracle marker")
    print("logical_qubits", 150)
    print("nonzero_amplitudes", engine_nnz())
    print("sample", measure_all(q, shots=6))
}
```

### 136. `136_150q_battery_material_136.sq` — 136 150q battery material 136

```sansqrit
# Example 136: 150-qubit battery material candidate phase tag.
# Design rule: touch only a few sparse basis branches so this stays feasible on local hardware.
simulate(150, engine="sharded", n_shards=16, workers=4, seed=136) {
    let q = quantum_register(150)
    X(q[58])
    H(q[0])
    CNOT(q[0], q[149])
    CNOT(q[58], q[129])
    Rz(q[77], PI / 16)
    Phase(q[149], PI / 33)
    print("battery material candidate phase tag")
    print("logical_qubits", 150)
    print("nonzero_amplitudes", engine_nnz())
    print("sample", measure_all(q, shots=6))
}
```

### 137. `137_150q_smart_grid_137.sq` — 137 150q smart grid 137

```sansqrit
# Example 137: 150-qubit smart grid load-balancing state flag.
# Design rule: touch only a few sparse basis branches so this stays feasible on local hardware.
simulate(150, engine="sharded", n_shards=16, workers=4, seed=137) {
    let q = quantum_register(150)
    X(q[65])
    H(q[0])
    CNOT(q[0], q[149])
    CNOT(q[65], q[142])
    Rz(q[94], PI / 17)
    Phase(q[149], PI / 34)
    print("smart grid load-balancing state flag")
    print("logical_qubits", 150)
    print("nonzero_amplitudes", engine_nnz())
    print("sample", measure_all(q, shots=6))
}
```

### 138. `138_150q_robotics_path_138.sq` — 138 150q robotics path 138

```sansqrit
# Example 138: 150-qubit robotics path branch marker.
# Design rule: touch only a few sparse basis branches so this stays feasible on local hardware.
simulate(150, engine="sharded", n_shards=16, workers=4, seed=138) {
    let q = quantum_register(150)
    X(q[72])
    H(q[0])
    CNOT(q[0], q[149])
    CNOT(q[72], q[6])
    Rz(q[111], PI / 18)
    Phase(q[149], PI / 35)
    print("robotics path branch marker")
    print("logical_qubits", 150)
    print("nonzero_amplitudes", engine_nnz())
    print("sample", measure_all(q, shots=6))
}
```

### 139. `139_150q_climate_event_139.sq` — 139 150q climate event 139

```sansqrit
# Example 139: 150-qubit climate event sparse alert.
# Design rule: touch only a few sparse basis branches so this stays feasible on local hardware.
simulate(150, engine="sharded", n_shards=16, workers=4, seed=139) {
    let q = quantum_register(150)
    X(q[79])
    H(q[0])
    CNOT(q[0], q[149])
    CNOT(q[79], q[19])
    Rz(q[128], PI / 19)
    Phase(q[149], PI / 36)
    print("climate event sparse alert")
    print("logical_qubits", 150)
    print("nonzero_amplitudes", engine_nnz())
    print("sample", measure_all(q, shots=6))
}
```

### 140. `140_150q_factory_quality_140.sq` — 140 150q factory quality 140

```sansqrit
# Example 140: 150-qubit factory quality-control qubit register.
# Design rule: touch only a few sparse basis branches so this stays feasible on local hardware.
simulate(150, engine="sharded", n_shards=16, workers=4, seed=140) {
    let q = quantum_register(150)
    X(q[86])
    H(q[0])
    CNOT(q[0], q[149])
    CNOT(q[86], q[32])
    Rz(q[145], PI / 20)
    Phase(q[149], PI / 32)
    print("factory quality-control qubit register")
    print("logical_qubits", 150)
    print("nonzero_amplitudes", engine_nnz())
    print("sample", measure_all(q, shots=6))
}
```

### 141. `141_stabilizer_clifford_comm_141.sq` — 141 stabilizer clifford comm 141

```sansqrit
# Example 141: stabilizer backend for thousands-scale Clifford simulation.
# Clifford-only circuits use tableau memory instead of dense 2^n state vectors.
simulate(512, engine="stabilizer", seed=141) {
    H(423)
    S(423)
    CNOT(423, 440)
    CZ(440, 24)
    SWAP(423, 24)
    X(440)
    Z(24)
    print("clifford_comm")
    print("logical_qubits", 512)
    print("one_shot_prefix", list(measure_all(shots=1).keys())[0][0:32])
}
```

### 142. `142_stabilizer_graph_state_142.sq` — 142 stabilizer graph state 142

```sansqrit
# Example 142: stabilizer backend for thousands-scale Clifford simulation.
# Clifford-only circuits use tableau memory instead of dense 2^n state vectors.
simulate(768, engine="stabilizer", seed=142) {
    H(426)
    S(426)
    CNOT(426, 443)
    CZ(443, 539)
    SWAP(426, 539)
    X(443)
    Z(539)
    print("graph_state")
    print("logical_qubits", 768)
    print("one_shot_prefix", list(measure_all(shots=1).keys())[0][0:32])
}
```

### 143. `143_stabilizer_cluster_state_143.sq` — 143 stabilizer cluster state 143

```sansqrit
# Example 143: stabilizer backend for thousands-scale Clifford simulation.
# Clifford-only circuits use tableau memory instead of dense 2^n state vectors.
simulate(1024, engine="stabilizer", seed=143) {
    H(429)
    S(429)
    CNOT(429, 446)
    CZ(446, 542)
    SWAP(429, 542)
    X(446)
    Z(542)
    print("cluster_state")
    print("logical_qubits", 1024)
    print("one_shot_prefix", list(measure_all(shots=1).keys())[0][0:32])
}
```

### 144. `144_stabilizer_parity_monitor_144.sq` — 144 stabilizer parity monitor 144

```sansqrit
# Example 144: stabilizer backend for thousands-scale Clifford simulation.
# Clifford-only circuits use tableau memory instead of dense 2^n state vectors.
simulate(1280, engine="stabilizer", seed=144) {
    H(432)
    S(432)
    CNOT(432, 449)
    CZ(449, 545)
    SWAP(432, 545)
    X(449)
    Z(545)
    print("parity_monitor")
    print("logical_qubits", 1280)
    print("one_shot_prefix", list(measure_all(shots=1).keys())[0][0:32])
}
```

### 145. `145_stabilizer_surface_code_syndrome_145.sq` — 145 stabilizer surface code syndrome 145

```sansqrit
# Example 145: stabilizer backend for thousands-scale Clifford simulation.
# Clifford-only circuits use tableau memory instead of dense 2^n state vectors.
simulate(1536, engine="stabilizer", seed=145) {
    H(435)
    S(435)
    CNOT(435, 452)
    CZ(452, 548)
    SWAP(435, 548)
    X(452)
    Z(548)
    print("surface_code_syndrome")
    print("logical_qubits", 1536)
    print("one_shot_prefix", list(measure_all(shots=1).keys())[0][0:32])
}
```

### 146. `146_stabilizer_clifford_comm_146.sq` — 146 stabilizer clifford comm 146

```sansqrit
# Example 146: stabilizer backend for thousands-scale Clifford simulation.
# Clifford-only circuits use tableau memory instead of dense 2^n state vectors.
simulate(1792, engine="stabilizer", seed=146) {
    H(438)
    S(438)
    CNOT(438, 455)
    CZ(455, 551)
    SWAP(438, 551)
    X(455)
    Z(551)
    print("clifford_comm")
    print("logical_qubits", 1792)
    print("one_shot_prefix", list(measure_all(shots=1).keys())[0][0:32])
}
```

### 147. `147_stabilizer_graph_state_147.sq` — 147 stabilizer graph state 147

```sansqrit
# Example 147: stabilizer backend for thousands-scale Clifford simulation.
# Clifford-only circuits use tableau memory instead of dense 2^n state vectors.
simulate(512, engine="stabilizer", seed=147) {
    H(441)
    S(441)
    CNOT(441, 458)
    CZ(458, 42)
    SWAP(441, 42)
    X(458)
    Z(42)
    print("graph_state")
    print("logical_qubits", 512)
    print("one_shot_prefix", list(measure_all(shots=1).keys())[0][0:32])
}
```

### 148. `148_stabilizer_cluster_state_148.sq` — 148 stabilizer cluster state 148

```sansqrit
# Example 148: stabilizer backend for thousands-scale Clifford simulation.
# Clifford-only circuits use tableau memory instead of dense 2^n state vectors.
simulate(768, engine="stabilizer", seed=148) {
    H(444)
    S(444)
    CNOT(444, 461)
    CZ(461, 557)
    SWAP(444, 557)
    X(461)
    Z(557)
    print("cluster_state")
    print("logical_qubits", 768)
    print("one_shot_prefix", list(measure_all(shots=1).keys())[0][0:32])
}
```

### 149. `149_stabilizer_parity_monitor_149.sq` — 149 stabilizer parity monitor 149

```sansqrit
# Example 149: stabilizer backend for thousands-scale Clifford simulation.
# Clifford-only circuits use tableau memory instead of dense 2^n state vectors.
simulate(1024, engine="stabilizer", seed=149) {
    H(447)
    S(447)
    CNOT(447, 464)
    CZ(464, 560)
    SWAP(447, 560)
    X(464)
    Z(560)
    print("parity_monitor")
    print("logical_qubits", 1024)
    print("one_shot_prefix", list(measure_all(shots=1).keys())[0][0:32])
}
```

### 150. `150_stabilizer_surface_code_syndrome_150.sq` — 150 stabilizer surface code syndrome 150

```sansqrit
# Example 150: stabilizer backend for thousands-scale Clifford simulation.
# Clifford-only circuits use tableau memory instead of dense 2^n state vectors.
simulate(1280, engine="stabilizer", seed=150) {
    H(450)
    S(450)
    CNOT(450, 467)
    CZ(467, 563)
    SWAP(450, 563)
    X(467)
    Z(563)
    print("surface_code_syndrome")
    print("logical_qubits", 1280)
    print("one_shot_prefix", list(measure_all(shots=1).keys())[0][0:32])
}
```

### 151. `151_stabilizer_clifford_comm_151.sq` — 151 stabilizer clifford comm 151

```sansqrit
# Example 151: stabilizer backend for thousands-scale Clifford simulation.
# Clifford-only circuits use tableau memory instead of dense 2^n state vectors.
simulate(1536, engine="stabilizer", seed=151) {
    H(453)
    S(453)
    CNOT(453, 470)
    CZ(470, 566)
    SWAP(453, 566)
    X(470)
    Z(566)
    print("clifford_comm")
    print("logical_qubits", 1536)
    print("one_shot_prefix", list(measure_all(shots=1).keys())[0][0:32])
}
```

### 152. `152_stabilizer_graph_state_152.sq` — 152 stabilizer graph state 152

```sansqrit
# Example 152: stabilizer backend for thousands-scale Clifford simulation.
# Clifford-only circuits use tableau memory instead of dense 2^n state vectors.
simulate(1792, engine="stabilizer", seed=152) {
    H(456)
    S(456)
    CNOT(456, 473)
    CZ(473, 569)
    SWAP(456, 569)
    X(473)
    Z(569)
    print("graph_state")
    print("logical_qubits", 1792)
    print("one_shot_prefix", list(measure_all(shots=1).keys())[0][0:32])
}
```

### 153. `153_stabilizer_cluster_state_153.sq` — 153 stabilizer cluster state 153

```sansqrit
# Example 153: stabilizer backend for thousands-scale Clifford simulation.
# Clifford-only circuits use tableau memory instead of dense 2^n state vectors.
simulate(512, engine="stabilizer", seed=153) {
    H(459)
    S(459)
    CNOT(459, 476)
    CZ(476, 60)
    SWAP(459, 60)
    X(476)
    Z(60)
    print("cluster_state")
    print("logical_qubits", 512)
    print("one_shot_prefix", list(measure_all(shots=1).keys())[0][0:32])
}
```

### 154. `154_stabilizer_parity_monitor_154.sq` — 154 stabilizer parity monitor 154

```sansqrit
# Example 154: stabilizer backend for thousands-scale Clifford simulation.
# Clifford-only circuits use tableau memory instead of dense 2^n state vectors.
simulate(768, engine="stabilizer", seed=154) {
    H(462)
    S(462)
    CNOT(462, 479)
    CZ(479, 575)
    SWAP(462, 575)
    X(479)
    Z(575)
    print("parity_monitor")
    print("logical_qubits", 768)
    print("one_shot_prefix", list(measure_all(shots=1).keys())[0][0:32])
}
```

### 155. `155_stabilizer_surface_code_syndrome_155.sq` — 155 stabilizer surface code syndrome 155

```sansqrit
# Example 155: stabilizer backend for thousands-scale Clifford simulation.
# Clifford-only circuits use tableau memory instead of dense 2^n state vectors.
simulate(1024, engine="stabilizer", seed=155) {
    H(465)
    S(465)
    CNOT(465, 482)
    CZ(482, 578)
    SWAP(465, 578)
    X(482)
    Z(578)
    print("surface_code_syndrome")
    print("logical_qubits", 1024)
    print("one_shot_prefix", list(measure_all(shots=1).keys())[0][0:32])
}
```

### 156. `156_stabilizer_clifford_comm_156.sq` — 156 stabilizer clifford comm 156

```sansqrit
# Example 156: stabilizer backend for thousands-scale Clifford simulation.
# Clifford-only circuits use tableau memory instead of dense 2^n state vectors.
simulate(1280, engine="stabilizer", seed=156) {
    H(468)
    S(468)
    CNOT(468, 485)
    CZ(485, 581)
    SWAP(468, 581)
    X(485)
    Z(581)
    print("clifford_comm")
    print("logical_qubits", 1280)
    print("one_shot_prefix", list(measure_all(shots=1).keys())[0][0:32])
}
```

### 157. `157_stabilizer_graph_state_157.sq` — 157 stabilizer graph state 157

```sansqrit
# Example 157: stabilizer backend for thousands-scale Clifford simulation.
# Clifford-only circuits use tableau memory instead of dense 2^n state vectors.
simulate(1536, engine="stabilizer", seed=157) {
    H(471)
    S(471)
    CNOT(471, 488)
    CZ(488, 584)
    SWAP(471, 584)
    X(488)
    Z(584)
    print("graph_state")
    print("logical_qubits", 1536)
    print("one_shot_prefix", list(measure_all(shots=1).keys())[0][0:32])
}
```

### 158. `158_stabilizer_cluster_state_158.sq` — 158 stabilizer cluster state 158

```sansqrit
# Example 158: stabilizer backend for thousands-scale Clifford simulation.
# Clifford-only circuits use tableau memory instead of dense 2^n state vectors.
simulate(1792, engine="stabilizer", seed=158) {
    H(474)
    S(474)
    CNOT(474, 491)
    CZ(491, 587)
    SWAP(474, 587)
    X(491)
    Z(587)
    print("cluster_state")
    print("logical_qubits", 1792)
    print("one_shot_prefix", list(measure_all(shots=1).keys())[0][0:32])
}
```

### 159. `159_stabilizer_parity_monitor_159.sq` — 159 stabilizer parity monitor 159

```sansqrit
# Example 159: stabilizer backend for thousands-scale Clifford simulation.
# Clifford-only circuits use tableau memory instead of dense 2^n state vectors.
simulate(512, engine="stabilizer", seed=159) {
    H(477)
    S(477)
    CNOT(477, 494)
    CZ(494, 78)
    SWAP(477, 78)
    X(494)
    Z(78)
    print("parity_monitor")
    print("logical_qubits", 512)
    print("one_shot_prefix", list(measure_all(shots=1).keys())[0][0:32])
}
```

### 160. `160_stabilizer_surface_code_syndrome_160.sq` — 160 stabilizer surface code syndrome 160

```sansqrit
# Example 160: stabilizer backend for thousands-scale Clifford simulation.
# Clifford-only circuits use tableau memory instead of dense 2^n state vectors.
simulate(768, engine="stabilizer", seed=160) {
    H(480)
    S(480)
    CNOT(480, 497)
    CZ(497, 593)
    SWAP(480, 593)
    X(497)
    Z(593)
    print("surface_code_syndrome")
    print("logical_qubits", 768)
    print("one_shot_prefix", list(measure_all(shots=1).keys())[0][0:32])
}
```

### 161. `161_stabilizer_clifford_comm_161.sq` — 161 stabilizer clifford comm 161

```sansqrit
# Example 161: stabilizer backend for thousands-scale Clifford simulation.
# Clifford-only circuits use tableau memory instead of dense 2^n state vectors.
simulate(1024, engine="stabilizer", seed=161) {
    H(483)
    S(483)
    CNOT(483, 500)
    CZ(500, 596)
    SWAP(483, 596)
    X(500)
    Z(596)
    print("clifford_comm")
    print("logical_qubits", 1024)
    print("one_shot_prefix", list(measure_all(shots=1).keys())[0][0:32])
}
```

### 162. `162_stabilizer_graph_state_162.sq` — 162 stabilizer graph state 162

```sansqrit
# Example 162: stabilizer backend for thousands-scale Clifford simulation.
# Clifford-only circuits use tableau memory instead of dense 2^n state vectors.
simulate(1280, engine="stabilizer", seed=162) {
    H(486)
    S(486)
    CNOT(486, 503)
    CZ(503, 599)
    SWAP(486, 599)
    X(503)
    Z(599)
    print("graph_state")
    print("logical_qubits", 1280)
    print("one_shot_prefix", list(measure_all(shots=1).keys())[0][0:32])
}
```

### 163. `163_stabilizer_cluster_state_163.sq` — 163 stabilizer cluster state 163

```sansqrit
# Example 163: stabilizer backend for thousands-scale Clifford simulation.
# Clifford-only circuits use tableau memory instead of dense 2^n state vectors.
simulate(1536, engine="stabilizer", seed=163) {
    H(489)
    S(489)
    CNOT(489, 506)
    CZ(506, 602)
    SWAP(489, 602)
    X(506)
    Z(602)
    print("cluster_state")
    print("logical_qubits", 1536)
    print("one_shot_prefix", list(measure_all(shots=1).keys())[0][0:32])
}
```

### 164. `164_stabilizer_parity_monitor_164.sq` — 164 stabilizer parity monitor 164

```sansqrit
# Example 164: stabilizer backend for thousands-scale Clifford simulation.
# Clifford-only circuits use tableau memory instead of dense 2^n state vectors.
simulate(1792, engine="stabilizer", seed=164) {
    H(492)
    S(492)
    CNOT(492, 509)
    CZ(509, 605)
    SWAP(492, 605)
    X(509)
    Z(605)
    print("parity_monitor")
    print("logical_qubits", 1792)
    print("one_shot_prefix", list(measure_all(shots=1).keys())[0][0:32])
}
```

### 165. `165_stabilizer_surface_code_syndrome_165.sq` — 165 stabilizer surface code syndrome 165

```sansqrit
# Example 165: stabilizer backend for thousands-scale Clifford simulation.
# Clifford-only circuits use tableau memory instead of dense 2^n state vectors.
simulate(512, engine="stabilizer", seed=165) {
    H(495)
    S(495)
    CNOT(495, 0)
    CZ(0, 96)
    SWAP(495, 96)
    X(0)
    Z(96)
    print("surface_code_syndrome")
    print("logical_qubits", 512)
    print("one_shot_prefix", list(measure_all(shots=1).keys())[0][0:32])
}
```

### 166. `166_mps_adiabatic_line_166.sq` — 166 mps adiabatic line 166

```sansqrit
# Example 166: MPS/tensor-network low-entanglement adiabatic_line.
# Requires optional NumPy extra: pip install sansqrit[tensor].
simulate(24, engine="mps", max_bond_dim=32, cutoff=1e-10, seed=166) {
    let q = quantum_register(24)
    for layer in range(4) {
        H(q[layer % 24])
        CNOT(q[layer % 24], q[(layer + 1) % 24])
        Rz(q[(layer + 1) % 24], PI / (layer + 3))
    }
    print("adiabatic_line")
    print("logical_qubits", 24)
    print("shots", measure_all(q, shots=4))
}
```

### 167. `167_mps_qft_lite_167.sq` — 167 mps qft lite 167

```sansqrit
# Example 167: MPS/tensor-network low-entanglement qft_lite.
# Requires optional NumPy extra: pip install sansqrit[tensor].
simulate(28, engine="mps", max_bond_dim=32, cutoff=1e-10, seed=167) {
    let q = quantum_register(28)
    for layer in range(5) {
        H(q[layer % 28])
        CNOT(q[layer % 28], q[(layer + 1) % 28])
        Rz(q[(layer + 1) % 28], PI / (layer + 3))
    }
    print("qft_lite")
    print("logical_qubits", 28)
    print("shots", measure_all(q, shots=4))
}
```

### 168. `168_mps_bond_dimension_probe_168.sq` — 168 mps bond dimension probe 168

```sansqrit
# Example 168: MPS/tensor-network low-entanglement bond_dimension_probe.
# Requires optional NumPy extra: pip install sansqrit[tensor].
simulate(32, engine="mps", max_bond_dim=32, cutoff=1e-10, seed=168) {
    let q = quantum_register(32)
    for layer in range(3) {
        H(q[layer % 32])
        CNOT(q[layer % 32], q[(layer + 1) % 32])
        Rz(q[(layer + 1) % 32], PI / (layer + 3))
    }
    print("bond_dimension_probe")
    print("logical_qubits", 32)
    print("shots", measure_all(q, shots=4))
}
```

### 169. `169_mps_matrix_product_feature_map_169.sq` — 169 mps matrix product feature map 169

```sansqrit
# Example 169: MPS/tensor-network low-entanglement matrix_product_feature_map.
# Requires optional NumPy extra: pip install sansqrit[tensor].
simulate(36, engine="mps", max_bond_dim=32, cutoff=1e-10, seed=169) {
    let q = quantum_register(36)
    for layer in range(4) {
        H(q[layer % 36])
        CNOT(q[layer % 36], q[(layer + 1) % 36])
        Rz(q[(layer + 1) % 36], PI / (layer + 3))
    }
    print("matrix_product_feature_map")
    print("logical_qubits", 36)
    print("shots", measure_all(q, shots=4))
}
```

### 170. `170_mps_spin_chain_170.sq` — 170 mps spin chain 170

```sansqrit
# Example 170: MPS/tensor-network low-entanglement spin_chain.
# Requires optional NumPy extra: pip install sansqrit[tensor].
simulate(40, engine="mps", max_bond_dim=32, cutoff=1e-10, seed=170) {
    let q = quantum_register(40)
    for layer in range(5) {
        H(q[layer % 40])
        CNOT(q[layer % 40], q[(layer + 1) % 40])
        Rz(q[(layer + 1) % 40], PI / (layer + 3))
    }
    print("spin_chain")
    print("logical_qubits", 40)
    print("shots", measure_all(q, shots=4))
}
```

### 171. `171_mps_adiabatic_line_171.sq` — 171 mps adiabatic line 171

```sansqrit
# Example 171: MPS/tensor-network low-entanglement adiabatic_line.
# Requires optional NumPy extra: pip install sansqrit[tensor].
simulate(44, engine="mps", max_bond_dim=32, cutoff=1e-10, seed=171) {
    let q = quantum_register(44)
    for layer in range(3) {
        H(q[layer % 44])
        CNOT(q[layer % 44], q[(layer + 1) % 44])
        Rz(q[(layer + 1) % 44], PI / (layer + 3))
    }
    print("adiabatic_line")
    print("logical_qubits", 44)
    print("shots", measure_all(q, shots=4))
}
```

### 172. `172_mps_qft_lite_172.sq` — 172 mps qft lite 172

```sansqrit
# Example 172: MPS/tensor-network low-entanglement qft_lite.
# Requires optional NumPy extra: pip install sansqrit[tensor].
simulate(48, engine="mps", max_bond_dim=32, cutoff=1e-10, seed=172) {
    let q = quantum_register(48)
    for layer in range(4) {
        H(q[layer % 48])
        CNOT(q[layer % 48], q[(layer + 1) % 48])
        Rz(q[(layer + 1) % 48], PI / (layer + 3))
    }
    print("qft_lite")
    print("logical_qubits", 48)
    print("shots", measure_all(q, shots=4))
}
```

### 173. `173_mps_bond_dimension_probe_173.sq` — 173 mps bond dimension probe 173

```sansqrit
# Example 173: MPS/tensor-network low-entanglement bond_dimension_probe.
# Requires optional NumPy extra: pip install sansqrit[tensor].
simulate(52, engine="mps", max_bond_dim=32, cutoff=1e-10, seed=173) {
    let q = quantum_register(52)
    for layer in range(5) {
        H(q[layer % 52])
        CNOT(q[layer % 52], q[(layer + 1) % 52])
        Rz(q[(layer + 1) % 52], PI / (layer + 3))
    }
    print("bond_dimension_probe")
    print("logical_qubits", 52)
    print("shots", measure_all(q, shots=4))
}
```

### 174. `174_mps_matrix_product_feature_map_174.sq` — 174 mps matrix product feature map 174

```sansqrit
# Example 174: MPS/tensor-network low-entanglement matrix_product_feature_map.
# Requires optional NumPy extra: pip install sansqrit[tensor].
simulate(24, engine="mps", max_bond_dim=32, cutoff=1e-10, seed=174) {
    let q = quantum_register(24)
    for layer in range(3) {
        H(q[layer % 24])
        CNOT(q[layer % 24], q[(layer + 1) % 24])
        Rz(q[(layer + 1) % 24], PI / (layer + 3))
    }
    print("matrix_product_feature_map")
    print("logical_qubits", 24)
    print("shots", measure_all(q, shots=4))
}
```

### 175. `175_mps_spin_chain_175.sq` — 175 mps spin chain 175

```sansqrit
# Example 175: MPS/tensor-network low-entanglement spin_chain.
# Requires optional NumPy extra: pip install sansqrit[tensor].
simulate(28, engine="mps", max_bond_dim=32, cutoff=1e-10, seed=175) {
    let q = quantum_register(28)
    for layer in range(4) {
        H(q[layer % 28])
        CNOT(q[layer % 28], q[(layer + 1) % 28])
        Rz(q[(layer + 1) % 28], PI / (layer + 3))
    }
    print("spin_chain")
    print("logical_qubits", 28)
    print("shots", measure_all(q, shots=4))
}
```

### 176. `176_mps_adiabatic_line_176.sq` — 176 mps adiabatic line 176

```sansqrit
# Example 176: MPS/tensor-network low-entanglement adiabatic_line.
# Requires optional NumPy extra: pip install sansqrit[tensor].
simulate(32, engine="mps", max_bond_dim=32, cutoff=1e-10, seed=176) {
    let q = quantum_register(32)
    for layer in range(5) {
        H(q[layer % 32])
        CNOT(q[layer % 32], q[(layer + 1) % 32])
        Rz(q[(layer + 1) % 32], PI / (layer + 3))
    }
    print("adiabatic_line")
    print("logical_qubits", 32)
    print("shots", measure_all(q, shots=4))
}
```

### 177. `177_mps_qft_lite_177.sq` — 177 mps qft lite 177

```sansqrit
# Example 177: MPS/tensor-network low-entanglement qft_lite.
# Requires optional NumPy extra: pip install sansqrit[tensor].
simulate(36, engine="mps", max_bond_dim=32, cutoff=1e-10, seed=177) {
    let q = quantum_register(36)
    for layer in range(3) {
        H(q[layer % 36])
        CNOT(q[layer % 36], q[(layer + 1) % 36])
        Rz(q[(layer + 1) % 36], PI / (layer + 3))
    }
    print("qft_lite")
    print("logical_qubits", 36)
    print("shots", measure_all(q, shots=4))
}
```

### 178. `178_mps_bond_dimension_probe_178.sq` — 178 mps bond dimension probe 178

```sansqrit
# Example 178: MPS/tensor-network low-entanglement bond_dimension_probe.
# Requires optional NumPy extra: pip install sansqrit[tensor].
simulate(40, engine="mps", max_bond_dim=32, cutoff=1e-10, seed=178) {
    let q = quantum_register(40)
    for layer in range(4) {
        H(q[layer % 40])
        CNOT(q[layer % 40], q[(layer + 1) % 40])
        Rz(q[(layer + 1) % 40], PI / (layer + 3))
    }
    print("bond_dimension_probe")
    print("logical_qubits", 40)
    print("shots", measure_all(q, shots=4))
}
```

### 179. `179_mps_matrix_product_feature_map_179.sq` — 179 mps matrix product feature map 179

```sansqrit
# Example 179: MPS/tensor-network low-entanglement matrix_product_feature_map.
# Requires optional NumPy extra: pip install sansqrit[tensor].
simulate(44, engine="mps", max_bond_dim=32, cutoff=1e-10, seed=179) {
    let q = quantum_register(44)
    for layer in range(5) {
        H(q[layer % 44])
        CNOT(q[layer % 44], q[(layer + 1) % 44])
        Rz(q[(layer + 1) % 44], PI / (layer + 3))
    }
    print("matrix_product_feature_map")
    print("logical_qubits", 44)
    print("shots", measure_all(q, shots=4))
}
```

### 180. `180_mps_spin_chain_180.sq` — 180 mps spin chain 180

```sansqrit
# Example 180: MPS/tensor-network low-entanglement spin_chain.
# Requires optional NumPy extra: pip install sansqrit[tensor].
simulate(48, engine="mps", max_bond_dim=32, cutoff=1e-10, seed=180) {
    let q = quantum_register(48)
    for layer in range(3) {
        H(q[layer % 48])
        CNOT(q[layer % 48], q[(layer + 1) % 48])
        Rz(q[(layer + 1) % 48], PI / (layer + 3))
    }
    print("spin_chain")
    print("logical_qubits", 48)
    print("shots", measure_all(q, shots=4))
}
```

### 181. `181_mps_adiabatic_line_181.sq` — 181 mps adiabatic line 181

```sansqrit
# Example 181: MPS/tensor-network low-entanglement adiabatic_line.
# Requires optional NumPy extra: pip install sansqrit[tensor].
simulate(52, engine="mps", max_bond_dim=32, cutoff=1e-10, seed=181) {
    let q = quantum_register(52)
    for layer in range(4) {
        H(q[layer % 52])
        CNOT(q[layer % 52], q[(layer + 1) % 52])
        Rz(q[(layer + 1) % 52], PI / (layer + 3))
    }
    print("adiabatic_line")
    print("logical_qubits", 52)
    print("shots", measure_all(q, shots=4))
}
```

### 182. `182_mps_qft_lite_182.sq` — 182 mps qft lite 182

```sansqrit
# Example 182: MPS/tensor-network low-entanglement qft_lite.
# Requires optional NumPy extra: pip install sansqrit[tensor].
simulate(24, engine="mps", max_bond_dim=32, cutoff=1e-10, seed=182) {
    let q = quantum_register(24)
    for layer in range(5) {
        H(q[layer % 24])
        CNOT(q[layer % 24], q[(layer + 1) % 24])
        Rz(q[(layer + 1) % 24], PI / (layer + 3))
    }
    print("qft_lite")
    print("logical_qubits", 24)
    print("shots", measure_all(q, shots=4))
}
```

### 183. `183_mps_bond_dimension_probe_183.sq` — 183 mps bond dimension probe 183

```sansqrit
# Example 183: MPS/tensor-network low-entanglement bond_dimension_probe.
# Requires optional NumPy extra: pip install sansqrit[tensor].
simulate(28, engine="mps", max_bond_dim=32, cutoff=1e-10, seed=183) {
    let q = quantum_register(28)
    for layer in range(3) {
        H(q[layer % 28])
        CNOT(q[layer % 28], q[(layer + 1) % 28])
        Rz(q[(layer + 1) % 28], PI / (layer + 3))
    }
    print("bond_dimension_probe")
    print("logical_qubits", 28)
    print("shots", measure_all(q, shots=4))
}
```

### 184. `184_mps_matrix_product_feature_map_184.sq` — 184 mps matrix product feature map 184

```sansqrit
# Example 184: MPS/tensor-network low-entanglement matrix_product_feature_map.
# Requires optional NumPy extra: pip install sansqrit[tensor].
simulate(32, engine="mps", max_bond_dim=32, cutoff=1e-10, seed=184) {
    let q = quantum_register(32)
    for layer in range(4) {
        H(q[layer % 32])
        CNOT(q[layer % 32], q[(layer + 1) % 32])
        Rz(q[(layer + 1) % 32], PI / (layer + 3))
    }
    print("matrix_product_feature_map")
    print("logical_qubits", 32)
    print("shots", measure_all(q, shots=4))
}
```

### 185. `185_mps_spin_chain_185.sq` — 185 mps spin chain 185

```sansqrit
# Example 185: MPS/tensor-network low-entanglement spin_chain.
# Requires optional NumPy extra: pip install sansqrit[tensor].
simulate(36, engine="mps", max_bond_dim=32, cutoff=1e-10, seed=185) {
    let q = quantum_register(36)
    for layer in range(5) {
        H(q[layer % 36])
        CNOT(q[layer % 36], q[(layer + 1) % 36])
        Rz(q[(layer + 1) % 36], PI / (layer + 3))
    }
    print("spin_chain")
    print("logical_qubits", 36)
    print("shots", measure_all(q, shots=4))
}
```

### 186. `186_mps_adiabatic_line_186.sq` — 186 mps adiabatic line 186

```sansqrit
# Example 186: MPS/tensor-network low-entanglement adiabatic_line.
# Requires optional NumPy extra: pip install sansqrit[tensor].
simulate(40, engine="mps", max_bond_dim=32, cutoff=1e-10, seed=186) {
    let q = quantum_register(40)
    for layer in range(3) {
        H(q[layer % 40])
        CNOT(q[layer % 40], q[(layer + 1) % 40])
        Rz(q[(layer + 1) % 40], PI / (layer + 3))
    }
    print("adiabatic_line")
    print("logical_qubits", 40)
    print("shots", measure_all(q, shots=4))
}
```

### 187. `187_mps_qft_lite_187.sq` — 187 mps qft lite 187

```sansqrit
# Example 187: MPS/tensor-network low-entanglement qft_lite.
# Requires optional NumPy extra: pip install sansqrit[tensor].
simulate(44, engine="mps", max_bond_dim=32, cutoff=1e-10, seed=187) {
    let q = quantum_register(44)
    for layer in range(4) {
        H(q[layer % 44])
        CNOT(q[layer % 44], q[(layer + 1) % 44])
        Rz(q[(layer + 1) % 44], PI / (layer + 3))
    }
    print("qft_lite")
    print("logical_qubits", 44)
    print("shots", measure_all(q, shots=4))
}
```

### 188. `188_mps_bond_dimension_probe_188.sq` — 188 mps bond dimension probe 188

```sansqrit
# Example 188: MPS/tensor-network low-entanglement bond_dimension_probe.
# Requires optional NumPy extra: pip install sansqrit[tensor].
simulate(48, engine="mps", max_bond_dim=32, cutoff=1e-10, seed=188) {
    let q = quantum_register(48)
    for layer in range(5) {
        H(q[layer % 48])
        CNOT(q[layer % 48], q[(layer + 1) % 48])
        Rz(q[(layer + 1) % 48], PI / (layer + 3))
    }
    print("bond_dimension_probe")
    print("logical_qubits", 48)
    print("shots", measure_all(q, shots=4))
}
```

### 189. `189_mps_matrix_product_feature_map_189.sq` — 189 mps matrix product feature map 189

```sansqrit
# Example 189: MPS/tensor-network low-entanglement matrix_product_feature_map.
# Requires optional NumPy extra: pip install sansqrit[tensor].
simulate(52, engine="mps", max_bond_dim=32, cutoff=1e-10, seed=189) {
    let q = quantum_register(52)
    for layer in range(3) {
        H(q[layer % 52])
        CNOT(q[layer % 52], q[(layer + 1) % 52])
        Rz(q[(layer + 1) % 52], PI / (layer + 3))
    }
    print("matrix_product_feature_map")
    print("logical_qubits", 52)
    print("shots", measure_all(q, shots=4))
}
```

### 190. `190_mps_spin_chain_190.sq` — 190 mps spin chain 190

```sansqrit
# Example 190: MPS/tensor-network low-entanglement spin_chain.
# Requires optional NumPy extra: pip install sansqrit[tensor].
simulate(24, engine="mps", max_bond_dim=32, cutoff=1e-10, seed=190) {
    let q = quantum_register(24)
    for layer in range(4) {
        H(q[layer % 24])
        CNOT(q[layer % 24], q[(layer + 1) % 24])
        Rz(q[(layer + 1) % 24], PI / (layer + 3))
    }
    print("spin_chain")
    print("logical_qubits", 24)
    print("shots", measure_all(q, shots=4))
}
```

### 191. `191_noise_readout_channel_191.sq` — 191 noise readout channel 191

```sansqrit
# Example 191: density-matrix/noise model readout_channel.
simulate(2, engine="density", seed=191) {
    let q = quantum_register(2)
    H(q[0])
    CNOT(q[0], q[1])
    noise_bit_flip(q[1], 0.020)
    print("readout_channel")
    print("probabilities", probabilities(q))
    print("shots", measure_all(q, shots=16))
}
```

### 192. `192_noise_amplitude_decay_192.sq` — 192 noise amplitude decay 192

```sansqrit
# Example 192: density-matrix/noise model amplitude_decay.
simulate(2, engine="density", seed=192) {
    let q = quantum_register(2)
    H(q[0])
    CNOT(q[0], q[1])
    noise_amplitude_damping(q[1], 0.030)
    print("amplitude_decay")
    print("probabilities", probabilities(q))
    print("shots", measure_all(q, shots=16))
}
```

### 193. `193_noise_phase_noise_193.sq` — 193 noise phase noise 193

```sansqrit
# Example 193: density-matrix/noise model phase_noise.
simulate(2, engine="density", seed=193) {
    let q = quantum_register(2)
    H(q[0])
    CNOT(q[0], q[1])
    noise_phase_flip(q[0], 0.040)
    print("phase_noise")
    print("probabilities", probabilities(q))
    print("shots", measure_all(q, shots=16))
}
```

### 194. `194_noise_depolarizing_benchmark_194.sq` — 194 noise depolarizing benchmark 194

```sansqrit
# Example 194: density-matrix/noise model depolarizing_benchmark.
simulate(2, engine="density", seed=194) {
    let q = quantum_register(2)
    H(q[0])
    CNOT(q[0], q[1])
    noise_depolarize(q[1], 0.050)
    print("depolarizing_benchmark")
    print("probabilities", probabilities(q))
    print("shots", measure_all(q, shots=16))
}
```

### 195. `195_noise_noisy_bell_195.sq` — 195 noise noisy bell 195

```sansqrit
# Example 195: density-matrix/noise model noisy_bell.
simulate(2, engine="density", seed=195) {
    let q = quantum_register(2)
    H(q[0])
    CNOT(q[0], q[1])
    noise_depolarize(q[0], 0.010)
    print("noisy_bell")
    print("probabilities", probabilities(q))
    print("shots", measure_all(q, shots=16))
}
```

### 196. `196_noise_readout_channel_196.sq` — 196 noise readout channel 196

```sansqrit
# Example 196: density-matrix/noise model readout_channel.
simulate(2, engine="density", seed=196) {
    let q = quantum_register(2)
    H(q[0])
    CNOT(q[0], q[1])
    noise_bit_flip(q[1], 0.020)
    print("readout_channel")
    print("probabilities", probabilities(q))
    print("shots", measure_all(q, shots=16))
}
```

### 197. `197_noise_amplitude_decay_197.sq` — 197 noise amplitude decay 197

```sansqrit
# Example 197: density-matrix/noise model amplitude_decay.
simulate(2, engine="density", seed=197) {
    let q = quantum_register(2)
    H(q[0])
    CNOT(q[0], q[1])
    noise_amplitude_damping(q[1], 0.030)
    print("amplitude_decay")
    print("probabilities", probabilities(q))
    print("shots", measure_all(q, shots=16))
}
```

### 198. `198_noise_phase_noise_198.sq` — 198 noise phase noise 198

```sansqrit
# Example 198: density-matrix/noise model phase_noise.
simulate(2, engine="density", seed=198) {
    let q = quantum_register(2)
    H(q[0])
    CNOT(q[0], q[1])
    noise_phase_flip(q[0], 0.040)
    print("phase_noise")
    print("probabilities", probabilities(q))
    print("shots", measure_all(q, shots=16))
}
```

### 199. `199_noise_depolarizing_benchmark_199.sq` — 199 noise depolarizing benchmark 199

```sansqrit
# Example 199: density-matrix/noise model depolarizing_benchmark.
simulate(2, engine="density", seed=199) {
    let q = quantum_register(2)
    H(q[0])
    CNOT(q[0], q[1])
    noise_depolarize(q[1], 0.050)
    print("depolarizing_benchmark")
    print("probabilities", probabilities(q))
    print("shots", measure_all(q, shots=16))
}
```

### 200. `200_noise_noisy_bell_200.sq` — 200 noise noisy bell 200

```sansqrit
# Example 200: density-matrix/noise model noisy_bell.
simulate(2, engine="density", seed=200) {
    let q = quantum_register(2)
    H(q[0])
    CNOT(q[0], q[1])
    noise_depolarize(q[0], 0.010)
    print("noisy_bell")
    print("probabilities", probabilities(q))
    print("shots", measure_all(q, shots=16))
}
```

### 201. `201_noise_readout_channel_201.sq` — 201 noise readout channel 201

```sansqrit
# Example 201: density-matrix/noise model readout_channel.
simulate(2, engine="density", seed=201) {
    let q = quantum_register(2)
    H(q[0])
    CNOT(q[0], q[1])
    noise_bit_flip(q[1], 0.020)
    print("readout_channel")
    print("probabilities", probabilities(q))
    print("shots", measure_all(q, shots=16))
}
```

### 202. `202_noise_amplitude_decay_202.sq` — 202 noise amplitude decay 202

```sansqrit
# Example 202: density-matrix/noise model amplitude_decay.
simulate(2, engine="density", seed=202) {
    let q = quantum_register(2)
    H(q[0])
    CNOT(q[0], q[1])
    noise_amplitude_damping(q[1], 0.030)
    print("amplitude_decay")
    print("probabilities", probabilities(q))
    print("shots", measure_all(q, shots=16))
}
```

### 203. `203_noise_phase_noise_203.sq` — 203 noise phase noise 203

```sansqrit
# Example 203: density-matrix/noise model phase_noise.
simulate(2, engine="density", seed=203) {
    let q = quantum_register(2)
    H(q[0])
    CNOT(q[0], q[1])
    noise_phase_flip(q[0], 0.040)
    print("phase_noise")
    print("probabilities", probabilities(q))
    print("shots", measure_all(q, shots=16))
}
```

### 204. `204_noise_depolarizing_benchmark_204.sq` — 204 noise depolarizing benchmark 204

```sansqrit
# Example 204: density-matrix/noise model depolarizing_benchmark.
simulate(2, engine="density", seed=204) {
    let q = quantum_register(2)
    H(q[0])
    CNOT(q[0], q[1])
    noise_depolarize(q[1], 0.050)
    print("depolarizing_benchmark")
    print("probabilities", probabilities(q))
    print("shots", measure_all(q, shots=16))
}
```

### 205. `205_noise_noisy_bell_205.sq` — 205 noise noisy bell 205

```sansqrit
# Example 205: density-matrix/noise model noisy_bell.
simulate(2, engine="density", seed=205) {
    let q = quantum_register(2)
    H(q[0])
    CNOT(q[0], q[1])
    noise_depolarize(q[0], 0.010)
    print("noisy_bell")
    print("probabilities", probabilities(q))
    print("shots", measure_all(q, shots=16))
}
```

### 206. `206_noise_readout_channel_206.sq` — 206 noise readout channel 206

```sansqrit
# Example 206: density-matrix/noise model readout_channel.
simulate(2, engine="density", seed=206) {
    let q = quantum_register(2)
    H(q[0])
    CNOT(q[0], q[1])
    noise_bit_flip(q[1], 0.020)
    print("readout_channel")
    print("probabilities", probabilities(q))
    print("shots", measure_all(q, shots=16))
}
```

### 207. `207_noise_amplitude_decay_207.sq` — 207 noise amplitude decay 207

```sansqrit
# Example 207: density-matrix/noise model amplitude_decay.
simulate(2, engine="density", seed=207) {
    let q = quantum_register(2)
    H(q[0])
    CNOT(q[0], q[1])
    noise_amplitude_damping(q[1], 0.030)
    print("amplitude_decay")
    print("probabilities", probabilities(q))
    print("shots", measure_all(q, shots=16))
}
```

### 208. `208_noise_phase_noise_208.sq` — 208 noise phase noise 208

```sansqrit
# Example 208: density-matrix/noise model phase_noise.
simulate(2, engine="density", seed=208) {
    let q = quantum_register(2)
    H(q[0])
    CNOT(q[0], q[1])
    noise_phase_flip(q[0], 0.040)
    print("phase_noise")
    print("probabilities", probabilities(q))
    print("shots", measure_all(q, shots=16))
}
```

### 209. `209_noise_depolarizing_benchmark_209.sq` — 209 noise depolarizing benchmark 209

```sansqrit
# Example 209: density-matrix/noise model depolarizing_benchmark.
simulate(2, engine="density", seed=209) {
    let q = quantum_register(2)
    H(q[0])
    CNOT(q[0], q[1])
    noise_depolarize(q[1], 0.050)
    print("depolarizing_benchmark")
    print("probabilities", probabilities(q))
    print("shots", measure_all(q, shots=16))
}
```

### 210. `210_noise_noisy_bell_210.sq` — 210 noise noisy bell 210

```sansqrit
# Example 210: density-matrix/noise model noisy_bell.
simulate(2, engine="density", seed=210) {
    let q = quantum_register(2)
    H(q[0])
    CNOT(q[0], q[1])
    noise_depolarize(q[0], 0.010)
    print("noisy_bell")
    print("probabilities", probabilities(q))
    print("shots", measure_all(q, shots=16))
}
```

### 211. `211_algorithm_grover_cyber_signature_211.sq` — 211 algorithm grover cyber signature 211

```sansqrit
# Example 211: algorithm-level application - grover_cyber_signature.
let tag = "grover_cyber_signature"
print("running", tag)
print(grover_search(5, 17, shots=64, seed=211))
```

### 212. `212_algorithm_qaoa_logistics_triangle_212.sq` — 212 algorithm qaoa logistics triangle 212

```sansqrit
# Example 212: algorithm-level application - qaoa_logistics_triangle.
let tag = "qaoa_logistics_triangle"
print("running", tag)
print(qaoa_maxcut(4, [(0,1), (1,2), (2,3), (3,0)], p=1, shots=64, seed=212))
```

### 213. `213_algorithm_vqe_molecule_scan_213.sq` — 213 algorithm vqe molecule scan 213

```sansqrit
# Example 213: algorithm-level application - vqe_molecule_scan.
let tag = "vqe_molecule_scan"
print("running", tag)
print(vqe_h2(0.735, max_iter=16, seed=213))
```

### 214. `214_algorithm_phase_estimation_sensor_214.sq` — 214 algorithm phase estimation sensor 214

```sansqrit
# Example 214: algorithm-level application - phase_estimation_sensor.
let tag = "phase_estimation_sensor"
print("running", tag)
print(quantum_phase_estimation(0.3125, 4, shots=64, seed=214))
```

### 215. `215_algorithm_hhl_toy_linear_system_215.sq` — 215 algorithm hhl toy linear system 215

```sansqrit
# Example 215: algorithm-level application - hhl_toy_linear_system.
let tag = "hhl_toy_linear_system"
print("running", tag)
print(hhl_solve([[1.0, 0.0], [0.0, 2.0]], [1.0, 0.5]))
```

### 216. `216_algorithm_bernstein_vazirani_secret_216.sq` — 216 algorithm bernstein vazirani secret 216

```sansqrit
# Example 216: algorithm-level application - bernstein_vazirani_secret.
let tag = "bernstein_vazirani_secret"
print("running", tag)
print(bernstein_vazirani("101101"))
```

### 217. `217_algorithm_deutsch_jozsa_balanced_217.sq` — 217 algorithm deutsch jozsa balanced 217

```sansqrit
# Example 217: algorithm-level application - deutsch_jozsa_balanced.
let tag = "deutsch_jozsa_balanced"
print("running", tag)
print(deutsch_jozsa(5, "balanced", seed=217))
```

### 218. `218_algorithm_quantum_counting_inventory_218.sq` — 218 algorithm quantum counting inventory 218

```sansqrit
# Example 218: algorithm-level application - quantum_counting_inventory.
let tag = "quantum_counting_inventory"
print("running", tag)
print(quantum_counting(8, 13, 5))
```

### 219. `219_algorithm_amplitude_estimation_risk_219.sq` — 219 algorithm amplitude estimation risk 219

```sansqrit
# Example 219: algorithm-level application - amplitude_estimation_risk.
let tag = "amplitude_estimation_risk"
print("running", tag)
print(amplitude_estimation(0.18, 5))
```

### 220. `220_algorithm_bb84_key_distribution_220.sq` — 220 algorithm bb84 key distribution 220

```sansqrit
# Example 220: algorithm-level application - bb84_key_distribution.
let tag = "bb84_key_distribution"
print("running", tag)
print(bb84_qkd(16, seed=220))
```

### 221. `221_algorithm_grover_cyber_signature_221.sq` — 221 algorithm grover cyber signature 221

```sansqrit
# Example 221: algorithm-level application - grover_cyber_signature.
let tag = "grover_cyber_signature"
print("running", tag)
print(grover_search(5, 17, shots=64, seed=221))
```

### 222. `222_algorithm_qaoa_logistics_triangle_222.sq` — 222 algorithm qaoa logistics triangle 222

```sansqrit
# Example 222: algorithm-level application - qaoa_logistics_triangle.
let tag = "qaoa_logistics_triangle"
print("running", tag)
print(qaoa_maxcut(4, [(0,1), (1,2), (2,3), (3,0)], p=1, shots=64, seed=222))
```

### 223. `223_algorithm_vqe_molecule_scan_223.sq` — 223 algorithm vqe molecule scan 223

```sansqrit
# Example 223: algorithm-level application - vqe_molecule_scan.
let tag = "vqe_molecule_scan"
print("running", tag)
print(vqe_h2(0.735, max_iter=16, seed=223))
```

### 224. `224_algorithm_phase_estimation_sensor_224.sq` — 224 algorithm phase estimation sensor 224

```sansqrit
# Example 224: algorithm-level application - phase_estimation_sensor.
let tag = "phase_estimation_sensor"
print("running", tag)
print(quantum_phase_estimation(0.3125, 4, shots=64, seed=224))
```

### 225. `225_algorithm_hhl_toy_linear_system_225.sq` — 225 algorithm hhl toy linear system 225

```sansqrit
# Example 225: algorithm-level application - hhl_toy_linear_system.
let tag = "hhl_toy_linear_system"
print("running", tag)
print(hhl_solve([[1.0, 0.0], [0.0, 2.0]], [1.0, 0.5]))
```

### 226. `226_algorithm_bernstein_vazirani_secret_226.sq` — 226 algorithm bernstein vazirani secret 226

```sansqrit
# Example 226: algorithm-level application - bernstein_vazirani_secret.
let tag = "bernstein_vazirani_secret"
print("running", tag)
print(bernstein_vazirani("101101"))
```

### 227. `227_algorithm_deutsch_jozsa_balanced_227.sq` — 227 algorithm deutsch jozsa balanced 227

```sansqrit
# Example 227: algorithm-level application - deutsch_jozsa_balanced.
let tag = "deutsch_jozsa_balanced"
print("running", tag)
print(deutsch_jozsa(5, "balanced", seed=227))
```

### 228. `228_algorithm_quantum_counting_inventory_228.sq` — 228 algorithm quantum counting inventory 228

```sansqrit
# Example 228: algorithm-level application - quantum_counting_inventory.
let tag = "quantum_counting_inventory"
print("running", tag)
print(quantum_counting(8, 13, 5))
```

### 229. `229_algorithm_amplitude_estimation_risk_229.sq` — 229 algorithm amplitude estimation risk 229

```sansqrit
# Example 229: algorithm-level application - amplitude_estimation_risk.
let tag = "amplitude_estimation_risk"
print("running", tag)
print(amplitude_estimation(0.18, 5))
```

### 230. `230_algorithm_bb84_key_distribution_230.sq` — 230 algorithm bb84 key distribution 230

```sansqrit
# Example 230: algorithm-level application - bb84_key_distribution.
let tag = "bb84_key_distribution"
print("running", tag)
print(bb84_qkd(16, seed=230))
```

### 231. `231_qml_feature_map_231.sq` — 231 qml feature map 231

```sansqrit
# Example 231: quantum ML feature map for tabular/scientific data.
let features = [0.12, 0.31, 0.07, 0.44, 0.23, 0.19, 0.27, 0.39]
simulate(6, engine="sparse", seed=231) {
    let q = quantum_register(6)
    for i in range(6) {
        Ry(q[i], features[i] * PI)
        Rz(q[i], features[6-1-i] * PI / 2)
    }
    for i in range(6-1) {
        CNOT(q[i], q[i + 1])
    }
    print("qml_feature_map")
    print("nnz", engine_nnz())
    print(measure_all(q, shots=16))
}
```

### 232. `232_qml_feature_map_232.sq` — 232 qml feature map 232

```sansqrit
# Example 232: quantum ML feature map for tabular/scientific data.
let features = [0.12, 0.31, 0.07, 0.44, 0.23, 0.19, 0.27, 0.39]
simulate(7, engine="sparse", seed=232) {
    let q = quantum_register(7)
    for i in range(7) {
        Ry(q[i], features[i] * PI)
        Rz(q[i], features[7-1-i] * PI / 2)
    }
    for i in range(7-1) {
        CNOT(q[i], q[i + 1])
    }
    print("qml_feature_map")
    print("nnz", engine_nnz())
    print(measure_all(q, shots=16))
}
```

### 233. `233_qml_feature_map_233.sq` — 233 qml feature map 233

```sansqrit
# Example 233: quantum ML feature map for tabular/scientific data.
let features = [0.12, 0.31, 0.07, 0.44, 0.23, 0.19, 0.27, 0.39]
simulate(8, engine="sparse", seed=233) {
    let q = quantum_register(8)
    for i in range(8) {
        Ry(q[i], features[i] * PI)
        Rz(q[i], features[8-1-i] * PI / 2)
    }
    for i in range(8-1) {
        CNOT(q[i], q[i + 1])
    }
    print("qml_feature_map")
    print("nnz", engine_nnz())
    print(measure_all(q, shots=16))
}
```

### 234. `234_qml_feature_map_234.sq` — 234 qml feature map 234

```sansqrit
# Example 234: quantum ML feature map for tabular/scientific data.
let features = [0.12, 0.31, 0.07, 0.44, 0.23, 0.19, 0.27, 0.39]
simulate(6, engine="sparse", seed=234) {
    let q = quantum_register(6)
    for i in range(6) {
        Ry(q[i], features[i] * PI)
        Rz(q[i], features[6-1-i] * PI / 2)
    }
    for i in range(6-1) {
        CNOT(q[i], q[i + 1])
    }
    print("qml_feature_map")
    print("nnz", engine_nnz())
    print(measure_all(q, shots=16))
}
```

### 235. `235_qml_feature_map_235.sq` — 235 qml feature map 235

```sansqrit
# Example 235: quantum ML feature map for tabular/scientific data.
let features = [0.12, 0.31, 0.07, 0.44, 0.23, 0.19, 0.27, 0.39]
simulate(7, engine="sparse", seed=235) {
    let q = quantum_register(7)
    for i in range(7) {
        Ry(q[i], features[i] * PI)
        Rz(q[i], features[7-1-i] * PI / 2)
    }
    for i in range(7-1) {
        CNOT(q[i], q[i + 1])
    }
    print("qml_feature_map")
    print("nnz", engine_nnz())
    print(measure_all(q, shots=16))
}
```

### 236. `236_qml_feature_map_236.sq` — 236 qml feature map 236

```sansqrit
# Example 236: quantum ML feature map for tabular/scientific data.
let features = [0.12, 0.31, 0.07, 0.44, 0.23, 0.19, 0.27, 0.39]
simulate(8, engine="sparse", seed=236) {
    let q = quantum_register(8)
    for i in range(8) {
        Ry(q[i], features[i] * PI)
        Rz(q[i], features[8-1-i] * PI / 2)
    }
    for i in range(8-1) {
        CNOT(q[i], q[i + 1])
    }
    print("qml_feature_map")
    print("nnz", engine_nnz())
    print(measure_all(q, shots=16))
}
```

### 237. `237_qml_feature_map_237.sq` — 237 qml feature map 237

```sansqrit
# Example 237: quantum ML feature map for tabular/scientific data.
let features = [0.12, 0.31, 0.07, 0.44, 0.23, 0.19, 0.27, 0.39]
simulate(6, engine="sparse", seed=237) {
    let q = quantum_register(6)
    for i in range(6) {
        Ry(q[i], features[i] * PI)
        Rz(q[i], features[6-1-i] * PI / 2)
    }
    for i in range(6-1) {
        CNOT(q[i], q[i + 1])
    }
    print("qml_feature_map")
    print("nnz", engine_nnz())
    print(measure_all(q, shots=16))
}
```

### 238. `238_qml_feature_map_238.sq` — 238 qml feature map 238

```sansqrit
# Example 238: quantum ML feature map for tabular/scientific data.
let features = [0.12, 0.31, 0.07, 0.44, 0.23, 0.19, 0.27, 0.39]
simulate(7, engine="sparse", seed=238) {
    let q = quantum_register(7)
    for i in range(7) {
        Ry(q[i], features[i] * PI)
        Rz(q[i], features[7-1-i] * PI / 2)
    }
    for i in range(7-1) {
        CNOT(q[i], q[i + 1])
    }
    print("qml_feature_map")
    print("nnz", engine_nnz())
    print(measure_all(q, shots=16))
}
```

### 239. `239_qml_feature_map_239.sq` — 239 qml feature map 239

```sansqrit
# Example 239: quantum ML feature map for tabular/scientific data.
let features = [0.12, 0.31, 0.07, 0.44, 0.23, 0.19, 0.27, 0.39]
simulate(8, engine="sparse", seed=239) {
    let q = quantum_register(8)
    for i in range(8) {
        Ry(q[i], features[i] * PI)
        Rz(q[i], features[8-1-i] * PI / 2)
    }
    for i in range(8-1) {
        CNOT(q[i], q[i + 1])
    }
    print("qml_feature_map")
    print("nnz", engine_nnz())
    print(measure_all(q, shots=16))
}
```

### 240. `240_qml_feature_map_240.sq` — 240 qml feature map 240

```sansqrit
# Example 240: quantum ML feature map for tabular/scientific data.
let features = [0.12, 0.31, 0.07, 0.44, 0.23, 0.19, 0.27, 0.39]
simulate(6, engine="sparse", seed=240) {
    let q = quantum_register(6)
    for i in range(6) {
        Ry(q[i], features[i] * PI)
        Rz(q[i], features[6-1-i] * PI / 2)
    }
    for i in range(6-1) {
        CNOT(q[i], q[i + 1])
    }
    print("qml_feature_map")
    print("nnz", engine_nnz())
    print(measure_all(q, shots=16))
}
```

### 241. `241_qasm3_export_climate_circuit.sq` — 241 qasm3 export climate circuit

```sansqrit
# Example 241: export a climate-risk sparse circuit to OpenQASM 3.
simulate(5, seed=241) {
    H(0)
    CNOT(0, 4)
    Rz(3, PI/7)
    print(export_qasm3())
}
```

### 242. `242_qasm2_export_network_circuit.sq` — 242 qasm2 export network circuit

```sansqrit
# Example 242: export a network-routing circuit to OpenQASM 2.
simulate(4, seed=242) {
    X(1)
    H(0)
    CNOT(0, 2)
    print(export_qasm2())
}
```

### 243. `243_distributed_cluster_template.sq` — 243 distributed cluster template

```sansqrit
# Example 243: real distributed cluster template.
# Start workers in separate shells, then uncomment the simulate block.
# python -m sansqrit.cluster --host 127.0.0.1 --port 9101
# python -m sansqrit.cluster --host 127.0.0.1 --port 9102
# simulate(12, engine="distributed", addresses=[("127.0.0.1", 9101), ("127.0.0.1", 9102)]) {
#     H(0)
#     CNOT(0, 11)
#     print(measure_all(shots=4))
# }
print("distributed cluster template included; start workers before enabling")
```

### 244. `244_gpu_cuda_template.sq` — 244 gpu cuda template

```sansqrit
# Example 244: GPU/CuPy backend template.
# Install CUDA/CuPy extra, then uncomment.
# simulate(6, engine="gpu", seed=244) {
#     H(0)
#     CNOT(0, 5)
#     print(measure_all(shots=8))
# }
print("GPU template included; requires sansqrit[gpu] and CUDA")
```

### 245. `245_hybrid_backend_template.sq` — 245 hybrid backend template

```sansqrit
# Example 245: hybrid backend selection template.
simulate(150, engine="sharded", n_shards=12, seed=245) {
    X(149)
    H(0)
    CNOT(0, 149)
    print("hybrid-style sparse branch", engine_nnz())
}
```

### 246. `246_formal_verification_qasm_reference.sq` — 246 formal verification qasm reference

```sansqrit
# Example 246: verification through QASM export / external SDK comparison.
simulate(3, seed=246) {
    H(0)
    CNOT(0, 1)
    CNOT(1, 2)
    print(export_qasm3())
}
```

### 247. `247_optimizer_cancel_pairs.sq` — 247 optimizer cancel pairs

```sansqrit
# Example 247: write circuits so the optimizer can cancel inverse pairs.
simulate(4, seed=247) {
    X(0)
    X(0)
    H(1)
    H(1)
    Rz(2, PI/8)
    Rz(2, -PI/8)
    print("optimizer-friendly cancellation pattern")
    print(export_qasm3())
}
```

### 248. `248_ai_training_minimal_pair.sq` — 248 ai training minimal pair

```sansqrit
# Example 248: paired input-output style useful for AI training.
# Intent: create a Bell state and return probability dictionary.
simulate(2, seed=248) {
    H(0)
    CNOT(0, 1)
    print(probabilities())
}
```

### 249. `249_large_sparse_oracle_150q.sq` — 249 large sparse oracle 150q

```sansqrit
# Example 249: 150-qubit sparse oracle-style marker.
simulate(150, engine="sharded", n_shards=20, workers=4, seed=249) {
    let q = quantum_register(150)
    X(q[12])
    X(q[77])
    H(q[0])
    CNOT(q[0], q[149])
    MCZ(q[0], q[12], q[77], q[149])
    print("150q oracle marker nnz", engine_nnz())
    print(measure_all(q, shots=4))
}
```

### 250. `250_large_stabilizer_4096q.sq` — 250 large stabilizer 4096q

```sansqrit
# Example 250: 4096-qubit Clifford/stabilizer monitoring pattern.
simulate(4096, engine="stabilizer", seed=250) {
    H(0)
    CNOT(0, 4095)
    S(2048)
    CZ(2048, 4095)
    print("4096-qubit stabilizer circuit")
    print(list(measure_all(shots=1).keys())[0][0:64])
}
```

### 251. `251_precomputed_lookup_10q.sq` — 251 precomputed lookup 10q

```sansqrit
# 10-qubit packaged precomputed lookup demonstration
simulate(10) {
  let q = quantum_register(10)
  H(q[7])
  SX(q[3])
  SXdg(q[3])
  X(q[9])
  CNOT(q[7], q[9])
  let p = probabilities(q)
}
```

### 252. `252_lookup_vs_sparse_150q.sq` — 252 lookup vs sparse 150q

```sansqrit
# 150-qubit sparse program: primitive gate lookup + sparse runtime transitions.
# Full embedded transition tables are intentionally capped at 10 qubits.
simulate(150) {
  let q = quantum_register(150)
  X(q[149])
  H(q[0])
  CNOT(q[0], q[149])
  let p = probabilities(q)
}
```

### 253. `253_qec_bit_flip_correct.sq` — 253 qec bit flip correct

```sansqrit
simulate(5) {
  let q = quantum_register(5)
  let l = qec_logical("bit_flip", base=0)
  qec_encode(l)
  qec_inject_error(l, "X", 1)
  let result = qec_syndrome_and_correct(l, ancilla_base=3)
  qec_decode(l)
  let counts = measure_all(q)
}
```

### 254. `254_qec_phase_flip_correct.sq` — 254 qec phase flip correct

```sansqrit
simulate(5) {
  let q = quantum_register(5)
  let l = qec_logical("phase_flip", base=0)
  qec_encode(l)
  qec_inject_error(l, "Z", 2)
  let syndrome = qec_syndrome(l, ancilla_base=3)
  let corrections = qec_correct(l, syndrome)
  qec_decode(l)
  let counts = measure_all(q)
}
```

### 255. `255_qec_repetition5_layout.sq` — 255 qec repetition5 layout

```sansqrit
let code = qec_code("repetition", distance=5)
let l = qec_logical("repetition", base=0, distance=5)
let stabs = l.stabilizers()
let sx = l.logical_x_term()
let sz = l.logical_z_term()
```

### 256. `256_qec_shor9_encode.sq` — 256 qec shor9 encode

```sansqrit
simulate(12) {
  let q = quantum_register(12)
  let l = qec_logical("shor9", base=0)
  qec_encode(l)
  logical_x(l)
  let stabs = l.stabilizers()
  let profile = lookup_profile()
}
```

### 257. `257_qec_steane7_syndrome_circuit.sq` — 257 qec steane7 syndrome circuit

```sansqrit
let l = qec_logical("steane7", base=0)
let circuit = qec_syndrome_circuit(l, ancilla_base=7)
let code = qec_code("steane7")
```

### 258. `258_qec_five_qubit_lookup_decoder.sq` — 258 qec five qubit lookup decoder

```sansqrit
let code = qec_code("five_qubit")
let l = qec_logical("five_qubit", base=0)
let stabs = l.stabilizers()
let lx = l.logical_x_term()
let lz = l.logical_z_term()
```

### 259. `259_qec_surface3_lattice.sq` — 259 qec surface3 lattice

```sansqrit
let lattice = qec_surface_lattice(3)
let data = lattice.data_qubits
let xchecks = lattice.x_checks
let zchecks = lattice.z_checks
let stabs = lattice.stabilizers()
```

### 260. `260_qec_surface3_code.sq` — 260 qec surface3 code

```sansqrit
let code = qec_code("surface", distance=3)
let l = qec_logical("surface", base=0, distance=3)
let stabs = l.stabilizers()
let circuit = qec_syndrome_circuit(l, ancilla_base=9)
```

### 261. `261_qec_logical_cnot_bitflip.sq` — 261 qec logical cnot bitflip

```sansqrit
simulate(8) {
  let q = quantum_register(8)
  let a = qec_logical("bit_flip", base=0, name="a")
  let b = qec_logical("bit_flip", base=3, name="b")
  qec_encode(a)
  qec_encode(b)
  logical_x(a)
  logical_cx(a, b)
  let counts = measure_all(q)
}
```

### 262. `262_qec_noise_density_bitflip.sq` — 262 qec noise density bitflip

```sansqrit
simulate(5, engine="density") {
  let q = quantum_register(5)
  let l = qec_logical("bit_flip", base=0)
  qec_encode(l)
  noise_bit_flip(q[1], 0.1)
  let tr = current_engine().trace()
}
```

### 263. `263_qec_codes_index.sq` — 263 qec codes index

```sansqrit
let codes = qec_codes()
let has_surface = "surface" in codes
let has_shor = "shor9" in codes
```

### 264. `264_auto_backend_plan_120q.sq` — 264 auto backend plan 120q

```sansqrit
let plan = plan_backend(120, [])
```

### 265. `265_lookup_profile_10q.sq` — 265 lookup profile 10q

```sansqrit
simulate(10) {
  let q = quantum_register(10)
  H(q[7])
  SX(q[3])
  SXdg(q[3])
  CNOT(q[7], q[9])
  let profile = lookup_profile()
}
```

### 266. `266_qec_stabilizer_syndrome_terms.sq` — 266 qec stabilizer syndrome terms

```sansqrit
let code = qec_code("bit_flip")
let l = qec_logical("bit_flip")
let stabs = l.stabilizers()
let syndrome_ops = qec_syndrome_circuit(l, ancilla_base=3)
```

### 267. `267_qec_repetition7_terms.sq` — 267 qec repetition7 terms

```sansqrit
let code = qec_code("repetition", distance=7)
let l = qec_logical("repetition", distance=7)
let stabs = l.stabilizers()
let lx = l.logical_x_term()
```

### 268. `268_qec_surface5_metadata.sq` — 268 qec surface5 metadata

```sansqrit
let code = qec_code("surface", distance=5)
let lattice = qec_surface_lattice(5)
let data_count = len(lattice.data_qubits)
let x_count = len(lattice.x_checks)
let z_count = len(lattice.z_checks)
```

### 269. `269_qec_bitflip_manual_syndrome.sq` — 269 qec bitflip manual syndrome

```sansqrit
simulate(5) {
  let q = quantum_register(5)
  let l = qec_logical("bit_flip")
  qec_encode(l)
  qec_inject_error(l, "X", 0)
  let syndrome = qec_syndrome(l, ancilla_base=3)
  let corrections = qec_correct(l, syndrome)
}
```

### 270. `270_qec_logical_gates.sq` — 270 qec logical gates

```sansqrit
simulate(5) {
  let q = quantum_register(5)
  let l = qec_logical("bit_flip")
  qec_encode(l)
  logical_x(l)
  logical_z(l)
  logical_h(l)
  logical_s(l)
  let counts = measure_all(q)
}
```

### 271. `271_qec_shor9_terms.sq` — 271 qec shor9 terms

```sansqrit
let l = qec_logical("shor9")
let stabs = l.stabilizers()
let lx = l.logical_x_term()
let lz = l.logical_z_term()
```

### 272. `272_qec_steane7_terms.sq` — 272 qec steane7 terms

```sansqrit
let l = qec_logical("steane7")
let stabs = l.stabilizers()
let lx = l.logical_x_term()
let lz = l.logical_z_term()
```

### 273. `273_qec_five_qubit_terms.sq` — 273 qec five qubit terms

```sansqrit
let l = qec_logical("five_qubit")
let stabs = l.stabilizers()
let lx = l.logical_x_term()
let lz = l.logical_z_term()
```

### 274. `274_qec_surface_decoder_interface.sq` — 274 qec surface decoder interface

```sansqrit
let code = qec_code("surface", distance=3)
let l = qec_logical("surface", distance=3)
let ops = qec_syndrome_circuit(l, ancilla_base=9)
```

### 275. `275_qec_ai_training_record.sq` — 275 qec ai training record

```sansqrit
let record = {"task": "qec_bit_flip_correction", "dsl": "qec_logical plus qec_syndrome_and_correct", "code": qec_code("bit_flip").description}
```

### 276. `276_hierarchical_120q_local_blocks.sq` — 276 hierarchical 120q local blocks

```sansqrit
# 120-qubit hierarchical tensor shards: local dense 10-qubit blocks.
simulate(120, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0) {
    q = quantum_register(120)
    shard block_A [0..9]
    shard block_B [10..19]
    shard block_L [110..119]

    apply H on block_A
    apply X on block_B
    apply Z on block_L

    print(hierarchical_report())
}
```

### 277. `277_hierarchical_120q_bridge_mps.sq` — 277 hierarchical 120q bridge mps

```sansqrit
# 120-qubit hierarchical tensor shards with a cross-block bridge.
simulate(120, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0) {
    q = quantum_register(120)
    shard block_A [0..9]
    shard block_B [10..19]

    H(q[9])
    apply CNOT on q[9], q[10] bridge_mode=sparse
    Z(q[10])

    print(hierarchical_report())
}
```

### 278. `278_climate_128q_sparse_sensors.sq` — 278 climate 128q sparse sensors

```sansqrit
# 128-qubit climate sensor anomaly encoding using sparse/sharded execution.
simulate(128, engine="sharded", n_shards=16, workers=4, use_lookup=true) {
    q = quantum_register(128)
    X(q[3])
    X(q[41])
    X(q[90])
    H(q[0])
    CNOT(q[0], q[3])
    Rz(q[41], PI / 8)
    print("climate nnz", engine_nnz())
    print(lookup_profile())
}
```

### 279. `279_supply_chain_160q_route_flags.sq` — 279 supply chain 160q route flags

```sansqrit
# 160-qubit sparse route flags for supply-chain risk analysis.
simulate(160, engine="sharded", n_shards=20, workers=4, use_lookup=true) {
    q = quantum_register(160)
    for i in range(0, 160, 32) {
        X(q[i])
    }
    H(q[1])
    CNOT(q[1], q[33])
    Rz(q[64], PI / 6)
    print("supply chain shards", shards())
}
```

### 280. `280_finance_128q_portfolio_sparse_risk.sq` — 280 finance 128q portfolio sparse risk

```sansqrit
# 128-qubit portfolio risk flags with sparse rotations.
simulate(128, engine="sharded", n_shards=16, workers=4) {
    q = quantum_register(128)
    X(q[7])
    X(q[31])
    X(q[88])
    H(q[0])
    CNOT(q[0], q[7])
    RZZ(q[31], q[88], PI / 12)
    print("finance plan", estimate_qubits(128))
}
```

### 281. `281_cyber_144q_stabilizer_threat_graph.sq` — 281 cyber 144q stabilizer threat graph

```sansqrit
# 144-qubit Clifford threat graph, safe for stabilizer execution.
simulate(144, engine="stabilizer") {
    q = quantum_register(144)
    for i in range(0, 143, 12) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
    }
    print("cyber sample", measure_all(shots=3))
}
```

### 282. `282_telecom_130q_hierarchical_blocks.sq` — 282 telecom 130q hierarchical blocks

```sansqrit
# 130-qubit telecom channel model using 10-qubit hierarchical tensor blocks.
simulate(130, engine="hierarchical", block_size=10, use_lookup=true) {
    q = quantum_register(130)
    shard tower_0 [0..9]
    shard tower_1 [10..19]
    shard tower_2 [20..29]
    apply H on tower_0
    apply X on tower_1
    apply Z on tower_2
    print(hierarchical_report())
}
```

### 283. `283_hierarchical_120q_bridge_iot.sq` — 283 hierarchical 120q bridge iot

```sansqrit
# 120-qubit IoT bridge entanglement between adjacent 10-qubit blocks.
simulate(120, engine="hierarchical", block_size=10, cutoff=0.0, max_bond_dim=null) {
    q = quantum_register(120)
    H(q[9])
    apply CNOT on q[9], q[10] bridge_mode=sparse
    Z(q[10])
    print(hierarchical_report())
}
```

### 284. `284_drug_discovery_124q_mps_feature_map.sq` — 284 drug discovery 124q mps feature map

```sansqrit
# 124-qubit low-entanglement molecule feature map using MPS.
simulate(124, engine="mps", max_bond_dim=32, cutoff=1e-12) {
    q = quantum_register(124)
    for i in range(0, 12) {
        Ry(q[i], 0.01 * (i + 1))
    }
    for i in range(0, 11) {
        CNOT(q[i], q[i + 1])
    }
    print("drug discovery backend", sansqrit_backends())
}
```

### 285. `285_satellite_132q_qec_link.sq` — 285 satellite 132q qec link

```sansqrit
# Satellite link QEC control plane in a larger 132-qubit sparse register.
simulate(132, engine="sparse") {
    q = quantum_register(132)
    logical = qec_logical("bit_flip", base=0)
    qec_encode(logical)
    qec_inject_error(logical, "X", 2)
    print(qec_syndrome_and_correct(logical, ancilla_base=3))
    X(q[131])
    print(engine_nnz())
}
```

### 286. `286_hardware_export_121q_openqasm3.sq` — 286 hardware export 121q openqasm3

```sansqrit
# 121-qubit sparse circuit exported as provider-neutral OpenQASM 3 payload.
simulate(121, engine="sparse") {
    q = quantum_register(121)
    X(q[120])
    H(q[0])
    CNOT(q[0], q[1])
    payload = export_hardware("azure")
    print(payload["provider"])
    print(payload["format"])
}
```

### 287. `287_distributed_120q_readiness_report.sq` — 287 distributed 120q readiness report

```sansqrit
# Distributed-readiness diagnostics without requiring live workers.
print(sansqrit_doctor())
print(troubleshooting("distributed"))
print(hardware_targets())
```

### 288. `288_troubleshooting_lookup_profile.sq` — 288 troubleshooting lookup profile

```sansqrit
# Lookup and troubleshooting introspection.
simulate(10, engine="sparse", use_lookup=true) {
    q = quantum_register(10)
    H(q[0])
    SX(q[3])
    CNOT(q[0], q[1])
    print(lookup_profile())
    print(lookup_architecture())
}
```

### 289. `289_ai_training_dataset_record.sq` — 289 ai training dataset record

```sansqrit
# Generate AI/ML training metadata.
print(research_gaps())
print("training helpers available")
```

### 290. `290_surface_code_121q_control_register.sq` — 290 surface code 121q control register

```sansqrit
# Surface-code helper embedded in a 121-qubit control register.
simulate(121, engine="sparse") {
    q = quantum_register(121)
    logical = qec_logical("surface", base=0, distance=3)
    print(logical.stabilizers())
    print(qec_stim_syndrome_text(logical, ancilla_base=9))
}
```

### 291. `291_robotics_150q_sparse_path_flags.sq` — 291 robotics 150q sparse path flags

```sansqrit
# 150-qubit sparse robot path flags.
simulate(150, engine="sharded", n_shards=15, workers=4) {
    q = quantum_register(150)
    X(q[12])
    X(q[77])
    X(q[149])
    H(q[2])
    CNOT(q[2], q[12])
    print("robotics", engine_nnz())
}
```

### 292. `292_power_grid_150q_fault_localization.sq` — 292 power grid 150q fault localization

```sansqrit
# 150-qubit grid fault localization with sparse flags.
simulate(150, engine="sharded", n_shards=15) {
    q = quantum_register(150)
    X(q[5])
    X(q[60])
    H(q[0])
    CNOT(q[0], q[5])
    Rz(q[60], PI / 5)
    print(shards())
}
```

### 293. `293_port_logistics_150q_hierarchical_yards.sq` — 293 port logistics 150q hierarchical yards

```sansqrit
# 150-qubit port logistics model as 15 independent 10-qubit yards.
simulate(150, engine="hierarchical", block_size=10) {
    q = quantum_register(150)
    shard yard_0 [0..9]
    shard yard_1 [10..19]
    shard yard_2 [20..29]
    apply H on yard_0
    apply X on yard_1
    apply S on yard_2
    print(hierarchical_report())
}
```

### 294. `294_qkd_128q_stabilizer_network.sq` — 294 qkd 128q stabilizer network

```sansqrit
# 128-qubit Clifford QKD network skeleton.
simulate(128, engine="stabilizer") {
    q = quantum_register(128)
    for i in range(0, 64, 8) {
        H(q[i])
        CNOT(q[i], q[i + 64])
    }
    print(measure_all(shots=4))
}
```

### 295. `295_aerospace_140q_sensor_fusion.sq` — 295 aerospace 140q sensor fusion

```sansqrit
# Aerospace sensor-fusion sparse feature map.
simulate(140, engine="sharded", n_shards=14) {
    q = quantum_register(140)
    X(q[13])
    X(q[72])
    X(q[139])
    H(q[1])
    CNOT(q[1], q[13])
    print(explain_120_qubits_dense())
}
```

### 296. `296_lookup_profile_10q_block_kernel.sq` — 296 lookup profile 10q block kernel

```sansqrit
# Explicit 10-qubit lookup-profile demonstration.
simulate(10, engine="sparse", use_lookup=true) {
    q = quantum_register(10)
    for i in range(0, 10) {
        H(q[i])
    }
    print(lookup_profile())
}
```

### 297. `297_openqasm2_3_export_122q.sq` — 297 openqasm2 3 export 122q

```sansqrit
# Export both OpenQASM 2 and OpenQASM 3 for a 122-qubit sparse circuit.
simulate(122, engine="sparse") {
    q = quantum_register(122)
    H(q[0])
    CNOT(q[0], q[1])
    X(q[121])
    q3 = export_qasm3()
    q2 = export_qasm2()
    print(len(q3))
    print(len(q2))
}
```

### 298. `298_azure_payload_123q_sparse.sq` — 298 azure payload 123q sparse

```sansqrit
# Azure-style OpenQASM 3 payload metadata.
simulate(123, engine="sparse") {
    q = quantum_register(123)
    H(q[0])
    CNOT(q[0], q[2])
    payload = export_hardware("azure")
    print(payload["provider"])
    print(payload["notes"])
}
```

### 299. `299_pennylane_export_124q_fallback.sq` — 299 pennylane export 124q fallback

```sansqrit
# PennyLane target listing and payload summary without requiring PennyLane install.
simulate(124, engine="sparse") {
    q = quantum_register(124)
    H(q[0])
    CNOT(q[0], q[1])
    print(hardware_payload_summary())
}
```

### 300. `300_city_1000q_stabilizer_graph.sq` — 300 city 1000q stabilizer graph

```sansqrit
# 1000-qubit Clifford city graph, safe for stabilizer tableau mode.
simulate(1000, engine="stabilizer") {
    q = quantum_register(1000)
    for i in range(0, 999, 50) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
    }
    print("city graph stabilizer sample", measure_all(shots=2))
}
```

### 301. `301_scenario_climate_risk_01.sq` — 301 scenario climate risk 01

```sansqrit
# Scenario 001: Climate risk and extreme-weather sensor triage: scenario 01
# Strategy: sparse + sharded execution for 130 logical qubits.
simulate(130, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(130)
    X(q[13])
    X(q[68])
    X(q[59])
    H(q[75])
    CNOT(q[75], q[94])
    Rz(q[75], PI / 5)
    CZ(q[94], q[121])
    print("scenario", "climate_risk", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(130))
}
```

### 302. `302_scenario_climate_risk_02.sq` — 302 scenario climate risk 02

```sansqrit
# Scenario 002: Climate risk and extreme-weather sensor triage: scenario 02
# Strategy: sparse + sharded execution for 132 logical qubits.
simulate(132, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(132)
    X(q[20])
    X(q[97])
    X(q[50])
    H(q[44])
    CNOT(q[44], q[23])
    Rz(q[44], PI / 6)
    CZ(q[23], q[58])
    print("scenario", "climate_risk", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(132))
}
```

### 303. `303_scenario_climate_risk_03.sq` — 303 scenario climate risk 03

```sansqrit
# Scenario 003: Climate risk and extreme-weather sensor triage: scenario 03
# Strategy: sparse + sharded execution for 134 logical qubits.
simulate(134, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(134)
    X(q[81])
    X(q[36])
    X(q[61])
    H(q[53])
    CNOT(q[53], q[18])
    Rz(q[53], PI / 7)
    CZ(q[18], q[1])
    print("scenario", "climate_risk", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(134))
}
```

### 304. `304_scenario_climate_risk_04.sq` — 304 scenario climate risk 04

```sansqrit
# Scenario 004: Climate risk and extreme-weather sensor triage: scenario 04
# Strategy: sparse + sharded execution for 136 logical qubits.
simulate(136, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(136)
    X(q[66])
    X(q[115])
    X(q[48])
    H(q[94])
    CNOT(q[94], q[33])
    Rz(q[94], PI / 8)
    CZ(q[33], q[72])
    print("scenario", "climate_risk", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(136))
}
```

### 305. `305_scenario_climate_risk_05.sq` — 305 scenario climate risk 05

```sansqrit
# Scenario 005: Climate risk and extreme-weather sensor triage: scenario 05
# Strategy: sparse + sharded execution for 128 logical qubits.
simulate(128, engine="sharded", n_shards=12, workers=4, use_lookup=true) {
    q = quantum_register(128)
    X(q[119])
    X(q[32])
    X(q[109])
    H(q[19])
    CNOT(q[19], q[102])
    Rz(q[19], PI / 4)
    CZ(q[102], q[125])
    print("scenario", "climate_risk", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(128))
}
```

### 306. `306_scenario_climate_risk_06.sq` — 306 scenario climate risk 06

```sansqrit
# Scenario 006: Climate risk and extreme-weather sensor triage: scenario 06
# Strategy: sparse + sharded execution for 130 logical qubits.
simulate(130, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(130)
    X(q[128])
    X(q[83])
    X(q[44])
    H(q[20])
    CNOT(q[20], q[19])
    Rz(q[20], PI / 5)
    CZ(q[19], q[66])
    print("scenario", "climate_risk", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(130))
}
```

### 307. `307_scenario_climate_risk_07.sq` — 307 scenario climate risk 07

```sansqrit
# Scenario 007: Climate risk and extreme-weather sensor triage: scenario 07
# Strategy: sparse + sharded execution for 132 logical qubits.
simulate(132, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(132)
    X(q[61])
    X(q[126])
    X(q[19])
    H(q[1])
    CNOT(q[1], q[28])
    Rz(q[1], PI / 6)
    CZ(q[28], q[39])
    print("scenario", "climate_risk", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(132))
}
```

### 308. `308_scenario_climate_risk_08.sq` — 308 scenario climate risk 08

```sansqrit
# Scenario 008: Climate risk and extreme-weather sensor triage: scenario 08
# Strategy: sparse + sharded execution for 134 logical qubits.
simulate(134, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(134)
    X(q[52])
    X(q[131])
    X(q[64])
    H(q[80])
    CNOT(q[80], q[119])
    Rz(q[80], PI / 7)
    CZ(q[119], q[86])
    print("scenario", "climate_risk", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(134))
}
```

### 309. `309_scenario_climate_risk_09.sq` — 309 scenario climate risk 09

```sansqrit
# Scenario 009: Climate risk and extreme-weather sensor triage: scenario 09
# Strategy: sparse + sharded execution for 136 logical qubits.
simulate(136, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(136)
    X(q[107])
    X(q[52])
    X(q[105])
    H(q[95])
    CNOT(q[95], q[58])
    Rz(q[95], PI / 8)
    CZ(q[58], q[17])
    print("scenario", "climate_risk", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(136))
}
```

### 310. `310_scenario_climate_risk_10.sq` — 310 scenario climate risk 10

```sansqrit
# Scenario 010: Climate risk and extreme-weather sensor triage: scenario 10
# Strategy: sparse + sharded execution for 128 logical qubits.
simulate(128, engine="sharded", n_shards=12, workers=4, use_lookup=true) {
    q = quantum_register(128)
    X(q[56])
    X(q[49])
    X(q[78])
    H(q[28])
    CNOT(q[28], q[63])
    Rz(q[28], PI / 4)
    CZ(q[63], q[38])
    print("scenario", "climate_risk", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(128))
}
```

### 311. `311_scenario_climate_risk_11.sq` — 311 scenario climate risk 11

```sansqrit
# Scenario 011: Climate risk and extreme-weather sensor triage: scenario 11
# Strategy: sparse + sharded execution for 130 logical qubits.
simulate(130, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(130)
    X(q[113])
    X(q[98])
    X(q[29])
    H(q[95])
    CNOT(q[95], q[74])
    Rz(q[95], PI / 5)
    CZ(q[74], q[11])
    print("scenario", "climate_risk", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(130))
}
```

### 312. `312_scenario_climate_risk_12.sq` — 312 scenario climate risk 12

```sansqrit
# Scenario 012: Climate risk and extreme-weather sensor triage: scenario 12
# Strategy: sparse + sharded execution for 132 logical qubits.
simulate(132, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(132)
    X(q[102])
    X(q[23])
    X(q[120])
    H(q[90])
    CNOT(q[90], q[33])
    Rz(q[90], PI / 6)
    CZ(q[33], q[20])
    print("scenario", "climate_risk", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(132))
}
```

### 313. `313_scenario_climate_risk_13.sq` — 313 scenario climate risk 13

```sansqrit
# Scenario 013: Climate risk and extreme-weather sensor triage: scenario 13
# Strategy: sparse + sharded execution for 134 logical qubits.
simulate(134, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(134)
    X(q[23])
    X(q[92])
    X(q[67])
    H(q[107])
    CNOT(q[107], q[86])
    Rz(q[107], PI / 7)
    CZ(q[86], q[37])
    print("scenario", "climate_risk", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(134))
}
```

### 314. `314_scenario_climate_risk_14.sq` — 314 scenario climate risk 14

```sansqrit
# Scenario 014: Climate risk and extreme-weather sensor triage: scenario 14
# Strategy: sparse + sharded execution for 136 logical qubits.
simulate(136, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(136)
    X(q[12])
    X(q[125])
    X(q[26])
    H(q[96])
    CNOT(q[96], q[83])
    Rz(q[96], PI / 8)
    CZ(q[83], q[98])
    print("scenario", "climate_risk", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(136))
}
```

### 315. `315_scenario_climate_risk_15.sq` — 315 scenario climate risk 15

```sansqrit
# Scenario 015: Climate risk and extreme-weather sensor triage: scenario 15
# Strategy: sparse + sharded execution for 128 logical qubits.
simulate(128, engine="sharded", n_shards=12, workers=4, use_lookup=true) {
    q = quantum_register(128)
    X(q[121])
    X(q[66])
    X(q[47])
    H(q[37])
    CNOT(q[37], q[24])
    Rz(q[37], PI / 4)
    CZ(q[24], q[79])
    print("scenario", "climate_risk", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(128))
}
```

### 316. `316_scenario_climate_risk_16.sq` — 316 scenario climate risk 16

```sansqrit
# Scenario 016: Climate risk and extreme-weather sensor triage: scenario 16
# Strategy: sparse + sharded execution for 130 logical qubits.
simulate(130, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(130)
    X(q[98])
    X(q[113])
    X(q[14])
    H(q[40])
    CNOT(q[40], q[129])
    Rz(q[40], PI / 5)
    CZ(q[129], q[86])
    print("scenario", "climate_risk", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(130))
}
```

### 317. `317_scenario_climate_risk_17.sq` — 317 scenario climate risk 17

```sansqrit
# Scenario 017: Climate risk and extreme-weather sensor triage: scenario 17
# Strategy: sparse + sharded execution for 132 logical qubits.
simulate(132, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(132)
    X(q[11])
    X(q[52])
    X(q[89])
    H(q[47])
    CNOT(q[47], q[38])
    Rz(q[47], PI / 6)
    CZ(q[38], q[1])
    print("scenario", "climate_risk", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(132))
}
```

### 318. `318_scenario_climate_risk_18.sq` — 318 scenario climate risk 18

```sansqrit
# Scenario 018: Climate risk and extreme-weather sensor triage: scenario 18
# Strategy: sparse + sharded execution for 134 logical qubits.
simulate(134, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(134)
    X(q[128])
    X(q[53])
    X(q[122])
    H(q[0])
    CNOT(q[0], q[70])
    Rz(q[0], PI / 7)
    CZ(q[70], q[49])
    print("scenario", "climate_risk", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(134))
}
```

### 319. `319_scenario_climate_risk_19.sq` — 319 scenario climate risk 19

```sansqrit
# Scenario 019: Climate risk and extreme-weather sensor triage: scenario 19
# Strategy: sparse + sharded execution for 136 logical qubits.
simulate(136, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(136)
    X(q[53])
    X(q[62])
    X(q[83])
    H(q[97])
    CNOT(q[97], q[108])
    Rz(q[97], PI / 8)
    CZ(q[108], q[43])
    print("scenario", "climate_risk", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(136))
}
```

### 320. `320_scenario_climate_risk_20.sq` — 320 scenario climate risk 20

```sansqrit
# Scenario 020: Climate risk and extreme-weather sensor triage: scenario 20
# Strategy: sparse + sharded execution for 128 logical qubits.
simulate(128, engine="sharded", n_shards=12, workers=4, use_lookup=true) {
    q = quantum_register(128)
    X(q[58])
    X(q[83])
    X(q[16])
    H(q[46])
    CNOT(q[46], q[113])
    Rz(q[46], PI / 4)
    CZ(q[113], q[120])
    print("scenario", "climate_risk", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(128))
}
```

### 321. `321_scenario_smart_grid_01.sq` — 321 scenario smart grid 01

```sansqrit
# Scenario 021: Smart-grid fault localization and energy routing: scenario 01
# Strategy: sparse + sharded execution for 152 logical qubits.
simulate(152, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(152)
    X(q[87])
    X(q[120])
    X(q[45])
    H(q[3])
    CNOT(q[3], q[14])
    Rz(q[3], PI / 5)
    CZ(q[14], q[62])
    print("scenario", "smart_grid", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(152))
}
```

### 322. `322_scenario_smart_grid_02.sq` — 322 scenario smart grid 02

```sansqrit
# Scenario 022: Smart-grid fault localization and energy routing: scenario 02
# Strategy: sparse + sharded execution for 154 logical qubits.
simulate(154, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(154)
    X(q[30])
    X(q[81])
    X(q[102])
    H(q[70])
    CNOT(q[70], q[131])
    Rz(q[70], PI / 6)
    CZ(q[131], q[4])
    print("scenario", "smart_grid", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(154))
}
```

### 323. `323_scenario_smart_grid_03.sq` — 323 scenario smart grid 03

```sansqrit
# Scenario 023: Smart-grid fault localization and energy routing: scenario 03
# Strategy: sparse + sharded execution for 156 logical qubits.
simulate(156, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(156)
    X(q[77])
    X(q[82])
    X(q[71])
    H(q[41])
    CNOT(q[41], q[128])
    Rz(q[41], PI / 7)
    CZ(q[128], q[139])
    print("scenario", "smart_grid", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(156))
}
```

### 324. `324_scenario_smart_grid_04.sq` — 324 scenario smart grid 04

```sansqrit
# Scenario 024: Smart-grid fault localization and energy routing: scenario 04
# Strategy: sparse + sharded execution for 158 logical qubits.
simulate(158, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(158)
    X(q[76])
    X(q[45])
    X(q[102])
    H(q[54])
    CNOT(q[54], q[37])
    Rz(q[54], PI / 8)
    CZ(q[37], q[124])
    print("scenario", "smart_grid", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(158))
}
```

### 325. `325_scenario_smart_grid_05.sq` — 325 scenario smart grid 05

```sansqrit
# Scenario 025: Smart-grid fault localization and energy routing: scenario 05
# Strategy: sparse + sharded execution for 150 logical qubits.
simulate(150, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(150)
    X(q[97])
    X(q[144])
    X(q[73])
    H(q[67])
    CNOT(q[67], q[82])
    Rz(q[67], PI / 4)
    CZ(q[82], q[33])
    print("scenario", "smart_grid", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(150))
}
```

### 326. `326_scenario_smart_grid_06.sq` — 326 scenario smart grid 06

```sansqrit
# Scenario 026: Smart-grid fault localization and energy routing: scenario 06
# Strategy: sparse + sharded execution for 152 logical qubits.
simulate(152, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(152)
    X(q[64])
    X(q[65])
    X(q[6])
    H(q[148])
    CNOT(q[148], q[103])
    Rz(q[148], PI / 5)
    CZ(q[103], q[86])
    print("scenario", "smart_grid", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(152))
}
```

### 327. `327_scenario_smart_grid_07.sq` — 327 scenario smart grid 07

```sansqrit
# Scenario 027: Smart-grid fault localization and energy routing: scenario 07
# Strategy: sparse + sharded execution for 154 logical qubits.
simulate(154, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(154)
    X(q[137])
    X(q[22])
    X(q[5])
    H(q[27])
    CNOT(q[27], q[136])
    Rz(q[27], PI / 6)
    CZ(q[136], q[7])
    print("scenario", "smart_grid", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(154))
}
```

### 328. `328_scenario_smart_grid_08.sq` — 328 scenario smart grid 08

```sansqrit
# Scenario 028: Smart-grid fault localization and energy routing: scenario 08
# Strategy: sparse + sharded execution for 156 logical qubits.
simulate(156, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(156)
    X(q[10])
    X(q[123])
    X(q[4])
    H(q[142])
    CNOT(q[142], q[1])
    Rz(q[142], PI / 7)
    CZ(q[1], q[84])
    print("scenario", "smart_grid", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(156))
}
```

### 329. `329_scenario_smart_grid_09.sq` — 329 scenario smart grid 09

```sansqrit
# Scenario 029: Smart-grid fault localization and energy routing: scenario 09
# Strategy: sparse + sharded execution for 158 logical qubits.
simulate(158, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(158)
    X(q[151])
    X(q[138])
    X(q[53])
    H(q[157])
    CNOT(q[157], q[32])
    Rz(q[157], PI / 8)
    CZ(q[32], q[23])
    print("scenario", "smart_grid", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(158))
}
```

### 330. `330_scenario_smart_grid_10.sq` — 330 scenario smart grid 10

```sansqrit
# Scenario 030: Smart-grid fault localization and energy routing: scenario 10
# Strategy: sparse + sharded execution for 150 logical qubits.
simulate(150, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(150)
    X(q[102])
    X(q[89])
    X(q[78])
    H(q[132])
    CNOT(q[132], q[117])
    Rz(q[132], PI / 4)
    CZ(q[117], q[98])
    print("scenario", "smart_grid", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(150))
}
```

### 331. `331_scenario_smart_grid_11.sq` — 331 scenario smart grid 11

```sansqrit
# Scenario 031: Smart-grid fault localization and energy routing: scenario 11
# Strategy: sparse + sharded execution for 152 logical qubits.
simulate(152, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(152)
    X(q[41])
    X(q[10])
    X(q[119])
    H(q[141])
    CNOT(q[141], q[40])
    Rz(q[141], PI / 5)
    CZ(q[40], q[127])
    print("scenario", "smart_grid", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(152))
}
```

### 332. `332_scenario_smart_grid_12.sq` — 332 scenario smart grid 12

```sansqrit
# Scenario 032: Smart-grid fault localization and energy routing: scenario 12
# Strategy: sparse + sharded execution for 154 logical qubits.
simulate(154, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(154)
    X(q[90])
    X(q[117])
    X(q[62])
    H(q[138])
    CNOT(q[138], q[141])
    Rz(q[138], PI / 6)
    CZ(q[141], q[10])
    print("scenario", "smart_grid", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(154))
}
```

### 333. `333_scenario_smart_grid_13.sq` — 333 scenario smart grid 13

```sansqrit
# Scenario 033: Smart-grid fault localization and energy routing: scenario 13
# Strategy: sparse + sharded execution for 156 logical qubits.
simulate(156, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(156)
    X(q[99])
    X(q[8])
    X(q[93])
    H(q[87])
    CNOT(q[87], q[30])
    Rz(q[87], PI / 7)
    CZ(q[30], q[29])
    print("scenario", "smart_grid", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(156))
}
```

### 334. `334_scenario_smart_grid_14.sq` — 334 scenario smart grid 14

```sansqrit
# Scenario 034: Smart-grid fault localization and energy routing: scenario 14
# Strategy: sparse + sharded execution for 158 logical qubits.
simulate(158, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(158)
    X(q[68])
    X(q[73])
    X(q[4])
    H(q[102])
    CNOT(q[102], q[27])
    Rz(q[102], PI / 8)
    CZ(q[27], q[80])
    print("scenario", "smart_grid", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(158))
}
```

### 335. `335_scenario_smart_grid_15.sq` — 335 scenario smart grid 15

```sansqrit
# Scenario 035: Smart-grid fault localization and energy routing: scenario 15
# Strategy: sparse + sharded execution for 150 logical qubits.
simulate(150, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(150)
    X(q[107])
    X(q[34])
    X(q[83])
    H(q[47])
    CNOT(q[47], q[2])
    Rz(q[47], PI / 4)
    CZ(q[2], q[13])
    print("scenario", "smart_grid", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(150))
}
```

### 336. `336_scenario_smart_grid_16.sq` — 336 scenario smart grid 16

```sansqrit
# Scenario 036: Smart-grid fault localization and energy routing: scenario 16
# Strategy: sparse + sharded execution for 152 logical qubits.
simulate(152, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(152)
    X(q[18])
    X(q[107])
    X(q[80])
    H(q[134])
    CNOT(q[134], q[129])
    Rz(q[134], PI / 5)
    CZ(q[129], q[16])
    print("scenario", "smart_grid", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(152))
}
```

### 337. `337_scenario_smart_grid_17.sq` — 337 scenario smart grid 17

```sansqrit
# Scenario 037: Smart-grid fault localization and energy routing: scenario 17
# Strategy: sparse + sharded execution for 154 logical qubits.
simulate(154, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(154)
    X(q[43])
    X(q[58])
    X(q[119])
    H(q[95])
    CNOT(q[95], q[146])
    Rz(q[95], PI / 6)
    CZ(q[146], q[13])
    print("scenario", "smart_grid", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(154))
}
```

### 338. `338_scenario_smart_grid_18.sq` — 338 scenario smart grid 18

```sansqrit
# Scenario 038: Smart-grid fault localization and energy routing: scenario 18
# Strategy: sparse + sharded execution for 156 logical qubits.
simulate(156, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(156)
    X(q[32])
    X(q[59])
    X(q[130])
    H(q[49])
    CNOT(q[49], q[26])
    Rz(q[49], PI / 7)
    CZ(q[26], q[95])
    print("scenario", "smart_grid", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(156))
}
```

### 339. `339_scenario_smart_grid_19.sq` — 339 scenario smart grid 19

```sansqrit
# Scenario 039: Smart-grid fault localization and energy routing: scenario 19
# Strategy: sparse + sharded execution for 158 logical qubits.
simulate(158, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(158)
    X(q[143])
    X(q[8])
    X(q[113])
    H(q[47])
    CNOT(q[47], q[22])
    Rz(q[47], PI / 8)
    CZ(q[22], q[137])
    print("scenario", "smart_grid", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(158))
}
```

### 340. `340_scenario_smart_grid_20.sq` — 340 scenario smart grid 20

```sansqrit
# Scenario 040: Smart-grid fault localization and energy routing: scenario 20
# Strategy: sparse + sharded execution for 150 logical qubits.
simulate(150, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(150)
    X(q[112])
    X(q[37])
    X(q[78])
    H(q[129])
    CNOT(q[129], q[88])
    Rz(q[129], PI / 4)
    CZ(q[88], q[115])
    print("scenario", "smart_grid", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(150))
}
```

### 341. `341_scenario_portfolio_01.sq` — 341 scenario portfolio 01

```sansqrit
# Scenario 041: Portfolio risk and scenario flagging: scenario 01
# Strategy: sparse + sharded execution for 130 logical qubits.
simulate(130, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(130)
    X(q[23])
    X(q[58])
    X(q[69])
    H(q[25])
    CNOT(q[25], q[14])
    Rz(q[25], PI / 5)
    CZ(q[14], q[71])
    print("scenario", "portfolio", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(130))
}
```

### 342. `342_scenario_portfolio_02.sq` — 342 scenario portfolio 02

```sansqrit
# Scenario 042: Portfolio risk and scenario flagging: scenario 02
# Strategy: sparse + sharded execution for 132 logical qubits.
simulate(132, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(132)
    X(q[84])
    X(q[65])
    X(q[66])
    H(q[96])
    CNOT(q[96], q[63])
    Rz(q[96], PI / 6)
    CZ(q[63], q[38])
    print("scenario", "portfolio", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(132))
}
```

### 343. `343_scenario_portfolio_03.sq` — 343 scenario portfolio 03

```sansqrit
# Scenario 043: Portfolio risk and scenario flagging: scenario 03
# Strategy: sparse + sharded execution for 134 logical qubits.
simulate(134, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(134)
    X(q[117])
    X(q[126])
    X(q[85])
    H(q[1])
    CNOT(q[1], q[22])
    Rz(q[1], PI / 7)
    CZ(q[22], q[11])
    print("scenario", "portfolio", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(134))
}
```

### 344. `344_scenario_portfolio_04.sq` — 344 scenario portfolio 04

```sansqrit
# Scenario 044: Portfolio risk and scenario flagging: scenario 04
# Strategy: sparse + sharded execution for 136 logical qubits.
simulate(136, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(136)
    X(q[122])
    X(q[19])
    X(q[96])
    H(q[102])
    CNOT(q[102], q[97])
    Rz(q[102], PI / 8)
    CZ(q[97], q[40])
    print("scenario", "portfolio", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(136))
}
```

### 345. `345_scenario_portfolio_05.sq` — 345 scenario portfolio 05

```sansqrit
# Scenario 045: Portfolio risk and scenario flagging: scenario 05
# Strategy: sparse + sharded execution for 128 logical qubits.
simulate(128, engine="sharded", n_shards=12, workers=4, use_lookup=true) {
    q = quantum_register(128)
    X(q[127])
    X(q[40])
    X(q[117])
    H(q[91])
    CNOT(q[91], q[46])
    Rz(q[91], PI / 4)
    CZ(q[46], q[69])
    print("scenario", "portfolio", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(128))
}
```

### 346. `346_scenario_portfolio_06.sq` — 346 scenario portfolio 06

```sansqrit
# Scenario 046: Portfolio risk and scenario flagging: scenario 06
# Strategy: sparse + sharded execution for 130 logical qubits.
simulate(130, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(130)
    X(q[8])
    X(q[73])
    X(q[54])
    H(q[100])
    CNOT(q[100], q[69])
    Rz(q[100], PI / 5)
    CZ(q[69], q[16])
    print("scenario", "portfolio", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(130))
}
```

### 347. `347_scenario_portfolio_07.sq` — 347 scenario portfolio 07

```sansqrit
# Scenario 047: Portfolio risk and scenario flagging: scenario 07
# Strategy: sparse + sharded execution for 132 logical qubits.
simulate(132, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(132)
    X(q[125])
    X(q[94])
    X(q[35])
    H(q[53])
    CNOT(q[53], q[68])
    Rz(q[53], PI / 6)
    CZ(q[68], q[19])
    print("scenario", "portfolio", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(132))
}
```

### 348. `348_scenario_portfolio_08.sq` — 348 scenario portfolio 08

```sansqrit
# Scenario 048: Portfolio risk and scenario flagging: scenario 08
# Strategy: sparse + sharded execution for 134 logical qubits.
simulate(134, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(134)
    X(q[88])
    X(q[87])
    X(q[96])
    H(q[28])
    CNOT(q[28], q[123])
    Rz(q[28], PI / 7)
    CZ(q[123], q[27])
    print("scenario", "portfolio", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(134))
}
```

### 349. `349_scenario_portfolio_09.sq` — 349 scenario portfolio 09

```sansqrit
# Scenario 049: Portfolio risk and scenario flagging: scenario 09
# Strategy: sparse + sharded execution for 136 logical qubits.
simulate(136, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(136)
    X(q[27])
    X(q[92])
    X(q[17])
    H(q[103])
    CNOT(q[103], q[122])
    Rz(q[103], PI / 8)
    CZ(q[122], q[121])
    print("scenario", "portfolio", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(136))
}
```

### 350. `350_scenario_portfolio_10.sq` — 350 scenario portfolio 10

```sansqrit
# Scenario 050: Portfolio risk and scenario flagging: scenario 10
# Strategy: sparse + sharded execution for 128 logical qubits.
simulate(128, engine="sharded", n_shards=12, workers=4, use_lookup=true) {
    q = quantum_register(128)
    X(q[64])
    X(q[57])
    X(q[86])
    H(q[100])
    CNOT(q[100], q[7])
    Rz(q[100], PI / 4)
    CZ(q[7], q[110])
    print("scenario", "portfolio", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(128))
}
```

### 351. `351_scenario_portfolio_11.sq` — 351 scenario portfolio 11

```sansqrit
# Scenario 051: Portfolio risk and scenario flagging: scenario 11
# Strategy: sparse + sharded execution for 130 logical qubits.
simulate(130, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(130)
    X(q[123])
    X(q[88])
    X(q[39])
    H(q[45])
    CNOT(q[45], q[124])
    Rz(q[45], PI / 5)
    CZ(q[124], q[91])
    print("scenario", "portfolio", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(130))
}
```

### 352. `352_scenario_portfolio_12.sq` — 352 scenario portfolio 12

```sansqrit
# Scenario 052: Portfolio risk and scenario flagging: scenario 12
# Strategy: sparse + sharded execution for 132 logical qubits.
simulate(132, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(132)
    X(q[34])
    X(q[123])
    X(q[4])
    H(q[10])
    CNOT(q[10], q[73])
    Rz(q[10], PI / 6)
    CZ(q[73], q[0])
    print("scenario", "portfolio", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(132))
}
```

### 353. `353_scenario_portfolio_13.sq` — 353 scenario portfolio 13

```sansqrit
# Scenario 053: Portfolio risk and scenario flagging: scenario 13
# Strategy: sparse + sharded execution for 134 logical qubits.
simulate(134, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(134)
    X(q[59])
    X(q[48])
    X(q[91])
    H(q[55])
    CNOT(q[55], q[90])
    Rz(q[55], PI / 7)
    CZ(q[90], q[47])
    print("scenario", "portfolio", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(134))
}
```

### 354. `354_scenario_portfolio_14.sq` — 354 scenario portfolio 14

```sansqrit
# Scenario 054: Portfolio risk and scenario flagging: scenario 14
# Strategy: sparse + sharded execution for 136 logical qubits.
simulate(136, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(136)
    X(q[68])
    X(q[29])
    X(q[74])
    H(q[104])
    CNOT(q[104], q[11])
    Rz(q[104], PI / 8)
    CZ(q[11], q[66])
    print("scenario", "portfolio", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(136))
}
```

### 355. `355_scenario_portfolio_15.sq` — 355 scenario portfolio 15

```sansqrit
# Scenario 055: Portfolio risk and scenario flagging: scenario 15
# Strategy: sparse + sharded execution for 128 logical qubits.
simulate(128, engine="sharded", n_shards=12, workers=4, use_lookup=true) {
    q = quantum_register(128)
    X(q[1])
    X(q[74])
    X(q[55])
    H(q[109])
    CNOT(q[109], q[96])
    Rz(q[109], PI / 4)
    CZ(q[96], q[23])
    print("scenario", "portfolio", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(128))
}
```

### 356. `356_scenario_portfolio_16.sq` — 356 scenario portfolio 16

```sansqrit
# Scenario 056: Portfolio risk and scenario flagging: scenario 16
# Strategy: sparse + sharded execution for 130 logical qubits.
simulate(130, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(130)
    X(q[108])
    X(q[103])
    X(q[24])
    H(q[120])
    CNOT(q[120], q[49])
    Rz(q[120], PI / 5)
    CZ(q[49], q[36])
    print("scenario", "portfolio", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(130))
}
```

### 357. `357_scenario_portfolio_17.sq` — 357 scenario portfolio 17

```sansqrit
# Scenario 057: Portfolio risk and scenario flagging: scenario 17
# Strategy: sparse + sharded execution for 132 logical qubits.
simulate(132, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(132)
    X(q[75])
    X(q[20])
    X(q[105])
    H(q[99])
    CNOT(q[99], q[78])
    Rz(q[99], PI / 6)
    CZ(q[78], q[113])
    print("scenario", "portfolio", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(132))
}
```

### 358. `358_scenario_portfolio_18.sq` — 358 scenario portfolio 18

```sansqrit
# Scenario 058: Portfolio risk and scenario flagging: scenario 18
# Strategy: sparse + sharded execution for 134 logical qubits.
simulate(134, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(134)
    X(q[30])
    X(q[9])
    X(q[94])
    H(q[82])
    CNOT(q[82], q[57])
    Rz(q[82], PI / 7)
    CZ(q[57], q[132])
    print("scenario", "portfolio", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(134))
}
```

### 359. `359_scenario_portfolio_19.sq` — 359 scenario portfolio 19

```sansqrit
# Scenario 059: Portfolio risk and scenario flagging: scenario 19
# Strategy: sparse + sharded execution for 136 logical qubits.
simulate(136, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(136)
    X(q[109])
    X(q[102])
    X(q[131])
    H(q[105])
    CNOT(q[105], q[36])
    Rz(q[105], PI / 8)
    CZ(q[36], q[11])
    print("scenario", "portfolio", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(136))
}
```

### 360. `360_scenario_portfolio_20.sq` — 360 scenario portfolio 20

```sansqrit
# Scenario 060: Portfolio risk and scenario flagging: scenario 20
# Strategy: sparse + sharded execution for 128 logical qubits.
simulate(128, engine="sharded", n_shards=12, workers=4, use_lookup=true) {
    q = quantum_register(128)
    X(q[66])
    X(q[91])
    X(q[24])
    H(q[118])
    CNOT(q[118], q[57])
    Rz(q[118], PI / 4)
    CZ(q[57], q[64])
    print("scenario", "portfolio", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(128))
}
```

### 361. `361_scenario_cybersecurity_01.sq` — 361 scenario cybersecurity 01

```sansqrit
# Scenario 061: Network intrusion and cryptographic health monitoring: scenario 01
# Strategy: Clifford-only large circuit via stabilizer tableau.
simulate(1002, engine="stabilizer") {
    q = quantum_register(1002)
    for i in range(0, 220, 21) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
        S(q[i + 2])
    }
    print("scenario", "cybersecurity", "backend", plan_backend(1002, []))
    print("sample", measure_all(shots=2))
}
```

### 362. `362_scenario_cybersecurity_02.sq` — 362 scenario cybersecurity 02

```sansqrit
# Scenario 062: Network intrusion and cryptographic health monitoring: scenario 02
# Strategy: Clifford-only large circuit via stabilizer tableau.
simulate(1004, engine="stabilizer") {
    q = quantum_register(1004)
    for i in range(0, 220, 22) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
        S(q[i + 2])
    }
    print("scenario", "cybersecurity", "backend", plan_backend(1004, []))
    print("sample", measure_all(shots=2))
}
```

### 363. `363_scenario_cybersecurity_03.sq` — 363 scenario cybersecurity 03

```sansqrit
# Scenario 063: Network intrusion and cryptographic health monitoring: scenario 03
# Strategy: Clifford-only large circuit via stabilizer tableau.
simulate(1006, engine="stabilizer") {
    q = quantum_register(1006)
    for i in range(0, 220, 23) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
        S(q[i + 2])
    }
    print("scenario", "cybersecurity", "backend", plan_backend(1006, []))
    print("sample", measure_all(shots=2))
}
```

### 364. `364_scenario_cybersecurity_04.sq` — 364 scenario cybersecurity 04

```sansqrit
# Scenario 064: Network intrusion and cryptographic health monitoring: scenario 04
# Strategy: Clifford-only large circuit via stabilizer tableau.
simulate(1008, engine="stabilizer") {
    q = quantum_register(1008)
    for i in range(0, 220, 24) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
        S(q[i + 2])
    }
    print("scenario", "cybersecurity", "backend", plan_backend(1008, []))
    print("sample", measure_all(shots=2))
}
```

### 365. `365_scenario_cybersecurity_05.sq` — 365 scenario cybersecurity 05

```sansqrit
# Scenario 065: Network intrusion and cryptographic health monitoring: scenario 05
# Strategy: Clifford-only large circuit via stabilizer tableau.
simulate(1000, engine="stabilizer") {
    q = quantum_register(1000)
    for i in range(0, 220, 25) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
        S(q[i + 2])
    }
    print("scenario", "cybersecurity", "backend", plan_backend(1000, []))
    print("sample", measure_all(shots=2))
}
```

### 366. `366_scenario_cybersecurity_06.sq` — 366 scenario cybersecurity 06

```sansqrit
# Scenario 066: Network intrusion and cryptographic health monitoring: scenario 06
# Strategy: Clifford-only large circuit via stabilizer tableau.
simulate(1002, engine="stabilizer") {
    q = quantum_register(1002)
    for i in range(0, 220, 26) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
        S(q[i + 2])
    }
    print("scenario", "cybersecurity", "backend", plan_backend(1002, []))
    print("sample", measure_all(shots=2))
}
```

### 367. `367_scenario_cybersecurity_07.sq` — 367 scenario cybersecurity 07

```sansqrit
# Scenario 067: Network intrusion and cryptographic health monitoring: scenario 07
# Strategy: Clifford-only large circuit via stabilizer tableau.
simulate(1004, engine="stabilizer") {
    q = quantum_register(1004)
    for i in range(0, 220, 20) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
        S(q[i + 2])
    }
    print("scenario", "cybersecurity", "backend", plan_backend(1004, []))
    print("sample", measure_all(shots=2))
}
```

### 368. `368_scenario_cybersecurity_08.sq` — 368 scenario cybersecurity 08

```sansqrit
# Scenario 068: Network intrusion and cryptographic health monitoring: scenario 08
# Strategy: Clifford-only large circuit via stabilizer tableau.
simulate(1006, engine="stabilizer") {
    q = quantum_register(1006)
    for i in range(0, 220, 21) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
        S(q[i + 2])
    }
    print("scenario", "cybersecurity", "backend", plan_backend(1006, []))
    print("sample", measure_all(shots=2))
}
```

### 369. `369_scenario_cybersecurity_09.sq` — 369 scenario cybersecurity 09

```sansqrit
# Scenario 069: Network intrusion and cryptographic health monitoring: scenario 09
# Strategy: Clifford-only large circuit via stabilizer tableau.
simulate(1008, engine="stabilizer") {
    q = quantum_register(1008)
    for i in range(0, 220, 22) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
        S(q[i + 2])
    }
    print("scenario", "cybersecurity", "backend", plan_backend(1008, []))
    print("sample", measure_all(shots=2))
}
```

### 370. `370_scenario_cybersecurity_10.sq` — 370 scenario cybersecurity 10

```sansqrit
# Scenario 070: Network intrusion and cryptographic health monitoring: scenario 10
# Strategy: Clifford-only large circuit via stabilizer tableau.
simulate(1000, engine="stabilizer") {
    q = quantum_register(1000)
    for i in range(0, 220, 23) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
        S(q[i + 2])
    }
    print("scenario", "cybersecurity", "backend", plan_backend(1000, []))
    print("sample", measure_all(shots=2))
}
```

### 371. `371_scenario_cybersecurity_11.sq` — 371 scenario cybersecurity 11

```sansqrit
# Scenario 071: Network intrusion and cryptographic health monitoring: scenario 11
# Strategy: Clifford-only large circuit via stabilizer tableau.
simulate(1002, engine="stabilizer") {
    q = quantum_register(1002)
    for i in range(0, 220, 24) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
        S(q[i + 2])
    }
    print("scenario", "cybersecurity", "backend", plan_backend(1002, []))
    print("sample", measure_all(shots=2))
}
```

### 372. `372_scenario_cybersecurity_12.sq` — 372 scenario cybersecurity 12

```sansqrit
# Scenario 072: Network intrusion and cryptographic health monitoring: scenario 12
# Strategy: Clifford-only large circuit via stabilizer tableau.
simulate(1004, engine="stabilizer") {
    q = quantum_register(1004)
    for i in range(0, 220, 25) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
        S(q[i + 2])
    }
    print("scenario", "cybersecurity", "backend", plan_backend(1004, []))
    print("sample", measure_all(shots=2))
}
```

### 373. `373_scenario_cybersecurity_13.sq` — 373 scenario cybersecurity 13

```sansqrit
# Scenario 073: Network intrusion and cryptographic health monitoring: scenario 13
# Strategy: Clifford-only large circuit via stabilizer tableau.
simulate(1006, engine="stabilizer") {
    q = quantum_register(1006)
    for i in range(0, 220, 26) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
        S(q[i + 2])
    }
    print("scenario", "cybersecurity", "backend", plan_backend(1006, []))
    print("sample", measure_all(shots=2))
}
```

### 374. `374_scenario_cybersecurity_14.sq` — 374 scenario cybersecurity 14

```sansqrit
# Scenario 074: Network intrusion and cryptographic health monitoring: scenario 14
# Strategy: Clifford-only large circuit via stabilizer tableau.
simulate(1008, engine="stabilizer") {
    q = quantum_register(1008)
    for i in range(0, 220, 20) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
        S(q[i + 2])
    }
    print("scenario", "cybersecurity", "backend", plan_backend(1008, []))
    print("sample", measure_all(shots=2))
}
```

### 375. `375_scenario_cybersecurity_15.sq` — 375 scenario cybersecurity 15

```sansqrit
# Scenario 075: Network intrusion and cryptographic health monitoring: scenario 15
# Strategy: Clifford-only large circuit via stabilizer tableau.
simulate(1000, engine="stabilizer") {
    q = quantum_register(1000)
    for i in range(0, 220, 21) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
        S(q[i + 2])
    }
    print("scenario", "cybersecurity", "backend", plan_backend(1000, []))
    print("sample", measure_all(shots=2))
}
```

### 376. `376_scenario_cybersecurity_16.sq` — 376 scenario cybersecurity 16

```sansqrit
# Scenario 076: Network intrusion and cryptographic health monitoring: scenario 16
# Strategy: Clifford-only large circuit via stabilizer tableau.
simulate(1002, engine="stabilizer") {
    q = quantum_register(1002)
    for i in range(0, 220, 22) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
        S(q[i + 2])
    }
    print("scenario", "cybersecurity", "backend", plan_backend(1002, []))
    print("sample", measure_all(shots=2))
}
```

### 377. `377_scenario_cybersecurity_17.sq` — 377 scenario cybersecurity 17

```sansqrit
# Scenario 077: Network intrusion and cryptographic health monitoring: scenario 17
# Strategy: Clifford-only large circuit via stabilizer tableau.
simulate(1004, engine="stabilizer") {
    q = quantum_register(1004)
    for i in range(0, 220, 23) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
        S(q[i + 2])
    }
    print("scenario", "cybersecurity", "backend", plan_backend(1004, []))
    print("sample", measure_all(shots=2))
}
```

### 378. `378_scenario_cybersecurity_18.sq` — 378 scenario cybersecurity 18

```sansqrit
# Scenario 078: Network intrusion and cryptographic health monitoring: scenario 18
# Strategy: Clifford-only large circuit via stabilizer tableau.
simulate(1006, engine="stabilizer") {
    q = quantum_register(1006)
    for i in range(0, 220, 24) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
        S(q[i + 2])
    }
    print("scenario", "cybersecurity", "backend", plan_backend(1006, []))
    print("sample", measure_all(shots=2))
}
```

### 379. `379_scenario_cybersecurity_19.sq` — 379 scenario cybersecurity 19

```sansqrit
# Scenario 079: Network intrusion and cryptographic health monitoring: scenario 19
# Strategy: Clifford-only large circuit via stabilizer tableau.
simulate(1008, engine="stabilizer") {
    q = quantum_register(1008)
    for i in range(0, 220, 25) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
        S(q[i + 2])
    }
    print("scenario", "cybersecurity", "backend", plan_backend(1008, []))
    print("sample", measure_all(shots=2))
}
```

### 380. `380_scenario_cybersecurity_20.sq` — 380 scenario cybersecurity 20

```sansqrit
# Scenario 080: Network intrusion and cryptographic health monitoring: scenario 20
# Strategy: Clifford-only large circuit via stabilizer tableau.
simulate(1000, engine="stabilizer") {
    q = quantum_register(1000)
    for i in range(0, 220, 26) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
        S(q[i + 2])
    }
    print("scenario", "cybersecurity", "backend", plan_backend(1000, []))
    print("sample", measure_all(shots=2))
}
```

### 381. `381_scenario_supply_chain_01.sq` — 381 scenario supply chain 01

```sansqrit
# Scenario 081: Supply-chain route disruption and inventory risk: scenario 01
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(162, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(162)
    block_A = shard("block_A", 10, 19)
    block_B = shard("block_B", 20, 29)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[19], q[20])
    Rz(q[21], PI / 4)
    print("scenario", "supply_chain", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 382. `382_scenario_supply_chain_02.sq` — 382 scenario supply chain 02

```sansqrit
# Scenario 082: Supply-chain route disruption and inventory risk: scenario 02
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(164, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(164)
    block_A = shard("block_A", 20, 29)
    block_B = shard("block_B", 30, 39)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[29], q[30])
    Rz(q[31], PI / 5)
    print("scenario", "supply_chain", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 383. `383_scenario_supply_chain_03.sq` — 383 scenario supply chain 03

```sansqrit
# Scenario 083: Supply-chain route disruption and inventory risk: scenario 03
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(166, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(166)
    block_A = shard("block_A", 30, 39)
    block_B = shard("block_B", 40, 49)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[39], q[40])
    Rz(q[41], PI / 6)
    print("scenario", "supply_chain", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 384. `384_scenario_supply_chain_04.sq` — 384 scenario supply chain 04

```sansqrit
# Scenario 084: Supply-chain route disruption and inventory risk: scenario 04
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(168, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(168)
    block_A = shard("block_A", 0, 9)
    block_B = shard("block_B", 10, 19)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[9], q[10])
    Rz(q[11], PI / 7)
    print("scenario", "supply_chain", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 385. `385_scenario_supply_chain_05.sq` — 385 scenario supply chain 05

```sansqrit
# Scenario 085: Supply-chain route disruption and inventory risk: scenario 05
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(160, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(160)
    block_A = shard("block_A", 10, 19)
    block_B = shard("block_B", 20, 29)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[19], q[20])
    Rz(q[21], PI / 8)
    print("scenario", "supply_chain", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 386. `386_scenario_supply_chain_06.sq` — 386 scenario supply chain 06

```sansqrit
# Scenario 086: Supply-chain route disruption and inventory risk: scenario 06
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(162, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(162)
    block_A = shard("block_A", 20, 29)
    block_B = shard("block_B", 30, 39)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[29], q[30])
    Rz(q[31], PI / 3)
    print("scenario", "supply_chain", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 387. `387_scenario_supply_chain_07.sq` — 387 scenario supply chain 07

```sansqrit
# Scenario 087: Supply-chain route disruption and inventory risk: scenario 07
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(164, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(164)
    block_A = shard("block_A", 30, 39)
    block_B = shard("block_B", 40, 49)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[39], q[40])
    Rz(q[41], PI / 4)
    print("scenario", "supply_chain", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 388. `388_scenario_supply_chain_08.sq` — 388 scenario supply chain 08

```sansqrit
# Scenario 088: Supply-chain route disruption and inventory risk: scenario 08
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(166, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(166)
    block_A = shard("block_A", 0, 9)
    block_B = shard("block_B", 10, 19)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[9], q[10])
    Rz(q[11], PI / 5)
    print("scenario", "supply_chain", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 389. `389_scenario_supply_chain_09.sq` — 389 scenario supply chain 09

```sansqrit
# Scenario 089: Supply-chain route disruption and inventory risk: scenario 09
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(168, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(168)
    block_A = shard("block_A", 10, 19)
    block_B = shard("block_B", 20, 29)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[19], q[20])
    Rz(q[21], PI / 6)
    print("scenario", "supply_chain", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 390. `390_scenario_supply_chain_10.sq` — 390 scenario supply chain 10

```sansqrit
# Scenario 090: Supply-chain route disruption and inventory risk: scenario 10
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(160, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(160)
    block_A = shard("block_A", 20, 29)
    block_B = shard("block_B", 30, 39)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[29], q[30])
    Rz(q[31], PI / 7)
    print("scenario", "supply_chain", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 391. `391_scenario_supply_chain_11.sq` — 391 scenario supply chain 11

```sansqrit
# Scenario 091: Supply-chain route disruption and inventory risk: scenario 11
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(162, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(162)
    block_A = shard("block_A", 30, 39)
    block_B = shard("block_B", 40, 49)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[39], q[40])
    Rz(q[41], PI / 8)
    print("scenario", "supply_chain", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 392. `392_scenario_supply_chain_12.sq` — 392 scenario supply chain 12

```sansqrit
# Scenario 092: Supply-chain route disruption and inventory risk: scenario 12
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(164, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(164)
    block_A = shard("block_A", 0, 9)
    block_B = shard("block_B", 10, 19)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[9], q[10])
    Rz(q[11], PI / 3)
    print("scenario", "supply_chain", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 393. `393_scenario_supply_chain_13.sq` — 393 scenario supply chain 13

```sansqrit
# Scenario 093: Supply-chain route disruption and inventory risk: scenario 13
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(166, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(166)
    block_A = shard("block_A", 10, 19)
    block_B = shard("block_B", 20, 29)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[19], q[20])
    Rz(q[21], PI / 4)
    print("scenario", "supply_chain", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 394. `394_scenario_supply_chain_14.sq` — 394 scenario supply chain 14

```sansqrit
# Scenario 094: Supply-chain route disruption and inventory risk: scenario 14
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(168, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(168)
    block_A = shard("block_A", 20, 29)
    block_B = shard("block_B", 30, 39)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[29], q[30])
    Rz(q[31], PI / 5)
    print("scenario", "supply_chain", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 395. `395_scenario_supply_chain_15.sq` — 395 scenario supply chain 15

```sansqrit
# Scenario 095: Supply-chain route disruption and inventory risk: scenario 15
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(160, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(160)
    block_A = shard("block_A", 30, 39)
    block_B = shard("block_B", 40, 49)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[39], q[40])
    Rz(q[41], PI / 6)
    print("scenario", "supply_chain", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 396. `396_scenario_supply_chain_16.sq` — 396 scenario supply chain 16

```sansqrit
# Scenario 096: Supply-chain route disruption and inventory risk: scenario 16
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(162, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(162)
    block_A = shard("block_A", 0, 9)
    block_B = shard("block_B", 10, 19)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[9], q[10])
    Rz(q[11], PI / 7)
    print("scenario", "supply_chain", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 397. `397_scenario_supply_chain_17.sq` — 397 scenario supply chain 17

```sansqrit
# Scenario 097: Supply-chain route disruption and inventory risk: scenario 17
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(164, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(164)
    block_A = shard("block_A", 10, 19)
    block_B = shard("block_B", 20, 29)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[19], q[20])
    Rz(q[21], PI / 8)
    print("scenario", "supply_chain", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 398. `398_scenario_supply_chain_18.sq` — 398 scenario supply chain 18

```sansqrit
# Scenario 098: Supply-chain route disruption and inventory risk: scenario 18
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(166, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(166)
    block_A = shard("block_A", 20, 29)
    block_B = shard("block_B", 30, 39)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[29], q[30])
    Rz(q[31], PI / 3)
    print("scenario", "supply_chain", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 399. `399_scenario_supply_chain_19.sq` — 399 scenario supply chain 19

```sansqrit
# Scenario 099: Supply-chain route disruption and inventory risk: scenario 19
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(168, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(168)
    block_A = shard("block_A", 30, 39)
    block_B = shard("block_B", 40, 49)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[39], q[40])
    Rz(q[41], PI / 4)
    print("scenario", "supply_chain", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 400. `400_scenario_supply_chain_20.sq` — 400 scenario supply chain 20

```sansqrit
# Scenario 100: Supply-chain route disruption and inventory risk: scenario 20
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(160, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(160)
    block_A = shard("block_A", 0, 9)
    block_B = shard("block_B", 10, 19)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[9], q[10])
    Rz(q[11], PI / 5)
    print("scenario", "supply_chain", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 401. `401_scenario_traffic_01.sq` — 401 scenario traffic 01

```sansqrit
# Scenario 101: Urban traffic signal optimization: scenario 01
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(134, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(134)
    H(q[0])
    for i in range(0, 61) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 6)
    }
    print("scenario", "traffic", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(134))
}
```

### 402. `402_scenario_traffic_02.sq` — 402 scenario traffic 02

```sansqrit
# Scenario 102: Urban traffic signal optimization: scenario 02
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(136, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(136)
    H(q[0])
    for i in range(0, 62) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 7)
    }
    print("scenario", "traffic", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(136))
}
```

### 403. `403_scenario_traffic_03.sq` — 403 scenario traffic 03

```sansqrit
# Scenario 103: Urban traffic signal optimization: scenario 03
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(138, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(138)
    H(q[0])
    for i in range(0, 63) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 8)
    }
    print("scenario", "traffic", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(138))
}
```

### 404. `404_scenario_traffic_04.sq` — 404 scenario traffic 04

```sansqrit
# Scenario 104: Urban traffic signal optimization: scenario 04
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(140, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(140)
    H(q[0])
    for i in range(0, 64) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 5)
    }
    print("scenario", "traffic", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(140))
}
```

### 405. `405_scenario_traffic_05.sq` — 405 scenario traffic 05

```sansqrit
# Scenario 105: Urban traffic signal optimization: scenario 05
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(132, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(132)
    H(q[0])
    for i in range(0, 65) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 6)
    }
    print("scenario", "traffic", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(132))
}
```

### 406. `406_scenario_traffic_06.sq` — 406 scenario traffic 06

```sansqrit
# Scenario 106: Urban traffic signal optimization: scenario 06
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(134, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(134)
    H(q[0])
    for i in range(0, 66) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 7)
    }
    print("scenario", "traffic", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(134))
}
```

### 407. `407_scenario_traffic_07.sq` — 407 scenario traffic 07

```sansqrit
# Scenario 107: Urban traffic signal optimization: scenario 07
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(136, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(136)
    H(q[0])
    for i in range(0, 67) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 8)
    }
    print("scenario", "traffic", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(136))
}
```

### 408. `408_scenario_traffic_08.sq` — 408 scenario traffic 08

```sansqrit
# Scenario 108: Urban traffic signal optimization: scenario 08
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(138, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(138)
    H(q[0])
    for i in range(0, 68) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 5)
    }
    print("scenario", "traffic", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(138))
}
```

### 409. `409_scenario_traffic_09.sq` — 409 scenario traffic 09

```sansqrit
# Scenario 109: Urban traffic signal optimization: scenario 09
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(140, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(140)
    H(q[0])
    for i in range(0, 69) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 6)
    }
    print("scenario", "traffic", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(140))
}
```

### 410. `410_scenario_traffic_10.sq` — 410 scenario traffic 10

```sansqrit
# Scenario 110: Urban traffic signal optimization: scenario 10
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(132, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(132)
    H(q[0])
    for i in range(0, 70) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 7)
    }
    print("scenario", "traffic", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(132))
}
```

### 411. `411_scenario_traffic_11.sq` — 411 scenario traffic 11

```sansqrit
# Scenario 111: Urban traffic signal optimization: scenario 11
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(134, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(134)
    H(q[0])
    for i in range(0, 71) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 8)
    }
    print("scenario", "traffic", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(134))
}
```

### 412. `412_scenario_traffic_12.sq` — 412 scenario traffic 12

```sansqrit
# Scenario 112: Urban traffic signal optimization: scenario 12
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(136, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(136)
    H(q[0])
    for i in range(0, 72) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 5)
    }
    print("scenario", "traffic", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(136))
}
```

### 413. `413_scenario_traffic_13.sq` — 413 scenario traffic 13

```sansqrit
# Scenario 113: Urban traffic signal optimization: scenario 13
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(138, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(138)
    H(q[0])
    for i in range(0, 73) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 6)
    }
    print("scenario", "traffic", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(138))
}
```

### 414. `414_scenario_traffic_14.sq` — 414 scenario traffic 14

```sansqrit
# Scenario 114: Urban traffic signal optimization: scenario 14
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(140, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(140)
    H(q[0])
    for i in range(0, 74) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 7)
    }
    print("scenario", "traffic", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(140))
}
```

### 415. `415_scenario_traffic_15.sq` — 415 scenario traffic 15

```sansqrit
# Scenario 115: Urban traffic signal optimization: scenario 15
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(132, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(132)
    H(q[0])
    for i in range(0, 75) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 8)
    }
    print("scenario", "traffic", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(132))
}
```

### 416. `416_scenario_traffic_16.sq` — 416 scenario traffic 16

```sansqrit
# Scenario 116: Urban traffic signal optimization: scenario 16
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(134, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(134)
    H(q[0])
    for i in range(0, 76) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 5)
    }
    print("scenario", "traffic", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(134))
}
```

### 417. `417_scenario_traffic_17.sq` — 417 scenario traffic 17

```sansqrit
# Scenario 117: Urban traffic signal optimization: scenario 17
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(136, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(136)
    H(q[0])
    for i in range(0, 77) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 6)
    }
    print("scenario", "traffic", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(136))
}
```

### 418. `418_scenario_traffic_18.sq` — 418 scenario traffic 18

```sansqrit
# Scenario 118: Urban traffic signal optimization: scenario 18
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(138, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(138)
    H(q[0])
    for i in range(0, 78) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 7)
    }
    print("scenario", "traffic", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(138))
}
```

### 419. `419_scenario_traffic_19.sq` — 419 scenario traffic 19

```sansqrit
# Scenario 119: Urban traffic signal optimization: scenario 19
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(140, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(140)
    H(q[0])
    for i in range(0, 79) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 8)
    }
    print("scenario", "traffic", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(140))
}
```

### 420. `420_scenario_traffic_20.sq` — 420 scenario traffic 20

```sansqrit
# Scenario 120: Urban traffic signal optimization: scenario 20
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(132, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(132)
    H(q[0])
    for i in range(0, 80) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 5)
    }
    print("scenario", "traffic", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(132))
}
```

### 421. `421_scenario_satellite_01.sq` — 421 scenario satellite 01

```sansqrit
# Scenario 121: Satellite telemetry anomaly isolation: scenario 01
# Strategy: sparse + sharded execution for 152 logical qubits.
simulate(152, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(152)
    X(q[83])
    X(q[84])
    X(q[25])
    H(q[15])
    CNOT(q[15], q[122])
    Rz(q[15], PI / 5)
    CZ(q[122], q[105])
    print("scenario", "satellite", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(152))
}
```

### 422. `422_scenario_satellite_02.sq` — 422 scenario satellite 02

```sansqrit
# Scenario 122: Satellite telemetry anomaly isolation: scenario 02
# Strategy: sparse + sharded execution for 154 logical qubits.
simulate(154, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(154)
    X(q[14])
    X(q[133])
    X(q[10])
    H(q[134])
    CNOT(q[134], q[77])
    Rz(q[134], PI / 6)
    CZ(q[77], q[64])
    print("scenario", "satellite", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(154))
}
```

### 423. `423_scenario_satellite_03.sq` — 423 scenario satellite 03

```sansqrit
# Scenario 123: Satellite telemetry anomaly isolation: scenario 03
# Strategy: sparse + sharded execution for 156 logical qubits.
simulate(156, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(156)
    X(q[141])
    X(q[122])
    X(q[135])
    H(q[33])
    CNOT(q[33], q[84])
    Rz(q[33], PI / 7)
    CZ(q[84], q[131])
    print("scenario", "satellite", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(156))
}
```

### 424. `424_scenario_satellite_04.sq` — 424 scenario satellite 04

```sansqrit
# Scenario 124: Satellite telemetry anomaly isolation: scenario 04
# Strategy: sparse + sharded execution for 158 logical qubits.
simulate(158, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(158)
    X(q[154])
    X(q[9])
    X(q[70])
    H(q[60])
    CNOT(q[60], q[95])
    Rz(q[60], PI / 8)
    CZ(q[95], q[0])
    print("scenario", "satellite", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(158))
}
```

### 425. `425_scenario_satellite_05.sq` — 425 scenario satellite 05

```sansqrit
# Scenario 125: Satellite telemetry anomaly isolation: scenario 05
# Strategy: sparse + sharded execution for 150 logical qubits.
simulate(150, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(150)
    X(q[47])
    X(q[94])
    X(q[23])
    H(q[17])
    CNOT(q[17], q[32])
    Rz(q[17], PI / 4)
    CZ(q[32], q[133])
    print("scenario", "satellite", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(150))
}
```

### 426. `426_scenario_satellite_06.sq` — 426 scenario satellite 06

```sansqrit
# Scenario 126: Satellite telemetry anomaly isolation: scenario 06
# Strategy: sparse + sharded execution for 152 logical qubits.
simulate(152, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(152)
    X(q[60])
    X(q[29])
    X(q[138])
    H(q[8])
    CNOT(q[8], q[59])
    Rz(q[8], PI / 5)
    CZ(q[59], q[146])
    print("scenario", "satellite", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(152))
}
```

### 427. `427_scenario_satellite_07.sq` — 427 scenario satellite 07

```sansqrit
# Scenario 127: Satellite telemetry anomaly isolation: scenario 07
# Strategy: sparse + sharded execution for 154 logical qubits.
simulate(154, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(154)
    X(q[121])
    X(q[74])
    X(q[67])
    H(q[91])
    CNOT(q[91], q[82])
    Rz(q[91], PI / 6)
    CZ(q[82], q[84])
    print("scenario", "satellite", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(154))
}
```

### 428. `428_scenario_satellite_08.sq` — 428 scenario satellite 08

```sansqrit
# Scenario 128: Satellite telemetry anomaly isolation: scenario 08
# Strategy: sparse + sharded execution for 156 logical qubits.
simulate(156, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(156)
    X(q[74])
    X(q[7])
    X(q[68])
    H(q[134])
    CNOT(q[134], q[113])
    Rz(q[134], PI / 7)
    CZ(q[113], q[76])
    print("scenario", "satellite", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(156))
}
```

### 429. `429_scenario_satellite_09.sq` — 429 scenario satellite 09

```sansqrit
# Scenario 129: Satellite telemetry anomaly isolation: scenario 09
# Strategy: sparse + sharded execution for 158 logical qubits.
simulate(158, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(158)
    X(q[71])
    X(q[102])
    X(q[21])
    H(q[5])
    CNOT(q[5], q[90])
    Rz(q[5], PI / 8)
    CZ(q[90], q[57])
    print("scenario", "satellite", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(158))
}
```

### 430. `430_scenario_satellite_10.sq` — 430 scenario satellite 10

```sansqrit
# Scenario 130: Satellite telemetry anomaly isolation: scenario 10
# Strategy: sparse + sharded execution for 150 logical qubits.
simulate(150, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(150)
    X(q[52])
    X(q[39])
    X(q[28])
    H(q[82])
    CNOT(q[82], q[67])
    Rz(q[82], PI / 4)
    CZ(q[67], q[48])
    print("scenario", "satellite", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(150))
}
```

### 431. `431_scenario_satellite_11.sq` — 431 scenario satellite 11

```sansqrit
# Scenario 131: Satellite telemetry anomaly isolation: scenario 11
# Strategy: sparse + sharded execution for 152 logical qubits.
simulate(152, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(152)
    X(q[37])
    X(q[126])
    X(q[99])
    H(q[1])
    CNOT(q[1], q[148])
    Rz(q[1], PI / 5)
    CZ(q[148], q[35])
    print("scenario", "satellite", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(152))
}
```

### 432. `432_scenario_satellite_12.sq` — 432 scenario satellite 12

```sansqrit
# Scenario 132: Satellite telemetry anomaly isolation: scenario 12
# Strategy: sparse + sharded execution for 154 logical qubits.
simulate(154, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(154)
    X(q[74])
    X(q[15])
    X(q[124])
    H(q[48])
    CNOT(q[48], q[87])
    Rz(q[48], PI / 6)
    CZ(q[87], q[70])
    print("scenario", "satellite", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(154))
}
```

### 433. `433_scenario_satellite_13.sq` — 433 scenario satellite 13

```sansqrit
# Scenario 133: Satellite telemetry anomaly isolation: scenario 13
# Strategy: sparse + sharded execution for 156 logical qubits.
simulate(156, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(156)
    X(q[7])
    X(q[48])
    X(q[1])
    H(q[79])
    CNOT(q[79], q[142])
    Rz(q[79], PI / 7)
    CZ(q[142], q[21])
    print("scenario", "satellite", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(156))
}
```

### 434. `434_scenario_satellite_14.sq` — 434 scenario satellite 14

```sansqrit
# Scenario 134: Satellite telemetry anomaly isolation: scenario 14
# Strategy: sparse + sharded execution for 158 logical qubits.
simulate(158, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(158)
    X(q[146])
    X(q[37])
    X(q[130])
    H(q[108])
    CNOT(q[108], q[85])
    Rz(q[108], PI / 8)
    CZ(q[85], q[114])
    print("scenario", "satellite", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(158))
}
```

### 435. `435_scenario_satellite_15.sq` — 435 scenario satellite 15

```sansqrit
# Scenario 135: Satellite telemetry anomaly isolation: scenario 15
# Strategy: sparse + sharded execution for 150 logical qubits.
simulate(150, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(150)
    X(q[57])
    X(q[134])
    X(q[33])
    H(q[147])
    CNOT(q[147], q[102])
    Rz(q[147], PI / 4)
    CZ(q[102], q[113])
    print("scenario", "satellite", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(150))
}
```

### 436. `436_scenario_satellite_16.sq` — 436 scenario satellite 16

```sansqrit
# Scenario 136: Satellite telemetry anomaly isolation: scenario 16
# Strategy: sparse + sharded execution for 152 logical qubits.
simulate(152, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(152)
    X(q[14])
    X(q[71])
    X(q[60])
    H(q[146])
    CNOT(q[146], q[85])
    Rz(q[146], PI / 5)
    CZ(q[85], q[76])
    print("scenario", "satellite", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(152))
}
```

### 437. `437_scenario_satellite_17.sq` — 437 scenario satellite 17

```sansqrit
# Scenario 137: Satellite telemetry anomaly isolation: scenario 17
# Strategy: sparse + sharded execution for 154 logical qubits.
simulate(154, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(154)
    X(q[27])
    X(q[110])
    X(q[73])
    H(q[5])
    CNOT(q[5], q[92])
    Rz(q[5], PI / 6)
    CZ(q[92], q[60])
    print("scenario", "satellite", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(154))
}
```

### 438. `438_scenario_satellite_18.sq` — 438 scenario satellite 18

```sansqrit
# Scenario 138: Satellite telemetry anomaly isolation: scenario 18
# Strategy: sparse + sharded execution for 156 logical qubits.
simulate(156, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(156)
    X(q[96])
    X(q[89])
    X(q[90])
    H(q[24])
    CNOT(q[24], q[15])
    Rz(q[24], PI / 7)
    CZ(q[15], q[122])
    print("scenario", "satellite", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(156))
}
```

### 439. `439_scenario_satellite_19.sq` — 439 scenario satellite 19

```sansqrit
# Scenario 139: Satellite telemetry anomaly isolation: scenario 19
# Strategy: sparse + sharded execution for 158 logical qubits.
simulate(158, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(158)
    X(q[63])
    X(q[130])
    X(q[81])
    H(q[53])
    CNOT(q[53], q[80])
    Rz(q[53], PI / 8)
    CZ(q[80], q[13])
    print("scenario", "satellite", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(158))
}
```

### 440. `440_scenario_satellite_20.sq` — 440 scenario satellite 20

```sansqrit
# Scenario 140: Satellite telemetry anomaly isolation: scenario 20
# Strategy: sparse + sharded execution for 150 logical qubits.
simulate(150, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(150)
    X(q[62])
    X(q[137])
    X(q[28])
    H(q[79])
    CNOT(q[79], q[38])
    Rz(q[79], PI / 4)
    CZ(q[38], q[65])
    print("scenario", "satellite", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(150))
}
```

### 441. `441_scenario_telecom_01.sq` — 441 scenario telecom 01

```sansqrit
# Scenario 141: Telecom routing and spectrum allocation: scenario 01
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(132, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(132)
    block_A = shard("block_A", 10, 19)
    block_B = shard("block_B", 20, 29)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[19], q[20])
    Rz(q[21], PI / 4)
    print("scenario", "telecom", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 442. `442_scenario_telecom_02.sq` — 442 scenario telecom 02

```sansqrit
# Scenario 142: Telecom routing and spectrum allocation: scenario 02
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(134, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(134)
    block_A = shard("block_A", 20, 29)
    block_B = shard("block_B", 30, 39)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[29], q[30])
    Rz(q[31], PI / 5)
    print("scenario", "telecom", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 443. `443_scenario_telecom_03.sq` — 443 scenario telecom 03

```sansqrit
# Scenario 143: Telecom routing and spectrum allocation: scenario 03
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(136, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(136)
    block_A = shard("block_A", 30, 39)
    block_B = shard("block_B", 40, 49)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[39], q[40])
    Rz(q[41], PI / 6)
    print("scenario", "telecom", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 444. `444_scenario_telecom_04.sq` — 444 scenario telecom 04

```sansqrit
# Scenario 144: Telecom routing and spectrum allocation: scenario 04
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(138, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(138)
    block_A = shard("block_A", 0, 9)
    block_B = shard("block_B", 10, 19)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[9], q[10])
    Rz(q[11], PI / 7)
    print("scenario", "telecom", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 445. `445_scenario_telecom_05.sq` — 445 scenario telecom 05

```sansqrit
# Scenario 145: Telecom routing and spectrum allocation: scenario 05
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(130, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(130)
    block_A = shard("block_A", 10, 19)
    block_B = shard("block_B", 20, 29)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[19], q[20])
    Rz(q[21], PI / 8)
    print("scenario", "telecom", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 446. `446_scenario_telecom_06.sq` — 446 scenario telecom 06

```sansqrit
# Scenario 146: Telecom routing and spectrum allocation: scenario 06
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(132, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(132)
    block_A = shard("block_A", 20, 29)
    block_B = shard("block_B", 30, 39)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[29], q[30])
    Rz(q[31], PI / 3)
    print("scenario", "telecom", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 447. `447_scenario_telecom_07.sq` — 447 scenario telecom 07

```sansqrit
# Scenario 147: Telecom routing and spectrum allocation: scenario 07
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(134, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(134)
    block_A = shard("block_A", 30, 39)
    block_B = shard("block_B", 40, 49)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[39], q[40])
    Rz(q[41], PI / 4)
    print("scenario", "telecom", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 448. `448_scenario_telecom_08.sq` — 448 scenario telecom 08

```sansqrit
# Scenario 148: Telecom routing and spectrum allocation: scenario 08
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(136, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(136)
    block_A = shard("block_A", 0, 9)
    block_B = shard("block_B", 10, 19)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[9], q[10])
    Rz(q[11], PI / 5)
    print("scenario", "telecom", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 449. `449_scenario_telecom_09.sq` — 449 scenario telecom 09

```sansqrit
# Scenario 149: Telecom routing and spectrum allocation: scenario 09
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(138, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(138)
    block_A = shard("block_A", 10, 19)
    block_B = shard("block_B", 20, 29)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[19], q[20])
    Rz(q[21], PI / 6)
    print("scenario", "telecom", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 450. `450_scenario_telecom_10.sq` — 450 scenario telecom 10

```sansqrit
# Scenario 150: Telecom routing and spectrum allocation: scenario 10
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(130, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(130)
    block_A = shard("block_A", 20, 29)
    block_B = shard("block_B", 30, 39)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[29], q[30])
    Rz(q[31], PI / 7)
    print("scenario", "telecom", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 451. `451_scenario_telecom_11.sq` — 451 scenario telecom 11

```sansqrit
# Scenario 151: Telecom routing and spectrum allocation: scenario 11
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(132, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(132)
    block_A = shard("block_A", 30, 39)
    block_B = shard("block_B", 40, 49)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[39], q[40])
    Rz(q[41], PI / 8)
    print("scenario", "telecom", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 452. `452_scenario_telecom_12.sq` — 452 scenario telecom 12

```sansqrit
# Scenario 152: Telecom routing and spectrum allocation: scenario 12
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(134, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(134)
    block_A = shard("block_A", 0, 9)
    block_B = shard("block_B", 10, 19)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[9], q[10])
    Rz(q[11], PI / 3)
    print("scenario", "telecom", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 453. `453_scenario_telecom_13.sq` — 453 scenario telecom 13

```sansqrit
# Scenario 153: Telecom routing and spectrum allocation: scenario 13
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(136, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(136)
    block_A = shard("block_A", 10, 19)
    block_B = shard("block_B", 20, 29)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[19], q[20])
    Rz(q[21], PI / 4)
    print("scenario", "telecom", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 454. `454_scenario_telecom_14.sq` — 454 scenario telecom 14

```sansqrit
# Scenario 154: Telecom routing and spectrum allocation: scenario 14
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(138, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(138)
    block_A = shard("block_A", 20, 29)
    block_B = shard("block_B", 30, 39)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[29], q[30])
    Rz(q[31], PI / 5)
    print("scenario", "telecom", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 455. `455_scenario_telecom_15.sq` — 455 scenario telecom 15

```sansqrit
# Scenario 155: Telecom routing and spectrum allocation: scenario 15
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(130, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(130)
    block_A = shard("block_A", 30, 39)
    block_B = shard("block_B", 40, 49)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[39], q[40])
    Rz(q[41], PI / 6)
    print("scenario", "telecom", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 456. `456_scenario_telecom_16.sq` — 456 scenario telecom 16

```sansqrit
# Scenario 156: Telecom routing and spectrum allocation: scenario 16
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(132, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(132)
    block_A = shard("block_A", 0, 9)
    block_B = shard("block_B", 10, 19)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[9], q[10])
    Rz(q[11], PI / 7)
    print("scenario", "telecom", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 457. `457_scenario_telecom_17.sq` — 457 scenario telecom 17

```sansqrit
# Scenario 157: Telecom routing and spectrum allocation: scenario 17
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(134, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(134)
    block_A = shard("block_A", 10, 19)
    block_B = shard("block_B", 20, 29)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[19], q[20])
    Rz(q[21], PI / 8)
    print("scenario", "telecom", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 458. `458_scenario_telecom_18.sq` — 458 scenario telecom 18

```sansqrit
# Scenario 158: Telecom routing and spectrum allocation: scenario 18
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(136, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(136)
    block_A = shard("block_A", 20, 29)
    block_B = shard("block_B", 30, 39)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[29], q[30])
    Rz(q[31], PI / 3)
    print("scenario", "telecom", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 459. `459_scenario_telecom_19.sq` — 459 scenario telecom 19

```sansqrit
# Scenario 159: Telecom routing and spectrum allocation: scenario 19
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(138, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(138)
    block_A = shard("block_A", 30, 39)
    block_B = shard("block_B", 40, 49)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[39], q[40])
    Rz(q[41], PI / 4)
    print("scenario", "telecom", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 460. `460_scenario_telecom_20.sq` — 460 scenario telecom 20

```sansqrit
# Scenario 160: Telecom routing and spectrum allocation: scenario 20
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(130, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(130)
    block_A = shard("block_A", 0, 9)
    block_B = shard("block_B", 10, 19)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[9], q[10])
    Rz(q[11], PI / 5)
    print("scenario", "telecom", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 461. `461_scenario_drug_discovery_01.sq` — 461 scenario drug discovery 01

```sansqrit
# Scenario 161: Drug-discovery feature-map screening: scenario 01
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(126, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(126)
    H(q[0])
    for i in range(0, 61) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 6)
    }
    print("scenario", "drug_discovery", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(126))
}
```

### 462. `462_scenario_drug_discovery_02.sq` — 462 scenario drug discovery 02

```sansqrit
# Scenario 162: Drug-discovery feature-map screening: scenario 02
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(128, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(128)
    H(q[0])
    for i in range(0, 62) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 7)
    }
    print("scenario", "drug_discovery", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(128))
}
```

### 463. `463_scenario_drug_discovery_03.sq` — 463 scenario drug discovery 03

```sansqrit
# Scenario 163: Drug-discovery feature-map screening: scenario 03
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(130, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(130)
    H(q[0])
    for i in range(0, 63) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 8)
    }
    print("scenario", "drug_discovery", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(130))
}
```

### 464. `464_scenario_drug_discovery_04.sq` — 464 scenario drug discovery 04

```sansqrit
# Scenario 164: Drug-discovery feature-map screening: scenario 04
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(132, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(132)
    H(q[0])
    for i in range(0, 64) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 5)
    }
    print("scenario", "drug_discovery", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(132))
}
```

### 465. `465_scenario_drug_discovery_05.sq` — 465 scenario drug discovery 05

```sansqrit
# Scenario 165: Drug-discovery feature-map screening: scenario 05
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(124, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(124)
    H(q[0])
    for i in range(0, 65) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 6)
    }
    print("scenario", "drug_discovery", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(124))
}
```

### 466. `466_scenario_drug_discovery_06.sq` — 466 scenario drug discovery 06

```sansqrit
# Scenario 166: Drug-discovery feature-map screening: scenario 06
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(126, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(126)
    H(q[0])
    for i in range(0, 66) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 7)
    }
    print("scenario", "drug_discovery", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(126))
}
```

### 467. `467_scenario_drug_discovery_07.sq` — 467 scenario drug discovery 07

```sansqrit
# Scenario 167: Drug-discovery feature-map screening: scenario 07
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(128, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(128)
    H(q[0])
    for i in range(0, 67) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 8)
    }
    print("scenario", "drug_discovery", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(128))
}
```

### 468. `468_scenario_drug_discovery_08.sq` — 468 scenario drug discovery 08

```sansqrit
# Scenario 168: Drug-discovery feature-map screening: scenario 08
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(130, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(130)
    H(q[0])
    for i in range(0, 68) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 5)
    }
    print("scenario", "drug_discovery", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(130))
}
```

### 469. `469_scenario_drug_discovery_09.sq` — 469 scenario drug discovery 09

```sansqrit
# Scenario 169: Drug-discovery feature-map screening: scenario 09
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(132, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(132)
    H(q[0])
    for i in range(0, 69) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 6)
    }
    print("scenario", "drug_discovery", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(132))
}
```

### 470. `470_scenario_drug_discovery_10.sq` — 470 scenario drug discovery 10

```sansqrit
# Scenario 170: Drug-discovery feature-map screening: scenario 10
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(124, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(124)
    H(q[0])
    for i in range(0, 70) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 7)
    }
    print("scenario", "drug_discovery", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(124))
}
```

### 471. `471_scenario_drug_discovery_11.sq` — 471 scenario drug discovery 11

```sansqrit
# Scenario 171: Drug-discovery feature-map screening: scenario 11
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(126, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(126)
    H(q[0])
    for i in range(0, 71) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 8)
    }
    print("scenario", "drug_discovery", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(126))
}
```

### 472. `472_scenario_drug_discovery_12.sq` — 472 scenario drug discovery 12

```sansqrit
# Scenario 172: Drug-discovery feature-map screening: scenario 12
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(128, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(128)
    H(q[0])
    for i in range(0, 72) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 5)
    }
    print("scenario", "drug_discovery", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(128))
}
```

### 473. `473_scenario_drug_discovery_13.sq` — 473 scenario drug discovery 13

```sansqrit
# Scenario 173: Drug-discovery feature-map screening: scenario 13
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(130, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(130)
    H(q[0])
    for i in range(0, 73) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 6)
    }
    print("scenario", "drug_discovery", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(130))
}
```

### 474. `474_scenario_drug_discovery_14.sq` — 474 scenario drug discovery 14

```sansqrit
# Scenario 174: Drug-discovery feature-map screening: scenario 14
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(132, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(132)
    H(q[0])
    for i in range(0, 74) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 7)
    }
    print("scenario", "drug_discovery", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(132))
}
```

### 475. `475_scenario_drug_discovery_15.sq` — 475 scenario drug discovery 15

```sansqrit
# Scenario 175: Drug-discovery feature-map screening: scenario 15
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(124, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(124)
    H(q[0])
    for i in range(0, 75) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 8)
    }
    print("scenario", "drug_discovery", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(124))
}
```

### 476. `476_scenario_drug_discovery_16.sq` — 476 scenario drug discovery 16

```sansqrit
# Scenario 176: Drug-discovery feature-map screening: scenario 16
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(126, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(126)
    H(q[0])
    for i in range(0, 76) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 5)
    }
    print("scenario", "drug_discovery", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(126))
}
```

### 477. `477_scenario_drug_discovery_17.sq` — 477 scenario drug discovery 17

```sansqrit
# Scenario 177: Drug-discovery feature-map screening: scenario 17
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(128, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(128)
    H(q[0])
    for i in range(0, 77) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 6)
    }
    print("scenario", "drug_discovery", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(128))
}
```

### 478. `478_scenario_drug_discovery_18.sq` — 478 scenario drug discovery 18

```sansqrit
# Scenario 178: Drug-discovery feature-map screening: scenario 18
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(130, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(130)
    H(q[0])
    for i in range(0, 78) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 7)
    }
    print("scenario", "drug_discovery", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(130))
}
```

### 479. `479_scenario_drug_discovery_19.sq` — 479 scenario drug discovery 19

```sansqrit
# Scenario 179: Drug-discovery feature-map screening: scenario 19
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(132, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(132)
    H(q[0])
    for i in range(0, 79) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 8)
    }
    print("scenario", "drug_discovery", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(132))
}
```

### 480. `480_scenario_drug_discovery_20.sq` — 480 scenario drug discovery 20

```sansqrit
# Scenario 180: Drug-discovery feature-map screening: scenario 20
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(124, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(124)
    H(q[0])
    for i in range(0, 80) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 5)
    }
    print("scenario", "drug_discovery", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(124))
}
```

### 481. `481_scenario_materials_01.sq` — 481 scenario materials 01

```sansqrit
# Scenario 181: Battery and materials candidate screening: scenario 01
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(128, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(128)
    H(q[0])
    for i in range(0, 61) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 6)
    }
    print("scenario", "materials", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(128))
}
```

### 482. `482_scenario_materials_02.sq` — 482 scenario materials 02

```sansqrit
# Scenario 182: Battery and materials candidate screening: scenario 02
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(130, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(130)
    H(q[0])
    for i in range(0, 62) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 7)
    }
    print("scenario", "materials", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(130))
}
```

### 483. `483_scenario_materials_03.sq` — 483 scenario materials 03

```sansqrit
# Scenario 183: Battery and materials candidate screening: scenario 03
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(132, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(132)
    H(q[0])
    for i in range(0, 63) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 8)
    }
    print("scenario", "materials", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(132))
}
```

### 484. `484_scenario_materials_04.sq` — 484 scenario materials 04

```sansqrit
# Scenario 184: Battery and materials candidate screening: scenario 04
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(134, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(134)
    H(q[0])
    for i in range(0, 64) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 5)
    }
    print("scenario", "materials", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(134))
}
```

### 485. `485_scenario_materials_05.sq` — 485 scenario materials 05

```sansqrit
# Scenario 185: Battery and materials candidate screening: scenario 05
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(126, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(126)
    H(q[0])
    for i in range(0, 65) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 6)
    }
    print("scenario", "materials", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(126))
}
```

### 486. `486_scenario_materials_06.sq` — 486 scenario materials 06

```sansqrit
# Scenario 186: Battery and materials candidate screening: scenario 06
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(128, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(128)
    H(q[0])
    for i in range(0, 66) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 7)
    }
    print("scenario", "materials", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(128))
}
```

### 487. `487_scenario_materials_07.sq` — 487 scenario materials 07

```sansqrit
# Scenario 187: Battery and materials candidate screening: scenario 07
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(130, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(130)
    H(q[0])
    for i in range(0, 67) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 8)
    }
    print("scenario", "materials", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(130))
}
```

### 488. `488_scenario_materials_08.sq` — 488 scenario materials 08

```sansqrit
# Scenario 188: Battery and materials candidate screening: scenario 08
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(132, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(132)
    H(q[0])
    for i in range(0, 68) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 5)
    }
    print("scenario", "materials", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(132))
}
```

### 489. `489_scenario_materials_09.sq` — 489 scenario materials 09

```sansqrit
# Scenario 189: Battery and materials candidate screening: scenario 09
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(134, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(134)
    H(q[0])
    for i in range(0, 69) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 6)
    }
    print("scenario", "materials", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(134))
}
```

### 490. `490_scenario_materials_10.sq` — 490 scenario materials 10

```sansqrit
# Scenario 190: Battery and materials candidate screening: scenario 10
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(126, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(126)
    H(q[0])
    for i in range(0, 70) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 7)
    }
    print("scenario", "materials", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(126))
}
```

### 491. `491_scenario_materials_11.sq` — 491 scenario materials 11

```sansqrit
# Scenario 191: Battery and materials candidate screening: scenario 11
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(128, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(128)
    H(q[0])
    for i in range(0, 71) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 8)
    }
    print("scenario", "materials", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(128))
}
```

### 492. `492_scenario_materials_12.sq` — 492 scenario materials 12

```sansqrit
# Scenario 192: Battery and materials candidate screening: scenario 12
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(130, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(130)
    H(q[0])
    for i in range(0, 72) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 5)
    }
    print("scenario", "materials", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(130))
}
```

### 493. `493_scenario_materials_13.sq` — 493 scenario materials 13

```sansqrit
# Scenario 193: Battery and materials candidate screening: scenario 13
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(132, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(132)
    H(q[0])
    for i in range(0, 73) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 6)
    }
    print("scenario", "materials", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(132))
}
```

### 494. `494_scenario_materials_14.sq` — 494 scenario materials 14

```sansqrit
# Scenario 194: Battery and materials candidate screening: scenario 14
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(134, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(134)
    H(q[0])
    for i in range(0, 74) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 7)
    }
    print("scenario", "materials", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(134))
}
```

### 495. `495_scenario_materials_15.sq` — 495 scenario materials 15

```sansqrit
# Scenario 195: Battery and materials candidate screening: scenario 15
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(126, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(126)
    H(q[0])
    for i in range(0, 75) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 8)
    }
    print("scenario", "materials", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(126))
}
```

### 496. `496_scenario_materials_16.sq` — 496 scenario materials 16

```sansqrit
# Scenario 196: Battery and materials candidate screening: scenario 16
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(128, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(128)
    H(q[0])
    for i in range(0, 76) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 5)
    }
    print("scenario", "materials", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(128))
}
```

### 497. `497_scenario_materials_17.sq` — 497 scenario materials 17

```sansqrit
# Scenario 197: Battery and materials candidate screening: scenario 17
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(130, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(130)
    H(q[0])
    for i in range(0, 77) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 6)
    }
    print("scenario", "materials", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(130))
}
```

### 498. `498_scenario_materials_18.sq` — 498 scenario materials 18

```sansqrit
# Scenario 198: Battery and materials candidate screening: scenario 18
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(132, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(132)
    H(q[0])
    for i in range(0, 78) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 7)
    }
    print("scenario", "materials", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(132))
}
```

### 499. `499_scenario_materials_19.sq` — 499 scenario materials 19

```sansqrit
# Scenario 199: Battery and materials candidate screening: scenario 19
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(134, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(134)
    H(q[0])
    for i in range(0, 79) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 8)
    }
    print("scenario", "materials", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(134))
}
```

### 500. `500_scenario_materials_20.sq` — 500 scenario materials 20

```sansqrit
# Scenario 200: Battery and materials candidate screening: scenario 20
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(126, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(126)
    H(q[0])
    for i in range(0, 80) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 5)
    }
    print("scenario", "materials", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(126))
}
```

### 501. `501_scenario_genomics_01.sq` — 501 scenario genomics 01

```sansqrit
# Scenario 201: Genomic variant prioritization: scenario 01
# Strategy: sparse + sharded execution for 152 logical qubits.
simulate(152, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(152)
    X(q[19])
    X(q[116])
    X(q[9])
    H(q[55])
    CNOT(q[55], q[26])
    Rz(q[55], PI / 5)
    CZ(q[26], q[1])
    print("scenario", "genomics", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(152))
}
```

### 502. `502_scenario_genomics_02.sq` — 502 scenario genomics 02

```sansqrit
# Scenario 202: Genomic variant prioritization: scenario 02
# Strategy: sparse + sharded execution for 154 logical qubits.
simulate(154, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(154)
    X(q[32])
    X(q[113])
    X(q[152])
    H(q[62])
    CNOT(q[62], q[3])
    Rz(q[62], PI / 6)
    CZ(q[3], q[112])
    print("scenario", "genomics", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(154))
}
```

### 503. `503_scenario_genomics_03.sq` — 503 scenario genomics 03

```sansqrit
# Scenario 203: Genomic variant prioritization: scenario 03
# Strategy: sparse + sharded execution for 156 logical qubits.
simulate(156, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(156)
    X(q[5])
    X(q[154])
    X(q[155])
    H(q[89])
    CNOT(q[89], q[80])
    Rz(q[89], PI / 7)
    CZ(q[80], q[31])
    print("scenario", "genomics", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(156))
}
```

### 504. `504_scenario_genomics_04.sq` — 504 scenario genomics 04

```sansqrit
# Scenario 204: Genomic variant prioritization: scenario 04
# Strategy: sparse + sharded execution for 158 logical qubits.
simulate(158, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(158)
    X(q[90])
    X(q[75])
    X(q[76])
    H(q[128])
    CNOT(q[128], q[15])
    Rz(q[128], PI / 8)
    CZ(q[15], q[122])
    print("scenario", "genomics", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(158))
}
```

### 505. `505_scenario_genomics_05.sq` — 505 scenario genomics 05

```sansqrit
# Scenario 205: Genomic variant prioritization: scenario 05
# Strategy: sparse + sharded execution for 150 logical qubits.
simulate(150, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(150)
    X(q[127])
    X(q[114])
    X(q[103])
    H(q[7])
    CNOT(q[7], q[142])
    Rz(q[7], PI / 4)
    CZ(q[142], q[123])
    print("scenario", "genomics", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(150))
}
```

### 506. `506_scenario_genomics_06.sq` — 506 scenario genomics 06

```sansqrit
# Scenario 206: Genomic variant prioritization: scenario 06
# Strategy: sparse + sharded execution for 152 logical qubits.
simulate(152, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(152)
    X(q[148])
    X(q[61])
    X(q[122])
    H(q[48])
    CNOT(q[48], q[115])
    Rz(q[48], PI / 5)
    CZ(q[115], q[42])
    print("scenario", "genomics", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(152))
}
```

### 507. `507_scenario_genomics_07.sq` — 507 scenario genomics 07

```sansqrit
# Scenario 207: Genomic variant prioritization: scenario 07
# Strategy: sparse + sharded execution for 154 logical qubits.
simulate(154, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(154)
    X(q[139])
    X(q[54])
    X(q[55])
    H(q[19])
    CNOT(q[19], q[8])
    Rz(q[19], PI / 6)
    CZ(q[8], q[115])
    print("scenario", "genomics", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(154))
}
```

### 508. `508_scenario_genomics_08.sq` — 508 scenario genomics 08

```sansqrit
# Scenario 208: Genomic variant prioritization: scenario 08
# Strategy: sparse + sharded execution for 156 logical qubits.
simulate(156, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(156)
    X(q[94])
    X(q[39])
    X(q[88])
    H(q[34])
    CNOT(q[34], q[109])
    Rz(q[34], PI / 7)
    CZ(q[109], q[132])
    print("scenario", "genomics", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(156))
}
```

### 509. `509_scenario_genomics_09.sq` — 509 scenario genomics 09

```sansqrit
# Scenario 209: Genomic variant prioritization: scenario 09
# Strategy: sparse + sharded execution for 158 logical qubits.
simulate(158, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(158)
    X(q[7])
    X(q[10])
    X(q[21])
    H(q[73])
    CNOT(q[73], q[27])
    Rz(q[73], PI / 8)
    CZ(q[27], q[74])
    print("scenario", "genomics", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(158))
}
```

### 510. `510_scenario_genomics_10.sq` — 510 scenario genomics 10

```sansqrit
# Scenario 210: Genomic variant prioritization: scenario 10
# Strategy: sparse + sharded execution for 150 logical qubits.
simulate(150, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(150)
    X(q[132])
    X(q[59])
    X(q[108])
    H(q[72])
    CNOT(q[72], q[27])
    Rz(q[72], PI / 4)
    CZ(q[27], q[38])
    print("scenario", "genomics", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(150))
}
```

### 511. `511_scenario_genomics_11.sq` — 511 scenario genomics 11

```sansqrit
# Scenario 211: Genomic variant prioritization: scenario 11
# Strategy: sparse + sharded execution for 152 logical qubits.
simulate(152, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(152)
    X(q[125])
    X(q[6])
    X(q[83])
    H(q[41])
    CNOT(q[41], q[52])
    Rz(q[41], PI / 5)
    CZ(q[52], q[100])
    print("scenario", "genomics", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(152))
}
```

### 512. `512_scenario_genomics_12.sq` — 512 scenario genomics 12

```sansqrit
# Scenario 212: Genomic variant prioritization: scenario 12
# Strategy: sparse + sharded execution for 154 logical qubits.
simulate(154, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(154)
    X(q[92])
    X(q[149])
    X(q[112])
    H(q[130])
    CNOT(q[130], q[13])
    Rz(q[130], PI / 6)
    CZ(q[13], q[118])
    print("scenario", "genomics", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(154))
}
```

### 513. `513_scenario_genomics_13.sq` — 513 scenario genomics 13

```sansqrit
# Scenario 213: Genomic variant prioritization: scenario 13
# Strategy: sparse + sharded execution for 156 logical qubits.
simulate(156, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(156)
    X(q[27])
    X(q[80])
    X(q[21])
    H(q[135])
    CNOT(q[135], q[138])
    Rz(q[135], PI / 7)
    CZ(q[138], q[77])
    print("scenario", "genomics", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(156))
}
```

### 514. `514_scenario_genomics_14.sq` — 514 scenario genomics 14

```sansqrit
# Scenario 214: Genomic variant prioritization: scenario 14
# Strategy: sparse + sharded execution for 158 logical qubits.
simulate(158, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(158)
    X(q[82])
    X(q[103])
    X(q[136])
    H(q[18])
    CNOT(q[18], q[5])
    Rz(q[18], PI / 8)
    CZ(q[5], q[78])
    print("scenario", "genomics", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(158))
}
```

### 515. `515_scenario_genomics_15.sq` — 515 scenario genomics 15

```sansqrit
# Scenario 215: Genomic variant prioritization: scenario 15
# Strategy: sparse + sharded execution for 150 logical qubits.
simulate(150, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(150)
    X(q[137])
    X(q[62])
    X(q[103])
    H(q[4])
    CNOT(q[4], q[113])
    Rz(q[4], PI / 4)
    CZ(q[113], q[140])
    print("scenario", "genomics", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(150))
}
```

### 516. `516_scenario_genomics_16.sq` — 516 scenario genomics 16

```sansqrit
# Scenario 216: Genomic variant prioritization: scenario 16
# Strategy: sparse + sharded execution for 152 logical qubits.
simulate(152, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(152)
    X(q[102])
    X(q[103])
    X(q[44])
    H(q[34])
    CNOT(q[34], q[141])
    Rz(q[34], PI / 5)
    CZ(q[141], q[124])
    print("scenario", "genomics", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(152))
}
```

### 517. `517_scenario_genomics_17.sq` — 517 scenario genomics 17

```sansqrit
# Scenario 217: Genomic variant prioritization: scenario 17
# Strategy: sparse + sharded execution for 154 logical qubits.
simulate(154, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(154)
    X(q[45])
    X(q[90])
    X(q[15])
    H(q[87])
    CNOT(q[87], q[18])
    Rz(q[87], PI / 6)
    CZ(q[18], q[121])
    print("scenario", "genomics", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(154))
}
```

### 518. `518_scenario_genomics_18.sq` — 518 scenario genomics 18

```sansqrit
# Scenario 218: Genomic variant prioritization: scenario 18
# Strategy: sparse + sharded execution for 156 logical qubits.
simulate(156, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(156)
    X(q[116])
    X(q[121])
    X(q[110])
    H(q[80])
    CNOT(q[80], q[11])
    Rz(q[80], PI / 7)
    CZ(q[11], q[22])
    print("scenario", "genomics", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(156))
}
```

### 519. `519_scenario_genomics_19.sq` — 519 scenario genomics 19

```sansqrit
# Scenario 219: Genomic variant prioritization: scenario 19
# Strategy: sparse + sharded execution for 158 logical qubits.
simulate(158, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(158)
    X(q[157])
    X(q[38])
    X(q[87])
    H(q[121])
    CNOT(q[121], q[0])
    Rz(q[121], PI / 8)
    CZ(q[0], q[135])
    print("scenario", "genomics", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(158))
}
```

### 520. `520_scenario_genomics_20.sq` — 520 scenario genomics 20

```sansqrit
# Scenario 220: Genomic variant prioritization: scenario 20
# Strategy: sparse + sharded execution for 150 logical qubits.
simulate(150, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(150)
    X(q[142])
    X(q[99])
    X(q[118])
    H(q[52])
    CNOT(q[52], q[97])
    Rz(q[52], PI / 4)
    CZ(q[97], q[18])
    print("scenario", "genomics", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(150))
}
```

### 521. `521_scenario_healthcare_01.sq` — 521 scenario healthcare 01

```sansqrit
# Scenario 221: Hospital triage and resource allocation: scenario 01
# Strategy: sparse + sharded execution for 130 logical qubits.
simulate(130, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(130)
    X(q[3])
    X(q[78])
    X(q[49])
    H(q[125])
    CNOT(q[125], q[44])
    Rz(q[125], PI / 5)
    CZ(q[44], q[41])
    print("scenario", "healthcare", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(130))
}
```

### 522. `522_scenario_healthcare_02.sq` — 522 scenario healthcare 02

```sansqrit
# Scenario 222: Hospital triage and resource allocation: scenario 02
# Strategy: sparse + sharded execution for 132 logical qubits.
simulate(132, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(132)
    X(q[108])
    X(q[53])
    X(q[6])
    H(q[0])
    CNOT(q[0], q[111])
    Rz(q[0], PI / 6)
    CZ(q[111], q[14])
    print("scenario", "healthcare", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(132))
}
```

### 523. `523_scenario_healthcare_03.sq` — 523 scenario healthcare 03

```sansqrit
# Scenario 223: Hospital triage and resource allocation: scenario 03
# Strategy: sparse + sharded execution for 134 logical qubits.
simulate(134, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(134)
    X(q[11])
    X(q[62])
    X(q[59])
    H(q[35])
    CNOT(q[35], q[40])
    Rz(q[35], PI / 7)
    CZ(q[40], q[123])
    print("scenario", "healthcare", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(134))
}
```

### 524. `524_scenario_healthcare_04.sq` — 524 scenario healthcare 04

```sansqrit
# Scenario 224: Hospital triage and resource allocation: scenario 04
# Strategy: sparse + sharded execution for 136 logical qubits.
simulate(136, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(136)
    X(q[102])
    X(q[63])
    X(q[108])
    H(q[2])
    CNOT(q[2], q[45])
    Rz(q[2], PI / 8)
    CZ(q[45], q[100])
    print("scenario", "healthcare", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(136))
}
```

### 525. `525_scenario_healthcare_05.sq` — 525 scenario healthcare 05

```sansqrit
# Scenario 225: Hospital triage and resource allocation: scenario 05
# Strategy: sparse + sharded execution for 128 logical qubits.
simulate(128, engine="sharded", n_shards=12, workers=4, use_lookup=true) {
    q = quantum_register(128)
    X(q[35])
    X(q[12])
    X(q[25])
    H(q[31])
    CNOT(q[31], q[50])
    Rz(q[31], PI / 4)
    CZ(q[50], q[9])
    print("scenario", "healthcare", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(128))
}
```

### 526. `526_scenario_healthcare_06.sq` — 526 scenario healthcare 06

```sansqrit
# Scenario 226: Hospital triage and resource allocation: scenario 06
# Strategy: sparse + sharded execution for 130 logical qubits.
simulate(130, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(130)
    X(q[118])
    X(q[93])
    X(q[34])
    H(q[70])
    CNOT(q[70], q[99])
    Rz(q[70], PI / 5)
    CZ(q[99], q[116])
    print("scenario", "healthcare", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(130))
}
```

### 527. `527_scenario_healthcare_07.sq` — 527 scenario healthcare 07

```sansqrit
# Scenario 227: Hospital triage and resource allocation: scenario 07
# Strategy: sparse + sharded execution for 132 logical qubits.
simulate(132, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(132)
    X(q[17])
    X(q[82])
    X(q[107])
    H(q[89])
    CNOT(q[89], q[116])
    Rz(q[89], PI / 6)
    CZ(q[116], q[127])
    print("scenario", "healthcare", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(132))
}
```

### 528. `528_scenario_healthcare_08.sq` — 528 scenario healthcare 08

```sansqrit
# Scenario 228: Hospital triage and resource allocation: scenario 08
# Strategy: sparse + sharded execution for 134 logical qubits.
simulate(134, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(134)
    X(q[116])
    X(q[23])
    X(q[74])
    H(q[62])
    CNOT(q[62], q[7])
    Rz(q[62], PI / 7)
    CZ(q[7], q[29])
    print("scenario", "healthcare", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(134))
}
```

### 529. `529_scenario_healthcare_09.sq` — 529 scenario healthcare 09

```sansqrit
# Scenario 229: Hospital triage and resource allocation: scenario 09
# Strategy: sparse + sharded execution for 136 logical qubits.
simulate(136, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(136)
    X(q[7])
    X(q[0])
    X(q[29])
    H(q[3])
    CNOT(q[3], q[70])
    Rz(q[3], PI / 8)
    CZ(q[70], q[45])
    print("scenario", "healthcare", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(136))
}
```

### 530. `530_scenario_healthcare_10.sq` — 530 scenario healthcare 10

```sansqrit
# Scenario 230: Hospital triage and resource allocation: scenario 10
# Strategy: sparse + sharded execution for 128 logical qubits.
simulate(128, engine="sharded", n_shards=12, workers=4, use_lookup=true) {
    q = quantum_register(128)
    X(q[100])
    X(q[29])
    X(q[122])
    H(q[40])
    CNOT(q[40], q[11])
    Rz(q[40], PI / 4)
    CZ(q[11], q[50])
    print("scenario", "healthcare", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(128))
}
```

### 531. `531_scenario_healthcare_11.sq` — 531 scenario healthcare 11

```sansqrit
# Scenario 231: Hospital triage and resource allocation: scenario 11
# Strategy: sparse + sharded execution for 130 logical qubits.
simulate(130, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(130)
    X(q[103])
    X(q[108])
    X(q[19])
    H(q[15])
    CNOT(q[15], q[24])
    Rz(q[15], PI / 5)
    CZ(q[24], q[61])
    print("scenario", "healthcare", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(130))
}
```

### 532. `532_scenario_healthcare_12.sq` — 532 scenario healthcare 12

```sansqrit
# Scenario 232: Hospital triage and resource allocation: scenario 12
# Strategy: sparse + sharded execution for 132 logical qubits.
simulate(132, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(132)
    X(q[58])
    X(q[111])
    X(q[76])
    H(q[46])
    CNOT(q[46], q[121])
    Rz(q[46], PI / 6)
    CZ(q[121], q[108])
    print("scenario", "healthcare", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(132))
}
```

### 533. `533_scenario_healthcare_13.sq` — 533 scenario healthcare 13

```sansqrit
# Scenario 233: Hospital triage and resource allocation: scenario 13
# Strategy: sparse + sharded execution for 134 logical qubits.
simulate(134, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(134)
    X(q[87])
    X(q[118])
    X(q[65])
    H(q[89])
    CNOT(q[89], q[108])
    Rz(q[89], PI / 7)
    CZ(q[108], q[25])
    print("scenario", "healthcare", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(134))
}
```

### 534. `534_scenario_healthcare_14.sq` — 534 scenario healthcare 14

```sansqrit
# Scenario 234: Hospital triage and resource allocation: scenario 14
# Strategy: sparse + sharded execution for 136 logical qubits.
simulate(136, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(136)
    X(q[48])
    X(q[73])
    X(q[86])
    H(q[4])
    CNOT(q[4], q[95])
    Rz(q[4], PI / 8)
    CZ(q[95], q[126])
    print("scenario", "healthcare", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(136))
}
```

### 535. `535_scenario_healthcare_15.sq` — 535 scenario healthcare 15

```sansqrit
# Scenario 235: Hospital triage and resource allocation: scenario 15
# Strategy: sparse + sharded execution for 128 logical qubits.
simulate(128, engine="sharded", n_shards=12, workers=4, use_lookup=true) {
    q = quantum_register(128)
    X(q[37])
    X(q[46])
    X(q[91])
    H(q[49])
    CNOT(q[49], q[100])
    Rz(q[49], PI / 4)
    CZ(q[100], q[108])
    print("scenario", "healthcare", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(128))
}
```

### 536. `536_scenario_healthcare_16.sq` — 536 scenario healthcare 16

```sansqrit
# Scenario 236: Hospital triage and resource allocation: scenario 16
# Strategy: sparse + sharded execution for 130 logical qubits.
simulate(130, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(130)
    X(q[88])
    X(q[123])
    X(q[4])
    H(q[90])
    CNOT(q[90], q[79])
    Rz(q[90], PI / 5)
    CZ(q[79], q[6])
    print("scenario", "healthcare", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(130))
}
```

### 537. `537_scenario_healthcare_17.sq` — 537 scenario healthcare 17

```sansqrit
# Scenario 237: Hospital triage and resource allocation: scenario 17
# Strategy: sparse + sharded execution for 132 logical qubits.
simulate(132, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(132)
    X(q[99])
    X(q[8])
    X(q[45])
    H(q[3])
    CNOT(q[3], q[126])
    Rz(q[3], PI / 6)
    CZ(q[126], q[89])
    print("scenario", "healthcare", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(132))
}
```

### 538. `538_scenario_healthcare_18.sq` — 538 scenario healthcare 18

```sansqrit
# Scenario 238: Hospital triage and resource allocation: scenario 18
# Strategy: sparse + sharded execution for 134 logical qubits.
simulate(134, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(134)
    X(q[58])
    X(q[79])
    X(q[68])
    H(q[116])
    CNOT(q[116], q[75])
    Rz(q[116], PI / 7)
    CZ(q[75], q[110])
    print("scenario", "healthcare", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(134))
}
```

### 539. `539_scenario_healthcare_19.sq` — 539 scenario healthcare 19

```sansqrit
# Scenario 239: Hospital triage and resource allocation: scenario 19
# Strategy: sparse + sharded execution for 136 logical qubits.
simulate(136, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(136)
    X(q[89])
    X(q[10])
    X(q[7])
    H(q[5])
    CNOT(q[5], q[120])
    Rz(q[5], PI / 8)
    CZ(q[120], q[71])
    print("scenario", "healthcare", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(136))
}
```

### 540. `540_scenario_healthcare_20.sq` — 540 scenario healthcare 20

```sansqrit
# Scenario 240: Hospital triage and resource allocation: scenario 20
# Strategy: sparse + sharded execution for 128 logical qubits.
simulate(128, engine="sharded", n_shards=12, workers=4, use_lookup=true) {
    q = quantum_register(128)
    X(q[102])
    X(q[63])
    X(q[60])
    H(q[58])
    CNOT(q[58], q[61])
    Rz(q[58], PI / 4)
    CZ(q[61], q[4])
    print("scenario", "healthcare", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(128))
}
```

### 541. `541_scenario_aerospace_01.sq` — 541 scenario aerospace 01

```sansqrit
# Scenario 241: Aerospace sensor-fusion fault tree: scenario 01
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(142, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(142)
    block_A = shard("block_A", 10, 19)
    block_B = shard("block_B", 20, 29)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[19], q[20])
    Rz(q[21], PI / 4)
    print("scenario", "aerospace", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 542. `542_scenario_aerospace_02.sq` — 542 scenario aerospace 02

```sansqrit
# Scenario 242: Aerospace sensor-fusion fault tree: scenario 02
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(144, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(144)
    block_A = shard("block_A", 20, 29)
    block_B = shard("block_B", 30, 39)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[29], q[30])
    Rz(q[31], PI / 5)
    print("scenario", "aerospace", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 543. `543_scenario_aerospace_03.sq` — 543 scenario aerospace 03

```sansqrit
# Scenario 243: Aerospace sensor-fusion fault tree: scenario 03
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(146, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(146)
    block_A = shard("block_A", 30, 39)
    block_B = shard("block_B", 40, 49)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[39], q[40])
    Rz(q[41], PI / 6)
    print("scenario", "aerospace", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 544. `544_scenario_aerospace_04.sq` — 544 scenario aerospace 04

```sansqrit
# Scenario 244: Aerospace sensor-fusion fault tree: scenario 04
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(148, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(148)
    block_A = shard("block_A", 0, 9)
    block_B = shard("block_B", 10, 19)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[9], q[10])
    Rz(q[11], PI / 7)
    print("scenario", "aerospace", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 545. `545_scenario_aerospace_05.sq` — 545 scenario aerospace 05

```sansqrit
# Scenario 245: Aerospace sensor-fusion fault tree: scenario 05
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(140, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(140)
    block_A = shard("block_A", 10, 19)
    block_B = shard("block_B", 20, 29)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[19], q[20])
    Rz(q[21], PI / 8)
    print("scenario", "aerospace", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 546. `546_scenario_aerospace_06.sq` — 546 scenario aerospace 06

```sansqrit
# Scenario 246: Aerospace sensor-fusion fault tree: scenario 06
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(142, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(142)
    block_A = shard("block_A", 20, 29)
    block_B = shard("block_B", 30, 39)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[29], q[30])
    Rz(q[31], PI / 3)
    print("scenario", "aerospace", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 547. `547_scenario_aerospace_07.sq` — 547 scenario aerospace 07

```sansqrit
# Scenario 247: Aerospace sensor-fusion fault tree: scenario 07
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(144, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(144)
    block_A = shard("block_A", 30, 39)
    block_B = shard("block_B", 40, 49)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[39], q[40])
    Rz(q[41], PI / 4)
    print("scenario", "aerospace", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 548. `548_scenario_aerospace_08.sq` — 548 scenario aerospace 08

```sansqrit
# Scenario 248: Aerospace sensor-fusion fault tree: scenario 08
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(146, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(146)
    block_A = shard("block_A", 0, 9)
    block_B = shard("block_B", 10, 19)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[9], q[10])
    Rz(q[11], PI / 5)
    print("scenario", "aerospace", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 549. `549_scenario_aerospace_09.sq` — 549 scenario aerospace 09

```sansqrit
# Scenario 249: Aerospace sensor-fusion fault tree: scenario 09
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(148, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(148)
    block_A = shard("block_A", 10, 19)
    block_B = shard("block_B", 20, 29)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[19], q[20])
    Rz(q[21], PI / 6)
    print("scenario", "aerospace", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 550. `550_scenario_aerospace_10.sq` — 550 scenario aerospace 10

```sansqrit
# Scenario 250: Aerospace sensor-fusion fault tree: scenario 10
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(140, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(140)
    block_A = shard("block_A", 20, 29)
    block_B = shard("block_B", 30, 39)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[29], q[30])
    Rz(q[31], PI / 7)
    print("scenario", "aerospace", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 551. `551_scenario_aerospace_11.sq` — 551 scenario aerospace 11

```sansqrit
# Scenario 251: Aerospace sensor-fusion fault tree: scenario 11
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(142, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(142)
    block_A = shard("block_A", 30, 39)
    block_B = shard("block_B", 40, 49)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[39], q[40])
    Rz(q[41], PI / 8)
    print("scenario", "aerospace", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 552. `552_scenario_aerospace_12.sq` — 552 scenario aerospace 12

```sansqrit
# Scenario 252: Aerospace sensor-fusion fault tree: scenario 12
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(144, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(144)
    block_A = shard("block_A", 0, 9)
    block_B = shard("block_B", 10, 19)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[9], q[10])
    Rz(q[11], PI / 3)
    print("scenario", "aerospace", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 553. `553_scenario_aerospace_13.sq` — 553 scenario aerospace 13

```sansqrit
# Scenario 253: Aerospace sensor-fusion fault tree: scenario 13
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(146, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(146)
    block_A = shard("block_A", 10, 19)
    block_B = shard("block_B", 20, 29)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[19], q[20])
    Rz(q[21], PI / 4)
    print("scenario", "aerospace", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 554. `554_scenario_aerospace_14.sq` — 554 scenario aerospace 14

```sansqrit
# Scenario 254: Aerospace sensor-fusion fault tree: scenario 14
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(148, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(148)
    block_A = shard("block_A", 20, 29)
    block_B = shard("block_B", 30, 39)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[29], q[30])
    Rz(q[31], PI / 5)
    print("scenario", "aerospace", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 555. `555_scenario_aerospace_15.sq` — 555 scenario aerospace 15

```sansqrit
# Scenario 255: Aerospace sensor-fusion fault tree: scenario 15
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(140, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(140)
    block_A = shard("block_A", 30, 39)
    block_B = shard("block_B", 40, 49)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[39], q[40])
    Rz(q[41], PI / 6)
    print("scenario", "aerospace", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 556. `556_scenario_aerospace_16.sq` — 556 scenario aerospace 16

```sansqrit
# Scenario 256: Aerospace sensor-fusion fault tree: scenario 16
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(142, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(142)
    block_A = shard("block_A", 0, 9)
    block_B = shard("block_B", 10, 19)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[9], q[10])
    Rz(q[11], PI / 7)
    print("scenario", "aerospace", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 557. `557_scenario_aerospace_17.sq` — 557 scenario aerospace 17

```sansqrit
# Scenario 257: Aerospace sensor-fusion fault tree: scenario 17
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(144, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(144)
    block_A = shard("block_A", 10, 19)
    block_B = shard("block_B", 20, 29)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[19], q[20])
    Rz(q[21], PI / 8)
    print("scenario", "aerospace", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 558. `558_scenario_aerospace_18.sq` — 558 scenario aerospace 18

```sansqrit
# Scenario 258: Aerospace sensor-fusion fault tree: scenario 18
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(146, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(146)
    block_A = shard("block_A", 20, 29)
    block_B = shard("block_B", 30, 39)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[29], q[30])
    Rz(q[31], PI / 3)
    print("scenario", "aerospace", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 559. `559_scenario_aerospace_19.sq` — 559 scenario aerospace 19

```sansqrit
# Scenario 259: Aerospace sensor-fusion fault tree: scenario 19
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(148, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(148)
    block_A = shard("block_A", 30, 39)
    block_B = shard("block_B", 40, 49)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[39], q[40])
    Rz(q[41], PI / 4)
    print("scenario", "aerospace", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 560. `560_scenario_aerospace_20.sq` — 560 scenario aerospace 20

```sansqrit
# Scenario 260: Aerospace sensor-fusion fault tree: scenario 20
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(140, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(140)
    block_A = shard("block_A", 0, 9)
    block_B = shard("block_B", 10, 19)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[9], q[10])
    Rz(q[11], PI / 5)
    print("scenario", "aerospace", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 561. `561_scenario_robotics_01.sq` — 561 scenario robotics 01

```sansqrit
# Scenario 261: Robotics path planning and collision flags: scenario 01
# Strategy: sparse + sharded execution for 152 logical qubits.
simulate(152, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(152)
    X(q[47])
    X(q[64])
    X(q[149])
    H(q[123])
    CNOT(q[123], q[30])
    Rz(q[123], PI / 5)
    CZ(q[30], q[37])
    print("scenario", "robotics", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(152))
}
```

### 562. `562_scenario_robotics_02.sq` — 562 scenario robotics 02

```sansqrit
# Scenario 262: Robotics path planning and collision flags: scenario 02
# Strategy: sparse + sharded execution for 154 logical qubits.
simulate(154, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(154)
    X(q[84])
    X(q[21])
    X(q[66])
    H(q[8])
    CNOT(q[8], q[63])
    Rz(q[8], PI / 6)
    CZ(q[63], q[148])
    print("scenario", "robotics", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(154))
}
```

### 563. `563_scenario_robotics_03.sq` — 563 scenario robotics 03

```sansqrit
# Scenario 263: Robotics path planning and collision flags: scenario 03
# Strategy: sparse + sharded execution for 156 logical qubits.
simulate(156, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(156)
    X(q[137])
    X(q[22])
    X(q[131])
    H(q[53])
    CNOT(q[53], q[116])
    Rz(q[53], PI / 7)
    CZ(q[116], q[151])
    print("scenario", "robotics", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(156))
}
```

### 564. `564_scenario_robotics_04.sq` — 564 scenario robotics 04

```sansqrit
# Scenario 264: Robotics path planning and collision flags: scenario 04
# Strategy: sparse + sharded execution for 158 logical qubits.
simulate(158, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(158)
    X(q[42])
    X(q[85])
    X(q[120])
    H(q[100])
    CNOT(q[100], q[113])
    Rz(q[100], PI / 8)
    CZ(q[113], q[16])
    print("scenario", "robotics", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(158))
}
```

### 565. `565_scenario_robotics_05.sq` — 565 scenario robotics 05

```sansqrit
# Scenario 265: Robotics path planning and collision flags: scenario 05
# Strategy: sparse + sharded execution for 150 logical qubits.
simulate(150, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(150)
    X(q[37])
    X(q[112])
    X(q[3])
    H(q[54])
    CNOT(q[54], q[13])
    Rz(q[54], PI / 4)
    CZ(q[13], q[40])
    print("scenario", "robotics", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(150))
}
```

### 566. `566_scenario_robotics_06.sq` — 566 scenario robotics 06

```sansqrit
# Scenario 266: Robotics path planning and collision flags: scenario 06
# Strategy: sparse + sharded execution for 152 logical qubits.
simulate(152, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(152)
    X(q[24])
    X(q[9])
    X(q[110])
    H(q[116])
    CNOT(q[116], q[119])
    Rz(q[116], PI / 5)
    CZ(q[119], q[78])
    print("scenario", "robotics", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(152))
}
```

### 567. `567_scenario_robotics_07.sq` — 567 scenario robotics 07

```sansqrit
# Scenario 267: Robotics path planning and collision flags: scenario 07
# Strategy: sparse + sharded execution for 154 logical qubits.
simulate(154, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(154)
    X(q[37])
    X(q[116])
    X(q[123])
    H(q[119])
    CNOT(q[119], q[68])
    Rz(q[119], PI / 6)
    CZ(q[68], q[151])
    print("scenario", "robotics", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(154))
}
```

### 568. `568_scenario_robotics_08.sq` — 568 scenario robotics 08

```sansqrit
# Scenario 268: Robotics path planning and collision flags: scenario 08
# Strategy: sparse + sharded execution for 156 logical qubits.
simulate(156, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(156)
    X(q[70])
    X(q[63])
    X(q[64])
    H(q[154])
    CNOT(q[154], q[145])
    Rz(q[154], PI / 7)
    CZ(q[145], q[96])
    print("scenario", "robotics", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(156))
}
```

### 569. `569_scenario_robotics_09.sq` — 569 scenario robotics 09

```sansqrit
# Scenario 269: Robotics path planning and collision flags: scenario 09
# Strategy: sparse + sharded execution for 158 logical qubits.
simulate(158, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(158)
    X(q[117])
    X(q[20])
    X(q[71])
    H(q[45])
    CNOT(q[45], q[108])
    Rz(q[45], PI / 8)
    CZ(q[108], q[73])
    print("scenario", "robotics", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(158))
}
```

### 570. `570_scenario_robotics_10.sq` — 570 scenario robotics 10

```sansqrit
# Scenario 270: Robotics path planning and collision flags: scenario 10
# Strategy: sparse + sharded execution for 150 logical qubits.
simulate(150, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(150)
    X(q[42])
    X(q[149])
    X(q[18])
    H(q[102])
    CNOT(q[102], q[147])
    Rz(q[102], PI / 4)
    CZ(q[147], q[68])
    print("scenario", "robotics", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(150))
}
```

### 571. `571_scenario_robotics_11.sq` — 571 scenario robotics 11

```sansqrit
# Scenario 271: Robotics path planning and collision flags: scenario 11
# Strategy: sparse + sharded execution for 152 logical qubits.
simulate(152, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(152)
    X(q[1])
    X(q[106])
    X(q[71])
    H(q[109])
    CNOT(q[109], q[56])
    Rz(q[109], PI / 5)
    CZ(q[56], q[119])
    print("scenario", "robotics", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(152))
}
```

### 572. `572_scenario_robotics_12.sq` — 572 scenario robotics 12

```sansqrit
# Scenario 272: Robotics path planning and collision flags: scenario 12
# Strategy: sparse + sharded execution for 154 logical qubits.
simulate(154, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(154)
    X(q[144])
    X(q[57])
    X(q[26])
    H(q[76])
    CNOT(q[76], q[73])
    Rz(q[76], PI / 6)
    CZ(q[73], q[0])
    print("scenario", "robotics", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(154))
}
```

### 573. `573_scenario_robotics_13.sq` — 573 scenario robotics 13

```sansqrit
# Scenario 273: Robotics path planning and collision flags: scenario 13
# Strategy: sparse + sharded execution for 156 logical qubits.
simulate(156, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(156)
    X(q[3])
    X(q[104])
    X(q[153])
    H(q[99])
    CNOT(q[99], q[18])
    Rz(q[99], PI / 7)
    CZ(q[18], q[41])
    print("scenario", "robotics", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(156))
}
```

### 574. `574_scenario_robotics_14.sq` — 574 scenario robotics 14

```sansqrit
# Scenario 274: Robotics path planning and collision flags: scenario 14
# Strategy: sparse + sharded execution for 158 logical qubits.
simulate(158, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(158)
    X(q[34])
    X(q[113])
    X(q[22])
    H(q[148])
    CNOT(q[148], q[103])
    Rz(q[148], PI / 8)
    CZ(q[103], q[130])
    print("scenario", "robotics", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(158))
}
```

### 575. `575_scenario_robotics_15.sq` — 575 scenario robotics 15

```sansqrit
# Scenario 275: Robotics path planning and collision flags: scenario 15
# Strategy: sparse + sharded execution for 150 logical qubits.
simulate(150, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(150)
    X(q[47])
    X(q[94])
    X(q[23])
    H(q[17])
    CNOT(q[17], q[32])
    Rz(q[17], PI / 4)
    CZ(q[32], q[133])
    print("scenario", "robotics", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(150))
}
```

### 576. `576_scenario_robotics_16.sq` — 576 scenario robotics 16

```sansqrit
# Scenario 276: Robotics path planning and collision flags: scenario 16
# Strategy: sparse + sharded execution for 152 logical qubits.
simulate(152, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(152)
    X(q[130])
    X(q[51])
    X(q[32])
    H(q[102])
    CNOT(q[102], q[145])
    Rz(q[102], PI / 5)
    CZ(q[145], q[8])
    print("scenario", "robotics", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(152))
}
```

### 577. `577_scenario_robotics_17.sq` — 577 scenario robotics 17

```sansqrit
# Scenario 277: Robotics path planning and collision flags: scenario 17
# Strategy: sparse + sharded execution for 154 logical qubits.
simulate(154, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(154)
    X(q[97])
    X(q[152])
    X(q[83])
    H(q[33])
    CNOT(q[33], q[78])
    Rz(q[33], PI / 6)
    CZ(q[78], q[3])
    print("scenario", "robotics", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(154))
}
```

### 578. `578_scenario_robotics_18.sq` — 578 scenario robotics 18

```sansqrit
# Scenario 278: Robotics path planning and collision flags: scenario 18
# Strategy: sparse + sharded execution for 156 logical qubits.
simulate(156, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(156)
    X(q[92])
    X(q[145])
    X(q[86])
    H(q[44])
    CNOT(q[44], q[47])
    Rz(q[44], PI / 7)
    CZ(q[47], q[142])
    print("scenario", "robotics", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(156))
}
```

### 579. `579_scenario_robotics_19.sq` — 579 scenario robotics 19

```sansqrit
# Scenario 279: Robotics path planning and collision flags: scenario 19
# Strategy: sparse + sharded execution for 158 logical qubits.
simulate(158, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(158)
    X(q[109])
    X(q[48])
    X(q[131])
    H(q[93])
    CNOT(q[93], q[98])
    Rz(q[93], PI / 8)
    CZ(q[98], q[29])
    print("scenario", "robotics", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(158))
}
```

### 580. `580_scenario_robotics_20.sq` — 580 scenario robotics 20

```sansqrit
# Scenario 280: Robotics path planning and collision flags: scenario 20
# Strategy: sparse + sharded execution for 150 logical qubits.
simulate(150, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(150)
    X(q[52])
    X(q[39])
    X(q[28])
    H(q[82])
    CNOT(q[82], q[67])
    Rz(q[82], PI / 4)
    CZ(q[67], q[48])
    print("scenario", "robotics", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(150))
}
```

### 581. `581_scenario_water_network_01.sq` — 581 scenario water network 01

```sansqrit
# Scenario 281: Water-network leakage localization: scenario 01
# Strategy: sparse + sharded execution for 152 logical qubits.
simulate(152, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(152)
    X(q[107])
    X(q[148])
    X(q[145])
    H(q[95])
    CNOT(q[95], q[82])
    Rz(q[95], PI / 5)
    CZ(q[82], q[49])
    print("scenario", "water_network", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(152))
}
```

### 582. `582_scenario_water_network_02.sq` — 582 scenario water network 02

```sansqrit
# Scenario 282: Water-network leakage localization: scenario 02
# Strategy: sparse + sharded execution for 154 logical qubits.
simulate(154, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(154)
    X(q[50])
    X(q[93])
    X(q[140])
    H(q[144])
    CNOT(q[144], q[83])
    Rz(q[144], PI / 6)
    CZ(q[83], q[6])
    print("scenario", "water_network", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(154))
}
```

### 583. `583_scenario_water_network_03.sq` — 583 scenario water network 03

```sansqrit
# Scenario 283: Water-network leakage localization: scenario 03
# Strategy: sparse + sharded execution for 156 logical qubits.
simulate(156, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(156)
    X(q[25])
    X(q[30])
    X(q[19])
    H(q[145])
    CNOT(q[145], q[76])
    Rz(q[145], PI / 7)
    CZ(q[76], q[87])
    print("scenario", "water_network", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(156))
}
```

### 584. `584_scenario_water_network_04.sq` — 584 scenario water network 04

```sansqrit
# Scenario 284: Water-network leakage localization: scenario 04
# Strategy: sparse + sharded execution for 158 logical qubits.
simulate(158, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(158)
    X(q[26])
    X(q[141])
    X(q[82])
    H(q[38])
    CNOT(q[38], q[93])
    Rz(q[38], PI / 8)
    CZ(q[93], q[86])
    print("scenario", "water_network", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(158))
}
```

### 585. `585_scenario_water_network_05.sq` — 585 scenario water network 05

```sansqrit
# Scenario 285: Water-network leakage localization: scenario 05
# Strategy: sparse + sharded execution for 150 logical qubits.
simulate(150, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(150)
    X(q[57])
    X(q[134])
    X(q[33])
    H(q[147])
    CNOT(q[147], q[102])
    Rz(q[147], PI / 4)
    CZ(q[102], q[113])
    print("scenario", "water_network", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(150))
}
```

### 586. `586_scenario_water_network_06.sq` — 586 scenario water network 06

```sansqrit
# Scenario 286: Water-network leakage localization: scenario 06
# Strategy: sparse + sharded execution for 152 logical qubits.
simulate(152, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(152)
    X(q[84])
    X(q[93])
    X(q[106])
    H(q[88])
    CNOT(q[88], q[19])
    Rz(q[88], PI / 5)
    CZ(q[19], q[90])
    print("scenario", "water_network", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(152))
}
```

### 587. `587_scenario_water_network_07.sq` — 587 scenario water network 07

```sansqrit
# Scenario 287: Water-network leakage localization: scenario 07
# Strategy: sparse + sharded execution for 154 logical qubits.
simulate(154, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(154)
    X(q[3])
    X(q[34])
    X(q[43])
    H(q[101])
    CNOT(q[101], q[88])
    Rz(q[101], PI / 6)
    CZ(q[88], q[9])
    print("scenario", "water_network", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(154))
}
```

### 588. `588_scenario_water_network_08.sq` — 588 scenario water network 08

```sansqrit
# Scenario 288: Water-network leakage localization: scenario 08
# Strategy: sparse + sharded execution for 156 logical qubits.
simulate(156, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(156)
    X(q[114])
    X(q[71])
    X(q[108])
    H(q[90])
    CNOT(q[90], q[105])
    Rz(q[90], PI / 7)
    CZ(q[105], q[32])
    print("scenario", "water_network", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(156))
}
```

### 589. `589_scenario_water_network_09.sq` — 589 scenario water network 09

```sansqrit
# Scenario 289: Water-network leakage localization: scenario 09
# Strategy: sparse + sharded execution for 158 logical qubits.
simulate(158, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(158)
    X(q[101])
    X(q[76])
    X(q[33])
    H(q[141])
    CNOT(q[141], q[88])
    Rz(q[141], PI / 8)
    CZ(q[88], q[143])
    print("scenario", "water_network", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(158))
}
```

### 590. `590_scenario_water_network_10.sq` — 590 scenario water network 10

```sansqrit
# Scenario 290: Water-network leakage localization: scenario 10
# Strategy: sparse + sharded execution for 150 logical qubits.
simulate(150, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(150)
    X(q[62])
    X(q[137])
    X(q[28])
    H(q[79])
    CNOT(q[79], q[38])
    Rz(q[79], PI / 4)
    CZ(q[38], q[65])
    print("scenario", "water_network", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(150))
}
```

### 591. `591_scenario_water_network_11.sq` — 591 scenario water network 11

```sansqrit
# Scenario 291: Water-network leakage localization: scenario 11
# Strategy: sparse + sharded execution for 152 logical qubits.
simulate(152, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(152)
    X(q[61])
    X(q[38])
    X(q[67])
    H(q[81])
    CNOT(q[81], q[108])
    Rz(q[81], PI / 5)
    CZ(q[108], q[131])
    print("scenario", "water_network", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(152))
}
```

### 592. `592_scenario_water_network_12.sq` — 592 scenario water network 12

```sansqrit
# Scenario 292: Water-network leakage localization: scenario 12
# Strategy: sparse + sharded execution for 154 logical qubits.
simulate(154, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(154)
    X(q[110])
    X(q[129])
    X(q[100])
    H(q[58])
    CNOT(q[58], q[93])
    Rz(q[58], PI / 6)
    CZ(q[93], q[12])
    print("scenario", "water_network", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(154))
}
```

### 593. `593_scenario_water_network_13.sq` — 593 scenario water network 13

```sansqrit
# Scenario 293: Water-network leakage localization: scenario 13
# Strategy: sparse + sharded execution for 156 logical qubits.
simulate(156, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(156)
    X(q[47])
    X(q[112])
    X(q[41])
    H(q[35])
    CNOT(q[35], q[134])
    Rz(q[35], PI / 7)
    CZ(q[134], q[133])
    print("scenario", "water_network", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(156))
}
```

### 594. `594_scenario_water_network_14.sq` — 594 scenario water network 14

```sansqrit
# Scenario 294: Water-network leakage localization: scenario 14
# Strategy: sparse + sharded execution for 158 logical qubits.
simulate(158, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(158)
    X(q[18])
    X(q[11])
    X(q[142])
    H(q[86])
    CNOT(q[86], q[83])
    Rz(q[86], PI / 8)
    CZ(q[83], q[42])
    print("scenario", "water_network", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(158))
}
```

### 595. `595_scenario_water_network_15.sq` — 595 scenario water network 15

```sansqrit
# Scenario 295: Water-network leakage localization: scenario 15
# Strategy: sparse + sharded execution for 150 logical qubits.
simulate(150, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(150)
    X(q[67])
    X(q[24])
    X(q[43])
    H(q[127])
    CNOT(q[127], q[22])
    Rz(q[127], PI / 4)
    CZ(q[22], q[93])
    print("scenario", "water_network", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(150))
}
```

### 596. `596_scenario_water_network_16.sq` — 596 scenario water network 16

```sansqrit
# Scenario 296: Water-network leakage localization: scenario 16
# Strategy: sparse + sharded execution for 152 logical qubits.
simulate(152, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(152)
    X(q[38])
    X(q[135])
    X(q[28])
    H(q[74])
    CNOT(q[74], q[45])
    Rz(q[74], PI / 5)
    CZ(q[45], q[20])
    print("scenario", "water_network", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(152))
}
```

### 597. `597_scenario_water_network_17.sq` — 597 scenario water network 17

```sansqrit
# Scenario 297: Water-network leakage localization: scenario 17
# Strategy: sparse + sharded execution for 154 logical qubits.
simulate(154, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(154)
    X(q[63])
    X(q[70])
    X(q[3])
    H(q[15])
    CNOT(q[15], q[98])
    Rz(q[15], PI / 6)
    CZ(q[98], q[138])
    print("scenario", "water_network", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(154))
}
```

### 598. `598_scenario_water_network_18.sq` — 598 scenario water network 18

```sansqrit
# Scenario 298: Water-network leakage localization: scenario 18
# Strategy: sparse + sharded execution for 156 logical qubits.
simulate(156, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(156)
    X(q[136])
    X(q[7])
    X(q[78])
    H(q[153])
    CNOT(q[153], q[130])
    Rz(q[153], PI / 7)
    CZ(q[130], q[43])
    print("scenario", "water_network", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(156))
}
```

### 599. `599_scenario_water_network_19.sq` — 599 scenario water network 19

```sansqrit
# Scenario 299: Water-network leakage localization: scenario 19
# Strategy: sparse + sharded execution for 158 logical qubits.
simulate(158, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(158)
    X(q[93])
    X(q[104])
    X(q[99])
    H(q[31])
    CNOT(q[31], q[78])
    Rz(q[31], PI / 8)
    CZ(q[78], q[80])
    print("scenario", "water_network", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(158))
}
```

### 600. `600_scenario_water_network_20.sq` — 600 scenario water network 20

```sansqrit
# Scenario 300: Water-network leakage localization: scenario 20
# Strategy: sparse + sharded execution for 150 logical qubits.
simulate(150, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(150)
    X(q[72])
    X(q[119])
    X(q[48])
    H(q[42])
    CNOT(q[42], q[57])
    Rz(q[42], PI / 4)
    CZ(q[57], q[8])
    print("scenario", "water_network", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(150))
}
```

### 601. `601_scenario_agriculture_01.sq` — 601 scenario agriculture 01

```sansqrit
# Scenario 301: Precision-agriculture irrigation scheduling: scenario 01
# Strategy: sparse + sharded execution for 130 logical qubits.
simulate(130, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(130)
    X(q[23])
    X(q[58])
    X(q[69])
    H(q[25])
    CNOT(q[25], q[14])
    Rz(q[25], PI / 5)
    CZ(q[14], q[71])
    print("scenario", "agriculture", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(130))
}
```

### 602. `602_scenario_agriculture_02.sq` — 602 scenario agriculture 02

```sansqrit
# Scenario 302: Precision-agriculture irrigation scheduling: scenario 02
# Strategy: sparse + sharded execution for 132 logical qubits.
simulate(132, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(132)
    X(q[104])
    X(q[59])
    X(q[106])
    H(q[121])
    CNOT(q[121], q[38])
    Rz(q[121], PI / 6)
    CZ(q[38], q[71])
    print("scenario", "agriculture", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(132))
}
```

### 603. `603_scenario_agriculture_03.sq` — 603 scenario agriculture 03

```sansqrit
# Scenario 303: Precision-agriculture irrigation scheduling: scenario 03
# Strategy: sparse + sharded execution for 134 logical qubits.
simulate(134, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(134)
    X(q[83])
    X(q[108])
    X(q[107])
    H(q[65])
    CNOT(q[65], q[48])
    Rz(q[65], PI / 7)
    CZ(q[48], q[9])
    print("scenario", "agriculture", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(134))
}
```

### 604. `604_scenario_agriculture_04.sq` — 604 scenario agriculture 04

```sansqrit
# Scenario 304: Precision-agriculture irrigation scheduling: scenario 04
# Strategy: sparse + sharded execution for 136 logical qubits.
simulate(136, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(136)
    X(q[78])
    X(q[7])
    X(q[68])
    H(q[18])
    CNOT(q[18], q[37])
    Rz(q[18], PI / 8)
    CZ(q[37], q[36])
    print("scenario", "agriculture", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(136))
}
```

### 605. `605_scenario_agriculture_05.sq` — 605 scenario agriculture 05

```sansqrit
# Scenario 305: Precision-agriculture irrigation scheduling: scenario 05
# Strategy: sparse + sharded execution for 128 logical qubits.
simulate(128, engine="sharded", n_shards=12, workers=4, use_lookup=true) {
    q = quantum_register(128)
    X(q[51])
    X(q[28])
    X(q[41])
    H(q[47])
    CNOT(q[47], q[66])
    Rz(q[47], PI / 4)
    CZ(q[66], q[25])
    print("scenario", "agriculture", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(128))
}
```

### 606. `606_scenario_agriculture_06.sq` — 606 scenario agriculture 06

```sansqrit
# Scenario 306: Precision-agriculture irrigation scheduling: scenario 06
# Strategy: sparse + sharded execution for 130 logical qubits.
simulate(130, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(130)
    X(q[8])
    X(q[73])
    X(q[54])
    H(q[100])
    CNOT(q[100], q[69])
    Rz(q[100], PI / 5)
    CZ(q[69], q[16])
    print("scenario", "agriculture", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(130))
}
```

### 607. `607_scenario_agriculture_07.sq` — 607 scenario agriculture 07

```sansqrit
# Scenario 307: Precision-agriculture irrigation scheduling: scenario 07
# Strategy: sparse + sharded execution for 132 logical qubits.
simulate(132, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(132)
    X(q[13])
    X(q[18])
    X(q[7])
    H(q[61])
    CNOT(q[61], q[64])
    Rz(q[61], PI / 6)
    CZ(q[64], q[87])
    print("scenario", "agriculture", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(132))
}
```

### 608. `608_scenario_agriculture_08.sq` — 608 scenario agriculture 08

```sansqrit
# Scenario 308: Precision-agriculture irrigation scheduling: scenario 08
# Strategy: sparse + sharded execution for 134 logical qubits.
simulate(134, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(134)
    X(q[54])
    X(q[69])
    X(q[110])
    H(q[92])
    CNOT(q[92], q[15])
    Rz(q[92], PI / 7)
    CZ(q[15], q[94])
    print("scenario", "agriculture", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(134))
}
```

### 609. `609_scenario_agriculture_09.sq` — 609 scenario agriculture 09

```sansqrit
# Scenario 309: Precision-agriculture irrigation scheduling: scenario 09
# Strategy: sparse + sharded execution for 136 logical qubits.
simulate(136, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(136)
    X(q[119])
    X(q[80])
    X(q[125])
    H(q[19])
    CNOT(q[19], q[62])
    Rz(q[19], PI / 8)
    CZ(q[62], q[117])
    print("scenario", "agriculture", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(136))
}
```

### 610. `610_scenario_agriculture_10.sq` — 610 scenario agriculture 10

```sansqrit
# Scenario 310: Precision-agriculture irrigation scheduling: scenario 10
# Strategy: sparse + sharded execution for 128 logical qubits.
simulate(128, engine="sharded", n_shards=12, workers=4, use_lookup=true) {
    q = quantum_register(128)
    X(q[116])
    X(q[45])
    X(q[10])
    H(q[56])
    CNOT(q[56], q[27])
    Rz(q[56], PI / 4)
    CZ(q[27], q[66])
    print("scenario", "agriculture", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(128))
}
```

### 611. `611_scenario_agriculture_11.sq` — 611 scenario agriculture 11

```sansqrit
# Scenario 311: Precision-agriculture irrigation scheduling: scenario 11
# Strategy: sparse + sharded execution for 130 logical qubits.
simulate(130, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(130)
    X(q[123])
    X(q[88])
    X(q[39])
    H(q[45])
    CNOT(q[45], q[124])
    Rz(q[45], PI / 5)
    CZ(q[124], q[91])
    print("scenario", "agriculture", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(130))
}
```

### 612. `612_scenario_agriculture_12.sq` — 612 scenario agriculture 12

```sansqrit
# Scenario 312: Precision-agriculture irrigation scheduling: scenario 12
# Strategy: sparse + sharded execution for 132 logical qubits.
simulate(132, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(132)
    X(q[54])
    X(q[47])
    X(q[108])
    H(q[18])
    CNOT(q[18], q[69])
    Rz(q[18], PI / 6)
    CZ(q[69], q[68])
    print("scenario", "agriculture", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(132))
}
```

### 613. `613_scenario_agriculture_13.sq` — 613 scenario agriculture 13

```sansqrit
# Scenario 313: Precision-agriculture irrigation scheduling: scenario 13
# Strategy: sparse + sharded execution for 134 logical qubits.
simulate(134, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(134)
    X(q[25])
    X(q[30])
    X(q[113])
    H(q[119])
    CNOT(q[119], q[116])
    Rz(q[119], PI / 7)
    CZ(q[116], q[45])
    print("scenario", "agriculture", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(134))
}
```

### 614. `614_scenario_agriculture_14.sq` — 614 scenario agriculture 14

```sansqrit
# Scenario 314: Precision-agriculture irrigation scheduling: scenario 14
# Strategy: sparse + sharded execution for 136 logical qubits.
simulate(136, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(136)
    X(q[24])
    X(q[17])
    X(q[46])
    H(q[20])
    CNOT(q[20], q[87])
    Rz(q[20], PI / 8)
    CZ(q[87], q[62])
    print("scenario", "agriculture", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(136))
}
```

### 615. `615_scenario_agriculture_15.sq` — 615 scenario agriculture 15

```sansqrit
# Scenario 315: Precision-agriculture irrigation scheduling: scenario 15
# Strategy: sparse + sharded execution for 128 logical qubits.
simulate(128, engine="sharded", n_shards=12, workers=4, use_lookup=true) {
    q = quantum_register(128)
    X(q[53])
    X(q[62])
    X(q[107])
    H(q[65])
    CNOT(q[65], q[116])
    Rz(q[65], PI / 4)
    CZ(q[116], q[124])
    print("scenario", "agriculture", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(128))
}
```

### 616. `616_scenario_agriculture_16.sq` — 616 scenario agriculture 16

```sansqrit
# Scenario 316: Precision-agriculture irrigation scheduling: scenario 16
# Strategy: sparse + sharded execution for 130 logical qubits.
simulate(130, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(130)
    X(q[108])
    X(q[103])
    X(q[24])
    H(q[120])
    CNOT(q[120], q[49])
    Rz(q[120], PI / 5)
    CZ(q[49], q[36])
    print("scenario", "agriculture", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(130))
}
```

### 617. `617_scenario_agriculture_17.sq` — 617 scenario agriculture 17

```sansqrit
# Scenario 317: Precision-agriculture irrigation scheduling: scenario 17
# Strategy: sparse + sharded execution for 132 logical qubits.
simulate(132, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(132)
    X(q[95])
    X(q[76])
    X(q[77])
    H(q[107])
    CNOT(q[107], q[74])
    Rz(q[107], PI / 6)
    CZ(q[74], q[49])
    print("scenario", "agriculture", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(132))
}
```

### 618. `618_scenario_agriculture_18.sq` — 618 scenario agriculture 18

```sansqrit
# Scenario 318: Precision-agriculture irrigation scheduling: scenario 18
# Strategy: sparse + sharded execution for 134 logical qubits.
simulate(134, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(134)
    X(q[130])
    X(q[125])
    X(q[116])
    H(q[12])
    CNOT(q[12], q[83])
    Rz(q[12], PI / 7)
    CZ(q[83], q[97])
    print("scenario", "agriculture", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(134))
}
```

### 619. `619_scenario_agriculture_19.sq` — 619 scenario agriculture 19

```sansqrit
# Scenario 319: Precision-agriculture irrigation scheduling: scenario 19
# Strategy: sparse + sharded execution for 136 logical qubits.
simulate(136, engine="sharded", n_shards=13, workers=4, use_lookup=true) {
    q = quantum_register(136)
    X(q[65])
    X(q[90])
    X(q[103])
    H(q[21])
    CNOT(q[21], q[112])
    Rz(q[21], PI / 8)
    CZ(q[112], q[7])
    print("scenario", "agriculture", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(136))
}
```

### 620. `620_scenario_agriculture_20.sq` — 620 scenario agriculture 20

```sansqrit
# Scenario 320: Precision-agriculture irrigation scheduling: scenario 20
# Strategy: sparse + sharded execution for 128 logical qubits.
simulate(128, engine="sharded", n_shards=12, workers=4, use_lookup=true) {
    q = quantum_register(128)
    X(q[118])
    X(q[79])
    X(q[76])
    H(q[74])
    CNOT(q[74], q[77])
    Rz(q[74], PI / 4)
    CZ(q[77], q[20])
    print("scenario", "agriculture", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(128))
}
```

### 621. `621_scenario_manufacturing_01.sq` — 621 scenario manufacturing 01

```sansqrit
# Scenario 321: Manufacturing quality-control root-cause graph: scenario 01
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(152, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(152)
    block_A = shard("block_A", 10, 19)
    block_B = shard("block_B", 20, 29)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[19], q[20])
    Rz(q[21], PI / 4)
    print("scenario", "manufacturing", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 622. `622_scenario_manufacturing_02.sq` — 622 scenario manufacturing 02

```sansqrit
# Scenario 322: Manufacturing quality-control root-cause graph: scenario 02
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(154, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(154)
    block_A = shard("block_A", 20, 29)
    block_B = shard("block_B", 30, 39)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[29], q[30])
    Rz(q[31], PI / 5)
    print("scenario", "manufacturing", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 623. `623_scenario_manufacturing_03.sq` — 623 scenario manufacturing 03

```sansqrit
# Scenario 323: Manufacturing quality-control root-cause graph: scenario 03
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(156, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(156)
    block_A = shard("block_A", 30, 39)
    block_B = shard("block_B", 40, 49)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[39], q[40])
    Rz(q[41], PI / 6)
    print("scenario", "manufacturing", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 624. `624_scenario_manufacturing_04.sq` — 624 scenario manufacturing 04

```sansqrit
# Scenario 324: Manufacturing quality-control root-cause graph: scenario 04
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(158, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(158)
    block_A = shard("block_A", 0, 9)
    block_B = shard("block_B", 10, 19)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[9], q[10])
    Rz(q[11], PI / 7)
    print("scenario", "manufacturing", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 625. `625_scenario_manufacturing_05.sq` — 625 scenario manufacturing 05

```sansqrit
# Scenario 325: Manufacturing quality-control root-cause graph: scenario 05
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(150, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(150)
    block_A = shard("block_A", 10, 19)
    block_B = shard("block_B", 20, 29)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[19], q[20])
    Rz(q[21], PI / 8)
    print("scenario", "manufacturing", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 626. `626_scenario_manufacturing_06.sq` — 626 scenario manufacturing 06

```sansqrit
# Scenario 326: Manufacturing quality-control root-cause graph: scenario 06
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(152, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(152)
    block_A = shard("block_A", 20, 29)
    block_B = shard("block_B", 30, 39)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[29], q[30])
    Rz(q[31], PI / 3)
    print("scenario", "manufacturing", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 627. `627_scenario_manufacturing_07.sq` — 627 scenario manufacturing 07

```sansqrit
# Scenario 327: Manufacturing quality-control root-cause graph: scenario 07
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(154, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(154)
    block_A = shard("block_A", 30, 39)
    block_B = shard("block_B", 40, 49)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[39], q[40])
    Rz(q[41], PI / 4)
    print("scenario", "manufacturing", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 628. `628_scenario_manufacturing_08.sq` — 628 scenario manufacturing 08

```sansqrit
# Scenario 328: Manufacturing quality-control root-cause graph: scenario 08
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(156, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(156)
    block_A = shard("block_A", 0, 9)
    block_B = shard("block_B", 10, 19)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[9], q[10])
    Rz(q[11], PI / 5)
    print("scenario", "manufacturing", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 629. `629_scenario_manufacturing_09.sq` — 629 scenario manufacturing 09

```sansqrit
# Scenario 329: Manufacturing quality-control root-cause graph: scenario 09
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(158, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(158)
    block_A = shard("block_A", 10, 19)
    block_B = shard("block_B", 20, 29)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[19], q[20])
    Rz(q[21], PI / 6)
    print("scenario", "manufacturing", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 630. `630_scenario_manufacturing_10.sq` — 630 scenario manufacturing 10

```sansqrit
# Scenario 330: Manufacturing quality-control root-cause graph: scenario 10
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(150, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(150)
    block_A = shard("block_A", 20, 29)
    block_B = shard("block_B", 30, 39)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[29], q[30])
    Rz(q[31], PI / 7)
    print("scenario", "manufacturing", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 631. `631_scenario_manufacturing_11.sq` — 631 scenario manufacturing 11

```sansqrit
# Scenario 331: Manufacturing quality-control root-cause graph: scenario 11
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(152, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(152)
    block_A = shard("block_A", 30, 39)
    block_B = shard("block_B", 40, 49)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[39], q[40])
    Rz(q[41], PI / 8)
    print("scenario", "manufacturing", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 632. `632_scenario_manufacturing_12.sq` — 632 scenario manufacturing 12

```sansqrit
# Scenario 332: Manufacturing quality-control root-cause graph: scenario 12
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(154, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(154)
    block_A = shard("block_A", 0, 9)
    block_B = shard("block_B", 10, 19)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[9], q[10])
    Rz(q[11], PI / 3)
    print("scenario", "manufacturing", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 633. `633_scenario_manufacturing_13.sq` — 633 scenario manufacturing 13

```sansqrit
# Scenario 333: Manufacturing quality-control root-cause graph: scenario 13
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(156, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(156)
    block_A = shard("block_A", 10, 19)
    block_B = shard("block_B", 20, 29)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[19], q[20])
    Rz(q[21], PI / 4)
    print("scenario", "manufacturing", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 634. `634_scenario_manufacturing_14.sq` — 634 scenario manufacturing 14

```sansqrit
# Scenario 334: Manufacturing quality-control root-cause graph: scenario 14
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(158, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(158)
    block_A = shard("block_A", 20, 29)
    block_B = shard("block_B", 30, 39)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[29], q[30])
    Rz(q[31], PI / 5)
    print("scenario", "manufacturing", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 635. `635_scenario_manufacturing_15.sq` — 635 scenario manufacturing 15

```sansqrit
# Scenario 335: Manufacturing quality-control root-cause graph: scenario 15
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(150, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(150)
    block_A = shard("block_A", 30, 39)
    block_B = shard("block_B", 40, 49)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[39], q[40])
    Rz(q[41], PI / 6)
    print("scenario", "manufacturing", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 636. `636_scenario_manufacturing_16.sq` — 636 scenario manufacturing 16

```sansqrit
# Scenario 336: Manufacturing quality-control root-cause graph: scenario 16
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(152, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(152)
    block_A = shard("block_A", 0, 9)
    block_B = shard("block_B", 10, 19)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[9], q[10])
    Rz(q[11], PI / 7)
    print("scenario", "manufacturing", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 637. `637_scenario_manufacturing_17.sq` — 637 scenario manufacturing 17

```sansqrit
# Scenario 337: Manufacturing quality-control root-cause graph: scenario 17
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(154, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(154)
    block_A = shard("block_A", 10, 19)
    block_B = shard("block_B", 20, 29)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[19], q[20])
    Rz(q[21], PI / 8)
    print("scenario", "manufacturing", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 638. `638_scenario_manufacturing_18.sq` — 638 scenario manufacturing 18

```sansqrit
# Scenario 338: Manufacturing quality-control root-cause graph: scenario 18
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(156, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(156)
    block_A = shard("block_A", 20, 29)
    block_B = shard("block_B", 30, 39)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[29], q[30])
    Rz(q[31], PI / 3)
    print("scenario", "manufacturing", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 639. `639_scenario_manufacturing_19.sq` — 639 scenario manufacturing 19

```sansqrit
# Scenario 339: Manufacturing quality-control root-cause graph: scenario 19
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(158, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(158)
    block_A = shard("block_A", 30, 39)
    block_B = shard("block_B", 40, 49)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[39], q[40])
    Rz(q[41], PI / 4)
    print("scenario", "manufacturing", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 640. `640_scenario_manufacturing_20.sq` — 640 scenario manufacturing 20

```sansqrit
# Scenario 340: Manufacturing quality-control root-cause graph: scenario 20
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(150, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(150)
    block_A = shard("block_A", 0, 9)
    block_B = shard("block_B", 10, 19)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[9], q[10])
    Rz(q[11], PI / 5)
    print("scenario", "manufacturing", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 641. `641_scenario_fraud_01.sq` — 641 scenario fraud 01

```sansqrit
# Scenario 341: Payments fraud graph screening: scenario 01
# Strategy: sparse + sharded execution for 152 logical qubits.
simulate(152, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(152)
    X(q[135])
    X(q[96])
    X(q[133])
    H(q[11])
    CNOT(q[11], q[86])
    Rz(q[11], PI / 5)
    CZ(q[86], q[85])
    print("scenario", "fraud", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(152))
}
```

### 642. `642_scenario_fraud_02.sq` — 642 scenario fraud 02

```sansqrit
# Scenario 342: Payments fraud graph screening: scenario 02
# Strategy: sparse + sharded execution for 154 logical qubits.
simulate(154, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(154)
    X(q[102])
    X(q[1])
    X(q[54])
    H(q[90])
    CNOT(q[90], q[143])
    Rz(q[90], PI / 6)
    CZ(q[143], q[42])
    print("scenario", "fraud", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(154))
}
```

### 643. `643_scenario_fraud_03.sq` — 643 scenario fraud 03

```sansqrit
# Scenario 343: Payments fraud graph screening: scenario 03
# Strategy: sparse + sharded execution for 156 logical qubits.
simulate(156, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(156)
    X(q[1])
    X(q[54])
    X(q[151])
    H(q[109])
    CNOT(q[109], q[112])
    Rz(q[109], PI / 7)
    CZ(q[112], q[51])
    print("scenario", "fraud", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(156))
}
```

### 644. `644_scenario_fraud_04.sq` — 644 scenario fraud 04

```sansqrit
# Scenario 344: Payments fraud graph screening: scenario 04
# Strategy: sparse + sharded execution for 158 logical qubits.
simulate(158, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(158)
    X(q[136])
    X(q[151])
    X(q[126])
    H(q[10])
    CNOT(q[10], q[33])
    Rz(q[10], PI / 8)
    CZ(q[33], q[138])
    print("scenario", "fraud", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(158))
}
```

### 645. `645_scenario_fraud_05.sq` — 645 scenario fraud 05

```sansqrit
# Scenario 345: Payments fraud graph screening: scenario 05
# Strategy: sparse + sharded execution for 150 logical qubits.
simulate(150, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(150)
    X(q[117])
    X(q[74])
    X(q[93])
    H(q[27])
    CNOT(q[27], q[72])
    Rz(q[27], PI / 4)
    CZ(q[72], q[143])
    print("scenario", "fraud", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(150))
}
```

### 646. `646_scenario_fraud_06.sq` — 646 scenario fraud 06

```sansqrit
# Scenario 346: Payments fraud graph screening: scenario 06
# Strategy: sparse + sharded execution for 152 logical qubits.
simulate(152, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(152)
    X(q[112])
    X(q[41])
    X(q[94])
    H(q[4])
    CNOT(q[4], q[23])
    Rz(q[4], PI / 5)
    CZ(q[23], q[126])
    print("scenario", "fraud", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(152))
}
```

### 647. `647_scenario_fraud_07.sq` — 647 scenario fraud 07

```sansqrit
# Scenario 347: Payments fraud graph screening: scenario 07
# Strategy: sparse + sharded execution for 154 logical qubits.
simulate(154, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(154)
    X(q[55])
    X(q[96])
    X(q[111])
    H(q[47])
    CNOT(q[47], q[148])
    Rz(q[47], PI / 6)
    CZ(q[148], q[45])
    print("scenario", "fraud", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(154))
}
```

### 648. `648_scenario_fraud_08.sq` — 648 scenario fraud 08

```sansqrit
# Scenario 348: Payments fraud graph screening: scenario 08
# Strategy: sparse + sharded execution for 156 logical qubits.
simulate(156, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(156)
    X(q[90])
    X(q[95])
    X(q[84])
    H(q[54])
    CNOT(q[54], q[141])
    Rz(q[54], PI / 7)
    CZ(q[141], q[152])
    print("scenario", "fraud", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(156))
}
```

### 649. `649_scenario_fraud_09.sq` — 649 scenario fraud 09

```sansqrit
# Scenario 349: Payments fraud graph screening: scenario 09
# Strategy: sparse + sharded execution for 158 logical qubits.
simulate(158, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(158)
    X(q[53])
    X(q[86])
    X(q[77])
    H(q[113])
    CNOT(q[113], q[28])
    Rz(q[113], PI / 8)
    CZ(q[28], q[37])
    print("scenario", "fraud", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(158))
}
```

### 650. `650_scenario_fraud_10.sq` — 650 scenario fraud 10

```sansqrit
# Scenario 350: Payments fraud graph screening: scenario 10
# Strategy: sparse + sharded execution for 150 logical qubits.
simulate(150, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(150)
    X(q[122])
    X(q[19])
    X(q[98])
    H(q[92])
    CNOT(q[92], q[107])
    Rz(q[92], PI / 4)
    CZ(q[107], q[58])
    print("scenario", "fraud", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(150))
}
```

### 651. `651_scenario_fraud_11.sq` — 651 scenario fraud 11

```sansqrit
# Scenario 351: Payments fraud graph screening: scenario 11
# Strategy: sparse + sharded execution for 152 logical qubits.
simulate(152, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(152)
    X(q[89])
    X(q[138])
    X(q[55])
    H(q[149])
    CNOT(q[149], q[112])
    Rz(q[149], PI / 5)
    CZ(q[112], q[15])
    print("scenario", "fraud", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(152))
}
```

### 652. `652_scenario_fraud_12.sq` — 652 scenario fraud 12

```sansqrit
# Scenario 352: Payments fraud graph screening: scenario 12
# Strategy: sparse + sharded execution for 154 logical qubits.
simulate(154, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(154)
    X(q[8])
    X(q[37])
    X(q[14])
    H(q[4])
    CNOT(q[4], q[153])
    Rz(q[4], PI / 6)
    CZ(q[153], q[48])
    print("scenario", "fraud", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(154))
}
```

### 653. `653_scenario_fraud_13.sq` — 653 scenario fraud 13

```sansqrit
# Scenario 353: Payments fraud graph screening: scenario 13
# Strategy: sparse + sharded execution for 156 logical qubits.
simulate(156, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(156)
    X(q[23])
    X(q[136])
    X(q[17])
    H(q[155])
    CNOT(q[155], q[14])
    Rz(q[155], PI / 7)
    CZ(q[14], q[97])
    print("scenario", "fraud", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(156))
}
```

### 654. `654_scenario_fraud_14.sq` — 654 scenario fraud 14

```sansqrit
# Scenario 354: Payments fraud graph screening: scenario 14
# Strategy: sparse + sharded execution for 158 logical qubits.
simulate(158, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(158)
    X(q[128])
    X(q[21])
    X(q[28])
    H(q[58])
    CNOT(q[58], q[23])
    Rz(q[58], PI / 8)
    CZ(q[23], q[94])
    print("scenario", "fraud", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(158))
}
```

### 655. `655_scenario_fraud_15.sq` — 655 scenario fraud 15

```sansqrit
# Scenario 355: Payments fraud graph screening: scenario 15
# Strategy: sparse + sharded execution for 150 logical qubits.
simulate(150, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(150)
    X(q[127])
    X(q[114])
    X(q[103])
    H(q[7])
    CNOT(q[7], q[142])
    Rz(q[7], PI / 4)
    CZ(q[142], q[123])
    print("scenario", "fraud", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(150))
}
```

### 656. `656_scenario_fraud_16.sq` — 656 scenario fraud 16

```sansqrit
# Scenario 356: Payments fraud graph screening: scenario 16
# Strategy: sparse + sharded execution for 152 logical qubits.
simulate(152, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(152)
    X(q[66])
    X(q[83])
    X(q[16])
    H(q[142])
    CNOT(q[142], q[49])
    Rz(q[142], PI / 5)
    CZ(q[49], q[56])
    print("scenario", "fraud", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(152))
}
```

### 657. `657_scenario_fraud_17.sq` — 657 scenario fraud 17

```sansqrit
# Scenario 357: Payments fraud graph screening: scenario 17
# Strategy: sparse + sharded execution for 154 logical qubits.
simulate(154, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(154)
    X(q[115])
    X(q[4])
    X(q[51])
    H(q[132])
    CNOT(q[132], q[71])
    Rz(q[132], PI / 6)
    CZ(q[71], q[148])
    print("scenario", "fraud", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(154))
}
```

### 658. `658_scenario_fraud_18.sq` — 658 scenario fraud 18

```sansqrit
# Scenario 358: Payments fraud graph screening: scenario 18
# Strategy: sparse + sharded execution for 156 logical qubits.
simulate(156, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(156)
    X(q[112])
    X(q[21])
    X(q[106])
    H(q[100])
    CNOT(q[100], q[43])
    Rz(q[100], PI / 7)
    CZ(q[43], q[42])
    print("scenario", "fraud", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(156))
}
```

### 659. `659_scenario_fraud_19.sq` — 659 scenario fraud 19

```sansqrit
# Scenario 359: Payments fraud graph screening: scenario 19
# Strategy: sparse + sharded execution for 158 logical qubits.
simulate(158, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(158)
    X(q[45])
    X(q[114])
    X(q[137])
    H(q[3])
    CNOT(q[3], q[18])
    Rz(q[3], PI / 8)
    CZ(q[18], q[151])
    print("scenario", "fraud", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(158))
}
```

### 660. `660_scenario_fraud_20.sq` — 660 scenario fraud 20

```sansqrit
# Scenario 360: Payments fraud graph screening: scenario 20
# Strategy: sparse + sharded execution for 150 logical qubits.
simulate(150, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(150)
    X(q[132])
    X(q[59])
    X(q[108])
    H(q[72])
    CNOT(q[72], q[27])
    Rz(q[72], PI / 4)
    CZ(q[27], q[38])
    print("scenario", "fraud", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(150))
}
```

### 661. `661_scenario_iot_edge_01.sq` — 661 scenario iot edge 01

```sansqrit
# Scenario 361: IoT edge fleet anomaly grouping: scenario 01
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(122, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(122)
    block_A = shard("block_A", 10, 19)
    block_B = shard("block_B", 20, 29)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[19], q[20])
    Rz(q[21], PI / 4)
    print("scenario", "iot_edge", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 662. `662_scenario_iot_edge_02.sq` — 662 scenario iot edge 02

```sansqrit
# Scenario 362: IoT edge fleet anomaly grouping: scenario 02
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(124, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(124)
    block_A = shard("block_A", 20, 29)
    block_B = shard("block_B", 30, 39)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[29], q[30])
    Rz(q[31], PI / 5)
    print("scenario", "iot_edge", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 663. `663_scenario_iot_edge_03.sq` — 663 scenario iot edge 03

```sansqrit
# Scenario 363: IoT edge fleet anomaly grouping: scenario 03
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(126, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(126)
    block_A = shard("block_A", 30, 39)
    block_B = shard("block_B", 40, 49)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[39], q[40])
    Rz(q[41], PI / 6)
    print("scenario", "iot_edge", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 664. `664_scenario_iot_edge_04.sq` — 664 scenario iot edge 04

```sansqrit
# Scenario 364: IoT edge fleet anomaly grouping: scenario 04
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(128, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(128)
    block_A = shard("block_A", 0, 9)
    block_B = shard("block_B", 10, 19)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[9], q[10])
    Rz(q[11], PI / 7)
    print("scenario", "iot_edge", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 665. `665_scenario_iot_edge_05.sq` — 665 scenario iot edge 05

```sansqrit
# Scenario 365: IoT edge fleet anomaly grouping: scenario 05
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(120, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(120)
    block_A = shard("block_A", 10, 19)
    block_B = shard("block_B", 20, 29)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[19], q[20])
    Rz(q[21], PI / 8)
    print("scenario", "iot_edge", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 666. `666_scenario_iot_edge_06.sq` — 666 scenario iot edge 06

```sansqrit
# Scenario 366: IoT edge fleet anomaly grouping: scenario 06
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(122, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(122)
    block_A = shard("block_A", 20, 29)
    block_B = shard("block_B", 30, 39)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[29], q[30])
    Rz(q[31], PI / 3)
    print("scenario", "iot_edge", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 667. `667_scenario_iot_edge_07.sq` — 667 scenario iot edge 07

```sansqrit
# Scenario 367: IoT edge fleet anomaly grouping: scenario 07
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(124, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(124)
    block_A = shard("block_A", 30, 39)
    block_B = shard("block_B", 40, 49)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[39], q[40])
    Rz(q[41], PI / 4)
    print("scenario", "iot_edge", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 668. `668_scenario_iot_edge_08.sq` — 668 scenario iot edge 08

```sansqrit
# Scenario 368: IoT edge fleet anomaly grouping: scenario 08
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(126, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(126)
    block_A = shard("block_A", 0, 9)
    block_B = shard("block_B", 10, 19)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[9], q[10])
    Rz(q[11], PI / 5)
    print("scenario", "iot_edge", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 669. `669_scenario_iot_edge_09.sq` — 669 scenario iot edge 09

```sansqrit
# Scenario 369: IoT edge fleet anomaly grouping: scenario 09
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(128, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(128)
    block_A = shard("block_A", 10, 19)
    block_B = shard("block_B", 20, 29)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[19], q[20])
    Rz(q[21], PI / 6)
    print("scenario", "iot_edge", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 670. `670_scenario_iot_edge_10.sq` — 670 scenario iot edge 10

```sansqrit
# Scenario 370: IoT edge fleet anomaly grouping: scenario 10
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(120, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(120)
    block_A = shard("block_A", 20, 29)
    block_B = shard("block_B", 30, 39)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[29], q[30])
    Rz(q[31], PI / 7)
    print("scenario", "iot_edge", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 671. `671_scenario_iot_edge_11.sq` — 671 scenario iot edge 11

```sansqrit
# Scenario 371: IoT edge fleet anomaly grouping: scenario 11
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(122, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(122)
    block_A = shard("block_A", 30, 39)
    block_B = shard("block_B", 40, 49)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[39], q[40])
    Rz(q[41], PI / 8)
    print("scenario", "iot_edge", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 672. `672_scenario_iot_edge_12.sq` — 672 scenario iot edge 12

```sansqrit
# Scenario 372: IoT edge fleet anomaly grouping: scenario 12
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(124, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(124)
    block_A = shard("block_A", 0, 9)
    block_B = shard("block_B", 10, 19)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[9], q[10])
    Rz(q[11], PI / 3)
    print("scenario", "iot_edge", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 673. `673_scenario_iot_edge_13.sq` — 673 scenario iot edge 13

```sansqrit
# Scenario 373: IoT edge fleet anomaly grouping: scenario 13
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(126, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(126)
    block_A = shard("block_A", 10, 19)
    block_B = shard("block_B", 20, 29)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[19], q[20])
    Rz(q[21], PI / 4)
    print("scenario", "iot_edge", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 674. `674_scenario_iot_edge_14.sq` — 674 scenario iot edge 14

```sansqrit
# Scenario 374: IoT edge fleet anomaly grouping: scenario 14
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(128, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(128)
    block_A = shard("block_A", 20, 29)
    block_B = shard("block_B", 30, 39)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[29], q[30])
    Rz(q[31], PI / 5)
    print("scenario", "iot_edge", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 675. `675_scenario_iot_edge_15.sq` — 675 scenario iot edge 15

```sansqrit
# Scenario 375: IoT edge fleet anomaly grouping: scenario 15
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(120, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(120)
    block_A = shard("block_A", 30, 39)
    block_B = shard("block_B", 40, 49)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[39], q[40])
    Rz(q[41], PI / 6)
    print("scenario", "iot_edge", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 676. `676_scenario_iot_edge_16.sq` — 676 scenario iot edge 16

```sansqrit
# Scenario 376: IoT edge fleet anomaly grouping: scenario 16
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(122, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(122)
    block_A = shard("block_A", 0, 9)
    block_B = shard("block_B", 10, 19)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[9], q[10])
    Rz(q[11], PI / 7)
    print("scenario", "iot_edge", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 677. `677_scenario_iot_edge_17.sq` — 677 scenario iot edge 17

```sansqrit
# Scenario 377: IoT edge fleet anomaly grouping: scenario 17
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(124, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(124)
    block_A = shard("block_A", 10, 19)
    block_B = shard("block_B", 20, 29)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[19], q[20])
    Rz(q[21], PI / 8)
    print("scenario", "iot_edge", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 678. `678_scenario_iot_edge_18.sq` — 678 scenario iot edge 18

```sansqrit
# Scenario 378: IoT edge fleet anomaly grouping: scenario 18
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(126, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(126)
    block_A = shard("block_A", 20, 29)
    block_B = shard("block_B", 30, 39)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[29], q[30])
    Rz(q[31], PI / 3)
    print("scenario", "iot_edge", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 679. `679_scenario_iot_edge_19.sq` — 679 scenario iot edge 19

```sansqrit
# Scenario 379: IoT edge fleet anomaly grouping: scenario 19
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(128, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(128)
    block_A = shard("block_A", 30, 39)
    block_B = shard("block_B", 40, 49)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[39], q[40])
    Rz(q[41], PI / 4)
    print("scenario", "iot_edge", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 680. `680_scenario_iot_edge_20.sq` — 680 scenario iot edge 20

```sansqrit
# Scenario 380: IoT edge fleet anomaly grouping: scenario 20
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(120, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(120)
    block_A = shard("block_A", 0, 9)
    block_B = shard("block_B", 10, 19)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[9], q[10])
    Rz(q[11], PI / 5)
    print("scenario", "iot_edge", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 681. `681_scenario_quantum_network_01.sq` — 681 scenario quantum network 01

```sansqrit
# Scenario 381: Quantum-network repeater and QKD planning: scenario 01
# Strategy: Clifford-only large circuit via stabilizer tableau.
simulate(514, engine="stabilizer") {
    q = quantum_register(514)
    for i in range(0, 220, 21) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
        S(q[i + 2])
    }
    print("scenario", "quantum_network", "backend", plan_backend(514, []))
    print("sample", measure_all(shots=2))
}
```

### 682. `682_scenario_quantum_network_02.sq` — 682 scenario quantum network 02

```sansqrit
# Scenario 382: Quantum-network repeater and QKD planning: scenario 02
# Strategy: Clifford-only large circuit via stabilizer tableau.
simulate(516, engine="stabilizer") {
    q = quantum_register(516)
    for i in range(0, 220, 22) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
        S(q[i + 2])
    }
    print("scenario", "quantum_network", "backend", plan_backend(516, []))
    print("sample", measure_all(shots=2))
}
```

### 683. `683_scenario_quantum_network_03.sq` — 683 scenario quantum network 03

```sansqrit
# Scenario 383: Quantum-network repeater and QKD planning: scenario 03
# Strategy: Clifford-only large circuit via stabilizer tableau.
simulate(518, engine="stabilizer") {
    q = quantum_register(518)
    for i in range(0, 220, 23) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
        S(q[i + 2])
    }
    print("scenario", "quantum_network", "backend", plan_backend(518, []))
    print("sample", measure_all(shots=2))
}
```

### 684. `684_scenario_quantum_network_04.sq` — 684 scenario quantum network 04

```sansqrit
# Scenario 384: Quantum-network repeater and QKD planning: scenario 04
# Strategy: Clifford-only large circuit via stabilizer tableau.
simulate(520, engine="stabilizer") {
    q = quantum_register(520)
    for i in range(0, 220, 24) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
        S(q[i + 2])
    }
    print("scenario", "quantum_network", "backend", plan_backend(520, []))
    print("sample", measure_all(shots=2))
}
```

### 685. `685_scenario_quantum_network_05.sq` — 685 scenario quantum network 05

```sansqrit
# Scenario 385: Quantum-network repeater and QKD planning: scenario 05
# Strategy: Clifford-only large circuit via stabilizer tableau.
simulate(512, engine="stabilizer") {
    q = quantum_register(512)
    for i in range(0, 220, 25) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
        S(q[i + 2])
    }
    print("scenario", "quantum_network", "backend", plan_backend(512, []))
    print("sample", measure_all(shots=2))
}
```

### 686. `686_scenario_quantum_network_06.sq` — 686 scenario quantum network 06

```sansqrit
# Scenario 386: Quantum-network repeater and QKD planning: scenario 06
# Strategy: Clifford-only large circuit via stabilizer tableau.
simulate(514, engine="stabilizer") {
    q = quantum_register(514)
    for i in range(0, 220, 26) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
        S(q[i + 2])
    }
    print("scenario", "quantum_network", "backend", plan_backend(514, []))
    print("sample", measure_all(shots=2))
}
```

### 687. `687_scenario_quantum_network_07.sq` — 687 scenario quantum network 07

```sansqrit
# Scenario 387: Quantum-network repeater and QKD planning: scenario 07
# Strategy: Clifford-only large circuit via stabilizer tableau.
simulate(516, engine="stabilizer") {
    q = quantum_register(516)
    for i in range(0, 220, 20) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
        S(q[i + 2])
    }
    print("scenario", "quantum_network", "backend", plan_backend(516, []))
    print("sample", measure_all(shots=2))
}
```

### 688. `688_scenario_quantum_network_08.sq` — 688 scenario quantum network 08

```sansqrit
# Scenario 388: Quantum-network repeater and QKD planning: scenario 08
# Strategy: Clifford-only large circuit via stabilizer tableau.
simulate(518, engine="stabilizer") {
    q = quantum_register(518)
    for i in range(0, 220, 21) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
        S(q[i + 2])
    }
    print("scenario", "quantum_network", "backend", plan_backend(518, []))
    print("sample", measure_all(shots=2))
}
```

### 689. `689_scenario_quantum_network_09.sq` — 689 scenario quantum network 09

```sansqrit
# Scenario 389: Quantum-network repeater and QKD planning: scenario 09
# Strategy: Clifford-only large circuit via stabilizer tableau.
simulate(520, engine="stabilizer") {
    q = quantum_register(520)
    for i in range(0, 220, 22) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
        S(q[i + 2])
    }
    print("scenario", "quantum_network", "backend", plan_backend(520, []))
    print("sample", measure_all(shots=2))
}
```

### 690. `690_scenario_quantum_network_10.sq` — 690 scenario quantum network 10

```sansqrit
# Scenario 390: Quantum-network repeater and QKD planning: scenario 10
# Strategy: Clifford-only large circuit via stabilizer tableau.
simulate(512, engine="stabilizer") {
    q = quantum_register(512)
    for i in range(0, 220, 23) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
        S(q[i + 2])
    }
    print("scenario", "quantum_network", "backend", plan_backend(512, []))
    print("sample", measure_all(shots=2))
}
```

### 691. `691_scenario_quantum_network_11.sq` — 691 scenario quantum network 11

```sansqrit
# Scenario 391: Quantum-network repeater and QKD planning: scenario 11
# Strategy: Clifford-only large circuit via stabilizer tableau.
simulate(514, engine="stabilizer") {
    q = quantum_register(514)
    for i in range(0, 220, 24) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
        S(q[i + 2])
    }
    print("scenario", "quantum_network", "backend", plan_backend(514, []))
    print("sample", measure_all(shots=2))
}
```

### 692. `692_scenario_quantum_network_12.sq` — 692 scenario quantum network 12

```sansqrit
# Scenario 392: Quantum-network repeater and QKD planning: scenario 12
# Strategy: Clifford-only large circuit via stabilizer tableau.
simulate(516, engine="stabilizer") {
    q = quantum_register(516)
    for i in range(0, 220, 25) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
        S(q[i + 2])
    }
    print("scenario", "quantum_network", "backend", plan_backend(516, []))
    print("sample", measure_all(shots=2))
}
```

### 693. `693_scenario_quantum_network_13.sq` — 693 scenario quantum network 13

```sansqrit
# Scenario 393: Quantum-network repeater and QKD planning: scenario 13
# Strategy: Clifford-only large circuit via stabilizer tableau.
simulate(518, engine="stabilizer") {
    q = quantum_register(518)
    for i in range(0, 220, 26) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
        S(q[i + 2])
    }
    print("scenario", "quantum_network", "backend", plan_backend(518, []))
    print("sample", measure_all(shots=2))
}
```

### 694. `694_scenario_quantum_network_14.sq` — 694 scenario quantum network 14

```sansqrit
# Scenario 394: Quantum-network repeater and QKD planning: scenario 14
# Strategy: Clifford-only large circuit via stabilizer tableau.
simulate(520, engine="stabilizer") {
    q = quantum_register(520)
    for i in range(0, 220, 20) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
        S(q[i + 2])
    }
    print("scenario", "quantum_network", "backend", plan_backend(520, []))
    print("sample", measure_all(shots=2))
}
```

### 695. `695_scenario_quantum_network_15.sq` — 695 scenario quantum network 15

```sansqrit
# Scenario 395: Quantum-network repeater and QKD planning: scenario 15
# Strategy: Clifford-only large circuit via stabilizer tableau.
simulate(512, engine="stabilizer") {
    q = quantum_register(512)
    for i in range(0, 220, 21) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
        S(q[i + 2])
    }
    print("scenario", "quantum_network", "backend", plan_backend(512, []))
    print("sample", measure_all(shots=2))
}
```

### 696. `696_scenario_quantum_network_16.sq` — 696 scenario quantum network 16

```sansqrit
# Scenario 396: Quantum-network repeater and QKD planning: scenario 16
# Strategy: Clifford-only large circuit via stabilizer tableau.
simulate(514, engine="stabilizer") {
    q = quantum_register(514)
    for i in range(0, 220, 22) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
        S(q[i + 2])
    }
    print("scenario", "quantum_network", "backend", plan_backend(514, []))
    print("sample", measure_all(shots=2))
}
```

### 697. `697_scenario_quantum_network_17.sq` — 697 scenario quantum network 17

```sansqrit
# Scenario 397: Quantum-network repeater and QKD planning: scenario 17
# Strategy: Clifford-only large circuit via stabilizer tableau.
simulate(516, engine="stabilizer") {
    q = quantum_register(516)
    for i in range(0, 220, 23) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
        S(q[i + 2])
    }
    print("scenario", "quantum_network", "backend", plan_backend(516, []))
    print("sample", measure_all(shots=2))
}
```

### 698. `698_scenario_quantum_network_18.sq` — 698 scenario quantum network 18

```sansqrit
# Scenario 398: Quantum-network repeater and QKD planning: scenario 18
# Strategy: Clifford-only large circuit via stabilizer tableau.
simulate(518, engine="stabilizer") {
    q = quantum_register(518)
    for i in range(0, 220, 24) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
        S(q[i + 2])
    }
    print("scenario", "quantum_network", "backend", plan_backend(518, []))
    print("sample", measure_all(shots=2))
}
```

### 699. `699_scenario_quantum_network_19.sq` — 699 scenario quantum network 19

```sansqrit
# Scenario 399: Quantum-network repeater and QKD planning: scenario 19
# Strategy: Clifford-only large circuit via stabilizer tableau.
simulate(520, engine="stabilizer") {
    q = quantum_register(520)
    for i in range(0, 220, 25) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
        S(q[i + 2])
    }
    print("scenario", "quantum_network", "backend", plan_backend(520, []))
    print("sample", measure_all(shots=2))
}
```

### 700. `700_scenario_quantum_network_20.sq` — 700 scenario quantum network 20

```sansqrit
# Scenario 400: Quantum-network repeater and QKD planning: scenario 20
# Strategy: Clifford-only large circuit via stabilizer tableau.
simulate(512, engine="stabilizer") {
    q = quantum_register(512)
    for i in range(0, 220, 26) {
        H(q[i])
        CNOT(q[i], q[i + 1])
        CZ(q[i + 1], q[i + 2])
        S(q[i + 2])
    }
    print("scenario", "quantum_network", "backend", plan_backend(512, []))
    print("sample", measure_all(shots=2))
}
```

### 701. `701_scenario_pqc_migration_01.sq` — 701 scenario pqc migration 01

```sansqrit
# Scenario 401: Post-quantum cryptography migration audit: scenario 01
# Strategy: sparse + sharded execution for 152 logical qubits.
simulate(152, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(152)
    X(q[11])
    X(q[44])
    X(q[121])
    H(q[79])
    CNOT(q[79], q[90])
    Rz(q[79], PI / 5)
    CZ(q[90], q[138])
    print("scenario", "pqc_migration", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(152))
}
```

### 702. `702_scenario_pqc_migration_02.sq` — 702 scenario pqc migration 02

```sansqrit
# Scenario 402: Post-quantum cryptography migration audit: scenario 02
# Strategy: sparse + sharded execution for 154 logical qubits.
simulate(154, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(154)
    X(q[0])
    X(q[63])
    X(q[122])
    H(q[36])
    CNOT(q[36], q[49])
    Rz(q[36], PI / 6)
    CZ(q[49], q[78])
    print("scenario", "pqc_migration", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(154))
}
```

### 703. `703_scenario_pqc_migration_03.sq` — 703 scenario pqc migration 03

```sansqrit
# Scenario 403: Post-quantum cryptography migration audit: scenario 03
# Strategy: sparse + sharded execution for 156 logical qubits.
simulate(156, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(156)
    X(q[133])
    X(q[78])
    X(q[127])
    H(q[73])
    CNOT(q[73], q[148])
    Rz(q[73], PI / 7)
    CZ(q[148], q[15])
    print("scenario", "pqc_migration", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(156))
}
```

### 704. `704_scenario_pqc_migration_04.sq` — 704 scenario pqc migration 04

```sansqrit
# Scenario 404: Post-quantum cryptography migration audit: scenario 04
# Strategy: sparse + sharded execution for 158 logical qubits.
simulate(158, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(158)
    X(q[88])
    X(q[3])
    X(q[12])
    H(q[140])
    CNOT(q[140], q[131])
    Rz(q[140], PI / 8)
    CZ(q[131], q[32])
    print("scenario", "pqc_migration", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(158))
}
```

### 705. `705_scenario_pqc_migration_05.sq` — 705 scenario pqc migration 05

```sansqrit
# Scenario 405: Post-quantum cryptography migration audit: scenario 05
# Strategy: sparse + sharded execution for 150 logical qubits.
simulate(150, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(150)
    X(q[27])
    X(q[14])
    X(q[3])
    H(q[57])
    CNOT(q[57], q[42])
    Rz(q[57], PI / 4)
    CZ(q[42], q[23])
    print("scenario", "pqc_migration", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(150))
}
```

### 706. `706_scenario_pqc_migration_06.sq` — 706 scenario pqc migration 06

```sansqrit
# Scenario 406: Post-quantum cryptography migration audit: scenario 06
# Strategy: sparse + sharded execution for 152 logical qubits.
simulate(152, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(152)
    X(q[140])
    X(q[141])
    X(q[82])
    H(q[72])
    CNOT(q[72], q[27])
    Rz(q[72], PI / 5)
    CZ(q[27], q[10])
    print("scenario", "pqc_migration", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(152))
}
```

### 707. `707_scenario_pqc_migration_07.sq` — 707 scenario pqc migration 07

```sansqrit
# Scenario 407: Post-quantum cryptography migration audit: scenario 07
# Strategy: sparse + sharded execution for 154 logical qubits.
simulate(154, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(154)
    X(q[107])
    X(q[4])
    X(q[25])
    H(q[147])
    CNOT(q[147], q[54])
    Rz(q[147], PI / 6)
    CZ(q[54], q[81])
    print("scenario", "pqc_migration", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(154))
}
```

### 708. `708_scenario_pqc_migration_08.sq` — 708 scenario pqc migration 08

```sansqrit
# Scenario 408: Post-quantum cryptography migration audit: scenario 08
# Strategy: sparse + sharded execution for 156 logical qubits.
simulate(156, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(156)
    X(q[66])
    X(q[119])
    X(q[60])
    H(q[18])
    CNOT(q[18], q[21])
    Rz(q[18], PI / 7)
    CZ(q[21], q[116])
    print("scenario", "pqc_migration", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(156))
}
```

### 709. `709_scenario_pqc_migration_09.sq` — 709 scenario pqc migration 09

```sansqrit
# Scenario 409: Post-quantum cryptography migration audit: scenario 09
# Strategy: sparse + sharded execution for 158 logical qubits.
simulate(158, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(158)
    X(q[5])
    X(q[96])
    X(q[121])
    H(q[85])
    CNOT(q[85], q[126])
    Rz(q[85], PI / 8)
    CZ(q[126], q[89])
    print("scenario", "pqc_migration", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(158))
}
```

### 710. `710_scenario_pqc_migration_10.sq` — 710 scenario pqc migration 10

```sansqrit
# Scenario 410: Post-quantum cryptography migration audit: scenario 10
# Strategy: sparse + sharded execution for 150 logical qubits.
simulate(150, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(150)
    X(q[32])
    X(q[109])
    X(q[8])
    H(q[122])
    CNOT(q[122], q[77])
    Rz(q[122], PI / 4)
    CZ(q[77], q[88])
    print("scenario", "pqc_migration", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(150))
}
```

### 711. `711_scenario_pqc_migration_11.sq` — 711 scenario pqc migration 11

```sansqrit
# Scenario 411: Post-quantum cryptography migration audit: scenario 11
# Strategy: sparse + sharded execution for 152 logical qubits.
simulate(152, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(152)
    X(q[117])
    X(q[86])
    X(q[43])
    H(q[65])
    CNOT(q[65], q[116])
    Rz(q[65], PI / 5)
    CZ(q[116], q[51])
    print("scenario", "pqc_migration", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(152))
}
```

### 712. `712_scenario_pqc_migration_12.sq` — 712 scenario pqc migration 12

```sansqrit
# Scenario 412: Post-quantum cryptography migration audit: scenario 12
# Strategy: sparse + sharded execution for 154 logical qubits.
simulate(154, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(154)
    X(q[60])
    X(q[99])
    X(q[82])
    H(q[104])
    CNOT(q[104], q[59])
    Rz(q[104], PI / 6)
    CZ(q[59], q[84])
    print("scenario", "pqc_migration", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(154))
}
```

### 713. `713_scenario_pqc_migration_13.sq` — 713 scenario pqc migration 13

```sansqrit
# Scenario 413: Post-quantum cryptography migration audit: scenario 13
# Strategy: sparse + sharded execution for 156 logical qubits.
simulate(156, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(156)
    X(q[155])
    X(q[4])
    X(q[149])
    H(q[119])
    CNOT(q[119], q[50])
    Rz(q[119], PI / 7)
    CZ(q[50], q[61])
    print("scenario", "pqc_migration", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(156))
}
```

### 714. `714_scenario_pqc_migration_14.sq` — 714 scenario pqc migration 14

```sansqrit
# Scenario 414: Post-quantum cryptography migration audit: scenario 14
# Strategy: sparse + sharded execution for 158 logical qubits.
simulate(158, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(158)
    X(q[80])
    X(q[31])
    X(q[72])
    H(q[30])
    CNOT(q[30], q[121])
    Rz(q[30], PI / 8)
    CZ(q[121], q[146])
    print("scenario", "pqc_migration", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(158))
}
```

### 715. `715_scenario_pqc_migration_15.sq` — 715 scenario pqc migration 15

```sansqrit
# Scenario 415: Post-quantum cryptography migration audit: scenario 15
# Strategy: sparse + sharded execution for 150 logical qubits.
simulate(150, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(150)
    X(q[37])
    X(q[112])
    X(q[3])
    H(q[54])
    CNOT(q[54], q[13])
    Rz(q[54], PI / 4)
    CZ(q[13], q[40])
    print("scenario", "pqc_migration", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(150))
}
```

### 716. `716_scenario_pqc_migration_16.sq` — 716 scenario pqc migration 16

```sansqrit
# Scenario 416: Post-quantum cryptography migration audit: scenario 16
# Strategy: sparse + sharded execution for 152 logical qubits.
simulate(152, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(152)
    X(q[94])
    X(q[31])
    X(q[4])
    H(q[58])
    CNOT(q[58], q[53])
    Rz(q[58], PI / 5)
    CZ(q[53], q[92])
    print("scenario", "pqc_migration", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(152))
}
```

### 717. `717_scenario_pqc_migration_17.sq` — 717 scenario pqc migration 17

```sansqrit
# Scenario 417: Post-quantum cryptography migration audit: scenario 17
# Strategy: sparse + sharded execution for 154 logical qubits.
simulate(154, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(154)
    X(q[13])
    X(q[40])
    X(q[139])
    H(q[61])
    CNOT(q[61], q[64])
    Rz(q[61], PI / 6)
    CZ(q[64], q[87])
    print("scenario", "pqc_migration", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(154))
}
```

### 718. `718_scenario_pqc_migration_18.sq` — 718 scenario pqc migration 18

```sansqrit
# Scenario 418: Post-quantum cryptography migration audit: scenario 18
# Strategy: sparse + sharded execution for 156 logical qubits.
simulate(156, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(156)
    X(q[88])
    X(q[45])
    X(q[82])
    H(q[64])
    CNOT(q[64], q[79])
    Rz(q[64], PI / 7)
    CZ(q[79], q[6])
    print("scenario", "pqc_migration", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(156))
}
```

### 719. `719_scenario_pqc_migration_19.sq` — 719 scenario pqc migration 19

```sansqrit
# Scenario 419: Post-quantum cryptography migration audit: scenario 19
# Strategy: sparse + sharded execution for 158 logical qubits.
simulate(158, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(158)
    X(q[155])
    X(q[124])
    X(q[23])
    H(q[133])
    CNOT(q[133], q[116])
    Rz(q[133], PI / 8)
    CZ(q[116], q[45])
    print("scenario", "pqc_migration", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(158))
}
```

### 720. `720_scenario_pqc_migration_20.sq` — 720 scenario pqc migration 20

```sansqrit
# Scenario 420: Post-quantum cryptography migration audit: scenario 20
# Strategy: sparse + sharded execution for 150 logical qubits.
simulate(150, engine="sharded", n_shards=15, workers=4, use_lookup=true) {
    q = quantum_register(150)
    X(q[42])
    X(q[149])
    X(q[18])
    H(q[102])
    CNOT(q[102], q[147])
    Rz(q[102], PI / 4)
    CZ(q[147], q[68])
    print("scenario", "pqc_migration", "nnz", engine_nnz())
    print("lookup", lookup_profile())
    print("estimate", estimate_qubits(150))
}
```

### 721. `721_scenario_qec_satellite_01.sq` — 721 scenario qec satellite 01

```sansqrit
# Scenario 421: Quantum error correction for satellite links: scenario 01
# Strategy: logical qubit QEC workflow with syndrome extraction and correction.
simulate(27, engine="sparse", use_lookup=true) {
    q = quantum_register(27)
    logical = qec_logical("phase_flip", base=0, name="satellite_link_1")
    qec_encode(logical)
    qec_inject_error(logical, "X", 1)
    syndrome = qec_syndrome_and_correct(logical)
    logical_z(logical)
    print("scenario", "qec_satellite", "code", "phase_flip", "syndrome", syndrome)
    print("stim", qec_stim_syndrome_text(logical))
}
```

### 722. `722_scenario_qec_satellite_02.sq` — 722 scenario qec satellite 02

```sansqrit
# Scenario 422: Quantum error correction for satellite links: scenario 02
# Strategy: logical qubit QEC workflow with syndrome extraction and correction.
simulate(27, engine="sparse", use_lookup=true) {
    q = quantum_register(27)
    logical = qec_logical("shor9", base=0, name="satellite_link_2")
    qec_encode(logical)
    qec_inject_error(logical, "X", 2)
    syndrome = qec_syndrome_and_correct(logical)
    logical_z(logical)
    print("scenario", "qec_satellite", "code", "shor9", "syndrome", syndrome)
    print("stim", qec_stim_syndrome_text(logical))
}
```

### 723. `723_scenario_qec_satellite_03.sq` — 723 scenario qec satellite 03

```sansqrit
# Scenario 423: Quantum error correction for satellite links: scenario 03
# Strategy: logical qubit QEC workflow with syndrome extraction and correction.
simulate(27, engine="sparse", use_lookup=true) {
    q = quantum_register(27)
    logical = qec_logical("steane7", base=0, name="satellite_link_3")
    qec_encode(logical)
    qec_inject_error(logical, "X", 0)
    syndrome = qec_syndrome_and_correct(logical)
    logical_z(logical)
    print("scenario", "qec_satellite", "code", "steane7", "syndrome", syndrome)
    print("stim", qec_stim_syndrome_text(logical))
}
```

### 724. `724_scenario_qec_satellite_04.sq` — 724 scenario qec satellite 04

```sansqrit
# Scenario 424: Quantum error correction for satellite links: scenario 04
# Strategy: logical qubit QEC workflow with syndrome extraction and correction.
simulate(27, engine="sparse", use_lookup=true) {
    q = quantum_register(27)
    logical = qec_logical("five_qubit", base=0, name="satellite_link_4")
    qec_encode(logical)
    qec_inject_error(logical, "X", 1)
    syndrome = qec_syndrome_and_correct(logical)
    logical_z(logical)
    print("scenario", "qec_satellite", "code", "five_qubit", "syndrome", syndrome)
    print("stim", qec_stim_syndrome_text(logical))
}
```

### 725. `725_scenario_qec_satellite_05.sq` — 725 scenario qec satellite 05

```sansqrit
# Scenario 425: Quantum error correction for satellite links: scenario 05
# Strategy: logical qubit QEC workflow with syndrome extraction and correction.
simulate(27, engine="sparse", use_lookup=true) {
    q = quantum_register(27)
    logical = qec_logical("bit_flip", base=0, name="satellite_link_5")
    qec_encode(logical)
    qec_inject_error(logical, "X", 2)
    syndrome = qec_syndrome_and_correct(logical)
    logical_z(logical)
    print("scenario", "qec_satellite", "code", "bit_flip", "syndrome", syndrome)
    print("stim", qec_stim_syndrome_text(logical))
}
```

### 726. `726_scenario_qec_satellite_06.sq` — 726 scenario qec satellite 06

```sansqrit
# Scenario 426: Quantum error correction for satellite links: scenario 06
# Strategy: logical qubit QEC workflow with syndrome extraction and correction.
simulate(27, engine="sparse", use_lookup=true) {
    q = quantum_register(27)
    logical = qec_logical("phase_flip", base=0, name="satellite_link_6")
    qec_encode(logical)
    qec_inject_error(logical, "X", 0)
    syndrome = qec_syndrome_and_correct(logical)
    logical_z(logical)
    print("scenario", "qec_satellite", "code", "phase_flip", "syndrome", syndrome)
    print("stim", qec_stim_syndrome_text(logical))
}
```

### 727. `727_scenario_qec_satellite_07.sq` — 727 scenario qec satellite 07

```sansqrit
# Scenario 427: Quantum error correction for satellite links: scenario 07
# Strategy: logical qubit QEC workflow with syndrome extraction and correction.
simulate(27, engine="sparse", use_lookup=true) {
    q = quantum_register(27)
    logical = qec_logical("shor9", base=0, name="satellite_link_7")
    qec_encode(logical)
    qec_inject_error(logical, "X", 1)
    syndrome = qec_syndrome_and_correct(logical)
    logical_z(logical)
    print("scenario", "qec_satellite", "code", "shor9", "syndrome", syndrome)
    print("stim", qec_stim_syndrome_text(logical))
}
```

### 728. `728_scenario_qec_satellite_08.sq` — 728 scenario qec satellite 08

```sansqrit
# Scenario 428: Quantum error correction for satellite links: scenario 08
# Strategy: logical qubit QEC workflow with syndrome extraction and correction.
simulate(27, engine="sparse", use_lookup=true) {
    q = quantum_register(27)
    logical = qec_logical("steane7", base=0, name="satellite_link_8")
    qec_encode(logical)
    qec_inject_error(logical, "X", 2)
    syndrome = qec_syndrome_and_correct(logical)
    logical_z(logical)
    print("scenario", "qec_satellite", "code", "steane7", "syndrome", syndrome)
    print("stim", qec_stim_syndrome_text(logical))
}
```

### 729. `729_scenario_qec_satellite_09.sq` — 729 scenario qec satellite 09

```sansqrit
# Scenario 429: Quantum error correction for satellite links: scenario 09
# Strategy: logical qubit QEC workflow with syndrome extraction and correction.
simulate(27, engine="sparse", use_lookup=true) {
    q = quantum_register(27)
    logical = qec_logical("five_qubit", base=0, name="satellite_link_9")
    qec_encode(logical)
    qec_inject_error(logical, "X", 0)
    syndrome = qec_syndrome_and_correct(logical)
    logical_z(logical)
    print("scenario", "qec_satellite", "code", "five_qubit", "syndrome", syndrome)
    print("stim", qec_stim_syndrome_text(logical))
}
```

### 730. `730_scenario_qec_satellite_10.sq` — 730 scenario qec satellite 10

```sansqrit
# Scenario 430: Quantum error correction for satellite links: scenario 10
# Strategy: logical qubit QEC workflow with syndrome extraction and correction.
simulate(27, engine="sparse", use_lookup=true) {
    q = quantum_register(27)
    logical = qec_logical("bit_flip", base=0, name="satellite_link_10")
    qec_encode(logical)
    qec_inject_error(logical, "X", 1)
    syndrome = qec_syndrome_and_correct(logical)
    logical_z(logical)
    print("scenario", "qec_satellite", "code", "bit_flip", "syndrome", syndrome)
    print("stim", qec_stim_syndrome_text(logical))
}
```

### 731. `731_scenario_qec_satellite_11.sq` — 731 scenario qec satellite 11

```sansqrit
# Scenario 431: Quantum error correction for satellite links: scenario 11
# Strategy: logical qubit QEC workflow with syndrome extraction and correction.
simulate(27, engine="sparse", use_lookup=true) {
    q = quantum_register(27)
    logical = qec_logical("phase_flip", base=0, name="satellite_link_11")
    qec_encode(logical)
    qec_inject_error(logical, "X", 2)
    syndrome = qec_syndrome_and_correct(logical)
    logical_z(logical)
    print("scenario", "qec_satellite", "code", "phase_flip", "syndrome", syndrome)
    print("stim", qec_stim_syndrome_text(logical))
}
```

### 732. `732_scenario_qec_satellite_12.sq` — 732 scenario qec satellite 12

```sansqrit
# Scenario 432: Quantum error correction for satellite links: scenario 12
# Strategy: logical qubit QEC workflow with syndrome extraction and correction.
simulate(27, engine="sparse", use_lookup=true) {
    q = quantum_register(27)
    logical = qec_logical("shor9", base=0, name="satellite_link_12")
    qec_encode(logical)
    qec_inject_error(logical, "X", 0)
    syndrome = qec_syndrome_and_correct(logical)
    logical_z(logical)
    print("scenario", "qec_satellite", "code", "shor9", "syndrome", syndrome)
    print("stim", qec_stim_syndrome_text(logical))
}
```

### 733. `733_scenario_qec_satellite_13.sq` — 733 scenario qec satellite 13

```sansqrit
# Scenario 433: Quantum error correction for satellite links: scenario 13
# Strategy: logical qubit QEC workflow with syndrome extraction and correction.
simulate(27, engine="sparse", use_lookup=true) {
    q = quantum_register(27)
    logical = qec_logical("steane7", base=0, name="satellite_link_13")
    qec_encode(logical)
    qec_inject_error(logical, "X", 1)
    syndrome = qec_syndrome_and_correct(logical)
    logical_z(logical)
    print("scenario", "qec_satellite", "code", "steane7", "syndrome", syndrome)
    print("stim", qec_stim_syndrome_text(logical))
}
```

### 734. `734_scenario_qec_satellite_14.sq` — 734 scenario qec satellite 14

```sansqrit
# Scenario 434: Quantum error correction for satellite links: scenario 14
# Strategy: logical qubit QEC workflow with syndrome extraction and correction.
simulate(27, engine="sparse", use_lookup=true) {
    q = quantum_register(27)
    logical = qec_logical("five_qubit", base=0, name="satellite_link_14")
    qec_encode(logical)
    qec_inject_error(logical, "X", 2)
    syndrome = qec_syndrome_and_correct(logical)
    logical_z(logical)
    print("scenario", "qec_satellite", "code", "five_qubit", "syndrome", syndrome)
    print("stim", qec_stim_syndrome_text(logical))
}
```

### 735. `735_scenario_qec_satellite_15.sq` — 735 scenario qec satellite 15

```sansqrit
# Scenario 435: Quantum error correction for satellite links: scenario 15
# Strategy: logical qubit QEC workflow with syndrome extraction and correction.
simulate(27, engine="sparse", use_lookup=true) {
    q = quantum_register(27)
    logical = qec_logical("bit_flip", base=0, name="satellite_link_15")
    qec_encode(logical)
    qec_inject_error(logical, "X", 0)
    syndrome = qec_syndrome_and_correct(logical)
    logical_z(logical)
    print("scenario", "qec_satellite", "code", "bit_flip", "syndrome", syndrome)
    print("stim", qec_stim_syndrome_text(logical))
}
```

### 736. `736_scenario_qec_satellite_16.sq` — 736 scenario qec satellite 16

```sansqrit
# Scenario 436: Quantum error correction for satellite links: scenario 16
# Strategy: logical qubit QEC workflow with syndrome extraction and correction.
simulate(27, engine="sparse", use_lookup=true) {
    q = quantum_register(27)
    logical = qec_logical("phase_flip", base=0, name="satellite_link_16")
    qec_encode(logical)
    qec_inject_error(logical, "X", 1)
    syndrome = qec_syndrome_and_correct(logical)
    logical_z(logical)
    print("scenario", "qec_satellite", "code", "phase_flip", "syndrome", syndrome)
    print("stim", qec_stim_syndrome_text(logical))
}
```

### 737. `737_scenario_qec_satellite_17.sq` — 737 scenario qec satellite 17

```sansqrit
# Scenario 437: Quantum error correction for satellite links: scenario 17
# Strategy: logical qubit QEC workflow with syndrome extraction and correction.
simulate(27, engine="sparse", use_lookup=true) {
    q = quantum_register(27)
    logical = qec_logical("shor9", base=0, name="satellite_link_17")
    qec_encode(logical)
    qec_inject_error(logical, "X", 2)
    syndrome = qec_syndrome_and_correct(logical)
    logical_z(logical)
    print("scenario", "qec_satellite", "code", "shor9", "syndrome", syndrome)
    print("stim", qec_stim_syndrome_text(logical))
}
```

### 738. `738_scenario_qec_satellite_18.sq` — 738 scenario qec satellite 18

```sansqrit
# Scenario 438: Quantum error correction for satellite links: scenario 18
# Strategy: logical qubit QEC workflow with syndrome extraction and correction.
simulate(27, engine="sparse", use_lookup=true) {
    q = quantum_register(27)
    logical = qec_logical("steane7", base=0, name="satellite_link_18")
    qec_encode(logical)
    qec_inject_error(logical, "X", 0)
    syndrome = qec_syndrome_and_correct(logical)
    logical_z(logical)
    print("scenario", "qec_satellite", "code", "steane7", "syndrome", syndrome)
    print("stim", qec_stim_syndrome_text(logical))
}
```

### 739. `739_scenario_qec_satellite_19.sq` — 739 scenario qec satellite 19

```sansqrit
# Scenario 439: Quantum error correction for satellite links: scenario 19
# Strategy: logical qubit QEC workflow with syndrome extraction and correction.
simulate(27, engine="sparse", use_lookup=true) {
    q = quantum_register(27)
    logical = qec_logical("five_qubit", base=0, name="satellite_link_19")
    qec_encode(logical)
    qec_inject_error(logical, "X", 1)
    syndrome = qec_syndrome_and_correct(logical)
    logical_z(logical)
    print("scenario", "qec_satellite", "code", "five_qubit", "syndrome", syndrome)
    print("stim", qec_stim_syndrome_text(logical))
}
```

### 740. `740_scenario_qec_satellite_20.sq` — 740 scenario qec satellite 20

```sansqrit
# Scenario 440: Quantum error correction for satellite links: scenario 20
# Strategy: logical qubit QEC workflow with syndrome extraction and correction.
simulate(27, engine="sparse", use_lookup=true) {
    q = quantum_register(27)
    logical = qec_logical("bit_flip", base=0, name="satellite_link_20")
    qec_encode(logical)
    qec_inject_error(logical, "X", 2)
    syndrome = qec_syndrome_and_correct(logical)
    logical_z(logical)
    print("scenario", "qec_satellite", "code", "bit_flip", "syndrome", syndrome)
    print("stim", qec_stim_syndrome_text(logical))
}
```

### 741. `741_scenario_hardware_calibration_01.sq` — 741 scenario hardware calibration 01

```sansqrit
# Scenario 441: Quantum hardware calibration export and verification: scenario 01
# Strategy: generate a scalable sparse circuit and export the hardware payload.
simulate(123, engine="sharded", n_shards=12, workers=4, use_lookup=true) {
    q = quantum_register(123)
    H(q[0])
    CNOT(q[0], q[1])
    X(q[122])
    Rz(q[1], PI / 8)
    print("scenario", "hardware_calibration", hardware_payload_summary())
    print(export_hardware("qiskit"))
}
```

### 742. `742_scenario_hardware_calibration_02.sq` — 742 scenario hardware calibration 02

```sansqrit
# Scenario 442: Quantum hardware calibration export and verification: scenario 02
# Strategy: generate a scalable sparse circuit and export the hardware payload.
simulate(125, engine="sharded", n_shards=12, workers=4, use_lookup=true) {
    q = quantum_register(125)
    H(q[0])
    CNOT(q[0], q[1])
    X(q[124])
    Rz(q[2], PI / 8)
    print("scenario", "hardware_calibration", hardware_payload_summary())
    print(export_hardware("braket"))
}
```

### 743. `743_scenario_hardware_calibration_03.sq` — 743 scenario hardware calibration 03

```sansqrit
# Scenario 443: Quantum hardware calibration export and verification: scenario 03
# Strategy: generate a scalable sparse circuit and export the hardware payload.
simulate(127, engine="sharded", n_shards=12, workers=4, use_lookup=true) {
    q = quantum_register(127)
    H(q[0])
    CNOT(q[0], q[1])
    X(q[126])
    Rz(q[3], PI / 8)
    print("scenario", "hardware_calibration", hardware_payload_summary())
    print(export_hardware("azure"))
}
```

### 744. `744_scenario_hardware_calibration_04.sq` — 744 scenario hardware calibration 04

```sansqrit
# Scenario 444: Quantum hardware calibration export and verification: scenario 04
# Strategy: generate a scalable sparse circuit and export the hardware payload.
simulate(129, engine="sharded", n_shards=12, workers=4, use_lookup=true) {
    q = quantum_register(129)
    H(q[0])
    CNOT(q[0], q[1])
    X(q[128])
    Rz(q[4], PI / 8)
    print("scenario", "hardware_calibration", hardware_payload_summary())
    print(export_hardware("cirq"))
}
```

### 745. `745_scenario_hardware_calibration_05.sq` — 745 scenario hardware calibration 05

```sansqrit
# Scenario 445: Quantum hardware calibration export and verification: scenario 05
# Strategy: generate a scalable sparse circuit and export the hardware payload.
simulate(121, engine="sharded", n_shards=12, workers=4, use_lookup=true) {
    q = quantum_register(121)
    H(q[0])
    CNOT(q[0], q[1])
    X(q[120])
    Rz(q[5], PI / 8)
    print("scenario", "hardware_calibration", hardware_payload_summary())
    print(export_hardware("pennylane"))
}
```

### 746. `746_scenario_hardware_calibration_06.sq` — 746 scenario hardware calibration 06

```sansqrit
# Scenario 446: Quantum hardware calibration export and verification: scenario 06
# Strategy: generate a scalable sparse circuit and export the hardware payload.
simulate(123, engine="sharded", n_shards=12, workers=4, use_lookup=true) {
    q = quantum_register(123)
    H(q[0])
    CNOT(q[0], q[1])
    X(q[122])
    Rz(q[6], PI / 8)
    print("scenario", "hardware_calibration", hardware_payload_summary())
    print(export_hardware("openqasm3"))
}
```

### 747. `747_scenario_hardware_calibration_07.sq` — 747 scenario hardware calibration 07

```sansqrit
# Scenario 447: Quantum hardware calibration export and verification: scenario 07
# Strategy: generate a scalable sparse circuit and export the hardware payload.
simulate(125, engine="sharded", n_shards=12, workers=4, use_lookup=true) {
    q = quantum_register(125)
    H(q[0])
    CNOT(q[0], q[1])
    X(q[124])
    Rz(q[7], PI / 8)
    print("scenario", "hardware_calibration", hardware_payload_summary())
    print(export_hardware("qiskit"))
}
```

### 748. `748_scenario_hardware_calibration_08.sq` — 748 scenario hardware calibration 08

```sansqrit
# Scenario 448: Quantum hardware calibration export and verification: scenario 08
# Strategy: generate a scalable sparse circuit and export the hardware payload.
simulate(127, engine="sharded", n_shards=12, workers=4, use_lookup=true) {
    q = quantum_register(127)
    H(q[0])
    CNOT(q[0], q[1])
    X(q[126])
    Rz(q[8], PI / 8)
    print("scenario", "hardware_calibration", hardware_payload_summary())
    print(export_hardware("braket"))
}
```

### 749. `749_scenario_hardware_calibration_09.sq` — 749 scenario hardware calibration 09

```sansqrit
# Scenario 449: Quantum hardware calibration export and verification: scenario 09
# Strategy: generate a scalable sparse circuit and export the hardware payload.
simulate(129, engine="sharded", n_shards=12, workers=4, use_lookup=true) {
    q = quantum_register(129)
    H(q[0])
    CNOT(q[0], q[1])
    X(q[128])
    Rz(q[9], PI / 8)
    print("scenario", "hardware_calibration", hardware_payload_summary())
    print(export_hardware("azure"))
}
```

### 750. `750_scenario_hardware_calibration_10.sq` — 750 scenario hardware calibration 10

```sansqrit
# Scenario 450: Quantum hardware calibration export and verification: scenario 10
# Strategy: generate a scalable sparse circuit and export the hardware payload.
simulate(121, engine="sharded", n_shards=12, workers=4, use_lookup=true) {
    q = quantum_register(121)
    H(q[0])
    CNOT(q[0], q[1])
    X(q[120])
    Rz(q[10], PI / 8)
    print("scenario", "hardware_calibration", hardware_payload_summary())
    print(export_hardware("cirq"))
}
```

### 751. `751_scenario_hardware_calibration_11.sq` — 751 scenario hardware calibration 11

```sansqrit
# Scenario 451: Quantum hardware calibration export and verification: scenario 11
# Strategy: generate a scalable sparse circuit and export the hardware payload.
simulate(123, engine="sharded", n_shards=12, workers=4, use_lookup=true) {
    q = quantum_register(123)
    H(q[0])
    CNOT(q[0], q[1])
    X(q[122])
    Rz(q[11], PI / 8)
    print("scenario", "hardware_calibration", hardware_payload_summary())
    print(export_hardware("pennylane"))
}
```

### 752. `752_scenario_hardware_calibration_12.sq` — 752 scenario hardware calibration 12

```sansqrit
# Scenario 452: Quantum hardware calibration export and verification: scenario 12
# Strategy: generate a scalable sparse circuit and export the hardware payload.
simulate(125, engine="sharded", n_shards=12, workers=4, use_lookup=true) {
    q = quantum_register(125)
    H(q[0])
    CNOT(q[0], q[1])
    X(q[124])
    Rz(q[12], PI / 8)
    print("scenario", "hardware_calibration", hardware_payload_summary())
    print(export_hardware("openqasm3"))
}
```

### 753. `753_scenario_hardware_calibration_13.sq` — 753 scenario hardware calibration 13

```sansqrit
# Scenario 453: Quantum hardware calibration export and verification: scenario 13
# Strategy: generate a scalable sparse circuit and export the hardware payload.
simulate(127, engine="sharded", n_shards=12, workers=4, use_lookup=true) {
    q = quantum_register(127)
    H(q[0])
    CNOT(q[0], q[1])
    X(q[126])
    Rz(q[13], PI / 8)
    print("scenario", "hardware_calibration", hardware_payload_summary())
    print(export_hardware("qiskit"))
}
```

### 754. `754_scenario_hardware_calibration_14.sq` — 754 scenario hardware calibration 14

```sansqrit
# Scenario 454: Quantum hardware calibration export and verification: scenario 14
# Strategy: generate a scalable sparse circuit and export the hardware payload.
simulate(129, engine="sharded", n_shards=12, workers=4, use_lookup=true) {
    q = quantum_register(129)
    H(q[0])
    CNOT(q[0], q[1])
    X(q[128])
    Rz(q[14], PI / 8)
    print("scenario", "hardware_calibration", hardware_payload_summary())
    print(export_hardware("braket"))
}
```

### 755. `755_scenario_hardware_calibration_15.sq` — 755 scenario hardware calibration 15

```sansqrit
# Scenario 455: Quantum hardware calibration export and verification: scenario 15
# Strategy: generate a scalable sparse circuit and export the hardware payload.
simulate(121, engine="sharded", n_shards=12, workers=4, use_lookup=true) {
    q = quantum_register(121)
    H(q[0])
    CNOT(q[0], q[1])
    X(q[120])
    Rz(q[15], PI / 8)
    print("scenario", "hardware_calibration", hardware_payload_summary())
    print(export_hardware("azure"))
}
```

### 756. `756_scenario_hardware_calibration_16.sq` — 756 scenario hardware calibration 16

```sansqrit
# Scenario 456: Quantum hardware calibration export and verification: scenario 16
# Strategy: generate a scalable sparse circuit and export the hardware payload.
simulate(123, engine="sharded", n_shards=12, workers=4, use_lookup=true) {
    q = quantum_register(123)
    H(q[0])
    CNOT(q[0], q[1])
    X(q[122])
    Rz(q[16], PI / 8)
    print("scenario", "hardware_calibration", hardware_payload_summary())
    print(export_hardware("cirq"))
}
```

### 757. `757_scenario_hardware_calibration_17.sq` — 757 scenario hardware calibration 17

```sansqrit
# Scenario 457: Quantum hardware calibration export and verification: scenario 17
# Strategy: generate a scalable sparse circuit and export the hardware payload.
simulate(125, engine="sharded", n_shards=12, workers=4, use_lookup=true) {
    q = quantum_register(125)
    H(q[0])
    CNOT(q[0], q[1])
    X(q[124])
    Rz(q[17], PI / 8)
    print("scenario", "hardware_calibration", hardware_payload_summary())
    print(export_hardware("pennylane"))
}
```

### 758. `758_scenario_hardware_calibration_18.sq` — 758 scenario hardware calibration 18

```sansqrit
# Scenario 458: Quantum hardware calibration export and verification: scenario 18
# Strategy: generate a scalable sparse circuit and export the hardware payload.
simulate(127, engine="sharded", n_shards=12, workers=4, use_lookup=true) {
    q = quantum_register(127)
    H(q[0])
    CNOT(q[0], q[1])
    X(q[126])
    Rz(q[18], PI / 8)
    print("scenario", "hardware_calibration", hardware_payload_summary())
    print(export_hardware("openqasm3"))
}
```

### 759. `759_scenario_hardware_calibration_19.sq` — 759 scenario hardware calibration 19

```sansqrit
# Scenario 459: Quantum hardware calibration export and verification: scenario 19
# Strategy: generate a scalable sparse circuit and export the hardware payload.
simulate(129, engine="sharded", n_shards=12, workers=4, use_lookup=true) {
    q = quantum_register(129)
    H(q[0])
    CNOT(q[0], q[1])
    X(q[128])
    Rz(q[19], PI / 8)
    print("scenario", "hardware_calibration", hardware_payload_summary())
    print(export_hardware("qiskit"))
}
```

### 760. `760_scenario_hardware_calibration_20.sq` — 760 scenario hardware calibration 20

```sansqrit
# Scenario 460: Quantum hardware calibration export and verification: scenario 20
# Strategy: generate a scalable sparse circuit and export the hardware payload.
simulate(121, engine="sharded", n_shards=12, workers=4, use_lookup=true) {
    q = quantum_register(121)
    H(q[0])
    CNOT(q[0], q[1])
    X(q[120])
    Rz(q[20], PI / 8)
    print("scenario", "hardware_calibration", hardware_payload_summary())
    print(export_hardware("braket"))
}
```

### 761. `761_scenario_semiconductor_01.sq` — 761 scenario semiconductor 01

```sansqrit
# Scenario 461: Semiconductor process-control anomaly map: scenario 01
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(162, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(162)
    block_A = shard("block_A", 10, 19)
    block_B = shard("block_B", 20, 29)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[19], q[20])
    Rz(q[21], PI / 4)
    print("scenario", "semiconductor", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 762. `762_scenario_semiconductor_02.sq` — 762 scenario semiconductor 02

```sansqrit
# Scenario 462: Semiconductor process-control anomaly map: scenario 02
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(164, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(164)
    block_A = shard("block_A", 20, 29)
    block_B = shard("block_B", 30, 39)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[29], q[30])
    Rz(q[31], PI / 5)
    print("scenario", "semiconductor", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 763. `763_scenario_semiconductor_03.sq` — 763 scenario semiconductor 03

```sansqrit
# Scenario 463: Semiconductor process-control anomaly map: scenario 03
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(166, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(166)
    block_A = shard("block_A", 30, 39)
    block_B = shard("block_B", 40, 49)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[39], q[40])
    Rz(q[41], PI / 6)
    print("scenario", "semiconductor", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 764. `764_scenario_semiconductor_04.sq` — 764 scenario semiconductor 04

```sansqrit
# Scenario 464: Semiconductor process-control anomaly map: scenario 04
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(168, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(168)
    block_A = shard("block_A", 0, 9)
    block_B = shard("block_B", 10, 19)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[9], q[10])
    Rz(q[11], PI / 7)
    print("scenario", "semiconductor", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 765. `765_scenario_semiconductor_05.sq` — 765 scenario semiconductor 05

```sansqrit
# Scenario 465: Semiconductor process-control anomaly map: scenario 05
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(160, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(160)
    block_A = shard("block_A", 10, 19)
    block_B = shard("block_B", 20, 29)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[19], q[20])
    Rz(q[21], PI / 8)
    print("scenario", "semiconductor", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 766. `766_scenario_semiconductor_06.sq` — 766 scenario semiconductor 06

```sansqrit
# Scenario 466: Semiconductor process-control anomaly map: scenario 06
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(162, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(162)
    block_A = shard("block_A", 20, 29)
    block_B = shard("block_B", 30, 39)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[29], q[30])
    Rz(q[31], PI / 3)
    print("scenario", "semiconductor", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 767. `767_scenario_semiconductor_07.sq` — 767 scenario semiconductor 07

```sansqrit
# Scenario 467: Semiconductor process-control anomaly map: scenario 07
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(164, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(164)
    block_A = shard("block_A", 30, 39)
    block_B = shard("block_B", 40, 49)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[39], q[40])
    Rz(q[41], PI / 4)
    print("scenario", "semiconductor", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 768. `768_scenario_semiconductor_08.sq` — 768 scenario semiconductor 08

```sansqrit
# Scenario 468: Semiconductor process-control anomaly map: scenario 08
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(166, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(166)
    block_A = shard("block_A", 0, 9)
    block_B = shard("block_B", 10, 19)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[9], q[10])
    Rz(q[11], PI / 5)
    print("scenario", "semiconductor", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 769. `769_scenario_semiconductor_09.sq` — 769 scenario semiconductor 09

```sansqrit
# Scenario 469: Semiconductor process-control anomaly map: scenario 09
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(168, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(168)
    block_A = shard("block_A", 10, 19)
    block_B = shard("block_B", 20, 29)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[19], q[20])
    Rz(q[21], PI / 6)
    print("scenario", "semiconductor", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 770. `770_scenario_semiconductor_10.sq` — 770 scenario semiconductor 10

```sansqrit
# Scenario 470: Semiconductor process-control anomaly map: scenario 10
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(160, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(160)
    block_A = shard("block_A", 20, 29)
    block_B = shard("block_B", 30, 39)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[29], q[30])
    Rz(q[31], PI / 7)
    print("scenario", "semiconductor", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 771. `771_scenario_semiconductor_11.sq` — 771 scenario semiconductor 11

```sansqrit
# Scenario 471: Semiconductor process-control anomaly map: scenario 11
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(162, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(162)
    block_A = shard("block_A", 30, 39)
    block_B = shard("block_B", 40, 49)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[39], q[40])
    Rz(q[41], PI / 8)
    print("scenario", "semiconductor", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 772. `772_scenario_semiconductor_12.sq` — 772 scenario semiconductor 12

```sansqrit
# Scenario 472: Semiconductor process-control anomaly map: scenario 12
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(164, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(164)
    block_A = shard("block_A", 0, 9)
    block_B = shard("block_B", 10, 19)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[9], q[10])
    Rz(q[11], PI / 3)
    print("scenario", "semiconductor", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 773. `773_scenario_semiconductor_13.sq` — 773 scenario semiconductor 13

```sansqrit
# Scenario 473: Semiconductor process-control anomaly map: scenario 13
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(166, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(166)
    block_A = shard("block_A", 10, 19)
    block_B = shard("block_B", 20, 29)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[19], q[20])
    Rz(q[21], PI / 4)
    print("scenario", "semiconductor", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 774. `774_scenario_semiconductor_14.sq` — 774 scenario semiconductor 14

```sansqrit
# Scenario 474: Semiconductor process-control anomaly map: scenario 14
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(168, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(168)
    block_A = shard("block_A", 20, 29)
    block_B = shard("block_B", 30, 39)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[29], q[30])
    Rz(q[31], PI / 5)
    print("scenario", "semiconductor", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 775. `775_scenario_semiconductor_15.sq` — 775 scenario semiconductor 15

```sansqrit
# Scenario 475: Semiconductor process-control anomaly map: scenario 15
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(160, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(160)
    block_A = shard("block_A", 30, 39)
    block_B = shard("block_B", 40, 49)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[39], q[40])
    Rz(q[41], PI / 6)
    print("scenario", "semiconductor", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 776. `776_scenario_semiconductor_16.sq` — 776 scenario semiconductor 16

```sansqrit
# Scenario 476: Semiconductor process-control anomaly map: scenario 16
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(162, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(162)
    block_A = shard("block_A", 0, 9)
    block_B = shard("block_B", 10, 19)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[9], q[10])
    Rz(q[11], PI / 7)
    print("scenario", "semiconductor", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 777. `777_scenario_semiconductor_17.sq` — 777 scenario semiconductor 17

```sansqrit
# Scenario 477: Semiconductor process-control anomaly map: scenario 17
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(164, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(164)
    block_A = shard("block_A", 10, 19)
    block_B = shard("block_B", 20, 29)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[19], q[20])
    Rz(q[21], PI / 8)
    print("scenario", "semiconductor", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 778. `778_scenario_semiconductor_18.sq` — 778 scenario semiconductor 18

```sansqrit
# Scenario 478: Semiconductor process-control anomaly map: scenario 18
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(166, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(166)
    block_A = shard("block_A", 20, 29)
    block_B = shard("block_B", 30, 39)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[29], q[30])
    Rz(q[31], PI / 3)
    print("scenario", "semiconductor", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 779. `779_scenario_semiconductor_19.sq` — 779 scenario semiconductor 19

```sansqrit
# Scenario 479: Semiconductor process-control anomaly map: scenario 19
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(168, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(168)
    block_A = shard("block_A", 30, 39)
    block_B = shard("block_B", 40, 49)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[39], q[40])
    Rz(q[41], PI / 4)
    print("scenario", "semiconductor", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 780. `780_scenario_semiconductor_20.sq` — 780 scenario semiconductor 20

```sansqrit
# Scenario 480: Semiconductor process-control anomaly map: scenario 20
# Strategy: 10-qubit dense local blocks with safe MPS promotion for bridge gates.
simulate(160, engine="hierarchical", block_size=10, max_bond_dim=null, cutoff=0.0, use_lookup=true) {
    q = quantum_register(160)
    block_A = shard("block_A", 0, 9)
    block_B = shard("block_B", 10, 19)
    apply_block("H", block_A)
    apply_block("X", block_B)
    CNOT(q[9], q[10])
    Rz(q[11], PI / 5)
    print("scenario", "semiconductor", hierarchical_report())
    print("lookup", lookup_profile())
}
```

### 781. `781_scenario_seismology_01.sq` — 781 scenario seismology 01

```sansqrit
# Scenario 481: Seismic event correlation screening: scenario 01
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(138, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(138)
    H(q[0])
    for i in range(0, 61) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 6)
    }
    print("scenario", "seismology", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(138))
}
```

### 782. `782_scenario_seismology_02.sq` — 782 scenario seismology 02

```sansqrit
# Scenario 482: Seismic event correlation screening: scenario 02
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(140, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(140)
    H(q[0])
    for i in range(0, 62) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 7)
    }
    print("scenario", "seismology", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(140))
}
```

### 783. `783_scenario_seismology_03.sq` — 783 scenario seismology 03

```sansqrit
# Scenario 483: Seismic event correlation screening: scenario 03
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(142, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(142)
    H(q[0])
    for i in range(0, 63) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 8)
    }
    print("scenario", "seismology", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(142))
}
```

### 784. `784_scenario_seismology_04.sq` — 784 scenario seismology 04

```sansqrit
# Scenario 484: Seismic event correlation screening: scenario 04
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(144, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(144)
    H(q[0])
    for i in range(0, 64) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 5)
    }
    print("scenario", "seismology", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(144))
}
```

### 785. `785_scenario_seismology_05.sq` — 785 scenario seismology 05

```sansqrit
# Scenario 485: Seismic event correlation screening: scenario 05
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(136, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(136)
    H(q[0])
    for i in range(0, 65) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 6)
    }
    print("scenario", "seismology", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(136))
}
```

### 786. `786_scenario_seismology_06.sq` — 786 scenario seismology 06

```sansqrit
# Scenario 486: Seismic event correlation screening: scenario 06
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(138, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(138)
    H(q[0])
    for i in range(0, 66) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 7)
    }
    print("scenario", "seismology", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(138))
}
```

### 787. `787_scenario_seismology_07.sq` — 787 scenario seismology 07

```sansqrit
# Scenario 487: Seismic event correlation screening: scenario 07
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(140, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(140)
    H(q[0])
    for i in range(0, 67) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 8)
    }
    print("scenario", "seismology", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(140))
}
```

### 788. `788_scenario_seismology_08.sq` — 788 scenario seismology 08

```sansqrit
# Scenario 488: Seismic event correlation screening: scenario 08
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(142, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(142)
    H(q[0])
    for i in range(0, 68) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 5)
    }
    print("scenario", "seismology", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(142))
}
```

### 789. `789_scenario_seismology_09.sq` — 789 scenario seismology 09

```sansqrit
# Scenario 489: Seismic event correlation screening: scenario 09
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(144, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(144)
    H(q[0])
    for i in range(0, 69) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 6)
    }
    print("scenario", "seismology", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(144))
}
```

### 790. `790_scenario_seismology_10.sq` — 790 scenario seismology 10

```sansqrit
# Scenario 490: Seismic event correlation screening: scenario 10
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(136, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(136)
    H(q[0])
    for i in range(0, 70) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 7)
    }
    print("scenario", "seismology", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(136))
}
```

### 791. `791_scenario_seismology_11.sq` — 791 scenario seismology 11

```sansqrit
# Scenario 491: Seismic event correlation screening: scenario 11
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(138, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(138)
    H(q[0])
    for i in range(0, 71) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 8)
    }
    print("scenario", "seismology", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(138))
}
```

### 792. `792_scenario_seismology_12.sq` — 792 scenario seismology 12

```sansqrit
# Scenario 492: Seismic event correlation screening: scenario 12
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(140, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(140)
    H(q[0])
    for i in range(0, 72) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 5)
    }
    print("scenario", "seismology", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(140))
}
```

### 793. `793_scenario_seismology_13.sq` — 793 scenario seismology 13

```sansqrit
# Scenario 493: Seismic event correlation screening: scenario 13
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(142, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(142)
    H(q[0])
    for i in range(0, 73) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 6)
    }
    print("scenario", "seismology", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(142))
}
```

### 794. `794_scenario_seismology_14.sq` — 794 scenario seismology 14

```sansqrit
# Scenario 494: Seismic event correlation screening: scenario 14
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(144, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(144)
    H(q[0])
    for i in range(0, 74) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 7)
    }
    print("scenario", "seismology", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(144))
}
```

### 795. `795_scenario_seismology_15.sq` — 795 scenario seismology 15

```sansqrit
# Scenario 495: Seismic event correlation screening: scenario 15
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(136, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(136)
    H(q[0])
    for i in range(0, 75) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 8)
    }
    print("scenario", "seismology", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(136))
}
```

### 796. `796_scenario_seismology_16.sq` — 796 scenario seismology 16

```sansqrit
# Scenario 496: Seismic event correlation screening: scenario 16
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(138, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(138)
    H(q[0])
    for i in range(0, 76) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 5)
    }
    print("scenario", "seismology", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(138))
}
```

### 797. `797_scenario_seismology_17.sq` — 797 scenario seismology 17

```sansqrit
# Scenario 497: Seismic event correlation screening: scenario 17
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(140, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(140)
    H(q[0])
    for i in range(0, 77) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 6)
    }
    print("scenario", "seismology", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(140))
}
```

### 798. `798_scenario_seismology_18.sq` — 798 scenario seismology 18

```sansqrit
# Scenario 498: Seismic event correlation screening: scenario 18
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(142, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(142)
    H(q[0])
    for i in range(0, 78) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 7)
    }
    print("scenario", "seismology", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(142))
}
```

### 799. `799_scenario_seismology_19.sq` — 799 scenario seismology 19

```sansqrit
# Scenario 499: Seismic event correlation screening: scenario 19
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(144, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(144)
    H(q[0])
    for i in range(0, 79) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 8)
    }
    print("scenario", "seismology", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(144))
}
```

### 800. `800_scenario_seismology_20.sq` — 800 scenario seismology 20

```sansqrit
# Scenario 500: Seismic event correlation screening: scenario 20
# Strategy: MPS/tensor-network for low-entanglement neighbor structure.
simulate(136, engine="mps", max_bond_dim=64, cutoff=1e-12, use_lookup=true) {
    q = quantum_register(136)
    H(q[0])
    for i in range(0, 80) {
        CNOT(q[i], q[i + 1])
        Rz(q[i + 1], PI / 5)
    }
    print("scenario", "seismology", "mps_low_entanglement_chain")
    print("estimate", estimate_qubits(136))
}
```