Install SansqritPy — pip package setup, CLI, examples, datasets

Installation Overview

SansqritPy is the Python package for the Sansqrit DSL. It includes a parser, CLI, sparse simulator, sharding and hierarchical tensor backends, lookup data files, QEC helpers, hardware export adapters, diagnostics, and AI training datasets.

0.3.6

Package version

820

Examples

37K+

Dataset records

10q

Embedded lookup blocks

Install from PyPI

python -m pip install --upgrade pip
python -m pip install sansqrit

Use a virtual environment for research notebooks, cloud machines, CI, or local development.

python -m venv .venv
source .venv/bin/activate
python -m pip install sansqrit

Optional Extras

Extra	Purpose	Install command
`qiskit`	Qiskit interop and optional verification	`pip install "sansqrit[qiskit]"`
`cirq`	Cirq export and optional simulator checks	`pip install "sansqrit[cirq]"`
`braket`	AWS Braket payload generation	`pip install "sansqrit[braket]"`
`pennylane`	QML/QNode style interop	`pip install "sansqrit[pennylane]"`
`gpu`	CuPy/cuQuantum planning paths	`pip install "sansqrit[gpu]"`
`all`	All optional integrations	`pip install "sansqrit[all]"`

Verify Installation

python -c "import sansqrit; print(sansqrit.__version__)"
sansqrit doctor
sansqrit backends
sansqrit estimate 120
sansqrit architecture

Expected output should report available Python modules, packaged lookup files, datasets, examples, optional dependencies, and backend capabilities.

CLI Commands

sansqrit run program.sq
sansqrit qasm program.sq --version 3 -o program.qasm
sansqrit translate program.sq
sansqrit dataset info
sansqrit dataset sample --split sft_train -n 3
sansqrit scenarios info
sansqrit plan program.sq
sansqrit gpu
sansqrit distributed
sansqrit qec-plan

First Program

simulate(2, engine="sparse") {
    q = quantum_register(2)
    H(q[0])
    CNOT(q[0], q[1])
    print(probabilities())
    print(measure_all(shots=20))
}

sansqrit run bell.sq

Packaged Dataset Verification

sansqrit dataset info
sansqrit dataset splits
sansqrit dataset sample --split real_world_scenarios -n 2
sansqrit dataset export --output ./sansqrit-training-export

The installed package includes SFT, evaluation, preference, and real-world scenario corpora for AI model training and prompt-to-Sansqrit code generation.

Troubleshooting

Large dense warning: 120-qubit dense state-vector simulation is not physically feasible on local hardware. Use engine("auto"), stabilizer, sparse, MPS, hierarchical tensor shards, or distributed sparse execution.

sansqrit troubleshoot lookup
sansqrit troubleshoot distributed
sansqrit troubleshoot qec
sansqrit doctor --json

Uploading a New Package Release

python -m pip install --upgrade build twine
python -m build --sdist --wheel
python -m twine check dist/*
python -m twine upload dist/*

If a version already exists on PyPI, bump the version number before uploading.