mit-han-lab/torchquantum
A PyTorch-based framework for Quantum Classical Simulation, Quantum Machine Learning, Quantum Neural Networks, Parameterized Quantum Circuits with support for easy deployments on real quantum computers.
This framework helps quantum computing researchers and algorithm designers quickly simulate quantum circuits and quantum machine learning models on classical hardware. You can input descriptions of quantum circuits or quantum neural networks using familiar PyTorch commands, and it outputs simulated quantum states, measurement results, and enables gradient calculations for optimization. It's designed for quantum algorithm researchers, quantum machine learning practitioners, and those working on quantum neural networks.
1,607 stars. Used by 1 other package. Available on PyPI.
Use this if you need to rapidly prototype, debug, and optimize quantum algorithms or quantum machine learning models using a classical simulator with GPU acceleration, before deploying to a real quantum computer.
Not ideal if your primary goal is to run computations exclusively on real quantum hardware without needing classical simulation and PyTorch integration for development.
Stars
1,607
Forks
245
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Oct 28, 2025
Commits (30d)
0
Dependencies
19
Reverse dependents
1
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