# 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.

Repository: https://github.com/mit-han-lab/torchquantum
Canonical: https://ross.abutalabs.com/products/torchquantum
Homepage: https://torchquantum.org
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: pytorch-quantum, quantum, quantum-machine-learning, neural-network, machine-learning, quantum-computing, pytorch, deep-learning, system, ml-for-systems, quantum-simulation, quantum-neural-network, parameterized-quantum-circuit
Last push: 2026-07-06T00:02:16+00:00

## Health v2 (maintenance only)
Score: 64/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 91, release rhythm 8, longevity 100
- inputs: {"age_days": 2036, "days_push": 59, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1662, forks 262 (observed 2026-08-28T04:05:18.655200+00:00)

## What it is
TorchQuantum is a PyTorch-based framework for quantum computing simulation, supporting statevector and pulse-level simulation on GPUs with automatic gradient computation for parameterized quantum circuits. It scales to 30+ qubits with multi-GPU support and integrates with the PyTorch and Qiskit ecosystems for deployment on real quantum computers.

## Use cases
- train quantum neural networks with PyTorch autograd
- simulate parameterized quantum circuits on GPU
- research quantum machine learning algorithms
- simulate 30+ qubit circuits with multiple GPUs
- deploy trained circuits to real quantum computers
- perform quantum optimal control with pulse-level simulation
- batch-simulate many quantum circuits in parallel

## When to choose
- you want quantum circuits as differentiable PyTorch modules with dynamic computation graphs
- you need fast GPU-accelerated or multi-GPU quantum simulation
- you are training parameterized quantum circuits or doing quantum machine learning research
- you want easy debugging of intermediate quantum states

## When to avoid
- you only need circuit construction and execution on IBM hardware without PyTorch training - Qiskit alone may suffice
- you need hybrid frameworks with extensive quantum algorithm libraries like PennyLane's ecosystem
- you need simulation beyond ~30 qubits
- you are not working in Python/PyTorch

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, simulation, deep-learning, sdk
- domain: quantum-computing, machine-learning, deep-learning
- platform: python, cross-platform
- tags: quantum-machine-learning, quantum-neural-networks, parameterized-quantum-circuits, pytorch, quantum-simulation, qiskit-ecosystem, statevector-simulation, pulse-simulation, algorithms, gpu

## Member repositories
- mit-han-lab/torchquantum (main) score 64

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:18.655200+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:44:35.068884+00:00, confidence not recorded.
  - readme: https://github.com/mit-han-lab/torchquantum (fetched 2026-08-28T04:05:18.655200+00:00, sha ef57a5170d38)
  - homepage: https://torchquantum.org (fetched 2026-08-29T11:16:51.648411+00:00, sha 8bc6c8b21364)
  - registry_pypi: https://pypi.org/pypi/torchquantum/json (fetched 2026-08-29T11:16:51.657750+00:00, sha 93940102f8f2)
- Data as of 2026-08-30T08:39:29.467469+00:00.