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TensorCircuit is the next generation of quantum software framework with support for automatic differentiation, just-in-time compiling, hardware acceleration, and vectorized parallelism.

TensorCircuit is built on top of modern machine learning frameworks: Jax, TensorFlow, and PyTorch. It is specifically suitable for highly efficient simulations of quantum-classical hybrid paradigm and variational quantum algorithms in ideal, noisy and approximate cases. It also supports real quantum hardware access and provides CPU/GPU/QPU hybrid deployment solutions since v0.9.

Getting Started

Please begin with Quick Start in the full documentation.

For more information on software usage, sota algorithm implementation and engineer paradigm demonstration, please refer to 70+ example scripts and 30+ tutorial notebooks. API docstrings and test cases in tests are also informative.

The following are some minimal demos.

• Circuit manipulation:

import tensorcircuit as tc
c = tc.Circuit(2)
c.H(0)
c.CNOT(0,1)
c.rx(1, theta=0.2)
print(c.wavefunction())
print(c.expectation_ps(z=[0, 1]))
print(c.sample(allow_state=True, batch=1024, format="count_dict_bin"))

• Runtime behavior customization:

tc.set_backend("tensorflow")
tc.set_dtype("complex128")
tc.set_contractor("greedy")

• Automatic differentiations with jit:

def forward(theta):
    c = tc.Circuit(2)
    c.R(0, theta=theta, alpha=0.5, phi=0.8)
    return tc.backend.real(c.expectation((tc.gates.z(), [0])))

g = tc.backend.grad(forward)
g = tc.backend.jit(g)
theta = tc.array_to_tensor(1.0)
print(g(theta))

• Sparse Hamiltonian generation and expectation evaluation:

n = 6
pauli_structures = []
weights = []
for i in range(n):
    pauli_structures.append(tc.quantum.xyz2ps({"z": [i, (i + 1) % n]}, n=n))
    weights.append(1.0)
for i in range(n):
    pauli_structures.append(tc.quantum.xyz2ps({"x": [i]}, n=n))
    weights.append(-1.0)
h = tc.quantum.PauliStringSum2COO(pauli_structures, weights)
print(h)
# BCOO(complex64[64, 64], nse=448)
c = tc.Circuit(n)
c.h(range(n))
energy = tc.templates.measurements.operator_expectation(c, h)
# -6

• Large-scale simulation with tensor network engine

# tc.set_contractor("cotengra-30-10")
n=500
c = tc.Circuit(n)
c.h(0)
c.cx(range(n-1), range(1, n))
c.expectation_ps(z=[0, n-1], reuse=False)

• Density matrix simulator and quantum info quantities

c = tc.DMCircuit(2)
c.h(0)
c.cx(0, 1)
c.depolarizing(1, px=0.1, py=0.1, pz=0.1)
dm = c.state()
print(tc.quantum.entropy(dm))
print(tc.quantum.entanglement_entropy(dm, [0]))
print(tc.quantum.entanglement_negativity(dm, [0]))
print(tc.quantum.log_negativity(dm, [0]))

Install

The package is written in pure Python and can be obtained via pip as:

pip install tensorcircuit

We recommend you install this package with tensorflow also installed as:

pip install tensorcircuit[tensorflow]

Other optional dependencies include [torch], [jax], [qiskit] and [cloud].

We also have Docker support.

Advantages

• Tensor network simulation engine based

• JIT, AD, vectorized parallelism compatible

• GPU support, quantum device access support, hybrid deployment support

• Efficiency

  • Time: 10 to 10^6+ times acceleration compared to TensorFlow Quantum, Pennylane or Qiskit

  • Space: 600+ qubits 1D VQE workflow (converged energy inaccuracy: < 1%)

• Elegance

  • Flexibility: customized contraction, multiple ML backend/interface choices, multiple dtype precisions, multiple QPU providers

  • API design: quantum for humans, less code, more power

• Batteries included

  <details> <summary> Tons of amazing features and built in tools for research (click for details) </summary>

  • Support super large circuit simulation using tensor network engine.

  • Support noisy simulation with both Monte Carlo and density matrix (tensor network powered) modes.

  • Support approximate simulation with MPS-TEBD modes.

  • Support analog/digital hybrid simulation (time dependent Hamiltonian evolution, pulse level simulation) with neural ode modes.

  • Support Fermion Gaussian state simulation with expectation, entanglement, measurement, ground state, real and imaginary time evolution.

  • Support qudits simulation.

  • Support parallel quantum circuit evaluation across multiple GPUs.

  • Highly customizable noise model with gate error and scalable readout error.

  • Support for non-unitary gate and post-selection simulation.

  • Support real quantum devices access from different providers.

  • Scalable readout error mitigation native to both bitstring and expectation level with automatic qubit mapping consideration.

  • Advanced quantum error mitigation methods and pipelines such as ZNE, DD, RC, etc.

  • Support MPS/MPO as representations for input states, quantum gates and observables to be measured.

  • Support vectorized parallelism on circuit inputs, circuit parameters, circuit structures, circuit measurements and these vectorization can be nested.

  • Gradients can be obtained with both automatic differenation and parameter shift (vmap accelerated) modes.

  • Machine learning interface/layer/model abstraction in both TensorFlow and PyTorch for both numerical simulation and real QPU experiments.

  • Circuit sampling supports both final state sampling and perfect sampling from tensor networks.

  • Light cone reduction support for local expectation calculation.

  • Highly customizable tensor network contraction path finder with opteinsum interface.

  • Observables are supported in measurement, sparse matrix, dense matrix and MPO format.

  • Super fast weighted sum Pauli string Hamiltonian matrix generation.

  • Reusable common circuit/measurement/problem templates and patterns.

  • Jittable classical shadow infrastructures.

  • SOTA quantum algorithm and model implementations.

  • Support hybrid workflows and pipelines with CPU/GPU/QPU hardware from local/cloud/hpc resources using tf/torch/jax/cupy/numpy frameworks all at the same time.

  </details>

Contributing

Status

This project is created and maintained by Shi-Xin Zhang with current core authors Shi-Xin Zhang and Yu-Qin Chen. We also thank contributions from the open source community.

Citation

If this project helps in your research, please cite our software whitepaper to acknowledge the work put into the development of TensorCircuit.

TensorCircuit: a Quantum Software Framework for the NISQ Era (published in Quantum)

which is also a good introduction to the software.

Research works citing TensorCircuit can be highlighted in Research and Applications section.

Guidelines

For contribution guidelines and notes, see CONTRIBUTING.

We welcome issues, PRs, and discussions from everyone, and these are all hosted on GitHub.

License

TensorCircuit is open source, released under the Apache License, Version 2.0.

Contributors

Research and Applications

DQAS

For the application of Differentiable Quantum Architecture Search, see applications.

Reference paper: https://arxiv.org/abs/2010.08561 (published in QST).

VQNHE

For the application of Variational Quantum-Neural Hybrid Eigensolver, see applications.

Reference paper: https://arxiv.org/abs/2106.05105 (published in PRL) and https://arxiv.org/abs/2112.10380 (published in AQT).

VQEX-MBL

For the application of VQEX on MBL phase identification, see the tutorial.

Reference paper: https://arxiv.org/abs/2111.13719 (published in PRB).

Stark-DTC

For the numerical demosntration of discrete time crystal enabled by Stark many-body localization, see the Floquet simulation demo.

Reference paper: https://arxiv.org/abs/2208.02866 (published in PRL).

RA-Training

For the numerical simulation of variational quantum algorithm training using random gate activation strategy by us, see the project repo.

Reference paper: https://arxiv.org/abs/2303.08154 (published in PRR as a Letter).

TenCirChem

TenCirChem is an efficient and versatile quantum computation package for molecular properties. TenCirChem is based on TensorCircuit and is optimized for chemistry applications.

Reference paper: https://arxiv.org/abs/2303.10825 (published in JCTC).

EMQAOA-DARBO

For the numerical simulation and hardware experiments with error mitigation on QAOA, see the project repo.

Reference paper: https://arxiv.org/abs/2303.14877 (published in Communications Physics).

NN-VQA

For the setup and simulation code of neural network encoded variational quantum eigensolver, see the demo.

Reference paper: https://arxiv.org/abs/2308.01068 (published in PRApplied).

More works

• Neural Predictor based Quantum Architecture Search: https://arxiv.org/abs/2103.06524 (published in Machine Learning: Science and Technology).

• Quantum imaginary-time control for accelerating the ground-state preparation: https://arxiv.org/abs/2112.11782 (published in PRR).

• Efficient Quantum Simulation of Electron-Phonon Systems by Variational Basis State Encoder: https://arxiv.org/abs/2301.01442 (published in PRR).

• Variational Quantum Simulations of Finite-Temperature Dynamical Properties via Thermofield Dynamics: https://arxiv.org/abs/2206.05571.

• Understanding quantum machine learning also requires rethinking generalization: https://arxiv.org/abs/2306.13461 (published in Nature Communications).

• Decentralized Quantum Federated Learning for Metaverse: Analysis, Design and Implementation: https://arxiv.org/abs/2306.11297. Code: https://github.com/s222416822/BQFL.

• Non-IID quantum federated learning with one-shot communication complexity: https://arxiv.org/abs/2209.00768 (published in Quantum Machine Intelligence). Code: https://github.com/JasonZHM/quantum-fed-infer.

• Quantum generative adversarial imitation learning: https://doi.org/10.1088/1367-2630/acc605 (published in New Journal of Physics).

• GSQAS: Graph Self-supervised Quantum Architecture Search: https://arxiv.org/abs/2303.12381 (published in Physica A: Statistical Mechanics and its Applications).

• Practical advantage of quantum machine learning in ghost imaging: https://www.nature.com/articles/s42005-023-01290-1 (published in Communications Physics).

• Zero and Finite Temperature Quantum Simulations Powered by Quantum Magic: https://arxiv.org/abs/2308.11616.

• Comparison of Quantum Simulators for Variational Quantum Search: A Benchmark Study: https://arxiv.org/abs/2309.05924.

• Statistical analysis of quantum state learning process in quantum neural networks: https://arxiv.org/abs/2309.14980 (published in NeurIPS).

• Generative quantum machine learning via denoising diffusion probabilistic models: https://arxiv.org/abs/2310.05866 (published in PRL).

• Quantum imaginary time evolution and quantum annealing meet topological sector optimization: https://arxiv.org/abs/2310.04291.

• Google Summer of Code 2023 Projects (QML4HEP): https://github.com/ML4SCI/QMLHEP, https://github.com/Gopal-Dahale/qgnn-hep, https://github.com/salcc/QuantumTransformers.

• Absence of barren plateaus in finite local-depth circuits with long-range entanglement: https://arxiv.org/abs/2311.01393 (published in PRL).

• Non-Markovianity benefits quantum dynamics simulation: https://arxiv.org/abs/2311.17622.

  </details>

If you want to highlight your research work or projects here, feel free to add by opening PR.