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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.

Please begin withQuick Start in thefull 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 intests are also informative.

The following are some minimal demos.

• Circuit manipulation:

importtensorcircuitastcc=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:

defforward(theta):c=tc.Circuit(2)c.R(0,theta=theta,alpha=0.5,phi=0.8)returntc.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))

n=6pauli_structures= []weights= []foriinrange(n):pauli_structures.append(tc.quantum.xyz2ps({"z": [i, (i+1)%n]},n=n))weights.append(1.0)foriinrange(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

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

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]))

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

pipinstalltensorcircuit

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

pipinstalltensorcircuit[tensorflow]

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

We also haveDocker support.

• 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

  Tons of amazing features and built in tools for research (click for details)

  • Supportsuper large circuit simulation using tensor network engine.

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

  • Supportapproximate simulation with MPS-TEBD modes.

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

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

  • Supportqudits simulation.

  • Supportparallel quantum circuit evaluation acrossmultiple GPUs.

  • Highly customizablenoise model with gate error and scalable readout error.

  • Support fornon-unitary gate and post-selection simulation.

  • Supportreal 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.

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

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

  • Gradients can be obtained with bothautomatic 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.

This project is created and maintained byShi-Xin Zhang with current core authorsShi-Xin Zhang andYu-Qin Chen. We also thankcontributions from the open source community.

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 inResearch and Applications section.

For contribution guidelines and notes, seeCONTRIBUTING.

We welcomeissues,PRs, anddiscussions from everyone, and these are all hosted on GitHub.

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

Shixin Zhang 💻📖💡🤔🚇🚧🔬👀🌍⚠️✅📢💬	Yuqin Chen 💻📖💡🤔🔬⚠️✅📢	Jiezhong Qiu 💻💡🤔🔬	Weitang Li 💻📖🤔🔬⚠️📢	Jiace Sun 💻📖💡🤔🔬⚠️	Zhouquan Wan 💻📖💡🤔🔬⚠️✅
Shuo Liu 💡🔬✅	Hao Yu 💻📖🚇⚠️✅	Xinghan Yang 📖🌍✅	JachyMeow ✅🌍	Zhaofeng Ye 🎨	erertertet 💻📖⚠️
Yicong Zheng ✅	Zixuan Song 📖🌍💻⚠️	Hao Xie 📖	Pramit Singh ⚠️	Jonathan Allcock 📖🤔📢	nealchen2003 📖
隐公观鱼 💻⚠️	WiuYuan 💡	Felix Xu ✅💻⚠️	Hong-Ye Hu 📖	peilin ✅💻⚠️📖	Cristian Emiliano Godinez Ramirez 💻⚠️
ztzhu 💻	Rabqubit 💡	Kazuki Tsuoka 💻⚠️📖💡	Gopal Ramesh Dahale 💡	Chanandellar Bong 💡

For the application of Differentiable Quantum Architecture Search, seeapplications.

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

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

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

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

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

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

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

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

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

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).

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

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

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

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

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