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

TensorCircuit is built on top of modern machine learning frameworks and is machine learning backend agnostic. It is specifically suitable for highly efficient simulations of quantum-classical hybrid paradigm and variational quantum algorithms.

Getting Started

Please begin with Quick Start.

For more information and introductions, please refer to helpful example scripts and full documentation. 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))

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] and [qiskit].

For the nightly build of tensorcircuit with new features, try:

pip uninstall tensorcircuit
pip install tensorcircuit-nightly

We also have Docker support.

Advantages

• Tensor network simulation engine based

• JIT, AD, vectorized parallelism compatible, GPU 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

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

Contributing

Status

This project is released by Tencent Quantum Lab and 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 lab and the open source community.

Citation

If this project helps in your research, please cite our software whitepaper published in Quantum:

TensorCircuit: a Quantum Software Framework for the NISQ Era

which is also a good introduction to the software.

Guidelines

For contribution guidelines and notes, see CONTRIBUTING.

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

Contributors

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 💻 ⚠️

Research and Applications

DQAS

For the application of Differentiable Quantum Architecture Search, see applications. Reference paper: https://arxiv.org/pdf/2010.08561.pdf (published in QST).

VQNHE

For the application of Variational Quantum-Neural Hybrid Eigensolver, see applications. Reference paper: https://arxiv.org/pdf/2106.05105.pdf (published in PRL) and https://arxiv.org/pdf/2112.10380.pdf.

VQEX - MBL

For the application of VQEX on MBL phase identification, see the tutorial. Reference paper: https://arxiv.org/pdf/2111.13719.pdf (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/pdf/2208.02866.pdf (published in PRL).