Skip to main content

Quantum circuits on top of tensor network

Project description


English | 简体中文

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 and Jupyter Tutorials.

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.rx(1, theta=0.2)
print(c.expectation((tc.gates.z(), [1])))

Runtime behavior customization:


Automatic differentiations with jit:

def forward(theta):
    c = tc.Circuit(n=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.gates.num_to_tensor(1.0)


pip install tensorcircuit(Extra package installation may be required for some features.)


For contribution guidelines and notes, see CONTRIBUTING.

For developers, we suggest first configuring a good conda environment. The versions of dependence packages may vary in terms of development requirements. The minimum requirement is the TensorNetwork package. Dockerfile is also provided.

Researches and applications


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


For the application of Variational Quantum-Neural Hybrid Eigensolver, see applications. Reference paper: and


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

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

tensorcircuit-0.0.220328.tar.gz (184.3 kB view hashes)

Uploaded source

Built Distribution

tensorcircuit-0.0.220328-py3-none-any.whl (178.1 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page