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Quantum circuits on top of tensor network

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TensorCircuit is the next generation of quantum circuit simulator 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 documentations. 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)


For contribution guidelines and notes, see CONTRIBUTING.


For users, pip install tensorcircuit is enough. (Extra package installation may be required for some features.)

For developers, we suggest to first configure a good conda environment. The versions of dependence packages may vary in terms of development requirements. The minimum requirement is the TensorNetwork package. Dockerfiles may also be helpful for building a good development enviroment.

Researches and applications


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


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


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

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