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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 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.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())

• 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 purely written in Python and can be obtained via pip as:

pip install tensorcircuit

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 tfq 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

For contribution guidelines and notes, see CONTRIBUTING.

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

Researches and Applications

DQAS

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

VQNHE

For the application of Variational Quantum-Neural Hybrid Eigensolver, see applications. Reference paper: https://arxiv.org/pdf/2106.05105.pdf 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.