TENSORCIRCUIT

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 has the beautiful backend agnostic feature. It is specifically suitable for simulations of quantum-classical hybrid paradigm and variational quantum algorithms.

Basic Usage

import tensorcircuit as tc
c = tc.Circuit(2)
c.H(0)
c.CNOT(0,1)
print(c.wavefunction())
print(c.expectation((tc.gates.z(), [1])))

Runtime behavior customization:

tc.set_backend("tensorflow")
tc.set_dtype("complex128")
tc.set_contractor("greedy")

Auto differentiations with jit (tf and jax backend currently supported):

@tc.backend.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.gates.num_to_tensor(1.0)
print(g(theta))

For tutorials on tensorcircuit, please refer to examples in this repo and separate tensorcircuit-tutorial repo for jupyter examples. Docstrings (incomplete for now) and test cases in tests are also helpful and informative.

Contributing

Guidelines

Please open issues or PRs.

NEVER directly push to this repo!

Keep the codebase private!

Install

For development workflow, we suggest to first configure a good conda environment. The versions of dependecy package may vary in terms of development requirements. The minimum requirement is the TensorNetwork package (pip install suggested).

For git workflow of contribution, see CONTRIBUTING.

Docs

cd docs
make html

Tests

pytest

Formatter

black .

Linter

pylint tensorcircuit tests

Type checker

mypy tensorcircuit

Integrated script

For now, we introduce one for all checker for development:

./check_all.sh

CI

We currently use GitHub Action for test CI, but it has limited quota for free private repo.

Research projects and application codes

DQAS

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

VQNHE

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