TENSORCIRCUIT

Build on top of TensorNetwork, differentiable quantum circuit simulator gains benefits from swift implementation to auto differentiation infrastructure.

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))

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).

Secondly, fork this repo to your GitHub account (make sure keeping the repo private!), and setup the SSH access to your GitHub account.

Lastly

git clone git@github.com:yourgithub/tensorcircuit-dev.git
export PYTHONPATH=/path/for/tensorcircuit/

or a better approach for the last step

python setup.py develop

Now, you are ready to import tensorcircuit and enjoy coding.

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.