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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 is machine learning backend agnostic. It is specifically suitable for 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 scripts examples and documentations. API docstrings (incomplete for now) 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((tc.gates.z(), [1])))
print(c.perfect_sampling())

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(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)
print(g(theta))

Contributing

For contribution guidelines and notes, see CONTRIBUTING.

Cautions

Please open issues or PRs.

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).

Docs

cd docs
make html

Tests

pytest

Formatter

black .

Linter

pylint tensorcircuit tests examples/*.py

Type checker

mypy tensorcircuit

Integrated script

For now, we introduce one-for-all checker for development before committing:

./check_all.sh

Researches and applications

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.