TENSORCIRCUIT

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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.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.

Install

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

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 and https://arxiv.org/pdf/2112.10380.pdf.

VQEX - MBL

For application of VQEX on MBL phase identification, see tutorial. Reference paper: https://arxiv.org/pdf/2111.13719.pdf.

Release files for tensorcircuit 0.0.220318

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for tensorcircuit 0.0.220318
File	Size	Uploaded
tensorcircuit-0.0.220318.tar.gz	180.1 kB	Mar 18, 2022	Details

Built distribution (wheel)

Table of built distributions (wheels) for tensorcircuit 0.0.220318
File	Interpreter	ABI	Platform	Reset
tensorcircuit-0.0.220318-py3-none-any.whl 177.0 kB Mar 18, 2022	Python 3	none	any	Details

Total release size:357.2 kB

Release files / tensorcircuit-0.0.220318.tar.gz

Download URL	tensorcircuit-0.0.220318.tar.gz
Size	180.1 kB
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SHA-256 checksum How to use checksums	`fb5abd514e284eae74b42aac333a3980c44847ebf9bab1719957626a58fc9597`
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Uploaded via	`twine/3.8.0 pkginfo/1.8.2 readme-renderer/33.0 requests/2.26.0 requests-toolbelt/0.9.1 urllib3/1.26.7 tqdm/4.62.3 importlib-metadata/4.11.2 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.8.0`

Release files / tensorcircuit-0.0.220318-py3-none-any.whl

Download URL	tensorcircuit-0.0.220318-py3-none-any.whl
Size	177.0 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`4030e7218e0d5a1adfe231c22dacc1c1b4de2e496aca9fbd354bb521534108c3`
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Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/3.8.0 pkginfo/1.8.2 readme-renderer/33.0 requests/2.26.0 requests-toolbelt/0.9.1 urllib3/1.26.7 tqdm/4.62.3 importlib-metadata/4.11.2 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.8.0`