TENSORCIRCUIT

English | 简体中文

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((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))

Install

pip install tensorcircuit(Extra package installation may be required for some features.)

Contributing

For contribution guidelines and notes, see CONTRIBUTING.

For developers, we suggest first configuring a good conda environment. The versions of dependence packages may vary in terms of development requirements. The minimum requirement is the TensorNetwork package. Dockerfile is also provided.

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.

Release files for tensorcircuit 0.0.220328

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 0.0.220328
File	Size	Uploaded
tensorcircuit-0.0.220328.tar.gz	184.3 kB	Mar 28, 2022	Details

Built distribution (wheel)

Table of built distributions (wheels) for tensorcircuit 0.0.220328
File	Interpreter	ABI	Platform	Reset
tensorcircuit-0.0.220328-py3-none-any.whl 178.1 kB Mar 28, 2022	Python 3	none	any	Details

Total release size:362.4 kB

Release files / tensorcircuit-0.0.220328.tar.gz

Download URL	tensorcircuit-0.0.220328.tar.gz
Size	184.3 kB
Tags	Source
SHA-256 checksum How to use checksums	`29bef87df016bf1676ec8fb97521ee38e8997b59446bf5c6895bd6d9e6936427`
BLAKE2b-256 checksum How to use checksums	`0ee3d20237789b52c450a7dd212fcf772618fb5dc350b43bb82f795a78d6b2de`
Upload date	Mar 28, 2022
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/3.8.0 pkginfo/1.8.2 readme-renderer/34.0 requests/2.26.0 requests-toolbelt/0.9.1 urllib3/1.26.7 tqdm/4.62.3 importlib-metadata/4.10.1 keyring/23.5.0 rfc3986/1.5.0 colorama/0.4.4 CPython/3.8.0`

Release files / tensorcircuit-0.0.220328-py3-none-any.whl

Download URL	tensorcircuit-0.0.220328-py3-none-any.whl
Size	178.1 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`596e4d1625f94c80bbb557d873b54d597272f6721f728edde523733709f8abed`
BLAKE2b-256 checksum How to use checksums	`86c244a1671681cfccab2d104f5d91ee90d318ee2b28abad9fa77a696f35cd43`
Upload date	Mar 28, 2022
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/3.8.0 pkginfo/1.8.2 readme-renderer/34.0 requests/2.26.0 requests-toolbelt/0.9.1 urllib3/1.26.7 tqdm/4.62.3 importlib-metadata/4.10.1 keyring/23.5.0 rfc3986/1.5.0 colorama/0.4.4 CPython/3.8.0`