tensorcircuit-nightly

Pre-release

This release is a pre-release and may not be stable for production use.

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.

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_ps(z=[0, 1]))
print(c.sample())

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(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.array_to_tensor(1.0)
print(g(theta))

Install

The package is purely written in Python and can be obtained via pip as:

pip install tensorcircuit

We also have Docker support.

Advantages

Tensor network simulation engine based
JIT, AD, vectorized parallelism compatible, GPU support
Efficiency
- Time: 10 to 10^6 times acceleration compared to tfq or qiskit
- Space: 600+ qubits 1D VQE workflow (converged energy inaccuracy: < 1%)
Elegance
- Flexibility: customized contraction, multiple ML backend/interface choices, multiple dtype precisions
- API design: quantum for humans, less code, more power

Citing TensorCircuit

This project is released by Tencent Quantum Lab and is currently maintained by Shi-Xin Zhang with contributions from the lab and open source community.

If this project helps in your research, please cite our software whitepaper:

TensorCircuit: a Quantum Software Framework for the NISQ Era

which is also a good introduction for the software.

Contributing

For contribution guidelines and notes, see CONTRIBUTING.

We welcome issues, PRs, and discussions from everyone, and these are all hosted on GitHub.

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-nightly 0.2.2.dev20220719

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-nightly 0.2.2.dev20220719
File	Size	Uploaded
tensorcircuit-nightly-0.2.2.dev20220719.tar.gz	210.7 kB	Jul 19, 2022	Details

Built distribution (wheel)

Table of built distributions (wheels) for tensorcircuit-nightly 0.2.2.dev20220719
File	Interpreter	ABI	Platform	Reset
tensorcircuit_nightly-0.2.2.dev20220719-py3-none-any.whl 210.7 kB Jul 19, 2022	Python 3	none	any	Details

Total release size: 421.4 kB

Release files / tensorcircuit-nightly-0.2.2.dev20220719.tar.gz

Download URL	tensorcircuit-nightly-0.2.2.dev20220719.tar.gz
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Release files / tensorcircuit_nightly-0.2.2.dev20220719-py3-none-any.whl

Download URL	tensorcircuit_nightly-0.2.2.dev20220719-py3-none-any.whl
Size	210.7 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`07bb308b639e61ff0263e7944c1cb0a1a377352f464f378a26878796b34f811f`
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