QuantumBFS/Yao.jl

Yao Extensible, Efficient Quantum Algorithm Design for Humans.

Yao is an open source framework that aims to empower quantum information research with software tools. It is designed with following in mind:

• quantum algorithm design;
• quantum software 2.0;
• quantum computation education.

We are in an early-release beta. Expect some adventures and rough edges.

A 3 line Quantum Fourier Transformation with Quantum Blocks:

A(i, j) = control(i, j=>shift(2π/(1<<(i-j+1))))
B(n, k) = chain(n, j==k ? put(k=>H) : A(j, k) for j in k:n)
qft(n) = chain(B(n, k) for k in 1:n)

Yao is a   Julia Language   package. To install Yao, please open Julia's interactive session (known as REPL) and press ] key in the REPL to use the package mode, then type the following command

For stable release

pkg> add Yao

For current master

pkg> add Yao#master

If you have problem to install the package, please file us an issue.

For CUDA support, see CuYao.jl.

For tensor network based simulations, see YaoToEinsum.jl.

Some quantum algorithms are implemented with Yao in QuAlgorithmZoo.

• STABLE — most recently tagged version of the documentation.
• LATEST — in-development version of the documentation.

We are running a monthly community call, please sign up in Julia slack channel by DM Roger-luo your email address. Or follow our twitter.

If you have anything interesting to share up to 40min, or just want to talk about your experience in a brief 10min, let us know! Please sign up with a topic using this Google sheet.

• Github issues: Please feel free to ask questions and report bugs, feature request in issues
• Slack: you can join julia's slack channel and ask Yao related questions in #yao-dev channel.
• Julia discourse: You can also ask questions on julia discourse or the Chinese discourse

This project is an effort of QuantumBFS, an open source organization for quantum science. Yao is currently maintained by Xiu-Zhe (Roger) Luo and Jin-Guo Liu with contributions from open source community. All the contributors are listed in the contributors.

If you use Yao in teaching and research, please cite our work:

@article{YaoFramework2019,
  title={Yao.jl: Extensible, Efficient Framework for Quantum Algorithm Design},
  author={Xiu-Zhe Luo and Jin-Guo Liu and Pan Zhang and Lei Wang},
  journal={arXiv preprint arXiv:1912.10877},
  year={2019}
}

Yao is released under the Apache 2 license.