Yao

Extensible, Efficient Quantum Algorithm Design for Humans.

Introduction

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.

Try your first Yao program

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)

Installation

Yao is a julia language package. To install Yao, please open Julia's interactive session (known as REPL) and type ] in the REPL to use the package mode, then type this 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.

Documentation

Getting Started

Examples: understand Yao's code for quantum algorithms

Algoritm Zoo

Some quantum algorithms are implemented with Yao in QuAlgorithmZoo.

Online Documentation

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

Communication

• 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

Contribution

Please read our contribution guide.

The Team

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

Papers Citing Yao

Variational Quantum Eigensolver with Fewer Qubits, Jin-Guo Liu, Yi-Hong Zhang, Yuan Wan, Lei Wang, https://arxiv.org/abs/1902.02663

Learning and inference on generative adversarial quantum circuits, Jinfeng Zeng, Yufeng Wu, Jin-Guo Liu, Lei Wang, and Jiangping Hu, Phys. Rev. A 99, 052306 – Published 6 May 2019

Parameterized quantum circuits as machine learning models, Marcello Benedetti, Erika Lloyd, and Stefan Sack https://arxiv.org/pdf/1906.07682.pdf

License

Yao is released under the Apache 2 license.