Yao.jl: Extensible, Efficient Quantum Algorithm Design for Humans.
2019-07-24, 11:00–11:30, Room 349

Quantum computation is the future of computing. However, writing quantum program can be hard for developers living in a classical world. We developed Yao.jl to help scientists test and explore their quantum ideas in a simple way.


Yao is an open source framework for

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


Comparing with state of art quantum simulators, our library is inspired by quantum circuit optimization.
Variational quantum optimization algorithms like quantum circuit Born machine (QCBM), quantum approximate optimization algorithm (QAOA), variational quantum eigensolver (VQE) and quantum circuit learning (QCL) et. al. are promising killer apps on a near term quantum computers.
These algorithms require the flexibility to tune parameters and have well defined patterns such as "Arbitrary Rotation Block" and "CNOT Entangler".

In Yao, we call these patterns "blocks". If we regard every gate or gate pattern as a "block", then the framework can

  • be flexible to dispatch parameters,
  • cache matrices of blocks to speed up future runs,
  • allow hierarchical design of quantum algorithms

Thanks to Julia's duck type and multiple dispatch features, user can

  • easily extend the block system by overloading specific interfaces
  • quantum circuit blocks can be dispatched to some special method to improve the performance in specific case (e.g. customized repeat block of H gate).


Yao is a framework that is about to have the following features:

  • Extensibility
  • define new operations with a minimum number of methods in principle.
  • extend with new operations on different hardware should be easy, (e.g GPUs, near term quantum devices, FPGAs, etc.)
  • Efficiency
  • comparing with python, julia have no significant overhead on small scale circuit.
  • special optimized methods are dispatched to frequently used blocks.
  • double interfaces "apply!" and "cache server + mat" allow us to choose freely when to sacrifice memory for faster simulation and when to sacrifice the speed to simulate more qubits.
  • Easy to Use
  • As a white-box simulator, rather than using a black box, users will be aware of what their simulation are doing right through the interface.
  • Hierarchical APIs from low abstraction quantum operators to highly abstract circuit block objects.

The whole framework is highly modularized, researchers can extend this framework for different purposes.


This project is an effort of QuantumBFS, an open source organization for quantum science. All the contributors are listed in the contributors.


Jinguo Liu