Yao.jl: Extensible, Efficient Quantum Algorithm Design for Humans

In this talk, I will introduce the design of Yao.jl and how it helps accelerate quantum algorithm design.

The study of variational quantum algorithms is gaining popularity. Variational quantum optimization algorithms such as quantum circuit Born machine, quantum approximation optimization algorithm, variational quantum eigensolver and quantum circuit learning are promising for near term quantum computers. These algorithms require the flexibility to tune parameters and to leverage circuit patterns such as "arbitrary rotation block" and "CNOT entangler". In a departure from traditional simulators and frameworks, we have designed and developed a framework along with an Intermediate Representation (IR) to represent, simulate and manipulate quantum circuits. Our design enables:

• Hierarchical design of quantum algorithms
• Heterogeneous computing
• Flexibility in dispatch parameters
• Caching matrix forms to speed up simulation
• Greater abstraction for quantum circuits

Yao.jl on Github: https://github.com/QuantumBFS/Yao.jl

Speaker

Xiuzhe Luo
Graduate Student
University of Waterloo

Co-authors

Jin-Guo Liu
Pan Zhang
Lei Wang

How to get here

The STAR Conference room (32-D463) is on the 4th floor of the Dreyfoos Wing in the Stata Center.

About us

C.A.J.U.N is dedicated to users and developers of the Julia language. Please see our Papercall https://www.papercall.io/cajun and submit your talk ideas for future events!