Xiuzhe Luo
Director Of Scientific Software at QuEra Computing Inc.
Old Toronto, Ontario, Canada
Summary
🤩
Rockstar🎓
Top SchoolXiuzhe Luo is a director of scientific software with a decade of experience building high-performance tooling for quantum research and ML-enabled physics, currently leading software and SDK development at QuEra Computing. He holds a PhD in physics from the University of Waterloo and won the 2020 Wittek Quantum Prize, combining deep domain research with production-grade engineering. As an early creator of Yao.jl and contributor to core Julia projects (Julia itself, CUDA.jl, Zygote, Flux), he has helped shape the Julia scientific stack for GPU-accelerated sparse linear algebra and automatic differentiation. Xiuzhe is known for turning advanced programming paradigms into practical compiler and emulation pipelines for neutral-atom hardware, and for maintaining robust testing and documentation across benchmarking and ML libraries. Colleagues describe his style as research-driven yet pragmatic—equally comfortable designing quantum gates and hardening CI for critical numerical code.
10 years of coding experience
5 years of employment as a software developer
University of Science and Technology of ChinaDoctor of Philosophy - PhD physics, Doctor of Philosophy - PhD physics at University of Waterloo
Stackoverflow
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Github Skills (29)
benchmark10
sparse-matrix10
machine-learning10
benchmarking10
gradient10
automatic-differentiation10
deep-learning10
gpu10
flux10
neural-network10
cuda10
fluxor10
linear-algebra10
numerical-computing10
julia10
Programming languages (19)
Github contributions (5)
QuantumBFS/Yao.jl
Apr 2018 - Dec 2022
Extensible, Efficient Quantum Algorithm Design for Humans.
Role in this project:
Backend Developer
Backend DeveloperContributions:19 releases, 91 reviews, 1393 commits in 4 years 8 months
Contributions summary:Xiuzhe contributed to the foundational design of the Yao.jl library by implementing essential components for quantum circuit simulation, including fundamental quantum gates (X, Y, Z, Hadamard) and their type hierarchies, which serve as the basis for representing and manipulating quantum operations within the framework. They also introduced the initial structure for memory representation and data processing, with files for basic blocks. The contributions included the test functions for those components.
quantum-computingquantum-circuitquantum-algorithmsyaomachine-learning
JuliaCI/BenchmarkTools.jl
Jan 2021 - May 2021
A benchmarking framework for the Julia language
Role in this project:QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:11 commits, 6 PRs, 7 comments in 4 months
Contributions summary:Xiuzhe's contributions primarily focus on maintaining and improving the testing infrastructure for the Julia benchmarking framework. They fixed tests to ensure compatibility with Julia version 1.6. Additionally, they added docstrings to the `@benchmarkset` and `@case` macros, and added documentation using Documenter, improving the usability and maintainability of the benchmarking tools. The user also contributed by adding benchmark sets and cases, indicating involvement in testing.
benchmarkingjulia-languagebenchmarkbenchmarksjulia
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