Juan Arrazola
Director Of Quantum Algorithms at Xanadu
Old Toronto, Ontario, Canada
Summary
🤩
Rockstar🎓
Top SchoolJuan Arrazola is Director of Quantum Algorithms at Xanadu, leading a 15-person team that translates quantum research into commercial software and partnerships worldwide. With a PhD in Quantum Information from the University of Waterloo and seven years of industry experience, he bridges quantum chemistry, machine learning, and full-stack quantum software engineering. He has authored dozens of highly cited papers, holds multiple patents, and contributes to flagship open-source projects like Strawberry Fields and PennyLane—adding graph-based algorithms, Monte Carlo feature pipelines, and mixed-state simulators. Known for turning theoretical ideas into production-ready tools, he combines deep academic rigor with practical developer chops and a knack for clear, tutorial-driven documentation.
7 years of coding experience
8 years of employment as a software developer
Universidad de los AndesMaster's Degree, Physics, Master's Degree, Physics at University of TorontoDoctor of Philosophy (Ph.D.), Physics - Quantum Information, Doctor of Philosophy (Ph.D.), Physics - Quantum Information at University of Waterloo
English, Spanish, French
Stackoverflow
101reputation
0reached
0answers
0questions
Github Skills (14)
unit-testing10
quantum-computing10
graph-algorithms10
quantum-chemistry10
machine-learning10
automatic-differentiation10
networkx10
tensorflow10
python10
numpy10
documentation9
plotly9
jax7
autograd6
Programming languages (3)
Github contributions (5)
XanaduAI/strawberryfields
Aug 2019 - Oct 2020
Strawberry Fields is a full-stack Python library for designing, simulating, and optimizing continuous variable (CV) quantum optical circuits.
Role in this project:
Full-stack Developer & ML Engineer
Full-stack Developer & ML EngineerContributions:17 reviews, 17 commits, 27 PRs in 1 year 1 month
Contributions summary:Juan significantly contributed to the `strawberryfields` repository, focusing on the implementation of new features and enhancements to the existing codebase. Their work included the addition of a `clique_shrink` function and the integration of Monte Carlo methods for feature vector calculation, demonstrating a focus on graph algorithms and machine learning. Additionally, the user added code examples to the docstrings, and added a graph visualization module, which provides useful tools for users. The user also fixed issues, fixed some broken docs building and implemented multiple tutorials about GBS applications.
pythoncircuitstensorflowoptimizingquantum-computing
PennyLaneAI/pennylane
Jan 2020 - Apr 2021
PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Train a quantum computer the same way as a neural network.
Role in this project:Back-end Developer
Back-end DeveloperContributions:444 reviews, 11 commits, 28 PRs in 1 year 3 months
Contributions summary:Juan primarily contributed to the `pennylane` library, focusing on quantum chemistry and related functionality. Their commits include refactoring function names, updating documentation, and modifying code within the `qchem` module. The user also contributed to implementing a mixed-state simulator, adding unit tests, and making changes to the core device logic. The user's work supports the development of quantum computing applications.
pythonautomatic-differentiationdifferentiable-computingcomputerstensorflow
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.