Christina Lee
Canada
Summary
🤩
Rockstar🎓
Top SchoolChristina Lee is a technical lead in quantum software at Xanadu with over 11 years of software engineering experience and more than five years focused specifically on quantum computing and research software. She guides the technical direction of PennyLane, enhancing its compiler, program representation, and device integrations to make quantum workflows more usable, extensible, and scalable for researchers and developers. Her hands-on contributions include implementing new quantum gates (like SX), improving core QNode and QuantumTape functionality, and upgrading QML tutorials for reproducibility and modern libraries—work that directly supports one of the field’s flagship open-source toolkits. Based in Canada with roots at Caltech, she blends deep systems design and UX sensibilities to deliver trustworthy, production-ready research software while mentoring teams to push practical boundaries in quantum applications.
11 years of coding experience
California Institute of Technology
Stackoverflow
81reputation
13kreached
2answers
7questions
Github Skills (26)
quantum-algorithms10
matrix10
learn-ruby-on-rails10
python10
user-manual10
basics10
information-theory10
automatic-differentiation10
mat10
quantum-computing10
linear-algebra10
unit-testing9
pytorch9
pytest9
tensorflow9
Programming languages (8)
Github contributions (5)
PennyLaneAI/pennylane
Oct 2020 - Jan 2023
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 & Quantum Computing Researcher
Back-end Developer & Quantum Computing ResearcherContributions:4 releases, 6114 reviews, 357 commits in 2 years 3 months
Contributions summary:Christina primarily focused on implementing and testing new quantum gates, specifically the square root of X gate (SX), within the PennyLane framework. They were involved in adding the gate to the default qubit device, defining its matrix representation, decomposition, and related tests. Furthermore, they contributed to improvements in the handling of the parameter-shift gradient method and worked on enhancing the functionality of the `QNode` and `QuantumTape` objects, which are central to PennyLane's quantum circuit construction.
pythonautomatic-differentiationdifferentiable-computingcomputerstensorflow
PennyLaneAI/qml
Jan 2021 - Oct 2022
Introductions to key concepts in quantum programming, as well as tutorials and implementations from cutting-edge quantum computing research.
Role in this project:ML Engineer
ML EngineerContributions:125 reviews, 13 commits, 48 PRs in 1 year 9 months
Contributions summary:Christina primarily focused on updating and improving existing quantum machine learning (QML) tutorial demonstrations within the repository. Their contributions included refactoring code, addressing deprecated functions, and converting existing tutorials into runnable demonstrations. They also updated the demo to use the newest versions of the underlying libraries and added documentation improvements. The user was also responsible for setting random seeds in the demonstrations to improve reproducibility.
automatic-differentiationtensorflowkey-conceptsqqml
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