Romain Moyard
Lead Software Engineer at ZuriQ
Zurich, Zurich, Switzerland
Summary
🤩
Rockstar🎓
Top SchoolRomain Moyard is a Lead Software Engineer based in Zurich with six years of hands-on experience building quantum-classical hybrid systems and differentiable programming tools. He combines deep expertise in PyTorch, JAX, TensorFlow and low-level compiler tech like C++, MLIR and LLVM to make cutting-edge research practical and accessible. At Xanadu and via contributions to the widely used PennyLane library he implemented core features such as density matrix support and partial trace operations, demonstrating both research-grade rigor and production-quality engineering. His background spans industry and academia—from Huawei and ETH Zürich to teaching roles—giving him a rare blend of theoretical grounding and systems-level implementation skill. Known for translating advanced quantum ideas into usable developer tools, he focuses on empowering others to solve complex problems with emerging compute paradigms.
6 years of coding experience
5 years of employment as a software developer
Bachelor of Science (BSc), Bachelor of Science (BSc) at Ecole polytechnique fédérale de LausanneBachelor's degree, Bachelor's degree at Université de Fribourg/Universität FreiburgMaster of Science - MS, Master of Science - MS at ETH ZürichErasmus Programme, Erasmus Programme at Technische Universität München
French, German, English
Github Skills (12)
autograd10
quantum-computing10
q-learning10
python10
numpy10
qml9
jax9
tensorflow9
differentiable-programming9
unit-testing9
pytorch9
automatic-differentiation9
Programming languages (5)
Github contributions (5)
PennyLaneAI/pennylane
Nov 2020 - Jan 2023
PennyLane is an open-source quantum software platform for quantum computing, quantum machine learning, and quantum chemistry. Create meaningful quantum algorithms, from inspiration to implementation.
Role in this project:
Backend Developer
Backend DeveloperContributions:4 releases, 1632 reviews, 842 commits in 2 years 2 months
Contributions summary:Romain primarily contributed to the development and maintenance of the PennyLane library, focusing on features related to quantum computation and machine learning. Their work included implementing density matrix capabilities within the `qml.density_matrix` function, including the addition of partial trace features. They also addressed bugs, improved code formatting, and updated dependency versions to ensure compatibility with other libraries.
machine-learningquantum-algorithmsquantum-chemistryquantum-computingquantum
rmoyard/Enzyme
Jan 2024 - Sep 2024
High-performance automatic differentiation of LLVM and MLIR.
Contributions:50 pushes, 5 branches in 8 months
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