Data Scientist with 8 years of experience focused on applied AI, quantum computing, and data science, blending back-end engineering and test automation expertise. Notable open-source contributions include enhancing PennyLane’s Device execution tracking and adding QAOA cycle functionality—work that bridges quantum algorithm design and reliable test coverage. Comfortable translating research-grade concepts into production-quality code, particularly around QNodes and device-wire mappings. Practical problem-solver who brings rigorous QA practices to cutting-edge quantum ML projects, ensuring correctness at the intersection of software and physics. Passionate about advancing quantum tooling while keeping systems maintainable and well-tested.
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 & QA Engineer / Test Automation Engineer
Contributions:230 reviews, 34 commits, 60 PRs in 1 year 5 months
Contributions summary:anthayes92 contributed significantly to the PennyLane library by adding features and tests related to the execution property of the Device class. Their work involved tracking device executions within QNodes, including adding new tests and modifying existing ones. Furthermore, the user was involved in the development of the QAOA cycle module, adding functionalities for mapping between edges and wires and writing relevant unit tests.
Contributions:51 pushes, 1 branch in 5 years 10 months
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