Maria Schuld
Independent Researcher at On Sabbatical
Durban, KwaZulu-Natal, South Africa
Summary
🤩
Rockstar🎓
Top SchoolMaria Schuld is a quantum machine learning researcher and leader with nine years of experience bridging academic research and industry productization, most recently leading Xanadu’s Quantum Machine Learning team. She holds a PhD in quantum machine learning and has deep expertise in translating research into practical tutorials, API docs, and developer-facing materials for prominent open-source projects like PennyLane, including contributions on quantum embeddings, variational circuits, and automatic differentiation. Based in Durban, South Africa, she combines hands-on ML engineering and technical writing to make cutting-edge quantum algorithms accessible to developers and researchers. Now on sabbatical as an independent researcher, she continues to explore how emerging quantum hardware can enable new AI paradigms while maintaining a track record of improving documentation and educational tooling for the wider quantum community.
9 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy - PhD, Quantum machine learning, Doctor of Philosophy - PhD, Quantum machine learning at Quantum Research Group, University of KwaZulu-NatalDipl Political Science, Political Science, Dipl Political Science, Political Science at Freie Universität BerlinMaster of Science (MSc), Physics, Master of Science (MSc), Physics at Technische Universität Berlin
German, Norwegian, English, Zulu, French
Github Skills (12)
quantum-computing10
lan10
develop10
q-learning10
ane10
documentation10
technical-writing10
machine-learning8
automatic-differentiation8
python8
tensorflow7
pytorch7
Programming languages (4)
Github contributions (5)
PennyLaneAI/pennylane
Apr 2018 - 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:
Technical Writer
Technical WriterContributions:1910 reviews, 334 commits, 341 PRs in 4 years 9 months
Contributions summary:Maria focused on enhancing the documentation of the PennyLane library. They added detailed documentation to the API and templates to the documentation. They also created new sections to help with development, compilation, and usage, while also addressing errors within the existing documentation.
pythonautomatic-differentiationdifferentiable-computingcomputerstensorflow
PennyLaneAI/qml
Nov 2019 - Feb 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 / Technical Writer
ML Engineer / Technical WriterContributions:360 reviews, 38 commits, 60 PRs in 2 years 3 months
Contributions summary:Maria's contributions primarily involve the development of quantum machine learning tutorials within the `qml` repository. They added a new tutorial on quantum embeddings and metric learning, moving it from a private repository and adapting it for public use. Further work included adding images and improving existing documentation, including articles on automatic differentiation, quantum embeddings, variational circuits, gradients, and quantum neural networks. Their work centers on practical applications of quantum machine learning concepts.
automatic-differentiationtensorflowkey-conceptsqqml
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