Korbinian Kottmann
Quantum Scientist - Software at Xanadu
Munich, Bavaria, Germany
Summary
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Rockstar🎓
Top SchoolKorbinian Kottmann is a Quantum Scientist and software engineer with seven years of experience at the intersection of quantum algorithms, differentiable programming, and open-source library design. Based in Munich, he contributes to PennyLane—one of the leading quantum machine learning frameworks—where he has improved core features like multi-dispatch, non-commuting observable support, and Fisher information tooling. With a PhD-focused trajectory (Marie Skłodowska–Curie fellow at ICFO) and hands-on roles at Xanadu, he bridges rigorous research with production-quality backend engineering. Notably, his work emphasizes making quantum workflows trainable like neural networks, translating complex quantum information concepts into robust, well-tested software.
7 years of coding experience
4 years of employment as a software developer
PhD, PhD at Universitat Polit��cnica de CatalunyaERASMUS, Mathematics, ERASMUS, Mathematics at Queen's University BelfastBachelor of Science, Physics, Bachelor of Science, Physics at Ulm UniversityMaster of Science, Physics, Master of Science, Physics at University of Ulm
Github Skills (13)
quantum-computing10
automatic-differentiation10
machine-learning10
differentiable-programming10
lan10
q-learning10
ane10
python10
autograd10
optimization9
jax8
tensorflow7
pytorch7
Programming languages (6)
Github contributions (5)
PennyLaneAI/pennylane
Jan 2022 - 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 & ML Engineer
Back-end Developer & ML EngineerContributions:1537 reviews, 218 commits, 223 PRs in 1 year
Contributions summary:Korbinian contributed significantly to the PennyLane library by implementing and enhancing core functionalities related to quantum machine learning and differentiable computing. Their work included adding a multi-dispatch decorator to handle argument dispatch across different interfaces, improving tests and documentation, and addressing issues related to non-commuting observables. Furthermore, they worked on integrating new features and improving the existing quantum information capabilities, including the classical and quantum Fisher information matrices. This suggests a strong understanding of quantum algorithms, differentiable programming, and library design within the context of quantum computing.
pythonautomatic-differentiationdifferentiable-computingcomputerstensorflow
Qottmann/phase-discovery-anomaly-detection
Jan 2020 - Feb 2020
Unsupervised phase discovery via anomaly detection using deep neural networks
Contributions:1 release, 35 commits, 3 PRs in 1 month
autoencoderanomalydeep-learningunsupervisedanomaly-detection
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