Stefan Woerner
Principal Research Staff Member at IBM
Zurich, Zurich, Switzerland
Summary
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RockstarStefan Woerner is a Principal Research Staff Member at IBM in Zurich with seven years of experience developing quantum algorithms and translating them into practical applications for finance and supply chain management. He focuses on the end-to-end implications of quantum hardware, blending algorithm design with realistic timelines to quantum advantage. A hands-on contributor to Qiskit, Stefan has implemented and refined optimization and machine-learning primitives—working on ADMM optimizers, eigensolver filters, and amplitude estimation—while improving library robustness and back-end infrastructure. His work spans coding, technical writing, and algorithm validation, highlighting an ability to both ship production-grade code and make complex ideas accessible through tutorials. Colleagues value his rigorous interplay of theory and engineering: he anticipates hardware constraints early and adapts algorithms accordingly. Based in Zurich, he pairs scientific depth with practical delivery to accelerate real-world quantum applications.
7 years of coding experience
Github Skills (23)
algorithm10
algorithms10
python10
optimizers10
testing10
user-manual10
machine-learning10
numpy10
optimisation10
neural-network10
quantum-computing10
jupyter-notebook10
quadratic-programming10
qiskit10
linear-algebra10
Programming languages (4)
Github contributions (5)
qiskit-community/qiskit-machine-learning
Apr 2019 - Apr 2021
Quantum Machine Learning
Role in this project:
ML Engineer
ML EngineerContributions:55 reviews, 17 commits, 8 PRs in 1 year 11 months
Contributions summary:Stefan contributed to the development and maintenance of the Qiskit Machine Learning library, specifically focusing on quantum machine learning algorithms. They implemented conditional imports for dependencies like Torch, which suggests involvement in integrating classical machine learning components. The user also addressed issues related to positive semi-definite kernel matrices within the QSVM algorithm, enhancing the reliability of the kernel calculations. Furthermore, they enabled the use of SparseArray for neural networks and updated the torch connector, and refined the implementation of loss functions, improving the flexibility and robustness of the library.
quantum-computingmachine-learningprototypequantum-programming-languagequantum
qiskit-community/qiskit-optimization
Apr 2020 - Oct 2020
Quantum Optimization
Role in this project:Back-end Developer
Back-end DeveloperContributions:33 reviews, 11 commits, 1 PR in 5 months
Contributions summary:Stefan primarily contributed to the back-end development of the Qiskit optimization library. Their commits focused on refactoring, renaming, and improving core components, such as the `OptimizationProblem` and converters. They also implemented and tested features like quadratic constraints and continuous variable handling, improving the library's capabilities. Furthermore, the user made critical changes to the ADMM and CPLEX optimizer, enhancing functionality and usability.
optimization-methodsquantum-computingautomatic-differentiationoptimizationprototype
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