Jonas M. Kübler

Jonas M. Kübler is a Doctoral Researcher at the Max Planck Institute for Intelligent Systems in the Empirical Inference department. His research focuses on the intersection of quantum computing and machine learning, particularly quantum kernels, hypothesis testing, and data-efficient learning methods.

Education

• International Max Planck Research School for Intelligent Systems (IMPRS-IS): Ph.D. candidate supervised by Bernhard Schölkopf, expected completion in 2022.
• LANL Quantum Computing Summer School: Participant (2019).
• Master’s in Quantum Causality (2018), published in New Journal of Physics.

Research Interests
Jonas investigates how quantum computers can redefine machine learning models, with a focus on theoretical rigor and empirical validation. His work spans quantum kernel methods, hypothesis testing, and variational quantum algorithms, emphasizing mathematical foundations and computational efficiency. Current projects include analyzing inductive biases in quantum models and optimizing kernel-based statistical tests.

Scientific Contributions
His publications highlight trends in quantum-enhanced learning, statistical inference, and data-efficient algorithm design. Notable themes include bridging quantum information theory with ML, advancing kernel methods for hypothesis testing, and exploring the limitations of quantum optimization techniques.

• Scientific Awards:
  • AISTATS 2022 Best Reviewer Award

Affiliations & Activities
Jonas is affiliated with the IMPRS-IS and has held internships at Amazon (2021) and Los Alamos National Laboratory (2019). He serves as a member of the Tübingen City Council, demonstrating interdisciplinary engagement beyond research.