QMATH Masterclass: Quantum Learning Theory
Quantum learning theory on the one hand poses fundamental questions within an elegant and abstract mathematical framework, while on the other hand has practical implications for current noisy devices. At this masterclass, experts will present the state-of-the-art in quantum learning theory and provide students as well as early-career researchers an opportunity to enter this vibrant area of quantum information science.
We are happy to have secured the participation of the following leading experts in quantum learning theory:
- Richard Küng (Johannes Keppler University Linz): one of the inventors of shadow tomography, as well as an expert in rigorous quantum machine learning.
- Jonas Helsen (Centre for Mathematics and Computer Science Amsterdam): has developed a general theory for randomized benchmarking and methods for studying statistical properties of random circuits.
- Yihui Quek (Free University Berlin): her work involves various aspects of quantum learning theory, such as learning circuits from output distributions and showing separations between quantum and classical learning.
These will be supplemented by local speakers:
- Albert H. Werner (University of Copenhagen): who specializes in open quantum systems and will lecture on learning problems in open quantum systems.
- Laura Mancinska (University of Copenhagen): has made fundamental contributions to the self-testing of quantum states.
Information on registration will follow