We invite you to attend (online-only) Episode XLVI of the Warsaw Quantum Computing Group meetings! "Kernels, tensors, matrices and reservoirs — the wild world of (Quantum) Machine Learning"
15.12.2022, 18:00 CET
Quantum Machine Learning is currently a very hot topic in both basic and applied research. We can have hope that QML-based systems will be able to outperform the classical ones someday. But it is important to notice that current ML systems have achieved amazing results already. During the talk I will present a collection of recent developments in QML such as Projected Quantum Kernels and Quantum Reservoir Computing. But I will also discuss relationships between tensor decompositions, matrix multiplication problem and Reinforcement Learning.
Piotr Gawron received his magister title in computer science from the Silesian University of Technology, Gliwice, Poland, his doctoral degree
in technical sciences from the Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Gliwice, Poland, and his
habilitation degree from the faculty of Automatic Control, Electronics, and Computer Science from the Silesian University of Technology in 2003, 2008, and 2014, respectively.
He is the leader of the Scientific Computing and Information Technology Group and institute professor at the Particle Astrophysics Science and Technology Centre (AstroCeNT) International Research Agenda, Nicolaus Copernicus Astronomical Center of the Polish Academy of Sciences. He is also the main specialist — software developer at the Laboratory of Quantum Computing of the Academic Computer Centre Cyfronet AGH, Cracow, Poland and visiting professor at the European Space Agency Φ-lab@ESRIN, Italy.
For eighteen years, he was a member of the Quantum Systems of Informatics Group at the Institute of Theoretical and Applied Informatics of the Polish Academy Sciences in Gliwice, Poland. He has been involved in quantum computer science research since the fourth year of his university studies. Previously, he was engaged in research on quantum games, quantum walks, simulation of noisy quantum computers, quantum programming languages, quantum control, numerical shadows, and tensor networks. Currently, he is studying applicability of quantum machine learning for Earth-observation imagery data processing, applications of quantum and classical machine learning techniques for gravitational waves, and dark matter detection.
This meeting is organized by the Quantum AI Foundation.