Quantum computing through the lens of control: Introduction and robustness properties

Abstract:
Quantum computing is a fascinating interdisciplinary research field that promises to revolutionize computing by efficiently solving previously intractable problems. Recent years have seen tremendous progress on both the experimental realization of quantum computing devices as well as the development and implementation of quantum algorithms. Yet, realizing computational advantages of quantum computers in practice remains a widely open problem due to numerous fundamental challenges. Interestingly, many of these challenges are connected to performance, robustness, scalability, optimization, or feedback, all of which are central concepts in control theory.

This talk will provide a tutorial introduction to quantum computing from the perspective of control theory. I will introduce the mathematical framework of quantum algorithms ranging from basic elements including quantum bits and quantum gates to more advanced concepts. In particular, I will present recent research results on the robustness of quantum algorithms and on quantum machine learning, covering both new theoretical insights as well as practical validations in simulation and on a real quantum computer.

Short bio:
Julian Berberich is a Lecturer (Akademischer Rat) at the Institute for Systems Theory and Automatic Control at the University of Stuttgart, Germany. He received his Ph.D. in Mechanical Engineering in 2022, and a Master’s degree in Engineering Cybernetics in 2018, both from the University of Stuttgart, Germany. In 2022, he was a visiting researcher at ETH Zürich, Switzerland. For his Ph.D. thesis, he received the Dr.-Klaus-Körper Prize by the International Association of Applied Mathematics and Mechanics, as well as the Bürkert University Prize by the Foundation of the University of Stuttgart. Furthermore, he is a recipient of the 2022 George S. Axelby Outstanding Paper Award as well as the Outstanding Student Paper Award at the 59th IEEE Conference on Decision and Control in 2020. His research interests include data-driven analysis and control as well as quantum computing.