Group of Julian Berberich

Quantum computers promise to revolutionize computing by efficiently solving previously intractable problems. They hold enormous potential for major technological advancements in fields such as simulation, optimization, and machine learning and their countless applications. Recent years have seen tremendous progress in quantum computing, however, realizing computational advantages in practice remains a widely open problem. A key obstacle is the presence of noise, e.g., resulting from an insufficient isolation from the environment. 

Our research group explores concepts and methods of systems and control theory in quantum computing, studying theoretical properties of quantum computing elements and finding practical methods for improving their reliability. Our scope encompasses both quantum algorithms as well as methods for quantum hardware which are relevant for real-world implementations of quantum devices. We are interested in understanding and improving intrinsic properties of quantum algorithms such as robustness, modularity, and feedback mechanisms, as well as specific algorithm classes, e.g., in quantum machine learning. Further, in the context of quantum hardware methods, we develop efficient and robust methods for estimation and control tasks, borrowing tools from robust optimization, robust control, and predictive control.

More information can be found on the pages linked below and associated with the macro-topics describing our research. 

This group is funded by the Deutsche Forschungsgemeinschaft (DFG) via the Emmy Noether Programme under grant 579821331 on Systems theory of quantum algorithms: Fundamentals and applications to noisy quantum computers. 

The group is also part of the Center for Integrated Quantum Science and Technology (IQST).

Current group members: Mirko Legnini, Eya Guizani (master student), Jonas Merklinger (master student)