Quantum technologies and machine learning are two groundbreaking fields of the future, which we bring together in our flaQship research group. We explore quantum computing-based data processing and develop innovative machine learning methods to support quantum technologies. Our goal is not only to advance Quantum Machine Learning (QML) theoretically but also to drive its industrial application.

Quantum computing unlocks new potential for machine learning, enabling novel applications in manufacturing, chemistry, and finance. In our research, we analyze which types of data are particularly well-suited for quantum-assisted processing and how QML models can be trained efficiently and robustly. Additionally, we investigate how classical machine learning algorithms can benefit from quantum computing.

A key focus is the automation of QML pipelines, making this technology accessible even without expert knowledge. We share our findings through scientific publications in journals and presentations at international conferences. Furthermore, we are committed to knowledge transfer, for example, through the Fraunhofer Academy and a lecture series on autonomous systems.

Our research thrives on international collaborations with renowned partners such as the University of Waterloo and Rigetti Computing. Through open-source software projects like AutoQML and sQUlearn, we actively contribute to the advancement of the field and accelerate the practical adoption of QML technologies.

By working at the intersection of quantum computing and machine learning, we are shaping the future of intelligent data processing.