Summer school on quantum machine learning: Registration now open!

Together with the institutes of the German Research Center for Artificial Intelligence in Kaiserslautern and Bremen, Fraunhofer Center for Logistics and Services is organizing a summer school on quantum machine learning. It will take place from Sunday, August 16, 17:00 to Friday, August 21, 2026, 14:00 in Bad Honnef and is sponsored by the German Physical Society (DPG). The language of instruction is English.

Quantum computing has evolved from a niche topic to a relevant research subject with many applications. Even though quantum computing is still a promise for the future, there are already industrial use cases that can benefit from this novel paradigm.

At the same time, artificial intelligence and machine learning are changing research and industry at an unprecedented pace. The interface between these two areas, quantum machine learning (QML), is a rapidly growing topic, but its concepts are still only partially understood and rarely taught in a coherent, practice-oriented framework.

Some of the most promising ideas in QML are based on methods and insights that are little known outside of specialized research groups. Despite their potential, these approaches are rarely presented at summer schools or general conferences on machine learning, resulting in a significant knowledge gap for young researchers entering the field.

The summer school on quantum machine learning aims to close this gap by offering young scientists — especially master’s students nearing graduation, doctoral students, and postdocs at the beginning of their careers — a structured introduction to quantum machine learning. The program covers both fundamental concepts, advanced topics and tensor networks in AI, thereby promoting interdisciplinary connections. Participants will acquire a solid theoretical foundation as well as practical experience, including the opportunity to conduct experiments on IQM hardware during the school.

Learn more and register: Quantum Machine Learning — DPG