Generative Modeling in Quantum Science

Recent years have witnessed remarkable progress in artificial intelligence driven by the advancement of generative modeling (GM) techniques. These developments have a transformative impact across application areas — including scientific research, where AlphaFold3's diffusion-based protein structure prediction is a striking example. This workshop will address the particularly compelling intersection of GM with quantum science.
On the one hand, GM techniques expand the scientific toolbox to investigate physics that is based on the fundamentally probabilistic laws of quantum mechanics. Conversely, the intrinsically probabilistic nature of quantum computation itself offers a new computational resource for generative modeling, potentially enabling quantum algorithms to tackle generative tasks with advantages over classical approaches. Developing a solid theoretical understanding remains an outstanding challenge in either case and statistical physics has proven an insightful tool to investigate the underlying principles of learning processes. Within this workshop we plan to discuss the bidirectional relationship between generative modeling and quantum science from the different perspectives of quantum many-body physics, quantum computing, and statistical physics. It will provide an opportunity for multi-disciplinary dialogue to explore emerging synergies and the transformative potential of GM for both fundamental research and technological applications.

The conference language will be English. The Wilhelm and Else Heraeus-Foundation bears the cost of full-board accommodation for all participants.