Machine Learning for Quantum Matter

MLQMAT25 report
Juan Felipe Carrasquilla Álvarez, Markus Schmitt, Filippo Vicentini
24th to 28th February 2025

 

Main focus of our event

The workshop Machine Learning for Quantum Matter explored cutting-edge advancements at the intersection of machine learning and quantum physics. The program covered several domains, including the control of quantum systems, machine learning applications in quantum experiments, analysis of experimental quantum data, and both classical and quantum variational algorithms. A significant portion of the workshop was dedicated to neural quantum states—classical representations of quantum states using neural networks — highlighting their increasing importance in the field. The workshop covered computational advancements, experiments, and theory.  

Distinguished Participants

The workshop attracted leading researchers from prestigious institutions worldwide both senior and junior. Notable speakers and participants included:
A. Bohrdt, M. Aidelsburger, and L. Pollet (Lya MU Munich), A. Georges (Collège de France and Flatiron Institute, France), L. Wang (CAS, China), G. Booth (King's College London, UK), G. Carleo and I. Romero (EPFL, Switzerland), A. Dawid (Leiden Univ., Netherlands), A. Valenti (CCQ - Flatiron Institute, USA), D. Luo (UCLA, USA), F. Marquardt (MPI, Germany), R. Melko (Univ. of Waterloo, Canada), S. Czischek (Univ. of Ottawa, Canada), M. Heyl (Univ. of Augsburg, Germany), R. Kueng (JKU Linz, Austria), J. Rigo (Forschungszentrum Jülich, Germany) and S. Bird (ETH Zurich, Switzerland), among many other established and junior researchers representing institutions across Europe, North America, and Asia.

Next Generation Scientists

The event provided an excellent platform for early-career researchers to showcase their work. The scientific newcomers demonstrated remarkable creativity and independence during poster sessions and presentations. We emphasize the junior participants' high level of scientific engagement and commitment to advancing the field, as well as their exceptional ability to develop constructive scientific and personal relationships throughout the event. Their independence and innovative approaches indicate a promising future for the interdisciplinary field of machine learning for quantum systems.

Scientific impact

The workshop facilitated significant scientific exchange between participants from diverse backgrounds and research perspectives. We hope that the attendees were exposed to state-of-the-art results and emerging directions in the application of machine learning to quantum many-body systems. We strove to create an inclusive discussion space where all participants felt welcomed and their contributions valued. The scientific coordinators have observed several new collaborative projects that originated from interactions during the workshop, demonstrating its tangible impact on the advancement of research in this rapidly evolving field.