2nd Machine Learning Methods for Complex and Quantum Systems (ML2026)

The ML2026 Workshop aims to foster an interactive and interdisciplinary environment for discussing recent advances and future directions in Machine Learning (ML), with a particular focus on its applications to complex and quantum physical systems. In addition to applied ML topics, we will explore foundational aspects of ML in relation to physics models (e.g., spin glasses) and their broader implications.

A distinctive feature of ML2026 is the active participation of entrepreneurs and private sector representatives, promoting cross-sector dialogue and collaboration. The workshop encourages cross-disciplinary exchange, methodological innovation, and capacity building in a highly interactive setting.

Key Topics

Machine Learning for Quantum Matter
Machine Learning and Quantum Computing; Quantum Machine Learning
Machine Learning for Complex Systems
Mathematical, Statistical and Physics Foundations of Machine Learning
Machine Learning and the Scientific Method
Industry Applications of Machine Learning for facing complexity
Artificial intelligence systems for learning and teaching: applications to university scientific studies

Learn more