Extracting Complexity of Quantum Dynamics Using Machine Learning - Zala Lenarcic

This course aims to teach learners how to extract complexity from quantum dynamics using machine learning. The course will focus on understanding universal aspects of many-body systems far from equilibrium, exploring topics such as short-time universality, entanglement dynamics, and mapping between classical and quantum non-equilibrium systems. The teaching method involves a conference format with presentations from experts in statistical physics, atomic, molecular, and optical physics, condensed matter physics, and high-energy physics. The intended audience for this course includes scientists and researchers interested in the intersection of quantum dynamics, machine learning, and non-equilibrium physics.