Extracting Complexity of Quantum Dynamics Using Machine Learning - Zala Lenarcic

Explore a conference talk on extracting complexity of quantum dynamics using machine learning, presented by Zala Lenarcic at the Kavli Institute for Theoretical Physics. Delve into cutting-edge research at the intersection of quantum physics and machine learning as part of the 2021 Non-Equilibrium Universality in Many-Body Physics KITP Conference. Gain insights into how machine learning techniques are being applied to understand complex quantum systems and their dynamics. Discover the latest advancements in non-equilibrium many-body physics and its connections to statistical physics, AMO, condensed matter, and high-energy physics. Learn about novel phases of matter far from equilibrium, short-time universality, entanglement dynamics, and the mapping between classical and quantum non-equilibrium systems. Understand the potential implications for realizing experiments that can enhance our understanding of far-from-equilibrium universality.

Extracting complexity of quantum dynamics using machine learning ▸ Zala Lenarcic