Dr. Markus Heyl
Max Planck Institute for the Physics of Complex Systems, Dresden
ABSTRACT: The efficient numerical simulation of nonequilibrium real-time evolution in isolated quantum matter constitutes a key challenge for current computational methods. This holds in particular in the regime of two spatial dimensions, whose experimental exploration is currently pursued with strong efforts in quantum simulators. In this talk I will present a versatile and efficient machine learning inspired approach based on a recently introduced artificial neural network encoding of quantum many-body wave functions. We identify and resolve some key challenges for the simulation of time evolution, which previously imposed significant limitations on the accurate description of large systems and long-time dynamics. As a concrete example, we study the dynamics of the paradigmatic two-dimensional transverse field Ising model, as recently also realized experimentally in systems of Rydberg atoms. Calculating the nonequilibrium real-time evolution across a broad range of parameters, we, for instance, observe collapse and revival oscillations of ferromagnetic order and demonstrate that the reached time scales are comparable to or exceed the capabilities of state-of-the-art tensor network methods.
BIOGRAPHY: Dr. Markus Heyl is the Leibniz Group Leader at the Max-Planck Institute for the Physics of Complex Systems (MPIPKS), Dresden. He did his PhD in theoretical physics at Ludwig-Maximilians-Universität Munich with Prof. Stefan Kehrein followed by postdocs at the Dresden University of Technology with Prof. Matthias Vojta (2012-2013) and the Institute for Quantum Optics and Quantum Information (IQOQI) in Innsbruch with Prof. Peter Zoller (2013-2015), where he was awarded the Leopoldina Fellowship. From 2015-2016, he worked at the Technical University of Munich with Prof. Wilhelm Zwerger under the Leopoldina Returning Fellowship. Since 2016, he has led a group at MPIPKS Dresden on Dynamics in Correlated Quantum Matter.