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FM: Fall Meeting
FM 79: Entanglement: Neural Networks for Many-Body Dynamics
FM 79.1: Talk
Donnerstag, 26. September 2019, 14:00–14:15, 2004
Quantum many-body dynamics with neural network states — •Markus Schmitt1 and Markus Heyl2 — 1Department of Physics, University of California, Berkeley, USA — 2Max-Planck-Institute for the Physics of Complex Systems, Dresden, Germany
The growth of entanglement during the non-equilibrium dynamics of quantum many-body systems constitutes a major challenge for numerical simulations on classical computers. We explore the possibility to compress the many-body wave function using artificial neural networks as a versatile approach for the efficient simulation of quantum dynamics. This method allows us to study two-dimensional systems far from equilibrium, which are realized, e.g., in quantum simulators based on ultracold atoms or Rydberg atoms. In our discussion we include subtleties of the time evolution algorithm and ways to assess the accuracy of the results.