BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Markus Heyl (Max-Planck Institute for the Physics of Complex Syste ms\, Dresden) DTSTART:20210322T170000Z DTEND:20210322T180000Z DTSTAMP:20260423T003259Z UID:MPML/39 DESCRIPTION:Title: Qu antum many-body dynamics in two dimensions with artificial neural networks \nby Markus Heyl (Max-Planck Institute for the Physics of Complex Syst ems\, Dresden) as part of Mathematics\, Physics and Machine Learning (IST\ , Lisbon)\n\n\nAbstract\nIn the last two decades the field of nonequilibri um quantum many-body physics has seen a rapid development driven\, in part icular\, by the remarkable progress in quantum simulators\, which today pr ovide access to dynamics in quantum matter with an unprecedented control. However\, the efficient numerical simulation of nonequilibrium real-time e volution in isolated quantum matter still remains a key challenge for curr ent computational methods especially beyond one spatial dimension. In this talk I will present a versatile and efficient machine learning inspired a pproach. I will first introduce the general idea of encoding quantum many- body wave functions into artificial neural networks. I will then identify and resolve key challenges for the simulation of real-time evolution\, whi ch previously imposed significant limitations on the accurate description of large systems and long-time dynamics. As a concrete example\, I will co nsider the dynamics of the paradigmatic two-dimensional transverse field I sing model\, where we observe collapse and revival oscillations of ferroma gnetic order and demonstrate that the reached time scales are comparable t o or exceed the capabilities of state-of-the-art tensor network methods.\n LOCATION:https://researchseminars.org/talk/MPML/39/ END:VEVENT END:VCALENDAR