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:20260423T024729Z UID:QM3/34 DESCRIPTION:Title: Qua ntum many-body dynamics in two dimensions with artificial neural networks< /a>\nby Markus Heyl (Max-Planck Institute for the Physics of Complex Syste ms\, Dresden) as part of Quantum Matter meets Maths (IST\, Lisbon)\n\n\nAb stract\nIn the last two decades the field of nonequilibrium quantum many-b ody physics has seen a rapid development driven\, in particular\, by the r emarkable progress in quantum simulators\, which today provide access to d ynamics in quantum matter with an unprecedented control. However\, the eff icient numerical simulation of nonequilibrium real-time evolution in isola ted quantum matter still remains a key challenge for current computational methods especially beyond one spatial dimension. In this talk I will pres ent a versatile and efficient machine learning inspired approach. I will f irst introduce the general idea of encoding quantum many-body wave functio ns into artificial neural networks. I will then identify and resolve key c hallenges for the simulation of real-time evolution\, which previously imp osed significant limitations on the accurate description of large systems and long-time dynamics. As a concrete example\, I will consider the dynami cs of the paradigmatic two-dimensional transverse field Ising model\, wher e we observe collapse and revival oscillations of ferromagnetic order and demonstrate that the reached time scales are comparable to or exceed the c apabilities of state-of-the-art tensor network methods.\n LOCATION:https://researchseminars.org/talk/QM3/34/ END:VEVENT END:VCALENDAR