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SUMMARY:Learning Solutions to the Schrödinger equation with Neural-Networ
 k Quantum States
DTSTART:20211215T161500
DTEND:20211215T171500
DTSTAMP:20260508T083627Z
UID:9f1387c0831d0e7710f5412c2d605cb54cbb858a2141a31655f068b0
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Giuseppe Carleo\, EPFL\nThe theoretical description of s
 everal complex quantum phenomena fundamentally relies on many-particle wav
 e functions and our ability to represent and manipulate them. Variational 
 methods in quantum mechanics aim at compact descriptions of many-body wave
  functions in terms of parameterised ansatz states\, and are at present li
 ving exciting transformative developments informed by ideas developed in m
 achine learning. In this presentation I will discuss variational represent
 ations of quantum states based on artificial neural networks [1] and their
  use in approximately solving the Schrödinger equation. I will further hi
 ghlight the general representation properties of such states\, the crucial
  role of physical symmetries\, as well as the connection with other known 
 representations based on tensor networks [2]. Finally\, I will discuss how
  some classic ideas in machine learning\, such as the Natural Gradient\, a
 re being used and re-purposed in quantum computing applications [3].\n\n[1
 ] Carleo and Troyer\, Science 365\, 602 (2017)\n[2] Sharir\, Shashua\, and
  Carleo\, arXiv:2103.10293 (2021)\n[3] Stokes\, Izaac\, Killoran\, and Car
 leo\, Quantum 4\, 269 (2020)
LOCATION:GA 3 21 https://plan.epfl.ch/?room==GA%203%2021
STATUS:CONFIRMED
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