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BEGIN:VEVENT
UID:DSC-21694
DTSTART;TZID=Europe/Berlin:20250224T163000
SEQUENCE:1740379028
TRANSP:OPAQUE
DTEND;TZID=Europe/Berlin:20250224T173000
URL:https://www.dresden-science-calendar.de/calendar/en/detail/21694
LOCATION:MPI-PKS\, Nöthnitzer Straße 3801187 Dresden
SUMMARY:Kueng: Provably efficient machine learning for quantum many-body pr
 oblems
CLASS:PUBLIC
DESCRIPTION:Speaker: Prof. Richard Kueng\nInstitute of Speaker: Johannes Ke
 pler Universität Linz\nTopics:\nPhysik\n Location:\n  Name: MPI-PKS ()\n 
  Street: Nöthnitzer Straße 38\n  City: 01187 Dresden\n  Phone: + 49 (0)3
 51 871 0\n  Fax: \nDescription: Classical machine learning (ML) provides a
  potentially powerful approach to solving challenging quantum many-body pr
 oblems in physics and chemistry. However\, the advantages of ML over tradi
 tional methods have not been firmly established.  In this work\, we prove 
 that classical ML algorithms **can** efficiently learn to predict importan
 t properties of a quantum many-body system. In particular\, ML can provabl
 y predict ground-state properties of gapped Hamiltonians after learning fr
 om other Hamiltonians in the same quantum phase of matter. Our proof techn
 ique combines signal processing with quantum many-body physics and also bu
 ilds upon the recently developed framework of classical shadows. I will tr
 y to convey the ideas and also present numerical experiments that confirm 
 our theoretical findings.  This colloquium talk is based on joint work wit
 h Hsin-Yuan (Robert) Huang\, Giacomo Torlai\, Victor Albert and John Presk
 ill\, see [Huang et al.\, Provably efficient machine learning for quantum 
 many-body problems\, Science 2022]
DTSTAMP:20260710T172723Z
CREATED:20250201T063600Z
LAST-MODIFIED:20250224T063708Z
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