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BEGIN:VEVENT
DTSTAMP:20210916T132450Z
LOCATION:Henry Dunant
DTSTART;TZID=Europe/Stockholm:20210707T113000
DTEND;TZID=Europe/Stockholm:20210707T120000
UID:submissions.pasc-conference.org_PASC21_sess143_msa264@linklings.com
SUMMARY:Many-Body Quantum Mechanics as a Machine Learning Problem
DESCRIPTION:Minisymposium\n\nMany-Body Quantum Mechanics as a Machine Lear
 ning Problem\n\nCarleo\n\nThe theoretical description of many-body quantum
  phenomena fundamentally relies on the solution of a “big-data&rdquo
 ; problem. In recent years, several machine learning approaches have been 
 developed in quantum physics, aiming at tackling the infamous quantum many
 -body problem from a new perspective. In this presentation I will discuss 
 how a systematic, and controlled machine learning of the many-body wave-fu
 nction can be realized. This goal is achieved by a variational representat
 ion of quantum states based on artificial neural networks. I will dis
 cuss succesfful selected applications of this ideas in condensed matter ph
 ysics and quantum computing.\n\nDomain: CS and Math, Chemistry and Materia
 ls, Physics, Life Sciences, Engineering
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