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UID:DSC-14116
DTSTART;VALUE=DATE:20180625
SEQUENCE:1518034952
TRANSP:OPAQUE
DTEND;VALUE=DATE:20180630
URL:https://www.dresden-science-calendar.de/calendar/en/detail/14116
LOCATION:MPI-PKS\, Nöthnitzer Straße 3801187 Dresden
SUMMARY:Melko: Machine Learning for Quantum Many-body Physics
CLASS:PUBLIC
DESCRIPTION:Speaker: Roger Melko\, Titus Neupert\, Simon Trebst\nInstitute 
 of Speaker: \nTopics:\nPhysik\, Informatik\nTime:\n9:00 AM-4:00 PM\n\n Loc
 ation:\n  Name: MPI-PKS (Sem. rooms 1+2)\n  Street: Nöthnitzer Straße 38
 \n  City: 01187 Dresden\n  Phone: + 49 (0)351 871 0\n  Fax: \nDescription:
  The workshop covers the emerging research area that applies machine learn
 ing techniques to analyze\, represent\, and solve quantum many-body system
 s in condensed matter physics. This includes problems of phase classificat
 ion and characterization\, state compression\, feature extraction\, wavefu
 nction representation using neural networks\, and connections between tens
 or networks and  machine learning.    Topics include        Supervised pha
 se classification      Unsupervised learning of quantum phases      Restri
 cted Boltzmann machines for representing wavefunctions      Solving quantu
 m many-body problems      Connections between the renormalization group an
 d deep learning      Machine learning and density functional theory      M
 aterial discovery using machine learning      Quantum neural networks     
  Quantum error correction and decoding with neural networks      Quantum s
 tate tomography with machine learning    
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DTSTAMP:20260523T001525Z
CREATED:20180207T202232Z
LAST-MODIFIED:20180207T202232Z
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