PhCo

Machine Learning for Quantum Many-body Physics

date
25.06.2018 - 29.06.2018 
time
09:00 AM - 04:00 PM 
speaker
Roger Melko, Titus Neupert, Simon Trebst 
part of series
MPI-PKS Workshops, Seminars and Conferences 
language
en 
main topic
Physics: Theoretical Physics, Quantum Physics
subtopics
Computer Science: Machine Learning
host
Mandy Lochar 
abstract

The workshop covers the emerging research area that applies machine learning techniques to analyze, represent, and solve quantum many-body systems in condensed matter physics. This includes problems of phase classification and characterization, state compression, feature extraction, wavefunction representation using neural networks, and connections between tensor networks and machine learning.

Topics include

Supervised phase classification
Unsupervised learning of quantum phases
Restricted Boltzmann machines for representing wavefunctions
Solving quantum many-body problems
Connections between the renormalization group and deep learning
Machine learning and density functional theory
Material discovery using machine learning
Quantum neural networks
Quantum error correction and decoding with neural networks
Quantum state tomography with machine learning

 

Last update: 07.02.2018 20:22.

venue 

Max-Planck-Institut für Physik komplexer Systeme (Sem. rooms 1+2) 
Nöthnitzer Straße 38
01187 Dresden
telefon
+ 49 (0)351 871 0 
e-mail
Max-Planck-Institut für Physik komplexer Systeme 
homepage
http://www.mpipks-dresden.mpg.de 

organizer 

Max-Planck-Institut für Physik komplexer Systeme (MPI-PKS)
Nöthnitzer Straße 38
01187 Dresden
telefon
+ 49 (0)351 871 0 
e-mail
Max-Planck-Institut für Physik komplexer Systeme (MPI-PKS) 
homepage
http://www.mpipks-dresden.mpg.de 
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