Machine Learning for Quantum Many-body Physics

Date

Jun 25, 2018 - Jun 29, 2018

Time

9:00 AM - 4:00 PM

Speaker

Roger Melko, Titus Neupert, Simon Trebst

Series

MPI-PKS Workshops, Seminare und Konferenzen

Language

en

Main Topic

Physik

Other Topics

Physik, Informatik

Host

Mandy Lochar

Description

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

Links

• Program and Registration

Last modified: Feb 7, 2018, 9:22:32 PM