Roger Melko
(University of Waterloo, Canada)
Titus Neupert
(University of Zurich, Switzerland)
Simon Trebst
(University of Cologne, Germany)
Machine Learning for Quantum Many-body Physics
International Workshop
25 - 29 June 2018
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
Invited speakers
Erez Berg (US)
Giuseppe Carleo (CH)
Juan Carrasquilla (CA)
Ignacio Cirac (DE)
Dong-Ling Deng (US)
Claudia Draxl (DE)
Jens Eisert (DE)
Luca Ghiringhelli (DE)
David Gross (DE)
Masatoshi Imada (JP)
Eun-Ah Kim (US)
Maciej Koch-Janusz (CH)
Nicolas Regnault (FR)
Matthias Rupp (DE)
Miles Stoudenmire (US)
Giacomo Torlai (CA)
Jordi Tura i Brugués (DE)
Evert van Nieuwenburg (US)
Frank Verstraete (BE)
Lei Wang (CN)
Yi Zhang (US)
