Lab Home | Phone | Search
Center for Nonlinear Studies  Center for Nonlinear Studies
 Home 
 People 
 Current 
 Executive Committee 
 Postdocs 
 Visitors 
 Students 
 Research 
 Publications 
 Conferences 
 Workshops 
 Sponsorship 
 Talks 
 Seminars 
 Postdoc Seminars Archive 
 Quantum Lunch 
 Quantum Lunch Archive 
 P/T Colloquia 
 Archive 
 Ulam Scholar 
 
 Postdoc Nominations 
 Student Requests 
 Student Program 
 Visitor Requests 
 Description 
 Past Visitors 
 Services 
 General 
 
 History of CNLS 
 
 Maps, Directions 
 CNLS Office 
 T-Division 
 LANL 
 
Thursday, September 07, 2017
12:30 PM - 1:30 PM
T-DO Conference Room

Quantum Lunch

Machine learning in classical and quantum many-body physics

Juan Carrasquilla
D-Wave systems

I will discuss how artificial neural networks can be used to identify phases and phase transitions in condensed matter systems via supervised learning. I will show that standard feed-forward neural networks can be trained to detect multiple types of order parameter directly from raw state configurations sampled with Monte Carlo. In addition, they can detect highly non-trivial states such as Coulomb phases, and if modified to a convolutional neural network, topological phases with no conventional order parameter. Furthermore, I will discuss the application of machine learning ideas to quantum systems. In particular, I will demonstrate that convolutional neural networks (CNN) have the potential to represent ground states of quantum many-body systems by showing that the ground state of Kitaev's toric code can be written as a CNN. Lastly, I will briefly show that machine learning devices such as the restricted Boltzmann machine can be efficiently used for quantum state tomography of highly-entangled states in arbitrary dimension.

Host: Lukasz Cincio