Bulletin of the American Physical Society
APS March Meeting 2018
Monday–Friday, March 5–9, 2018; Los Angeles, California
Session A32: Machine Learning in Classical and Quantum Manybody PhysicsInvited

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Sponsoring Units: DCOMP DCMP Chair: Lei Wang, Chinese Academy of Sciences Room: LACC 408A 
Monday, March 5, 2018 8:00AM  8:36AM 
A32.00001: Modeling ManyBody Physics with Restricted Boltzmann Machines Invited Speaker: Roger Melko Neural networks hold the potential to significantly improve the efficiency of various simulation strategies in condensed matter and statistical physics. Foremost is the idea of generative modeling, or sampling an approximate probability distribution or wavefunction, using stochastic neural networks. In this talk, we survey the uses of one such neural network called a Restricted Boltzmann Machine (RBM) in the field of manybody physics. We illustrate the ability of RBMs to "learn" by being trained with data from finitesize lattice Hamiltonians, and ask whether the resulting model is an efficient and faithful representation of the original system. We explore various quantum and classical examples, including systems with conventional phases and phase transitions, as well as unconventional and topological order. Finally, we discuss the potential for RBMs to augment traditional Monte Carlo approaches, examine their representational efficiency for compressing quantum wavefunctions, and discuss connections to Tensor Networks and related numerical methods. 
Monday, March 5, 2018 8:36AM  9:12AM 
A32.00002: Neuralnetwork Quantum States Invited Speaker: Giuseppe Carleo , Matthias Troyer , Giacomo Torlai , Roger Melko , Juan Carrasquilla , Guglielmo Mazzola Artificial intelligence is living truly exciting times thanks to the fast advancements in the field of machine learning. Machinelearningbased approaches, routinely adopted in cuttingedge industrial applications, are being increasingly adopted to study fundamental problems in science as well. Very recently, their effectiveness has been demonstrated also for manybody physics [13]. 
Monday, March 5, 2018 9:12AM  9:48AM 
A32.00003: Machine learning and the magnetic phases of correlated fermions Invited Speaker: Ehsan Khatami , Kelvin Ch'ng , Nick Vasquez , Juan Carrasquilla , Roger G. Melko Machine learning has emerged as an exciting new tool to study phases and phase transitions of models in statistical mechanics and condensed matter physics in the past couple of years. In this talk, I will discuss the application of artificial neural network machine learning techniques to predict the finitetemperature magnetic phase diagram of strongly correlated lattice fermions. I will show results for the classification of auxiliary field configurations produced by quantum Monte Carlo simulations of the Hubbard model at commensurate filling, and discuss how the learning can be transferred to gain insight about the fate of the ordered phase as the system is doped. In the last part, I will present results from several unsupervised machine learning and dimension reduction algorithms and show that they capture the physics of the model as the temperature is varied in the weakcoupling region. 
Monday, March 5, 2018 9:48AM  10:24AM 
A32.00004: Machine learning of quantum manyfermion systems Invited Speaker: Simon Trebst The application of stateoftheart machine learning (ML) techniques to statistical physics problems has seen a surge of interest for their ability to discriminate phases of matter by extracting essential features in the manybody wavefunction or the ensemble of correlators sampled in Monte Carlo simulations. 
Monday, March 5, 2018 10:24AM  11:00AM 
A32.00005: SelfLearning Monte Carlo Methods Invited Speaker: Yang Qi Monte Carlo simulation is an unbiased numerical tool for studying classical and quantum manybody systems. Despite a polynomial complexity in theory (when the model is signproblem free), one of its bottlenecks is the lack of general and efficient update algorithm, especially for large size systems close to phase transition or with strong frustrations, for which local updates perform badly. 
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