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 Many-body Physics
8:00 AM–11:00 AM,
Monday, March 5, 2018
LACC Room: 408A
Sponsoring Units: DCOMP DCMP
Chair: Lei Wang, Chinese Academy of Sciences
Abstract: A32.00004 : Machine learning of quantum many-fermion systems
9:48 AM–10:24 AM
The application of state-of-the-art 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 many-body wavefunction or the ensemble of correlators sampled in Monte Carlo simulations.
In this talk, I will focus on quantum many-fermion problems and demonstrate that convolutional neural networks (CNNs) can identify a plethora of collective states including metals, spin-density and charge-density wave ordered phases as well non-trivial states such as superconductors and topologically ordered states. Both supervised and unsupervised ML approaches will be introduced. I will further elucidate how CNNs can also be used to alleviate the notorious sign problem in fermionic quantum Monte Carlo techniques.
Joint work with Peter Broecker.
 P. Broecker et al., Scientific Reports 7, 8823 (2017)
 P. Broecker et al., arXiv:1707.00663
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