Bulletin of the American Physical Society
APS March Meeting 2020
Volume 65, Number 1
Monday–Friday, March 2–6, 2020; Denver, Colorado
Session L39: Machine Learning for Quantum Matter I
8:00 AM–11:00 AM,
Wednesday, March 4, 2020
Sponsoring Units: DCOMP GDS DMP
Chair: Miles Stoudenmire, Simons Foundation
Abstract: L39.00008 : Adversarial machine learning for modeling the distribution of large-scale ultracold atom experiments*
(Ontario Tech University)
(National Research Council of Canada)
We present how artificial neural networks allow for the direct and targeted generation of large-scale microstates, while restricting the time-consuming simulations or measurements to a small number of particles. Their potential is illustrated on a data set of experimental snapshots of a doped Fermi-Hubbard model realized by ultracold atoms trapped in an optical lattice. The adversarial machine learning method we develop here is broadly applicable and can also be used for speeding up computer simulations of both equilibrium and nonequilibrium physical systems.
*This research is supported by an NVIDIA hardware grant.
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