Bulletin of the American Physical Society
APS March Meeting 2021
Volume 66, Number 1
Monday–Friday, March 15–19, 2021; Virtual; Time Zone: Central Daylight Time, USA
Session A21: Machine Learning for Quantum Matter I
8:00 AM–11:00 AM,
Monday, March 15, 2021
Sponsoring
Units:
DCOMP GDS DMP
Chair: Stefanie Czischek, University of Waterloo
Abstract: A21.00011 : Quantum Ground States from Reinforcement Learning*
10:48 AM–11:00 AM
On Demand
Presenter:
Ariel Barr
(Materials Science and Engineering, Massachusetts Institute of Technology)
Authors:
Ariel Barr
(Materials Science and Engineering, Massachusetts Institute of Technology)
Willem Gispen
(Physics, University of Cambridge)
Austen Lamacraft
(Physics, University of Cambridge)
We demonstrate how reinforcement learning may be used to solve problems in quantum systems via the path integral representation. Our work provides a novel neural approach to many-body quantum mechanics that leverages optimal control to approximate the Feynman–Kac path measure. The drift of the quantum stochastic control process is chosen to match the distribution of paths in the Feynman–Kac representation of the solution of the imaginary time Schrodinger equation. This provides a variational principle that can be used for reinforcement learning of a neural representation and a new alternative to prior deep learning approaches that start from the Schrodinger picture. Further, our approach learns an optimal importance sampler for the FK trajectories, providing a drop-in replacement for path integral Monte Carlo.
*We acknowledge support from a NSF GRFP Fellowship under Grant No. DGE-174530 (ARB) and EPSRC Grant No. EP/P034616/1 (AL)
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