Quantum Ground States from Reinforcement Learning

Autor:	Barr, Ariel, Gispen, Willem, Lamacraft, Austen
Rok vydání:	2020
Předmět:	Quantum Physics Condensed Matter - Disordered Systems and Neural Networks
Druh dokumentu:	Working Paper
Popis:	Finding the ground state of a quantum mechanical system can be formulated as an optimal control problem. In this formulation, the drift of the optimally controlled process is chosen to match the distribution of paths in the Feynman--Kac (FK) representation of the solution of the imaginary time Schr\"odinger equation. This provides a variational principle that can be used for reinforcement learning of a neural representation of the drift. Our approach is a drop-in replacement for path integral Monte Carlo, learning an optimal importance sampler for the FK trajectories. We demonstrate the applicability of our approach to several problems of one-, two-, and many-particle physics. Comment: Accepted at MSML 2020
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2006.09044 Zobrazit plný text záznamu View this record from Arxiv