Deep autoregressive models for the efficient variational simulation of many-body quantum systems
Abstract
Artificial Neural Networks were recently shown to be an efficient representation of highly-entangled many-body quantum states. In practical applications, neural-network states inherit numerical schemes used in Variational Monte Carlo, most notably the use of Markov-Chain Monte-Carlo (MCMC) sampling to estimate quantum expectations. The local stochastic sampling in MCMC caps the potential advantages of neural networks in two ways: (i) Its intrinsic computational cost sets stringent practical limits on the width and depth of the networks, and therefore limits their expressive capacity; (ii) Its difficulty in generating precise and uncorrelated samples can result in estimations of observables that are very far from their true value. Inspired by the state-of-the-art generative models used in machine learning, we propose a specialized Neural Network architecture that supports efficient and exact sampling, completely circumventing the need for Markov Chain sampling. We demonstrate our approach for two-dimensional interacting spin models, showcasing the ability to obtain accurate results on larger system sizes than those currently accessible to neural-network quantum states.
Cite
@article{arxiv.1902.04057,
title = {Deep autoregressive models for the efficient variational simulation of many-body quantum systems},
author = {Or Sharir and Yoav Levine and Noam Wies and Giuseppe Carleo and Amnon Shashua},
journal= {arXiv preprint arXiv:1902.04057},
year = {2020}
}