Deep Autoregressive Models for the Efficient Variational Simulation of Many-Body Quantum Systems

Or Sharir*, Yoav Levine, Noam Wies, Giuseppe Carleo, Amnon Shashua

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

120 Scopus citations

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 method, 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.

Original languageAmerican English
Article number020503
JournalPhysical Review Letters
Volume124
Issue number2
DOIs
StatePublished - 16 Jan 2020

Bibliographical note

Publisher Copyright:
© 2020 American Physical Society.

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