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Deep autoregressive models for the efficient variational simulation of many-body quantum systems | | Or Sharir
; Yoav Levine
; Noam Wies
; Giuseppe Carleo
; Amnon Shashua
; | | Date: |
11 Feb 2019 | | 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 a two-dimensional interacting spin model,
showcasing the ability to obtain accurate results on larger system sizes than
those currently accessible to neural-network quantum states. | | Source: | arXiv, 1902.4057 | | Services: | Forum | Review | PDF | Favorites | |
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