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• W2935584311 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." @default.
• W2935584311 created "2019-04-11" @default.
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• W2935584311 date "2020-01-16" @default.
• W2935584311 modified "2023-10-04" @default.
• W2935584311 title "Deep Autoregressive Models for the Efficient Variational Simulation of Many-Body Quantum Systems" @default.
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• W2935584311 doi "https://doi.org/10.1103/physrevlett.124.020503" @default.
• W2935584311 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32004039" @default.
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