The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Deep Autoregressive Models for the Efficient Variational Simulation of Many-Body Quantum Systems
Open accessAbstract
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.
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Cited by
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Metadata
- Authors
- Sharir, O., Levine, Y., Wies, N., Carleo, G., & Shashua, A.
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2020-01-16
- Volume
- 124
- Issue
- 2
- Pages
- 020503-020503
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W2935584311
- Cited by
- 256
- References
- 32
- FWCI
- 15.85
- Type
- journal article
- Language
- en
- Open access
- green
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