AI Article Synopsis
- A long-standing challenge in physics and chemistry is accurately obtaining properties of many-body interacting quantum matter due to the complexity of the many-body wave-function.
- This paper presents a new method using artificial neural networks, specifically a deep Boltzmann machine architecture, to create classical representations of these quantum systems.
- The approach can effectively reproduce imaginary-time evolution for many-body lattice Hamiltonians, operates deterministically, requires a manageable number of neurons, and can improve existing models, offering a new way to analyze quantum many-body states.
Article Abstract
Obtaining accurate properties of many-body interacting quantum matter is a long-standing challenge in theoretical physics and chemistry, rooting into the complexity of the many-body wave-function. Classical representations of many-body states constitute a key tool for both analytical and numerical approaches to interacting quantum problems. Here, we introduce a technique to construct classical representations of many-body quantum systems based on artificial neural networks. Our constructions are based on the deep Boltzmann machine architecture, in which two layers of hidden neurons mediate quantum correlations. The approach reproduces the exact imaginary-time evolution for many-body lattice Hamiltonians, is completely deterministic, and yields networks with a polynomially-scaling number of neurons. We provide examples where physical properties of spin Hamiltonians can be efficiently obtained. Also, we show how systematic improvements upon existing restricted Boltzmann machines ansatze can be obtained. Our method is an alternative to the standard path integral and opens new routes in representing quantum many-body states.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6294148 | PMC |
| http://dx.doi.org/10.1038/s41467-018-07520-3 | DOI Listing |
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