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Approximating Ground States by Neural Network Quantum States
Version 1
: Received: 17 December 2018 / Approved: 18 December 2018 / Online: 18 December 2018 (10:39:29 CET)
A peer-reviewed article of this Preprint also exists.
Yang, Y.; Zhang, C.; Cao, H. Approximating Ground States by Neural Network Quantum States. Entropy 2019, 21, 82. Yang, Y.; Zhang, C.; Cao, H. Approximating Ground States by Neural Network Quantum States. Entropy 2019, 21, 82.
Abstract
The many-body problem in quantum physics originates from the difficulty of describing the non-trivial correlations encoded in the exponential complexity of the many-body wave function. Motivated by the Giuseppe Carleo's work titled solving the quantum many-body problem with artificial neural networks [Science, 2017, 355: 602], we focus on finding the NNQS approximation of the unknown ground state of a given Hamiltonian $H$ in terms of the best relative error and explore the influences of sum, tensor product, local unitary of Hamiltonians on the best relative error. Besides, we illustrate our method with some examples.
Keywords
approximation; ground state; neural network quantum state
Subject
Physical Sciences, Mathematical Physics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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