Preprint Article Version 1 This version is not peer-reviewed

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.

Journal reference: Entropy 2019, 21, 82
DOI: 10.3390/e21010082

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.

Subject Areas

approximation; ground state; neural network quantum state

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