Approximating Ground States by Neural Network Quantum States

Approximating Ground States by Neural Network Quantum States

Motivated by the Carleo’s work [Science, 2017, 355: 602], we focus on finding the neural network quantum statesapproximation 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 Hamilton...

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Bibliographic Details
Main Authors: Ying Yang, Chengyang Zhang, Huaixin Cao
Format: Article
Language:English
Published: MDPI AG 2019-01-01
Series:Entropy
Subjects:
approximation
ground state
neural network quantum state
Online Access:http://www.mdpi.com/1099-4300/21/1/82
Description
Summary:Motivated by the Carleo’s work [Science, 2017, 355: 602], we focus on finding the neural network quantum statesapproximation 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.
ISSN:1099-4300

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