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...
Main Authors: | , , |
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Format: | Article |
Language: | English |
Published: |
MDPI AG
2019-01-01
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Series: | Entropy |
Subjects: | |
Online Access: | http://www.mdpi.com/1099-4300/21/1/82 |
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. |
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ISSN: | 1099-4300 |