Approximating Ground States by Neural Network Quantum States
1
School of Mathematics and Information Science, Shaanxi Normal University, Xi’an 710119, China
2
School of Mathematics and Information Technology, Yuncheng University, Yuncheng 044000, China
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(1), 82; https://doi.org/10.3390/e21010082
Received: 16 December 2018 / Revised: 6 January 2019 / Accepted: 16 January 2019 / Published: 17 January 2019
(This article belongs to the Collection Quantum Information)
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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Keywords:
approximation; ground state; neural network quantum state
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MDPI and ACS Style
Yang, Y.; Zhang, C.; Cao, H. Approximating Ground States by Neural Network Quantum States. Entropy 2019, 21, 82.
AMA Style
Yang Y, Zhang C, Cao H. Approximating Ground States by Neural Network Quantum States. Entropy. 2019; 21(1):82.
Chicago/Turabian StyleYang, Ying; Zhang, Chengyang; Cao, Huaixin. 2019. "Approximating Ground States by Neural Network Quantum States" Entropy 21, no. 1: 82.
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