Solving the Electronic Schrödinger Equation by Pairing Tensor-Network State with Neural Network Quantum State

Bowen Kan, Yingqi Tian, Daiyou Xie, Yangjun Wu, Yi Fan and Honghui Shang ()
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Bowen Kan: Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100045, China
Yingqi Tian: Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100045, China
Daiyou Xie: Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei 230026, China
Yangjun Wu: Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100045, China
Yi Fan: Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei 230026, China
Honghui Shang: Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei 230026, China

Mathematics, 2024, vol. 12, issue 3, 1-16

Abstract: Neural network methods have shown promise for solving complex quantum many-body systems. In this study, we develop a novel approach through incorporating the density-matrix renormalization group (DMRG) method with the neural network quantum state method. The results demonstrate that, when tensor-network pre-training is introduced into the neural network, a high efficiency can be achieved for quantum many-body systems with strong correlations.

Keywords: quantum mechanics; neural network quantum state; high-performance simulations; strongly correlated materials (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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