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

Author

Listed:
  • 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
    School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210093, 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)

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.

Suggested Citation

  • Bowen Kan & Yingqi Tian & Daiyou Xie & Yangjun Wu & Yi Fan & Honghui Shang, 2024. "Solving the Electronic Schrödinger Equation by Pairing Tensor-Network State with Neural Network Quantum State," Mathematics, MDPI, vol. 12(3), pages 1-16, January.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:3:p:433-:d:1329006
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    References listed on IDEAS

    as
    1. Yangjun Wu & Xiansong Xu & Dario Poletti & Yi Fan & Chu Guo & Honghui Shang, 2023. "A Real Neural Network State for Quantum Chemistry," Mathematics, MDPI, vol. 11(6), pages 1-10, March.
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