Bridging the Gap between Deep Learning and Frustrated Quantum Spin System for Extreme-Scale Simulations on New Generation of Sunway Supercomputer

Efficient numerical methods are promising tools for delivering unique insights into the fascinating properties of physics, such as the highly frustrated quantum many-body systems. However, the computational complexity of obtaining the wave functions for accurately describing the quantum states incre...

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Veröffentlicht in:	IEEE transactions on parallel and distributed systems 2022-11, Vol.33 (11), p.2846-2859
Hauptverfasser:	Li, Mingfan, Chen, Junshi, Xiao, Qian, Wang, Fei, Jiang, Qingcai, Zhao, Xuncheng, Lin, Rongfen, An, Hong, Liang, Xiao, He, Lixin
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Convolutional neural networks Deep learning Heisenberg theory Lattices Monte Carlo methods new generation sunway supercomputer Numerical methods Quantum system Quantum theory Simulation spin-1/2 J1 – J2 Heisenberg model Stationary state Statistical models Supercomputers Wave functions
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