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

@inproceedings{Li2021BridgingTG, title={Bridging the Gap between Deep Learning and Frustrated Quantum Spin System for Extreme-scale Simulations on New Generation of Sunway Supercomputer}, author={Mingfan Li and Junshi Chen and Qian Xiao and Qingcai Jiang and Xuncheng Zhao and Rongfen Lin and Fei Wang and Xiao Liang and Lixin He and Hong An}, year={2021} }

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 increases exponentially with respect to particle number. Here we present a novel convolutional neural network (CNN) for simulating the two-dimensional highly frustrated spin-1/2 J1 − J2 Heisenberg model…

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