Deep learning representations for quantum many-body systems on heterogeneous hardware

Deep learning representations for quantum many-body systems on heterogeneous hardware

The quantum many-body problems are important for condensed matter physics, however solving the problems are challenging because the Hilbert space grows exponentially with the size of the problem. The recently developed deep learning methods provide a promising new route to solve long-standing quantu...

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Bibliographic Details
Main Authors: Xiao Liang, Mingfan Li, Qian Xiao, Junshi Chen, Chao Yang, Hong An, Lixin He
Format: Article
Language:English
Published: IOP Publishing 2023-01-01
Series:Machine Learning: Science and Technology
Subjects:
neural network quantum state
deep learning
stochastic reconfiguration
Lanczos
Sunway
heterogeneous architecture
Online Access:https://doi.org/10.1088/2632-2153/acc56a

Internet

https://doi.org/10.1088/2632-2153/acc56a

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