摘要
Deep learning methods have been shown to be effective in representing ground-state wavefunctions of quantum many-body systems,however the existing approaches cannot be easily used for non-square like or large systems.Here.we propose a variational ansatz based on the graph attention network(GAT)which learns distributed latent representations and can be used on non-square lattices.
作者
于启航
林子敬
Qi-Hang Yu;Zi-Jing Lin(Department of Physics,University of Science and Technology of China,Hefei 230026,China;Hefei National Laboratory,University of Science and Technology of China,Hefei 230088,China)
基金
supported by the National Natural Science Foundation of China(Grant Nos.12374017 and 12074362)
the Innovation Program for Quantum Science and Technology(Grant No.2021ZD0303303)。
