Abstract
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.The GAT-based ansatz has a computational complexity that grows linearly with the system size and can be extended to large systems naturally.Numerical results show that our method achieves the state-of-the-art results on spin-1/2 J1-J2 Heisenberg models over the square,honeycomb,triangular,and kagome lattices with different interaction strengths and lattice sizes(up to 24 x 24 for square lattice).The method also provides excellent results for the ground states of transverse field Ising models on square lattices.The GAT-based techniques are efficient and versatile and hold promise for studying large quantum many-body systems with exponentially sized objects.
基金项目
国家自然科学基金(12374017)
国家自然科学基金(12074362)
Innovation Program for Quantum Science and Technology(2021ZD0303303)
维普
万方数据