首页|Solving Quantum Many-Particle Models with Graph Attention Network
Solving Quantum Many-Particle Models with Graph Attention Network
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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.
于启航、林子敬
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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
国家自然科学基金国家自然科学基金Innovation Program for Quantum Science and Technology
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