期刊
PHYSICAL REVIEW RESEARCH
卷 2, 期 3, 页码 -出版社
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevResearch.2.033075
关键词
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资金
- Engineering and Physical Sciences Research Council (EPSRC) [EP/M007065/1, EP/P034616/1]
- EPSRC [EP/M007065/1, EP/P034616/1, EP/K028960/1] Funding Source: UKRI
Neural quantum states (NQS) are a promising approach to study many-body quantum physics. However, they face a major challenge when applied to lattice models: convolutional networks struggle to converge to ground states with a nontrivial sign structure. We tackle this problem by proposing a neural network architecture with a simple, explicit, and interpretable phase Ansatz, which can robustly represent such states and achieve state-of-the-art variational energies for both conventional and frustrated antiferromagnets. In the latter case, our approach uncovers low-energy states that exhibit the Marshall sign rule and are therefore inconsistent with the expected ground state. Such states are the likely cause of the obstruction for NQS-based variational Monte Carlo to access the true ground states of these systems. We discuss the implications of this observation and suggest potential strategies to overcome the problem.
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