论文标题
神经网络波功能和标志问题
Neural network wave functions and the sign problem
论文作者
论文摘要
神经量子状态（NQS）是研究多体量子物理学的有前途的方法。但是，当应用于晶格模型时，它们面临着一个重大挑战：卷积网络难以将非平凡的标志结构汇聚到基础状态。我们通过提出一个简单，明确且可解释的ANSATZ的神经网络体系结构来解决这个问题，该阶段可以坚固地代表此类状态并实现常规和沮丧的反铁磁体的最新变化能量。在后一种情况下，我们的方法发现了表现出马歇尔标志规则的低能状态，因此与预期的基态不一致。这种状态可能是妨碍基于NQS的变异蒙特卡洛进入这些系统的真实接地状态的原因。我们讨论了这一观察结果的含义，并提出了克服问题的潜在策略。
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.
