研究人员开发了一种新颖的规范不变图神经网络（GNN）架构，旨在处理阿贝尔格点规范模型。该GNN使用威尔逊环等局部规范不变输入显式强制执行对称性，并在整个消息传递过程中保持对称性。该方法已在$\mathbb{Z}_2$和$\mathrm{U}(1)$格点规范模型上成功进行了基准测试，证明了对全局和空间分辨可观测量进行了准确预测。 AI
影响 为模拟复杂的物理系统引入了一种新的GNN架构，有可能在量子链模型中实现更有效和可扩展的时间演化。
排序理由 详细介绍格点规范理论新GNN架构的学术论文。
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arXiv:2605.03901v1 Announce Type: cross Abstract: Local gauge structures play a central role in a wide range of condensed matter systems and synthetic quantum platforms, where they emerge as effective descriptions of strongly correlated phases and engineered dynamics. We introduc…
Local gauge structures play a central role in a wide range of condensed matter systems and synthetic quantum platforms, where they emerge as effective descriptions of strongly correlated phases and engineered dynamics. We introduce a gauge-invariant graph neural network (GNN) arc…