机器学习辅助的局部拓扑不变量测量
Machine learning assisted measurement of local topological invariants
摘要：
拓扑量子器件的不断发展需要一种有效的、非侵入性的方法来评估不同拓扑相的组件的一致性。在这里，我们展示了机器学习为非侵入性拓扑质量控制铺平了道路。为此，我们使用局部拓扑标记，能够区分一维导线的拓扑相位。在固态系统中直接观察这种标记是具有挑战性的，但我们表明，人工神经网络可以学习近似它从实验可访问的局部状态密度。我们的方法区分不同的非平凡的阶段，即使是直接运输测量的系统是不可用的，复合系统。这种新方法可以在实验中找到重要的用途，从新型拓扑材料的研究到高通量自动化材料设计。
英文摘要：
The continuous effort towards topological quantum devices calls for an efficient and non-invasive method to assess the conformity of components in different topological phases. Here, we show that machine learning paves the way towards non-invasive topological quality control. To do so, we use a local topological marker, able to discriminate between topological phases of one-dimensional wires. The direct observation of this marker in solid state systems is challenging, but we show that an artificial neural network can learn to approximate it from the experimentally accessible local density of states. Our method distinguishes different non-trivial phases, even for systems where direct transport measurements are not available and for composite systems. This new approach could find significant use in experiments, ranging from the study of novel topological materials to high-throughput automated material design.
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