We show how machine learning techniques can be applied for the classification of topological phases in leaky photonic lattices using limited measurement data. We propose an approach based solely on bulk intensity measurements, thus exempt from the need for complicated phase retrieval procedures. In particular, we design a fully connected neural network that accurately determines topological properties from the output intensity distribution in dimerized waveguide arrays with leaky channels, after propagation of a spatially localized initial excitation at a finite distance, in a setting that closely emulates realistic experimental conditions.

翻译：我们展示了如何将机器学习技术应用于利用有限测量数据对泄漏光子晶格中的拓扑相进行分类。我们提出了一种仅基于体强度测量的方法，从而无需复杂的相位恢复过程。具体而言，我们设计了一个全连接神经网络，该网络能够从具有泄漏通道的二聚化波导阵列的输出强度分布中准确确定拓扑性质，该阵列在有限距离内传播空间局域化初始激发后工作，其设置紧密模拟了真实实验条件。