Identifying topology of leaky photonic lattices with machine learning

TL;DR

This paper demonstrates how machine learning can classify topological phases in leaky photonic lattices using only bulk intensity measurements, simplifying experimental procedures.

Contribution

It introduces a neural network approach that accurately identifies topological properties from intensity data without phase retrieval, applicable to realistic photonic experiments.

Findings

Neural network accurately classifies topological phases.

Method works with limited measurement data.

Applicable to realistic experimental conditions.

Abstract

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.