Identifying topology of leaky photonic lattices with machine learning
Ekaterina O. Smolina Lev A. Smirnov Daniel Leykam Franco Nori Daria A. Smirnova
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.