Identifying topology of leaky photonic lattices with machine learning.

ABSTRACT: We show how machine learning techniques can be applied for the classification of topological phases in finite leaky photonic lattices using limited measurement data. We propose an approach based solely on a single real-space bulk intensity image, thus exempt from 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.

SUBMITTER: Smolina E 

PROVIDER: S-EPMC11501594 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature