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 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.

9 pages, 8 figures

References in corpus (4)

• Topological Transition in a Non-Hermitian Quantum Walk
• Unsupervised machine learning of topological phase transitions from experimental data
• Identifying Topological Phase Transitions in Experiments Using Manifold Learning
• Experimental demonstration of adversarial examples in learning topological phases

Cited by in corpus (2)

• Exponentially improved efficient machine learning for quantum many-body states with provable guarantees
• Quantum circuit complexity and unsupervised machine learning of topological order