# Identifying topology of leaky photonic lattices with machine learning

arXiv: https://arxiv.org/abs/2308.14407 · HTML: https://mlsanity.com/paper/2308.14407/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 35 references that are in the map.
- First posted on arXiv 28 Aug 2023.

## Builds on (most cited first, 2 of 2)

- [Machine Learning Topological Invariants with Neural Networks](https://mlsanity.com/paper/1708.09401/) (2017)
- [Machine learning topological phases in real space](https://mlsanity.com/paper/1901.01963/) (2019)

## Built on by (most cited first, 1 of 1)

- [Quantum circuit complexity and unsupervised machine learning of topological order](https://mlsanity.com/paper/2508.04486/) (2025)

## Abstract (arXiv)

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.