Machine learning-based classification of vector vortex beams
Abstract
Structured light is attracting significant attention for its diverse applications in both classical and quantum optics. The so-called vector vortex beams display peculiar properties in both contexts due to the non-trivial correlations between optical polarization and orbital angular momentum. Here we demonstrate a new, flexible experimental approach to the classification of vortex vector beams. We first describe a platform for generating arbitrary complex vector vortex beams inspired to photonic quantum walks. We then exploit recent machine learning methods -- namely convolutional neural networks and principal component analysis -- to recognize and classify specific polarization patterns. Our study demonstrates the significant advantages resulting from the use of machine learning-based protocols for the construction and characterization of high-dimensional resources for quantum protocols.
Cite
@article{arxiv.2005.07949,
title = {Machine learning-based classification of vector vortex beams},
author = {Taira Giordani and Alessia Suprano and Emanuele Polino and Francesca Acanfora and Luca Innocenti and Alessandro Ferraro and Mauro Paternostro and Nicolò Spagnolo and Fabio Sciarrino},
journal= {arXiv preprint arXiv:2005.07949},
year = {2020}
}