Machine Learning-Based Classification of Vector Vortex Beams.
Phys Rev Lett
; 124(16): 160401, 2020 Apr 24.
Article
en En
| MEDLINE
| ID: mdl-32383956
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 nontrivial 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.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
Phys Rev Lett
Año:
2020
Tipo del documento:
Article
País de afiliación:
Italia
Pais de publicación:
Estados Unidos