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.

Machine learning-based classification of vector vortex beams / Giordani, Taira; Suprano, Alessia; Polino, Emanuele; Acanfora, Francesca; Innocenti, Luca; Ferraro, Alessandro; Paternostro, Mauro; Spagnolo, Nicolò; Sciarrino, Fabio. - In: PHYSICAL REVIEW LETTERS. - ISSN 0031-9007. - 124:16(2020). [10.1103/PhysRevLett.124.160401]

Machine learning-based classification of vector vortex beams

Giordani, Taira;Suprano, Alessia;Polino, Emanuele;Spagnolo, Nicolò;Sciarrino, Fabio
2020

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 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.
2020
orbital angular momentum; machine learning; vector vortex beams
01 Pubblicazione su rivista::01a Articolo in rivista
Machine learning-based classification of vector vortex beams / Giordani, Taira; Suprano, Alessia; Polino, Emanuele; Acanfora, Francesca; Innocenti, Luca; Ferraro, Alessandro; Paternostro, Mauro; Spagnolo, Nicolò; Sciarrino, Fabio. - In: PHYSICAL REVIEW LETTERS. - ISSN 0031-9007. - 124:16(2020). [10.1103/PhysRevLett.124.160401]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1395305
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