Machine learning-based classification of vector vortex beams

Taira Giordani, Alessia Suprano, Emanuele Polino, Francesca Acanfora, Luca Innocenti, Alessandro Ferraro, Mauro Paternostro, Nicolò Spagnolo, Fabio Sciarrino

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)
204 Downloads (Pure)


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.
Original languageEnglish
JournalPhys. Rev. Lett.
Publication statusPublished - 16 May 2020


  • quant-ph


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