Machine Learning-Based Classification of Vector Vortex Beams

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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.
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Funding Information
  • H2020 European Research Council (828978, 766900)
  • European Cooperation in Science and Technology (CA15220)
  • Royal Society (RSWF\R3\183013)
  • Leverhulme Trust (RGP-2018-266)
  • Fondazione Angelo Della Riccia (15/IA/2864)

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