Journal
PHYSICAL REVIEW LETTERS
Volume 124, Issue 16, Pages -Publisher
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevLett.124.160401
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Funding
- ERC Advanced grant PHOSPhOR (Photonics of Spin-Orbit Optical Phenomena) [828978]
- EU Collaborative project TEQ [766900]
- Fondazione Angelo della Riccia
- DfE-SFI Investigator Programme [15/IA/2864]
- COST Action [CA15220]
- Royal Society Wolfson Research Fellowship [RSWF\R3\183013]
- Leverhulme Trust Research Project Grant [RGP-2018-266]
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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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