6533b835fe1ef96bd129f528
RESEARCH PRODUCT
Machine Learning-Based Classification of Vector Vortex Beams.
Francesca AcanforaLuca InnocentiAlessandro FerraroFabio SciarrinoMauro PaternostroAlessia SupranoNicolò SpagnoloEmanuele PolinoTaira Giordanisubject
Angular momentumComputer sciencequantum opticquanutm informationphotonicsPrincipal component analysisGeneral Physics and AstronomyFOS: Physical sciencesMachine learningcomputer.software_genre01 natural sciencesConvolutional neural networkSettore FIS/03 - Fisica Della Materiaquant-phPolarization0103 physical sciencesQuantum walk010306 general physicsQuantum opticsorbital angular momentum; machine learning; vector vortex beamsQuantum PhysicsQuantum opticsbusiness.industryVortex flowOptical polarizationVectorsVortexmachine learningConvolutional neural networksArtificial intelligencePhotonicsbusinessQuantum Physics (quant-ph)computerStructured lightdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2020-05-16 | Physical review letters |