Machine learning-based classification of vector vortex beams

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 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.

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Cited by in corpus (4)

• Deep learning enhanced Rydberg multifrequency microwave recognition
• Dynamical learning of a photonics quantum-state engineering process
• Enhanced detection techniques of Orbital Angular Momentum states in the classical and quantum regimes
• Deep-learning-based recognition of multi-singularity structured light