Machine Learning-Based Classification of Vector Vortex Beams

Area of Science:

• Optics and Photonics
• Quantum Information Science
• Machine Learning Applications

Background:

• Structured light, particularly vector vortex beams, exhibits unique properties due to coupled polarization and orbital angular momentum.
• These beams are crucial for advancements in classical and quantum optics, necessitating efficient characterization methods.

Purpose of the Study:

• To introduce a novel and adaptable experimental technique for classifying complex vector vortex beams.
• To leverage machine learning for automated recognition and categorization of intricate polarization states.

Main Methods:

• Generation of arbitrary complex vector vortex beams using a platform inspired by photonic quantum walks.
• Application of machine learning algorithms, specifically convolutional neural networks and principal component analysis, for pattern recognition.

Main Results:

• Successful demonstration of a flexible platform for generating diverse vector vortex beams.
• Effective classification of specific polarization patterns using machine learning algorithms.
• Validation of machine learning's utility in handling high-dimensional optical states.

Conclusions:

• Machine learning-based protocols offer significant advantages for the construction and characterization of high-dimensional quantum resources.
• The developed approach provides a powerful tool for advancing research in structured light and quantum information processing.