Classification of 270 classes of vector vortex beams using Machine learning-based methods

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Vector vortex beams (VVBs) are a promising type of structured light that combine the orbital angular momentum (OAM) and the polarization states of light. Due to their intrinsic high dimensionality, VVBs show great advantages in applications like optical communications, information encryption, and quantum information processing. However, the high dimensionality presents a challenge for pattern detection. In this paper, we compare different machine learning-based methods for classifying 270 classes of VVB using basic CNN, MobileNet, and ResNet18 neural networks. We visualize the VVB modes using a color-coding method with Stokes parameters, and the neural networks’ performance is tested in a 1 km free space communication link with four atmospheric turbulence strengths. The results demonstrate that neural networks can recognize large datasets of laser modes with good accuracies, even under turbulence environments. We also propose an image encryption scheme using the VVB dataset to encode an RGB figure which is transmitted through the turbulence channel and successfully recovered by the pre-trained neural networks. Our study highlights the potential of artificial intelligence for VVB pattern recognition and could have a significant impact on the design of future optical communications systems and information encryption protocols.