Identification of diffracted vortex beams at different propagation distances using deep learning

TL;DR

This paper presents a deep learning approach to accurately identify the topological charge and propagation distance of vortex beams with orbital angular momentum, even under phase distortions, enhancing quantum communication and sensing.

Contribution

The study introduces an enhanced neural network model capable of recognizing OAM modes at various distances with high accuracy despite phase distortions, advancing optical communication technologies.

Findings

Achieved 97% accuracy in identifying vortex beam properties.

Effective recognition under phase distortions and varying propagation distances.

Implications for improved quantum communication and sensing protocols.

Abstract

Orbital angular momentum of light is regarded as a valuable resource in quantum technology, especially in quantum communication and quantum sensing and ranging. However, the OAM state of light is susceptible to undesirable experimental conditions such as propagation distance and phase distortions, which hinders the potential for the realistic implementation of relevant technologies. In this article, we exploit an enhanced deep learning neural network to identify different OAM modes of light at multiple propagation distances with phase distortions. Specifically, our trained deep learning neural network can efficiently identify the vortex beam's topological charge and propagation distance with 97% accuracy. Our technique has important implications for OAM based communication and sensing protocols.