Identification of diffracted vortex beams at different propagation distances using deep learning
This work addresses a challenge in quantum communication and sensing by improving OAM state identification, though it is incremental as it applies an enhanced deep learning method to a known bottleneck.
The researchers tackled the problem of identifying orbital angular momentum (OAM) modes of light under distortions like propagation distance and phase changes, achieving 97% accuracy in identifying topological charge and propagation distance using a deep learning neural network.
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.