On the use of deep neural networks in optical communications

TL;DR

This paper demonstrates that deep neural networks can accurately classify complex, noisy Laguerre-Gauss modes of light using only intensity profiles, potentially simplifying and enhancing optical communication systems.

Contribution

It introduces a neural network-based method for classifying high-dimensional, noisy optical modes using simulated training, reducing measurement complexity.

Findings

Near-unity fidelity in classifying modes up to 100 quanta of orbital angular momentum

Effective classification of experimental superpositions trained solely on simulated data

Potential for significant improvements in optical communication technology

Abstract

Information transfer rates in optical communications may be dramatically increased by making use of spatially non-Gaussian states of light. Here we demonstrate the ability of deep neural networks to classify numerically-generated, noisy Laguerre-Gauss modes of up to 100 quanta of orbital angular momentum with near-unity fidelity. The scheme relies only on the intensity profile of the detected modes, allowing for considerable simplification of current measurement schemes required to sort the states containing increasing degrees of orbital angular momentum. We also present results that show the strength of deep neural networks in the classification of experimental superpositions of Laguerre-Gauss modes when the networks are trained solely using simulated images. It is anticipated that these results will allow for an enhancement of current optical communications technologies.