On the use of deep neural networks in optical communications

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.

arXiv comment10 pages, 15 figures. ©2018 Optical Society of America. Users may use, reuse, and build upon the article, or use the article for text or data mining, so long as such uses are for non-commercial purposes and appropriate attribution is maintained. All other rights are reserved

References in corpus (8)

• Entanglement of Orbital Angular Momentum States of Photons
• Quantum Entanglement of High Angular Momenta
• High-dimensional quantum cryptography with twisted light
• Communication with spatially modulated Light through turbulent Air across Vienna
• Higher-dimensional orbital angular momentum based quantum key distribution with mutually unbiased bases
• Superpositions of the Orbital Angular Momentum for Applications in Quantum Experiments
• Orbital angular momentum entanglement in turbulence
• Four-photon orbital angular momentum entanglement

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