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Proceedings Paper

Deep learning as a tool to distinguish between high orbital angular momentum optical modes
Author(s): E. M. Knutson; Sanjaya Lohani; Onur Danaci; Sean D. Huver; Ryan T. Glasser
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Paper Abstract

The generation of light containing large degrees of orbital angular momentum (OAM) has recently been demon- strated in both the classical and quantum regimes. Since there is no fundamental limit to how many quanta of OAM a single photon can carry, optical states with an arbitrarily high difference in this quantum number may, in principle, be entangled. This opens the door to investigations into high-dimensional entanglement shared between states in superpositions of nonzero OAM. Additionally, making use of non-zero OAM states can allow for a dramatic increase in the amount of information carried by a single photon, thus increasing the information capacity of a communication channel. In practice, however, it is difficult to differentiate between states with high OAM numbers with high precision. Here we investigate the ability of deep neural networks to differentiate between states that contain large values of OAM. We show that such networks may be used to differentiate be- tween nearby OAM states that contain realistic amounts of noise, with OAM values of up to 100. Additionally, we examine how the classification accuracy scales with the signal-to-noise ratio of images that are used to train the network, as well as those being tested. Finally, we demonstrate the simultaneous classification of < 100 OAM states with greater than 70 % accuracy. We intend to verify our system with experimentally-produced classi- cal OAM states, as well as investigate possibilities that would allow this technique to work in the few-photon quantum regime.

Paper Details

Date Published: 14 September 2016
PDF: 7 pages
Proc. SPIE 9970, Optics and Photonics for Information Processing X, 997013 (14 September 2016); doi: 10.1117/12.2242115
Show Author Affiliations
E. M. Knutson, Tulane Univ. (United States)
Sanjaya Lohani, Tulane Univ. (United States)
Onur Danaci, Tulane Univ. (United States)
Sean D. Huver, Deep Science AI (United States)
Ryan T. Glasser, Tulane Univ. (United States)


Published in SPIE Proceedings Vol. 9970:
Optics and Photonics for Information Processing X
Khan M. Iftekharuddin; Abdul A. S. Awwal; Mireya García Vázquez; Andrés Márquez; Mohammad A. Matin, Editor(s)

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