Advancing classical and quantum communication systems with machine learning

Darko Zibar*, Uiara Celine de Moura, Hou-Man Chin, Ann M. Rosa Brusin, Nitin Jain, Francesco Da Ros, Sebastian Kleis, C. Schaeffer, Tobias Gehring, Ulrik Lund Andersen, Andrea Carena

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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Abstract

A perspective on how machine learning can aid the next–generation of classical and quantum optical communication systems is given. We focus on the design of Raman amplifiers and phase tracking at the quantum limit.

Original languageEnglish
Title of host publicationOptical Fiber Communication Conference 2020
Number of pages3
PublisherOptical Society of America (OSA)
Publication date2020
Article numberW1K.1
ISBN (Print)978-1-943580-71-2
DOIs
Publication statusPublished - 2020
EventOptical Fiber Communication Conference 2020 - San Diego Convention Center, San Diego, United States
Duration: 8 Mar 202012 Mar 2020

Conference

ConferenceOptical Fiber Communication Conference 2020
LocationSan Diego Convention Center
CountryUnited States
CitySan Diego
Period08/03/202012/03/2020

Bibliographical note

From the session: Machine Learning for Optical Communication Systems (W1K)

Cite this

Zibar, D., Moura, U. C. D., Chin, H-M., Brusin, A. M. R., Jain, N., Da Ros, F., Kleis, S., Schaeffer, C., Gehring, T., Andersen, U. L., & Carena, A. (2020). Advancing classical and quantum communication systems with machine learning. In Optical Fiber Communication Conference 2020 [W1K.1] Optical Society of America (OSA). https://doi.org/10.1364/OFC.2020.W1K.1