Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
Conferences > OFC > 2020 > W1K > W1K.1

Advancing classical and quantum communication systems with machine learning

D. Zibar, U. C. Moura, H. M. Chin, A. M. Rosa Brusin, N. Jain, F. Da Ros, S. Kleis, C. Schaeffer, T. Gehring, U. L. Andersen, and A. Carena

Not Accessible

Your library or personal account may give you access

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.

© 2020 The Author(s)

PDF Article
More Like This
Machine Learning in Quantum Communication

Max Rückmann, Sebastian Kleis, Christian G. Schaeffer, and Darko Zibar
SpTu3I.6 Signal Processing in Photonic Communications (SPPCom) 2020

A Machine Learning-Assisted Quantum and Classical Co-existence System

R. Yang, R. Wang, A. Seferidis, T. Omigbodun, S. Bahrani, R. D. Oliveira, R. Nejabati, and D. Simeonidou
M2J.2 Optical Fiber Communication Conference (OFC) 2024

Workshop on Machine Learning for Optical Communication Systems: a summary

Josh Gordon, Abdella Battou, and Dan Kilper
M3E.3 Optical Fiber Communication Conference (OFC) 2020

View More...
Select as filters
Select Topics Cancel
Top
       
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.
Privacy | Terms of Use