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

doi:10.1364/OFC.2020.W1K.1

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 proceeding › Article in proceedings › Research › peer-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 language	English
Title of host publication	Optical Fiber Communication Conference 2020
Number of pages	3
Publisher	IEEE
Publication date	2020
Article number	W1K.1
ISBN (Print)	978-1-943580-71-2
DOIs	https://doi.org/10.1364/OFC.2020.W1K.1
Publication status	Published - 2020
Event	Optical Fiber Communication Conference 2020 - San Diego Convention Center, San Diego, United States Duration: 8 Mar 2020 → 12 Mar 2020

Conference

Conference	Optical Fiber Communication Conference 2020
Location	San Diego Convention Center
Country/Territory	United States
City	San Diego
Period	08/03/2020 → 12/03/2020
Sponsor	Acacia Communications Inc., AC Photonics, Inc., Alibaba Group, Ciena Corporation, Cisco Systems

Bibliographical note

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

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10.1364/OFC.2020.W1K.1

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title = "Advancing classical and quantum communication systems with machine learning",

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.",

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

note = "From the session: Machine Learning for Optical Communication Systems (W1K); Optical Fiber Communication Conference 2020, OFC 2020 ; Conference date: 08-03-2020 Through 12-03-2020",

year = "2020",

doi = "10.1364/OFC.2020.W1K.1",

language = "English",

isbn = "978-1-943580-71-2",

booktitle = "Optical Fiber Communication Conference 2020",

publisher = "IEEE",

address = "United States",

}

Zibar, D, Moura, UCD, Chin, H-M, Brusin, AMR, Jain, N , Da Ros, F, Kleis, S, Schaeffer, C, Gehring, T , Andersen, UL & Carena, A 2020, Advancing classical and quantum communication systems with machine learning. in Optical Fiber Communication Conference 2020., W1K.1, IEEE, Optical Fiber Communication Conference 2020, San Diego, California, United States, 08/03/2020. https://doi.org/10.1364/OFC.2020.W1K.1

TY - GEN

T1 - Advancing classical and quantum communication systems with machine learning

AU - Zibar, Darko

AU - Moura, Uiara Celine de

AU - Chin, Hou-Man

AU - Brusin, Ann M. Rosa

AU - Jain, Nitin

AU - Da Ros, Francesco

AU - Kleis, Sebastian

AU - Schaeffer, C.

AU - Gehring, Tobias

AU - Andersen, Ulrik Lund

AU - Carena, Andrea

N1 - From the session: Machine Learning for Optical Communication Systems (W1K)

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

U2 - 10.1364/OFC.2020.W1K.1

DO - 10.1364/OFC.2020.W1K.1

M3 - Article in proceedings

SN - 978-1-943580-71-2

BT - Optical Fiber Communication Conference 2020

PB - IEEE

T2 - Optical Fiber Communication Conference 2020

Y2 - 8 March 2020 through 12 March 2020

ER -

Advancing classical and quantum communication systems with machine learning

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