Advancing classical and quantum communication systems with machine learning | Zendy

Darko Zibar | Zendy; Uiara Celine de Moura | Zendy; Hou-Man Chin | Zendy; Ann Margareth Rosa Brusin | Zendy; Nitin Jain | Zendy; Francesco Da Ros | Zendy; Sebastian Kleis | Zendy; Christian Schaeffer | Zendy; Tobias Gehring | Zendy; Ulrik L. Andersen | Zendy; Andrea Carena | Zendy

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Advancing classical and quantum communication systems with machine learning

Author(s) -

Darko Zibar,

Uiara Celine de Moura,

Hou-Man Chin,

Ann Margareth Rosa Brusin,

Nitin Jain,

Francesco Da Ros,

Sebastian Kleis,

Christian Schaeffer,

Tobias Gehring,

Ulrik L. Andersen,

Andrea Carena

Publication year - 2020

Publication title -

optical fiber communication conference (ofc) 2022

Language(s) - English

Resource type - Conference proceedings

DOI - 10.1364/ofc.2020.w1k.1

Subject(s) - perspective (graphical) , quantum , computer science , focus (optics) , limit (mathematics) , classical limit , quantum information science , artificial intelligence , physics , quantum entanglement , quantum mechanics , optics , mathematics , mathematical analysis

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.

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