Quantum advantages for data transmission in future networks: An overview

We review recent advancements in the domain of Joint Detection Receivers and Entanglement-Assisted Data transmission links with an emphasis on their potential use in future networks. Both data transmission techniques can surpass the Shannon limit by significant amounts in situations where either the number of photons per received information carrier or the number of transmitted photons per information carrier is extremely small. To obtain an advantage from shared entanglement, significant noise levels are needed as well. These fundamental constraints dictate that only certain application scenarios are of relevance to the new technology. We discuss these constraints in detail in the context of the current network architecture and stress the relation to optical computation. Based on our discussion, we go on to propose potential domains of application for the new technology.

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Publication Details

Journal
Computer Networks
Published
2024-08-30
DOI
https://doi.org/10.1016/j.comnet.2024.110727
Citations
7
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
2.43

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Quantum advantages for data transmission in future networks: An overview

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article en
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Abstract

We review recent advancements in the domain of Joint Detection Receivers and Entanglement-Assisted Data transmission links with an emphasis on their potential use in future networks. Both data transmission techniques can surpass the Shannon limit by significant amounts in situations where either the number of photons per received information carrier or the number of transmitted photons per information carrier is extremely small. To obtain an advantage from shared entanglement, significant noise levels are needed as well. These fundamental constraints dictate that only certain application scenarios are of relevance to the new technology. We discuss these constraints in detail in the context of the current network architecture and stress the relation to optical computation. Based on our discussion, we go on to propose potential domains of application for the new technology.

Computer NetworksVol. 254
Technical University of Munich (DE)
Deutsche Forschungsgemeinschaft, Bundesministerium für Bildung und Forschung
Openalex Percentile: Top 10%
Neural Networks and Reservoir Computing
2.43
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