Resilient Graph Partitioning for Quantum Networks

Quantum entanglement is important for quantum communications for long range transmission. Such long transmission is achieved through entanglement swapping, a method monopolizing considerable resources as the quantum network topology expands. Therefore, in this work we propose a method to reduce the resources required for entanglement swapping using network partitioning while ensuring resiliency. This method involves the fidelity of distributing entangled states that depends on the source, the link and the bell state analyzer utilized to quantify the required fidelity for a functioning network. By partitioning the network to redundant sub-graphs, we show the advantage of such approach in reducing the required functional fidelity.

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Publication Details

Published
2024-10-15
DOI
https://doi.org/10.1109/fnwf63303.2024.11028850
Primary Topic
Advanced Optical Network Technologies
Type
article
Field-Weighted Citation Impact
0.00

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article

Resilient Graph Partitioning for Quantum Networks

Riccardo Bassoli, Frank H. P. Fitzek, Ayse Nur Yilmaz, Abdelkrim Menina
Advanced Optical Network Technologies
article

Resilient Graph Partitioning for Quantum Networks

Riccardo Bassoli, Frank H. P. Fitzek, Ayse Nur Yilmaz, Abdelkrim Menina
article en

Abstract

Quantum entanglement is important for quantum communications for long range transmission. Such long transmission is achieved through entanglement swapping, a method monopolizing considerable resources as the quantum network topology expands. Therefore, in this work we propose a method to reduce the resources required for entanglement swapping using network partitioning while ensuring resiliency. This method involves the fidelity of distributing entangled states that depends on the source, the link and the bell state analyzer utilized to quantify the required fidelity for a functioning network. By partitioning the network to redundant sub-graphs, we show the advantage of such approach in reducing the required functional fidelity.

Deutsche Telekom (Germany) (DE), Hasan Kalyoncu University (TR)
Ministry of Education, Deutsche Forschungsgemeinschaft
Openalex Percentile: Top 72%
Advanced Optical Network Technologies
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