Requirements for upgrading trusted nodes to a repeater chain over 900 km of optical fiber

Journal Article (2024)
Author(s)

F. Horta Ferreira da Silva (Kavli institute of nanoscience Delft, TU Delft - QuTech Advanced Research Centre, TU Delft - QID/Wehner Group)

G. Avis (Kavli institute of nanoscience Delft, TU Delft - QID/Wehner Group, TU Delft - QuTech Advanced Research Centre)

J.A. Slater (TU Delft - QuTech Advanced Research Centre, TU Delft - BUS/Quantum Delft)

S.D.C. Wehner (TU Delft - QuTech Advanced Research Centre, TU Delft - Quantum Computer Science, Kavli institute of nanoscience Delft, TU Delft - QID/Wehner Group)

Research Group
QID/Wehner Group
DOI related publication
https://doi.org/10.1088/2058-9565/ad7499
More Info
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Publication Year
2024
Language
English
Research Group
QID/Wehner Group
Issue number
4
Volume number
9
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Abstract

We perform a numerical study of the distribution of entanglement on a real-world fiber grid connecting the German cities of Bonn and Berlin. The connection is realized using a chain of processing-node quantum repeaters spanning roughly 900 kilometers. Their placement is constrained by the fiber grid we consider, resulting in asymmetric links. We investigate how minimal hardware requirements depend on the target application, as well as on the number of repeaters in the chain. We find that requirements for blind quantum computing are markedly different than those for quantum key distribution, with the required coherence time being around two and a half times larger for the former. Further, we observe a trade-off regarding how target secret-key rates are achieved when using different numbers of repeaters: comparatively low-quality entangled states generated at a high rate are preferred for higher numbers of repeaters, whereas comparatively high-quality states generated at a lower rate are favored for lower numbers of repeaters. To obtain our results we employ an extensive simulation framework implemented using NetSquid, a discrete-event simulator for quantum networks. These are combined with an optimization methodology based on genetic algorithms to determine minimal hardware requirements.