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This research introduces Quantum Utility Routing (QUR), a framework for continuous-variable quantum key distribution networks that optimizes secret key generation while managing relay trust and resource constraints. By incorporating machine learning to streamline weight optimization, the proposed method improves cryptographic performance over traditional routing algorithms without excessive resource consumption. The study establishes a configurable trade-off between security metrics and network efficiency, advancing practical deployment strategies for hybrid trusted and untrusted quantum infrastructures.
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Quantum utility routing in continuous-variable QKD networks with trusted and untrusted relays

arXiv2 min read

arXiv:2610.03382v1 Announce Type: new Abstract: Continuous-variable quantum key distribution (CV-QKD) networks require routing strategies that account simultaneously for secret key generation, relay trust and finite network resources. We introduce Quantum Utility Routing (QUR), a weighted routing framework for networks combining trusted point-to-point links with untrusted CV Bell measurement relays. Transition level composable finite size secret key rates (SKRs) are incorporated directly into the route search, allowing QUR to balance bottleneck SKR against route geometry and resource use. We consider graph optimized QUR (QUR$_{\mathrm{go}}$) and a fixed weight implementation (QUR$_{\mathrm{fw}}$) that uses machine learning to avoid repeated graph specific weight optimization, and benchmark both against A* and Max--Min routing. At the default network configuration, QUR$_{\mathrm{go}}$ and QUR$_{\mathrm{fw}}$ improve the mean path SKR relative to A* while requiring substantially fewer network resources than Max--Min. The framework also maintains secure connectivity over a broad range of trusted node availability by exploiting untrusted infrastructure as measurement relays. These results demonstrate that routing in CV-QKD networks can be treated as a configurable trade off between cryptographic performance and network resource consumption.

Abstract:Continuous-variable quantum key distribution (CV-QKD) networks require routing strategies that account simultaneously for secret key generation, relay trust and finite network resources. We introduce Quantum Utility Routing (QUR), a weighted routing framework for networks combining trusted point-to-point links with untrusted CV Bell measurement relays. Transition level composable finite size secret key rates (SKRs) are incorporated directly into the route search, allowing QUR to balance bottleneck SKR against route geometry and resource use. We consider graph optimized QUR (QUR$_{\mathrm{go}}$) and a fixed weight implementation (QUR$_{\mathrm{fw}}$) that uses machine learning to avoid repeated graph specific weight optimization, and benchmark both against A* and Max--Min routing. At the default network configuration, QUR$_{\mathrm{go}}$ and QUR$_{\mathrm{fw}}$ improve the mean path SKR relative to A* while requiring substantially fewer network resources than Max--Min. The framework also maintains secure connectivity over a broad range of trusted node availability by exploiting untrusted infrastructure as measurement relays. These results demonstrate that routing in CV-QKD networks can be treated as a configurable trade off between cryptographic performance and network resource consumption.

arXiv:2610.03382 [quant-ph]

(or arXiv:2610.03382v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2610.03382 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 262 words

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