Adaptive Resource and Memory Control for Stability in Quantum Entanglement Distribution
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Quantum Physics arXiv:2603.24874 (quant-ph) [Submitted on 25 Mar 2026] Title:Adaptive Resource and Memory Control for Stability in Quantum Entanglement Distribution Authors:Nicolò Lo Piparo, William J. Munro, Kae Nemoto View a PDF of the paper titled Adaptive Resource and Memory Control for Stability in Quantum Entanglement Distribution, by Nicol\`o Lo Piparo and 1 other authors View PDF HTML (experimental) Abstract:We investigate congestion-aware control of quantum repeater nodes operating under stochastic traffic and finite memory coherence. Entanglement generation is modeled as a probabilistic process producing Werner states subject to depolarizing memory decoherence, while entanglement requests arrive according to Poisson and bursty ON--OFF processes. Using a queueing-theoretic framework, we couple physical-layer memory dynamics with congestion-dependent service behavior to analyze stability, delay, and fidelity trade-offs. Operating regimes are characterized in terms of the load parameter, showing that fixed cutoff policies impose a fundamental fidelity--latency trade-off together with strict stability limits. Queue-aware adaptive control strategies are then introduced that dynamically adjust memory cutoff times and the number of parallel entanglement-generation channels. Cutoff adaptation restores stability near critical load by trading fidelity for service capacity, whereas resource scaling increases capacity without degrading entanglement quality. Under bursty traffic, joint adaptation suppresses delay spikes while activating additional channels only during congestion periods. The framework is further extended to a two-user shared-resource scenario in which independent traffic flows compete for a common resource pool. Stability is determined by aggregate load, while adaptive resource redistribution stabilizes queues that diverge under fixed partitioning. These results provide a queue-aware congestion-control perspective for adaptive resource management in quantum networks. Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2603.24874 [quant-ph] (or arXiv:2603.24874v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2603.24874 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Nicolò Lo Piparo [view email] [v1] Wed, 25 Mar 2026 23:36:38 UTC (2,794 KB) Full-text links: Access Paper: View a PDF of the paper titled Adaptive Resource and Memory Control for Stability in Quantum Entanglement Distribution, by Nicol\`o Lo Piparo and 1 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-03 References & Citations INSPIRE HEP NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) Links to Code Toggle Papers with Code (What is Papers with Code?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)