From Provable to Practical: A Problem-Driven Survey of Classical and Machine-Learning Defenses for DV/CV Quantum Key Distribution
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Quantum Physics arXiv:2605.27497 (quant-ph) [Submitted on 26 May 2026] Title:From Provable to Practical: A Problem-Driven Survey of Classical and Machine-Learning Defenses for DV/CV Quantum Key Distribution Authors:Hasan Abbas Al-Mohammed, Afnan S. Al-Ali View a PDF of the paper titled From Provable to Practical: A Problem-Driven Survey of Classical and Machine-Learning Defenses for DV/CV Quantum Key Distribution, by Hasan Abbas Al-Mohammed and Afnan S. Al-Ali View PDF HTML (experimental) Abstract:Quantum key distribution (QKD) promises information-theoretic security, yet practical deployments in discrete-variable (DV) and continuous-variable (CV) settings remain exposed to device imperfections, channel manipulation, finite-key effects, and vulnerabilities in machine-learning (ML) components used for adaptation and monitoring. This survey adopts a problem-driven perspective based on nine practical problem classes (P1-P9) spanning device, channel, protocol, ML, and network layers. For each class, we compare classical defenses with ML-enabled solutions including anomaly detection, parameter prediction, noise estimation, adversarial purification, and resource allocation. Reported results include DBSCAN-based CV attack detection at P=99.7%, R=99.8%, F1=0.998, adversarial robustness recovery up to 79.5%, channel-amplification detection at 100%/91.26% under low/high-noise conditions, and LightGBM-based noise prediction reducing evaluation time by up to 98.8%. The survey further proposes a benchmarking framework combining datasets, stress protocols, and unified evaluation metrics including SKR impact, maximum distance, latency, and robustness. Finally, we provide defense-in-depth deployment guidelines and outline future research directions for secure and practical QKD systems. Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2605.27497 [quant-ph] (or arXiv:2605.27497v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2605.27497 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Hasan Al-Mohammed Dr [view email] [v1] Tue, 26 May 2026 17:44:51 UTC (842 KB) Full-text links: Access Paper: View a PDF of the paper titled From Provable to Practical: A Problem-Driven Survey of Classical and Machine-Learning Defenses for DV/CV Quantum Key Distribution, by Hasan Abbas Al-Mohammed and Afnan S. Al-AliView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-05 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?) 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?)