UAV-Deployed OAM-BB84 QKD: Turbulence- and Misalignment-Resilient Decoy-State Finite-Key Security with AI-Assisted Calibration
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Quantum Physics arXiv:2601.11117 (quant-ph) [Submitted on 16 Jan 2026] Title:UAV-Deployed OAM-BB84 QKD: Turbulence- and Misalignment-Resilient Decoy-State Finite-Key Security with AI-Assisted Calibration Authors:Linxier Deng View a PDF of the paper titled UAV-Deployed OAM-BB84 QKD: Turbulence- and Misalignment-Resilient Decoy-State Finite-Key Security with AI-Assisted Calibration, by Linxier Deng View PDF HTML (experimental) Abstract:We present a theoretical framework for quantum key distribution (QKD) using orbital angular momentum (OAM) encoded BB84 on an unmanned aerial vehicle (UAV) platform. A unified channel model captures Kolmogorov turbulence, pointing induced misalignment, and finite aperture clipping, enabling quantitative predictions of inter mode crosstalk and the resulting quantum bit error rate (QBER). Using a weak plus vacuum decoy state formulation, we derive composable finite key lower bounds on the secret key rate that incorporate statistical fluctuations, detector dark counts, efficiency mismatch, and error correction leakage. To stabilize performance under non stationary flight conditions, we introduce a lightweight physics informed learning module that combines physical priors with measured link statistics to classify valid pulses, reject corrupted data, and recommend decoding strategies. We outline a complete evaluation pipeline including UAV system architecture, turbulence driven QBER maps, decoy optimization, finite key scaling, and AI calibration metrics. Simulations indicate that under moderate turbulence and milliradian level pointing jitter, the proposed AI assisted method can improve the secret key rate by 10 percent to 30 percent while preserving composable security. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2601.11117 [quant-ph] (or arXiv:2601.11117v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2601.11117 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Linxier Deng [view email] [v1] Fri, 16 Jan 2026 09:23:33 UTC (2,165 KB) Full-text links: Access Paper: View a PDF of the paper titled UAV-Deployed OAM-BB84 QKD: Turbulence- and Misalignment-Resilient Decoy-State Finite-Key Security with AI-Assisted Calibration, by Linxier DengView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-01 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?)