AI Optimized Routing and Resource Allocation for Quantum Enabled Non Terrestrial Industrial Networks
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Quantum Physics arXiv:2601.20877 (quant-ph) [Submitted on 21 Jan 2026] Title:AI Optimized Routing and Resource Allocation for Quantum Enabled Non Terrestrial Industrial Networks Authors:Sathish Krishna Anumula, Harinatha Reddy Chennam, Ranganath Nagesh Taware, Balakumar Ravindranath Kunthu View a PDF of the paper titled AI Optimized Routing and Resource Allocation for Quantum Enabled Non Terrestrial Industrial Networks, by Sathish Krishna Anumula and 3 other authors View PDF Abstract:The industrial transformation of Industry 4 and 5 results in cyber physical production systems that require secure resilient and energy efficient connectivity over integrated terrestrial and nonterrestrial networks NTNs Since its operation over fiber spans over 5G or 6G infrastructures to Low Earth Orbit LEO satellites quantum communication techniques enabled by Quantum Key Distribution QKD together with entanglement assisted links have the potential for high assurance security as well as synchronization But quantum channels are extremely vulnerable to any kind of impairment be it environmental or physicalsuch as effects induced by atmospheric turbulence pointing errors Doppler shifts satellite motion restricted optical power and limited quantum memory All these factors make for a tightly coupled routing and resource allocation problem that unfortunately cannot be addressed at scale by existing approaches to network control. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2601.20877 [quant-ph] (or arXiv:2601.20877v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2601.20877 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Sathish Anumula [view email] [v1] Wed, 21 Jan 2026 14:11:27 UTC (707 KB) Full-text links: Access Paper: View a PDF of the paper titled AI Optimized Routing and Resource Allocation for Quantum Enabled Non Terrestrial Industrial Networks, by Sathish Krishna Anumula and 3 other authorsView PDF 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?)