Quantum-Inspired Hamiltonian Optimization, Stochastic Tensor Networks and Adaptive Congestion Routing for Large-Scale QKD Networks
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Quantum Physics arXiv:2605.27425 (quant-ph) [Submitted on 19 May 2026] Title:Quantum-Inspired Hamiltonian Optimization, Stochastic Tensor Networks and Adaptive Congestion Routing for Large-Scale QKD Networks Authors:Jose Luis Rosales View a PDF of the paper titled Quantum-Inspired Hamiltonian Optimization, Stochastic Tensor Networks and Adaptive Congestion Routing for Large-Scale QKD Networks, by Jose Luis Rosales View PDF HTML (experimental) Abstract:Quantum Key Distribution (QKD) networks require routing methodologies capable of jointly optimizing latency, secret key generation rate, congestion, finite capacity and operational security constraints under dynamically evolving traffic conditions. In this work we introduce a quantum-inspired optimization framework for adaptive multi-demand routing in QKD communication networks based on effective Hamiltonian modelling, Quantum Monte Carlo inspired annealing and stochastic Tensor-Network State (TNS) compression. The communication network is represented as a stochastic interacting graph whose routing configurations evolve under an effective Hamiltonian containing latency, keyrate, congestion, risk and capacity terms. The resulting optimization landscape is explored through two complementary approaches: a stochastic Metropolis annealer based on incremental local Hamiltonian updates, and a stochastic boundary-MPS tensor-network approximation that compresses the low-energy routing sector through thermal branch selection. The resulting framework establishes a scalable bridge between QKD network orchestration, statistical-physics-inspired optimization, tensor-network compression and future quantum-native routing systems. Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2605.27425 [quant-ph] (or arXiv:2605.27425v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2605.27425 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Jose Luis Rosales B. [view email] [v1] Tue, 19 May 2026 22:05:10 UTC (19 KB) Full-text links: Access Paper: View a PDF of the paper titled Quantum-Inspired Hamiltonian Optimization, Stochastic Tensor Networks and Adaptive Congestion Routing for Large-Scale QKD Networks, by Jose Luis RosalesView 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?)