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Toward Quantum-Optimized Flow Scheduling in Multi-Beam Digital Satellites

arXiv Quantum Physics
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⚡ Quantum Brief
Researchers from Michigan Tech and NASA propose a hybrid quantum-classical solution to optimize data flow scheduling in next-gen multi-beam satellites, addressing a computationally intractable NP-hard problem that stymies classical methods. The team reformulates satellite slot assignment as a QUBO problem, embedding throughput maximization and operational constraints into a quantum-friendly framework via parameter rescaling to maintain computational feasibility. A novel layer-wise training approach mitigates common quantum optimization pitfalls like barren plateaus, progressively deepening circuits while refining solutions to escape rugged loss landscapes in variational algorithms. Benchmark tests on quantum hardware using simulated satellite traffic show improved solution quality and runtime compared to classical Mixed-Integer Linear Programming and heuristic schedulers. The study marks a critical step toward real-world quantum advantage in satellite communications, demonstrating robustness under realistic workload conditions while balancing optimization quality and operational speed.
Toward Quantum-Optimized Flow Scheduling in Multi-Beam Digital Satellites

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Quantum Physics arXiv:2603.00701 (quant-ph) [Submitted on 28 Feb 2026] Title:Toward Quantum-Optimized Flow Scheduling in Multi-Beam Digital Satellites Authors:Qiben Yan, John P. T. Stenger, Daniel Gunlycke View a PDF of the paper titled Toward Quantum-Optimized Flow Scheduling in Multi-Beam Digital Satellites, by Qiben Yan and 2 other authors View PDF HTML (experimental) Abstract:Data flow scheduling for high-throughput multibeam satellites is a challenging NP-hard combinatorial optimization problem. As the problem scales, traditional methods, such as Mixed-Integer Linear Programming and heuristic schedulers, often face a trade-off between solution quality and real-time feasibility. In this paper, we present a hybrid quantum-classical framework that improves scheduling efficiency by casting Multi-Beam Time-Frequency Slot Assignment (MB-TFSA) as a Quadratic Unconstrained Binary Optimization (QUBO) problem. We incorporate the throughput-maximization objective and operational constraints into a compact QUBO via parameter rescaling to keep the formulation tractable. To address optimization challenges in variational quantum algorithms, such as barren plateaus and rugged loss landscapes, we introduce a layer-wise training strategy that gradually increases circuit depth while iteratively refining the solution. We evaluate solution quality, runtime, and robustness on quantum hardware, and benchmark against classical and hybrid baselines using realistic, simulated satellite traffic workloads. Subjects: Quantum Physics (quant-ph); Emerging Technologies (cs.ET); Networking and Internet Architecture (cs.NI) ACM classes: C.2.1; C.2.3; F.1.1 Cite as: arXiv:2603.00701 [quant-ph] (or arXiv:2603.00701v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2603.00701 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Qiben Yan [view email] [v1] Sat, 28 Feb 2026 15:21:38 UTC (1,270 KB) Full-text links: Access Paper: View a PDF of the paper titled Toward Quantum-Optimized Flow Scheduling in Multi-Beam Digital Satellites, by Qiben Yan and 2 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-03 Change to browse by: cs cs.ET cs.NI 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?)

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quantum-machine-learning
quantum-optimization
quantum-algorithms
quantum-hardware

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Source: arXiv Quantum Physics