QUACOD: Quantum Optimization via Coordinate Descent for Scalable Drone Scheduling
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Quantum Physics arXiv:2605.14001 (quant-ph) [Submitted on 13 May 2026] Title:QUACOD: Quantum Optimization via Coordinate Descent for Scalable Drone Scheduling Authors:Van-Quang-Huy Nguyen, Hoang-Quan Nguyen, Samee U. Khan, Ilya Safro, Khoa Luu View a PDF of the paper titled QUACOD: Quantum Optimization via Coordinate Descent for Scalable Drone Scheduling, by Van-Quang-Huy Nguyen and 4 other authors View PDF HTML (experimental) Abstract:Quantum computing has demonstrated its potential to solve various optimization problems, including drone scheduling, which is important not only for drone delivery but also for logistics in general. However, one of the main obstacles is that practical drone scheduling settings typically require quantum resources that current hardware cannot provide. Therefore, in this work, we introduce a new Quantum Optimization via Coordinate Descent (QUACOD) approach to address this problem under the constraint of a limited number of available qubits. By leveraging coordinate descent, QUACOD decomposes the original high-complexity problem into multiple subproblems, which are then solved using quantum optimization. In our experiments, QUACOD outperforms the state-of-the-art (SOTA) quantum-based drone scheduling method not only in optimized drone completion times but also in scalability, handling up to 5 times more drones and 35 times more routes. In addition, QUACOD demonstrates that hardware-efficient circuits are effective for optimization problems. Together, these contributions advance quantum computing toward practical applications in the noisy intermediate-scale quantum (NISQ) era. Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2605.14001 [quant-ph] (or arXiv:2605.14001v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2605.14001 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Van Quang Huy Nguyen [view email] [v1] Wed, 13 May 2026 18:08:52 UTC (4,098 KB) Full-text links: Access Paper: View a PDF of the paper titled QUACOD: Quantum Optimization via Coordinate Descent for Scalable Drone Scheduling, by Van-Quang-Huy Nguyen and 4 other authorsView 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?)