Reducing QUBO Complexity in Drone Delivery Through Classical Feasibility Preprocessing

Quantum Physics arXiv:2610.21003 (quant-ph) [Submitted on 14 Oct 2026] Title:Reducing QUBO Complexity in Drone Delivery Through Classical Feasibility Preprocessing Authors:Silvie Illésová, Vojtěch Novák, Martin Beseda View a PDF of the paper titled Reducing QUBO Complexity in Drone Delivery Through Classical Feasibility Preprocessing, by Silvie Ill\'esov\'a and Vojt\v{e}ch Nov\'ak and Martin Beseda View PDF HTML (experimental) Abstract:Drone-delivery planning combines routing decisions with operational constraints such as payload capacity and limited onboard energy, resulting in combinatorial optimization problems that can become costly to encode directly in binary form. This work investigates a hybrid quantum--classical approach in which candidate drone missions are generated and evaluated classically before constructing a quadratic unconstrained binary optimization (QUBO) model for mission selection. Payload- and energy-infeasible missions are removed during preprocessing, while the reduced QUBO is solved using exact enumeration, simulated annealing (SA), and the Quantum Approximate Optimization Algorithm (QAOA), with mixed-integer linear programming (MILP) used as a classical reference. Experiments on controlled synthetic delivery instances evaluate solution quality, the effect of preprocessing, and computational scaling. Exact QUBO optimization reproduces the MILP optimum on the evaluated small instances, while SA achieves $100\%$ feasibility with a median optimality gap of $0\%$. Combined payload-and-energy preprocessing reduces the mean number of QUBO variables by $29.60\%$ and quadratic interactions by $49.20\%$, while reducing mean SA runtime by $31.26\%$. In contrast, the evaluated shallow statevector-based QAOA configuration achieves lower feasibility and is restricted to small QUBO instances. These results indicate that, in the considered setting, the main practical benefit of the hybrid architecture comes from classical feasibility preprocessing that reduces the binary search space before QUBO-based optimization. Subjects: Quantum Physics (quant-ph); Emerging Technologies (cs.ET) Cite as: arXiv:2610.21003 [quant-ph] (or arXiv:2610.21003v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2610.21003 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Martin Beseda [view email] [v1] Wed, 14 Oct 2026 22:53:39 UTC (76 KB) Full-text links: Access Paper: View a PDF of the paper titled Reducing QUBO Complexity in Drone Delivery Through Classical Feasibility Preprocessing, by Silvie Ill\'esov\'a and Vojt\v{e}ch Nov\'ak and Martin BesedaView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-10 Change to browse by: cs cs.ET 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?)