001051617 001__ 1051617 001051617 005__ 20260116204431.0 001051617 0247_ $$2doi$$a10.1109/QCE65121.2025.00235 001051617 037__ $$aFZJ-2026-00539 001051617 1001_ $$0P:(DE-HGF)0$$aOnah, Chinonso$$b0 001051617 1112_ $$a2025 IEEE International Conference on Quantum Computing and Engineering (QCE)$$cAlbuquerque$$d2025-08-30 - 2025-09-05$$wNM 001051617 245__ $$aQUEST: QUantum-Enhanced Shared Transportation 001051617 260__ $$aAlbuquerque, NM, USA$$bIEEE$$c2025 001051617 300__ $$a2149 - 2160 001051617 3367_ $$2ORCID$$aCONFERENCE_PAPER 001051617 3367_ $$033$$2EndNote$$aConference Paper 001051617 3367_ $$2BibTeX$$aINPROCEEDINGS 001051617 3367_ $$2DRIVER$$aconferenceObject 001051617 3367_ $$2DataCite$$aOutput Types/Conference Paper 001051617 3367_ $$0PUB:(DE-HGF)8$$2PUB:(DE-HGF)$$aContribution to a conference proceedings$$bcontrib$$mcontrib$$s1768573657_26728 001051617 520__ $$aWe introduce “Windbreaking-as-a-Service” (WaaS) as an innovative approach to shared transportation in which larger “windbreaker” vehicles provide aerodynamic shelter for “windsurfer” vehicles, thereby reducing drag and energy consumption. As a computational framework to solve the largescale matching and assignment problems that arise in WaaS, we present QUEST (Quantum-Enhanced Shared Transportation). Specifically, wef ormulate t he p airing of windbreakers and windsurfers - subject to timing, speed, and vehicle-class constraints - as a mixed-integer quadratic problem (MIQP). Focusing on a single-segment prototype, we verify the solution classically via the Hungarian Algorithm, a Gurobi-based solver, and brute-force enumeration of binary vectors. We then encode the problem as a Quadratic Unconstrained Binary Optimization (QUBO) and map it to an Ising Hamiltonian, enabling the use of the Quantum Approximate Optimization Algorithm (QAOA) and other quantum and classical annealing technologies. Our quantum implementation successfully recovers the optimal assignment identified by the classical methods, c onfirming the so undness of the QUEST pipeline for a controlled prototype. While QAOA and other quantum heuristics do not guarantee a resolution of the fundamental complexity barriers, this study illustrates how the WaaS problem can be systematically translated into a quantumready model. It also lays the groundwork for addressing multisegment scenarios and potentially leveraging quantum advantage for large-scale shared-transportation instances. 001051617 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0 001051617 588__ $$aDataset connected to CrossRef Conference 001051617 7001_ $$0P:(DE-HGF)0$$aMisciasci, Neel$$b1 001051617 7001_ $$0P:(DE-HGF)0$$aOthmer, Carsten$$b2 001051617 7001_ $$0P:(DE-Juel1)138295$$aMichielsen, Kristel$$b3$$ufzj 001051617 770__ $$z979-8-3315-5736-2 001051617 773__ $$a10.1109/QCE65121.2025.00235 001051617 8564_ $$uhttps://juser.fz-juelich.de/record/1051617/files/QUEST_QUantum-Enhanced_Shared_Transportation.pdf$$yRestricted 001051617 909CO $$ooai:juser.fz-juelich.de:1051617$$pVDB 001051617 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)138295$$aForschungszentrum Jülich$$b3$$kFZJ 001051617 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5111$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x0 001051617 920__ $$lyes 001051617 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0 001051617 980__ $$acontrib 001051617 980__ $$aVDB 001051617 980__ $$aI:(DE-Juel1)JSC-20090406 001051617 980__ $$aUNRESTRICTED