QUEST: QUantum-Enhanced Shared Transportation

Abstract

We 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.