QUEST：量子增强的共享交通
QUEST: QUantum-Enhanced Shared Transportation
AI总结：
提出QUEST量子计算框架，将共享交通中的破风车与冲浪车配对问题转化为QUBO模型，并利用QAOA成功恢复经典算法最优解，为大规模共享交通利用量子优势奠定基础。
摘要：
我们引入了“破风即服务”（WaaS）这一共享交通创新方法，其中较大的“破风车”为“冲浪车”提供空气动力学庇护，从而减少阻力和燃料消耗。作为解决WaaS中产生的大规模匹配与分配问题的计算框架，我们提出了QUEST（量子增强的共享交通）。具体而言，我们将破风车与冲浪车的配对问题——受限于时间、速度和车辆类别约束——表述为混合整数二次问题（MIQP）。针对单段原型，我们通过匈牙利算法、基于Gurobi的求解器以及二元向量的暴力枚举来经典地验证解。随后，我们将该问题编码为二次无约束二元优化（QUBO），并将其映射至Ising哈密顿量，从而支持使用量子近似优化算法（QAOA）及其他量子和经典退火技术。我们的量子实现成功恢复了经典方法识别的最优分配，证实了QUEST流程在受控原型上的可靠性。尽管QAOA等量子启发式方法无法保证突破基本复杂性障碍，但本研究展示了如何将WaaS问题系统转化为量子就绪模型。它也为处理多段场景及在大型共享交通实例中利用量子优势奠定了基础。
英文摘要：
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 fuel consumption. As a computational framework to solve the large-scale matching and assignment problems that arise in WaaS, we present \textbf{QUEST} (Quantum-Enhanced Shared Transportation). Specifically, we formulate the pairing 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, confirming the soundness 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 quantum-ready model. It also lays the groundwork for addressing multi-segment scenarios and potentially leveraging quantum advantage for large-scale shared-transportation instances.
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