Study on Quantum Approximation Optimization Algorithm in Airport Cargo Transportation Problem

Abstract

The vehicle routing problem (VRP) is a core NP-hard combinatorial optimization problem in logistics and supply chain management. Quantum computing, particularly the Quantum Approximate Optimization Algorithm (QAOA), is being explored as a promising heuristic tool for tackling such problems. Therefore, this study employs the QAOA approach to solve the airport cargo VRP. Subsequently, through a concrete airport cargo case study, the paper analyzes the process of solving single-vehicle TSP and multivehicle VRP problems using QAOA. It compares the performance of different classical optimizers and discusses the impact of parameter settings (e.g., penalty factors, QAOA depth, and number of iterations) on the solution quality and feasibility, especially comparing the convergence speed of dynamic and fixed penalty coefficients. The paper also describes the design principles of QAOA quantum circuits for such VRP problems. Furthermore, the study presents a preliminary benchmark on a real quantum computer, demonstrating that its computation time for the problem’s QUBO matrix (14 ms) is approximately comparable to that of classical algorithms. Finally, it summarizes the current scalability challenges and limitations faced by QAOA in VRP applications concerning qubit count, circuit depth, and classical optimizer performance, and offers an outlook on future development directions. The research indicates that although QAOA demonstrates solution capabilities on specific small-scale VRP instances, the complexity of parameter tuning and sensitivity to the spatial distribution of aircraft stands are critical considerations before practical application.