Tang Liang, Yang Chao, Wen Kai, Wu Wei, Guo Yiyun
College of Transportation Engineering, Dalian Maritime University, Dalian, China.
Beijing QBoson Quantum Technology Co., Ltd, Beijing, China.
Sci Rep. 2024 May 28;14(1):12205. doi: 10.1038/s41598-024-62821-6.
Due to the high degree of automation, automated guided vehicles (AGVs) have been widely used in many scenarios for transportation, and traditional computing power is stretched in large-scale AGV scheduling. In recent years, quantum computing has shown incomparable performance advantages in solving specific problems, especially Combinatorial optimization problem. In this paper, quantum computing technology is introduced into the study of the AGV scheduling problem. Additionally two types of quadratic unconstrained binary optimisation (QUBO) models suitable for different scheduling objectives are constructed, and the scheduling scheme is coded into the ground state of Hamiltonian operator, and the problem is solved by using optical coherent Ising machine (CIM). The experimental results show that compared with the traditional calculation method, the optical quantum computer can save 92% computation time on average. It has great application potential.
由于高度自动化，自动导引车（AGV）已在许多场景中广泛用于运输，而传统计算能力在大规模AGV调度中捉襟见肘。近年来，量子计算在解决特定问题，尤其是组合优化问题方面展现出无与伦比的性能优势。本文将量子计算技术引入AGV调度问题的研究中。此外，构建了两种适用于不同调度目标的二次无约束二进制优化（QUBO）模型，并将调度方案编码到哈密顿算子的基态中，通过光学相干伊辛机（CIM）解决该问题。实验结果表明，与传统计算方法相比，光学量子计算机平均可节省92%的计算时间。它具有巨大的应用潜力。
