论文标题
使用量子近似优化算法解决车辆路由问题
Solving Vehicle Routing Problem Using Quantum Approximate Optimization Algorithm
论文作者
论文摘要
在本文中，我们描述了量子近似优化算法（QAOA）的用法，该算法是一种量子古典启发式，以解决合并优化和整数编程任务，称为车辆路由问题（VRP）。我们概述了VRP的ISING公式，并提出了一个详细的程序来解决VRP，通过使用IBM Qiskit平台最大程度地减少其模拟的Ising Hamiltonian。在这里，我们尝试找到有关VRP问题的解决方案：（4,2），（5,2）和（5,3），其中每个位置和K车辆都代表VRP问题。我们发现，QAOA的性能不仅取决于所使用的经典优化器，实现绝热路径的步骤P的数量，或者初始化参数的方式，还取决于问题实例本身。
In this paper, we describe the usage of the Quantum Approximate Optimization Algorithm (QAOA), which is a quantum-classical heuristic, to solve a combinatorial optimization and integer programming task known as Vehicle Routing Problem (VRP). We outline the Ising formulation for VRP and present a detailed procedure to solve VRP by minimizing its simulated Ising Hamiltonian using the IBM Qiskit platform. Here, we attempt to find solutions for the VRP problems: (4,2), (5,2), and (5,3), where each (n, k) represents a VRP problem with n locations and k vehicles. We find that the performance of QAOA is not just dependent upon the classical optimizer used, the number of steps p in which an adiabatic path is realized, or the way parameters are initialized, but also on the problem instance itself.
