论文标题
在存在嘈杂通道的情况下通过混合量子算法解决的车辆路由问题的分析
Analysis of The Vehicle Routing Problem Solved via Hybrid Quantum Algorithms in Presence of Noisy Channels
论文作者
论文摘要
车辆路由问题（VRP）是NP坚硬的优化问题，在科学和工业中一直是研究的兴趣。目的是规划车辆路线，以最佳的效率将货物运送到固定数量的客户。经典工具和方法提供了良好的近似值，以达到最佳的全局解决方案。量子计算和量子机器学习提供了一种新方法来解决由于量子效应的固有加速而更快地解决问题的组合优化。使用混合算法（例如量子近似优化算法和二次无约束的二进制优化），在不同的量子计算平台上提供了许多VRP解决方案。在这项工作中，我们使用固定的Ansatz上的变异量子质量器为3和4个城市构建了一个基本的VRP求解器。在几个嘈杂的量子通道的示例中，进一步扩展了工作以评估溶液的鲁棒性。我们发现，量子算法的性能在很大程度上取决于使用了哪些噪声模型。通常，噪声是有害的，但在不同的噪声源之间并非如此。
The vehicle routing problem (VRP) is an NP-hard optimization problem that has been an interest of research for decades in science and industry. The objective is to plan routes of vehicles to deliver goods to a fixed number of customers with optimal efficiency. Classical tools and methods provide good approximations to reach the optimal global solution. Quantum computing and quantum machine learning provide a new approach to solving combinatorial optimization of problems faster due to inherent speedups of quantum effects. Many solutions of VRP are offered across different quantum computing platforms using hybrid algorithms such as quantum approximate optimization algorithm and quadratic unconstrained binary optimization. In this work, we build a basic VRP solver for 3 and 4 cities using the variational quantum eigensolver on a fixed ansatz. The work is further extended to evaluate the robustness of the solution in several examples of noisy quantum channels. We find that the performance of the quantum algorithm depends heavily on what noise model is used. In general, noise is detrimental, but not equally so among different noise sources.
