论文标题
使用混合限制的量子退火优化测试车辆的生产
Optimizing the Production of Test Vehicles using Hybrid Constrained Quantum Annealing
论文作者
论文摘要
优化预生产车辆配置是汽车行业的挑战之一。给定一系列需要具有某些功能的汽车的测试列表，希望找到涵盖测试的最小汽车数量并遵守配置规则。在本文中，我们在满意度框架中对问题进行了建模，并通过使用D-Wave提供的新引入的混合限制性二次模型（CQM）求解器来解决它。问题定义基于宝马量子计算挑战中给出的“优化测试车辆的生产”。我们为问题制定了约束的二次模型，并使用贪婪的算法来配置汽车。我们基准从CQM求解器获得的结果，其结果来自经典求解器（例如CBC（Coin-Or Branch and Cut）和Gurobi）和Gurobi。我们得出的结论是，CQM求解器的性能可与经典求解器优化测试车辆的数量相当。作为问题的扩展，我们描述了如何将测试的调度纳入模型。
Optimization of pre-production vehicle configurations is one of the challenges in the automotive industry. Given a list of tests requiring cars with certain features, it is desirable to find the minimum number of cars that cover the tests and obey the configuration rules. In this paper, we model the problem in the framework of satisfiability and solve it by utilizing the newly introduced hybrid constrained quadratic model (CQM) solver provided by D-Wave. The problem definition is based on the "Optimizing the Production of Test Vehicles" use case given in the BMW Quantum Computing Challenge. We formulate a constrained quadratic model for the problem and use a greedy algorithm to configure the cars. We benchmark the results obtained from the CQM solver with the results from the classical solvers like CBC (Coin-or branch and cut) and Gurobi. We conclude that the performance of the CQM solver is comparable to classical solvers in optimizing the number of test vehicles. As an extension to the problem, we describe how the scheduling of the tests can be incorporated into the model.
