为硬操作规划问题编程量子退火器的案例研究
A case study in programming a quantum annealer for hard operational planning problems
摘要：
我们报告的一个案例研究编程的早期量子退火攻击优化问题的运营规划。虽然一些研究已经研究了量子退火机在其体系结构固有问题上的性能，其他研究也研究了来自应用领域的选择问题的性能，但我们的研究是量子退火机在实际领域的参数化硬问题家族上的性能的首批研究之一。我们探讨了两种不同的一般映射的规划问题的二次无约束二元优化（QUBO）问题，并将其应用到两个参数化的家庭规划问题，导航型和导航型。我们还研究了两个更紧凑，但问题类型的具体，映射到QUBO，一个导航型规划问题和一个导航型规划问题。我们研究嵌入属性和参数设置，并检查它们的效率与量子退火解决这些问题的效果。从这些结果中，我们得到的见解有用的编程和设计未来的量子退火：问题的选择，使用的映射，嵌入的属性，和退火配置文件都很重要，每个显着影响性能。
英文摘要：
We report on a case study in programming an early quantum annealer to attack optimization problems related to operational planning. While a number of studies have looked at the performance of quantum annealers on problems native to their architecture, and others have examined performance of select problems stemming from an application area, ours is one of the first studies of a quantum annealer's performance on parametrized families of hard problems from a practical domain. We explore two different general mappings of planning problems to quadratic unconstrained binary optimization (QUBO) problems, and apply them to two parametrized families of planning problems, navigation-type and scheduling-type. We also examine two more compact, but problem-type specific, mappings to QUBO, one for the navigation-type planning problems and one for the scheduling-type planning problems. We study embedding properties and parameter setting, and examine their effect on the efficiency with which the quantum annealer solves these problems. From these results we derive insights useful for the programming and design of future quantum annealers: problem choice, the mapping used, the properties of the embedding, and the annealing profile all matter, each significantly affecting the performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
