探讨量子退火炉的退火过程

Elijah Pelofske, Georg Hahn, H. Djidjev
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引用次数: 5

摘要

商用绝热量子退火炉有潜力有效地解决重要的NP-hard优化问题。最新一代的机器还允许用户自定义退火计划,即退火开始到结束时退火分数变化的计划。在这项工作中,我们使用上述D-Wave 2000Q的功能来试图监控退火过程中退火解决方案的演变。这个过程我们称之为切片:在退火过程中的每个时间切片中,我们都能够获得退火解的近似分布。我们使用我们的技术来获得对D-Wave 2000Q的各种见解。例如,我们观察到在退火过程中单个比特何时翻转,何时稳定,这使我们能够单独确定每个量子位的冰点。我们使用随机QUBO(二次无约束二进制优化)实例和为了更好地可视化,我们特别优化的实例(使用我们自己的遗传算法)来突出我们的结果,以在退火过程中展示其解决方案的显着进化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Peering Into the Anneal Process of a Quantum Annealer
Commercial adiabatic quantum annealers have the potential to solve important NP-hard optimization problems efficiently. The newest generation of those machines additionally allows the user to customize the anneal schedule, that is, the schedule with which the anneal fraction is changed from the start to the end of the annealing. In this work we use the aforementioned feature of the D-Wave 2000Q to attempt to monitor how the anneal solution evolves during the anneal process. This process we call slicing: at each time slice during the anneal, we are able to obtain an approximate distribution of anneal solutions. We use our technique to obtain a variety of insights into the D-Wave 2000Q. For example, we observe when individual bits flip during the anneal process and when they stabilize, which allows us to determine the freeze-out point for each qubit individually. We highlight our results using both random QUBO (quadratic unconstrained binary optimization) instances and, for better visualization, instances which we specifically optimize (using our own genetic algorithm) to exhibit a pronounced evolution of its solution during the anneal.
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