量子实用公路上的里程碑:量子退火案例研究

Catherine C. McGeoch, Pau Farré
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引用次数: 1

摘要

我们介绍了量子效用,这是一种评估量子性能的新方法,旨在通过考虑与量子计算相关的开销成本来捕捉用户体验。量子处理单元(QPU)的量子效用演示表明,当考虑计算开销成本时,QPU可以在从业者感兴趣的某些任务上优于经典求解器。里程碑是对量子效用的测试,它被限制在开销成本和输入类型的特定子集中。我们通过对基于D-Wave退火的QPU与七个经典求解器的基准研究来说明这种方法,用于启发式优化中的各种问题。我们考虑了在独立使用D-Wave QPU时产生的开销成本(与混合计算相反)。我们在通往大规模量子应用的道路上定义了三个早期里程碑。里程碑0是没有开销成本的纯量子计算,并且在其他里程碑上得到了隐含的积极结果。我们根据里程碑1和里程碑2评估了D-Wave Advantage QPU的性能:对于里程碑1,在99%的测试中,QPU的性能优于所有经典求解器。对于里程碑2,在19%的测试中,QPU的性能优于所有经典解算器,并且QPU成功的场景对应于经典解算器最常失败的情况。这种分离开销子集进行单独分析的方法揭示了量子性能与经典性能的不同机制,这解释了观察到的成功和失败模式的差异。我们提出了基于证据的论点,这些区别预示着退火量子处理器在不久的将来将在不断扩大的输入类别和更具挑战性的里程碑上支持量子效用的演示。
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Milestones on the Quantum Utility Highway: Quantum Annealing Case Study
We introduce quantum utility , a new approach to evaluating quantum performance that aims to capture the user experience by considering the overhead costs associated with a quantum computation. A demonstration of quantum utility by the quantum processing unit (QPU) shows that the QPU can outperform classical solvers at some tasks of interest to practitioners, when considering the costs of computational overheads. A milestone is a test of quantum utility that is restricted to a specific subset of overhead costs and input types. We illustrate this approach with a benchmark study of a D-Wave annealing-based QPU versus seven classical solvers, for a variety of problems in heuristic optimization. We consider overhead costs that arise in standalone use of the D-Wave QPU (as opposed to a hybrid computation). We define three early milestones on the path to broad-scale quantum utility. Milestone 0 is the purely quantum computation with no overhead costs, and is demonstrated implicitly by positive results on other milestones. We evaluate performance of a D-Wave Advantage QPU with respect to milestones 1 and 2: For milestone 1, the QPU outperformed all classical solvers in 99% of our tests. For milestone 2, the QPU outperformed all classical solvers in 19% of our tests, and the scenarios in which the QPU found success correspond to cases where classical solvers most frequently failed. This approach isolating subsets of overheads for separate analysis reveals distinct mechanisms in quantum versus classical performance, which explain the observed differences in patterns of success and failure. We present evidence-based arguments that these distinctions bode well for annealing quantum processors to support demonstrations of quantum utility on ever-expanding classes of inputs and with more challenging milestones, in the very near future.
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