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Single-step quantum search using problem structure

机译:使用问题结构的单步量子搜索

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The structure of satisfiability problems is used to improve search algorithms for quantum computers and to reduce their required coherence times by using only a single coherent evaluation of problem properties. The structure of random k-SAT allows the determination of the asymptotic average behavior of these algorithms, showing they improve on the quantum algorithms, such as amplitude amplification, that ignore detailed problem structure but remain exponential for hard problem instances. Compared to good classical methods, the algorithm performs better, on average, for weakly and highly constrained problems, but worse for hard cases. The analytic techniques introduced here apply also to other quantum algorithms, supplementing the limited evaluation possible with classical simulations, and showing how quantum computing can use the ensemble properties of NP search problems. [References: 52]
机译:可满足性问题的结构用于改进量子计算机的搜索算法,并通过仅使用问题属性的单个相干评估来减少其所需的相干时间。随机k-SAT的结构允许确定这些算法的渐近平均行为,表明它们在诸如幅度放大之类的量子算法上有所改进,该算法忽略了详细的问题结构,但在困难问题实例中仍保持指数级。与优良的经典方法相比,该算法在弱和高度约束的问题上平均表现更好,而在困难情况下则表现较差。这里介绍的分析技术也适用于其他量子算法,以经典的模拟方法补充了有限的评估,并展示了量子计算如何利用NP搜索问题的整体性质。 [参考:52]

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