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arXiv 0908.3066quant-ph
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量子搜索与建议

Quantum search with advice

Ashley Montanaro

摘要:

我们考虑的问题,搜索的非结构化列表中的一个标记的元素,当一个被告知这个元素可能位于何处,在概率分布的形式。目标是最小化针对该分布对列表进行的查找标记元素的查询的预期数量。我们提出了一个量子算法,它解决了这个问题,使用最佳数量的查询,一个常数因子。对于输入上的某些分布,例如某些幂律分布,该算法可以实现指数加速超过最佳可能的经典算法。我们还给出了一个有效的量子算法的一个变种,这个任务的分布是事先不知道的,但必须在一个额外的成本查询。该算法是基于使用Grover的量子搜索算法和振幅放大作为子程序。

英文摘要:

We consider the problem of search of an unstructured list for a marked element, when one is given advice as to where this element might be located, in the form of a probability distribution. The goal is to minimise the expected number of queries to the list made to find the marked element, with respect to this distribution. We present a quantum algorithm which solves this problem using an optimal number of queries, up to a constant factor. For some distributions on the input, such as certain power law distributions, the algorithm can achieve exponential speed-ups over the best possible classical algorithm. We also give an efficient quantum algorithm for a variant of this task where the distribution is not known in advance, but must be queried at an additional cost. The algorithms are based on the use of Grover's quantum search algorithm and amplitude amplification as subroutines.

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。

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