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Nearly optimal separations between communication (or query) complexity and partitions

机译:通信(或查询)复杂度与分区之间的最佳分离

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We show a nearly quadratic separation between deterministic communication complexity and the logarithm of the partition number, which is essentially optimal. This improves upon a recent power 1.5 separation of G??s, Pitassi, and Watson (FOCS 2015). In query complexity, we establish a nearly quadratic separation between deterministic (and even randomized) query complexity and subcube partition complexity, which is also essentially optimal. We also establish a nearly power 1.5 separation between quantum query complexity and subcube partition complexity, the first superlinear separation between the two measures. Lastly, we show a quadratic separation between quantum query complexity and one-sided subcube partition complexity.Our query complexity separations use the recent cheat sheet framework of Aaronson, Ben-David, and the author. Our query functions are built up in stages by alternating function composition with the cheat sheet construction. The communication complexity separation follows from lifting the query separation to communication complexity.
机译:我们显示了确定性通信复杂性和分区号的对数之间的几乎二次分离,这实际上是最佳的。这是基于最近的G ?? s,Pitassi和Watson的1.5幂分离(FOCS 2015)而改进的。在查询复杂度方面,我们在确定性(甚至随机化)查询复杂度和子多维数据集分区复杂度之间建立了几乎二次的分离,这实际上也是最佳的。我们还在量子查询复杂度和子多维数据集分区复杂度之间建立了接近幂1.5的分隔,这是两个量度之间的第一个超线性分隔。最后,我们展示了量子查询复杂度与单面子多维数据集分区复杂度之间的二次分离。我们的查询复杂度分离使用了Aaronson,Ben-David和作者的最新备忘单框架。我们的查询功能是通过将功能组成与备忘单结构交替使用来逐步构建的。通信复杂度分离是从将查询分离提升为通信复杂度之后进行的。

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