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Quantum speed-up of Markov chain based algorithms

机译:基于马尔可夫链的算法的量子加速

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摘要

We develop a generic method for quantizing classical algorithms based on random walks. We show that under certain conditions, the quantum version gives rise to a quadratic speed-up. This is the case, in particular, when the Markov chain is ergodic and its transition matrix is symmetric. This generalizes the celebrated result of L. K. Grover (1996)and a number of more recent results, including the element distinctness result of Ambainis and the result of Ambainis, Kempe and Rivosh that computes properties of quantum walks on the d-dimensional torus. Among the consequences is a faster search for multiple marked items. We show that the quantum escape time, just like its classical version, depends on the spectral properties of the transition matrix with the marked rows and columns deleted.
机译:我们开发了一种基于随机游走量化经典算法的通用方法。我们表明,在某些条件下,量子形式会引起二次加速。当马尔可夫链是遍历遍历且其转移矩阵是对称的时,尤其是这种情况。这概括了L.K.格罗弗(L.K.格罗弗(L.K.后果之一就是更快地搜索多个带标记的项目。我们表明,量子逃逸时间与其经典版本一样,取决于过渡矩阵的光谱特性,其中标记的行和列被删除。

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