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Coined Quantum Walks Lift the Cospectrality of Graphs and Trees

机译:量子量子漫步提升了图和树的共谱性

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In this paper we consider the problem of distinguishing graphs that are cospectral with respect to the standard adjacency and Laplacian matrix representations. Borrowing ideas from the field of quantum computing, we define a new matrix based on paths of the coined quantum walk. Quantum walks exhibit interference effects and their behaviour is markedly different to that of classical random walks. We show that the spectrum of this new matrix is able to distinguish many graphs which cannot be distinguished by standard spectral methods. We pay particular attention to strongly regular graphs; if a pair of strongly regular graphs share the same parameter set then there is no efficient algorithm that is proven to be able distinguish them. We have tested the method on large families of co-parametric strongly regular graphs and found it to be successful in every case. We have also tested the spectra's performance when used to give a distance measure for inexact graph matching tasks.
机译:在本文中,我们考虑了区分与标准邻接关系和Laplacian矩阵表示相关的图的问题。借用量子计算领域的思想,我们基于造币的量子游动路径定义了一个新矩阵。量子步态具有干扰效应,其行为与经典随机步态明显不同。我们表明,这种新矩阵的光谱能够区分许多标准光谱方法无法分辨的图形。我们特别注意强正则图;如果一对强正则图共享相同的参数集,则没有有效的算法被证明能够区分它们。我们已经在大系列的参数化强正则图上测试了该方法,发现它在每种情况下都是成功的。我们还测试了光谱的性能,该光谱用于为不精确的图形匹配任务提供距离度量。

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