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Coined Quantum Walks Lift the Cospectrality of Graphs and Trees

机译:被创造的量子散步提升了图形和树木的焦切

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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,我们考虑了相对于标准邻接和拉普拉斯矩阵表示具有C的曲线谱的图表的问题。从量子计算领域借用思路,我们基于被创建量子步行的路径定义了一种新的矩阵。量子散步表现出干扰效应,它们的行为明显不同于古典随机散步。我们表明,这种新矩阵的频谱能够区分许多无法通过标准光谱方法区分的图表。我们特别注意强烈规则的图表;如果一对强烈的常规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COMACETRIC的大家庭中测试了该方法,强烈规则的图表,并发现其在每种情况下都成功。我们还在使用时测试了Spectra的性能,以便为不准确的图形匹配任务提供距离测量值。

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