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arXiv:1910.12536 · Quantum Information Processing

Quantum walks defined by digraphs and generalized Hermitian adjacency matrices

Sho Kubota, Etsuo Segawa, Tetsuji Taniguchi

Published Mar 1, 2021• 16 citations
arXivHTMLPDF
DOI
10.1007/s11128-021-03033-z

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Abstract. In this note, we give a characterization of the adjacency matrix of the line digraph of a regular digraph and we discuss a generalization.On the light of the characterization given, we remark that the underlying digraph of a coined quantum random walk is the line digraph of a regular digraph. 1. A characterization of the adjacency matrix of the line digraph of a regular digraph 1.1. Set-up. The notion of line digraph has been introduced by Harary and Norman [HN60] in 1960. A classic survey on line graphs and digraphs is [HB78]; a recent one is [P95]. Line digraphs are used in the design and analysis of interconnection networks (see e.g. [FYA84]). Line digraphs are also used in algorithms for DIRECTED MAX-CUT [CE90] and TRAVELLING SALESMAN [GKWS98]. Definition 1 (Line digraph). The line digraph − → LD of a digraph ( D = (V, A) is the
We consider a discrete-time quantum walk, called the Grover walk, on a distance regular graph XXX. Given that XXX has diameter ddd and invertible adjacency matrix, we show that the square of the transition matrix of the Grover walk on XXX is a product of at most ddd commuting transition matrices of continuous-time quantum walks, each on some distance digraph of the line digraph of XXX. We also obtain a similar factorization for any graph XXX in a Bose Mesner algebra.
We consider the continuous-time quantum walk defined on the adjacency matrix of a graph. At each instant, the walk defines a mixing matrix which is doubly-stochastic. The average of the mixing matrices contains relevant information about the quantum walk and about the graph. We show that it is the matrix of transformation of the orthogonal projection onto the commutant algebra of the adjacency matrix, restricted to diagonal matrices. Using this formulation of the average mixing matrix, we find connections between its rank and automorphisms of the graph.