Abstract
Adding self-loops at each vertex of a graph improves the performance of quantum walks algorithms over loopless algorithms. Many works approach quantum walks to search for a single marked vertex. In this article, we experimentally address several problems related to quantum walk in the hypercube with self-loops to search for multiple marked vertices. We first investigate the quantum walk in the loopless hypercube. We saw that neighbor vertices are also amplified and that approximately 1/2 of the system energy is concentrated in them. We show that the optimal value of l for a single marked vertex is not optimal for multiple marked vertices. We define a new value of \(l = (n/N)\cdot k\) to search multiple marked vertices. Next, we use this new value of l found to analyze the search for multiple marked vertices non-adjacent and show that the probability of success is close to 1. We also use the new value of l found to analyze the search for several marked vertices that are adjacent and show that the probability of success is directly proportional to the density of marked vertices in the neighborhood. We also show that, in the case where neighbors are marked, if there is at least one non-adjacent marked vertex, the probability of success increases to close to 1. The results found show that the self-loop value for the quantum walk in the hypercube to search for several marked vertices is \(l = (n / N) \cdot k \).
Acknowledgments to the Science and Technology Support Foundation of Pernambuco (FACEPE) Brazil, Brazilian National Council for Scientific and Technological Development (CNPq), and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001 by their financial support to the development of this research.
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de Souza, L.S., de Carvalho, J.H.A., Ferreira, T.A.E. (2021). Lackadaisical Quantum Walk in the Hypercube to Search for Multiple Marked Vertices. In: Britto, A., Valdivia Delgado, K. (eds) Intelligent Systems. BRACIS 2021. Lecture Notes in Computer Science(), vol 13073. Springer, Cham. https://doi.org/10.1007/978-3-030-91702-9_17
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