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Quantum Machine Learning

Multi-target quantum walk search on Johnson graph

arXiv
Authors: Pulak Ranjan Giri

Year

2025

Paper ID

51831

Status

Preprint

Abstract Read

~2 min

Abstract Words

207

Citations

N/A

Abstract

The discrete-time quantum walk on the Johnson graph J(n,k) is a useful tool for performing target vertex searches with high success probability. This graph is defined by n distinct elements, with vertices being all the binom{n}{k} k-element subsets and two vertices are connected by an edge if they differ exactly by one element. However, most works in the literature focus solely on the search for a single target vertex on the Johnson graph. In this article, we utilize lackadaisical quantum walk--a form of discrete-time coined quantum walk with a wighted self-loop at each vertex of the graph--along with our recently proposed modified coin operator, mathcal{C}g, to find multiple target vertices on the Johnson graph J(n,k) for various values of k. Additionally, a comparison based on the numerical analysis of the performance of the mathcal{C}g coin operator in searching for multiple target vertices on the Johnson graph, against various other frequently used coin operators by the discrete-time quantum walk search algorithms, shows that only mathcal{C}g coin can search for multiple target vertices with a very high success probability in all the scenarios discussed in this article, outperforming other widely used coin operators in the literature.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2025 reference point for readers tracking recent quantum research.
  • The discrete-time quantum walk on the Johnson graph J(n,k) is a useful tool for performing target vertex searches with high success probability.

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