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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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