A mathematical framework for maze solving using quantum walks

Abstract We provide a mathematical framework for identifying the shortest path in a maze using a Grover walk, which becomes non-unitary by introducing absorbing holes. In this study, we define the maze as a network with vertices connected by unweighted edges. Our analysis of the stationary state of the truncated Grover walk on finite graphs, where we strategically place absorbing holes and self-loops on specific vertices, demonstrates that this approach can effectively solve mazes. By setting arbitrary start and goal vertices in the underlying graph, we obtain the following long-time results: (i) in tree structures, the probability amplitude is concentrated exclusively along the shortest path between start and goal; (ii) in ladder-like structures with additional paths, the probability amplitude is maximized near the shortest path.

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Publication Details

Journal
Quantum Information Processing
Published
2025-03-17
DOI
https://doi.org/10.1007/s11128-025-04711-y
Citations
1
Primary Topic
Quantum Computing Algorithms and Architecture
Type
article
Field-Weighted Citation Impact
2.48

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article

A mathematical framework for maze solving using quantum walks

Etsuo Segawa, Hiromichi Ohno, Leo Matsuoka
1 citations
Quantum Information Processing
Quantum Computing Algorithms and Architecture
2.48
article

A mathematical framework for maze solving using quantum walks

Etsuo Segawa, Hiromichi Ohno, Leo Matsuoka
article en
1 citations

Abstract

Abstract We provide a mathematical framework for identifying the shortest path in a maze using a Grover walk, which becomes non-unitary by introducing absorbing holes. In this study, we define the maze as a network with vertices connected by unweighted edges. Our analysis of the stationary state of the truncated Grover walk on finite graphs, where we strategically place absorbing holes and self-loops on specific vertices, demonstrates that this approach can effectively solve mazes. By setting arbitrary start and goal vertices in the underlying graph, we obtain the following long-time results: (i) in tree structures, the probability amplitude is concentrated exclusively along the shortest path between start and goal; (ii) in ladder-like structures with additional paths, the probability amplitude is maximized near the shortest path.

Quantum Information ProcessingVol. 24(3)
Shinshu University (JP), Hiroshima Institute of Technology (JP), Yokohama National University (JP)
Shinshu University, Japan Society for the Promotion of Science
Openalex Percentile: Top 11%
Quantum Computing Algorithms and Architecture
2.48
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