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Quantum Machine Learning

Parameterized Quantum Query Algorithms for Graph Problems

arXiv

Authors: Tatsuya Terao, Ryuhei Mori

Year

2024

Paper ID

64496

Status

Preprint

Abstract Read

~2 min

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Abstract

In this paper, we consider the parameterized quantum query complexity for graph problems. We design parameterized quantum query algorithms for k-vertex cover and k-matching problems, and present lower bounds on the parameterized quantum query complexity. Then, we show that our quantum query algorithms are optimal up to a constant factor when the parameters are small.

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This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
It adds a 2024 reference point for readers tracking recent quantum research.
In this paper, we consider the parameterized quantum query complexity for graph problems.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2408.03864 [2] arXiv https://arxiv.org/abs/2408.03864 [3] Publisher https://arxiv.org/abs/2408.03864

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