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Quantum Information Processing

A quantum algorithm of K-means toward practical use

  • 01-04-2022
Published in:
Author
Hiroshi Ohno
Published in
Quantum Information Processing | Issue 4/2022

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Abstract

The article introduces a quantum-enhanced K-means algorithm designed to address the computational demands and energy consumption challenges of classical machine learning algorithms. By leveraging quantum subroutines for distance calculations, minimum finding, and mean calculation, the proposed algorithm aims to provide significant speedup for large datasets. The authors demonstrate the feasibility of the algorithm through numerical experiments on synthetic, Iris, and image datasets, showcasing comparable performance to classical K-means algorithms. The study highlights the potential of quantum machine learning in practical applications, paving the way for future advancements in this field.
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previous article Realistic continuous-variable quantum teleportation using a displaced Fock state channel
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Title
A quantum algorithm of K-means toward practical use
Author
Hiroshi Ohno
Publication date
01-04-2022
Publisher
Springer US
Published in
Quantum Information Processing / Issue 4/2022
Print ISSN: 1570-0755
Electronic ISSN: 1573-1332
DOI
https://doi.org/10.1007/s11128-022-03485-x
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