Quantum clustering with k-Means: A hybrid approach

Quantum computing, based on quantum theory, holds great promise as an advanced computational paradigm for achieving fast computations. Quantum algorithms are expected to surpass their classical counterparts in terms of computational complexity for certain tasks, including machine learning. In this p...

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Bibliographic Details
Published in:	Theoretical computer science Vol. 992; p. 114466
Main Authors:	Poggiali, Alessandro, Berti, Alessandro, Bernasconi, Anna, Del Corso, Gianna M., Guidotti, Riccardo
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 21.04.2024
Subjects:	Clustering Data mining Quantum machine learning Clustering Data mining Quantum machine learning
ISSN:	0304-3975, 1879-2294
Online Access:	Get full text
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Summary:	Quantum computing, based on quantum theory, holds great promise as an advanced computational paradigm for achieving fast computations. Quantum algorithms are expected to surpass their classical counterparts in terms of computational complexity for certain tasks, including machine learning. In this paper, we design, implement, and evaluate three hybrid quantum k-Means algorithms, exploiting different degrees of parallelism. Indeed, each algorithm incrementally leverages quantum parallelism to reduce the complexity of the cluster assignment step up to a constant cost. In particular, we exploit quantum phenomena to speed up the computation of distances. The core idea is that the computation of distances between records and centroids can be executed simultaneously, thus saving time, especially for big datasets. We show that our hybrid quantum k-Means algorithms are theoretically faster than the classical algorithm, while experiments suggest that it is possible to obtain comparable clustering results.
ISSN:	0304-3975 1879-2294
DOI:	10.1016/j.tcs.2024.114466