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Quantum k-means based on Quantum Local Sensitive Hashing with Application to Irrigation Machine Sensors Data Clustering

Year: 2026

Type: conference-paper

Abstract: A k-means algorithm is one of the popular algorithms of Machine learning for clustering problem. In this paper, we present a quantum version of modification of the algorithm that has O(N$\sqrt {dk} $+Nm) query complexity for each iteration and a small size of quantum memory, which is O(logd+logk+logm). Here N is the number of objects, d is the number of attributes (or features), k is the number of clusters, and m is the number of bits in the binary representation of one attribute. We apply the algorithm for clustering data that coming from sensors of an irrigation machine.

Source: IEEE International Conference on Smart Computing Workshops and Other Affiliated Events (SMARTCOMP Companion)

DOI: 10.1109/smartcomp-companion70724.2026.00039

Authors: Kamil Khadiev, Aliya Khadieva

Institution: Kazan Federal University

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W7171978836

FWCI:

Cites: 74

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Related to: 10