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This work aims to improve quantum k-means clustering algorithms for efficient data handling.

June 3, 2026ComputersOpen Access

Quantum k-Means Clustering Using Hadamard-Test-Based Similarity Estimation

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Randomized trial demonstrates improved clustering results in quantum machine learning, implying potential for effective data management.

This work aims to improve quantum k-means clustering algorithms for efficient data handling.
Introduced two quantum-inspired k-means algorithms: quantum subtraction operation and rotational-difference dissimilarity measure.
Employed a Hadamard-test-based circuit design for dissimilarity measurement.
Evaluated on six datasets including Iris and Breast Cancer Wisconsin.
Clustering results using the rotational-based dissimilarity metric were comparable to classical methods.
Achieved improved clustering validity and classification performance metrics.

Alaeiyan et al. (2026) studied this question.

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