Quantum-Inspired Clustering Algorithms for Big Data

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Traditional clustering algorithms, such as k-means and DBSCAN, often face significant challenges when applied to big data. The computational complexity associated with distance calculations and nearest neighbor searches scales poorly with dataset size, limiting their effectiveness. This paper proposes a novel approach to clustering inspired by quantum measurement theory, specifically quantum-inspired nearest neighbor search. We leverage concepts from quantum mechanics, such as superposition and measurement, to design clustering algorithms that can efficiently identify clusters within high-dimensional datasets. The core idea is to treat the clustering problem as a measurement process, where candidate cluster centers are repeatedly measured against data points, and the most likely cluster centers are selected. The resulting algorithms aim to provide scalability improvements over conventional methods, particularly for datasets with a large number of features and data points. The effectiveness of the proposed algorithms is demonstrated through theoretical analysis and conceptual explanations, highlighting the potential benefits and future research directions.

DOI: 10.5281/zenodo.22197111

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