Quantum-Inspired Clustering with Entangled Feature Representations

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper introduces a novel clustering approach termed Quantum-Inspired Clustering (QIC) leveraging concepts derived from quantum mechanics, specifically entanglement, to enhance feature representation and cluster discovery. Traditional clustering algorithms often face challenges when dealing with high-dimensional data and intricate feature dependencies. QIC addresses this by encoding features into entangled states, allowing the algorithm to capture and exploit correlated relationships within the data that are typically overlooked by conventional methods. The core mechanism relies on identifying clusters based on the shared information encoded within these entangled representations. The resulting algorithm demonstrates the potential to improve clustering performance, particularly in scenarios with complex datasets where linear separation is difficult or non-existent. We present a formal description of the algorithm, including the feature encoding process and the clustering objective function. The performance of the proposed method is discussed, highlighting its advantages and potential limitations.

DOI: 10.5281/zenodo.22452694

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