Quantum-Correlated Feature Extraction

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper explores the potential of utilizing quantum entanglement for feature extraction from complex datasets. The core concept involves representing dataset features using entangled qubits. Measurements of these qubits, governed by their entangled correlations, generate a compressed and highly informative data representation. This approach contrasts with traditional feature extraction techniques, which often suffer from computational bottlenecks when dealing with high-dimensional data. The proposed method aims to dramatically reduce computational complexity by exploiting the inherent parallelism and correlations within quantum systems. We present a theoretical framework outlining the algorithm and its expected performance, demonstrating the potential for significantly faster and more efficient feature extraction. The results suggest a promising avenue for future research in areas such as machine learning and data analysis.

DOI: 10.5281/zenodo.22627547

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