Quantized Entropy-Driven Algorithm

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum information theory offers a unique perspective on data compression and feature extraction, leveraging the inherent properties of quantum entanglement. This paper introduces a novel algorithm, the Quantized Entropy-Driven Algorithm, that utilizes quantum information entropy as a guiding principle to significantly enhance data compression and feature extraction. We demonstrate the effectiveness of this approach through rigorous analysis and comparative experiments, showcasing its potential to surpass traditional methods in efficiency and accuracy. The core mechanism centers around the application of quantum entanglement to optimize data storage and transfer, ultimately accelerating the extraction of crucial features. This research explores the theoretical and practical implications of this approach, highlighting its potential to revolutionize data management and analysis.

DOI: 10.5281/zenodo.22247343

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