Dynamic Geometric Mapping for Quantum Feature Extraction

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel approach to quantum feature extraction based on dynamic geometric mapping. Traditional feature extraction methods often rely on classical computational techniques, limiting their ability to fully exploit the inherent quantum properties of data. We propose a new method that leverages quantum entanglement to represent and extract features, offering a fundamentally different perspective. This approach utilizes a dynamic geometric mapping to translate data into a quantum state, enabling a more nuanced and efficient extraction process. The core mechanism centers around simulating quantum feature extraction through the interplay of entangled geometric relationships. We demonstrate the efficacy of this method through theoretical analysis and preliminary experimental results, showcasing a significant improvement in feature extraction accuracy and speed compared to conventional approaches. The fundamental insight lies in harnessing quantum entanglement to guide the data transformation, providing a powerful tool for quantum data analysis.

DOI: 10.5281/zenodo.22202711

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