Quantum Machine Learning for Feature Extraction and Modeling

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) offers a paradigm shift in computational modeling, potentially surpassing the limitations of classical algorithms. This paper investigates the application of QML techniques to enhance feature extraction and modeling, focusing on efficient data processing and improved model performance, particularly in scenarios involving high-dimensional data and complex datasets. We explore the core mechanism of leveraging quantum properties to accelerate feature extraction, refine model representations, and ultimately boost the effectiveness of machine learning models. The research details a novel approach to feature representation using quantum algorithms, demonstrating significant improvements across various datasets. This work presents a framework for intelligently extracting key features from data, enabling the development of more robust and accurate models. The core claim is that QML can significantly enhance machine learning performance, particularly with complex datasets and high dimensionality.

DOI: 10.5281/zenodo.22152857

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