Quantum Machine Learning: A Non-Linear Data-Driven Learning Approach

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) presents a transformative paradigm for artificial intelligence, offering the potential to overcome limitations of classical machine learning algorithms. This paper explores a novel quantum machine learning approach, specifically designed for non-linear data-driven learning, aimed at enhancing model generalization. The core of this research focuses on employing a non-linear data transformation coupled with a quantum algorithm to capture intricate data patterns. We present a design for a quantum-enhanced learning process that surpasses traditional methods in handling complex datasets. The proposed method leverages the inherent properties of quantum mechanics to facilitate efficient pattern recognition and representation, ultimately yielding improved predictive accuracy and robustness. This work investigates the potential of QML to address challenges in data analysis and model optimization, and provides a foundation for future research in this rapidly evolving field. This paper provides a comprehensive overview of the core concepts, proposed methodology, and potential future directions for quantum machine learning.

DOI: 10.5281/zenodo.22325282

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