Quantum Machine Learning: A Novel Approach to Non-Linear Geometric Function Modeling

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) is a rapidly emerging field that leverages the unique properties of quantum mechanics to develop new algorithms and computational models. This paper presents a novel quantum machine learning approach centered on the modeling of non-linear geometric functions. Traditional machine learning struggles with complex, non-linear data, and QML offers the potential to overcome these limitations through inherent quantum properties. This research explores the integration of quantum algorithms into a framework designed to effectively represent and manipulate geometric relationships. We propose a specific model architecture incorporating quantum entanglement and superposition to enhance the capacity of the system to capture the essential features of these functions. The core mechanism centers around the application of quantum-enhanced geometric transformations, aiming to surpass the capabilities of classical methods. This work investigates the potential of such a model for a range of geometric tasks, demonstrating its promise as a powerful tool for advanced geometric analysis and modeling. The study presents a preliminary exploration of this approach and highlights the key challenges and opportunities for future development.

DOI: 10.5281/zenodo.22192138

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