Quantum Machine Learning in Non-Linear Decision Trees

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) has emerged as a promising field, offering the potential to revolutionize various computational tasks, including machine learning. This paper investigates the application of QML to decision tree algorithms, specifically focusing on the development of a novel non-linear decision tree model designed for optimized decision-solving. We explore how quantum computation can enhance the optimization process inherent in decision tree construction, leading to improved efficiency and accuracy. The core claim is that leveraging quantum mechanics allows for automatic parameter adjustment within the decision tree, significantly increasing its effectiveness. This work introduces a framework where quantum algorithms are employed to refine the decision tree structure, addressing limitations of traditional methods. The research also investigates the potential benefits of this approach in complex decision scenarios, highlighting the advantages over conventional techniques. The paper concludes with a discussion of future research directions and the expected impact of QML on the field of decision tree learning.

DOI: 10.5281/zenodo.22296460

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