Quantum Machine Learning in Dynamic Decision Trees for Optimization and Planning

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper investigates the application of quantum machine learning to dynamic decision tree models, aiming to enhance optimization processes and achieve efficient planning. Traditional decision trees often rely on fixed parameters, limiting their adaptability to complex scenarios. We propose a novel approach utilizing quantum computing to dynamically adjust decision tree parameters, fostering improved accuracy and efficiency. The core mechanism centers around leveraging quantum properties to guide the tree's growth and refinement, offering a significant advancement over conventional methods. This work explores the potential of quantum-enhanced decision tree optimization for various applications, including complex logistics and resource allocation.

DOI: 10.5281/zenodo.22296526

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