Title: Quantum Machine Learning for Dynamic System Modeling: Adaptive Parameter Adjustment

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper presents a novel approach to dynamic system modeling utilizing quantum machine learning (QML) for adaptive parameter adjustment. Traditional system modeling often relies on fixed parameters, limiting the model's ability to accurately represent complex dynamics. We propose a QML framework where the system is treated as a dynamic process, and the algorithm dynamically adjusts parameters to enhance the representation of system behavior. This approach offers enhanced precision and adaptability compared to conventional methods. The core mechanism involves employing a quantum reinforcement learning algorithm to iteratively refine the model's parameters, leading to improved predictive capabilities. This research explores the potential of QML to overcome limitations inherent in traditional system modeling and provides a foundation for future advancements in dynamic system analysis.

DOI: 10.5281/zenodo.22290906

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