Dynamic System Modeling with Quantum Optimization Algorithms

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper presents a novel dynamic system modeling approach utilizing quantum optimization algorithms to enhance the accuracy of system simulations and accelerate the modeling process. Traditional optimization methods often struggle with complex systems exhibiting dynamic behavior, limiting their capabilities. We introduce a framework based on quantum optimization, which dynamically adjusts system parameters to optimize model fidelity and simulation speed. The core mechanism involves iteratively refining the model through quantum optimization, allowing for a more precise representation of dynamic systems. The proposed method offers a significant improvement in system understanding and simulation performance compared to existing techniques. This work contributes to the advancement of computational modeling, particularly for systems requiring dynamic analysis.

DOI: 10.5281/zenodo.22263877

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