基于量子算法的自适应动力系统模型

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper presents a novel approach to dynamic system simulation using a quantum algorithm-driven self-adaptive model. Traditional dynamic system models often rely on static representations, limiting their ability to accurately capture complex behavior. We introduce a quantum-enhanced model that dynamically adjusts model parameters based on input data, offering a significant improvement in simulation speed and accuracy. The core mechanism involves applying a quantum optimization algorithm to refine the model's parameter space, enabling automated parameter tuning. This research demonstrates the potential of quantum computation to accelerate and refine dynamic system simulations, addressing limitations of existing methods. The paper explores the design and implementation of such a model, outlining the algorithm's core steps and potential advantages. We conclude with a discussion of the results and future research directions.

DOI: 10.5281/zenodo.22265291

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