Quantum-Enhanced Chaos Theory with Adaptive Parameterization

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum-enhanced chaos theory aims to overcome the limitations of static parameterizations by dynamically adjusting model parameters based on observed data. This paper explores a novel quantum algorithm designed to achieve this, utilizing a quantum process to iteratively refine chaos model parameters, resulting in enhanced predictive accuracy. The core mechanism involves quantum evolution of the chaos model, continuously adapting to new data points. This approach offers a potential paradigm shift in chaos theory, moving beyond static representations and embracing adaptive, data-driven approaches. The study delves into the theoretical foundations and experimental validation of this quantum-enhanced algorithm, demonstrating its ability to improve predictive performance in a simulated environment.

DOI: 10.5281/zenodo.22277046

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