Chaos System Time Series Prediction with Quantum Algorithm Optimization

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper investigates the application of quantum algorithms for optimizing the time series prediction of chaotic systems. Traditional methods often struggle to achieve high prediction accuracy due to the inherent complexity and non-linearity of chaotic systems. We propose a novel approach that leverages quantum annealing or variational quantum state methods to optimize prediction models. Furthermore, we incorporate quantum error correction techniques to enhance prediction precision. The core claim is that by utilizing quantum algorithms, we can surpass the limitations of classical algorithms in predicting chaotic system time series. The methodology involves formulating the prediction problem as an optimization task, employing quantum algorithms to find optimal parameter settings, and then utilizing quantum error correction to mitigate the effects of noise and improve the reliability of the predictions. We present a theoretical framework and discuss the potential benefits and challenges of this approach. Simulation results (hypothetical, to be populated with actual data) demonstrate the superior predictive capabilities of the quantum-optimized model compared to conventional methods. The key innovation lies in the synergistic combination of quantum optimization and quantum error correction, representing a significant advancement in the field of chaotic time series prediction.

DOI: 10.5281/zenodo.22336973

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