Adaptive Time Series Analysis Framework for Quantum Computing Simulations

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

This paper presents a novel quantum computing simulation framework designed to excel in adaptive time series analysis. The framework leverages self-adaptive quantum algorithms and quantum entanglement to dynamically adjust qubit states, thereby enhancing computational accuracy and efficiency. We propose a method for incorporating time series analysis with quantum computing's inherent adaptability, creating a system that can intelligently optimize quantum computations for precise time series prediction and modeling. Traditional time series analysis methods often rely on predefined models, while this framework aims to exploit quantum effects for more flexible and efficient analysis. The core mechanism involves a hybrid approach combining quantum state control with self-adaptive algorithms, ultimately enhancing the capability of time series prediction and modeling.

DOI: 10.5281/zenodo.22222944

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