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Hierarchical Fractal Dimension Discovery via Quantum Iteration

Jincheng Zhang

Published Sep 7, 2026• 0 citations
Publisher

Abstract

This paper explores the potential of quantum algorithms to revolutionize the discovery of hierarchical fractal dimension properties within data sets. Traditional methods often struggle to capture the intricate complexities of fractal patterns, limiting our ability to accurately characterize data. We propose a novel quantum iteration technique designed to uncover hidden structures and patterns within data, offering a fundamentally different approach to characterization. The core mechanism leverages quantum superposition and entanglement to explore a vast space of potential fractal dimensions, potentially surpassing the limitations of classical computational approaches. This work investigates the feasibility and potential impact of this quantum algorithm on data analysis and characterization. We present a preliminary analysis of the algorithm's potential and outline future research directions.

DOI
10.5281/zenodo.22638777

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