Quantum-Driven Fractal Dimension Optimization

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum-driven fractal dimension optimization presents a novel approach to determining the fractal dimension of shapes by employing quantum algorithms. This paper explores the potential of quantum annealing and other quantum optimization techniques to enhance the search process for optimal fractal dimensions. We analyze the advantages and challenges of utilizing quantum mechanics to achieve a more efficient and accurate optimization process compared to classical methods. The core mechanism involves leveraging quantum properties to explore the solution space more effectively, ultimately leading to improved results in fractal dimension estimation. The paper concludes with a discussion of the future research directions and potential impact of this technology.

DOI: 10.5281/zenodo.22497381

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