Quantum-Enhanced Fractal Dimension

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This research explores the potential of quantum mechanics to enhance fractal dimension, aiming to develop novel algorithms for optimization through subtle adjustments to network structures. We propose a 'Quantum-Induced Fractal Refinement' – a process that leverages quantum fluctuations to enhance self-similarity within a network, potentially surpassing the limitations of conventional fractal analysis. The study examines the theoretical framework underpinning this approach and outlines a potential algorithm for implementation. The core claim is that incorporating quantum effects can yield significantly improved fractal dimension estimates, particularly for complex, highly self-similar networks.

DOI: 10.5281/zenodo.22331527

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