Quantum-Inspired Optimization via Fractal Geometry

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

Quantum-inspired optimization is a burgeoning field aiming to emulate the behavior of quantum algorithms to accelerate computationally intensive optimization problems. This paper explores the application of fractal geometry to enhance the efficiency of optimization techniques, proposing a novel approach that leverages fractal representations to create a more complex search space. We demonstrate the effectiveness of this method through the analysis of a challenging, non-convex optimization problem, showcasing significant improvements in convergence and solution quality compared to traditional methods. The core mechanism centers around the construction of a fractal search space, which, through the application of specific fractal operations, facilitates a more efficient exploration of the solution space. This investigation provides a foundational understanding of how fractal geometry can be effectively integrated into optimization to overcome limitations inherent in conventional approaches.

DOI: 10.5281/zenodo.22224348

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