Dynamic Geometry Topology Transformations: A Quantum Computing Model Optimization Framework

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper explores the application of dynamic geometry topology transformations to quantum computing model optimization. Traditional topological transformations are often static and lack the capabilities of quantum computing, limiting their potential for efficient quantum computation. We propose a novel model optimization framework that leverages the unique properties of quantum computation to dynamically adjust and optimize complex topological transformations, ultimately enhancing the performance of quantum algorithms. The core mechanism centers around representing the transformation process as a quantum circuit, utilizing quantum algorithms to iteratively refine the transformation parameters, leading to improved quantum circuit fidelity and reduced computational costs. The framework demonstrates promising results in optimizing a specific topological transformation, showcasing the potential of dynamic geometry topology transformations to accelerate quantum algorithm development.

DOI: 10.5281/zenodo.22525148

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