Dynamic Topology Quantum Computing Adaptive Learning Algorithm

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Quantum computing offers the potential for unprecedented computational power, but realizing this potential requires overcoming fundamental limitations in existing computational paradigms. This paper introduces a novel dynamic topology quantum computing adaptive learning algorithm, designed to dynamically adjust quantum system parameters in response to environmental changes. Traditional machine learning algorithms rely on pre-defined models, while this approach leverages the inherent adaptability of quantum topology to achieve enhanced scalability and flexibility. The algorithm's core mechanism involves utilizing the self-adaptive nature of quantum topology to iteratively refine the quantum system's state, leading to improved performance in complex quantum computations. We present a comprehensive theoretical analysis and initial experimental validation demonstrating the algorithm's efficacy in solving specific quantum algorithms.

DOI: 10.5281/zenodo.22613212

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