基于动态拓扑的量子自适应计算

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper introduces a novel quantum self-adaptive computing framework based on dynamic topology. Traditional quantum computation relies on the efficiency of classical computation, but this framework leverages quantum mechanics' inherent adaptability to significantly enhance computational efficiency. The framework utilizes a dynamically evolving topological structure to simulate learning processes, effectively optimizing the quantum algorithm's performance. The core mechanism centers on the dynamic adjustment of topological parameters, mirroring the iterative refinement of a machine learning model. We demonstrate the framework's efficacy through a series of numerical experiments, showcasing improved performance compared to traditional quantum algorithms in specific scenarios. The work presents a fundamentally new approach to quantum self-adaptation, offering a promising pathway towards more efficient and versatile quantum computation.

DOI: 10.5281/zenodo.22158768

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