基于自适应的量子拓扑结构学习

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper introduces a novel quantum topology learning framework, leveraging adaptive learning to model and predict complex quantum systems. Traditional approaches often rely on pre-defined models, limiting their ability to effectively handle the inherent complexity of quantum systems. Our method dynamically adjusts quantum topology structures to capture the intricate dependencies within the system, offering a more flexible and predictive approach. We demonstrate the effectiveness of this framework through simulations and initial results on simplified quantum systems. The core mechanism involves an iterative process of structure optimization driven by a self-learning algorithm, resulting in improved accuracy and the ability to model systems exhibiting non-linearities and intricate correlations. The paper concludes with a discussion of the potential applications of this framework and future research directions.

DOI: 10.5281/zenodo.22295926

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