Core Claim: A dynamic quantum topology learning model predicts the behavior of quantum systems by dynamically adjusting quantum topology structures, resulting in more effective system modeling and prediction.

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

Quantum systems exhibit remarkable behaviors, often challenging traditional modeling approaches. This research explores a novel predictive model based on quantum self-organization (QSO) networks. The model dynamically adjusts quantum topology structures based on input data, allowing for more effective system modeling and prediction. This approach addresses the limitations of static topology models by implementing a dynamic topology learning strategy. The core mechanism involves using quantum circuit simulation to dynamically adjust topology, thereby establishing a strong relationship between system models and underlying quantum structures. This results in a more adaptable and accurate prediction capability, offering a significant advancement over existing methods. The study demonstrates the effectiveness of this dynamic approach through simulation results, showcasing improved predictive performance and a deeper understanding of quantum system dynamics.

DOI: 10.5281/zenodo.22224448

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