基于量子自适应网络的动态参数调整

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel dynamic parameter adjustment algorithm based on quantum self-adaptation networks. Traditional network training methods rely on classical parameter adjustments, which often struggle to adapt to complex, dynamic system behavior. This work proposes a quantum self-adaptation network architecture that leverages quantum computing principles to dynamically adjust network parameters, resulting in enhanced model prediction accuracy and generalization capabilities. The core mechanism of this algorithm centers around the implementation of a "quantum self-adaptation adjustment" process, utilizing quantum computation to learn and refine parameter adjustments more effectively. We demonstrate the effectiveness of this approach through simulations and preliminary experiments, highlighting its potential to surpass existing network architectures in terms of predictive performance and robustness.

DOI: 10.5281/zenodo.22203292

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