基于量子自适应的神经网络架构优化

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper presents a novel neural network architecture optimization framework based on quantum self-adaptation. Traditional neural network training often relies on manually tuning hyperparameters, which can be time-consuming and suboptimal. We introduce a quantum self-adaptation mechanism that dynamically adjusts network parameters during training, optimizing the model's performance and efficiency. This framework leverages the inherent advantages of quantum computation to explore a larger parameter space and identify optimal configurations more efficiently than classical methods. We demonstrate the effectiveness of this approach through extensive experiments on several benchmark datasets. The results indicate a significant improvement in model accuracy and training speed compared to state-of-the-art methods. We also explore the potential of this quantum-inspired optimization technique for a broader range of neural network architectures.

DOI: 10.5281/zenodo.22509365

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