Quantum Neural Network Adaptive Weight Adjustment

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Quantum Neural Network Adaptive Weight Adjustment addresses the limitations of traditional quantum neural networks by introducing a dynamic weight adjustment mechanism. This paper presents a novel framework that dynamically adjusts the weights of the neural network based on the input data, enhancing model generalization and performance. We detail the proposed method, including the underlying mathematical principles and a detailed explanation of the implementation process. The core mechanism leverages adaptive weight adjustments to optimize the network's parameters, leading to improved robustness and accuracy. This research demonstrates the effectiveness of the proposed approach through theoretical analysis and experimental validation.

DOI: 10.5281/zenodo.22636361

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