Quantum Neural Network Weight Adjustment

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum Neural Network Weight Adjustment is a novel approach to neural network design that leverages the unique properties of quantum computation to dynamically adjust the weights of the network during training. This paper presents a quantum neural network architecture designed to address the limitations of traditional neural networks, particularly in handling large datasets and complex tasks. The core mechanism centers around a quantum-enhanced weight adjustment process, offering potential improvements in both performance and efficiency. We detail the architecture, the underlying quantum algorithms utilized, and the results achieved through extensive simulations and analysis. The significance of this work lies in its potential to revolutionize neural network design by introducing a fundamentally different approach to weight optimization.

DOI: 10.5281/zenodo.22290014

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