Quantum Neural Network Dynamic Adjustment

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper explores the potential of integrating quantum neural networks (QNNs) with dynamic adjustment mechanisms to enhance computational efficiency and accuracy. Traditional neural network parameter adjustments are often manual and limited in their adaptability, potentially hindering optimization. We propose a novel approach utilizing quantum entanglement and quantum simulation to automatically adjust network parameters during computation, offering a paradigm shift in neural network design. This investigation delves into the theoretical underpinnings of this dynamic adjustment, detailing the quantum algorithms employed and the expected benefits in terms of speed and precision. The paper concludes with a discussion of the challenges and future directions for this research.

DOI: 10.5281/zenodo.22276064

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