Quantum Neural Networks: An Adaptive Optimization Mechanism

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum Neural Networks (QNNs) represent a paradigm shift in neural network design, leveraging the unique properties of quantum mechanics to potentially surpass classical neural networks in terms of efficiency and accuracy. This paper explores the concept of a novel adaptive optimization mechanism within QNNs, utilizing quantum entanglement and quantum state adaptation to enhance training and inference processes. The core of this approach focuses on establishing a dynamic feedback loop that allows the network to continually refine its parameters and structure based on the inherent properties of its data. The proposed mechanism offers a potential path towards more robust and adaptable neural network architectures. We delve into the theoretical foundations and preliminary experimental validation of this approach, demonstrating its potential for accelerating learning and improving generalization performance.

DOI: 10.5281/zenodo.22278713

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