Quantum Adaptive Neural Networks: Adaptive Learning Strategies Through Quantum Entanglement and Quantum Computing

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

Quantum Adaptive Neural Networks (QANNs) represent a novel approach to neural network training, leveraging the unique properties of quantum mechanics, specifically quantum entanglement and quantum computing. This paper explores the design and implementation of a QANN architecture that dynamically adjusts learning rate and regularization parameters, offering a potential solution to challenges inherent in traditional deep learning models. The core mechanism centers on utilizing quantum entanglement to create a dynamic feedback loop, enabling the network to adapt to data patterns in a more robust and efficient manner. We propose a framework that employs quantum state manipulation to guide the learning process, leading to improved generalization performance. This work demonstrates the potential of incorporating quantum principles into neural network training, offering a pathway towards more stable and adaptable models.

DOI: 10.5281/zenodo.22225571

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