Quantum Adaptive Neural Networks

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum Adaptive Neural Networks (QANNs) represents a novel approach to neural network architecture, designed to address the limitations of traditional models in handling high-dimensional data and complex tasks. This paper explores the potential of leveraging quantum computing's unique properties, specifically quantum self-adaptation algorithms, to enhance both training and inference processes. The core mechanism centers around dynamically adjusting network parameters using quantum optimization techniques, resulting in improved performance across various scenarios. This research investigates the fundamental principles of QANNs and their potential impact on machine learning applications.

DOI: 10.5281/zenodo.22195521

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