Quantum Neural Networks: Adaptive Learning Strategies

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum Neural Networks (QNNs) represent a promising paradigm shift in machine learning, leveraging the unique properties of quantum mechanics to potentially surpass classical neural networks. This paper explores the application of QNNs to adaptive learning, proposing a novel self-adaptive learning strategy that dynamically adjusts network parameters and structural configurations based on the network's evolving state. We present a framework centered around quantum entanglement and superposition, enabling a continuous, nuanced adjustment of the learning process. The core mechanism focuses on leveraging the inherent quantum properties to optimize learning efficiency. We demonstrate the efficacy of this approach through simulations and preliminary experiments, illustrating a significant improvement in learning speed and robustness compared to traditional approaches. The resulting QNN demonstrates a key advantage: a more flexible and adaptable learning process, offering a pathway towards truly intelligent and efficient learning.

DOI: 10.5281/zenodo.22268454

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