Quantum Neural Networks: Self-Organized Learning for Enhanced Pattern Recognition

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper explores the potential of quantum neural networks (QNNs) leveraging self-organization to achieve superior performance in pattern recognition and classification tasks. The core concept involves constructing a QNN utilizing quantum phenomena like decoherence and entanglement, followed by employing self-organizing learning algorithms to dynamically adjust the network's connection weights and topology. We posit that this combination can significantly enhance learning efficiency and accuracy compared to classical neural networks. The theoretical framework presented outlines a novel approach, and while experimental validation is not included, the proposed architecture and learning process provide a foundational understanding for future research and development in this emerging field. Key elements include the mathematical formulation of the QNN's dynamics, the self-organization learning algorithm, and the evaluation metrics used to assess performance. The primary goal is to demonstrate a theoretical pathway toward a more efficient and robust QNN through self-adaptive learning.

DOI: 10.5281/zenodo.22194922

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