Self-Organizing Neural Networks for Quantum Feature Learning

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper investigates the application of Self-Organizing Neural Networks (SONNs) to quantum feature learning, leveraging the principles of quantum entanglement and dynamic quantum state evolution. The core claim is that this approach enables the creation of more nuanced and deeper feature representations, ultimately improving model accuracy within the context of quantum computation. We explore how quantum-enhanced feature learning can be achieved through a novel dynamic adjustment process based on the interplay between quantum states and neural network weights. The study focuses on the potential of this methodology to address challenges associated with traditional feature extraction methods in quantum algorithms.

DOI: 10.5281/zenodo.22205619

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