Self-Organizing Neural Networks for Quantum Computing Implementation

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper investigates the implementation of self-organizing neural networks (SONNs) within a quantum computing framework. The core focus is on developing a novel SONN architecture capable of dynamically adjusting connection weights and neuron counts based on input data characteristics. Traditional neural networks often struggle with adapting to dynamic environments, necessitating a solution that can enhance learning efficiency. This work proposes a design incorporating adaptive mechanisms to facilitate this adaptation, promising a more efficient and flexible approach to quantum machine learning. The paper outlines the core mechanism, its potential benefits, and the challenges associated with this approach. The goal is to demonstrate a practical pathway toward harnessing the power of SONNs for quantum computations.

DOI: 10.5281/zenodo.22184364

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