Non-linear Self-Organizing Neural Network Model for Quantum Computing (NSNQN)

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces the Non-linear Self-Organizing Neural Network Model (NSNQN) – a novel framework for quantum computing that leverages the principles of self-organization and quantum entanglement to simulate complex biological neural systems. Traditional neural networks often struggle to capture the intricate self-organization observed in biological neurons, limiting their ability to model intricate biological processes. The NSNQN utilizes quantum entanglement to enable self-learning and adaptive network behavior, offering a pathway toward advanced quantum computing applications. We detail the model's architecture, the integration of quantum entanglement, and the potential for achieving self-aware and responsive neural networks. The paper explores the theoretical foundations and preliminary results demonstrating the NSNQN's capability to emulate biological neural network dynamics. We conclude by highlighting the significant potential of this approach for advancing quantum computation and neuroscience research.

DOI: 10.5281/zenodo.22187784

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