Nonlinear Topological Quantum Computing Adaptive Learning Algorithm

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper introduces a novel adaptive learning algorithm for nonlinear topological quantum computing (NTQC) based on self-organized quantum systems. Traditional machine learning approaches rely on pre-defined models, while this algorithm leverages the inherent adaptability of non-linear topological quantum systems. The core mechanism involves dynamically adjusting quantum system parameters to optimize performance in response to environmental changes. This approach offers enhanced scalability, flexibility, and potential for efficient quantum computation. We present a theoretical framework and initial implementation demonstrating the algorithm's capabilities in a simplified model. The paper concludes with a discussion of the challenges and future directions for this research.

DOI: 10.5281/zenodo.22612416

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