基于自组织网络的量子机器学习

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper introduces a novel quantum machine learning framework, termed "Self-Organized Network Quantum Machine Learning" (SON-QML), leveraging quantum entanglement and self-symmetry to accelerate and refine quantum machine learning algorithms. Traditional quantum machine learning often suffers from issues of decoherence, noise, and limited expressiveness. SON-QML employs a dynamically structured quantum network, guided by quantum entanglement, to enhance data representation and facilitate efficient learning. The core mechanism centers on utilizing the principles of quantum self-symmetry to improve model accuracy and reduce computational complexity. We present a theoretical framework and initial implementation demonstrating the potential of this approach in various quantum machine learning scenarios. The paper concludes by exploring future research directions and highlighting the advantages of SON-QML over existing methods.

DOI: 10.5281/zenodo.22510780

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