基于自组织矩阵的量子机器学习

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

This paper introduces a novel quantum machine learning framework based on self-organizing matrices. We propose a method for automated quantum machine learning model construction and optimization, leveraging the inherent properties of self-organizing matrices to achieve more efficient training. Traditional quantum machine learning often relies on manual design, while our approach offers a paradigm shift toward automated and enhanced learning capabilities. The core mechanism involves utilizing the self-organizing matrix structure to inherently guide the model's evolution, leading to improved training speed and potentially higher accuracy. We detail the framework's architecture, demonstrate its efficacy through a series of benchmark tasks, and analyze its potential impact on quantum machine learning. The research explores the practical implications of this new approach, aiming to address key challenges in the field.

DOI: 10.5281/zenodo.22225253

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