基于量子纠缠的自适应机器学习的优化器

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper introduces a novel quantum-enhanced machine learning optimization algorithm based on the principles of quantum entanglement. We propose a system that utilizes entangled qubits to dynamically adjust learning rates and regularization parameters during the training process, resulting in enhanced model performance compared to traditional optimization techniques. The core mechanism involves leveraging the inherent correlations within entangled states to facilitate faster and more effective gradient propagation and parameter updates. This approach offers a potential paradigm shift in machine learning optimization, capitalizing on the unique properties of quantum mechanics to improve model efficiency and generalization. The research explores the theoretical framework and initial implementation of this algorithm, demonstrating its potential for accelerating and improving the training of complex machine learning models.

DOI: 10.5281/zenodo.22268619

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