基于量子信息的自适应学习框架

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

This paper presents a novel learning framework leveraging quantum information theory for adaptive learning. Traditional machine learning models often rely on static parameter adjustments, limiting their ability to efficiently adapt to complex data and optimize performance. We propose a quantum-inspired approach that dynamically adjusts model parameters based on quantum state changes during the learning process. This dynamic adjustment significantly enhances learning efficiency and generalization capabilities. The framework employs a novel quantum state evolution mechanism, allowing for a more nuanced and responsive learning process compared to conventional approaches. The core mechanism focuses on harnessing the inherent quantum properties of data to guide the learning algorithm. We demonstrate the effectiveness of this framework through a series of experiments, showcasing improved learning speed and robustness in a variety of machine learning tasks.

DOI: 10.5281/zenodo.22224785

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