基于量子自适应的机器学习 - 量子特征的动态优化

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper introduces a novel machine learning approach, termed "Quantum Adaptive Machine Learning" (QAML), that leverages quantum feature adaptation to enhance model generalization and efficiency. Traditional machine learning often relies on static, pre-defined features, limiting the model's ability to adapt to complex data patterns. QAML utilizes quantum feature adaptation to dynamically optimize the model's feature space, allowing for more flexible and robust representations. The paper details the fundamental principles, experimental setup, and results demonstrating the superior performance of QAML compared to conventional methods across a range of datasets. The core mechanism involves the application of quantum algorithms to iteratively refine feature representations, leading to a significant improvement in model accuracy and computational cost.

DOI: 10.5281/zenodo.22264989

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