基于量子自适应的机器学习框架

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper introduces a novel quantum-enhanced machine learning framework, termed "Quantum Adaptive Machine Learning" (QAML), leveraging quantum self-adaptation to enhance the performance of various machine learning algorithms. Traditional machine learning often struggles with complex, high-dimensional data, requiring extensive parameter tuning. QAML addresses this challenge by employing quantum self-adaptation, a quantum mechanical phenomenon allowing the system to dynamically adjust its parameters based on the data itself. We detail the framework's architecture, explain the core mechanisms driving its enhanced performance, and provide a comparative analysis with existing techniques. The study demonstrates significant improvements in accuracy and efficiency across several benchmark datasets, highlighting the potential of this approach for tackling complex machine learning tasks.

DOI: 10.5281/zenodo.22592690

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