基于自适应的量子机器学习的参数调整策略

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper introduces a novel approach to quantum machine learning, termed "Adaptive Quantum Machine Learning" (AQML), that leverages self-adaptation to dynamically adjust model parameters during training based on both model error and gradient information. Traditional parameter adjustment methods often rely on static adjustments, failing to fully exploit the complexities of quantum systems. AQML employs a quantum algorithm to iteratively refine model parameters, effectively optimizing the training process. The core mechanism involves a quantum optimization loop, combined with a robust error analysis framework. We present a preliminary implementation demonstrating the efficacy of this approach in a simulated quantum learning scenario. This work addresses the limitations of existing parameter adjustment techniques by introducing a dynamic, self-optimizing process that can significantly improve model performance.

DOI: 10.5281/zenodo.22299002

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