Adaptive Quantum Machine Learning Parameter Adjustment Strategy Optimization

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper investigates the application of quantum machine learning (QML) to parameter adjustment within the training process of classical machine learning models. Traditional parameter tuning methods often rely on fixed adjustments, failing to effectively leverage complex, dynamic information derived from model training. We propose a novel approach that leverages quantum algorithms to dynamically adjust model parameters based on both model training error and gradient information, promoting more robust and accurate model performance. This method integrates quantum error analysis into the parameter adjustment process, offering a fundamentally new approach to optimization. The research explores the potential of quantum mechanics to enhance the efficiency and adaptability of parameter optimization, potentially surpassing the limitations of classical methods. This work focuses on the design and implementation of a QML-based parameter adjustment strategy, incorporating quantum algorithms for error assessment and dynamic parameter refinement. We demonstrate the efficacy of this approach through simulations and preliminary experiments, highlighting its potential to improve model generalization and robustness.

DOI: 10.5281/zenodo.22299026

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