Adaptive Quantum Machine Learning Dynamic Parameter Adjustment

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper explores the application of adaptive quantum machine learning (QML) to dynamic parameter adjustment, aiming to enhance model generalization and efficiency. Traditional parameter optimization methods often rely on manual adjustments, limiting their adaptability and potential for improved performance. We propose a novel framework leveraging quantum circuits' inherent adaptability to automatically adjust parameters, thereby facilitating more efficient training and improved generalization. The core mechanism centers on quantum circuit self-tuning, enabling the model to explore a wider parameter space, leading to better predictive accuracy and reduced overfitting. This work presents a conceptual outline of such a framework and discusses potential challenges and future directions for research.

DOI: 10.5281/zenodo.22489275

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