Quantum Machine Learning: Adaptive Model Parameter Optimization

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) has emerged as a rapidly advancing field, offering the potential to revolutionize various computational tasks. Within QML, adaptive model parameter optimization stands as a critical area of focus, aiming to dynamically adjust model parameters to achieve optimal performance across diverse datasets. This paper presents a novel adaptive model parameter optimization algorithm based on quantum mechanics principles, leveraging quantum states to enhance the training process. The core mechanism involves utilizing quantum algorithms to iteratively refine model parameters, ultimately leading to improved machine learning model performance. The research addresses the limitations of traditional parameter tuning methods by presenting a fundamentally different approach, promising enhanced efficiency and potential for superior results. This work introduces a quantum-enhanced optimization strategy designed to tackle the inherent challenges of model parameter adjustment.

DOI: 10.5281/zenodo.22248302

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