Quantum Machine Learning: Adaptive Training Strategies

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) represents a paradigm shift in machine learning, leveraging the principles of quantum mechanics to potentially unlock unprecedented computational power and algorithmic efficiency. This paper investigates a novel adaptive training strategy for QML models, focusing on the dynamic nature of quantum states. We propose a system that automatically adjusts training parameters based on the quantum state's evolution, fostering improved training efficiency and model generalization. The core mechanism centers around exploiting quantum state transformations to dynamically optimize model parameters during training. The research explores the potential of this approach to overcome the limitations of traditional static training methods and advance the capabilities of QML models. This work introduces a framework for adaptive parameter adjustment predicated on the quantum state, demonstrating a promising path toward enhanced model performance and reduced computational costs.

DOI: 10.5281/zenodo.22154346

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