Quantum Machine Learning Adaptive Optimization

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) offers the potential to revolutionize machine learning through the utilization of quantum phenomena. This paper investigates a novel adaptive optimization framework for QML, specifically focusing on dynamically adjusting the learning rate and optimization parameters during training. We propose a quantum algorithm that leverages quantum state manipulation to achieve enhanced model performance and generalization compared to classical approaches. This work addresses the limitations of static learning rate strategies by introducing a self-optimizing mechanism. The core claim is that this framework significantly improves model accuracy and robustness through adaptive optimization, offering a crucial advancement in QML.

DOI: 10.5281/zenodo.22281292

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