Quantum Machine Learning: An Adaptive Optimization Algorithm

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) is an emerging field that explores the potential of quantum computing to enhance machine learning algorithms. This paper proposes a novel adaptive optimization algorithm for QML, designed to dynamically adjust model parameters to achieve improved efficiency and enhanced generalization capabilities. The core mechanism of this algorithm involves a self-tuning parameter set, allowing for automatic adaptation to complex datasets and minimizing the need for manual parameter tuning. The proposed method offers a promising path towards more robust and efficient QML models, potentially surpassing the limitations of traditional approaches. The paper delves into the theoretical foundations and practical implications of this adaptive optimization strategy, highlighting its potential impact on the advancement of QML research and applications.

DOI: 10.5281/zenodo.22184061

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