Quantum Machine Learning Parameter Optimization

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum Machine Learning Parameter Optimization (QMLPO) presents a novel approach to parameter optimization within quantum machine learning models, leveraging the unique properties of quantum mechanics, specifically quantum entanglement and quantum algorithms. This paper explores the potential of utilizing quantum entanglement to accelerate parameter search and enhance model performance. We detail the core mechanism, outlining how quantum algorithms are integrated to overcome the limitations of classical optimization techniques. The research investigates the impact of quantum entanglement on the efficiency of parameter adjustments, ultimately aiming to improve the robustness and accuracy of quantum machine learning models. The paper concludes with a discussion of the current challenges and future directions for QMLPO.

DOI: 10.5281/zenodo.22296524

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