Quantum Machine Learning Parameter Adjustment Algorithm

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) has emerged as a promising field, offering potential advantages over classical machine learning in terms of speed, scalability, and the ability to model complex systems. One significant challenge lies in optimizing model parameters—a crucial step in achieving optimal performance. Traditional parameter tuning methods often rely on grid search, random search, or gradient-based optimization, which can be computationally expensive and time-consuming. This paper introduces a novel quantum machine learning parameter adjustment algorithm, leveraging quantum computing's unique properties to automate the parameter optimization process. The proposed algorithm utilizes a quantum circuit to efficiently explore the parameter space, leading to improved model accuracy and generalization capabilities. This work explores the potential of quantum computing to revolutionize the parameter tuning process, offering a more efficient and robust approach compared to classical methods. The core mechanism involves quantum state manipulation to represent and update model parameters, achieving a significant improvement in performance.

DOI: 10.5281/zenodo.22253370

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