Quantum Machine Learning: A Bayesian Optimization-Based Algorithm for Automated Model Parameter Tuning

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper introduces a novel quantum machine learning algorithm based on Bayesian optimization, designed to automatically adjust model parameters for optimal performance. Traditional machine learning model parameter optimization often relies on manual adjustments, which struggles to adapt to evolving data. Our algorithm leverages the probabilistic nature of model parameters, employing Bayesian optimization to dynamically refine the model, ultimately leading to improved accuracy and generalization. This work presents a fundamentally new approach to model parameter tuning, offering enhanced adaptability and potential for superior results compared to existing methods.

DOI: 10.5281/zenodo.22299215

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