Quantum Machine Learning: Adaptive Hyperparameter Optimization

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) offers the potential to revolutionize various computational tasks, particularly in optimization problems. This paper explores the application of QML to adaptive hyperparameter optimization, a crucial step in training machine learning models. We propose a novel QML-based algorithm that leverages quantum state dynamics to automatically adjust hyperparameters, leading to improved model performance. This approach addresses the limitations of traditional static hyperparameter optimization methods by dynamically responding to the data and model itself. The core mechanism involves the quantum state evolution driven by the learning process, enabling a more efficient and robust optimization process. This work demonstrates the potential of QML to significantly enhance model accuracy and generalization capabilities.

DOI: 10.5281/zenodo.22154338

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