Quantum Machine Learning: Adaptive Model Tuning

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) represents a paradigm shift in computational science, promising breakthroughs across diverse fields. Within the realm of machine learning, adaptive model tuning is a critical process, often requiring extensive manual intervention. This paper explores a novel quantum machine learning model adjustment framework, leveraging quantum computation's unique properties to automate this crucial step. We propose a Quantum-Enhanced Parameter Adjustment (QEPA) algorithm, designed to dynamically optimize model parameters, ultimately enhancing both model performance and generalization capabilities. The core mechanism relies on quantum optimization techniques to efficiently explore the parameter space and identify optimal configurations. This approach contrasts with traditional methods, offering the potential for substantial improvements in model accuracy and robustness.

DOI: 10.5281/zenodo.22253420

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