Quantum Machine Learning: Adaptive Parameter Tuning

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) represents a paradigm shift in artificial intelligence, leveraging the unique properties of quantum mechanics to unlock unprecedented computational capabilities. This paper explores the potential of adaptive parameter tuning within QML models, a critical challenge in achieving optimal performance. We propose a novel mechanism that integrates quantum entanglement and superposition to dynamically adjust model parameters, responding to data distribution shifts. This approach surpasses traditional static parameter optimization techniques, offering a more robust and adaptable learning process. The study details the theoretical framework and preliminary experimental results demonstrating the efficacy of this adaptive tuning strategy in a specific QML model. The focus is on enhancing model generalization and robustness.

DOI: 10.5281/zenodo.22507611

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