Quantum Machine Learning: Adaptive Parameter Adjustment Algorithm

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) is a rapidly evolving field that leverages the unique properties of quantum systems to solve complex computational problems. Traditional machine learning algorithms often struggle with scaling to larger datasets and complex models, limiting their applicability in diverse domains. This paper introduces a novel adaptive parameter adjustment algorithm for quantum machine learning, aiming to address these limitations by dynamically adjusting model parameters during training. The core mechanism involves a feedback loop that continuously optimizes parameters based on observed performance, leading to improved model accuracy and generalization capabilities. The proposed algorithm offers a significant advancement over existing techniques by providing a more flexible and responsive approach to parameter tuning. This research explores the potential of this adaptive approach to enhance the performance of various quantum machine learning models.

DOI: 10.5281/zenodo.22302212

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