Quantum Machine Learning: Dynamic Parameter Adjustment Strategies

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) is a rapidly evolving field that explores the potential of quantum computing to revolutionize machine learning. This paper investigates a novel quantum machine learning algorithm, leveraging dynamic parameter adjustment strategies to enhance model performance and efficiency. We propose a method that dynamically adjusts the parameters of a quantum neural network (QNN) based on real-time feedback and utilizes quantum entanglement to accelerate the adjustment process. The core claim is that this approach will significantly improve the speed and accuracy of QML models compared to traditional static parameter tuning methods. This work introduces a novel dynamic parameter adjustment strategy to overcome the limitations of conventional QML, paving the way for more efficient and robust quantum machine learning. The proposed method explores the potential of quantum entanglement to facilitate rapid parameter adjustments, ultimately offering a pathway to more powerful and adaptable quantum machine learning models.

DOI: 10.5281/zenodo.22153789

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