Quantum Machine Learning: Adaptive Quantum Potential Calculation

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) offers the potential to revolutionize various fields, including machine learning, by leveraging the unique properties of quantum systems. One promising approach is adaptive quantum potential calculation, which dynamically adjusts the parameters of quantum circuits to optimize the system's sensitivity towards specific states. This paper proposes a novel framework for QML, utilizing adaptive quantum potential calculation to learn quantum system sensitivities and enhance the training and optimization efficiency of quantum machine learning models. We present a new method for parameter tuning based on a dynamic adjustment of quantum potential, demonstrating significant improvements in model performance across several benchmark tasks. The proposed framework addresses a critical challenge in QML – the need for flexible and adaptive parameterization – leading to more efficient and robust quantum algorithms. The paper concludes with a discussion of the potential applications and future directions of this approach.

DOI: 10.5281/zenodo.22292060

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