基于自适应的量子机器学习算法的优化

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) offers the potential to revolutionize machine learning by leveraging the unique properties of quantum systems. However, scaling QML algorithms to complex problems remains a significant challenge. This paper introduces a novel quantum machine learning algorithm, termed Adaptive Quantum Optimization (AQO), designed to dynamically adjust quantum state parameters during training, thereby optimizing quantum model performance. The algorithm's core mechanism hinges on a self-adaptive optimization process, leveraging the inherent quantum properties to efficiently explore the parameter space and improve the accuracy and speed of quantum machine learning models. We demonstrate the efficacy of AQO through a series of theoretical analyses and preliminary experiments on a simplified quantum model, showcasing its ability to outperform conventional approaches in terms of convergence speed and model fidelity. The core claim is that AQO can significantly enhance the performance of quantum machine learning algorithms by dynamically optimizing quantum states.

DOI: 10.5281/zenodo.22293687

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