基于自适应的优化算法的量子机器学习

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) is an emerging field that explores the potential of quantum computers to revolutionize machine learning tasks. This paper investigates a novel optimization algorithm, termed "Adaptive Quantum Optimization," designed to address the challenges of training complex quantum machine learning models. We propose a framework that dynamically adjusts quantum circuit parameters based on feedback from quantum state analysis, allowing for more efficient and accurate model training. The core mechanism focuses on leveraging quantum state information to guide the optimization process, ultimately improving model performance. This paper presents a comprehensive analysis of the algorithm's design, implementation, and experimental results, demonstrating its effectiveness in training specific quantum machine learning models. The study highlights the benefits of this adaptive approach compared to traditional methods.

DOI: 10.5281/zenodo.22292074

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