Comparison of classical machine learning approaches with hybrid quantum approaches in applied problems

S.K. Akhmed
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Abstract

The work is aimed at analyzing the potential advantages of using quantum approaches in applied problems of artificial intelligence. In this paper, the task of classifying medical images extracted from histopathological images of sections of lymph nodes is set. The theoretical basis used for the construction of quantum and hybrid-quantum computing elements used in the article will be given. Quantum analogues of classical machine learning algorithms and neural networks will be considered. The paper will give a step-by-step description of the data transformation, the construction of models and their training, followed by an analysis of the results obtained and the performance of the simulation of quantum computing.

经典机器学习方法与混合量子方法在应用问题中的比较
这项工作旨在分析在人工智能应用问题中使用量子方法的潜在优势。本文设定了从淋巴结切片的组织病理图像中提取医学图像进行分类的任务。本文将给出用于构建量子和混合量子计算元件的理论基础。将考虑经典机器学习算法和神经网络的量子类似物。本文将逐步描述数据转换,模型的构建及其训练,然后分析所获得的结果和量子计算模拟的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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