标题： 经典与量子机器学习相结合用于腺癌和鳞状细胞癌患者的分类 显示英文标题

标题： An Amalgamation of Classical and Quantum Machine Learning For the Classification of Adenocarcinoma and Squamous Cell Carcinoma Patients

摘要： 准确分类疾病亚型的能力至关重要，尤其是在肿瘤学中，这种能力可能具有挽救生命的影响。 在这里，我们报告了非小细胞肺癌两种亚型之间的分类，即腺癌与鳞状细胞癌。 数据包括104名患者的约20,000个基因表达值。 数据来源于[1] [2]。 我们使用了经典和量子机器学习模型的组合，成功地对这些患者进行了分类。 我们除了使用XGBoost [3]外，还采用了基于单变量统计的特征选择方法。 还使用了一位作者开发的一种新颖且专有的数据表示方法，称为QCrush，因为它设计用于在D-Wave量子退火计算机的尺寸限制下包含最大量的信息。 机器学习是由一个量子玻尔兹曼机完成的。 本文将报告我们的结果、各种经典方法以及我们采用的量子机器学习方法。 显示英文摘要

摘要： The ability to accurately classify disease subtypes is of vital importance, especially in oncology where this capability could have a life saving impact. Here we report a classification between two subtypes of non-small cell lung cancer, namely Adeno- carcinoma vs Squamous cell carcinoma. The data consists of approximately 20,000 gene expression values for each of 104 patients. The data was curated from [1] [2]. We used an amalgamation of classical and and quantum machine learning models to successfully classify these patients. We utilized feature selection methods based on univariate statistics in addition to XGBoost [3]. A novel and proprietary data representation method developed by one of the authors called QCrush was also used as it was designed to incorporate a maximal amount of information under the size constraints of the D-Wave quantum annealing computer. The machine learning was performed by a Quantum Boltzmann Machine. This paper will report our results, the various classical methods, and the quantum machine learning approach we utilized.