[论文解读] Evaluating the Impact of Different Quantum Kernels on the Classification Performance of Support Vector Machine Algorithm: A Medical Dataset Application

Emine Akpinar, Sardar M. N. Islam+1 more|arXiv (Cornell University)|Jul 13, 2024

Artificial Intelligence in Healthcare被引用 5

一句话总结

该论文评估不同量子特征映射在两份医学数据集上的 QSVM-Kernel 分类准确度和运行时的影响，识别能带来最佳性能的特征映射。

ABSTRACT

The support vector machine algorithm with a quantum kernel estimator (QSVM-Kernel), as a leading example of a quantum machine learning technique, has undergone significant advancements. Nevertheless, its integration with classical data presents unique challenges. While quantum computers primarily interact with data in quantum states, embedding classical data into quantum states using feature mapping techniques is essential for leveraging quantum algorithms Despite the recognized importance of feature mapping, its specific impact on data classification outcomes remains largely unexplored. This study addresses this gap by comprehensively assessing the effects of various feature mapping methods on classification results, taking medical data analysis as a case study. In this study, the QSVM-Kernel method was applied to classification problems in two different and publicly available medical datasets, namely, the Wisconsin Breast Cancer (original) and The Cancer Genome Atlas (TCGA) Glioma datasets. In the QSVM-Kernel algorithm, quantum kernel matrices obtained from 9 different quantum feature maps were used. Thus, the effects of these quantum feature maps on the classification results of the QSVM-Kernel algorithm were examined in terms of both classifier performance and total execution time. As a result, in the Wisconsin Breast Cancer (original) and TCGA Glioma datasets, when Rx and Ry rotational gates were used, respectively, as feature maps in the QSVM-Kernel algorithm, the best classification performances were achieved both in terms of classification performance and total execution time. The contributions of this study are that (1) it highlights the significant impact of feature mapping techniques on medical data classification outcomes using the QSVM-Kernel algorithm, and (2) it also guides undertaking research for improved QSVM classification performance.

研究动机与目标

• 通过解决特征映射如何影响量子核方法在与经典数据配对时的研究动机。
• 使用九种不同的量子特征映射在医学数据集上评估 QSVM-Kernel 性能。
• 在特征映射之间比较分类准确性和总执行时间。
• 为在医学数据中改进 QSVM 分类结果提供选择特征映射的指导。

提出的方法

• 应用 QSVM-Kernel，使用来自九种不同量子特征映射的量子核矩阵。
• 使用两份公开可用的医学数据集：Wisconsin Breast Cancer (original) 和 TCGA Glioma。
• 评估每个特征映射的分类性能和总执行时间。
• 确定哪些特征映射（例如 Rx 和 Ry 旋转门）在每个数据集上产生最佳结果。
• 分析特征映射选择与计算效率之间的关系。

实验结果

研究问题

• RQ1 不同的量子特征映射如何影响医学数据上的 QSVM-Kernel 分类性能？
• RQ2 在所考察数据集上，哪些特征映射在准确性和执行时间之间实现权衡的优化？
• RQ3 具体门（例如 Rx、Ry）是否在不同数据集上始终产生更优的结果？

主要发现

• Rx 与 Ry 旋转门在 Wisconsin Breast Cancer 数据集和 TCGA Glioma 数据集上实现了最佳分类性能和执行时间。
• 本研究显示特征映射技术对使用 QSVM-Kernel 的医学数据分类结果具有显著影响。
• 提供了通过合适的特征映射设计 QSVM 分类以提高性能的指导。