发表机构
School of Engineering & Technology (UIET), CSJM University（CSJM大学工程技术学院（UIET））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文在乳腺癌数据集上比较量子与经典分类器，发现经典逻辑回归准确率最高（97.8%），量子模型训练慢且未超越经典基线，但良性召回率有竞争力。
AI 中文摘要
一个潜在的前进方向是量子机器学习（QML），其旨在利用量子计算与经典机器学习相结合，以提高计算效率和模型的表达能力。本文在威斯康星州乳腺癌数据集上，将两种量子分类器——变分量子分类器（VQC）和量子核支持向量机（QSVM）——与三种经典分类器作为基线分类器——逻辑回归、支持向量机（SVM）和多层感知器（MLP）——进行了比较。量子电路在PennyLane框架中创建，并在经典后端上进行模拟。然而，在准确性方面，经典逻辑回归表现更好，准确率为97.8%，经典SVM和QSVM的准确率均为95.6%，而量子VQC的准确率较低，为88.9%，且对良性类别的召回率为100%，但仅正确识别了17个恶性病例中的12个（恶性类别召回率约为70.6%）。量子模型的缺点是训练时间较长；然而，由于量子电路需要经典模拟，量子SVM耗时23.29秒，而经典线性模型耗时不到0.01秒。这些结果表明，对于小型结构化数据集，基于量子计算的分类器尚未超越调优良好的经典对应模型。在某些方面（如良性类别召回率），它们表现具有竞争力，但在恶性类别召回率上则不然，尤其是VQC的表现差于经典基线，这值得在真实量子计算机上进一步研究。
英文摘要
A potential path forward is Quantum Machine Learning (QML), which aims to leverage quantum computing in conjunction with classical machine learning to enhance computing efficiency and the expressiveness of models. In this paper, two different quantum classifiers - Variational Quantum Classifier (VQC) and Quantum Kernel Support Vector Machine (QSVM) - are compared with three classical classifiers as baseline classifiers - Logistic Regression, Support Vector Machine (SVM), and a Multi-Layer Perceptron (MLP) - on the Breast Cancer Wisconsin dataset. The quantum circuits were created in the PennyLane framework and simulated on a classical backend. However, in terms of accuracy, classical Logistic Regression performed better with an accuracy of 97.8%, classical SVM and QSVM with an accuracy of 95.6% each, although the Quantum VQC achieved a lower accuracy of 88.9% and had a recall of 100% for the benign class, though it correctly identified only 12 of the 17 malignant cases (a malignant-class recall of approximately 70.6%). The drawback of quantum models is the higher training time; however, since the quantum circuit needs to be classically simulated, the quantum SVM took 23.29 seconds compared to less than 0.01 seconds for the classical linear models. These results indicate that for small structured datasets, classifiers based on quantum computing have not yet surpassed well-tuned classical counterparts. In some respects (e.g., benign-class recall), they perform competitively, though not on malignant-class recall, where the VQC in particular performed worse than the classical baselines, which is worth further investigation on real quantum computers.
