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Comparative Study of Quantum and Classical Machine Learning Models in Binary Classification

arXiv · AI, language, vision and robotics · article · Sep 20, 2026 · UTC

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 PennyL

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Evidence & attribution

First collected: 2026-09-23T10:01:48.231Z. This is not the publication date.