Comparative Study of Quantum to Classical Machine Learning Algorithms for Breast Cancer Classification

Current research paper presents a comparative analysis of classical and quantum machine learning algorithms for breast cancer diagnosis. The study focuses on two classical machine learning algorithms, XGBoost and SVM, along with two quantum models, Variational Quantum Classifier (VQC) and Quantum Support Vector Classifier (QSVC). The performance of the models is evaluated based on F1 score and recall. Results indicated that classic SVM and QSVC showed competitive results considering the other two algorithms regarding F1 score. This work contributes to the growing research on the application of classical and quantum ML algorithms in breast cancer classification.