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Lecture notes in networks and systems

Beyond Classical Limits: How Quantum SVMs and Feature Maps Revolutionize Machine Learning

Asif Mohammed Saad, Kh Ehsanur Rahman, Md. Farhan Shahriyar, Yuldasheva Gulora, Ha Meem Hossain

Published Jan 1, 2026• 0 citations
Publisher
DOI
10.1007/978-3-032-15764-5_44

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This research study reviews the quantum kernel techniques and discusses about the application of a quantum machine learning model for the classification of breast cancer using Qiskit Machine learning version 0.4.0. This study applies ZZ-Feature Map to transform classical computer data into quantum states through the use of entanglement and superposition qualities characteristic of quantum mechanics. Then, a Quantum Kernel was created using the feature map and quantum instance (simulator) to compute similarity in data points in the space of a quantum state. It utilized this kernel to train QSVC - a quantum analogue of classical Support Vector Machine (SVM) with breast cancer data. With just two pivoting features for categorization, the resource limits obtained 70% overall accuracy. Preliminary Findings Using quantum kernel techniques improves the classification jobs via quantum computation.