What question did this study set out to answer?

The aim is to enhance breast cancer classification using a hybrid quantum model that combines QSVM and QNN.

March 5, 2026Open Access

A hybrid QSVM-QNN approach for breast cancer diagnosis using quantum feature maps

Key Points

The aim is to enhance breast cancer classification using a hybrid quantum model that combines QSVM and QNN.
Proposed a hybrid QSVM and QNN model for classification on the Breast Cancer Wisconsin Dataset.
Utilized fidelity quantum kernels with three feature maps: Z, ZZ, and Pauli.
Implemented a variational quantum classifier via EstimatorQNN for optimal feature extraction.
Conducted experiments on IBM’s Qiskit Aer simulator to assess performance with seven principal features.
Evaluated performance metrics including accuracy, recall, precision, F1-score, and confusion matrix.
Achieved above 90% classification accuracy with the hybrid model.
Demonstrated improvements in criteria such as recall and precision over classical methods.
Reduced computational costs while maintaining high accuracy compared to traditional models.

Abstract

Quantum assisted machine learning (QML) have the potential to outperform the classical Machine Learning models by utilizing the quantum feature maps embeddings and variational circuits. In this study, a Hybrid Quantum Support Vector Machine (QSVM) and Quantum Neural Network (QNN) is proposed for supervised classification applied to the Breast Cancer Wisconsin Dataset. The QSVM component employs fidelity quantum kernels with three distinct feature maps—Z, ZZ and Pauli—to transform classical data into quantum states. The QNN is implemented using a variational quantum classifier based on the EstimatorQNN, which enables quantum-trainable parameters for enhanced feature extraction. The proposed hybrid methodology is evaluated using seven principal features, and experiments are conducted on IBM’s state vector Qiskit Aer local simulator. Performance is assessed based on accuracy, recall, precision, F1-score, and confusion matrix. Additionally, statistical significance is evaluated using paired t test and F test to compare the performance of different quantum feature maps and to validate improvements made in the Hybrid Model. The results demonstrate that the hybrid QSVM/QNN approach improves classification performance not only in terms of accuracy by achieving above 90% accuracy but also by reducing computational cost. This work contributes to the advancement of quantum enhanced machine learning (ML) algorithms by demonstrating the hybrid quantum-classical approach in structured data classification.

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