Cancer classification based on gene expression increases early diagnosis and recovery, but high-dimensional genes with a small number of samples are a major challenge. This work introduces a new hybrid quantum-kernel support vector machine (QKSVM) combined with a Binary Harris hawk optimization (BHHO)-based gene selection for cancer classification on a quantum simulator. This study aims to improve the microarray cancer prediction performance with the quantum kernel estimation based on the informative genes by BHHO. The feature selection is a critical step in large-dimensional features, and BHHO is used to select important features. The BHHO mimics the behavior of the cooperative action of Harris hawks in nature. The principal component analysis (PCA) is applied to reduce the selected genes to match the qubit numbers. After which, the quantum computer is used to estimate the kernel with the training data of the reduced genes and generate the quantum kernel matrix. Moreover, the classical computer is used to draw the support vectors based on the quantum kernel matrix. Also, the prediction stage is performed with the classical device. Finally, the proposed approach is applied to colon and breast microarray datasets and evaluated with all genes and the selected genes by BHHO. The proposed approach (QKSVM{PCA{BHHO) is found to enhance the overall performance with two datasets. Also, the proposed approach is evaluated with different quantum feature maps (kernels) and classical kernel (RBF).
Cancer classification based on gene expression increases early diagnosis and recovery, but high-dimensional genes with a small number of samples are a major challenge. This work introduces a new hybrid quantum-kernel support vector machine (QKSVM) combined with a Binary Harris hawk optimization (BHHO)-based gene selection for cancer classification on a quantum simulator. This study aims to improve the microarray cancer prediction performance with the quantum kernel estimation based on the informative genes by BHHO. The feature selection is a critical step in large-dimensional features, and BHHO is used to select important features. The BHHO mimics the behavior of the cooperative action of Harris hawks in nature. The principal component analysis (PCA) is applied to reduce the selected genes to match the qubit numbers. After which, the quantum computer is used to estimate the kernel with the training data of the reduced genes and generate the quantum kernel matrix. Moreover, the classical computer is used to draw the support vectors based on the quantum kernel matrix. Also, the prediction stage is performed with the classical device. Finally, the proposed approach is applied to colon and breast microarray datasets and evaluated with all genes and the selected genes by BHHO. The proposed approach (QKSVM{PCA{BHHO) is found to enhance the overall performance with two datasets. Also, the proposed approach is evaluated with different quantum feature maps (kernels) and classical kernel (RBF).
The integration of classical machine learning with quantum computing has sparked a remarkable revolution, opening avenues to tackle previously insurmountable challenges in diverse fields, particularly in clinical applications. One such challenging task is to predict lung cancer with minimal clinical samples and features. The prominent objective of this proposed research is to introduce a novel Quantum Kernel Support Vector Machine (QKSVM) constructed with the Quantum Kernel Circuit (QKC) enriched with Quantum Approximation Optimization Algorithm (QAOA) embedding circuit to classify highly complex lung cancer using a minimal number of samples and minimal feature dimensions. In this context, to compare the classification performance of the transformative potential of the proposed QAOA embedding QKSVM classifier model, we implemented two other variants of the QKC leveraging Angle embedding (Angle Embedding QKSVM) and Instantaneous Quantum Polynomial embedding (IQP Embedding QKSVM). We also compared their performance with that of a classical SVM classifier. In this study, we delve into the transformative potential classification performance of the proposed QAOA Embedding-based QKSVM classifier, which is contrasted particularly in scenarios with limited sample size and feature dimensions. Our investigation reveals the quantum kernel’s superiority, even when operating with smaller datasets. This remarkable performance is attributed to the quantum models’ robustness in extracting intricate diagnosing features, enabling it to extract more pertinent insights from fewer data points. Notably, the QKSVM model trained with a significantly selected feature vector of 4-Dimensions (4D) with Principal Component Analysis (PCA) emerges as the frontrunner in lung cancer predictions, achieving an outstanding test accuracy of 94.64% and the highest area under curve (AUC) metric of 82.33%.
This paper presents an empirical comparison of pure quantum, hybrid quantum-classical, and classical support vector machines (SVMs) for binary medical classification on the Breast Cancer Wisconsin dataset. Taking advantage of uniform data preprocessing and feature selection, our hybrid QSVM with quantum kernel mapping coupled with classical optimization achieves accuracy of 98.25% compared with the classical SVM (94.74%) and pure quantum SVM (93.86%). In-depth benchmarking incorporates accuracy, recall, F1-score, and training time, exposing accuracy/computational cost trade-offs. We discuss how dimensionality of features and kernel selection influence model scalability and performance. Albeit constrained to a single dataset and simulation, this work enhances knowledge about hybrid quantum SVM benefits and real-world implications. Future efforts will seek to expand benchmarking to other datasets, include real quantum hardware assessment, and create explanation methods to improve interpretability and clinical usability.
Quantum Support Vector Machines (QSVMs) have recently emerged as a promising approach for biomedical classification tasks. However, their practical deployment remains constrained by limited qubit availability and sensitivity to high-dimensional feature spaces. This study proposes a hybrid framework integrating Pearson correlation–based feature selection and Principal Component Analysis (PCA) with QSVM for breast cancer diagnosis. First, Pearson correlation analysis is employed to remove redundant and weakly relevant features. Subsequently, PCA projects the selected attributes into a compact subspace while preserving most of the original data variance. This dimensionality reduction strategy decreases the number of qubits required for quantum encoding and improves computational efficiency. Experiments conducted on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset demonstrate that the proposed hybrid QSVM achieves 98% classification accuracy, outperforming or matching existing classical and quantum-based approaches. The results confirm that combining classical preprocessing techniques with quantum classifiers provides a robust and resource-efficient solution for biomedical data analysis.
Subtyping of lung cancer is crucial for individualized treatment and optimizing outcomes. Computational modeling allows data analysis over large scales, which helps in making precision classification in large datasets or high-dimensional datasets. Conventional machine learning methods perform well, but integration of multi-omic data (DNA, RNA, miRNA) is challenging in cancer subtyping because of high dimensionality and complex inter-omic relationships. To bypass this Quantum machine learning (QML) provides better ability to navigate high-dimensional biological data with many variables using quantum computational concepts that can identify fine patterns or small-scale patterns beyond the capability of classical approaches. Through the integration of quantum computing principles with the machine learning framework, the study highlights the improvement of computer technology. The framework shows how the system is optimized with the help of quantum principles, how it can improve accuracy, parallel processing and reducing latency. In this work, we have combined data on miRNA, RNA expression, and DNA methylation to create a complete workflow for classifying cancer. This method uses a wide variety of feature selection techniques, such as random forest-based selection, chi-square, mutual information(MI), and principal component analysis(PCA).In order to illustrate the notion of quantum advantage, we carefully compare the performance metrics of the Quantum Support Vector Classifier (QSVC) to the classical Support Vector Machine (SVM), using a ZZ feature map quantum kernel as our baseline for analysis. Using five-fold crossvalidation, we have also verified that the quantum has an advantage with consistently lower variance and higher accuracy.
Early and accurate diagnosis of breast cancer plays a critical role in improving patient survival rates and reducing treatment costs. Classical machine learning algorithms such as Logistic Regression and Support Vector Machines (SVM) have demonstrated strong performance in medical diagnosis tasks; however, their scalability and representational limits motivate the exploration of emerging paradigms. Quantum Machine Learning (QML), particularly Variational Quantum Classifiers (VQC), has recently gained attention due to its potential to model complex decision boundaries using quantum circuits. This paper presents a comparative analysis between a Variational Quantum Classifier and classical machine learning models for binary breast cancer classification. The study employs dimensionality-reduced clinical data and evaluates model performance using accuracy, confusion matrices, decision boundary visualization, Principal Component Analysis (PCA), and Receiver Operating Characteristic (ROC) curves. Experimental results indicate that the VQC achieves competitive performance with classical approaches, demonstrating its feasibility for small-scale medical datasets and highlighting the promise of quantum-enhanced learning in healthcare applications.
The application of machine learning has shown significant progress in various fields, including clinical data classification. This study presents a comparative analysis between the classical Support Vector Machine (SVM) and Quantum Support Vector Machine (QSVM) for breast cancer classification using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. Principal Component Analysis (PCA) was applied as dimension reduction strategy across three configurations (PCA = 2, PCA = 4, and PCA = 6) to evaluate the sensitivity of both models to the feature space dimension. Preprocessing included log1p transformation on 22 skewed features, Manual SMOTE balancing, and MinMaxScaler normalization. SVM was optimized via GridSearchCV (80 parameter combinations, cv = 5), while QSVM employed a ZZFeatureMap quantum kernel (reps = 2, entanglement = full) on qasm_simulator. Experimental results demonstrate that SVM achieved superior accuracy across all configurations, with peak performance at PCA = 6 (Accuracy= 98.83%, AUC = 0.9985, Specificity = 1.0000, Sensitivity = 0.9688, Precision = 1.0000, NPV = 0.9817, and F1-score for malignant = 0.98). QSVM achieved its highest accuracy at PCA = 4 (92.98%), AUC = 0.9622, Sensitivity = 0.9219, Precision = 0.8939, NPV = 0.9524, and F1-score for malignant = 0.91. Across the three PCA configurations, QSVM exhibited a non-monotonic relationship between PCA dimensions and quantum kernel performance . These findings provide a quantitative benchmark for comparing quantum and classical machine learning in breast cancer diagnosis.