Quantum Kernel based Support Vector Machine with Quantum Approximate Optimization Algorithm Embedding for improved Lung Cancer Prediction

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%.