Quantum-enhanced brain tumor detection using QSVM and VQC

Accurate disease diagnosis is becoming a critical challenge owing to the limited success of classical machine-learning approaches, especially in classification problems. Accurate and rapid brain tumor classification in medical diagnosis could aid in early detection and treatment planning. This paper presents a novel quantum-enhanced machine learning approach using a Quantum Support Vector Machine (QSVM) and Variational Quantum Classifier (VQC) models for improved tumor classification. ZFeatureMap is deployed for the conversion of classical data points to quantum space, as it is found to be most suitable for the characteristics of the brain tumor dataset and provides a balance between accuracy, efficiency, and computational feasibility. The QSVM and VQC models are implemented on a publicly available brain tumor dataset, demonstrating significant improvement over classical algorithms. Both models were implemented on a 32-qubit quantum simulator and a seven-qubit quantum computer. The simulation results indicate that the QSVM-based model is 3.62% more accurate and 60.4 times faster, while the VQC-based model is 2.49% more accurate and twice as fast as classical SVM. The findings suggest that quantum-enhanced machine learning has the scope to revolutionize medical diagnosis with more precise and faster results.