Enhancing quantum support vector machine for healthcare applications using custom feature maps

Quantum support vector machine (QSVM), based on the principles of quantum mechanics has revolutionized complex data processing tasks in several healthcare applications. Feature maps play a crucial role in transforming input data into a higher-dimensional space, enabling QSVM to capture intricate patterns and improve classification performance. This study intends to further enhance the performance of the QSVM by introducing five new custom feature maps. Furthermore, the study assesses the performance of these enhancements to the QSVM by empirically validating it for classification on four medical open-source datasets. The performance of QSVM using the custom feature maps is also compared with two standard feature maps (ZFeatureMap and ZZFeatureMap) available in Qiskit framework. The results indicate that custom feature maps outperform standard ones with an increase of up to 5% in Area Under Receiver Operating Characteristic Curve (AUC) values, 18% in F1-score, and 25% in Matthews Correlation Coefficient (MCC) values.