Quantum Enhanced Support Vector Machine with Instantaneous Quantum Polynomial Encoding for Improved Cyclone Classification

The paradigm of classical machine learning has been greatly revolutionized by the integration of the quantum computing paradigm, which has made it possible to solve complicated issues that were previously unsolvable by conventional computers. One such problem is accurately classifying cyclones with minimal data samples and feature parameters. In this paper, we investigate the superior learning capabilities of the Instantaneous Quantum Polynomial Embedding-based kernel Support Vector Machine classifier when compared to the classical Support Vector Machine (SVM) classifier, especially in scenarios with less sample size and feature vector size. Surprisingly, our outcomes reveal that the quantum kernel strategy outperforms its traditional counterpart, even with a smaller sample-based dataset. This incredible performance can be attributed to the quantum core strategy’s exceptional ability to extract complex attributes, which enables it to extract more relevant insights from a smaller number of data points. We present a Quantum Kernel Support Vector Machine (QKSVM) built as a Quantum Kernel Circuit (QKC) in three configurations with varied qubit counts utilizing distinct embedding techniques: Angle embedding (QKSVM Angle), Instantaneous Quantum Polynomial embedding (QKSVM IQP), and Quantum Approximation Optimization Algorithm (QKSVM QAOA) embedding. Our results demonstrate that the QKSVM approach surpasses traditional SVM, especially when IQP embedding is employed. The QSVM model trained with four Principal Component Analysis (PCA) based selected relevant features outperforms the other QKC variations in cyclone classification, obtaining an excellent test accuracy rate of 91.66%.