Design and Evaluation of a Quantum Kernel Support Vector Classifier using Qiskit for Hybrid Quantum–Classical Binary Classification

Abstract

Over The past decade, quantum computing and machine learning have each witnessed tremendous growth in their respective fields. While quantum computing promises a completely novel paradigm for computation, based on the principles of superposition, entanglement, and interference, machine learning has firmly cemented its place in the sphere of data-driven decision-making. This paper seeks to provide a practical solution to this question, implementing a quantum classical learning system that is capable of performing binary classification tasks. We achieve this through the creation of a Quantum Kernel Support Vector Classifier (QSVC) using the Qiskit framework. While the classical feature set from the Iris dataset is scaled appropriately for the quantum system, the feature set is then represented in the quantum state through the ZZFeatureMap circuit. The QSVC utilizes the kernel-based similarity between quantum states. The results were promising, with the QSVC obtaining a classification accuracy of 92% over a test set, with the model trained in 6.32 seconds on a simulated quantum backend. The decision boundaries learned by the model were complex, and the quantum kernel representation of the feature set was able to capture the underlying class structure, which would have been difficult to achieve in a classical low-dimensional feature space. Our method, with quantum circuits relegated to state preparation and measurement while leaving the optimization to classical processes, is designed to operate within these limits. This makes our method not only theoretically interesting but also immediately applicable to existing simulators and quantum hardware.