Insights into Quantum Support Vector Machine

Abstract Machine learning through Quantum computing principles has been found to take an edge in the artificial intelligence and machine learning domains. Both domains have similarities in terms of higher-dimensional computing through complex linear algebra. This has given way to Quantum Machine learning. To work with available quantum computers to a greater extent, quantum machine learning algorithms are designed to work on Nearly Intermediate State Quantum (NISQ) devices using hybrid quantum-classical methods of execution. IBM Qiskit quantum framework with existing classical machine learning frameworks like SK-Learn and tensor-flow is used for this purpose of the study on how a Quantum support vector machine algorithm can be more effectively used over the classical support vector machine when classical support vector machines lack the computation power. In this paper, how quantum principles are involved in gaining the advantages and limitations of using quantum machines are discussed with an example of experimentation run on quantum machines and classical machines using the IBM Qiskit framework.