Pattern recognition with Quantum Support Vector Machine(QSVM) on near term quantum processors.

Abstract

Machine Learning is boosting up the advancement in the eld of Arti cial Intelligence these days. However, almost every machine learning algorithm contains an optimization problem to solve. Inspired by quantum mechanics, quantum computing is quite a promising approach to solve high complexity optimization problems signi cantly faster and more e cient than classical computers. In this paper, we have worked on a very fundamental supervised learning problem. First, we discuss an approach to map the classical feature points on a quantum computer. Then we propose a Quantum Support Vector Machine(QSVM) model that runs on near term superconducting quantum processors. We show that using quantum optimization it is possible to train a discriminative SVM model that is capable of recognising patterns.