Supervised Machine Learning Strategies for Investigation of Weird Pattern Formulation from Large Volume Data Using Quantum Computing

Abstract

AbstractQuantum machine learning [QML] is a new formulation on quantum hardware platform will try to achieve more enhanced data analysis and prediction which classical computer will not be able to generate. Classical computers are having computational limitations in terms of large volume data processing. Quantum machine are not to replace classical machines but quantum computers will solve operational difficulties of classical machines in terms of computational time. Quantum machine learning accelerates the supervised, unsupervised, and reinforcement learning methods. Classical ML methods such as SVM, PCA, Clustering, Neural networks are giving promising results but classical machines are inadequate to perform certain computations. Proposed Quantum machine learning would result in complex and weird patterns. Quantum support vector machine(QSVM) is a method used in supervised learning for classification and regression. QSVM uses high-dimensional feature space possibly on infinite dimension called as enhanced feature space for generating hyperplane. This hyperplane will classify non-linear and complex data on multiclass domain to achieve improved accuracy in less computational time. This paper investigates various strategies for quantum enhanced machine learning algorithm in supervised learning method. KeywordsQuantum computingMachine learningQubitQuantum machine learningSupport vector machine