Comparative Analysis of Classification Efficiency of Quantum Machine Learning Algorithms

Machine learning has achieved outstanding results across various applications. The collaboration between quantum physics and machine learning can potentially deliver new and groundbreaking opportunities for scientific exploration and business applications, creating a new and exciting field of research known as quantum machine learning (QML). The quantum devices such as quantum classifiers, designed to address classification challenges in machine learning, have recently garnered significant interest. This study thoroughly examines the research on quantum classifiers with particular emphasis on recent advancements. Various quantum classification algorithms, such as Quantum KNN, Quantum Neural Networks, and Quantum Support Vector Machines, are explored in depth. A detailed comparative analysis of these techniques is conducted to understand real-time scenario. The study is anticipated to come out with insight into the best QML algorithm for classification problems.