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Performance Analysis of Quantum Machine Learning Classifiers
Pablo Rivas · Javier Orduz

Tue Dec 07 01:21 PM -- 01:26 PM (PST) @

In recent years, researchers have started looking into data transformations in quantum computation. They want to see how quantum computing affects the robustness and performance of machine learning methods. Quantum mechanics succeed in explaining some phenomena where classical formulas failed in the past. Thus, it expanded in analytical research fields such as Quantum Machine Learning (QML) over the years. The developing QML discipline has proven solutions to issues that are equivalent (or comparable) to those addressed by classical machine learning, including classification and prediction problems using quantum classifiers. As a result of these factors, quantum classifier analysis has become one of the most important topics in QML. This paper studies four quantum classifiers: Support Vector Classification with Quantum Kernel (SVCQK), Quantum Support Vector Classifier (QSVC), Variational Quantum Classifier (VQC), and Circuit Quantum Neural Network Classifier (CQNNC). We also report case study outcomes and results analysis utilizing linearly and non-linearly separable datasets generated. Our research is to explore if quantum information may aid learning or convergence.

Author Information

Pablo Rivas (Baylor University)
Javier Orduz (Baylor University)

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