Analysis of Quantum Support Vector Machines in Classification Problems

A typical problem that is solved by supervised machine learning techniques is classification. There are many types of this problem, such as binary classification, multi-classification, and multi-labels. The development of techniques that perform well in different types of classification problems, and for datasets with many or few variables, or many or few data, remains a challenge. On the other hand, recent work indicates that machine learning can benefit from quantum computing in terms of reducing computational complexity and improving its performance. The purpose of this research is to analyze the use of Quantum Support Vector Machines (QSVM) for different types of classification problems. For this, several kernels (linear and Gaussian) are considered. In turn, a comparison is made with Random Forest. Also, a detailed analysis is carried out for the binary classification of coffee beans, obtaining a precision of 95% and an accuracy of 79%.