Automation of the Quantum Algorithm HHL for implementing two-dimensional SVMs

Support Vector Machine (SVM) is considered one of the main classification Machine Learning algorithms. Following the original formulation,an SVM generation has quadratic complexity, leaving room for exploring resolution methods with better performance. One way to enhance its efficiency is by utilizing Quantum Computing algorithms, such as the HHL. This work presents an automation of a Quantum Machine Learning algorithm that uses HHL to generate SVMs fixed at the origin of a two-dimensional hyperplane.