Abstract Least Squares Support Vector Machine (LS-SVM) is a reformulation of the Support Vector Machine(SVM) algorithm that uses a hyperplane to classify elements in a dataset, with the coefficients of the hyperplane being determined by solving a linear system of equations. A quantum algorithm, the Harrow-Hassidim-Lloyd (HHL) algorithm, has the potential to offer exponential advantages over classical algorithms for solving linear systems, although an efficient implementation is yet to be developed. The current era of quantum computing presents a series of technical challenges for implementing and executing circuits, with smaller circuits being less susceptible to noise. In response to this scenario, circuit reduction optimizations have been developed to enhance execution quality. This paper presents the process of using optimized HHL quantum circuits to generate LS-SVMs applied to the classification of pulsar candidates, where the quantum generated SVMs closely matched the performance of their classical counterparts and obtained competitive results against other Quantum Machine Learning methods even when using a real quantum device, with the optimized circuits having a significant execution speedup over the original quantum circuits.
Pinheiro et al. (Wed,) studied this question.