Research on accelerated solution of SVM classification task based on quantum computing

Classification is a fundamental task in machine learning that aims to categorize input samples into predefined classes based on their features. It has widespread applications in areas such as spam detection and image classification. Support Vector Machines (SVM) are a widely-used supervised learning method that seeks to maximize the margin between decision boundaries, particularly excelling in non-linear classification tasks through kernel functions. In this study, the SVM classification problem is reformulated as an optimization problem and represented using Quadratic Unconstrained Binary Optimization (QUBO) to leverage quantum computing for solving. To enhance the solution process, the simulated annealing algorithm is employed, improving convergence speed and boosting both the efficiency and performance of the classification task.