Optimization is a fundamental process in mathematics and computer science that involves finding the most effective and optimal solution from all possible solutions to a problem. It plays a crucial role in various fields, such as engineering, finance, logistics, and machine learning, by enhancing efficiency, reducing costs, and improving performance. Optimization problems can be categorized into single-objective problems, where a single criterion is maximized or minimized, and multi-objective problems, which involve balancing multiple, often conflicting, objectives. Traditional optimization methods, such as linear, nonlinear, and integer programming, have been extensively used to address such problems. However, with growing problem size and complexity, these methods are at a disadvantage with regard to issues such as computational cost, scalability, and escaping local minima in non-convex spaces. This has necessitated the development of new methods, such as quantum optimization, which promises to eliminate these problems by leveraging the potential of quantum computing.