Optimization is fundamental in engineering and other scientific disciplines, addressing problems, such as resource allocation, process efficiency, and optimal design. Classical optimization methods rely on iterative computations and often struggle with complex, high-dimensional problems due to exponential scaling. Quantum optimization leverages principles of quantum mechanics, such as superposition and entanglement, to explore solutions more efficiently. This section introduces the quantum approximate optimization algorithm (QAOA), compares classical and quantum optimization methods, and explores quantum-inspired techniques for classical systems.