QUANTUM OPTIMIZATION APPLIED TO OPERATIONS RESEARCH: AN OVERVIEW

This article presents an overview of quantum optimization and its applications in operations research. We explain in an accessible way the main quantum algorithms for combinatorial optimization, such as the Quantum Approximate Optimization Algorithm (QAOA) and Quantum Annealing, and their applications in classic problems such as the traveling salesman problem and the backpack problem. We also discuss the current advantages and limitations of quantum computing to solve optimization problems, and present the future perspectives of this emerging area.