Quantum Annealing

Quantum annealing is a metaheuristic optimization technique leveraging quantum mechanics to solve complex combinatorial problems, primarily those expressible as Quadratic Unconstrained Binary Optimization (QUBO) problems. Current research focuses on improving the efficiency and solution quality of quantum annealing through advancements in algorithms like Quantum Approximate Optimization Algorithm (QAOA), hybrid quantum-classical approaches, and refined problem formulations (e.g., incorporating adaptive learning or function smoothing). These efforts aim to enhance the applicability of quantum annealing to diverse fields, including machine learning, computer vision, and logistics optimization, by addressing limitations in scalability and solution accuracy compared to classical methods.