Quantum annealing is a quantum computing technique designed to solve optimization problems by finding the lowest energy state of a system. Unlike classical optimization methods, which often require iterative processes to converge on a solution, quantum annealing leverages quantum mechanics principles to explore potential solutions more efficiently. In essence, it uses a process similar to thermal annealing in metallurgy to guide a system from a higher energy state to a lower one, where the optimal solution resides.
The process begins with the formulation of the optimization problem as a Hamiltonian, a mathematical representation that describes the system's energy. Developers set up a quantum system based on this Hamiltonian, where each possible solution corresponds to a quantum state. The quantum annealer starts in a superposition of all potential states, allowing it to explore multiple solutions simultaneously. As the system is gradually cooled down, the probabilities of the quantum states evolve, allowing the system to settle into the lowest energy state, which is the solution to the optimization problem.
For example, imagine a traveling salesman problem, where the goal is to determine the shortest possible route visiting a set of cities. A quantum annealer would encode the different routes into the Hamiltonian, explore all possibilities at once, and then transition through states as it finds lower energy configurations. At the end of the annealing process, the system collapses into a state representing the optimal route. This ability to examine multiple solutions at once can significantly reduce the time needed to solve complex optimization problems compared to traditional methods.