Quantum annealing is a quantum computing method used to find optimal or near-optimal solutions to complex optimization problems by exploiting quantum mechanics—specifically, the adiabatic theorem.
How It Works:
The process involves preparing a quantum system in the ground state (lowest energy configuration) of a simple initial Hamiltonian (energy landscape). Then, over time, the system’s Hamiltonian is slowly evolved into one that encodes the problem to be solved.
According to the adiabatic theorem, if this evolution is done slowly enough, the system remains in its ground state throughout. When the process ends, measuring the final state yields the optimal solution to the problem.
Key Features:
- Used for optimization: Especially suited for problems like the traveling salesman, portfolio optimization, or protein folding.
- Relies on tunneling: Quantum systems can tunnel through energy barriers, potentially escaping local minima more effectively than classical systems.
- Implemented in hardware: D-Wave Systems has built commercial quantum annealers using this principle.
Limitations:
- Unlike universal quantum computers, quantum annealers are specialized for specific optimization tasks.
- Success depends on slow evolution, noise control, and problem mapping quality.
Quantum annealing offers a practical approach to solving difficult problems by harnessing the unique behavior of quantum systems during energy minimization.