A machine that uses quantum effects to find the minimum energy state of a system.A quantum annealer is a specialized type of quantum computer designed to solve optimization problems by finding the lowest energy state of a physical system. Unlike universal quantum computers that perform general-purpose logic gates, a quantum annealer is tailored for a specific task: mapping a problem onto a set of interacting quantum bits (qubits) and then letting the system evolve to its ground state, which corresponds to the optimal solution. It is particularly effective for problems that can be expressed as finding the minimum of a cost function, such as portfolio optimization, traffic routing, or protein folding.
The concept was introduced in 1998 by Tadashi Kadowaki and Hidetoshi Nishimori, who proposed quantum annealing as a way to solve optimization problems, building on earlier ideas of quantum adiabatic computation by Edward Farhi and colleagues.
The machine starts with all qubits in a superposition of states, representing all possible solutions simultaneously. It then slowly turns on a problem-specific Hamiltonian—the energy function encoding the constraints and objectives—while gradually reducing a transverse field that keeps the qubits in superposition. If the process is slow enough (adiabatic), the system remains in its ground state and ends in the lowest energy configuration, which encodes the answer. The qubits are typically superconducting loops, and the interactions are programmed via magnetic couplings.
Quantum annealers are used in industry for logistics, financial modeling, and machine learning, with commercial devices like those from D-Wave Systems deployed in research labs and companies.
Quantum annealers can sometimes find solutions faster than classical computers, but they are not always faster—the advantage depends on the problem's structure and the noise in the hardware.

