Quantum annealing and adiabatic quantum computation (AQC) are two related but distinct models of quantum computation. While they both employ the principles of adiabatic evolution, they have differences in terms of their goals and specific implementations.
Goal:
- Quantum Annealing: Quantum annealing is primarily designed to solve optimization problems. It focuses on finding the global minimum of a given objective function by mapping it to the ground state of a corresponding quantum system.
- Adiabatic Quantum Computation: AQC, on the other hand, is a more general model of quantum computation that can potentially solve a broader range of computational problems, including optimization problems as a subset. AQC aims to find the ground state of a quantum system corresponding to the solution of the computational problem.
Algorithmic Approach:
- Quantum Annealing: Quantum annealing uses a technique inspired by classical simulated annealing, where a quantum system starts in a simple initial state and evolves gradually towards the target state (ground state) by slowly modifying the system's Hamiltonian.
- Adiabatic Quantum Computation: AQC follows the adiabatic theorem of quantum mechanics, which states that if a system is prepared in the ground state of a known Hamiltonian and the Hamiltonian is slowly changed, the system will remain in the instantaneous ground state. In AQC, the quantum system starts in a known initial state (usually a superposition of all possible states) and evolves adiabatically to the final state corresponding to the solution of the computational problem.
Hardware Implementations:
- Quantum Annealing: Quantum annealing machines, such as those developed by D-Wave Systems, are specifically designed to implement quantum annealing. They utilize superconducting qubits and programmable couplers to solve optimization problems.
- Adiabatic Quantum Computation: AQC can be implemented on various types of quantum hardware, including superconducting qubits, trapped ions, or topological qubits. The implementation requires careful engineering of the quantum system's Hamiltonian and the control over the parameters governing the adiabatic evolution.
Computational Power:
- Quantum Annealing: Quantum annealing is expected to excel in certain optimization problems, such as finding the lowest energy state of a complex system or optimizing a given objective function with many variables. However, it may not be as versatile for general-purpose computation as other models.
- Adiabatic Quantum Computation: AQC has the potential to solve a broader class of problems beyond optimization, making it more suitable for general-purpose quantum computation. However, the computational power and efficiency of AQC depend on the specific problem and the quality of the hardware implementation.
In summary, quantum annealing is a specific application of adiabatic quantum computation that focuses on solving optimization problems, while AQC is a more general model that encompasses quantum annealing but has a broader scope for solving various computational problems.