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Published in Splinter Robert, Illustrated Encyclopedia of Applied and Engineering Physics, 2017
[computational, engineering, solid-state] Two different meanings are associated with annealing, one for the solid-state engineering application and the other in computational science. In material properties, annealing references a heat treatment. The surface may be chemically altered to make it more resistant to scratches or thermally modified to make the material resilient to applied force in order to induce geometric modifications. Rapid heating and slow cooling will reduce the mechanical strength, whereas regular heating and fast cooling can increase the mechanical strength, as used to give hardness to the blade of a sword. In computational sciences, annealing of data refers to the process of reducing the solution domain in order to limit the complexity of a system, making it unsolvable. In quantum mechanics, the quantum annealing technique provides the perturbation mechanism of action for combinatorial optimization of a ground state problem for systems with a crystal structure. The crystalline, or glassy dynamics, refers to developing process at extreme slow incremental evolution, also referred to as “relaxation.” The relaxed state will have amorphous quantum states on macroscopic scale. In quantum mechanics, the quantum annealing provides the tools to solve for the time-dependent Schrödinger equation in a real-time process within a range of constraints and approximations.
Application in Adiabatic Quantum Annealing
Published in Edward Wolf, Gerald Arnold, Michael Gurvitch, John Zasadzinski, Josephson Junctions, 2017
Adiabatic quantum computing is a viable alternative to gate model quantum computing. In AQC the system stays closely to the timedependent instantaneous ground state and thus is expected to be more robust than GMQC against environment noise which ultimately destroy quantum coherence and entanglement. Quantum annealing is a special type of ground state quantum computing. Although QA is not equivalent to universal GMQC it is an effective strategy for solving difficult optimization problems such as ISG and problems with a large number of binary variables.
Evolutionary Computation and Swarm Intelligence
Published in Soumya D. Mohanty, Swarm Intelligence Methods for Statistical Regression, 2018
A promising new physics-based approach, called quantum annealing, is emerging on the horizon that has the potential to revolutionize how optimization problems are solved [3]. This approach is based on programming an optimization problem onto a new type of computing hardware [22], called a quantum annealer, that exploits the laws of quantum mechanics. While major corporations are already working on testing this approach, it remains to be seen whether and when it becomes mainstream.
Quantum computing to solve scenario-based stochastic time-dependent shortest path routing
Published in Transportation Letters, 2023
Vinayak V. Dixit, Chence Niu, David Rey, S. Travis Waller, Michael W. Levin
Quantum computational engines based on quantum annealing are fundamentally different, for example D-Wave quantum computers. In classical computing, a bit can only be in one of two states – either 0 or 1. Therefore, two classical bits can represent four possible combinations: (0,0), (0,1), (1,0), and (1,1). However, in quantum computing, a qubit can be in a superposition of both 0 and 1 at the same time. This means that two qubits can represent four possible combinations of both 0 and 1, namely (0,0), (1,0), (0,1), and (1,1). The number of possible energy states grows exponentially with the number of qubits, which gives quantum computing significant advantages over classical computing. For example, a quantum computer with 20 qubits can represent 2^20 (or about 1 million) different energy states simultaneously. Therefore, quantum computing has the potential to perform certain types of calculations much faster than classical computing, because it can perform many calculations at the same time due to the exponential growth of energy states with the number of qubits. Quantum annealing uses quantum physics to find the solutions with the lowest energy states (the objective function). In the D-Wave quantum computer, the energy of each state is dependent on the biases of the qubits and the coupling between them which are decided by the problem formulation. The quantum annealing is to start from a particular system state to that of the final state defined by a Hamiltonian defining the feasible states. As is well known, finding minimum energy states in non-convex Hamiltonians is an NP-hard problem that classical computers take a long time to solve. A D-Wave’s quantum annealer (QA) implements the optimization problem as a following time-dependent Ising Hamiltonian:
Expressing multiagent coalition structure problems for optimisation by quantum annealing
Published in Enterprise Information Systems, 2019
Quantum annealing is believed to be superior in performance to classic simulated annealing. Despite initial scepticism of whether D-Wave truly exploits quantum effects, a recent study (Denchev et al. 2016) has shown that it is indeed capable of using quantum tunnelling effects in order to speed up computation beyond the capabilities of classic computers. It has indicated that, for a specific set of problems, the quantum version can be eight orders of magnitude faster than simulated annealing.