57
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Quantum Annealing in the Transverse Ising Model

      Preprint

      Read this article at

          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          We introduce quantum fluctuations into the simulated annealing process of optimization problems, aiming at faster convergence to the optimal state. Quantum fluctuations cause transitions between states and thus play the same role as thermal fluctuations in the conventional approach. The idea is tested by the transverse Ising model, in which the transverse field is a function of time similar to the temperature in the conventional method. The goal is to find the ground state of the diagonal part of the Hamiltonian with high accuracy as quickly as possible. We have solved the time-dependent Schr\"odinger equation numerically for small size systems with various exchange interactions. Comparison with the results of the corresponding classical (thermal) method reveals that the quantum annealing leads to the ground state with much larger probability in almost all cases if we use the same annealing schedule.

          Related collections

          Most cited references10

          • Record: found
          • Abstract: not found
          • Article: not found

          Solvable Model of a Spin-Glass

            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Dynamics of the Magnetization with an Inversion of the Magnetic Field

              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Generalized Simulated Annealing

              We propose a new stochastic algorithm (generalized simulated annealing) for computationally finding the global minimum of a given (not necessarily convex) energy/cost function defined in a continuous D-dimensional space. This algorithm recovers, as particular cases, the so called classical ("Boltzmann machine") and fast ("Cauchy machine") simulated annealings, and can be quicker than both. Key-words: simulated annealing; nonconvex optimization; gradient descent; generalized statistical mechanics.
                Bookmark

                Author and article information

                Journal
                10.1103/PhysRevE.58.5355
                cond-mat/9804280

                Comments

                Comment on this article