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Py-dwave-neal
Jul 20, 2023
DWave: Simulated annealing sampler for general Ising model graphs
An implementation of a simulated annealing sampler.
A simulated annealing sampler can be used for approximate Boltzmann sampling or heuristic optimization. This implementation approaches the equilibrium distribution by performing updates at a sequence of increasing beta values, beta_schedule, terminating at the target beta. Each spin is updated once in a fixed order per point in the beta_schedule according to a Metropolis- Hastings update. When beta is large the target distribution concentrates, at equilibrium, over ground states of the model. Samples are guaranteed to match the equilibrium for long ‘smooth’ beta schedules.
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