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General Ising graph simulated annealing solver

Project description

> :warning: dwave-neal is deprecated in favor of dwave-samplers.


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.

For more information, see Kirkpatrick, S.; Gelatt Jr, C. D.; Vecchi, M. P. (1983). “Optimization by Simulated Annealing”. Science. 220 (4598): 671–680

Example Usage

import neal

sampler = neal.SimulatedAnnealingSampler()

h = {0: -1, 1: -1}
J = {(0, 1): -1}
sampleset = sampler.sample_ising(h, J)


To install:

pip install dwave-neal

To build from source:

pip install -r requirements.txt
python build_ext --inplace
python install


Released under the Apache License 2.0. See LICENSE file.


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