dimod.reference.samplers.SimulatedAnnealingSampler.sample¶

SimulatedAnnealingSampler.sample(bqm, beta_range=None, num_reads=10, num_sweeps=1000)[source]¶

Sample from low-energy spin states using simulated annealing.

Parameters:	bqm (`BinaryQuadraticModel`) – Binary quadratic model to be sampled from. beta_range (tuple, optional) – Beginning and end of the beta schedule (beta is the inverse temperature) as a 2-tuple. The schedule is applied linearly in beta. Default is chosen based on the total bias associated with each node. num_reads (int, optional) – Number of reads. Each sample is the result of a single run of the simulated annealing algorithm. num_sweeps (int, optional) – Number of sweeps or steps. Default is 1000.
Returns:	A dimod `Response` object.
Return type:	`Response`

Note

This is a reference implementation, not optimized for speed and therefore not an appropriate sampler for benchmarking.

Examples

This example provides samples for a two-variable QUBO model.

>>> import dimod
>>> sampler = dimod.SimulatedAnnealingSampler()
>>> Q = {(0, 0): -1, (1, 1): -1, (0, 1): 2}
>>> bqm = dimod.BinaryQuadraticModel.from_qubo(Q, offset = 0.0)
>>> response = sampler.sample(bqm, num_reads=2)
>>> response.data_vectors['energy']        
array([-1., -1.])