Samplers

The dimod package includes several example samplers.

Exact Solver

A solver that calculates the energy of all possible samples.

Note

This sampler is designed for use in testing. Because it calculates the energy for every possible sample, it is very slow.

Class

class ExactSolver[source]

A simple exact solver for testing and debugging code using your local CPU.

Notes

This solver becomes slow for problems with 18 or more variables.

Examples

This example solves a two-variable Ising model.

>>> h = {'a': -0.5, 'b': 1.0}
>>> J = {('a', 'b'): -1.5}
>>> sampleset = dimod.ExactSolver().sample_ising(h, J)
>>> print(sampleset)
   a  b energy num_oc.
0 -1 -1   -2.0       1
2 +1 +1   -1.0       1
1 +1 -1    0.0       1
3 -1 +1    3.0       1
['SPIN', 4 rows, 4 samples, 2 variables]

This example solves a two-variable QUBO.

>>> Q = {('a', 'b'): 2.0, ('a', 'a'): 1.0, ('b', 'b'): -0.5}
>>> sampleset = dimod.ExactSolver().sample_qubo(Q)

This example solves a two-variable binary quadratic model

>>> bqm = dimod.BinaryQuadraticModel({'a': 1.5}, {('a', 'b'): -1}, 0.0, 'SPIN')
>>> sampleset = dimod.ExactSolver().sample(bqm)

Methods

ExactSolver.sample(bqm) Sample from a binary quadratic model.
ExactSolver.sample_ising(h, J, **parameters) Sample from an Ising model using the implemented sample method.
ExactSolver.sample_qubo(Q, **parameters) Sample from a QUBO using the implemented sample method.

Null Sampler

A sampler that always returns an empty sample set.

Class

class NullSampler(parameters=None)[source]

A sampler that always returns an empty sample set.

This sampler is useful for writing unit tests where the result is not important.

Parameters:parameters (iterable/dict, optional) – If provided, sets the parameters accepted by the sample methods. The values given in these parameters are ignored.

Examples

>>> bqm = dimod.BinaryQuadraticModel.from_qubo({('a', 'b'): 1})
>>> sampler = dimod.NullSampler()
>>> sampleset = sampler.sample(bqm)
>>> len(sampleset)
0

Setting additional parameters for the null sampler.

>>> bqm = dimod.BinaryQuadraticModel.from_qubo({('a', 'b'): 1})
>>> sampler = dimod.NullSampler(parameters=['a'])
>>> sampleset = sampler.sample(bqm, a=5)

Properties

NullSampler.parameters Keyword arguments accepted by the sampling methods

Methods

NullSampler.sample(bqm, **kwargs) Return an empty sample set.
NullSampler.sample_ising(h, J, **parameters) Sample from an Ising model using the implemented sample method.
NullSampler.sample_qubo(Q, **parameters) Sample from a QUBO using the implemented sample method.

Random Sampler

A sampler that gives random samples.

Class

class RandomSampler[source]

A sampler that gives random samples for testing.

Properties

RandomSampler.parameters Keyword arguments accepted by the sampling methods.

Methods

RandomSampler.sample(bqm[, num_reads]) Give random samples for a binary quadratic model.
RandomSampler.sample_ising(h, J, **parameters) Sample from an Ising model using the implemented sample method.
RandomSampler.sample_qubo(Q, **parameters) Sample from a QUBO using the implemented sample method.

Simulated Annealing Sampler

A reference implementation of a simulated annealing sampler.

neal.sampler.SimulatedAnnealingSampler is a more performant implementation of simulated annealing you can use for solving problems.

Class

class SimulatedAnnealingSampler[source]

A simple simulated annealing sampler for testing and debugging code.

Examples

This example solves a two-variable Ising model.

>>> h = {'a': -0.5, 'b': 1.0}
>>> J = {('a', 'b'): -1.5}
>>> sampleset = dimod.SimulatedAnnealingSampler().sample_ising(h, J)

Properties

SimulatedAnnealingSampler.parameters Keyword arguments accepted by the sampling methods.

Methods

SimulatedAnnealingSampler.sample(bqm[, …]) Sample from low-energy spin states using simulated annealing.
SimulatedAnnealingSampler.sample_ising(h, J, …) Sample from an Ising model using the implemented sample method.
SimulatedAnnealingSampler.sample_qubo(Q, …) Sample from a QUBO using the implemented sample method.