dwave.system.samplers.LeapHybridSampler.sample¶

LeapHybridSampler.
sample
(bqm, time_limit=None, **kwargs)[source]¶ Sample from the specified binary quadratic model.
Parameters:  bqm (
dimod.BinaryQuadraticModel
) – The binary quadratic model.  time_limit (int) – Maximum run time, in seconds, to allow the solver to work on the problem.
Must be at least the minimum required for the number of problem variables,
which is calculated and set by default.
The minimum time for a hybrid solver is specified as a piecewiselinear
curve defined by a set of floatingpoint pairs, the minimum_time_limit
field under
LeapHybridSampler.properties
. The first element in each pair is the number of problem variables; the second is the minimum required time. The minimum time for any particular number of variables is a linear interpolation calculated on two pairs that represent the relevant range for the given number of variables. For example, if LeapHybridSampler().properties[“minimum_time_limit”] returns [[1, 0.1], [100, 10.0], [1000, 20.0]], then the minimum time for a 50variable problem is 5 seconds, the linear interpolation of the first two pairs that represent problems with between 1 to 100 variables.  **kwargs – Optional keyword arguments for the solver, specified in
LeapHybridSampler.parameters
.
Returns: A dimod
SampleSet
object.Return type: Examples
This example builds a random sparse graph and uses a hybrid solver to find a maximum independent set.
>>> import dimod >>> import networkx as nx >>> import dwave_networkx as dnx >>> import numpy as np ... >>> # Create a maximumindependent set problem from a random graph >>> problem_node_count = 300 >>> G = nx.random_geometric_graph(problem_node_count, radius=0.0005*problem_node_count) >>> qubo = dnx.algorithms.independent_set.maximum_weighted_independent_set_qubo(G) >>> bqm = dimod.BQM.from_qubo(qubo) ... >>> # Find a good solution >>> sampler = LeapHybridSampler() # doctest: +SKIP >>> sampleset = sampler.sample(bqm) # doctest: +SKIP
 bqm (