# Default sampler¶

Sets a binary quadratic model sampler used by default for functions that require a sample when none is specified.

A sampler is a process that samples from low-energy states in models defined by an Ising equation or a Quadratic Unconstrained Binary Optimization Problem (QUBO).

## Sampler API¶

• Required Methods: ‘sample_qubo’ and ‘sample_ising’
• Return value: iterable of samples, in order of increasing energy

See dimod for details.

Example

This example creates and uses a placeholder for binary quadratic model samplers that returns a correct response only in the case of finding an independent set on a complete graph (where one node is always an independent set). The placeholder sampler can be used to test the simple examples of the functions for configuring a default sampler.

>>> # Create a placeholder sampler
>>> class ExampleSampler:
...     # an example sampler, only works for independent set on complete
...     # graphs
...     def __init__(self, name):
...         self.name = name
...     def sample_ising(self, h, J):
...         sample = {v: -1 for v in h}
...         sample[0] = 1  # set one node to true
...         return [sample]
...     def sample_qubo(self, Q):
...         sample = {v: 0 for v in set().union(*Q)}
...         sample[0] = 1  # set one node to true
...         return [sample]
...     def __str__(self):
...         return self.name
...
>>> # Identify the new sampler as the default sampler
>>> sampler0 = ExampleSampler('sampler0')
>>> dnx.set_default_sampler(sampler0)
>>> # Find an independent set using the default sampler
>>> G = nx.complete_graph(5)
>>> dnx.maximum_independent_set(G)
[0]


## Functions¶

 set_default_sampler(sampler) Sets a default binary quadratic model sampler. unset_default_sampler() Resets the default sampler back to None. get_default_sampler() Queries the current default sampler.