def __init__(self, sampler, embedding,
chain_strength=None,
flux_biases=None,
flux_bias_num_reads=1000,
flux_bias_max_age=3600):
child, = self.children = [FixedEmbeddingComposite(sampler, embedding)]
self.nodelist, self.edgelist, self.adjacency = child.structure
self.embedding = child.embedding
self.parameters = parameters = {'apply_flux_bias_offsets': []}
parameters.update(child.parameters)
self.properties = child.properties.copy() # shallow copy
# Validate the chain strength, or obtain it from J-range if chain strength is not provided.
self.chain_strength = _validate_chain_strength(sampler, chain_strength)
if flux_biases is False: # use 'is' because bool(None) is False
# in this case we are done
self.flux_biases = None
return
if FLUX_BIAS_KWARG not in sampler.parameters:
raise ValueError("Given child sampler does not accept flux_biases.")
# come back as a dict
flux_biases = get_flux_biases(sampler, embedding,
num_reads=flux_bias_num_reads,
chain_strength=self.chain_strength,
max_age=flux_bias_max_age)
self.flux_biases = [flux_biases.get(v, 0.0) for v in range(sampler.properties['num_qubits'])]
return
def __init__(self, sampler, embedding,
chain_strength=None,
flux_biases=None,
flux_bias_num_reads=1000,
flux_bias_max_age=3600):
super(VirtualGraphComposite, self).__init__(sampler, embedding)
self.parameters.update(apply_flux_bias_offsets=[])
# Validate the chain strength, or obtain it from J-range if chain strength is not provided.
self.chain_strength = _validate_chain_strength(sampler, chain_strength)
if flux_biases is False: # use 'is' because bool(None) is False
# in this case we are done
self.flux_biases = None
return
if FLUX_BIAS_KWARG not in sampler.parameters:
raise ValueError("Given child sampler does not accept flux_biases.")
# come back as a dict
flux_biases = get_flux_biases(sampler, embedding,
num_reads=flux_bias_num_reads,
chain_strength=self.chain_strength,
max_age=flux_bias_max_age)
self.flux_biases = [flux_biases.get(v, 0.0) for v in range(sampler.properties['num_qubits'])]
return
def step2(self, sampler, embedding):
"""get flux biases from cache"""
return fb.get_flux_biases(sampler, embedding, chain_strength=1)
def step1(self, sampler, embedding):
"""get new flux bias"""
return fb.get_flux_biases(sampler,
embedding,
chain_strength=1,
max_age=0)
def __init__(self, sampler, embedding,
chain_strength=None,
flux_biases=None, flux_bias_num_reads=1000, flux_bias_max_age=3600):
self.children = [sampler]
self.parameters = parameters = {'apply_flux_bias_offsets': []}
parameters.update(sampler.parameters)
self.properties = {'child_properties': sampler.properties.copy()}
#
# Get the adjacency of the child sampler (this is the target for our embedding)
#
try:
target_nodelist, target_edgelist, target_adjacency = sampler.structure
except:
# todo, better exception catching
raise
#
# Validate the chain strength, or obtain it from J-range if chain strength is not provided.
#
self.chain_strength = self._validate_chain_strength(chain_strength)
#
# We want to track the persistent embedding so that we can map input problems
# to the child sampler.
#
if isinstance(embedding, str):
embedding = get_embedding_from_tag(embedding, target_nodelist, target_edgelist)
elif not isinstance(embedding, dict):
raise TypeError("expected input `embedding` to be a dict.")
self.embedding = embedding
#
# Derive the structure of our composed from the target graph and the embedding
#
source_adjacency = dimod.embedding.target_to_source(target_adjacency, embedding)
try:
nodelist = sorted(source_adjacency)
edgelist = sorted(_adjacency_to_edges(source_adjacency))
except TypeError:
# python3 does not allow sorting of unlike types, so if nodes have
# different type names just choose an arbitrary order
nodelist = list(source_adjacency)
edgelist = list(_adjacency_to_edges(source_adjacency))
self.nodelist = nodelist
self.edgelist = edgelist
self.adjacency = source_adjacency
#
# If the sampler accepts flux bias offsets, we'll want to set them
#
if flux_biases is None and FLUX_BIAS_KWARG in sampler.parameters:
# If nothing is provided, then we either get them from the cache or generate them
flux_biases = get_flux_biases(sampler, embedding, num_reads=flux_bias_num_reads,
max_age=flux_bias_max_age)
elif flux_biases:
if FLUX_BIAS_KWARG not in sampler.accepted_kwargs:
raise ValueError("Given child sampler does not accept flux_biases.")
# something provided, error check
if not isinstance(flux_biases, list):
flux_biases = list(flux_biases) # cast to a list
else:
# disabled, empty or not available for this sampler so do nothing
flux_biases = None
self.flux_biases = flux_biases