def get_embedding_from_tag(embedding_tag,
target_nodelist,
target_edgelist,
presorted=False):
with cache_connect() as cur:
embedding = select_embedding_from_tag(cur, embedding_tag,
target_nodelist, target_edgelist)
return embedding
def get_flux_biases(sampler,
embedding,
num_reads,
chain_strength=1,
max_age=3600):
# try to read the chip_id, otherwise get the name
system_name = sampler.properties.get('chip_id', str(sampler.__class__))
try:
with cache_connect() as cur:
fbo = get_flux_biases_from_cache(cur,
embedding.values(),
system_name,
chain_strength=chain_strength)
except MissingFluxBias:
# if dwave-system-tuning is not available, then we can't calculate the biases
try:
import dwave.system.tuning as dst
except ImportError:
return []
fbo = dst.oneshot_flux_bias(sampler,
embedding.values(),
num_reads=num_reads,
chain_strength=chain_strength)
# store them in the cache
fbo_dict = dict(fbo)
with cache_connect() as cur:
for chain in embedding.values():
flux_bias = fbo_dict[next(iter(chain))]
insert_flux_bias(cur, chain, system_name, flux_bias,
chain_strength)
return fbo
def load_embedding(target_nodelist, target_edgelist, embedding, embedding_tag):
target_adjacency = {v: set() for v in target_nodelist}
for u, v in target_edgelist:
target_adjacency[u].add(v)
target_adjacency[v].add(u)
source_adjacency = embutil.target_to_source(target_adjacency, embedding)
source_nodelist = sorted(source_adjacency)
source_edgelist = sorted(
sorted(edge) for edge in _adjacency_to_edges(source_adjacency))
with cache_connect() as cur:
insert_embedding(cur, source_nodelist, source_edgelist,
target_nodelist, target_edgelist, embedding,
embedding_tag)
def get_flux_biases(sampler,
embedding,
chain_strength,
num_reads=1000,
max_age=3600):
"""Get the flux bias offsets for sampler and embedding.
Args:
sampler (:obj:`.DWaveSampler`):
A D-Wave sampler.
embedding (dict[hashable, iterable]):
Mapping from a source graph to the specified sampler’s graph (the target graph). The
keys of embedding should be nodes in the source graph, the values should be an iterable
of nodes in the target graph.
chain_strength (number):
Desired chain coupling strength. This is the magnitude of couplings between qubits
in a chain.
num_reads (int, optional, default=1000):
The number of reads per system call if new flux biases need to be calculated.
max_age (int, optional, default=3600):
The maximum age (in seconds) allowed for previously calculated flux bias offsets.
Returns:
dict: A dict where the keys are the nodes in the chains and the values are the flux biases.
"""
if not isinstance(sampler, dimod.Sampler):
raise TypeError("input sampler should be DWaveSampler")
# try to read the chip_id, otherwise get the name
system_name = sampler.properties.get('chip_id', str(sampler.__class__))
try:
with cache_connect() as cur:
fbo = get_flux_biases_from_cache(cur,
embedding.values(),
system_name,
chain_strength=chain_strength,
max_age=max_age)
return fbo
except MissingFluxBias:
pass
# if dwave-drivers is not available, then we can't calculate the biases
try:
import dwave.drivers as drivers
except ImportError:
msg = (
"dwave-drivers not found, cannot calculate flux biases. dwave-drivers can be "
"installed with "
"'pip install dwave-drivers --extra-index-url https://pypi.dwavesys.com/simple'. "
"See documentation for dwave-drivers license.")
raise RuntimeError(msg)
fbo = drivers.oneshot_flux_bias(
sampler,
embedding.values(),
num_reads=num_reads,
chain_strength=chain_strength,
label='VirtualGraph flux bias measurements')
# store them in the cache
with cache_connect() as cur:
for chain in embedding.values():
v = next(iter(chain))
flux_bias = fbo.get(v, 0.0)
insert_flux_bias(cur, chain, system_name, flux_bias,
chain_strength)
return fbo