def solve(self, matrix, initial=()):
'''
returns: a solution
solution is a tuple (dict, float) representing sample and energy.
'''
if bool(initial):
initial_sample = list(initial[0].values())
print("initial sample: ",initial_sample)
print("solver starts the process...")
mtx = matrix.copy()
print("converting to upper triangular...")
mtx = mt.to_upper_triangular(mtx)
print("matrix has %d zeros out of %d" % (np.count_nonzero(mtx==0), mtx.shape[0]*mtx.shape[1]))
np.savetxt("mtx.txt", mtx, fmt='%d')
Q = {}
size = mtx.shape[0]
for i,j in itertools.product(range(size),range(size)):
if i==j:
if mtx[i][i]:
Q[(i,i)] = mtx[i][i]
elif i<j:
if mtx[i][j]:
Q[(i,j)] = mtx[i][j]
else:
pass
print("Solver engages Dwave quantum hardware!")
sampler = dimod.ScaleComposite(EmbeddingComposite(DWaveSampler()))
anneal_schedule = [(0,1),(4,0.5),(14,0.5),(20,1)]
if bool(initial):
print(initial)
initial_state_dict = initial[0]
response = sampler.sample_qubo(Q,num_reads=50, anneal_schedule=anneal_schedule, initial_state=initial_state_dict)
else:
response = sampler.sample_qubo(Q,num_reads=50, annealing_time=20)
timing_iter = (response.info['timing']['qpu_sampling_time'] / 1000000)
self.timing += timing_iter
print(timing_iter)
print(response.info)
for datum in response.data(fields=['sample','energy','num_occurrences']):
print(datum)
break
return (response.first.sample, response.first.energy)
def test_scale_aware_scale_composite(self):
nodelist = [0, 1, 2]
edgelist = [(0, 1), (1, 2), (0, 2)]
embedding = {'a': [0], 'b': [1, 2]}
sampler = FixedEmbeddingComposite(dimod.TrackingComposite(
dimod.ScaleComposite(
dimod.StructureComposite(dimod.NullSampler(), nodelist,
edgelist))),
embedding=embedding,
scale_aware=True)
_ = sampler.sample_ising({'a': 100, 'b': -100}, {'ab': 300})
self.assertIn('ignored_interactions', sampler.child.input)
ignored = sampler.child.input['ignored_interactions']
self.assertTrue(ignored == [(1, 2)] or ignored == [(2, 1)])
def sample(self, bqm, chain_strength=None, **kwargs):
"""Sample from the specified binary quadratic model.
Args:
bqm (:class:`~dimod.BinaryQuadraticModel`):
Any binary quadratic model with up to
:attr:`.largest_clique_size` variables. This BQM is embedded
using a dense clique embedding.
chain_strength (float, optional):
The (relative) chain strength to use in the embedding. By
default a chain strength of `1.5sqrt(N)` where `N` is the size
of the largest clique, as returned by
:attr:`.largest_clique_size`.
**kwargs:
Optional keyword arguments for the sampling method, specified
per solver in :attr:`.DWaveCliqueSampler.parameters`.
D-Wave System Documentation's
`solver guide <https://docs.dwavesys.com/docs/latest/doc_solver_ref.html>`_
describes the parameters and properties supported on the D-Wave
system. Note that `auto_scale` is not supported by this
sampler, because it scales the problem as part of the embedding
process.
"""
# some arguments should not be overwritten
if 'auto_scale' in kwargs:
raise TypeError("sample() got an unexpected keyword argument "
"'auto_scale'")
if 'bias_range' in kwargs:
raise TypeError("sample() got an unexpected keyword argument "
"'bias_range'")
if 'quadratic_range' in kwargs:
raise TypeError("sample() got an unexpected keyword argument "
"'quadratic_range'")
# handle circular import. todo: fix
from dwave.system.composites.embedding import FixedEmbeddingComposite
# get the embedding
embedding = find_clique_embedding(bqm.variables, self.target_graph,
use_cache=True)
# returns an empty embedding when the BQM is too large
if not embedding and bqm.num_variables:
raise ValueError("Cannot embed given BQM (size {}), sampler can "
"only handle problems of size {}".format(
len(bqm.variables), self.largest_clique_size))
assert bqm.num_variables == len(embedding) # sanity check
if chain_strength is None:
# chain length determines chain strength
if embedding and bqm.num_interactions > 0:
squared_j = (j ** 2 for j in bqm.quadratic.values())
rms = math.sqrt(sum(squared_j)/bqm.num_interactions)
chain_strength = 1.5 * rms * math.sqrt(bqm.num_variables)
else:
chain_strength = 1 # doesn't matter
# scaling only make sense in Ising space
original_bqm = bqm
if bqm.vartype is not dimod.SPIN:
bqm = bqm.change_vartype(dimod.SPIN, inplace=False)
sampler = FixedEmbeddingComposite(
dimod.ScaleComposite(self.child),
embedding)
if 'auto_scale' in self.child.parameters:
kwargs['auto_scale'] = False
sampleset = sampler.sample(bqm,
bias_range=self.qpu_linear_range,
quadratic_range=self.qpu_quadratic_range,
chain_strength=chain_strength,
**kwargs
)
# change_vartype is non-blocking
return sampleset.change_vartype(original_bqm.vartype)
def sample(self, bqm, chain_strength=None, **kwargs):
"""Sample from the specified binary quadratic model.
Args:
bqm (:class:`~dimod.BinaryQuadraticModel`):
Any binary quadratic model with up to
:attr:`.largest_clique_size` variables. This BQM is embedded
using a clique embedding.
chain_strength (float/mapping/callable, optional):
Sets the coupling strength between qubits representing variables
that form a :term:`chain`. Mappings should specify the required
chain strength for each variable. Callables should accept the BQM
and embedding and return a float or mapping. By default,
`chain_strength` is calculated with
:func:`~dwave.embedding.chain_strength.uniform_torque_compensation`.
**kwargs:
Optional keyword arguments for the sampling method, specified
per solver in :attr:`.parameters`.
D-Wave System Documentation's
`solver guide <https://docs.dwavesys.com/docs/latest/doc_solver_ref.html>`_
describes the parameters and properties supported on the D-Wave
system. Note that `auto_scale` is not supported by this
sampler, because it scales the problem as part of the embedding
process.
"""
# some arguments should not be overwritten
if 'auto_scale' in kwargs:
raise TypeError("sample() got an unexpected keyword argument "
"'auto_scale'")
if 'bias_range' in kwargs:
raise TypeError("sample() got an unexpected keyword argument "
"'bias_range'")
if 'quadratic_range' in kwargs:
raise TypeError("sample() got an unexpected keyword argument "
"'quadratic_range'")
# handle circular import. todo: fix
from dwave.system.composites.embedding import FixedEmbeddingComposite
# get the embedding
embedding = find_clique_embedding(bqm.variables,
self.target_graph,
use_cache=True)
# returns an empty embedding when the BQM is too large
if not embedding and bqm.num_variables:
raise ValueError("Cannot embed given BQM (size {}), sampler can "
"only handle problems of size {}".format(
len(bqm.variables), self.largest_clique_size))
assert bqm.num_variables == len(embedding) # sanity check
# scaling only make sense in Ising space
original_bqm = bqm
if bqm.vartype is not dimod.SPIN:
bqm = bqm.change_vartype(dimod.SPIN, inplace=False)
sampler = FixedEmbeddingComposite(dimod.ScaleComposite(self.child),
embedding)
if 'auto_scale' in self.child.parameters:
kwargs['auto_scale'] = False
sampleset = sampler.sample(bqm,
bias_range=self.qpu_linear_range,
quadratic_range=self.qpu_quadratic_range,
chain_strength=chain_strength,
**kwargs)
# change_vartype is non-blocking
return sampleset.change_vartype(original_bqm.vartype)
