def sample_qubo(self, qubo, offset=0, label=None, **params):
"""Sample from the specified :term:`QUBO`.
Args:
qubo (dict[(int, int), float]):
Coefficients of a quadratic unconstrained binary optimization
(QUBO) problem. Should be a dict of the form `{(u, v): bias, ...}`
where `u`, `v`, are binary-valued variables and `bias` is their
associated coefficient.
offset (optional, default=0):
Constant offset applied to the model.
label (str, optional):
Problem label you can optionally tag submissions with for ease
of identification.
**params:
Parameters for the sampling method, solver-specific.
Returns:
:class:`~dwave.cloud.computation.Future`
Examples:
This example creates a client using the local system's default D-Wave Cloud Client
configuration file, which is configured to access a D-Wave 2000Q QPU, submits
a :term:`QUBO` problem (a Boolean NOT gate represented by a penalty model), and
samples 5 times.
>>> from dwave.cloud import Client
>>> with Client.from_config() as client: # doctest: +SKIP
... solver = client.get_solver()
... u, v = next(iter(solver.edges))
... Q = {(u, u): -1, (u, v): 0, (v, u): 2, (v, v): -1}
... computation = solver.sample_qubo(Q, num_reads=5)
... for i in range(5):
... print(computation.samples[i][u], computation.samples[i][v])
...
...
(0, 1)
(1, 0)
(1, 0)
(0, 1)
(1, 0)
"""
linear, quadratic = reformat_qubo_as_ising(qubo)
return self._sample('qubo',
linear,
quadratic,
offset,
params,
label=label)
def sample_qubo(self, qubo, **params):
"""Sample from the specified :term:`QUBO`.
Args:
qubo (dict[(int, int), float]):
Coefficients of a quadratic unconstrained binary optimization
(QUBO) model.
**params:
Parameters for the sampling method, solver-specific.
Returns:
:class:`Future`
Examples:
This example creates a client using the local system's default D-Wave Cloud Client
configuration file, which is configured to access a D-Wave 2000Q QPU, submits
a :term:`QUBO` problem (a Boolean NOT gate represented by a penalty model), and
samples 5 times.
>>> from dwave.cloud import Client
>>> with Client.from_config() as client: # doctest: +SKIP
... solver = client.get_solver()
... u, v = next(iter(solver.edges))
... Q = {(u, u): -1, (u, v): 0, (v, u): 2, (v, v): -1}
... computation = solver.sample_qubo(Q, num_reads=5)
... for i in range(5):
... print(computation.samples[i][u], computation.samples[i][v])
...
...
(0, 1)
(1, 0)
(1, 0)
(0, 1)
(1, 0)
"""
linear, quadratic = reformat_qubo_as_ising(qubo)
return self._sample('qubo', linear, quadratic, params)
def from_qmi_response(problem,
response,
embedding_context=None,
warnings=None,
params=None,
sampleset=None):
"""Construct problem data for visualization based on the low-level sampling
problem definition and the low-level response.
Args:
problem ((list/dict, dict[(int, int), float]) or dict[(int, int), float]):
Problem in Ising or QUBO form, conforming to solver graph.
Note: if problem is given as tuple, it is assumed to be in Ising
variable space, and if given as a dict, Binary variable space is
assumed. Zero energy offset is always implied.
response (:class:`dwave.cloud.computation.Future`):
Sampling response, as returned by the low-level sampling interface
in the Cloud Client (e.g. :meth:`dwave.cloud.Solver.sample_ising`
for Ising problems).
embedding_context (dict, optional):
A map containing an embedding of logical problem onto the
solver's graph (the ``embedding`` key) and embedding parameters
used (e.g. ``chain_strength``).
warnings (list[dict], optional):
Optional list of warnings.
params (dict, optional):
Sampling parameters used.
sampleset (:class:`dimod.SampleSet`, optional):
Optional unembedded sampleset.
"""
logger.debug("from_qmi_response({!r})".format(
dict(problem=problem,
response=response,
response_energies=response['energies'],
embedding_context=embedding_context,
warnings=warnings,
params=params,
sampleset=sampleset)))
try:
linear, quadratic = problem
except:
linear, quadratic = reformat_qubo_as_ising(problem)
# make sure lin/quad are not dimod views (that handle directed edges)
if isinstance(linear, BQMView):
linear = dict(linear)
if isinstance(quadratic, BQMView):
quadratic = dict(quadratic)
solver = response.solver
if not isinstance(response.solver, StructuredSolver):
raise TypeError("only structured solvers are supported")
topology = _get_solver_topology(solver)
if topology['type'] not in SUPPORTED_SOLVER_TOPOLOGY_TYPES:
raise TypeError("unsupported solver topology type")
solver_id = solver.id
problem_type = response.problem_type
variables = list(response.variables)
active = active_qubits(linear, quadratic)
# filter out invalid values (user's error in problem definition), since
# SAPI ignores them too
active = {q for q in active if q in solver.variables}
# sanity check
active_variables = response['active_variables']
assert set(active) == set(active_variables)
solutions = list(map(itemsgetter(*active_variables),
response['solutions']))
energies = response['energies']
num_occurrences = response.num_occurrences
num_variables = solver.num_qubits
timing = response.timing
# note: we can't use encode_problem_as_qp(solver, linear, quadratic) because
# visualizer accepts decoded lists (and nulls instead of NaNs)
problem_data = {
"format":
"qp", # SAPI non-conforming (nulls vs nans)
"lin": [
uniform_get(linear, v, 0 if v in active else None)
for v in solver._encoding_qubits
],
"quad": [
quadratic.get((q1, q2), 0) + quadratic.get((q2, q1), 0)
for (q1, q2) in solver._encoding_couplers
if q1 in active and q2 in active
]
}
# include optional embedding
if embedding_context is not None and 'embedding' in embedding_context:
problem_data['embedding'] = embedding_context['embedding']
# try to reconstruct sampling params
if params is None:
params = {'num_reads': int(sum(num_occurrences))}
# expand with defaults
params = _expand_params(solver, params, timing)
# construct problem stats
problem_stats = _problem_stats(response=response,
sampleset=sampleset,
embedding_context=embedding_context)
data = {
"ready":
True,
"details":
_details_dict(response),
"data":
_problem_dict(solver_id, problem_type, problem_data, params,
problem_stats),
"answer":
_answer_dict(solutions, active_variables, energies, num_occurrences,
timing, num_variables),
"warnings":
_warnings(warnings),
"rel":
dict(solver=solver),
}
if sampleset is not None:
data["unembedded_answer"] = _unembedded_answer_dict(sampleset)
logger.trace("from_qmi_response returned %r", data)
return data
def verify_data_encoding(self,
problem,
response,
solver,
params,
data,
embedding_context=None):
# avoid persistent data modification
data = data.copy()
# make sure data correct after JSON decoding (minus the 'rel' data)
del data['rel']
data = json.loads(json.dumps(data))
# test structure
self.assertIsInstance(data, dict)
self.assertTrue(
all(k in data for k in 'details data answer warnings'.split()))
# .details
self.assertIn('id', data['details'])
self.assertIn('label', data['details'])
self.assertEqual(data['details']['solver'], solver.id)
# .problem
self.assertEqual(data['data']['type'], response.problem_type)
# .problem.params, smoke tests
self.assertIn('params', data['data'])
self.assertEqual(data['data']['params']['num_reads'],
params['num_reads'])
self.assertIn('annealing_time', data['data']['params'])
self.assertIn('programming_thermalization', data['data']['params'])
if response.problem_type == 'ising':
linear, quadratic = problem
elif response.problem_type == 'qubo':
linear, quadratic = reformat_qubo_as_ising(problem)
else:
self.fail("Unknown problem type")
active_variables = response['active_variables']
problem_data = {
"format":
"qp",
"lin": [
uniform_get(linear, v, 0 if v in active_variables else None)
for v in solver._encoding_qubits
],
"quad": [
quadratic.get((q1, q2), 0) + quadratic.get((q2, q1), 0)
for (q1, q2) in solver._encoding_couplers
if q1 in active_variables and q2 in active_variables
]
}
if embedding_context is not None:
problem_data['embedding'] = embedding_context['embedding']
self.assertDictEqual(data['data']['data'], problem_data)
# .answer
self.assertEqual(sum(data['answer']['num_occurrences']),
params['num_reads'])
self.assertEqual(data['answer']['num_occurrences'],
response['num_occurrences'])
self.assertEqual(data['answer']['num_variables'],
response['num_variables'])
self.assertEqual(data['answer']['active_variables'], active_variables)
solutions = [[sol[idx] for idx in active_variables]
for sol in response['solutions']]
self.assertEqual(data['answer']['solutions'], solutions)
self.assertEqual(data['answer']['energies'], response['energies'])
self.assertEqual(data['answer']['timing'], response['timing'])
