def check_problem(self, linear, quadratic):
"""Test if an Ising model matches the graph provided by the solver.
Args:
linear (list/dict): Linear terms of the model (h).
quadratic (dict of (int, int):float): Quadratic terms of the model (J).
Returns:
boolean
"""
for key, value in uniform_iterator(linear):
if value != 0 and key not in self.nodes:
return False
for key, value in uniform_iterator(quadratic):
if value != 0 and tuple(key) not in self.edges:
return False
return True
def sample_qubo(self, qubo, **params):
"""Draw samples from the provided QUBO.
To submit a problem: ``POST /problems/``
Args:
qubo (dict of (int, int):float): Terms of the model.
**params: Parameters for the sampling method, specified per solver.
Returns:
:obj:`Future`
"""
# In a QUBO the linear and quadratic terms in the objective are mixed into
# a matrix. For the sake of encoding, we will separate them before calling `_sample`
linear = {i1: v for (i1, i2), v in uniform_iterator(qubo) if i1 == i2}
quadratic = {(i1, i2): v for (i1, i2), v in uniform_iterator(qubo) if i1 != i2}
return self._sample('qubo', linear, quadratic, params)
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)
"""
# In a QUBO the linear and quadratic terms in the objective are mixed into
# a matrix. For the sake of encoding, we will separate them before calling `_sample`
linear = {i1: v for (i1, i2), v in uniform_iterator(qubo) if i1 == i2}
quadratic = {(i1, i2): v for (i1, i2), v in uniform_iterator(qubo) if i1 != i2}
return self._sample('qubo', linear, quadratic, params)
def check_problem(self, linear, quadratic):
"""Test if an Ising model matches the graph provided by the solver.
Args:
linear (list/dict):
Linear terms of the model (h).
quadratic (dict[(int, int), float]):
Quadratic terms of the model (J).
Returns:
boolean
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, and
tests a simple :term:`Ising` model for two target embeddings (that is, representations
of the model's graph by coupled qubits on the QPU's sparsely connected graph),
where only the second is valid.
>>> from dwave.cloud import Client
>>> print((0, 1) in solver.edges) # doctest: +SKIP
False
>>> print((0, 4) in solver.edges) # doctest: +SKIP
True
>>> with Client.from_config() as client: # doctest: +SKIP
... solver = client.get_solver()
... print(solver.check_problem({0: -1, 1: 1},{(0, 1):0.5}))
... print(solver.check_problem({0: -1, 4: 1},{(0, 4):0.5}))
...
False
True
"""
for key, value in uniform_iterator(linear):
if value != 0 and key not in self.nodes:
return False
for key, value in uniform_iterator(quadratic):
if value != 0 and tuple(key) not in self.edges:
return False
return True
def test_uniform_iterator(self):
items = [('a', 1), ('b', 2)]
self.assertEqual(list(uniform_iterator(OrderedDict(items))), items)
self.assertEqual(list(uniform_iterator('ab')), list(enumerate('ab')))
