def new_f(sampler, bqm, **kwargs):
try:
structure = sampler.structure
adjacency = structure.adjacency
except AttributeError:
if isinstance(sampler, Structured):
raise RuntimeError("something is wrong with the structured sampler")
else:
raise TypeError("sampler does not have a structure property")
if not all(v in adjacency for v in bqm.linear):
# todo: better error message
raise BinaryQuadraticModelStructureError("given bqm does not match the sampler's structure")
if not all(u in adjacency[v] for u, v in bqm.quadratic):
# todo: better error message
raise BinaryQuadraticModelStructureError("given bqm does not match the sampler's structure")
return f(sampler, bqm, **kwargs)
def sample_ising(self, h, *args, **kwargs):
# to be consistent with the cloud-client, we ignore the 0 biases
# on missing nodes for lists
if isinstance(h, list):
if len(h) > self.solver.num_qubits:
msg = "Problem graph incompatible with solver."
raise BinaryQuadraticModelStructureError(msg)
nodes = self.solver.nodes
h = dict((v, b) for v, b in enumerate(h) if b and v in nodes)
return super().sample_ising(h, *args, **kwargs)
def sample_qubo(self, Q, **kwargs):
"""Sample from the specified QUBO.
Args:
Q (dict):
Coefficients of a quadratic unconstrained binary optimization (QUBO) model.
**kwargs:
Optional keyword arguments for the sampling method, specified per solver in
:attr:`.DWaveSampler.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.
Returns:
:class:`dimod.SampleSet`: A `dimod` :obj:`~dimod.SampleSet` object.
In it this sampler also provides timing information in the `info`
field as described in the D-Wave System Documentation's
`timing guide <https://docs.dwavesys.com/docs/latest/doc_timing.html>`_.
Examples:
This example submits a two-variable QUBO mapped directly to qubits
0 and 4 on a D-Wave system.
>>> from dwave.system.samplers import DWaveSampler
>>> sampler = DWaveSampler()
>>> Q = {(0, 0): -1, (4, 4): -1, (0, 4): 2}
>>> sampleset = sampler.sample_qubo(Q)
>>> for sample in sampleset.samples(): # doctest: +SKIP
... print(sample)
...
{0: 0, 4: 1}
See `Ocean Glossary <https://docs.ocean.dwavesys.com/en/latest/glossary.html>`_
for explanations of technical terms in descriptions of Ocean tools.
"""
variables = set().union(*Q)
# developer note: in the future we should probably catch exceptions
# from the cloud client, but for now this is simpler/cleaner. We use
# the solver's nodes/edges because they are a set, so faster lookup
nodes = self.solver.nodes
edges = self.solver.edges
if not all(
u in nodes if u == v else ((u, v) in edges or (v, u) in edges)
for u, v in Q):
msg = "Problem graph incompatible with solver."
raise BinaryQuadraticModelStructureError(msg)
future = self.solver.sample_qubo(Q, **kwargs)
hook = _result_to_response_hook(variables, dimod.BINARY)
return dimod.SampleSet.from_future(future, hook)
def sample(self, bqm, warnings=None, **kwargs):
"""Sample from the specified binary quadratic model.
Args:
bqm (:class:`~dimod.BinaryQuadraticModel`):
The binary quadratic model. Must match :attr:`.nodelist` and
:attr:`.edgelist`.
warnings (:class:`~dwave.system.warnings.WarningAction`, optional):
Defines what warning action to take, if any. See
:ref:`warnings_system`. The default behaviour is to
ignore warnings.
**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.
Returns:
:class:`~dimod.SampleSet`: Sample set constructed from a (non-blocking)
:class:`~concurrent.futures.Future`-like object.
In it this sampler also provides timing information in the `info`
field as described in the D-Wave System Documentation's
:ref:`sysdocs_gettingstarted:qpu_sapi_qpu_timing`.
Examples:
This example submits a two-variable Ising problem mapped directly to two
adjacent qubits on a D-Wave system. ``qubit_a`` is the first qubit in
the QPU's indexed list of qubits and ``qubit_b`` is one of the qubits
coupled to it. Given sufficient reads (here 100), the quantum
computer should return the best solution, :math:`{1, -1}` on ``qubit_a`` and
``qubit_b``, respectively, as its first sample (samples are ordered from
lowest energy).
>>> from dwave.system import DWaveSampler
...
>>> sampler = DWaveSampler()
...
>>> qubit_a = sampler.nodelist[0]
>>> qubit_b = next(iter(sampler.adjacency[qubit_a]))
>>> sampleset = sampler.sample_ising({qubit_a: -1, qubit_b: 1},
... {},
... num_reads=100)
>>> sampleset.first.sample[qubit_a] == 1 and sampleset.first.sample[qubit_b] == -1
True
See `Ocean Glossary <https://docs.ocean.dwavesys.com/en/stable/concepts/index.html>`_
for explanations of technical terms in descriptions of Ocean tools.
"""
solver = self.solver
try:
future = solver.sample_bqm(bqm, **kwargs)
except ProblemStructureError as exc:
msg = ("Problem graph incompatible with solver. Please use 'EmbeddingComposite' "
"to map the problem graph to the solver.")
raise BinaryQuadraticModelStructureError(msg) from exc
if warnings is None:
warnings = self.warnings_default
warninghandler = WarningHandler(warnings)
warninghandler.energy_scale(bqm)
# need a hook so that we can check the sampleset (lazily) for
# warnings
def _hook(computation):
sampleset = computation.sampleset
if warninghandler is not None:
warninghandler.too_few_samples(sampleset)
if warninghandler.action is WarningAction.SAVE:
sampleset.info['warnings'] = warninghandler.saved
return sampleset
return dimod.SampleSet.from_future(future, _hook)
def sample(self, bqm, warnings=None, **kwargs):
"""Sample from the specified Ising model.
Args:
bqm (:class:`~dimod.BinaryQuadraticModel`):
The binary quadratic model. Must match :attr:`.nodelist` and
:attr:`.edgelist`.
warnings (:class:`~dwave.system.warnings.WarningAction`, optional):
Defines what warning action to take, if any. See
:mod:`~dwave.system.warnings`. The default behaviour is defined
by :attr:`warnings_default`, which itself defaults to
:class:`~dwave.system.warnings.IGNORE`
**kwargs:
Optional keyword arguments for the sampling method, specified per solver in
:attr:`.DWaveSampler.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.
Returns:
:class:`dimod.SampleSet`: A `dimod` :obj:`~dimod.SampleSet` object.
In it this sampler also provides timing information in the `info`
field as described in the D-Wave System Documentation's
`timing guide <https://docs.dwavesys.com/docs/latest/doc_timing.html>`_.
Examples:
This example submits a two-variable Ising problem mapped directly to qubits
0 and 1 on a D-Wave system. Given sufficient reads (here 100), the quantum
computer should return the best solution, :math:`{1, -1}` on qubits 0 and 1,
respectively, as its first sample (samples are ordered from lowest energy).
>>> from dwave.system import DWaveSampler
>>> sampler = DWaveSampler(solver={'qubits__issuperset': {0, 1}})
>>> sampleset = sampler.sample_ising({0: -1, 1: 1}, {}, num_reads=100)
>>> sampleset.first.sample[0] == 1 and sampleset.first.sample[1] == -1
True
See `Ocean Glossary <https://docs.ocean.dwavesys.com/en/latest/glossary.html>`_
for explanations of technical terms in descriptions of Ocean tools.
"""
solver = self.solver
if not (solver.nodes.issuperset(bqm.linear)
and solver.edges.issuperset(bqm.quadratic)):
msg = "Problem graph incompatible with solver."
raise BinaryQuadraticModelStructureError(msg)
future = solver.sample_bqm(bqm, **kwargs)
if warnings is None:
warnings = self.warnings_default
warninghandler = WarningHandler(warnings)
warninghandler.energy_scale(bqm)
# need a hook so that we can check the sampleset (lazily) for
# warnings
def _hook(computation):
sampleset = computation.sampleset
if warninghandler is not None:
warninghandler.too_few_samples(sampleset)
if warninghandler.action is WarningAction.SAVE:
sampleset.info['warnings'] = warninghandler.saved
return sampleset
return dimod.SampleSet.from_future(future, _hook)
def sample_ising(self, h, J, **kwargs):
"""Sample from the specified Ising model.
Args:
h (dict/list):
Linear biases of the Ising model. If a dict, should be of the
form `{v: bias, ...}` where `v` is a spin-valued variable and
`bias` is its associated bias. If a list, it is treated as a
list of biases where the indices are the variable labels,
except in the case of missing qubits in which case 0 biases are
ignored while a non-zero bias set on a missing qubit raises an
error.
J (dict[(int, int): float]):
Quadratic biases of the Ising model.
**kwargs:
Optional keyword arguments for the sampling method, specified per solver in
:attr:`.DWaveSampler.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.
Returns:
:class:`dimod.SampleSet`: A `dimod` :obj:`~dimod.SampleSet` object.
In it this sampler also provides timing information in the `info`
field as described in the D-Wave System Documentation's
`timing guide <https://docs.dwavesys.com/docs/latest/doc_timing.html>`_.
Examples:
This example submits a two-variable Ising problem mapped directly to qubits
0 and 1 on a D-Wave system.
>>> from dwave.system.samplers import DWaveSampler
>>> sampler = DWaveSampler()
>>> sampleset = sampler.sample_ising({0: -1, 1: 1}, {})
>>> for sample in sampleset.samples(): # doctest: +SKIP
... print(sample)
...
{0: 1, 1: -1}
See `Ocean Glossary <https://docs.ocean.dwavesys.com/en/latest/glossary.html>`_
for explanations of technical terms in descriptions of Ocean tools.
"""
nodes = self.solver.nodes # set rather than .nodelist which is a list
if isinstance(h, list):
# to be consistent with the cloud-client, we ignore the 0 biases
# on missing nodes.
h = dict((v, b) for v, b in enumerate(h) if b or v in nodes)
variables = set(h).union(*J)
# developer note: in the future we should probably catch exceptions
# from the cloud client, but for now this is simpler/cleaner. We use
# the solver's nodes/edges because they are a set, so faster lookup
edges = self.solver.edges
if not (all(v in nodes for v in h) and
all((u, v) in edges or (v, u) in edges for u, v in J)):
msg = "Problem graph incompatible with solver."
raise BinaryQuadraticModelStructureError(msg)
future = self.solver.sample_ising(h, J, **kwargs)
hook = _result_to_response_hook(variables, dimod.SPIN)
return dimod.SampleSet.from_future(future, hook)
