def test_vartype_dimod_smoke(self):
StructuredSolver.reformat_parameters('SPIN', {}, {})
StructuredSolver.reformat_parameters('BINARY', {}, {})
StructuredSolver.reformat_parameters(dimod.BINARY, {}, {})
StructuredSolver.reformat_parameters(dimod.SPIN, {}, {})
with self.assertRaises(ValueError):
StructuredSolver.reformat_parameters("INTEGER", {}, {})
def test_initial_states(self):
doc = {'initial_state': {0: 0, 4: 1}}
self.assertEqual(StructuredSolver.reformat_parameters('ising', doc, dict(num_qubits=9)),
dict(initial_state=[-1, 3, 3, 3, 1, 3, 3, 3, 3]))
self.assertEqual(StructuredSolver.reformat_parameters('qubo', doc, dict(num_qubits=9)),
dict(initial_state=[0, 3, 3, 3, 1, 3, 3, 3, 3]))
if dimod:
self.assertEqual(StructuredSolver.reformat_parameters('SPIN', doc, dict(num_qubits=9)),
dict(initial_state=[-1, 3, 3, 3, 1, 3, 3, 3, 3]))
self.assertEqual(StructuredSolver.reformat_parameters('BINARY', doc, dict(num_qubits=9)),
dict(initial_state=[0, 3, 3, 3, 1, 3, 3, 3, 3]))
self.assertEqual(doc, {'initial_state': {0: 0, 4: 1}})
def setUp(self):
# mock client
self.client = Client(endpoint='e',
token='t',
solver=dict(name__contains='test'))
self.client._fetch_solvers = lambda **kw: self.solvers
self.client._submit = lambda *pa, **kw: None
self.client.upload_problem_encoded = lambda *pa, **kw: Present(
result=mock_problem_id)
# mock solvers
self.structured_solver = StructuredSolver(
client=self.client,
data={
"properties": {
"supported_problem_types": ["qubo", "ising"],
"qubits": [0, 1, 2],
"couplers": [[0, 1], [0, 2], [1, 2]],
"num_qubits": 3,
"num_reads_range": [0, 100],
"parameters": {
"num_reads": "Number of samples to return.",
"postprocess": "either 'sampling' or 'optimization'"
},
"topology": {
"type": "chimera",
"shape": [16, 16, 4]
},
"category": "qpu",
"tags": ["lower_noise"]
},
"id": "test-qpu-solver",
"description": "A test solver 1",
"status": "online"
})
self.unstructured_solver = UnstructuredSolver(
client=self.client,
data={
"properties": {
"supported_problem_types": ["bqm"],
"parameters": {
"num_reads": "Number of samples to return."
},
"category": "hybrid",
},
"id": "test-unstructured-solver",
"description": "A test unstructured solver"
})
self.solvers = [self.structured_solver]
# we can't use unstructured solvers without dimod installed,
# so don't even try testing it
if dimod:
self.solvers.append(self.unstructured_solver)
# reset all event handlers
from dwave.cloud.events import _client_event_hooks_registry as reg
reg.update({k: [] for k in reg})
def __init__(self, **kwargs):
kwargs.setdefault(
'solver',
StructuredSolver(client=None, data=qpu_clique_solver_data(5)))
kwargs.setdefault(
'problem', generate_const_ising_problem(kwargs['solver'],
h=1,
j=-1))
kwargs.setdefault('problem_type', 'ising')
kwargs.setdefault('answer', self._problem_answer())
super().__init__(**kwargs)
def get_structured_solver():
data = {
"properties": {
"supported_problem_types": ["qubo", "ising"],
"qubits": [0, 1, 2, 3],
"couplers": [(0, 1), (1, 2), (2, 3), (3, 0)],
"num_qubits": 4,
"parameters": {"num_reads": "Number of samples to return."}
},
"id": "test-structured-solver",
"description": "A test structured solver"
}
return StructuredSolver(client=None, data=data)
def setUp(self):
# mock solvers
self.qpu1 = StructuredSolver(
client=None,
data={
"properties": {
"supported_problem_types": ["qubo", "ising"],
"qubits": [0, 1, 2],
"couplers": [[0, 1], [0, 2], [1, 2]],
"num_qubits": 3,
"num_reads_range": [0, 100],
"parameters": {
"num_reads": "Number of samples to return.",
"postprocess": "either 'sampling' or 'optimization'"
},
"topology": {
"type": "chimera",
"shape": [16, 16, 4]
},
"category": "qpu",
"tags": ["lower_noise"]
},
"id": "qpu1",
"description": "QPU Chimera solver",
"status": "online",
"avg_load": 0.1
})
self.qpu2 = StructuredSolver(
client=None,
data={
"properties": {
"supported_problem_types": ["qubo", "ising"],
"qubits": [0, 1, 2, 3, 4],
"couplers": [[0, 1], [0, 2], [1, 2], [2, 3], [3, 4]],
"num_qubits": 5,
"num_reads_range": [0, 200],
"parameters": {
"num_reads": "Number of samples to return.",
"flux_biases": "Supported ...",
"anneal_schedule": "Supported ..."
},
"topology": {
"type": "pegasus",
"shape": [6, 6, 12]
},
"category": "qpu",
"vfyc": True
},
"id": "qpu2",
"description": "QPU Pegasus solver",
"avg_load": 0.2
})
self.software = StructuredSolver(
client=None,
data={
"properties": {
"supported_problem_types": ["qubo", "ising"],
"qubits": [0, 1],
"couplers": [[0, 1]],
"num_qubits": 7,
"num_reads_range": [0, 1000],
"parameters": {
"num_reads": "Number of samples to return."
},
"vfyc": False,
"topology": {
"type": "chimera",
"shape": [4, 4, 4]
},
"category": "software",
# the following are only present in this solver
"some_set": [1, 2],
"some_range": [1, 2],
"some_string": "x",
"tags": ["tag"]
},
"id": "sw_solver1",
"description": "Software solver",
"avg_load": 0.7
})
self.hybrid = UnstructuredSolver(client=None,
data={
"properties": {
"supported_problem_types":
["bqm"],
"maximum_number_of_variables":
10000,
"maximum_time_limit_hrs":
24.0,
"minimum_time_limit":
[[1, 3.0], [1024, 3.0],
[4096, 10.0], [10000, 40.0]],
"quota_conversion_rate":
20,
"parameters": {
"time_limit": ""
},
"category":
"hybrid",
},
"id": "hybrid_v1",
"description": "Hybrid solver"
})
self.qpu_solvers = [self.qpu1, self.qpu2]
self.software_solvers = [self.software]
self.hybrid_solvers = [self.hybrid]
self.structured_solvers = self.qpu_solvers + self.software_solvers
self.unstructured_solvers = self.hybrid_solvers
self.solvers = self.structured_solvers + self.unstructured_solvers
# mock client
self.client = Client('endpoint', 'token')
self.client._fetch_solvers = lambda **kw: self.solvers
def test_initial_states_sequence(self):
doc = {'initial_state': [-1, 3, 3, 3, 1, 3, 3, 3, 3]}
self.assertEqual(StructuredSolver.reformat_parameters('ising', doc, dict(num_qubits=9)),
dict(initial_state=[-1, 3, 3, 3, 1, 3, 3, 3, 3]))
def test_initial_states_inplace(self):
doc = {'initial_state': {0: 0, 4: 1}}
StructuredSolver.reformat_parameters('ising', doc, dict(num_qubits=9), inplace=True)
self.assertEqual(doc, dict(initial_state=[-1, 3, 3, 3, 1, 3, 3, 3, 3]))
def test_empty(self):
self.assertEqual(StructuredSolver.reformat_parameters('ising', {}, {}), {})
def setUpClass(cls):
try:
cls.solver_mock = StructuredSolver(data=mocks.qpu_pegasus_solver_data(16,
problem_timing_data=mocks.qpu_problem_timing_data(qpu='advantage')), client=None)
except RuntimeError:
raise cls.skipTest(cls, "structured solver mock not available")
def test_vartype_smoke(self):
for vt in StructuredSolver._handled_problem_types:
StructuredSolver.reformat_parameters(vt, {}, {})
with self.assertRaises(ValueError):
StructuredSolver.reformat_parameters('not a type', {}, {})
import copy
import networkx as nx
from parameterized import parameterized, parameterized_class
try:
import dimod
except ImportError:
dimod = None
from dwave.cloud.client import Client
from dwave.cloud.solver import StructuredSolver
from dwave.cloud.testing import mocks
try:
C16 = StructuredSolver(data=mocks.qpu_chimera_solver_data(16), client=None)
P16 = StructuredSolver(data=mocks.qpu_pegasus_solver_data(16), client=None)
solvers = [(C16,), (P16,)]
except RuntimeError:
solvers = []
from tests import config
@parameterized_class(("solver", ), solvers)
@unittest.skipUnless(dimod, "dimod not installed")
class TestCheckProblem(unittest.TestCase):
def test_identity(self):
# NOTE: cast to list can be removed once we drop support for dimod 0.8.x
bqm = dimod.generators.ran_r(1, list(self.solver.edges))
self.assertTrue(self.solver.check_problem(bqm.linear, bqm.quadratic))
