def test_unstructured_sampler(self):
with self.assertRaises(ValueError):
dimod.child_structure_dfs(dimod.NullSampler())
nested = dimod.TrackingComposite(dimod.NullSampler())
with self.assertRaises(ValueError):
dimod.child_structure_dfs(nested)
def test_composed_sampler(self):
nodelist = list(range(5))
edgelist = list(itertools.combinations(nodelist, 2))
structured_sampler = dimod.StructureComposite(dimod.NullSampler(),
nodelist, edgelist)
sampler = dimod.TrackingComposite(structured_sampler)
structure = dimod.child_structure_dfs(sampler)
self.assertEqual(structure.nodelist, nodelist)
self.assertEqual(structure.edgelist, edgelist)
def embed_graph(self):
"""
Separate function as graph embedding can be reused so long as self.dim, self.k remain constant
Embed graph maps the fully connected Ising model we have defined to the D-Wave Chimera topology
"""
source_edgelist = [(i, j) for i in range(int(self.nqubits))
for j in range(i, int(self.nqubits))]
dws = DWaveSampler(qpu=True) # instantiate DWaveSampler
dws_structure = dimod.child_structure_dfs(dws)
target_edgelist = dws_structure.edgelist
self.embedding = mm.find_embedding(source_edgelist, target_edgelist)
physical_qubits = []
for qubit_list in self.embedding.values(
): # this loop just puts all the physical qubit indices into one list
physical_qubits += qubit_list
print("Number of physical qubits: ", len(physical_qubits))
def test_sampler(self):
# not a composed sampler
nodelist = list(range(5))
edgelist = list(itertools.combinations(nodelist, 2))
class Dummy(dimod.Structured):
@property
def nodelist(self):
return nodelist
@property
def edgelist(self):
return edgelist
sampler = Dummy()
structure = dimod.child_structure_dfs(sampler)
self.assertEqual(structure.nodelist, nodelist)
self.assertEqual(structure.edgelist, edgelist)