def __init__(self, problem_instance: dimod.BinaryQuadraticModel, qpu: Type[QPU], initial_mapping: Mapping = None):
self.problem = problem_instance
self.remaining_interactions = problem_instance.to_networkx_graph()
self.qpu = qpu
if initial_mapping is None:
self.initial_mapping = Mapping(self.qpu, self.problem)
else:
self.initial_mapping = initial_mapping
self.mapping = cp.deepcopy(self.initial_mapping)
self.layers = [Layer(self.qpu)]
def decompose_into_chains(problem_instance: dimod.BinaryQuadraticModel):
problem_graph = problem_instance.to_networkx_graph()
for log_qb in problem_graph:
problem_graph.add_node(log_qb, endnode=False)
problem_size = len(problem_graph)
color_sets = find_edge_coloring(problem_graph)
color_sets = sorted(color_sets,
key=lambda color_set: len(color_set),
reverse=True)
for log_qb0, log_qb1 in problem_graph.edges():
if frozenset((log_qb0, log_qb1)) in color_sets[0]:
color0 = problem_graph[log_qb0][log_qb1]['color']
break
for log_qb0, log_qb1 in problem_graph.edges():
if frozenset((log_qb0, log_qb1)) in color_sets[1]:
color1 = problem_graph[log_qb0][log_qb1]['color']
break
def filter_edge(log_qb0, log_qb1) -> bool:
if problem_graph[log_qb0][log_qb1]['color'] == color0:
return True
elif problem_graph[log_qb0][log_qb1]['color'] == color1:
return True
else:
return False
twocolor_graph = nx.subgraph_view(problem_graph, filter_edge=filter_edge)
components = [
twocolor_graph.subgraph(c).copy()
for c in nx.connected_components(twocolor_graph)
]
chains, loops = [], []
for component in components:
deg = 3
for node in component:
if component.degree(node) < deg:
node0 = node
deg = component.degree(node)
for neighbor in component.neighbors(node0):
node1 = neighbor
break
lst = [node0, node1]
for _ in range(len(component) - 2):
for neighbor in component.neighbors(node1):
if neighbor is not node0:
node0 = node1
node1 = neighbor
lst.append(node1)
break
if deg == 1:
chains.append(lst)
elif deg == 2:
loops.append(lst)
return chains, loops
