def test_nice_coordinates(self):
p = pegasus_graph(3, nice_coordinates=True)
c = chimera_graph(24, coordinates=True)
num_edges = 0
for u, v in fragmented_edges(p):
self.assertTrue(c.has_edge(u, v))
num_edges += 1
#This is a weird edgecount: each node produces 5 extra edges for the internal connections
#between fragments corresponding to a pegasus qubit. But then we need to delete the odd
#couplers, which aren't included in the chimera graph -- odd couplers make a perfect
#matching, so thats 1/2 an edge per node.
self.assertEqual(p.number_of_edges() + 9 * p.number_of_nodes()//2, num_edges)
def draw_chimera_yield(G, **kwargs):
"""Draws the given graph G with highlighted faults, according to layout.
Parameters
----------
G : NetworkX graph
The graph to be parsed for faults
unused_color : tuple or color string (optional, default (0.9,0.9,0.9,1.0))
The color to use for nodes and edges of G which are not faults.
If unused_color is None, these nodes and edges will not be shown at all.
fault_color : tuple or color string (optional, default (1.0,0.0,0.0,1.0))
A color to represent nodes absent from the graph G. Colors should be
length-4 tuples of floats between 0 and 1 inclusive.
fault_shape : string, optional (default='x')
The shape of the fault nodes. Specification is as matplotlib.scatter
marker, one of 'so^>v<dph8'.
fault_style : string, optional (default='dashed')
Edge fault line style (solid|dashed|dotted|dashdot)
kwargs : optional keywords
See networkx.draw_networkx() for a description of optional keywords,
with the exception of the `pos` parameter which is not used by this
function. If `linear_biases` or `quadratic_biases` are provided,
any provided `node_color` or `edge_color` arguments are ignored.
"""
try:
assert (G.graph["family"] == "chimera")
m = G.graph["rows"]
n = G.graph["columns"]
t = G.graph["tile"]
coordinates = G.graph["labels"] == "coordinate"
except:
raise ValueError("Target chimera graph needs to have columns, rows, \
tile, and label attributes to be able to identify faulty qubits.")
perfect_graph = chimera_graph(m, n, t, coordinates=coordinates)
draw_yield(G, chimera_layout(perfect_graph), perfect_graph, **kwargs)
def find_clique_embedding(k, m=None, target_graph=None):
"""Find an embedding of a k-sized clique on a Pegasus graph (target_graph).
This clique is found by transforming the Pegasus graph into a K2,2 Chimera graph and then
applying a Chimera clique finding algorithm. The results are then converted back in terms of
Pegasus coordinates.
Note: If target_graph is None, m will be used to generate a m-by-m Pegasus graph. Hence m and
target_graph cannot both be None.
Args:
k (int/iterable/:obj:`networkx.Graph`): Number of members in the requested clique; list of nodes;
a complete graph that you want to embed onto the target_graph
m (int): Number of tiles in a row of a square Pegasus graph
target_graph (:obj:`networkx.Graph`): A Pegasus graph
Returns:
dict: A dictionary representing target_graphs's clique embedding. Each dictionary key
represents a node in said clique. Each corresponding dictionary value is a list of pegasus
coordinates that should be chained together to represent said node.
"""
# Organize parameter values
if target_graph is None:
if m is None:
raise TypeError("m and target_graph cannot both be None.")
target_graph = pegasus_graph(m)
m = target_graph.graph['rows'] # We only support square Pegasus graphs
_, nodes = k
# Deal with differences in ints vs coordinate target_graphs
if target_graph.graph['labels'] == 'nice':
fwd_converter = get_nice_to_pegasus_fn(m=m)
back_converter = get_pegasus_to_nice_fn(m=m)
pegasus_coords = [fwd_converter(*p) for p in target_graph.nodes]
back_translate = lambda embedding: {
key: [back_converter(*p) for p in chain]
for key, chain in embedding.items()
}
elif target_graph.graph['labels'] == 'int':
# Convert nodes in terms of Pegasus coordinates
coord_converter = pegasus_coordinates(m)
pegasus_coords = map(coord_converter.tuple, target_graph.nodes)
# A function to convert our final coordinate embedding to an ints embedding
back_translate = lambda embedding: {
key: list(coord_converter.ints(chain))
for key, chain in embedding.items()
}
else:
pegasus_coords = target_graph.nodes
back_translate = lambda embedding: embedding
# Break each Pegasus qubits into six Chimera fragments
# Note: By breaking the graph in this way, you end up with a K2,2 Chimera graph
fragment_tuple = get_tuple_fragmentation_fn(target_graph)
fragments = fragment_tuple(pegasus_coords)
# Create a K2,2 Chimera graph
# Note: 6 * m because Pegasus qubits split into six pieces, so the number of rows and columns
# get multiplied by six
chim_m = 6 * m
chim_graph = chimera_graph(chim_m, t=2, coordinates=True)
# Determine valid fragment couplers in a K2,2 Chimera graph
edges = chim_graph.subgraph(fragments).edges()
# Find clique embedding in K2,2 Chimera graph
embedding_processor = processor(edges,
M=chim_m,
N=chim_m,
L=2,
linear=False)
chimera_clique_embedding = embedding_processor.tightestNativeClique(
len(nodes))
# Convert chimera fragment embedding in terms of Pegasus coordinates
defragment_tuple = get_tuple_defragmentation_fn(target_graph)
pegasus_clique_embedding = map(defragment_tuple, chimera_clique_embedding)
pegasus_clique_embedding = dict(zip(nodes, pegasus_clique_embedding))
pegasus_clique_embedding = back_translate(pegasus_clique_embedding)
if len(pegasus_clique_embedding) != len(nodes):
raise ValueError("No clique embedding found")
return pegasus_clique_embedding
def find_clique_embedding(k, m=None, target_graph=None):
"""Find an embedding for a clique in a Pegasus graph.
Given a clique (fully connected graph) and target Pegasus graph, attempts
to find an embedding by transforming the Pegasus graph into a :math:`K_{2,2}`
Chimera graph and then applying a Chimera clique-finding algorithm. Results
are converted back to Pegasus coordinates.
Args:
k (int/iterable/:obj:`networkx.Graph`): A complete graph to embed,
formatted as a number of nodes, node labels, or a NetworkX graph.
m (int): Number of tiles in a row of a square Pegasus graph. Required to
generate an m-by-m Pegasus graph when `target_graph` is None.
target_graph (:obj:`networkx.Graph`): A Pegasus graph. Required when `m`
is None.
Returns:
dict: An embedding as a dict, where keys represent the clique's nodes and
values, formatted as lists, represent chains of pegasus coordinates.
Examples:
This example finds an embedding for a :math:`K_3` complete graph in a
2-by-2 Pegaus graph.
>>> from dwave.embedding.pegasus import find_clique_embedding
...
>>> print(find_clique_embedding(3, 2)) # doctest: +SKIP
{0: [10, 34], 1: [35, 11], 2: [32, 12]}
"""
# Organize parameter values
if target_graph is None:
if m is None:
raise TypeError("m and target_graph cannot both be None.")
target_graph = pegasus_graph(m)
m = target_graph.graph['rows'] # We only support square Pegasus graphs
_, nodes = k
# Deal with differences in ints vs coordinate target_graphs
if target_graph.graph['labels'] == 'nice':
fwd_converter = get_nice_to_pegasus_fn()
back_converter = get_pegasus_to_nice_fn()
pegasus_coords = [fwd_converter(*p) for p in target_graph.nodes]
back_translate = lambda embedding: {key: [back_converter(*p) for p in chain]
for key, chain in embedding.items()}
elif target_graph.graph['labels'] == 'int':
# Convert nodes in terms of Pegasus coordinates
coord_converter = pegasus_coordinates(m)
pegasus_coords = map(coord_converter.tuple, target_graph.nodes)
# A function to convert our final coordinate embedding to an ints embedding
back_translate = lambda embedding: {key: list(coord_converter.ints(chain))
for key, chain in embedding.items()}
else:
pegasus_coords = target_graph.nodes
back_translate = lambda embedding: embedding
# Break each Pegasus qubits into six Chimera fragments
# Note: By breaking the graph in this way, you end up with a K2,2 Chimera graph
fragment_tuple = get_tuple_fragmentation_fn(target_graph)
fragments = fragment_tuple(pegasus_coords)
# Create a K2,2 Chimera graph
# Note: 6 * m because Pegasus qubits split into six pieces, so the number of rows and columns
# get multiplied by six
chim_m = 6 * m
chim_graph = chimera_graph(chim_m, t=2, coordinates=True)
# Determine valid fragment couplers in a K2,2 Chimera graph
edges = chim_graph.subgraph(fragments).edges()
# Find clique embedding in K2,2 Chimera graph
embedding_processor = processor(edges, M=chim_m, N=chim_m, L=2, linear=False)
chimera_clique_embedding = embedding_processor.tightestNativeClique(len(nodes))
# Convert chimera fragment embedding in terms of Pegasus coordinates
defragment_tuple = get_tuple_defragmentation_fn(target_graph)
pegasus_clique_embedding = map(defragment_tuple, chimera_clique_embedding)
pegasus_clique_embedding = dict(zip(nodes, pegasus_clique_embedding))
pegasus_clique_embedding = back_translate(pegasus_clique_embedding)
if len(pegasus_clique_embedding) != len(nodes):
raise ValueError("No clique embedding found")
return pegasus_clique_embedding