def test_row_identity(self):
C41 = dnx.chimera_graph(4, 1)
coord = dnx.chimera_coordinates(4, 1, 4)
labels = dnx.canonical_chimera_labeling(C41)
labels = {v: coord.chimera_to_linear(labels[v]) for v in labels}
G = nx.relabel_nodes(C41, labels, copy=True)
self.assertTrue(nx.is_isomorphic(G, C41))
def test_3x3_identity(self):
C33 = dnx.chimera_graph(3, 3)
coord = dnx.chimera_coordinates(3, 3, 4)
labels = dnx.canonical_chimera_labeling(C33)
labels = {v: coord.chimera_to_linear(labels[v]) for v in labels}
G = nx.relabel_nodes(C33, labels, copy=True)
self.assertTrue(nx.is_isomorphic(G, C33))
def __init__(self, guest: Graph, host: ChimeraGraph):
if host.faulty_nodes or host.faulty_edges:
raise NotImplementedError(
"Chimera graphs with faults are not supported by these algorithms")
self.guest = guest
self.host = host
m, l = host.params
dnx_coords = dnx.chimera_coordinates(m, t=l)
self.linear_to_chimera = dnx_coords.linear_to_chimera
self.chimera_to_linear = dnx_coords.chimera_to_linear
self.forward_embed = None
def test_tile_identity(self):
C1 = dnx.chimera_graph(1)
coord = dnx.chimera_coordinates(1, 1, 4)
labels = dnx.canonical_chimera_labeling(C1)
labels = {v: coord.chimera_to_linear(labels[v]) for v in labels}
G = nx.relabel_nodes(C1, labels, copy=True)
self.assertTrue(nx.is_isomorphic(G, C1))
self.assertEqual(set(G), set(C1))
def test_bqm_tile_identity(self):
J = {e: -1 for e in dnx.chimera_graph(1).edges}
C1bqm = dimod.BinaryQuadraticModel.from_ising({}, J)
coord = dnx.chimera_coordinates(1, 1, 4)
labels = dnx.canonical_chimera_labeling(C1bqm)
labels = {v: coord.chimera_to_linear(labels[v]) for v in labels}
bqm = C1bqm.relabel_variables(labels, inplace=False)
self.assertEqual(bqm, C1bqm)
def test_reversed(self):
C33 = nx.OrderedGraph()
C33.add_nodes_from(reversed(range(3 * 3 * 4)))
C33.add_edges_from(dnx.chimera_graph(3, 3, 4).edges)
coord = dnx.chimera_coordinates(3, 3, 4)
labels = dnx.canonical_chimera_labeling(C33)
labels = {v: coord.chimera_to_linear(labels[v]) for v in labels}
G = nx.relabel_nodes(C33, labels, copy=True)
self.assertTrue(nx.is_isomorphic(G, C33))
def double_triangle_clique(chimera_graph: ChimeraGraph) -> Dict[int, List[int]]:
"""
Performs a double-sided triangle embedding in a similar fashion as described by the PSSA paper.
'Graph Minors from Simulated Annealing for Annealing Machines with Sparse Connectivity'
Reference:
Y. Sugie, Y. Yoshida, N. Mertig, T. Takemoto, H. Teramoto, A. Nakamura, I. Takigawa, S.-i.
Minato, M.Yamaoka, and T. Komatsuzaki, Minor-embedding heuristics for large-scale
annealing processors with sparse hardware graphs of up to 102,400 nodes,
2020. arXiv:2004.03819 [quant-ph]:
Args:
chimera_graph: Chimera host to transform.
Returns: an embedding.
!NOTE: The vertex sets are represented as lists but the ordering matters.
The nodes should be ordered according to the chain formed on the hardware hardware.
"""
m, l = chimera_graph.params
to_linear = dnx.chimera_coordinates(m, t=l).chimera_to_linear
# Embed the upper triangular
top_embed = [[] for _ in range(m * l)]
for i in range(m * l):
cell, unit = i // l, i % l
# Add the nodes above diagonal cell
for j in range(cell):
top_embed[i].append(to_linear((j, cell, 0, unit)))
# Add the two nodes in the diagonal cell
top_embed[i].extend((to_linear(
(cell, cell, 0, unit)), to_linear((cell, cell, 1, unit))))
# Add the nodes to right of diagonal cell
for j in range(cell + 1, m):
top_embed[i].append(to_linear((cell, j, 1, unit)))
# Embed the lower triangular
bot_embed = [[] for _ in range((m - 1) * l)]
for i in range((m - 1) * l):
cell, unit = i // l, i % l
# Add the nodes to left of diagonal cell
for j in range(cell):
bot_embed[i].append(to_linear((cell + 1, j, 1, unit)))
# Add the two nodes in the diagonal cell
bot_embed[i].extend((to_linear(
(cell + 1, cell, 1, unit)), to_linear((cell + 1, cell, 0, unit))))
# Add the nodes below diagonal cell
for j in range(cell + 1, m - 1):
bot_embed[i].append(to_linear((j + 1, cell, 0, unit)))
combined = top_embed + bot_embed
return {i: combined[i] for i in range(len(combined))}
def _initialize_weights_chimera(chimera_graph, size, draw_inter_weight,
draw_intra_weight, draw_other_weight):
c_coor = dwave.chimera_coordinates(size)
for _from, _to in chimera_graph.edges:
_from_nice = c_coor.linear_to_chimera(_from)
_to_nice = c_coor.linear_to_chimera(_to)
if in_same_chimera_tile(_from_nice, _to_nice):
# edge from one side to the other (internal edge)
if not on_same_side(_from_nice, _to_nice):
chimera_graph.add_edge(_from, _to, weight=draw_intra_weight())
else: # odd couplers
chimera_graph.add_edge(_from, _to, weight=draw_other_weight())
else:
chimera_graph.add_edge(_from, _to, weight=draw_inter_weight())
def __init__(self, *args, **kwargs):
super(TestBusclique, self).__init__(*args, **kwargs)
self.c16 = dnx.chimera_graph(16)
self.p16 = dnx.pegasus_graph(16)
self.c16c = dnx.chimera_graph(16, coordinates=True, data=False)
self.c428 = dnx.chimera_graph(4, n=2, t=8)
self.c248 = dnx.chimera_graph(2, n=4, t=8)
self.c42A = dnx.chimera_graph(4, n=2, t=9)
c4c_0 = subgraph_node_yield(dnx.chimera_graph(4, coordinates=True),
.95)
p4c_0 = subgraph_node_yield(dnx.pegasus_graph(4, coordinates=True),
.95)
c4c = [
c4c_0,
subgraph_edge_yield(c4c_0, .95),
subgraph_edge_yield_few_bad(c4c_0, .95, 6)
]
p4c = [
p4c_0,
subgraph_edge_yield(p4c_0, .95),
subgraph_edge_yield_few_bad(p4c_0, .95, 6)
]
p4coords = dnx.pegasus_coordinates(4)
c4coords = dnx.chimera_coordinates(4, 4, 4)
c4 = [
relabel(c, c4coords.iter_chimera_to_linear,
c4coords.iter_chimera_to_linear_pairs, 4) for c in c4c
]
p4 = [
relabel(p, p4coords.iter_pegasus_to_linear,
p4coords.iter_pegasus_to_linear_pairs, 4) for p in p4c
]
p4n = [
relabel(p,
p4coords.iter_pegasus_to_nice,
p4coords.iter_pegasus_to_nice_pairs,
4,
nice_coordinates=True) for p in p4c
]
self.c4, self.c4_nd, self.c4_d = list(zip(c4, c4c))
self.p4, self.p4_nd, self.p4_d = list(zip(p4, p4c, p4n))
def bipartite_with_faults(chimera_graph: ChimeraGraph):
"""
Create a bipartite template embedding which allows for faulty nodes and edges.
Args:
chimera_graph: Chimera host to transform.
Returns: Tuple (left, right) for embedding in each respective partition.
"""
def append_nonempty(super, sub):
if sub:
super.append(sub)
m, l = chimera_graph.params
faulty = chimera_graph.faulty_nodes
to_linear = dnx.chimera_coordinates(m, t=l).chimera_to_linear
h_embed = []
for i in range(m * l):
chain = []
cell, unit = i // l, i % l
for j in range(m):
ln = to_linear((cell, j, 1, unit))
if ln in faulty:
append_nonempty(h_embed, chain)
chain = []
else:
chain.append(ln)
append_nonempty(h_embed, chain)
v_embed = []
for i in range(m * l):
chain = []
cell, unit = i // l, i % l
for j in range(m):
ln = to_linear((j, cell, 0, unit))
if ln in faulty:
append_nonempty(v_embed, chain)
chain = []
else:
chain.append(ln)
append_nonempty(v_embed, chain)
return h_embed, v_embed
def test_coordinate_generator(self):
G = dnx.chimera_graph(4, 2, coordinates=True, data=True)
H = dnx.chimera_graph(4, 2, coordinates=False, data=True)
self.assertTrue(nx.is_isomorphic(G, H))
Gnodes = set(G.nodes)
Glabels = set(n['linear_index'] for n in G.nodes.values())
Hnodes = set(H.nodes)
Hlabels = set(n['chimera_index'] for n in H.nodes.values())
self.assertEqual(Gnodes, Hlabels)
self.assertEqual(Hnodes, Glabels)
coords = dnx.chimera_coordinates(2, 1)
f = nx.relabel_nodes(g, coords.chimera_to_linear, copy=True)
self.assertEqual(set(f.edges), set(h.edges))
e = nx.relabel_nodes(h, coords.linear_to_chimera, copy=True)
self.assertEqual(set(e.edges), set(g.edges))
def test_construction_string_labels(self):
C22 = dnx.chimera_graph(2, 2, 3)
coord = dnx.chimera_coordinates(2, 2, 3)
alpha = 'abcdefghijklmnopqrstuvwxyz'
bqm = dimod.BinaryQuadraticModel.empty(dimod.BINARY)
for u, v in reversed(list(C22.edges)):
bqm.add_interaction(alpha[u], alpha[v], 1)
assert len(bqm.quadratic) == len(C22.edges)
assert len(bqm) == len(C22)
labels = dnx.canonical_chimera_labeling(bqm)
labels = {v: alpha[coord.chimera_to_linear(labels[v])] for v in labels}
bqm2 = bqm.relabel_variables(labels, inplace=False)
self.assertEqual(bqm, bqm2)
def overlap_clique(chimera_graph: ChimeraGraph):
"""
Returns a clique overlap template embedding as described in 'Template-based minor embedding
for adiabatic quantum optimization'.
Reference:
T. Serra, T. Huang, A. Raghunathan, and D. Bergman, Template-based minor embedding for
adiabatic quantum optimization, 2019. arXiv:1910.02179 [cs.DS].
Args:
chimera_graph: Chimera host to transform.
Returns: an embedding.
"""
m, l = chimera_graph.params
to_linear = dnx.chimera_coordinates(m, t=l).chimera_to_linear
# Embed the clique major
top_embed = [[] for _ in range(m * l)]
for i in range(m * l):
cell, unit = i // l, i % l
# Add the nodes above diagonal cell
for j in range(cell):
top_embed[i].append(to_linear((j, cell, 0, unit)))
# Add the two nodes in the diagonal cell
top_embed[i].extend((to_linear(
(cell, cell, 0, unit)), to_linear((cell, cell, 1, unit))))
# Add the entire row
for j in range(0, m):
top_embed[i].append(to_linear((cell, j, 1, unit)))
# Embed the clique minor
bot_embed = [[] for _ in range((m - 1) * l)]
for i in range((m - 1) * l):
cell, unit = i // l, i % l
for j in range(cell, m - 1):
bot_embed[i].append(to_linear((j + 1, cell, 0, unit)))
combined = top_embed + bot_embed
return {i: combined[i] for i in range(len(combined))}
def test_graph_relabeling(self):
def graph_equal(g, h):
self.assertEqual(set(g), set(h))
self.assertEqual(set(map(tuple, map(sorted, g.edges))),
set(map(tuple, map(sorted, g.edges))))
for v, d in g.nodes(data=True):
self.assertEqual(h.nodes[v], d)
coords = dnx.chimera_coordinates(3)
for data in True, False:
c3l = dnx.chimera_graph(3, data=data)
c3c = dnx.chimera_graph(3, data=data, coordinates=True)
graph_equal(c3l, coords.graph_to_linear(c3c))
graph_equal(c3l, coords.graph_to_linear(c3l))
graph_equal(c3c, coords.graph_to_chimera(c3l))
graph_equal(c3c, coords.graph_to_chimera(c3c))
h = dnx.chimera_graph(2)
del h.graph['labels']
with self.assertRaises(ValueError):
coords.graph_to_linear(h)
with self.assertRaises(ValueError):
coords.graph_to_chimera(h)
def test_nonsquare_coordinate_generator(self):
#issue 149 found an issue with non-square generators -- let's be extra careful here
for (m, n) in [(2, 4), (4, 2)]:
G = dnx.chimera_graph(m, n, coordinates=True, data=True)
H = dnx.chimera_graph(m, n, coordinates=False, data=True)
self.assertTrue(nx.is_isomorphic(G, H))
Gnodes = set(G.nodes)
Glabels = set(q['linear_index'] for q in G.nodes.values())
Hnodes = set(H.nodes)
Hlabels = set(q['chimera_index'] for q in H.nodes.values())
self.assertEqual(Gnodes, Hlabels)
self.assertEqual(Hnodes, Glabels)
coords = dnx.chimera_coordinates(m, n)
F = nx.relabel_nodes(G, coords.chimera_to_linear, copy=True)
self.assertEqual(set(map(frozenset, F.edges)),
set(map(frozenset, H.edges)))
E = nx.relabel_nodes(H, coords.linear_to_chimera, copy=True)
self.assertEqual(set(map(frozenset, E.edges)),
set(map(frozenset, G.edges)))
def _lookup_intersection_coordinates(G):
"""For a dwave_networkx graph G, this returns a dictionary mapping the
lattice points to sets of vertices of G.
For Chimera, Pegasus and Zephyr, each lattice point corresponds to the 2
qubits intersecting at that point.
"""
graph_data = G.graph
family = graph_data.get("family")
data_key = None
intersection_points = defaultdict(set)
if family == "chimera":
shore = graph_data.get("tile")
collect_intersection_points = _chimera_all_intersection_points
if graph_data["labels"] == "coordinate":
def get_coords(v):
return v
elif graph_data["data"]:
data_key = "chimera_index"
else:
coords = dnx.chimera_coordinates(graph_data['rows'],
n=graph_data['columns'],
t=shore)
get_coords = coords.linear_to_chimera
elif family == "pegasus":
shore = [
graph_data['vertical_offsets'], graph_data['horizontal_offsets']
]
collect_intersection_points = _pegasus_all_intersection_points
if graph_data["labels"] == "coordinate":
def get_coords(v):
return v
elif graph_data["data"]:
data_key = "pegasus_index"
else:
coords = dnx.pegasus_coordinates(graph_data['rows'])
if graph_data['labels'] == 'int':
get_coords = coords.linear_to_pegasus
elif graph_data['labels'] == 'nice':
get_coords = coords.nice_to_pegasus
elif family == "zephyr":
shore = graph_data.get("tile")
collect_intersection_points = _zephyr_all_intersection_points
if graph_data["labels"] == "coordinate":
def get_coords(v):
return v
elif graph_data["data"]:
data_key = "zephyr_index"
else:
coords = dnx.zephyr_coordinates(graph_data['rows'], t=shore)
get_coords = coords.linear_to_zephyr
if data_key is None:
for v in G:
collect_intersection_points(intersection_points, shore, v,
*get_coords(v))
else:
for v, d in G.nodes(data=True):
collect_intersection_points(intersection_points, shore, v,
*d[data_key])
return intersection_points
def quadripartite_with_faults(chimera_graph: ChimeraGraph):
"""
Create a quadripartite template embedding which allows for faulty nodes and edges.
Args:
chimera_graph: Chimera host to transform.
Returns: Tuple (U1, U2, U3, U4) for embedding in each respective partition.
"""
def append_nonempty(super, sub):
if sub:
super.append(sub)
m, l = chimera_graph.params
faulty = chimera_graph.faulty_nodes
to_linear = dnx.chimera_coordinates(m, t=l).chimera_to_linear
U1, U4 = [], []
for i in range(m * l):
chain1, chain4 = [], []
cell, unit = i // l, i % l
for j in range(m):
ln = to_linear((cell, j, 1, unit))
if ln in faulty:
if i < m * l / 2:
append_nonempty(U1, chain1)
chain1 = []
else:
append_nonempty(U4, chain4)
chain4 = []
else:
if i < m * l / 2:
chain1.append(ln)
else:
chain4.append(ln)
append_nonempty(U1, chain1)
append_nonempty(U4, chain4)
U2, U3 = [], []
for i in range(m * l):
chain2, chain3 = [], []
cell, unit = i // l, i % l
for j in range(m):
ln = to_linear((j, cell, 0, unit))
if ln in faulty:
if j < m / 2:
append_nonempty(U2, chain2)
chain2 = []
else:
append_nonempty(U3, chain3)
chain3 = []
else:
if j < m / 2:
chain2.append(ln)
else:
chain3.append(ln)
append_nonempty(U2, chain2)
append_nonempty(U3, chain3)
return U1, U2, U3, U4
def make_origin_embeddings(qpu_sampler=None, lattice_type=None):
"""Creates optimal embeddings for a lattice.
The embeddings created are compatible with the topology and shape of a
specified ``qpu_sampler``.
Args:
qpu_sampler (:class:`dimod.Sampler`, optional):
Quantum sampler such as a D-Wave system. If not specified, the
:class:`~dwave.system.samplers.DWaveSampler` sampler class is used
to select a QPU solver with a topology compatible with the specified
``lattice_type`` (e.g. an Advantage system for a 'pegasus' lattice
type).
lattice_type (str, optional, default=qpu_sampler.properties['topology']['type']):
Options are:
* "cubic"
Embeddings compatible with the schemes arXiv:2009.12479 and
arXiv:2003.00133 are created for a ``qpu_sampler`` of
topology type either 'pegasus' or 'chimera'.
* "pegasus"
Embeddings are chain length one (minimal and native).
If ``qpu_sampler`` topology type is 'pegasus', maximum
scale subgraphs are embedded using the ``nice_coordinates``
vector labeling scheme for variables.
* "chimera"
Embeddings are chain length one (minimal and native).
If ``qpu_sampler`` topology type is 'chimera', maximum
scale chimera subgraphs are embedded using the chimera
vector labeling scheme for variables.
Returns:
A list of embeddings. Each embedding is a dictionary, mapping
geometric problem keys to sets of qubits (chains) compatible with
the ``qpu_sampler``.
Examples:
This example creates a list of three cubic lattice embeddings
compatible with the default online system. These three embeddings are
related by rotation of the lattice: for a Pegasus P16 system the
embeddings are for lattices of size (15,15,12), (12,15,15) and (15,12,15)
respectively.
>>> from dwave.system.samplers import DWaveSampler # doctest: +SKIP
>>> sampler = DWaveSampler() # doctest: +SKIP
>>> embeddings = make_origin_embeddings(qpu_sampler=sampler,
... lattice_type='cubic') # doctest: +SKIP
"""
if qpu_sampler is None:
if lattice_type == 'pegasus' or lattice_type == 'chimera':
qpu_sampler = DWaveSampler(solver={'topology__type': lattice_type})
else:
qpu_sampler = DWaveSampler()
qpu_type = qpu_sampler.properties['topology']['type']
if lattice_type is None:
lattice_type = qpu_type
qpu_shape = qpu_sampler.properties['topology']['shape']
target = nx.Graph()
target.add_edges_from(qpu_sampler.edgelist)
if qpu_type == lattice_type:
# Fully yielded fully utilized native topology problem.
# This method is also easily adapted to work for any chain-length 1
# embedding
origin_embedding = {q: [q] for q in qpu_sampler.properties['qubits']}
if lattice_type == 'pegasus':
# Trimming to nice_coordinate supported embeddings is not a unique,
# options, it has some advantages and some disadvantages:
proposed_source = dnx.pegasus_graph(qpu_shape[0],
nice_coordinates=True)
proposed_source = nx.relabel_nodes(
proposed_source, {
q: dnx.pegasus_coordinates(qpu_shape[0]).nice_to_linear(q)
for q in proposed_source.nodes()
})
lin_to_vec = dnx.pegasus_coordinates(qpu_shape[0]).linear_to_nice
elif lattice_type == 'chimera':
proposed_source = dnx.chimera_graph(qpu_shape[0], qpu_shape[1],
qpu_shape[2])
lin_to_vec = dnx.chimera_coordinates(
qpu_shape[0]).linear_to_chimera
else:
raise ValueError(
f'Unsupported native processor topology {qpu_type}. '
'Support for Zephyr and other topologies is straightforward to '
'add subject to standard dwave_networkx library tool availability.'
)
elif lattice_type == 'cubic':
if qpu_type == 'pegasus':
vec_to_lin = dnx.pegasus_coordinates(
qpu_shape[0]).pegasus_to_linear
L = qpu_shape[0] - 1
dimensions = [L, L, 12]
# See arXiv:2003.00133
origin_embedding = {
(x, y, z): [
vec_to_lin((0, x, z + 4, y)),
vec_to_lin((1, y + 1, 7 - z, x))
]
for x in range(L) for y in range(L) for z in range(8)
if target.has_edge(vec_to_lin((
0, x, z + 4, y)), vec_to_lin((1, y + 1, 7 - z, x)))
}
origin_embedding.update({
(x, y, z): [
vec_to_lin((0, x + 1, z - 8, y)),
vec_to_lin((1, y, 19 - z, x))
]
for x in range(L) for y in range(L) for z in range(8, 12)
if target.has_edge(vec_to_lin((
0, x + 1, z - 8, y)), vec_to_lin((1, y, 19 - z, x)))
})
elif qpu_type == 'chimera':
vec_to_lin = dnx.chimera_coordinates(
qpu_shape[0], qpu_shape[1], qpu_shape[2]).chimera_to_linear
L = qpu_shape[0] // 2
dimensions = [L, L, 8]
# See arxiv:2009.12479, one choice amongst many
origin_embedding = {
(x, y, z): [
vec_to_lin(coord)
for coord in [(2 * x + 1, 2 * y, 0,
z), (2 * x, 2 * y, 0,
z), (2 * x, 2 * y, 1,
z), (2 * x, 2 * y + 1, 1, z)]
]
for x in range(L) for y in range(L) for z in range(4)
if target.has_edge(vec_to_lin((
2 * x + 1, 2 * y, 0, z)), vec_to_lin((2 * x, 2 * y, 0, z)))
and target.has_edge(vec_to_lin((
2 * x, 2 * y, 0, z)), vec_to_lin((2 * x, 2 * y, 1, z)))
and target.has_edge(vec_to_lin((
2 * x, 2 * y, 1, z)), vec_to_lin((2 * x, 2 * y + 1, 1, z)))
}
origin_embedding.update({
(x, y, 4 + z): [
vec_to_lin(coord)
for coord in [(2 * x + 1, 2 * y, 1,
z), (2 * x + 1, 2 * y + 1, 1,
z), (2 * x + 1, 2 * y + 1, 0,
z), (2 * x, 2 * y + 1, 0, z)]
]
for x in range(L) for y in range(L) for z in range(4)
if target.has_edge(vec_to_lin((
2 * x + 1, 2 * y, 1,
z)), vec_to_lin((2 * x + 1, 2 * y + 1, 1, z)))
and target.has_edge(vec_to_lin((
2 * x + 1, 2 * y + 1, 1,
z)), vec_to_lin((2 * x + 1, 2 * y + 1, 0, z))) and target.
has_edge(vec_to_lin((2 * x + 1, 2 * y + 1, 0,
z)), vec_to_lin((2 * x, 2 * y + 1, 0, z)))
})
else:
raise ValueError(f'Unsupported qpu_sampler topology {qpu_type} '
'for cubic lattice solver')
proposed_source = _make_cubic_lattice(dimensions)
else:
raise ValueError('Unsupported combination of lattice_type '
'and qpu_sampler topology')
origin_embedding = _yield_limited_origin_embedding(origin_embedding,
proposed_source, target)
if qpu_type == lattice_type:
# Convert keys to standard vector scheme:
origin_embedding = {
lin_to_vec(node): origin_embedding[node]
for node in origin_embedding
}
# We can propose additional embeddings. Or we can use symmetries of the
# target graph (automorphisms), to create additional embedding options.
# This is important in the cubic case, because the subregion shape and
# embedding features are asymmetric in the x, y and z directions.
# Various symmetries can be exploited in all lattices.
origin_embeddings = [origin_embedding]
if lattice_type == 'cubic':
# A rotation is sufficient for demonstration purposes:
origin_embeddings.append({(key[2], key[0], key[1]): value
for key, value in origin_embedding.items()})
origin_embeddings.append({(key[1], key[2], key[0]): value
for key, value in origin_embedding.items()})
elif lattice_type == 'pegasus':
# A horizontal to vertical flip is sufficient for demonstration purposes: # Flip north-east to south-west axis (see draw_pegasus):
L = qpu_shape[0]
origin_embeddings.append({(key[0], L - 2 - key[2], L - 2 - key[1],
1 - key[3], 3 - key[4]): value
for key, value in origin_embedding.items()})
else:
# A horizontal to vertical flip is sufficient for demonstration purposes:
origin_embeddings.append({(key[1], key[0], 1 - key[2], key[3]): value
for key, value in origin_embedding.items()})
return origin_embeddings
