def weighted_maximum_cut(G, sampler=None, **sampler_args):
"""Returns an approximate weighted maximum cut.
Defines an Ising problem with ground states corresponding to
a weighted maximum cut and uses the sampler to sample from it.
A weighted maximum cut is a subset S of the vertices of G that
maximizes the sum of the edge weights between S and its
complementary subset.
Parameters
----------
G : NetworkX graph
The graph on which to find a weighted maximum cut. Each edge in G should
have a numeric `weight` attribute.
sampler
A binary quadratic model sampler. A sampler is a process that
samples from low energy states in models defined by an Ising
equation or a Quadratic Unconstrained Binary Optimization
Problem (QUBO). A sampler is expected to have a 'sample_qubo'
and 'sample_ising' method. A sampler is expected to return an
iterable of samples, in order of increasing energy. If no
sampler is provided, one must be provided using the
`set_default_sampler` function.
sampler_args
Additional keyword parameters are passed to the sampler.
Returns
-------
S : set
A maximum cut of G.
Notes
-----
Samplers by their nature may not return the optimal solution. This
function does not attempt to confirm the quality of the returned
sample.
"""
# In order to form the Ising problem, we want to increase the
# energy by 1 for each edge between two nodes of the same color.
# The linear biases can all be 0.
h = {v: 0. for v in G}
try:
J = {(u, v): G[u][v]['weight'] for u, v in G.edges}
except KeyError:
raise DWaveNetworkXException("edges must have 'weight' attribute")
# draw the lowest energy sample from the sampler
response = sampler.sample_ising(h, J, **sampler_args)
sample = next(iter(response))
return set(v for v in G if sample[v] >= 0)
def find_chimera_indices(G):
"""Attempts to determine the Chimera indices of the nodes in graph G.
See the :func:`~chimera_graph()` function for a definition of a Chimera graph and Chimera
indices.
Parameters
----------
G : NetworkX graph
Should be a single-tile Chimera graph.
Returns
-------
chimera_indices : dict
A dict of the form {node: (i, j, u, k), ...} where (i, j, u, k)
is a 4-tuple of integer Chimera indices.
Examples
--------
>>> G = dnx.chimera_graph(1, 1, 4)
>>> chimera_indices = dnx.find_chimera_indices(G)
>>> G = nx.Graph()
>>> G.add_edges_from([(0, 2), (1, 2), (1, 3), (0, 3)])
>>> chimera_indices = dnx.find_chimera_indices(G)
>>> nx.set_node_attributes(G, chimera_indices, 'chimera_index')
"""
# if the nodes are orderable, we want the lowest-order one.
try:
nlist = sorted(G.nodes)
except TypeError:
nlist = G.nodes()
n_nodes = len(nlist)
# create the object that will store the indices
chimera_indices = {}
# ok, let's first check for the simple cases
if n_nodes == 0:
return chimera_indices
elif n_nodes == 1:
raise DWaveNetworkXException(
'Singleton graphs are not Chimera-structured')
elif n_nodes == 2:
return {nlist[0]: (0, 0, 0, 0), nlist[1]: (0, 0, 1, 0)}
# next, let's get the bicoloring of the graph; this raises an exception if the graph is
# not bipartite
coloring = color(G)
# we want the color of the node to be the u term in the Chimera-index, so we want the
# first node in nlist to be color 0
if coloring[nlist[0]] == 1:
coloring = {v: 1 - coloring[v] for v in coloring}
# we also want the diameter of the graph
# claim: diameter(G) == m + n for |G| > 2
dia = diameter(G)
# we have already handled the |G| <= 2 case, so we know, for diameter == 2, that the Chimera
# graph is a single tile
if dia == 2:
shore_indices = [0, 0]
for v in nlist:
u = coloring[v]
chimera_indices[v] = (0, 0, u, shore_indices[u])
shore_indices[u] += 1
return chimera_indices
# NB: max degree == shore size <==> one tile
raise Exception(
'not yet implemented for Chimera graphs with more than one tile')
def pegasus_graph(m,
create_using=None,
node_list=None,
edge_list=None,
data=True,
offset_lists=None,
offsets_index=None,
coordinates=False,
fabric_only=True):
"""
Creates a Pegasus graph with size parameter m. The Pegasus topology produced
by this generator with default parameters is one member of a large family of
topologies under consideration, and may not be reflected in future products.
A Pegasus lattice is a graph minor of a lattice similar to Chimera,
where unit tiles are completely connected. In the most generality, our
prelattice Q(N0,N1) contains nodes of the form
(i, j, 0, k) with 0 <= k < 2 [vertical nodes]
and
(i, j, 1, k) with 0 <= k < 2 [horizontal nodes]
for 0 <= i <= N0 and 0 <= j < N1; and edges of the form
(i, j, u, k) ~ (i+u, j+1-u, u, k) [external edges]
(i, j, 0, k) ~ (i, j, 1, k) [internal edges]
(i, j, u, 0) ~ (i, j, u, 1) [odd edges]
The minor is specified by two lists of offsets; S0 and S1 of length L0 and L1
(where L0 and L1, and the entries of S0 and S1, must be divisible by 2).
From these offsets, we construct our minor, a Pegasus lattice, by contracting
the complete intervals of external edges,
I(0, w, k, z) = [(L1*w + k, L0*z + S0[k] + r, 0, k % 2) for 0 <= r < L0]
I(1, w, k, z) = [(L1*z + S1[k] + r, L0*w + k, 1, k % 2) for 0 <= r < L1]
and deleting the prelattice nodes of any interval not fully contained in
Q(N0, N1).
This generator is specialized to L0 = L1 = 12; N0 = N1 = 12m.
The notation (u, w, k, z) is called the pegasus index of a node in a pegasus
lattice. The entries can be interpreted as following,
u : qubit orientation (0 = vertical, 1 = horizontal)
w : orthogonal major offset
k : orthogonal minor offset
z : parallel offset
and the edges in the minor have the form
(u, w, k, z) ~ (u, w, k, z+1) [external edges]
(0, w0, k0, z0) ~ (1, w1, k1, z1) [internal edges, see below]
(u, w, 2k, z) ~ (u, w, 2k+1, z) [odd edges]
where internal edges only exist when
w1 = z0 + (1 if k1 < S0[k0] else 0), and
z1 = w0 - (1 if k0 < S1[k1] else 0)
linear indices are computed from pegasus indices by the formula
q = ((u * m + w) * 12 + k) * (m - 1) + z
Parameters
----------
m : int
The size parameter for the Pegasus lattice.
create_using : Graph, optional (default None)
If provided, this graph is cleared of nodes and edges and filled
with the new graph. Usually used to set the type of the graph.
node_list : iterable, optional (default None)
Iterable of nodes in the graph. If None, calculated from m.
Note that this list is used to remove nodes, so any nodes specified
not in range(24 * m * (m-1)) will not be added.
edge_list : iterable, optional (default None)
Iterable of edges in the graph. If None, edges are generated as
described above. The nodes in each edge must be integer-labeled in
range(24 * m * (m-1)).
data : bool, optional (default True)
If True, each node has a pegasus_index attribute. The attribute
is a 4-tuple Pegasus index as defined above. (if coordinates = True,
we set a linear_index, which is an integer)
coordinates : bool, optional (default False)
If True, node labels are 4-tuple Pegasus indices
offset_lists : pair of lists, optional (default None)
Used to directly control the offsets, each list in the pair should
have length 12, and contain even ints. If offset_lists is not None,
then offsets_index must be None.
offsets_index : int, optional (default None)
A number between 0 and 7 inclusive, to select a preconfigured
set of topological parameters. If both offsets_index and
offset_lists are None, then we set offsets_index = 0. At least
one of these two parameters must be None.
fabric_only: bool, optional (default True)
The Pegasus graph, by definition, will have some disconnected
components. If this True, we will only construct nodes from the
largest component. Otherwise, the full disconnected graph will be
constructed. Ignored if edge_lists is not None
"""
warnings.warn(
"The Pegasus topology produced by this generator with default parameters is one member of a large family of topologies under consideration, and may not be reflected in future products"
)
if offset_lists is None:
offsets_descriptor = offsets_index = offsets_index or 0
offset_lists = [
[(
2,
2,
2,
2,
10,
10,
10,
10,
6,
6,
6,
6,
), (
6,
6,
6,
6,
2,
2,
2,
2,
10,
10,
10,
10,
)],
[(
2,
2,
2,
2,
10,
10,
10,
10,
6,
6,
6,
6,
), (
2,
2,
2,
2,
10,
10,
10,
10,
6,
6,
6,
6,
)],
[(
2,
2,
2,
2,
10,
10,
10,
10,
6,
6,
6,
6,
), (
10,
10,
10,
10,
6,
6,
6,
6,
2,
2,
2,
2,
)],
[(
10,
10,
10,
10,
6,
6,
6,
6,
2,
2,
2,
2,
), (
10,
10,
10,
10,
6,
6,
6,
6,
2,
2,
2,
2,
)],
[(
10,
10,
10,
10,
6,
6,
6,
6,
2,
2,
2,
2,
), (
2,
2,
2,
2,
6,
6,
6,
6,
10,
10,
10,
10,
)],
[(
6,
6,
2,
2,
2,
2,
10,
10,
10,
10,
6,
6,
), (
6,
6,
2,
2,
2,
2,
10,
10,
10,
10,
6,
6,
)],
[(
6,
6,
2,
2,
2,
2,
10,
10,
10,
10,
6,
6,
), (
6,
6,
10,
10,
10,
10,
2,
2,
2,
2,
6,
6,
)],
[(
6,
6,
6,
6,
6,
6,
6,
6,
6,
6,
6,
6,
), (
6,
6,
6,
6,
6,
6,
6,
6,
6,
6,
6,
6,
)],
][offsets_index]
elif offsets_index is not None:
raise DWaveNetworkXException(
"provide at most one of offsets_index and offset_lists")
else:
for ori in 0, 1:
for x, y in zip(offset_lists[ori][::2], offset_lists[ori][1::2]):
if x != y:
warnings.warn(
"The offets list you've provided is possibly non-physical. Odd-coupled qubits should have the same value."
)
offsets_descriptor = offset_lists
G = nx.empty_graph(0, create_using)
G.name = "pegasus_graph(%s, %s)" % (m, offsets_descriptor)
construction = (("family", "pegasus"), ("rows", m), ("columns", m),
("tile", 12), ("vertical_offsets", offset_lists[0]),
("horizontal_offsets", offset_lists[1]), ("data", data),
("labels", "coordinate" if coordinates else "int"))
G.graph.update(construction)
max_size = m * (m - 1) * 24 # max number of nodes G can have
m1 = m - 1
if coordinates:
c2i = lambda *q: q
else:
def c2i(u, w, k, z):
return u * 12 * m * m1 + w * 12 * m1 + k * m1 + z
if edge_list is None:
if fabric_only:
fabric_start = min(s for s in offset_lists[1]), min(
s for s in offset_lists[0])
fabric_end = 12 - max(s for s in offset_lists[1]), 12 - max(
s for s in offset_lists[0])
else:
fabric_end = fabric_start = 0, 0
G.add_edges_from((c2i(u, w, k, z), c2i(u, w, k, z + 1)) for u in (0, 1)
for w in range(m)
for k in range(fabric_start[u] if w == 0 else 0, 12 -
(fabric_end[u] if w == m1 else 0))
for z in range(m1 - 1))
G.add_edges_from((c2i(u, w, k, z), c2i(u, w, k + 1, z)) for u in (0, 1)
for w in range(m)
for k in range(fabric_start[u] if w == 0 else 0, 12 -
(fabric_end[u] if w == m1 else 0), 2)
for z in range(m1))
off0, off1 = offset_lists
G.add_edges_from(
(c2i(0, w, k, z),
c2i(1, z + (kk < off0[k]), kk, w - (k < off1[kk])))
for w in range(m) for kk in range(12)
for k in range(0 if w else off1[kk], 12 if w < m1 else off1[kk])
for z in range(m1))
else:
G.add_edges_from(edge_list)
if node_list is not None:
nodes = set(node_list)
G.remove_nodes_from(set(G) - nodes)
G.add_nodes_from(nodes) # for singleton nodes
if data:
v = 0
for u in range(2):
for w in range(m):
for k in range(12):
for z in range(m1):
q = u, w, k, z
if coordinates:
if q in G:
G.node[q]['linear_index'] = v
else:
if v in G:
G.node[v]['pegasus_index'] = q
v += 1
return G
def pegasus_graph(m, create_using=None, node_list=None, edge_list=None, data=True,
offset_lists=None, offsets_index=None, coordinates=False, fabric_only=True,
nice_coordinates=False):
"""
Creates a Pegasus graph with size parameter `m`.
Parameters
----------
m : int
Size parameter for the Pegasus lattice.
create_using : Graph, optional (default None)
If provided, this graph is cleared of nodes and edges and filled
with the new graph. Usually used to set the type of the graph.
node_list : iterable, optional (default None)
Iterable of nodes in the graph. If None, calculated from `m`.
Note that this list is used to remove nodes, so any nodes specified
not in ``range(24 * m * (m-1))`` are not added.
edge_list : iterable, optional (default None)
Iterable of edges in the graph. If None, edges are generated as
described below. The nodes in each edge must be integer-labeled in
``range(24 * m * (m-1))``.
data : bool, optional (default True)
If True, each node has a pegasus_index attribute. The attribute
is a 4-tuple Pegasus index as defined below. If the `coordinates` parameter
is True, a linear_index, which is an integer, is used.
coordinates : bool, optional (default False)
If True, node labels are 4-tuple Pegasus indices. Ignored if the
`nice_coordinates` parameter is True.
offset_lists : pair of lists, optional (default None)
Directly controls the offsets. Each list in the pair must have length 12
and contain even ints. If `offset_lists` is not None, the `offsets_index`
parameter must be None.
offsets_index : int, optional (default None)
A number between 0 and 7, inclusive, that selects a preconfigured
set of topological parameters. If both the `offsets_index` and
`offset_lists` parameters are None, the `offsets_index` parameters is set
to zero. At least one of these two parameters must be None.
fabric_only: bool, optional (default True)
The Pegasus graph, by definition, has some disconnected
components. If True, the generator only constructs nodes from the
largest component. If False, the full disconnected graph is
constructed. Ignored if the `edge_lists` parameter is not None or
`nice_coordinates` is True
nice_coordinates: bool, optional (default False)
If the `offsets_index` parameter is 0, the graph uses a "nicer"
coordinate system, more compatible with Chimera addressing.
These coordinates are 5-tuples taking the form :math:`(t, y, x, u, k)` where
:math:`0 <= x < M-1`, :math:`0 <= y < M-1`, :math:`0 <= u < 2`,
:math:`0 <= k < 4`, and :math:`0 <= t < 3`.
For any given :math:`0 <= t0 < 3`, the subgraph of nodes with :math:`t = t0`
has the structure of `chimera(M-1, M-1, 4)` with the addition of odd couplers.
Supercedes both the `fabric_only` and `coordinates` parameters.
Returns
-------
G : NetworkX Graph
A Pegasus lattice for size parameter `m`.
The maximum degree of this graph is 15. The number of nodes depends on multiple
parameters; for example,
* `pegasus_graph(1)`: zero nodes
* `pegasus_graph(m, fabric_only=False)`: :math:`24m(m-1)` nodes
* `pegasus_graph(m, fabric_only=True)`: :math:`24m(m-1)-8(m-1)` nodes
* `pegasus_graph(m, nice_coordinates=True)`: :math:`24(m-1)^2` nodes
Counting formulas for edges have a complicated dependency on parameter settings.
Some example upper bounds are:
* `pegasus_graph(1, fabric_only=False)`: zero edges
* `pegasus_graph(m, fabric_only=False)`: :math:`12*(15*(m-1)^2 + m - 3)`
edges if m > 1
Note that the formulas above are valid for default offset parameters.
A Pegasus lattice is a graph minor of a lattice similar
to Chimera, where unit tiles are completely connected. In its most general
definition, prelattice :math:`Q(N0,N1)` contains nodes of the form
* vertical nodes: :math:`(i, j, 0, k)` with :math:`0 <= k < 2`
* horizontal nodes: :math:`(i, j, 1, k)` with :math:`0 <= k < 2`
for :math:`0 <= i <= N0` and :math:`0 <= j < N1`, and edges of the form
* external: :math:`(i, j, u, k)` ~ :math:`(i+u, j+1-u, u, k)`
* internal: :math:`(i, j, 0, k)` ~ :math:`(i, j, 1, h)`
* odd: :math:`(i, j, u, 0)` ~ :math:`(i, j, u, 1)`
Given two lists of offsets, :math:`S0` and :math:`S1`, of length
:math:`L0` and :math:`L1`, where both lengths and values must be divisible by
2, the minor---a Pegasus lattice---is constructed by contracting the complete
intervals of external edges::
I(0, w, k, z) = [(L1*w + k, L0*z + S0[k] + r, 0, k % 2) for 0 <= r < L0]
I(1, w, k, z) = [(L1*z + S1[k] + r, L0*w + k, 1, k % 2) for 0 <= r < L1]
and deleting the prelattice nodes of any interval not fully contained in
:math:`Q(N0, N1)`.
This generator, 'pegasus_graph()', is specialized for the minor constructed by
prelattice and offset parameters :math:`L0 = L1 = 12` and :math:`N0 = N1 = 12m`.
The *Pegasus index* of a node in a Pegasus lattice, :math:`(u, w, k, z)`, can be
interpreted as:
* :math:`u`: qubit orientation (0 = vertical, 1 = horizontal)
* :math:`w`: orthogonal major offset
* :math:`k`: orthogonal minor offset
* :math:`z`: parallel offset
Edges in the minor have the form
* external: :math:`(u, w, k, z)` ~ :math:`(u, w, k, z+1)`
* internal: :math:`(0, w0, k0, z0)` ~ :math:`(1, w1, k1, z1)`
* odd: :math:`(u, w, 2k, z)` ~ :math:`(u, w, 2k+1, z)`
where internal edges only exist when
1. w1 = z0 + (1 if k1 < S0[k0] else 0)
2. z1 = w0 - (1 if k0 < S1[k1] else 0)
Linear indices are computed from Pegasus indices by the formula::
q = ((u * m + w) * 12 + k) * (m - 1) + z
Examples
========
>>> G = dnx.pegasus_graph(2, nice_coordinates=True)
>>> G.nodes(data=True)[(0, 0, 0, 0, 0)] # doctest: +SKIP
{'linear_index': 4, 'pegasus_index': (0, 0, 4, 0)}
"""
if offset_lists is None:
offsets_descriptor = offsets_index = offsets_index or 0
offset_lists = [
[(2, 2, 2, 2, 10, 10, 10, 10, 6, 6, 6, 6,), (6, 6, 6, 6, 2, 2, 2, 2, 10, 10, 10, 10,)],
[(2, 2, 2, 2, 10, 10, 10, 10, 6, 6, 6, 6,), (2, 2, 2, 2, 10, 10, 10, 10, 6, 6, 6, 6,)],
[(2, 2, 2, 2, 10, 10, 10, 10, 6, 6, 6, 6,), (10, 10, 10, 10, 6, 6, 6, 6, 2, 2, 2, 2,)],
[(10, 10, 10, 10, 6, 6, 6, 6, 2, 2, 2, 2,), (10, 10, 10, 10, 6, 6, 6, 6, 2, 2, 2, 2,)],
[(10, 10, 10, 10, 6, 6, 6, 6, 2, 2, 2, 2,), (2, 2, 2, 2, 6, 6, 6, 6, 10, 10, 10, 10,)],
[(6, 6, 2, 2, 2, 2, 10, 10, 10, 10, 6, 6,), (6, 6, 2, 2, 2, 2, 10, 10, 10, 10, 6, 6,)],
[(6, 6, 2, 2, 2, 2, 10, 10, 10, 10, 6, 6,), (6, 6, 10, 10, 10, 10, 2, 2, 2, 2, 6, 6,)],
[(6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,), (6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,)],
][offsets_index]
elif offsets_index is not None:
raise DWaveNetworkXException("provide at most one of offsets_index and offset_lists")
else:
for ori in 0, 1:
for x, y in zip(offset_lists[ori][::2], offset_lists[ori][1::2]):
if x != y:
warnings.warn("The offets list you've provided is possibly non-physical. Odd-coupled qubits should have the same value.")
offsets_descriptor = offset_lists
G = nx.empty_graph(0, create_using)
G.name = "pegasus_graph(%s, %s)" % (m, offsets_descriptor)
m1 = m - 1
if nice_coordinates:
if offsets_index != 0:
raise NotImplementedError("nice coordinate system is only implemented for offsets_index 0")
labels = 'nice'
pegasus_to_nice = pegasus_coordinates.pegasus_to_nice
nice_to_pegasus = pegasus_coordinates.nice_to_pegasus
label = lambda *q: pegasus_to_nice(q)
elif coordinates:
label = lambda *q: q
labels = 'coordinate'
else:
labels = 'int'
def label(u, w, k, z):
return u * 12 * m * m1 + w * 12 * m1 + k * m1 + z
construction = (("family", "pegasus"), ("rows", m), ("columns", m),
("tile", 12), ("vertical_offsets", offset_lists[0]),
("horizontal_offsets", offset_lists[1]), ("data", data),
("labels", labels))
G.graph.update(construction)
max_size = m * (m - 1) * 24 # max number of nodes G can have
if edge_list is None:
if nice_coordinates:
fabric_start = 4,8
fabric_end = 8, 4
elif fabric_only:
fabric_start = min(s for s in offset_lists[1]), min(s for s in offset_lists[0])
fabric_end = 12 - max(s for s in offset_lists[1]), 12 - max(s for s in offset_lists[0])
else:
fabric_end = fabric_start = 0, 0
G.add_edges_from((label(u, w, k, z), label(u, w, k, z + 1))
for u in (0, 1)
for w in range(m)
for k in range(fabric_start[u] if w == 0 else 0, 12 - (fabric_end[u] if w == m1 else 0))
for z in range(m1 - 1))
G.add_edges_from((label(u, w, k, z), label(u, w, k + 1, z))
for u in (0, 1)
for w in range(m)
for k in range(fabric_start[u] if w == 0 else 0, 12 - (fabric_end[u] if w == m1 else 0), 2)
for z in range(m1))
off0, off1 = offset_lists
def qfilter(u, w, k, z):
if w == 0: return k >= fabric_start[u]
if w == m1: return k < 12-fabric_end[u]
return True
def efilter(e): return qfilter(*e[0]) and qfilter(*e[1])
internal_couplers = (((0, w, k, z), (1, z + (kk < off0[k]), kk, w - (k < off1[kk])))
for w in range(m)
for kk in range(12)
for k in range(0 if w else off1[kk], 12 if w < m1 else off1[kk])
for z in range(m1))
G.add_edges_from((label(*e[0]), label(*e[1])) for e in internal_couplers if efilter(e))
else:
G.add_edges_from(edge_list)
if node_list is not None:
nodes = set(node_list)
G.remove_nodes_from(set(G) - nodes)
G.add_nodes_from(nodes) # for singleton nodes
if data:
v = 0
if nice_coordinates:
def fill_data():
q = (u, w, k, z)
d = get_node_data(pegasus_to_nice(q))
if d is not None:
d['linear_index'] = v
d['pegasus_index'] = q
elif coordinates:
def fill_data():
d = get_node_data((u, w, k, z))
if d is not None:
d['linear_index'] = v
else:
def fill_data():
d = get_node_data(v)
if d is not None:
d['pegasus_index'] = (u, w, k, z)
get_node_data = G.nodes.get
for u in range(2):
for w in range(m):
for k in range(12):
for z in range(m1):
fill_data()
v += 1
return G
def pegasus_graph(m,
create_using=None,
node_list=None,
edge_list=None,
data=True,
offset_lists=None,
offsets_index=None,
coordinates=False,
fabric_only=True,
nice_coordinates=False):
"""
Creates a Pegasus graph with size parameter m. The number of nodes and edges varies
according to multiple parameters, for example,
pegasus_graph(1) contains zero nodes,
pegasus_graph(m, fabric_only=False) contains :math:`24m(m-1)` nodes,
pegasus_graph(m, fabric_only=True) contains :math:`24m(m-1)-8(m-1)` nodes, and
pegasus_graph(m, nice_coordinates=True) contains :math:`24(m-1)^2` nodes.
The maximum degree of these graph is 15, and counting formulas are more complicated
for edges given most parameter settings. Upper bounds are given below,
pegasus_graph(1, fabric_only=False) has zero edges,
pegasus_graph(m, fabric_only=False) has :math:`12*(15*(m-1)^2 + m - 3)` edges if m > 1
Note that the above are valid with default offset parameters.
A Pegasus lattice is a graph minor of a lattice similar to Chimera,
where unit tiles are completely connected. In the most generality,
prelattice :math:`Q(N0,N1)` contains nodes of the form
:math:`(i, j, 0, k)` with :math:`0 <= k < 2` [vertical nodes]
and
:math:`(i, j, 1, k)` with :math:`0 <= k < 2` [horizontal nodes]
for :math:`0 <= i <= N0` and :math:`0 <= j < N1`; and edges of the form
:math:`(i, j, u, k)` ~ :math:`(i+u, j+1-u, u, k)` [external edges]
:math:`(i, j, 0, k)` ~ :math:`(i, j, 1, k)` [internal edges]
:math:`(i, j, u, 0)` ~ :math:`(i, j, u, 1)` [odd edges]
The minor is specified by two lists of offsets; :math:`S0` and :math:`S1` of length
:math:`L0` and :math:`L1` (where :math:`L0` and :math:`L1`, and the entries of
:math:`S0` and :math:`S1`, must be divisible by 2).
From these offsets, we construct our minor, a Pegasus lattice, by contracting
the complete intervals of external edges::
I(0, w, k, z) = [(L1*w + k, L0*z + S0[k] + r, 0, k % 2) for 0 <= r < L0]
I(1, w, k, z) = [(L1*z + S1[k] + r, L0*w + k, 1, k % 2) for 0 <= r < L1]
and deleting the prelattice nodes of any interval not fully contained in
:math:`Q(N0, N1)`.
This generator is specialized to :math:`L0 = L1 = 12`; :math:`N0 = N1 = 12m`.
The notation :math:`(u, w, k, z)` is called the Pegasus index of a node in a Pegasus
lattice. The entries can be interpreted as following,
:math:`u`: qubit orientation (0 = vertical, 1 = horizontal)
:math:`w`: orthogonal major offset
:math:`k`: orthogonal minor offset
:math:`z`: parallel offset
and the edges in the minor have the form
:math:`(u, w, k, z)` ~ :math:`(u, w, k, z+1)` [external edges]
:math:`(0, w0, k0, z0)` ~ :math:`(1, w1, k1, z1)` [internal edges, see below]
:math:`(u, w, 2k, z)` ~ :math:`(u, w, 2k+1, z)` [odd edges]
where internal edges only exist when::
w1 = z0 + (1 if k1 < S0[k0] else 0), and
z1 = w0 - (1 if k0 < S1[k1] else 0)
linear indices are computed from pegasus indices by the formula::
q = ((u * m + w) * 12 + k) * (m - 1) + z
Parameters
----------
m : int
The size parameter for the Pegasus lattice.
create_using : Graph, optional (default None)
If provided, this graph is cleared of nodes and edges and filled
with the new graph. Usually used to set the type of the graph.
node_list : iterable, optional (default None)
Iterable of nodes in the graph. If None, calculated from m.
Note that this list is used to remove nodes, so any nodes specified
not in range(24 * m * (m-1)) will not be added.
edge_list : iterable, optional (default None)
Iterable of edges in the graph. If None, edges are generated as
described above. The nodes in each edge must be integer-labeled in
range(24 * m * (m-1)).
data : bool, optional (default True)
If True, each node has a pegasus_index attribute. The attribute
is a 4-tuple Pegasus index as defined above. (if coordinates = True,
we set a linear_index, which is an integer)
coordinates : bool, optional (default False)
If True, node labels are 4-tuple Pegasus indices. Ignored if
nice_coordinates is True
offset_lists : pair of lists, optional (default None)
Used to directly control the offsets, each list in the pair should
have length 12, and contain even ints. If offset_lists is not None,
then offsets_index must be None.
offsets_index : int, optional (default None)
A number between 0 and 7 inclusive, to select a preconfigured
set of topological parameters. If both offsets_index and
offset_lists are None, then we set offsets_index = 0. At least
one of these two parameters must be None.
fabric_only: bool, optional (default True)
The Pegasus graph, by definition, will have some disconnected
components. If this True, we will only construct nodes from the
largest component. Otherwise, the full disconnected graph will be
constructed. Ignored if edge_lists is not None or nice_coordinates
is True
nice_coordinates: bool, optional (default False)
In the case that offsets_index = 0, generate the graph with a nicer
coordinate system which is more compatible with Chimera addressing.
These coordinates are 5-tuples taking the form (t, y, x, u, k) where
0 <= x < M-1, 0 <= y < M-1, 0 <= u < 2, 0 <= k < 4 and 0 <= t < 3.
For any given 0 <= t0 < 3, the subgraph of nodes with t = t0 has the
structure of chimera(M-1, M-1, 4) with the addition of odd couplers.
Supercedes both the fabric_only and coordinates parameters.
"""
if offset_lists is None:
offsets_descriptor = offsets_index = offsets_index or 0
offset_lists = [
[(
2,
2,
2,
2,
10,
10,
10,
10,
6,
6,
6,
6,
), (
6,
6,
6,
6,
2,
2,
2,
2,
10,
10,
10,
10,
)],
[(
2,
2,
2,
2,
10,
10,
10,
10,
6,
6,
6,
6,
), (
2,
2,
2,
2,
10,
10,
10,
10,
6,
6,
6,
6,
)],
[(
2,
2,
2,
2,
10,
10,
10,
10,
6,
6,
6,
6,
), (
10,
10,
10,
10,
6,
6,
6,
6,
2,
2,
2,
2,
)],
[(
10,
10,
10,
10,
6,
6,
6,
6,
2,
2,
2,
2,
), (
10,
10,
10,
10,
6,
6,
6,
6,
2,
2,
2,
2,
)],
[(
10,
10,
10,
10,
6,
6,
6,
6,
2,
2,
2,
2,
), (
2,
2,
2,
2,
6,
6,
6,
6,
10,
10,
10,
10,
)],
[(
6,
6,
2,
2,
2,
2,
10,
10,
10,
10,
6,
6,
), (
6,
6,
2,
2,
2,
2,
10,
10,
10,
10,
6,
6,
)],
[(
6,
6,
2,
2,
2,
2,
10,
10,
10,
10,
6,
6,
), (
6,
6,
10,
10,
10,
10,
2,
2,
2,
2,
6,
6,
)],
[(
6,
6,
6,
6,
6,
6,
6,
6,
6,
6,
6,
6,
), (
6,
6,
6,
6,
6,
6,
6,
6,
6,
6,
6,
6,
)],
][offsets_index]
elif offsets_index is not None:
raise DWaveNetworkXException(
"provide at most one of offsets_index and offset_lists")
else:
for ori in 0, 1:
for x, y in zip(offset_lists[ori][::2], offset_lists[ori][1::2]):
if x != y:
warnings.warn(
"The offets list you've provided is possibly non-physical. Odd-coupled qubits should have the same value."
)
offsets_descriptor = offset_lists
G = nx.empty_graph(0, create_using)
G.name = "pegasus_graph(%s, %s)" % (m, offsets_descriptor)
m1 = m - 1
if nice_coordinates:
if offsets_index != 0:
raise NotImplementedError(
"nice coordinate system is only implemented for offsets_index 0"
)
labels = 'nice'
c2i = get_pegasus_to_nice_fn()
elif coordinates:
c2i = lambda *q: q
labels = 'coordinate'
else:
labels = 'int'
def c2i(u, w, k, z):
return u * 12 * m * m1 + w * 12 * m1 + k * m1 + z
construction = (("family", "pegasus"), ("rows", m), ("columns", m),
("tile", 12), ("vertical_offsets", offset_lists[0]),
("horizontal_offsets",
offset_lists[1]), ("data", data), ("labels", labels))
G.graph.update(construction)
max_size = m * (m - 1) * 24 # max number of nodes G can have
if edge_list is None:
if nice_coordinates:
fabric_start = 4, 8
fabric_end = 8, 4
elif fabric_only:
fabric_start = min(s for s in offset_lists[1]), min(
s for s in offset_lists[0])
fabric_end = 12 - max(s for s in offset_lists[1]), 12 - max(
s for s in offset_lists[0])
else:
fabric_end = fabric_start = 0, 0
G.add_edges_from((c2i(u, w, k, z), c2i(u, w, k, z + 1)) for u in (0, 1)
for w in range(m)
for k in range(fabric_start[u] if w == 0 else 0, 12 -
(fabric_end[u] if w == m1 else 0))
for z in range(m1 - 1))
G.add_edges_from((c2i(u, w, k, z), c2i(u, w, k + 1, z)) for u in (0, 1)
for w in range(m)
for k in range(fabric_start[u] if w == 0 else 0, 12 -
(fabric_end[u] if w == m1 else 0), 2)
for z in range(m1))
off0, off1 = offset_lists
def qfilter(u, w, k, z):
if w == 0: return k >= fabric_start[u]
if w == m1: return k < 12 - fabric_end[u]
return True
def efilter(e):
return qfilter(*e[0]) and qfilter(*e[1])
internal_couplers = (
((0, w, k, z), (1, z + (kk < off0[k]), kk, w - (k < off1[kk])))
for w in range(m) for kk in range(12)
for k in range(0 if w else off1[kk], 12 if w < m1 else off1[kk])
for z in range(m1))
G.add_edges_from(
(c2i(*e[0]), c2i(*e[1])) for e in internal_couplers if efilter(e))
else:
G.add_edges_from(edge_list)
if node_list is not None:
nodes = set(node_list)
G.remove_nodes_from(set(G) - nodes)
G.add_nodes_from(nodes) # for singleton nodes
if data:
v = 0
for u in range(2):
for w in range(m):
for k in range(12):
for z in range(m1):
q = u, w, k, z
if nice_coordinates:
p = c2i(*q)
if p in G:
G.nodes[p]['linear_index'] = v
G.nodes[p]['pegasus_index'] = q
elif coordinates:
if q in G:
G.nodes[q]['linear_index'] = v
else:
if v in G:
G.nodes[v]['pegasus_index'] = q
v += 1
return G
def weighted_maximum_cut(G, sampler=None, **sampler_args):
"""Returns an approximate weighted maximum cut.
Defines an Ising problem with ground states corresponding to
a weighted maximum cut and uses the sampler to sample from it.
A weighted maximum cut is a subset S of the vertices of G that
maximizes the sum of the edge weights between S and its
complementary subset.
Parameters
----------
G : NetworkX graph
The graph on which to find a weighted maximum cut. Each edge in G should
have a numeric `weight` attribute.
sampler
A binary quadratic model sampler. A sampler is a process that
samples from low energy states in models defined by an Ising
equation or a Quadratic Unconstrained Binary Optimization
Problem (QUBO). A sampler is expected to have a 'sample_qubo'
and 'sample_ising' method. A sampler is expected to return an
iterable of samples, in order of increasing energy. If no
sampler is provided, one must be provided using the
`set_default_sampler` function.
sampler_args
Additional keyword parameters are passed to the sampler.
Returns
-------
S : set
A maximum cut of G.
Example
-------
This example uses a sampler from
`dimod <https://github.com/dwavesystems/dimod>`_ to find a weighted maximum
cut for a graph of a Chimera unit cell. The graph is created using the
`chimera_graph()` function with weights added to all its edges such that
those incident to nodes {6, 7} have weight -1 while the others are +1. A
weighted maximum cut should cut as many of the latter and few of the former
as possible.
>>> import dimod
>>> import dwave_networkx as dnx
>>> samplerSA = dimod.SimulatedAnnealingSampler()
>>> G = dnx.chimera_graph(1, 1, 4)
>>> for u, v in G.edges:
....: if (u >= 6) | (v >=6):
....: G[u][v]['weight']=-1
....: else: G[u][v]['weight']=1
....:
>>> dnx.weighted_maximum_cut(G, samplerSA)
{4, 5}
Notes
-----
Samplers by their nature may not return the optimal solution. This
function does not attempt to confirm the quality of the returned
sample.
"""
# In order to form the Ising problem, we want to increase the
# energy by 1 for each edge between two nodes of the same color.
# The linear biases can all be 0.
h = {v: 0. for v in G}
try:
J = {(u, v): G[u][v]['weight'] for u, v in G.edges}
except KeyError:
raise DWaveNetworkXException("edges must have 'weight' attribute")
# draw the lowest energy sample from the sampler
response = sampler.sample_ising(h, J, **sampler_args)
sample = next(iter(response))
return set(v for v in G if sample[v] >= 0)
