def _build_network(
tensors: Sequence[Tensor], network_structure: Sequence[Sequence],
backend: Text
) -> Tuple[List[network_components.BaseNode], Dict[Any,
network_components.Edge]]:
nodes = []
edges = {}
for i, (tensor, edge_lbls) in enumerate(zip(tensors, network_structure)):
if len(tensor.shape) != len(edge_lbls):
raise ValueError(
"Incorrect number of edge labels specified tensor {}".format(
i))
if isinstance(tensor, network_components.BaseNode):
node = tensor
else:
node = network_components.Node(tensor,
name="tensor_{}".format(i),
backend=backend)
nodes.append(node)
for (axis_num, edge_lbl) in enumerate(edge_lbls):
if edge_lbl not in edges:
e = node[axis_num]
e.set_name(str(edge_lbl))
edges[edge_lbl] = e
else:
# This will raise an error if the edges are not dangling.
e = network_components.connect(edges[edge_lbl],
node[axis_num],
name=str(edge_lbl))
edges[edge_lbl] = e
return nodes, edges
def test_cnot_gate(): # Prepare input state: |11> q0_in = Node(np.array([0, 1], dtype=np.float64)) q1_in = Node(np.array([0, 1], dtype=np.float64)) # Prepare output state: |10> q0_out = Node(np.array([0, 1], dtype=np.float64)) q1_out = Node(np.array([1, 0], dtype=np.float64)) # Build quantum circuit copy_node, q0_t1, q1_t1 = add_cnot(q0_in[0], q1_in[0]) network_components.connect(q0_t1, q0_out[0]) network_components.connect(q1_t1, q1_out[0]) # Contract the network, first using Bucket Elimination, then once # no more copy tensors are left to exploit, fall back to the naive # contractor. contraction_order = (copy_node,) net = bucket([q0_in, q1_in, q0_out, q1_out, copy_node], contraction_order) result = greedy(net) # Verify that CNOT has turned |11> into |10>. np.testing.assert_allclose(result.get_tensor(), 1.0)
def test_swap_gate(): # Prepare input state: 0.6|00> + 0.8|10> q0_in = Node(np.array([0.6, 0.8], dtype=np.float64), backend="jax") q1_in = Node(np.array([1, 0], dtype=np.float64), backend="jax") # Prepare output state: 0.6|00> + 0.8|01> q0_out = Node(np.array([1, 0], dtype=np.float64), backend="jax") q1_out = Node(np.array([0.6, 0.8], dtype=np.float64), backend="jax") # Build quantum circuit: three CNOTs implement a SWAP copy_node_1, q0_t1, q1_t1 = add_cnot(q0_in[0], q1_in[0], backend="jax") copy_node_2, q1_t2, q0_t2 = add_cnot(q1_t1, q0_t1, backend="jax") copy_node_3, q0_t3, q1_t3 = add_cnot(q0_t2, q1_t2, backend="jax") network_components.connect(q0_t3, q0_out[0]) network_components.connect(q1_t3, q1_out[0]) # Contract the network, first Bucket Elimination, then greedy to complete. contraction_order = (copy_node_1, copy_node_2, copy_node_3) nodes = [q0_in, q0_out, q1_in, q1_out, copy_node_1, copy_node_2, copy_node_3] net = bucket(nodes, contraction_order) result = greedy(net) # Verify that SWAP has turned |10> into |01> and kept |00> unchanged. np.testing.assert_allclose(result.get_tensor(), 1.0)
def add_cnot(q0: network_components.Edge,
q1: network_components.Edge,
backend: str = "numpy"
) -> Tuple[network_components.CopyNode, network_components.Edge,
network_components.Edge]:
"""Adds the CNOT quantum gate to tensor network.
CNOT consists of two rank-3 tensors: a COPY tensor on the control qubit and
a XOR tensor on the target qubit.
Args:
q0: Input edge for the control qubit.
q1: Input edge for the target qubit.
backend: backend to use
Returns:
Tuple with three elements:
- copy tensor corresponding to the control qubit
- output edge for the control qubit and
- output edge for the target qubit.
"""
control = CopyNode(rank=3, dimension=2, backend=backend)
xor = np.array([[[1, 0], [0, 1]], [[0, 1], [1, 0]]], dtype=np.float64)
target = Node(xor, backend=backend)
network_components.connect(q0, control[0])
network_components.connect(q1, target[0])
network_components.connect(control[1], target[1])
return (control, control[2], target[2])
def connect(self,
edge1: network_components.Edge,
edge2: network_components.Edge,
name: Optional[Text] = None) -> network_components.Edge:
"""Join two dangling edges into a new edge.
Args:
edge1: The first dangling edge.
edge2: The second dangling edge.
name: Optional name to give the new edge.
Returns:
A new edge created by joining the two dangling edges together.
Raises:
ValueError: If either edge1 or edge2 is not a dangling edge or if edge1
and edge2 are the same edge.
"""
name = self._new_edge_name(name)
new_edge = network_components.connect(edge1, edge2, name)
new_edge.set_signature(self.edge_increment)
return new_edge
def eliminate_identities(nodes: Collection[BaseNode]) -> Tuple[dict, dict]:
"""Eliminates any connected CopyNodes that are identity matrices.
This will modify the network represented by `nodes`.
Only identities that are connected to other nodes are eliminated.
Args:
nodes: Collection of nodes to search.
Returns:
nodes_dict: Dictionary mapping remaining Nodes to any replacements.
dangling_edges_dict: Dictionary specifying all dangling-edge replacements.
"""
nodes_dict = {}
dangling_edges_dict = {}
for n in nodes:
if isinstance(n, CopyNode) and n.get_rank() == 2 and not (
n[0].is_dangling() and n[1].is_dangling()):
old_edges = [n[0], n[1]]
_, new_edges = remove_node(n)
if 0 in new_edges and 1 in new_edges:
e = connect(new_edges[0], new_edges[1])
elif 0 in new_edges: # 1 was dangling
dangling_edges_dict[old_edges[1]] = new_edges[0]
elif 1 in new_edges: # 0 was dangling
dangling_edges_dict[old_edges[0]] = new_edges[1]
else:
# Trace of identity, so replace with a scalar node!
d = n.get_dimension(0)
# NOTE: Assume CopyNodes have numpy dtypes.
nodes_dict[n] = Node(np.array(d, dtype=n.dtype),
backend=n.backend)
else:
for e in n.get_all_dangling():
dangling_edges_dict[e] = e
nodes_dict[n] = n
return nodes_dict, dangling_edges_dict
def split_node_full_svd(
node: BaseNode,
left_edges: List[Edge],
right_edges: List[Edge],
max_singular_values: Optional[int] = None,
max_truncation_err: Optional[float] = None,
left_name: Optional[Text] = None,
middle_name: Optional[Text] = None,
right_name: Optional[Text] = None,
left_edge_name: Optional[Text] = None,
right_edge_name: Optional[Text] = None,
) -> Tuple[BaseNode, BaseNode, BaseNode, Tensor]:
"""Split a node by doing a full singular value decomposition.
Let M be the matrix created by flattening left_edges and right_edges into
2 axes. Let :math:`U S V^* = M` be the Singular Value Decomposition of
:math:`M`.
The left most node will be :math:`U` tensor of the SVD, the middle node is
the diagonal matrix of the singular values, ordered largest to smallest,
and the right most node will be the :math:`V*` tensor of the SVD.
The singular value decomposition is truncated if `max_singular_values` or
`max_truncation_err` is not `None`.
The truncation error is the 2-norm of the vector of truncated singular
values. If only `max_truncation_err` is set, as many singular values will
be truncated as possible while maintaining:
`norm(truncated_singular_values) <= max_truncation_err`.
If only `max_singular_values` is set, the number of singular values kept
will be `min(max_singular_values, number_of_singular_values)`, so that
`max(0, number_of_singular_values - max_singular_values)` are truncated.
If both `max_truncation_err` and `max_singular_values` are set,
`max_singular_values` takes priority: The truncation error may be larger
than `max_truncation_err` if required to satisfy `max_singular_values`.
Args:
node: The node you want to split.
left_edges: The edges you want connected to the new left node.
right_edges: The edges you want connected to the new right node.
max_singular_values: The maximum number of singular values to keep.
max_truncation_err: The maximum allowed truncation error.
left_name: The name of the new left node. If None, a name will be
generated automatically.
middle_name: The name of the new center node. If None, a name will be
generated automatically.
right_name: The name of the new right node. If None, a name will be
generated automatically.
left_edge_name: The name of the new left `Edge` connecting
the new left node (`U`) and the new central node (`S`).
If `None`, a name will be generated automatically.
right_edge_name: The name of the new right `Edge` connecting
the new central node (`S`) and the new right node (`V*`).
If `None`, a name will be generated automatically.
Returns:
A tuple containing:
left_node:
A new node created that connects to all of the `left_edges`.
Its underlying tensor is :math:`U`
singular_values_node:
A new node that has 2 edges connecting `left_node` and `right_node`.
Its underlying tensor is :math:`S`
right_node:
A new node created that connects to all of the `right_edges`.
Its underlying tensor is :math:`V^*`
truncated_singular_values:
The vector of truncated singular values.
"""
if not hasattr(node, 'backend'):
raise TypeError('Node {} of type {} has no `backend`'.format(
node, type(node)))
if node.axis_names and left_edge_name and right_edge_name:
left_axis_names = []
right_axis_names = [right_edge_name]
for edge in left_edges:
left_axis_names.append(node.axis_names[edge.axis1] if edge.node1 is
node else node.axis_names[edge.axis2])
for edge in right_edges:
right_axis_names.append(node.axis_names[edge.axis1] if edge.node1
is node else node.axis_names[edge.axis2])
left_axis_names.append(left_edge_name)
center_axis_names = [left_edge_name, right_edge_name]
else:
left_axis_names = None
center_axis_names = None
right_axis_names = None
backend = node.backend
node.reorder_edges(left_edges + right_edges)
u, s, vh, trun_vals = backend.svd_decomposition(node.tensor,
len(left_edges),
max_singular_values,
max_truncation_err)
left_node = Node(u,
name=left_name,
axis_names=left_axis_names,
backend=backend.name)
singular_values_node = Node(backend.diag(s),
name=middle_name,
axis_names=center_axis_names,
backend=backend.name)
right_node = Node(vh,
name=right_name,
axis_names=right_axis_names,
backend=backend.name)
for i, edge in enumerate(left_edges):
left_node.add_edge(edge, i)
edge.update_axis(i, node, i, left_node)
for i, edge in enumerate(right_edges):
# i + 1 to account for the new edge.
right_node.add_edge(edge, i + 1)
edge.update_axis(i + len(left_edges), node, i + 1, right_node)
connect(left_node.edges[-1],
singular_values_node.edges[0],
name=left_edge_name)
connect(singular_values_node.edges[1],
right_node.edges[0],
name=right_edge_name)
return left_node, singular_values_node, right_node, trun_vals
def split_node_rq(
node: BaseNode,
left_edges: List[Edge],
right_edges: List[Edge],
left_name: Optional[Text] = None,
right_name: Optional[Text] = None,
edge_name: Optional[Text] = None,
) -> Tuple[BaseNode, BaseNode]:
"""Split a `Node` using RQ (reversed QR) decomposition
Let M be the matrix created by flattening left_edges and right_edges into
2 axes. Let :math:`QR = M^*` be the QR Decomposition of
:math:`M^*`. This will split the network into 2 nodes. The left node's
tensor will be :math:`R^*` (a lower triangular matrix) and the right
node's tensor will be :math:`Q^*` (an orthonormal matrix)
Args:
node: The node you want to split.
left_edges: The edges you want connected to the new left node.
right_edges: The edges you want connected to the new right node.
left_name: The name of the new left node. If `None`, a name will be
generated automatically.
right_name: The name of the new right node. If `None`, a name will be
generated automatically.
edge_name: The name of the new `Edge` connecting the new left and
right node. If `None`, a name will be generated automatically.
Returns:
A tuple containing:
left_node:
A new node created that connects to all of the `left_edges`.
Its underlying tensor is :math:`Q`
right_node:
A new node created that connects to all of the `right_edges`.
Its underlying tensor is :math:`R`
"""
if not hasattr(node, 'backend'):
raise TypeError('Node {} of type {} has no `backend`'.format(
node, type(node)))
if node.axis_names and edge_name:
left_axis_names = []
right_axis_names = [edge_name]
for edge in left_edges:
left_axis_names.append(node.axis_names[edge.axis1] if edge.node1 is
node else node.axis_names[edge.axis2])
for edge in right_edges:
right_axis_names.append(node.axis_names[edge.axis1] if edge.node1
is node else node.axis_names[edge.axis2])
left_axis_names.append(edge_name)
else:
left_axis_names = None
right_axis_names = None
backend = node.backend
node.reorder_edges(left_edges + right_edges)
r, q = backend.rq_decomposition(node.tensor, len(left_edges))
left_node = Node(r,
name=left_name,
axis_names=left_axis_names,
backend=backend.name)
for i, edge in enumerate(left_edges):
left_node.add_edge(edge, i)
edge.update_axis(i, node, i, left_node)
right_node = Node(q,
name=right_name,
axis_names=right_axis_names,
backend=backend.name)
for i, edge in enumerate(right_edges):
# i + 1 to account for the new edge.
right_node.add_edge(edge, i + 1)
edge.update_axis(i + len(left_edges), node, i + 1, right_node)
connect(left_node.edges[-1], right_node.edges[0], name=edge_name)
return left_node, right_node
def split_node(
node: BaseNode,
left_edges: List[Edge],
right_edges: List[Edge],
max_singular_values: Optional[int] = None,
max_truncation_err: Optional[float] = None,
left_name: Optional[Text] = None,
right_name: Optional[Text] = None,
edge_name: Optional[Text] = None,
) -> Tuple[BaseNode, BaseNode, Tensor]:
"""Split a `Node` using Singular Value Decomposition.
Let M be the matrix created by flattening left_edges and right_edges into
2 axes. Let :math:`U S V^* = M` be the Singular Value Decomposition of
:math:`M`. This will split the network into 2 nodes. The left node's
tensor will be :math:`U \\sqrt{S}` and the right node's tensor will be
:math:`\\sqrt{S} V^*` where :math:`V^*` is
the adjoint of :math:`V`.
The singular value decomposition is truncated if `max_singular_values` or
`max_truncation_err` is not `None`.
The truncation error is the 2-norm of the vector of truncated singular
values. If only `max_truncation_err` is set, as many singular values will
be truncated as possible while maintaining:
`norm(truncated_singular_values) <= max_truncation_err`.
If only `max_singular_values` is set, the number of singular values kept
will be `min(max_singular_values, number_of_singular_values)`, so that
`max(0, number_of_singular_values - max_singular_values)` are truncated.
If both `max_truncation_err` and `max_singular_values` are set,
`max_singular_values` takes priority: The truncation error may be larger
than `max_truncation_err` if required to satisfy `max_singular_values`.
Args:
node: The node you want to split.
left_edges: The edges you want connected to the new left node.
right_edges: The edges you want connected to the new right node.
max_singular_values: The maximum number of singular values to keep.
max_truncation_err: The maximum allowed truncation error.
left_name: The name of the new left node. If `None`, a name will be
generated automatically.
right_name: The name of the new right node. If `None`, a name will be
genenerated automatically.
edge_name: The name of the new `Edge` connecting the new left and
right node. If `None`, a name will be generated automatically.
The new axis will get the same name as the edge.
Returns:
A tuple containing:
left_node:
A new node created that connects to all of the `left_edges`.
Its underlying tensor is :math:`U \\sqrt{S}`
right_node:
A new node created that connects to all of the `right_edges`.
Its underlying tensor is :math:`\\sqrt{S} V^*`
truncated_singular_values:
The vector of truncated singular values.
"""
if not hasattr(node, 'backend'):
raise TypeError('Node {} of type {} has no `backend`'.format(
node, type(node)))
if node.axis_names and edge_name:
left_axis_names = []
right_axis_names = [edge_name]
for edge in left_edges:
left_axis_names.append(node.axis_names[edge.axis1] if edge.node1 is
node else node.axis_names[edge.axis2])
for edge in right_edges:
right_axis_names.append(node.axis_names[edge.axis1] if edge.node1
is node else node.axis_names[edge.axis2])
left_axis_names.append(edge_name)
else:
left_axis_names = None
right_axis_names = None
backend = node.backend
node.reorder_edges(left_edges + right_edges)
u, s, vh, trun_vals = backend.svd_decomposition(node.tensor,
len(left_edges),
max_singular_values,
max_truncation_err)
sqrt_s = backend.sqrt(s)
u_s = u * sqrt_s
# We have to do this since we are doing element-wise multiplication against
# the first axis of vh. If we don't, it's possible one of the other axes of
# vh will be the same size as sqrt_s and would multiply across that axis
# instead, which is bad.
sqrt_s_broadcast_shape = backend.concat(
[backend.shape(sqrt_s), [1] * (len(vh.shape) - 1)], axis=-1)
vh_s = vh * backend.reshape(sqrt_s, sqrt_s_broadcast_shape)
left_node = Node(u_s,
name=left_name,
axis_names=left_axis_names,
backend=backend.name)
for i, edge in enumerate(left_edges):
left_node.add_edge(edge, i)
edge.update_axis(i, node, i, left_node)
right_node = Node(vh_s,
name=right_name,
axis_names=right_axis_names,
backend=backend.name)
for i, edge in enumerate(right_edges):
# i + 1 to account for the new edge.
right_node.add_edge(edge, i + 1)
edge.update_axis(i + len(left_edges), node, i + 1, right_node)
connect(left_node.edges[-1], right_node.edges[0], name=edge_name)
node.fresh_edges(node.axis_names)
return left_node, right_node, trun_vals
def split_node_qr(
node: BaseNode,
left_edges: List[Edge],
right_edges: List[Edge],
left_name: Optional[Text] = None,
right_name: Optional[Text] = None,
edge_name: Optional[Text] = None,
) -> Tuple[BaseNode, BaseNode]:
"""Split a `node` using QR decomposition.
Let :math:`M` be the matrix created by
flattening `left_edges` and `right_edges` into 2 axes.
Let :math:`QR = M` be the QR Decomposition of :math:`M`.
This will split the network into 2 nodes.
The `left node`'s tensor will be :math:`Q` (an orthonormal matrix)
and the `right node`'s tensor will be :math:`R` (an upper triangular matrix)
Args:
node: The node you want to split.
left_edges: The edges you want connected to the new left node.
right_edges: The edges you want connected to the new right node.
left_name: The name of the new left node. If `None`, a name will be
generated automatically.
right_name: The name of the new right node. If `None`, a name will be
generated automatically.
edge_name: The name of the new `Edge` connecting the new left and right
node. If `None`, a name will be generated automatically.
Returns:
A tuple containing:
left_node:
A new node created that connects to all of the `left_edges`.
Its underlying tensor is :math:`Q`
right_node:
A new node created that connects to all of the `right_edges`.
Its underlying tensor is :math:`R`
Raises:
AttributeError: If `node` has no backend attribute
"""
if not hasattr(node, 'backend'):
raise AttributeError('Node {} of type {} has no `backend`'.format(
node, type(node)))
if node.axis_names and edge_name:
left_axis_names = []
right_axis_names = [edge_name]
for edge in left_edges:
left_axis_names.append(node.axis_names[edge.axis1] if edge.node1 is node
else node.axis_names[edge.axis2])
for edge in right_edges:
right_axis_names.append(node.axis_names[edge.axis1] if edge.node1 is node
else node.axis_names[edge.axis2])
left_axis_names.append(edge_name)
else:
left_axis_names = None
right_axis_names = None
backend = node.backend
transp_tensor = node.tensor_from_edge_order(left_edges + right_edges)
q, r = backend.qr_decomposition(transp_tensor, len(left_edges))
left_node = Node(
q, name=left_name, axis_names=left_axis_names, backend=backend)
left_axes_order = [
edge.axis1 if edge.node1 is node else edge.axis2 for edge in left_edges
]
for i, edge in enumerate(left_edges):
left_node.add_edge(edge, i)
edge.update_axis(left_axes_order[i], node, i, left_node)
right_node = Node(
r, name=right_name, axis_names=right_axis_names, backend=backend)
right_axes_order = [
edge.axis1 if edge.node1 is node else edge.axis2 for edge in right_edges
]
for i, edge in enumerate(right_edges):
# i + 1 to account for the new edge.
right_node.add_edge(edge, i + 1)
edge.update_axis(right_axes_order[i], node, i + 1, right_node)
connect(left_node.edges[-1], right_node.edges[0], name=edge_name)
node.fresh_edges(node.axis_names)
return left_node, right_node
def split_node(
node: BaseNode,
left_edges: List[Edge],
right_edges: List[Edge],
max_singular_values: Optional[int] = None,
max_truncation_err: Optional[float] = None,
relative: Optional[bool] = False,
left_name: Optional[Text] = None,
right_name: Optional[Text] = None,
edge_name: Optional[Text] = None,
) -> Tuple[BaseNode, BaseNode, Tensor]:
"""Split a `node` using Singular Value Decomposition.
Let :math:`M` be the matrix created by flattening `left_edges` and
`right_edges` into 2 axes.
Let :math:`U S V^* = M` be the SVD of :math:`M`.
This will split the network into 2 nodes.
The left node's tensor will be :math:`U \\sqrt{S}`
and the right node's tensor will be
:math:`\\sqrt{S} V^*` where :math:`V^*` is the adjoint of :math:`V`.
The singular value decomposition is truncated if `max_singular_values` or
`max_truncation_err` is not `None`.
The truncation error is the 2-norm of the vector of truncated singular
values. If only `max_truncation_err` is set, as many singular values will
be truncated as possible while maintaining:
`norm(truncated_singular_values) <= max_truncation_err`.
If `relative` is set `True` then `max_truncation_err` is understood
relative to the largest singular value.
If only `max_singular_values` is set, the number of singular values kept
will be `min(max_singular_values, number_of_singular_values)`, so that
`max(0, number_of_singular_values - max_singular_values)` are truncated.
If both `max_truncation_err` and `max_singular_values` are set,
`max_singular_values` takes priority: The truncation error may be larger
than `max_truncation_err` if required to satisfy `max_singular_values`.
Args:
node: The node you want to split.
left_edges: The edges you want connected to the new left node.
right_edges: The edges you want connected to the new right node.
max_singular_values: The maximum number of singular values to keep.
max_truncation_err: The maximum allowed truncation error.
relative: Multiply `max_truncation_err` with the largest singular value.
left_name: The name of the new left node. If `None`, a name will be
generated automatically.
right_name: The name of the new right node. If `None`, a name will be
generated automatically.
edge_name: The name of the new `Edge` connecting the new left and
right node. If `None`, a name will be generated automatically.
The new axis will get the same name as the edge.
Returns:
A tuple containing:
left_node:
A new node created that connects to all of the `left_edges`.
Its underlying tensor is :math:`U \\sqrt{S}`
right_node:
A new node created that connects to all of the `right_edges`.
Its underlying tensor is :math:`\\sqrt{S} V^*`
truncated_singular_values:
The vector of truncated singular values.
Raises:
AttributeError: If `node` has no backend attribute
"""
if not hasattr(node, 'backend'):
raise AttributeError('Node {} of type {} has no `backend`'.format(
node, type(node)))
if node.axis_names and edge_name:
left_axis_names = []
right_axis_names = [edge_name]
for edge in left_edges:
left_axis_names.append(node.axis_names[edge.axis1] if edge.node1 is node
else node.axis_names[edge.axis2])
for edge in right_edges:
right_axis_names.append(node.axis_names[edge.axis1] if edge.node1 is node
else node.axis_names[edge.axis2])
left_axis_names.append(edge_name)
else:
left_axis_names = None
right_axis_names = None
backend = node.backend
transp_tensor = node.tensor_from_edge_order(left_edges + right_edges)
u, s, vh, trun_vals = backend.svd_decomposition(
transp_tensor,
len(left_edges),
max_singular_values,
max_truncation_err,
relative=relative)
sqrt_s = backend.sqrt(s)
u_s = backend.broadcast_right_multiplication(u, sqrt_s)
vh_s = backend.broadcast_left_multiplication(sqrt_s, vh)
left_node = Node(
u_s, name=left_name, axis_names=left_axis_names, backend=backend)
left_axes_order = [
edge.axis1 if edge.node1 is node else edge.axis2 for edge in left_edges
]
for i, edge in enumerate(left_edges):
left_node.add_edge(edge, i)
edge.update_axis(left_axes_order[i], node, i, left_node)
right_node = Node(
vh_s, name=right_name, axis_names=right_axis_names, backend=backend)
right_axes_order = [
edge.axis1 if edge.node1 is node else edge.axis2 for edge in right_edges
]
for i, edge in enumerate(right_edges):
# i + 1 to account for the new edge.
right_node.add_edge(edge, i + 1)
edge.update_axis(right_axes_order[i], node, i + 1, right_node)
connect(left_node.edges[-1], right_node.edges[0], name=edge_name)
node.fresh_edges(node.axis_names)
return left_node, right_node, trun_vals
