qiskit.dagcircuit.DAGDependency

class DAGDependency

Object to represent a quantum circuit as a directed acyclic graph via operation dependencies (i.e. lack of commutation).

The nodes in the graph are operations represented by quantum gates. The edges correspond to non-commutation between two operations (i.e. a dependency). A directed edge from node A to node B means that operation A does not commute with operation B. The object’s methods allow circuits to be constructed.

The nodes in the graph have the following attributes: ‘operation’, ‘successors’, ‘predecessors’.

Example:

Bell circuit with no measurement.

      ┌───┐
qr_0: ┤ H ├──■──
      └───┘┌─┴─┐
qr_1: ─────┤ X ├
           └───┘

The dependency DAG for the above circuit is represented by two nodes. The first one corresponds to Hadamard gate, the second one to the CNOT gate as the gates do not commute there is an edge between the two nodes.

Reference:

[1] Iten, R., Moyard, R., Metger, T., Sutter, D. and Woerner, S., 2020. Exact and practical pattern matching for quantum circuit optimization. arXiv:1909.05270

Create an empty DAGDependency.

__init__()

Create an empty DAGDependency.

Methods

__init__()	Create an empty DAGDependency.
add_clbits(clbits)	Add individual clbit wires.
add_creg(creg)	Add clbits in a classical register.
add_op_node(operation, qargs, cargs)	Add a DAGDepNode to the graph and update the edges.
add_qreg(qreg)	Add qubits in a quantum register.
add_qubits(qubits)	Add individual qubit wires.
copy()	Function to copy a DAGDependency object.
depth()	Return the circuit depth.
direct_predecessors(node_id)	Direct predecessors id of a given node as sorted list.
direct_successors(node_id)	Direct successors id of a given node as sorted list.
draw([scale, filename, style])	Draws the DAGDependency graph.
get_all_edges()	Enumeration of all edges.
get_edges(src_id, dest_id)	Edge enumeration between two nodes through method get_all_edge_data.
get_in_edges(node_id)	Enumeration of all incoming edges for a given node.
get_node(node_id)	param node_id label of considered node.
get_nodes()	returns iterator over all the nodes.
get_out_edges(node_id)	Enumeration of all outgoing edges for a given node.
predecessors(node_id)	Predecessors id of a given node as sorted list.
size()	Returns the number of gates in the circuit
successors(node_id)	Successors id of a given node as sorted list.
to_networkx()	Returns a copy of the DAGDependency in networkx format.
to_retworkx()	Returns the DAGDependency in retworkx format.
topological_nodes()	Yield nodes in topological order.

Attributes

calibrations	Return calibration dictionary.
global_phase	Return the global phase of the circuit.

add_clbits(clbits)

Add individual clbit wires.

add_creg(creg)

Add clbits in a classical register.

add_op_node(operation, qargs, cargs)

Add a DAGDepNode to the graph and update the edges.

Parâmetros

• operation (qiskit.circuit.Instruction) – operation as a quantum gate.

• qargs (list[Qubit]) – list of qubits on which the operation acts

• cargs (list[Clbit]) – list of classical wires to attach to.

add_qreg(qreg)

Add qubits in a quantum register.

add_qubits(qubits)

Add individual qubit wires.

property calibrations

Return calibration dictionary.

The custom pulse definition of a given gate is of the form

{‘gate_name’: {(qubits, params): schedule}}

copy()

Function to copy a DAGDependency object. :returns: a copy of a DAGDependency object. :rtype: DAGDependency

depth()

Return the circuit depth. :returns: the circuit depth :rtype: int

direct_predecessors(node_id)

Direct predecessors id of a given node as sorted list.

Parâmetros

node_id (int) – label of considered node.

Retorna

direct predecessors id as a sorted list

Tipo de retorno

List

direct_successors(node_id)

Direct successors id of a given node as sorted list.

Parâmetros

node_id (int) – label of considered node.

Retorna

direct successors id as a sorted list

Tipo de retorno

List

draw(scale=0.7, filename=None, style='color')

Draws the DAGDependency graph.

This function needs pydot <https://github.com/erocarrera/pydot>, which in turn needs Graphviz <https://www.graphviz.org/>` to be installed.

Parâmetros

• scale (float) – scaling factor

• filename (str) – file path to save image to (format inferred from name)

• style (str) – ‘plain’: B&W graph ‘color’ (default): color input/output/op nodes

Retorna

if in Jupyter notebook and not saving to file,

otherwise None.

Tipo de retorno

Ipython.display.Image

get_all_edges()

Enumeration of all edges.

Retorna

corresponding to the label.

Tipo de retorno

List

get_edges(src_id, dest_id)

Edge enumeration between two nodes through method get_all_edge_data.

Parâmetros

• src_id (int) – label of the first node.

• dest_id (int) – label of the second node.

Retorna

corresponding to all edges between the two nodes.

Tipo de retorno

List

get_in_edges(node_id)

Enumeration of all incoming edges for a given node.

Parâmetros

node_id (int) – label of considered node.

Retorna

corresponding incoming edges data.

Tipo de retorno

List

get_node(node_id)

Parâmetros

node_id (int) – label of considered node.

Retorna

corresponding to the label.

Tipo de retorno

node

get_nodes()

Retorna

iterator over all the nodes.

Tipo de retorno

generator(dict)

get_out_edges(node_id)

Enumeration of all outgoing edges for a given node.

Parâmetros

node_id (int) – label of considered node.

Retorna

corresponding outgoing edges data.

Tipo de retorno

List

property global_phase

Return the global phase of the circuit.

predecessors(node_id)

Predecessors id of a given node as sorted list.

Parâmetros

node_id (int) – label of considered node.

Retorna

all predecessors id as a sorted list

Tipo de retorno

List

size()

Returns the number of gates in the circuit

successors(node_id)

Successors id of a given node as sorted list.

Parâmetros

node_id (int) – label of considered node.

Retorna

all successors id as a sorted list

Tipo de retorno

List

to_networkx()

Returns a copy of the DAGDependency in networkx format.

to_retworkx()

Returns the DAGDependency in retworkx format.

topological_nodes()

Yield nodes in topological order.

Retorna

node in topological order.

Tipo de retorno

generator(DAGNode)