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TemplateOptimization

class qiskit.transpiler.passes.TemplateOptimization(*args, **kwargs)

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Bases: TransformationPass

Class for the template optimization pass.

Parameters

  • template_list (list[QuantumCircuit()]) – list of the different template circuit to apply.
  • heuristics_backward_param (list[int]) – [length, survivor] Those are the parameters for applying heuristics on the backward part of the algorithm. This part of the algorithm creates a tree of matching scenario. This tree grows exponentially. The heuristics evaluate which scenarios have the longest match and keep only those. The length is the interval in the tree for cutting it and survivor is the number of scenarios that are kept. We advise to use l=3 and s=1 to have serious time advantage. We remind that the heuristics implies losing a part of the maximal matches. Check reference for more details.
  • heuristics_qubits_param (list[int]) – [length] The heuristics for the qubit choice make guesses from the dag dependency of the circuit in order to limit the number of qubit configurations to explore. The length is the number of successors or not predecessors that will be explored in the dag dependency of the circuit, each qubits of the nodes are added to the set of authorized qubits. We advise to use length=1. Check reference for more details.
  • user_cost_dict (Dict[str, int]) – quantum cost dictionary passed to TemplateSubstitution to configure its behavior. This will override any default values if None is not given. The key is the name of the gate and the value its quantum cost.

Attributes

is_analysis_pass

Check if the pass is an analysis pass.

If the pass is an AnalysisPass, that means that the pass can analyze the DAG and write the results of that analysis in the property set. Modifications on the DAG are not allowed by this kind of pass.

is_transformation_pass

Check if the pass is a transformation pass.

If the pass is a TransformationPass, that means that the pass can manipulate the DAG, but cannot modify the property set (but it can be read).


Methods

execute

execute(passmanager_ir, state, callback=None)

Execute optimization task for input Qiskit IR.

Parameters

  • passmanager_ir (Any) – Qiskit IR to optimize.
  • state (PassManagerState) – State associated with workflow execution by the pass manager itself.
  • callback (Callable | None) – A callback function which is caller per execution of optimization task.

Returns

Optimized Qiskit IR and state of the workflow.

Return type

tuple[Any, qiskit.passmanager.compilation_status.PassManagerState]

name

name()

Name of the pass.

Return type

str

run

run(dag)

Parameters

dag (DAGCircuit) – DAG circuit.

Returns

optimized DAG circuit.

Return type

DAGCircuit

Raises

TranspilerError – If the template has not the right form or if the output circuit acts differently as the input circuit.

update_status

update_status(state, run_state)

Update workflow status.

Parameters

  • state (PassManagerState) – Pass manager state to update.
  • run_state (RunState) – Completion status of current task.

Returns

Updated pass manager state.

Return type

PassManagerState

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