OptimizeCliffords

class qiskit.transpiler.passes.OptimizeCliffords(*args, **kwargs)[source]

Bases: TransformationPass

Combine consecutive Cliffords over the same qubits. This serves as an example of extra capabilities enabled by storing Cliffords natively on the circuit.

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(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 of the pass.

Return type:

str

run(dag)[source]

Run the OptimizeCliffords pass on dag.

Parameters:

dag (DAGCircuit) – the DAG to be optimized.

Returns:

the optimized DAG.

Return type:

DAGCircuit

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