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OptimizeAnnotated

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

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Bases: TransformationPass

Optimization pass on circuits with annotated operations.

Implemented optimizations:

  • For each annotated operation, converting the list of its modifiers to a canonical form. For example, consecutively applying inverse(), control(2) and inverse() is equivalent to applying control(2).
  • Removing annotations when possible. For example, AnnotatedOperation(SwapGate(), [InverseModifier(), InverseModifier()]) is equivalent to SwapGate().
  • Recursively combining annotations. For example, if g1 = AnnotatedOperation(SwapGate(), InverseModifier()) and g2 = AnnotatedOperation(g1, ControlModifier(2)), then g2 can be replaced with AnnotatedOperation(SwapGate(), [InverseModifier(), ControlModifier(2)]).
  • Applies conjugate reduction to annotated operations. As an example, control - [P -- Q -- P^{-1}] can be rewritten as P -- control - [Q] -- P^{-1}, that is, only the middle part needs to be controlled. This also works for inverse and power modifiers.

OptimizeAnnotated initializer.

Parameters

  • target – Optional, the backend target to use for this pass.
  • equivalence_library – The equivalence library used (instructions in this library will not be optimized by this pass).
  • basis_gates – Optional, target basis names to unroll to, e.g. [‘u3’, ‘cx’] (instructions in this list will not be optimized by this pass). Ignored if target is also specified.
  • recurse – By default, when either target or basis_gates is specified, the pass recursively descends into gate definitions (and the recursion is not applied when neither is specified since such objects do not need to be synthesized). Setting this value to False precludes the recursion in every case.
  • do_conjugate_reduction – controls whether conjugate reduction should be performed.

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)

GitHub

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()

GitHub

Name of the pass.

Return type

str

run

run(dag)

GitHub

Run the OptimizeAnnotated pass on dag.

Parameters

dag (DAGCircuit) – input dag.

Returns

Output dag with higher-level operations optimized.

Raises

TranspilerError – when something goes wrong.

update_status

update_status(state, run_state)

GitHub

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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