qiskit_noise_learning.experiment_builder.EvenDepthVanillaPaths

class qiskit_noise_learning.experiment_builder.EvenDepthVanillaPaths(*, prep_gate: ModelGate | None = None, meas_gate: ModelGate | None = None, gates: list[ModelGate] | None = None, input_paulis: dict[str, QubitSparsePauliList] | None = None)

GitHub

Bases: AddPaths

Generate unbound vanilla paths with repetitions of two gate applications.

For each target gate, generates paths where the repeatable fragment consists of two applications of the gate.

Parameters

prep_gate – The preparation gate. If None, defaults to the gate named "P".
meas_gate – The measurement gate. If None, defaults to the gate named "M".
gates – Gates to generate paths for; these must be unitary (no preparation or measurement component). If None, defaults to all unitary gates in the gate set.
input_paulis – Optional mapping from gate name to the Paulis to use. If not specified, defaults to the gate generators if the fidelity model contains a PauliLindbladModel, otherwise a ValueError is raised.

init

__init__(*, prep_gate: ModelGate | None = None, meas_gate: ModelGate | None = None, gates: list[ModelGate] | None = None, input_paulis: dict[str, QubitSparsePauliList] | None = None)

Methods

Column 1	Column 2
`__init__`(*[, prep_gate, meas_gate, gates, ...])
`run`(experiment)	Validate required fields, then apply this stage.

Attributes

Column 1	Column 2
`populates_fields`
`required_fields`	Built-in immutable sequence.

required_fields

Type: tuple[str, ...]

Built-in immutable sequence.

If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable’s items.

If the argument is a tuple, the return value is the same object.

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