qiskit_noise_learning.experiment_builder.VanillaInstructionSequences

class qiskit_noise_learning.experiment_builder.VanillaInstructionSequences(*, prep_gate: ModelGate | None = None, meas_gate: ModelGate | None = None, gates: list[ModelGate] | None = None, coupling_map: CouplingMap | None = None, randomization_multiplier: int = 1)

GitHub

Bases: AddInstructionSequences

Generate vanilla instruction sequences for each target 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 sequences for. If None, defaults to all non-SPAM gates.
coupling_map – The coupling map. If None, defaults to the gate set’s coupling map.
randomization_multiplier – Randomization multiplier applied to all generated sequences.

init

__init__(*, prep_gate: ModelGate | None = None, meas_gate: ModelGate | None = None, gates: list[ModelGate] | None = None, coupling_map: CouplingMap | None = None, randomization_multiplier: int = 1)

Methods

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

Attributes

Chronique « 1 »	Chronique « 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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