qiskit_noise_learning.analysis.FlipPostSelect
class qiskit_noise_learning.analysis.FlipPostSelect(creg_pair_identifier: Callable[[list[str]], Iterator[tuple[str, str]]] | None = None, mode: Literal['node', 'edge'] = 'edge')
Bases: AnalysisStage
Apply a mask to raw data based on bit flips across measurement outcomes.
This post selection stage is based on identifying successful bit flips on the same qubit(s) between two measurements. It can be configured to operate in one of two modes:
"node": Shots are discarded if at least one bit failed to flip."edge": Shots are discarded if there exists a pair of neighbouring qubits in the measurement for which both bits failed to flip.
Parameters
- creg_pair_identifier – A callable that, given a list of present creg names, returns an iterator over pairs of creg names for which to do the flip-based post selection on. Defaults to returning pairs of cregs with names of the form
"*"and"*_ps". - mode – Post-selection mode; either
"node"or"edge".
__init__
__init__(creg_pair_identifier: Callable[[list[str]], Iterator[tuple[str, str]]] | None = None, mode: Literal['node', 'edge'] = 'edge')
Methods
Column 1 | Column 2 |
|---|---|
__init__([creg_pair_identifier, mode]) | |
run(fit) | Run this stage, returning a new Fit with the output level populated. |
Attributes
Column 1 | Column 2 |
|---|---|
creg_pair_identifier | |
input_level | The data level this stage reads. |
mode | |
output_level | The data level this stage writes. |
input_level
The data level this stage reads.
output_level
The data level this stage writes.
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