qiskit_noise_learning.circuit_generator.ExecutorDataMapper
class qiskit_noise_learning.circuit_generator.ExecutorDataMapper(item_sequence_indices: list[list[int]], creg_names: list[list[str]], measurement_maps: list[dict[str, ndarray[int]]], instruction_sequences: list[InstructionSequence], num_randomizations: int, fidelity_model: LinearMap[Hashable, FidelityIndex] | None = None, paths: list[Path] | None = None, relations: set[tuple[int, int]] | None = None)
Bases: object
Map executor results into standard results.
As instruction sequences with similar structure are generated together with a single template circuit and different samplex arguments, the order of input sequences to ExecutorCircuitGenerator.generate() is not preserved during execution. This class contains properties to format the results of a qiskit_ibm_runtime.results.QuantumProgramResult to the order of the input sequences.
Parameters
- item_sequence_indices – For each program item, an ordered list of instruction sequence indices. Position in the list corresponds to the configuration index within the result item.
- creg_names – The name of classical registers in each program item.
- measurement_maps – For each program item, a dictionary from creg names to an ordered array of measured qubit indices.
- instruction_sequences – The instruction sequences associated with the data.
- num_randomizations – The number of randomizations used per experiment.
- fidelity_model – The fidelity model used in the experiment.
- paths – The analysis paths.
- relations – Path-to-sequence relations.
__init__
__init__(item_sequence_indices: list[list[int]], creg_names: list[list[str]], measurement_maps: list[dict[str, ndarray[int]]], instruction_sequences: list[InstructionSequence], num_randomizations: int, fidelity_model: LinearMap[Hashable, FidelityIndex] | None = None, paths: list[Path] | None = None, relations: set[tuple[int, int]] | None = None)
Methods
Column 1 | Column 2 |
|---|---|
__init__(item_sequence_indices, creg_names, ...) |
Attributes
Column 1 | Column 2 |
|---|---|
creg_names | List of names of the classical registers contained in the results. |
fidelity_model | The fidelity model used in the experiment. |
instruction_sequences | The instruction sequences corresponding to the sequence indices in the sequence map. |
item_sequence_indices | Per program item, the instruction sequence indices corresponding to each config. |
measurement_maps | A per-program-item map from creg name to an ordered array of measured qubit indices. |
num_randomizations | The number of randomizations used per experiment. |
paths | The analysis paths. |
relations | Path-to-sequence relations. |
item_sequence_indices
Per program item, the instruction sequence indices corresponding to each config.
creg_names
List of names of the classical registers contained in the results.
The list at a given index corresponds to names expected in the data of the qiskit_ibm_runtime.results.QuantumProgramResult at the same index.
measurement_maps
Type: list[dict[str, ndarray[int]]]
A per-program-item map from creg name to an ordered array of measured qubit indices.
instruction_sequences
Type: list
The instruction sequences corresponding to the sequence indices in the sequence map.
num_randomizations
Type: int
The number of randomizations used per experiment.
fidelity_model
Type: LinearMap[Hashable, FidelityIndex] | None
The fidelity model used in the experiment.