---
title: ExecutorDataMapper (latest version)
description: API reference for qiskit_noise_learning.circuit_generator.ExecutorDataMapper in the latest version of qiskit-noise-learning
source: https://quantum.cloud.ibm.com/docs/en/api/qiskit-noise-learning/generated/circuit-generator-executor-data-mapper
---

# 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)`

[GitHub](https://github.com/Qiskit/qiskit-noise-learning/tree/stable/0.1/qiskit_noise_learning/circuit_generator/executor_data_mapper.py)

Bases: [`object`](https://docs.python.org/3/library/functions.html#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()`](/docs/api/qiskit-noise-learning/generated/circuit-generator-executor-circuit-generator#generate "qiskit_noise_learning.circuit_generator.ExecutorCircuitGenerator.generate") is not preserved during execution. This class contains properties to format the results of a [`qiskit_ibm_runtime.results.QuantumProgramResult`](/docs/api/qiskit-ibm-runtime/results-quantum-program-result "(in Qiskit Runtime IBM Client)") 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

|                                                                                                                                                                                                     |   |
| --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | - |
| [`__init__`](#qiskit_noise_learning.circuit_generator.ExecutorDataMapper.__init__ "qiskit_noise_learning.circuit_generator.ExecutorDataMapper.__init__")(item\_sequence\_indices, creg\_names, ...) |   |

## Attributes

|                                                                                                                                                                                                 |                                                                                      |
| ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------ |
| [`creg_names`](#qiskit_noise_learning.circuit_generator.ExecutorDataMapper.creg_names "qiskit_noise_learning.circuit_generator.ExecutorDataMapper.creg_names")                                  | List of names of the classical registers contained in the results.                   |
| [`fidelity_model`](#qiskit_noise_learning.circuit_generator.ExecutorDataMapper.fidelity_model "qiskit_noise_learning.circuit_generator.ExecutorDataMapper.fidelity_model")                      | The fidelity model used in the experiment.                                           |
| [`instruction_sequences`](#qiskit_noise_learning.circuit_generator.ExecutorDataMapper.instruction_sequences "qiskit_noise_learning.circuit_generator.ExecutorDataMapper.instruction_sequences") | The instruction sequences corresponding to the sequence indices in the sequence map. |
| [`item_sequence_indices`](#qiskit_noise_learning.circuit_generator.ExecutorDataMapper.item_sequence_indices "qiskit_noise_learning.circuit_generator.ExecutorDataMapper.item_sequence_indices") | Per program item, the instruction sequence indices corresponding to each config.     |
| [`measurement_maps`](#qiskit_noise_learning.circuit_generator.ExecutorDataMapper.measurement_maps "qiskit_noise_learning.circuit_generator.ExecutorDataMapper.measurement_maps")                | A per-program-item map from creg name to an ordered array of measured qubit indices. |
| [`num_randomizations`](#qiskit_noise_learning.circuit_generator.ExecutorDataMapper.num_randomizations "qiskit_noise_learning.circuit_generator.ExecutorDataMapper.num_randomizations")          | The number of randomizations used per experiment.                                    |
| [`paths`](#qiskit_noise_learning.circuit_generator.ExecutorDataMapper.paths "qiskit_noise_learning.circuit_generator.ExecutorDataMapper.paths")                                                 | The analysis paths.                                                                  |
| [`relations`](#qiskit_noise_learning.circuit_generator.ExecutorDataMapper.relations "qiskit_noise_learning.circuit_generator.ExecutorDataMapper.relations")                                     | Path-to-sequence relations.                                                          |

### item\_sequence\_indices

Type: [`list`](https://docs.python.org/3/library/stdtypes.html#list)\[[`list`](https://docs.python.org/3/library/stdtypes.html#list)\[[`int`](https://docs.python.org/3/library/functions.html#int)]]

Per program item, the instruction sequence indices corresponding to each config.

### creg\_names

Type: [`list`](https://docs.python.org/3/library/stdtypes.html#list)\[[`list`](https://docs.python.org/3/library/stdtypes.html#list)\[[`str`](https://docs.python.org/3/library/stdtypes.html#str)]]

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`](/docs/api/qiskit-ibm-runtime/results-quantum-program-result "(in Qiskit Runtime IBM Client)") at the same index.

### measurement\_maps

Type: [`list`](https://docs.python.org/3/library/stdtypes.html#list)\[[`dict`](https://docs.python.org/3/library/stdtypes.html#dict)\[[`str`](https://docs.python.org/3/library/stdtypes.html#str), [`ndarray`](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)\[[`int`](https://docs.python.org/3/library/functions.html#int)]]]

A per-program-item map from creg name to an ordered array of measured qubit indices.

### instruction\_sequences

Type: [`list`](https://docs.python.org/3/library/stdtypes.html#list)

The instruction sequences corresponding to the sequence indices in the sequence map.

### num\_randomizations

Type: [`int`](https://docs.python.org/3/library/functions.html#int)

The number of randomizations used per experiment.

### fidelity\_model

Type: [`LinearMap`](/docs/api/qiskit-noise-learning/generated/math-linear-map "qiskit_noise_learning.math.linear_map.LinearMap")\[[`Hashable`](https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable), [`FidelityIndex`](/docs/api/qiskit-noise-learning/generated/sequences-fidelity-index "qiskit_noise_learning.sequences.fidelity_index.FidelityIndex")] | [`None`](https://docs.python.org/3/library/constants.html#None)

The fidelity model used in the experiment.

### paths

Type: [`list`](https://docs.python.org/3/library/stdtypes.html#list)\[[`Path`](/docs/api/qiskit-noise-learning/generated/sequences-path "qiskit_noise_learning.sequences.path.Path")] | [`None`](https://docs.python.org/3/library/constants.html#None)

The analysis paths.

### relations

Type: [`set`](https://docs.python.org/3/library/stdtypes.html#set)\[[`tuple`](https://docs.python.org/3/library/stdtypes.html#tuple)\[[`int`](https://docs.python.org/3/library/functions.html#int), [`int`](https://docs.python.org/3/library/functions.html#int)]] | [`None`](https://docs.python.org/3/library/constants.html#None)

Path-to-sequence relations.