---
title: LogPathMap (latest version)
description: API reference for qiskit_noise_learning.sequences.LogPathMap in the latest version of qiskit-noise-learning
source: https://quantum.cloud.ibm.com/docs/en/api/qiskit-noise-learning/generated/sequences-log-path-map
---

# qiskit\_noise\_learning.sequences.LogPathMap

*class* `qiskit_noise_learning.sequences.LogPathMap(fidelity_space: IndexedSpace[FidelityIndex])`

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

Bases: [`LinearMap`](/docs/api/qiskit-noise-learning/generated/math-linear-map "qiskit_noise_learning.math.linear_map.LinearMap")\[[`FidelityIndex`](/docs/api/qiskit-noise-learning/generated/sequences-fidelity-index "qiskit_noise_learning.sequences.fidelity_index.FidelityIndex"), [`Path`](/docs/api/qiskit-noise-learning/generated/sequences-path "qiskit_noise_learning.sequences.path.Path")]

The linear map from a space of log fidelities to its associated log path space.

This map is purely combinatorial: the row of a path is the fragment-depth-weighted multiplicity of each fidelity index appearing in the path. It does not depend on any noise model, only on the [`Path`](/docs/api/qiskit-noise-learning/generated/sequences-path "qiskit_noise_learning.sequences.Path") structure and the fidelity indices’ membership in the input space.

**Parameters**

**fidelity\_space** – The space of log fidelities.

### \_\_init\_\_

`__init__(fidelity_space: IndexedSpace[FidelityIndex])`

## Methods

|                                                                                                                                           |                                                                   |
| ----------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------- |
| [`__init__`](#qiskit_noise_learning.sequences.LogPathMap.__init__ "qiskit_noise_learning.sequences.LogPathMap.__init__")(fidelity\_space) |                                                                   |
| `compose`(outer)                                                                                                                          | Post-compose: self maps I->O, outer maps O->C, result maps I->C.  |
| `left_multiply`(matrix)                                                                                                                   | Multiply on the left by an explicit matrix.                       |
| `pre_compose`(inner)                                                                                                                      | Pre-compose: inner maps A->I, self maps I->O, result maps A->O.   |
| `projected_output`(output\_indices, vector)                                                                                               | Compute a projection of the map applied to a vector.              |
| [`rows`](#qiskit_noise_learning.sequences.LogPathMap.rows "qiskit_noise_learning.sequences.LogPathMap.rows")(output\_indices)             | Construct the sub-matrix whose rows are the given output indices. |

## Attributes

|                |                   |
| -------------- | ----------------- |
| `input_space`  | The input space.  |
| `output_space` | The output space. |

### rows

`rows(output_indices: Iterable[Path]) → IndexedMatrix[Path, FidelityIndex]`

Construct the sub-matrix whose rows are the given output indices.

**Parameters**

**output\_indices** – The labels for the desired rows of the matrix.

**Returns**

[`IndexedMatrix`](/docs/api/qiskit-noise-learning/generated/math-indexed-matrix "qiskit_noise_learning.math.IndexedMatrix")