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

# qiskit\_noise\_learning.sequences.LogPathSpace

*class* `qiskit_noise_learning.sequences.LogPathSpace(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: [`IndexedSpace`](/docs/api/qiskit-noise-learning/generated/math-indexed-space "qiskit_noise_learning.math.indexed_space.IndexedSpace")\[[`Path`](/docs/api/qiskit-noise-learning/generated/sequences-path "qiskit_noise_learning.sequences.path.Path")]

The (infinite-dimensional) space of log path-fidelities.

For a [`Path`](/docs/api/qiskit-noise-learning/generated/sequences-path "qiskit_noise_learning.sequences.Path"), the “path-fidelity” is:

- If the path is unbound, the product of the fidelities in the repeatable fragment.
- If the path is bound, the product of all fidelities in the path (counting multiplicities).

This corresponds to the sign-corrected observable of an experiment traversing the path.

The log path space represents the vector space of such log path-fidelities, indexed by the paths themselves. It is defined relative to a space of log fidelities: a path is a member if all of the fidelity indices in its fragments are members of that fidelity space.

**Parameters**

**fidelity\_space** – The space of log fidelities whose fidelity indices the paths are built from.

### \_\_init\_\_

`__init__(fidelity_space: IndexedSpace[FidelityIndex])`

## Methods

|                                                                                                                                               |   |
| --------------------------------------------------------------------------------------------------------------------------------------------- | - |
| [`__init__`](#qiskit_noise_learning.sequences.LogPathSpace.__init__ "qiskit_noise_learning.sequences.LogPathSpace.__init__")(fidelity\_space) |   |

## Attributes

|                                                                                                                                                |                                                                              |
| ---------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------- |
| [`dim`](#qiskit_noise_learning.sequences.LogPathSpace.dim "qiskit_noise_learning.sequences.LogPathSpace.dim")                                  | The dimension (cardinality) of the space.                                    |
| [`fidelity_space`](#qiskit_noise_learning.sequences.LogPathSpace.fidelity_space "qiskit_noise_learning.sequences.LogPathSpace.fidelity_space") | The space of log fidelities whose fidelity indices the paths are built from. |

### fidelity\_space

Type: [`IndexedSpace`](/docs/api/qiskit-noise-learning/generated/math-indexed-space "qiskit_noise_learning.math.indexed_space.IndexedSpace")\[[`FidelityIndex`](/docs/api/qiskit-noise-learning/generated/sequences-fidelity-index "qiskit_noise_learning.sequences.fidelity_index.FidelityIndex")]

The space of log fidelities whose fidelity indices the paths are built from.

### dim

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

The dimension (cardinality) of the space.

May be `math.inf` for infinite-dimensional spaces.