--- title: LearningOptions (latest version) description: API reference for qiskit_noise_learning.noise_learner.LearningOptions in the latest version of qiskit-noise-learning source: https://quantum.cloud.ibm.com/docs/en/api/qiskit-noise-learning/generated/noise-learner-learning-options --- # qiskit\_noise\_learning.noise\_learner.LearningOptions *class* `qiskit_noise_learning.noise_learner.LearningOptions(*, num_randomizations: Annotated[int, Ge(ge=1)] = 32, shots_per_randomizations: Annotated[int, Ge(ge=1)] = 128, fragment_depths: list[int] = [0, 1, 2, 4, 16, 32], k_locality: Annotated[int, Ge(ge=0)] = 2, path_generator: Literal['even_depth'] = 'even_depth', analyzer: Literal['standard'] = 'standard')` [GitHub](https://github.com/Qiskit/qiskit-noise-learning/tree/stable/0.1/qiskit_noise_learning/noise_learner/learning_options.py) Bases: `BaseModel` Options for the noise learner. ### \_\_init\_\_ `__init__(**data: Any) → None` Create a new model by parsing and validating input data from keyword arguments. Raises \[ValidationError]\[pydantic\_core.ValidationError] if the input data cannot be validated to form a valid model. self is explicitly positional-only to allow self as a field name. ## Methods | | | | ---------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------- | | [`__init__`](#qiskit_noise_learning.noise_learner.LearningOptions.__init__ "qiskit_noise_learning.noise_learner.LearningOptions.__init__")(\*\*data) | Create a new model by parsing and validating input data from keyword arguments. | | `construct`(\[\_fields\_set]) | | | `copy`(\*\[, include, exclude, update, deep]) | Returns a copy of the model. | | `dict`(\*\[, include, exclude, by\_alias, ...]) | | | `from_orm`(obj) | | | `json`(\*\[, include, exclude, by\_alias, ...]) | | | `model_construct`(\[\_fields\_set]) | Creates a new instance of the Model class with validated data. | | `model_copy`(\*\[, update, deep]) | !!! abstract "Usage Documentation" | | `model_dump`(\*\[, mode, include, exclude, ...]) | !!! abstract "Usage Documentation" | | `model_dump_json`(\*\[, indent, ensure\_ascii, ...]) | !!! abstract "Usage Documentation" | | `model_json_schema`(by\_alias, ref\_template, ...) | Generates a JSON schema for a model class. | | `model_parametrized_name`(params) | Compute the class name for parametrizations of generic classes. | | `model_post_init`(context, /) | Override this method to perform additional initialization after \_\_init\_\_ and model\_construct. | | `model_rebuild`(\*\[, force, raise\_errors, ...]) | Try to rebuild the pydantic-core schema for the model. | | `model_validate`(obj, \*\[, strict, extra, ...]) | Validate a pydantic model instance. | | `model_validate_json`(json\_data, \*\[, strict, ...]) | !!! abstract "Usage Documentation" | | `model_validate_strings`(obj, \*\[, strict, ...]) | Validate the given object with string data against the Pydantic model. | | `parse_file`(path, \*\[, content\_type, ...]) | | | `parse_obj`(obj) | | | `parse_raw`(b, \*\[, content\_type, encoding, ...]) | | | `schema`(\[by\_alias, ref\_template]) | | | `schema_json`(\*\[, by\_alias, ref\_template]) | | | `update_forward_refs`(\*\*localns) | | | `validate`(value) | | ## Attributes | | | | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------- | | `model_computed_fields` | | | [`model_config`](#qiskit_noise_learning.noise_learner.LearningOptions.model_config "qiskit_noise_learning.noise_learner.LearningOptions.model_config") | Configuration for the model, should be a dictionary conforming to \[ConfigDict]\[pydantic.config.ConfigDict]. | | `model_extra` | Get extra fields set during validation. | | `model_fields` | | | `model_fields_set` | Returns the set of fields that have been explicitly set on this model instance. | | [`num_randomizations`](#qiskit_noise_learning.noise_learner.LearningOptions.num_randomizations "qiskit_noise_learning.noise_learner.LearningOptions.num_randomizations") | The number of randomizations to use per learning circuit. | | [`shots_per_randomizations`](#qiskit_noise_learning.noise_learner.LearningOptions.shots_per_randomizations "qiskit_noise_learning.noise_learner.LearningOptions.shots_per_randomizations") | The number of shots to use per randomization. | | [`fragment_depths`](#qiskit_noise_learning.noise_learner.LearningOptions.fragment_depths "qiskit_noise_learning.noise_learner.LearningOptions.fragment_depths") | The fragment depths to use (number of repetitions of each path's repeatable fragment). | | [`k_locality`](#qiskit_noise_learning.noise_learner.LearningOptions.k_locality "qiskit_noise_learning.noise_learner.LearningOptions.k_locality") | The locality of the terms to include in the noise model. | | [`path_generator`](#qiskit_noise_learning.noise_learner.LearningOptions.path_generator "qiskit_noise_learning.noise_learner.LearningOptions.path_generator") | The path generator to use. | | [`analyzer`](#qiskit_noise_learning.noise_learner.LearningOptions.analyzer "qiskit_noise_learning.noise_learner.LearningOptions.analyzer") | The analyzer to use. | ### num\_randomizations Type: [`int`](https://docs.python.org/3/library/functions.html#int) The number of randomizations to use per learning circuit. ### shots\_per\_randomizations Type: [`int`](https://docs.python.org/3/library/functions.html#int) The number of shots to use per randomization. ### fragment\_depths Type: [`list`](https://docs.python.org/3/library/stdtypes.html#list)\[[`int`](https://docs.python.org/3/library/functions.html#int)] The fragment depths to use (number of repetitions of each path’s repeatable fragment). ### model\_config Default value: `{}` Configuration for the model, should be a dictionary conforming to \[ConfigDict]\[pydantic.config.ConfigDict]. ### k\_locality Type: [`int`](https://docs.python.org/3/library/functions.html#int) The locality of the terms to include in the noise model. ### path\_generator Type: [`Literal`](https://docs.python.org/3/library/typing.html#typing.Literal)\[`'even_depth'`] The path generator to use. By default, the generator produces even depth paths for each gate for which to learn the noise. ### analyzer Type: [`Literal`](https://docs.python.org/3/library/typing.html#typing.Literal)\[`'standard'`] The analyzer to use. By default, the analyzer pipeline first computes observables, then curve fits exponentials, and finally uses non-negative least squares to solve for model parameters.