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')
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
Column 1 | Column 2 |
|---|---|
__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
Column 1 | Column 2 |
|---|---|
model_computed_fields | |
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 | The number of randomizations to use per learning circuit. |
shots_per_randomizations | The number of shots to use per randomization. |
fragment_depths | The fragment depths to use (number of repetitions of each path's repeatable fragment). |
k_locality | The locality of the terms to include in the noise model. |
path_generator | The path generator to use. |
analyzer | The analyzer to use. |
num_randomizations
Type: int
The number of randomizations to use per learning circuit.
shots_per_randomizations
Type: int
The number of shots to use per randomization.
fragment_depths
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
The locality of the terms to include in the noise model.
path_generator
Type: 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['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.