qxmt.configs module

qxmt.configs module#

class qxmt.configs.DatasetConfig(*, openml=None, file=None, generate=None, split, features=None, raw_preprocess_logic=None, transform_logic=None)

Bases: BaseModel

Parameters:

  • openml (OpenMLConfig | None)

  • file (FileConfig | None)

  • generate (GenerateDataConfig | None)

  • split (SplitConfig)

  • features (list[str] | None)

  • raw_preprocess_logic (list[dict[str, Any]] | dict[str, Any] | None)

  • transform_logic (list[dict[str, Any]] | dict[str, Any] | None)

features: list[str] | None
file: FileConfig | None
generate: GenerateDataConfig | None
model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

openml: OpenMLConfig | None
raw_preprocess_logic: list[dict[str, Any]] | dict[str, Any] | None
split: SplitConfig
transform_logic: list[dict[str, Any]] | dict[str, Any] | None
class qxmt.configs.DeviceConfig(*, platform, device_name, backend_name=None, n_qubits, shots=None, random_seed=None, save_shots_results=False)

Bases: BaseModel

Parameters:

  • platform (str)

  • device_name (str)

  • backend_name (str | None)

  • n_qubits (int)

  • shots (int | None)

  • random_seed (int | None)

  • save_shots_results (bool)

backend_name: str | None
check_real_machine_setting()
Return type:

DeviceConfig

check_save_shots()
Return type:

DeviceConfig

classmethod check_shots(value)
Parameters:

value (int)

Return type:

int

device_name: str
model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

n_qubits: int
platform: str
random_seed: int | None
save_shots_results: bool
shots: int | None
class qxmt.configs.EvaluationConfig(*, default_metrics, custom_metrics=None)

Bases: BaseModel

Parameters:

  • default_metrics (list[str])

  • custom_metrics (list[dict[str, Any]] | None)

custom_metrics: list[dict[str, Any]] | None
default_metrics: list[str]
model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class qxmt.configs.ExperimentConfig(*, path='', description='', global_settings, dataset, device, feature_map=None, kernel=None, model, evaluation)

Bases: BaseModel

Parameters:
__init__(**data)

Initialize the experiment configuration.

Case 1:

Load the configuration from a file path. This case the data is a dictionary with a single key “path”.

Case 2:

Load the configuration from a dictionary. This case the data is a dictionary with the configuration data.

Parameters:

data (Any)

Return type:

None

dataset: DatasetConfig
description: str
device: DeviceConfig
evaluation: EvaluationConfig
feature_map: FeatureMapConfig | None
global_settings: GlobalSettingsConfig
kernel: KernelConfig | None
load_from_path(path)
Parameters:

path (str)

Return type:

dict[str, Any]

model: ModelConfig
model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

path: Path | str
class qxmt.configs.FeatureMapConfig(*, module_name, implement_name, params=None)

Bases: BaseModel

Parameters:

  • module_name (str)

  • implement_name (str)

  • params (dict[str, Any] | None)

implement_name: str
model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

module_name: str
params: dict[str, Any] | None
class qxmt.configs.FileConfig(*, data_path, label_path, label_name)

Bases: BaseModel

Parameters:

  • data_path (Path | str)

  • label_path (Path | str | None)

  • label_name (str | None)

data_path: Path | str
label_name: str | None
label_path: Path | str | None
model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_post_init(_FileConfig__context)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Parameters:

_FileConfig__context (dict[str, Any])

Return type:

None

class qxmt.configs.GenerateDataConfig(*, generate_method, params={})

Bases: BaseModel

Parameters:

  • generate_method (Literal['linear'])

  • params (dict[str, Any] | None)

generate_method: Literal['linear']
model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

params: dict[str, Any] | None
class qxmt.configs.GlobalSettingsConfig(*, random_seed, task_type)

Bases: BaseModel

Parameters:

  • random_seed (int)

  • task_type (Literal['classification', 'regression'])

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

random_seed: int
task_type: Literal['classification', 'regression']
class qxmt.configs.KernelConfig(*, module_name, implement_name, params=None)

Bases: BaseModel

Parameters:

  • module_name (str)

  • implement_name (str)

  • params (dict[str, Any] | None)

implement_name: str
model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

module_name: str
params: dict[str, Any] | None
class qxmt.configs.ModelConfig(*, name, params, feature_map=None, kernel=None)

Bases: BaseModel

Parameters:
feature_map: FeatureMapConfig | None
kernel: KernelConfig | None
model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str
params: dict[str, Any]
class qxmt.configs.OpenMLConfig(*, name=None, id=None, return_format='numpy', save_path=None)

Bases: BaseModel

Parameters:

  • name (str | None)

  • id (int | None)

  • return_format (str)

  • save_path (Path | str | None)

id: int | None
model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_post_init(_OpenMLConfig__context)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Parameters:

_OpenMLConfig__context (dict[str, Any])

Return type:

None

name: str | None
return_format: str
save_path: Path | str | None
class qxmt.configs.SplitConfig(*, train_ratio, validation_ratio=0.0, test_ratio, shuffle=True)

Bases: BaseModel

Parameters:

  • train_ratio (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

  • validation_ratio (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

  • test_ratio (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

  • shuffle (bool)

check_ratio()
Return type:

SplitConfig

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

shuffle: bool
test_ratio: float
train_ratio: float
validation_ratio: float