modules.applications.qml.generative_modeling.training.inference.Inference

class Inference

Bases: TrainingGenerative

This module executes a quantum circuit with parameters of a pretrained model.

__init__(): Constructor method.

Methods

`__init__`()	Constructor method.
`get_available_submodule_options`()	Gets the list of available options.
`get_available_submodules`(option)	If the module has submodules depending on certain options, this method should adjust the submodule_options accordingly.
`get_default_submodule`(option)	Raises ValueError as this module has no submodules.
`get_depending_parameters`(option, config)	If the module has parameters depending on certain options, this method should return the parameters for the given option.
`get_parameter_options`()	Returns the configurable settings for this circuit.
`get_requirements`()	Returns requirements of this module.
`get_submodule`(option)	Submodule is instantiated according to the information given in self.sub_options.
`kl_divergence`(pmf_model, pmf_target)	This function calculates the Kullback-Leibler divergence, that is used as a loss function.
`mmd`(pmf_model, pmf_target)	This function calculates the maximum mean discrepancy, that is used as a loss function.
`nll`(pmf_model, pmf_target)	This function calculates th negative log likelihood, that is used as a loss function.
`postprocess`(input_data, config, **kwargs)	Perform the actual training of the machine learning model.
`preprocess`(input_data, config, **kwargs)	Essential method for the benchmarking process.
`sample_from_pmf`(pmf, n_shots)	This function samples from the probability mass function generated by the quantum circuit.
`start_training`(input_data, config, **kwargs)	Method that uses a pretrained model for inference.

class Config

Bases: TypedDict

Attributes of a valid config.

pretrained: str

clear() → None. Remove all items from D.

copy() → a shallow copy of D

fromkeys(value=None, /): Create a new dictionary with keys from iterable and values set to value.

get(key, default=None, /): Return the value for key if key is in the dictionary, else default.

items() → a set-like object providing a view on D's items

keys() → a set-like object providing a view on D's keys

pop(k[, d]) → v, remove specified key and return the corresponding value.: If the key is not found, return the default if given; otherwise, raise a KeyError.

popitem()

Remove and return a (key, value) pair as a 2-tuple.

Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty.

setdefault(key, default=None, /)

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

update([E, ]**F) → None. Update D from mapping/iterable E and F.: If E is present and has a .keys() method, then does: for k in E.keys(): D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values() → an object providing a view on D's values

class Timing

Bases: object

This module is an abstraction of time measurement for both CPU and GPU processes.

start_recording_cpu() → None: This is a function to start time measurement on the CPU.

start_recording_gpu() → None: This is a function to start time measurement on the GPU.

stop_recording_cpu() → float

This is a function to stop time measurement on the CPU.

.return: Elapsed time in milliseconds

stop_recording_gpu() → float

This is a function to stop time measurement on the GPU.

Returns:: Elapsed time in milliseconds

get_available_submodule_options() → list

Gets the list of available options.

Returns:: List of module options

get_available_submodules(option: list) → list

If the module has submodules depending on certain options, this method should adjust the submodule_options accordingly.

Parameters:: option -- List of chosen options
Returns:: List of available submodules

get_default_submodule(option: str) → Core

Raises ValueError as this module has no submodules.

Parameters:: option -- Option name
Raises:: ValueError -- If called, since this module has no submodules

get_depending_parameters(option: str, config: dict) → dict

If the module has parameters depending on certain options, this method should return the parameters for the given option.

Parameters:

option -- The chosen option
config -- Current config dictionary

Returns:

The parameters for the given option

get_parameter_options() → dict

Returns the configurable settings for this circuit.

Returns:: Configuration settings for the pretrained model

return {
        "pretrained": {
            "values": [False],
            "custom_input": True,
            "postproc": str,
            "description": "Please provide the parameters of a pretrained model."
        }
    }

static get_requirements() → list[dict]

Returns requirements of this module.

Returns:: List of dict with requirements of this module.

get_submodule(option: str) → Core

Submodule is instantiated according to the information given in self.sub_options. If self.sub_options is None, get_default_submodule is called as a fallback.

Parameters:: option -- String with the options
Returns:: Instance of a module

kl_divergence(pmf_model: ndarray, pmf_target: ndarray) → ndarray

This function calculates the Kullback-Leibler divergence, that is used as a loss function.

Parameters:

pmf_model -- Probability mass function generated by the quantum circuit
pmf_target -- Probability mass function of the target distribution

Returns:

Kullback-Leibler divergence

mmd(pmf_model: ndarray, pmf_target: ndarray) → ndarray

This function calculates the maximum mean discrepancy, that is used as a loss function.

Parameters:

pmf_model -- Probability mass function generated by the quantum circuit
pmf_target -- Probability mass function of the target distribution

Returns:

Maximum mean discrepancy

nll(pmf_model: ndarray, pmf_target: ndarray) → ndarray

This function calculates th negative log likelihood, that is used as a loss function.

Parameters:

pmf_model -- Probability mass function generated by the quantum circuit
pmf_target -- Probability mass function of the target distribution

Returns:

Negative log likelihood

postprocess(input_data: dict, config: dict, **kwargs) → tuple[dict, float]

Perform the actual training of the machine learning model.

Parameters:

input_data -- Collected information of the benchmarking process
config -- Training settings
kwargs -- Optional additional arguments

Returns:

Training results and the postprocessing time

preprocess(input_data: any, config: dict, **kwargs) → tuple[any, float]

Essential method for the benchmarking process. This is always executed before traversing down to the next module, passing the data returned by this function.

Parameters:

input_data -- Data for the module, comes from the parent module if that exists
config -- Config for the module
kwargs -- Optional keyword arguments

Returns:

The output of the preprocessing and the time it took to preprocess

sample_from_pmf(pmf: ndarray, n_shots: int) → ndarray

This function samples from the probability mass function generated by the quantum circuit.

Parameters:

pmf -- Probability mass function generated by the quantum circuit
n_shots -- Number of shots

Returns:

Number of counts in the 2**n_qubits bins

start_training(input_data: dict, config: Config, **kwargs: dict) → dict

Method that uses a pretrained model for inference.

Parameters:

input_data -- Dictionary with information needed for inference
config -- Inference settings
kwargs -- Optional additional arguments

Returns:

Dictionary including the information of previous modules as well as of this module