modules.applications.qml.generative_modeling.training.training_generative.TrainingGenerative

class TrainingGenerative(name: str)

Bases: Core, Training, ABC

The Training module is the base class fot both finding (QCBM) and executing trained models (Inference).

__init__(name: str)

Constructor method.

Parameters:

name -- Name of the training instance

Methods

__init__(name)

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)

Given an option string by the user, this returns a submodule.

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 parameters for a given module.

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)

This function starts the training of QML model or deploys a pretrained model.
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

abstract get_default_submodule(option: str) → Core

Given an option string by the user, this returns a submodule.

Parameters:

option -- String with the chosen submodule

Returns:

Module of type Core

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

abstract get_parameter_options() → dict

Returns the parameters for a given module.

Should always be in this format:

{
   "parameter_name":{
      "values":[1, 2, 3],
      "description":"How many nodes do you need?"
   },
    "parameter_name_2":{
      "values":["x", "y"],
      "description":"Which type of problem do you want?"
   }
}
Returns:

Available settings for this application

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

abstract start_training(input_data: dict, config: any, **kwargs: dict) → dict

This function starts the training of QML model or deploys a pretrained model.

Parameters:

  • input_data -- A representation of the quantum machine learning model that will be trained

  • config -- Config specifying the parameters of the training (dict-like Config type defined in children)

  • kwargs -- Optional additional settings

Returns:

Solution, the time it took to compute it and some optional additional information