modules.applications.qml.generative_modeling.mappings.library_pennylane.LibraryPennylane

class LibraryPennylane

Bases: LibraryGenerative

__init__()

Constructor method.

Parameters:: name -- Name of the model

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)	Returns the default submodule based on the provided option.
`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_execute_circuit`(circuit, backend, ...)	This method combines the PennyLane circuit implementation and the selected backend and returns a function that will be called during training.
`get_parameter_options`()	Returns the configurable settings for the PennyLane Library.
`get_requirements`()	Returns requirements of this module.
`get_submodule`(option)	Submodule is instantiated according to the information given in self.sub_options.
`postprocess`(input_data, config, **kwargs)	This method corresponds to the identity and passes the information of the subsequent module back to the preceding module in the benchmarking process.
`preprocess`(input_data, config, **kwargs)	Base class for mapping the gate sequence to a library such as Qiskit.
`select_backend`(config, n_qubits)	This method configures the backend.
`sequence_to_circuit`(input_data)	Method that maps the gate sequence, that specifies the architecture of a quantum circuit to its PennyLane implementation.

class Config

Bases: TypedDict

Attributes of a valid config.

backend: str
n_shots: int

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

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) → QCBM | QGAN | Inference

Returns the default submodule based on the provided option.

Parameters:: option -- The option to select the submodule
Returns:: The selected submodule
Raises:: NotImplemented -- If the provided option is not implemented

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

static get_execute_circuit(circuit: callable, backend: device, config: str, config_dict: dict) → tuple[any, any]

This method combines the PennyLane circuit implementation and the selected backend and returns a function that will be called during training.

Parameters:

circuit -- PennyLane implementation of the quantum circuit
backend -- Configured qiskit backend
config -- Name of a backend
config_dict -- Dictionary including the number of shots

Returns:

Tuple that contains a method that executes the quantum circuit for a given set of parameters twice

get_parameter_options() → dict

Returns the configurable settings for the PennyLane Library.

Returns:: Dictionary with configurable settings.

return {
    "backend": {
        "values": ["default.qubit", "default.qubit.jax", "lightning.qubit", "lightning.gpu"],
        "description": "Which backend do you want to use?"
    },

    "n_shots": {
        "values": [100, 1000, 10000, 1000000],
        "description": "How many shots do you want use for estimating the PMF of the 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

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

This method corresponds to the identity and passes the information of the subsequent module back to the preceding module in the benchmarking process.

Parameters:

input_data -- Collected information of the benchmarking procesS
config -- Config specifying the number of qubits of the circuit
kwargs -- Optional keyword arguments

Returns:

Tuple with input dictionary and the computation time of the function

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

Base class for mapping the gate sequence to a library such as Qiskit.

Parameters:

input_data -- Collection of information from the previous modules
config -- Config specifying the number of qubits of the circuit
kwargs -- Optional keyword arguments

Returns:

Tuple including dictionary with the function to execute the quantum circuit on a simulator or quantum hardware and the computation time of the function

static select_backend(config: str, n_qubits: int) → any

This method configures the backend.

Parameters:

config -- Name of a backend
n_qubits -- Number of qubits

Returns:

Configured backend

sequence_to_circuit(input_data: dict) → dict

Method that maps the gate sequence, that specifies the architecture of a quantum circuit to its PennyLane implementation.

Parameters:: input_data -- Collected information of the benchmarking process
Returns:: Same dictionary but the gate sequence is replaced by its PennyLane implementation