modules.applications.qml.generative_modeling.mappings.preset_qiskit_noisy_backend.PresetQiskitNoisyBackend

class PresetQiskitNoisyBackend

Bases: LibraryGenerative

This module maps a library-agnostic gate sequence to a qiskit circuit.

__init__(): Constructor method.

Methods

`__init__`()	Constructor method.
`configure_backend`(backend, device, ...)	This method configures the backend with the specified device and simulation method.
`decompile_noisy_config`(config_dict, num_qubits)	This method processes a configuration dictionary.
`get_FakeBackend`(noise_configuration, num_qubits)	This method returns a fake backend based on the provided noise configuration and number of qubits.
`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 given 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 qiskit 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 Qiskit Library.
`get_requirements`()	Returns requirements of this module.
`get_simulation_method_and_device`(device, ...)	This method determines the simulation method and device based on the provided configuration.
`get_submodule`(option)	Submodule is instantiated according to the information given in self.sub_options.
`get_transpile_routine`(transpile_config)	This method returns the transpile routine based on the provided configuration.
`log_backend_info`(backend)
`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.
`select_backend_configuration`(...)	This method selects the backend configuration based on the provided noise configuration.
`sequence_to_circuit`(input_data)	Maps the gate sequence, that specifies the architecture of a quantum circuit to its Qiskit implementation.
`split_string`(s)

Attributes

circuit_transpiled

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

configure_backend(backend: Backend, device: str, simulation_method: str) → None

This method configures the backend with the specified device and simulation method.

Parameters:

backend -- Backend to be configured
device -- Device type (CPU/GPU)
simulation_method -- Simulation method

decompile_noisy_config(config_dict: dict, num_qubits: int) → Backend

This method processes a configuration dictionary. If a custom noise configuration is specified, it creates a custom backend configuration; otherwise, it defaults to the 'aer_simulator' backend. It returns the configured backend.

Parameters:

config_dict -- Contains information about config
num_qubits -- Number of qubits

Returns:

Configured qiskit backend

get_FakeBackend(noise_configuration: str, num_qubits: int) → Backend

This method returns a fake backend based on the provided noise configuration and number of qubits.

Parameters:

noise_configuration -- Noise configuration type
num_qubits -- Number of qubits

Returns:

Fake backend simulator

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 | Inference

Returns the default submodule based on the given option.

Parameters:: option -- The submodule option to select
Returns:: Instance of 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

get_execute_circuit(circuit: QuantumCircuit, backend: Backend, config: str, config_dict: dict) → tuple[any, any]

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

Parameters:

circuit -- Qiskit implementation of the quantum circuit
backend -- Configured qiskit backend
config -- Name of a backend
config_dict -- Contains information about config

Returns:

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

transpiled circuit

get_parameter_options() → dict

Returns the configurable settings for the Qiskit Library.

return:: Dictionary with configurable settings.

{
"backend": {
    "values": ["aer_simulator_gpu", "aer_simulator_cpu"],
    "description": "Which backend do you want to use? "
                   "In the NoisyQiskit Module only aer_simulators can be used."
},

"simulation_method": {
    "values": ["automatic", "statevector", "density_matrix", "cpu_mps"],  # TODO Change names
    "description": "What simulation methode should be used"
},

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

"transpile_optimization_level": {
    "values": [1, 2, 3, 0],
    "description": "Switch between different optimization levels in the Qiskit transpile routine. "
                   "1: light optimization, 2: heavy optimization, 3: even heavier optimization, "
                   "0: no optimization. Level 1 recommended as standard option."
},

"noise_configuration": {
    "values": value_list,
    "description": "What noise configuration do you want to use?"
}

}

static get_requirements() → list[dict]

Returns requirements of this module.

Returns:: List of dict with requirements of this module

get_simulation_method_and_device(device: str, simulation_config: str) → tuple[str, str]

This method determines the simulation method and device based on the provided configuration.

Parameters:

device -- Contains information about processing unit
simulation_config -- Contains information about qiskit simulation method

Returns:

Tuple containing the simulation method and device

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

get_transpile_routine(transpile_config: int) → int

This method returns the transpile routine based on the provided configuration.

Parameters:: transpile_config -- Configuration for transpile routine
Returns:: Transpile routine level

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) → Backend

This method configures the backend.

Parameters:

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

Returns:

Configured qiskit backend

select_backend_configuration(noise_configuration: str, num_qubits: int) → Backend

This method selects the backend configuration based on the provided noise configuration.

Parameters:

noise_configuration -- Noise configuration type
num_qubits -- Number of qubits

Returns:

Selected backend configuration

sequence_to_circuit(input_data: dict) → dict

Maps the gate sequence, that specifies the architecture of a quantum circuit to its Qiskit implementation.

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