modules.solvers.qaoa.QAOA

class QAOA

Bases: Solver

QAOA with some parts copied/derived from https://github.com/aws/amazon-braket-examples.

__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)	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_parameter_options`()	Returns the configurable settings for this solver.
`get_requirements`()	Return requirements of this module.
`get_submodule`(option)	Submodule is instantiated according to the information given in self.sub_options.
`postprocess`(input_data, config, **kwargs)	The actual solving process is done here, using the device which is provided by the device submodule and the problem data provided by the parent module.
`preprocess`(input_data, config, **kwargs)	Essential method for the benchmarking process.
`run`(mapped_problem, device_wrapper, config, ...)	Run QAOA algorithm on Ising.

class Config

Bases: TypedDict

Attributes of a valid config.

shots: int
opt_method: str
depth: 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) → Core

Returns the default submodule based on the provided option.

Parameters:: option -- The name of the submodule
Returns:: Instance of the default submodule

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 solver.

Returns:: Dictionary of parameter settings

return {
        "shots": {  # number measurements to make on circuit
            "values": list(range(10, 500, 30)),
            "description": "How many shots do you need?"
        },
        "opt_method": {
            "values": ["Powell", "Nelder-Mead"],
            "description": "Which optimization method do you want?"
        },
        "depth": {
            "values": [3],
            "description": "Which circuit depth for QAOA do you want?"
        }
    }

static get_requirements() → list[dict]

Return 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: any, config: dict, **kwargs) → tuple[any, float]

The actual solving process is done here, using the device which is provided by the device submodule and the problem data provided by the parent module.

Parameters:

input_data -- Data passed to the run function of the solver
config -- Solver config
kwargs -- Optional keyword arguments

Returns:

Output and time needed

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

run(mapped_problem: dict, device_wrapper: any, config: Config, **kwargs: dict) → tuple[any, float, dict]

Run QAOA algorithm on Ising.

Parameters:

mapped_problem -- Dict containing problem parameters mapped to the Ising model
device_wrapper -- Instance of device
config -- Solver configuration settings
kwargs -- No additionally settings needed

Returns:

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