modules.applications.optimization.tsp.mappings.ising.Ising

class Ising

Bases: Mapping

Ising formulation for the TSP.

__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)

Get 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_parameter_options()

Returns the configurable settings for this mapping.

get_requirements()

Return requirements of this module.

get_submodule(option)

Submodule is instantiated according to the information given in self.sub_options.

map(problem, config)

Maps the networkx graph to an Ising formulation.

postprocess(input_data, config, **kwargs)

Reverse transformation/mapping from the submodule's format to the mathematical formulation suitable for the parent module.

preprocess(input_data, config, **kwargs)

Maps the data to the correct target format.

reverse_map(solution)

Maps the solution back to the representation needed by the TSP class for validation/evaluation.
class Config

Bases: TypedDict

Attributes of a valid config.

lagrange_factor: float
mapping: 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
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

Get the default submodule based on the given option.

Parameters:

option -- Submodule option

Returns:

Corresponding 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_parameter_options() → dict

Returns the configurable settings for this mapping.

Returns:

Dictionary containing parameter options.

return {
        "lagrange_factor": {
            "values": [0.75, 1.0, 1.25],
            "description": "By which factor would you like to multiply your lagrange?"
        },
        "mapping": {
            "values": ["ocean", "qiskit"],
            "description": "Which Ising formulation of the TSP problem should be used?"
        }
    }
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

map(problem: Graph, config: Config) → tuple[dict, float]

Maps the networkx graph to an Ising formulation.

Parameters:

  • problem -- Networkx graph

  • config -- Config with the parameters specified in Config class

Returns:

Dict with Ising, time it took to map it

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

Reverse transformation/mapping from the submodule's format to the mathematical formulation suitable for the parent module.

Parameters:

  • input_data -- Data which should be reverse-mapped

  • config -- Config of the reverse mapping

  • kwargs -- Optional keyword arguments

Returns:

Tuple with reverse-mapped problem and the time it took to map it

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

Maps the data to the correct target format.

Parameters:

  • input_data -- Data which should be mapped

  • config -- Config of the mapping

  • kwargs -- Optional keyword arguments

Returns:

Tuple with mapped problem and the time it took to map it

reverse_map(solution: any) → tuple[dict, float]

Maps the solution back to the representation needed by the TSP class for validation/evaluation.

Parameters:

solution -- List or array containing the solution

Returns:

Solution mapped accordingly, time it took to map it