quantum_launcher.base.base

Summary

Classes:

Algorithm	Abstract class for Algorithms.
Backend	Abstract class representing a backend for quantum computing.
Problem	Abstract class for defining Problems.
Result	Result(best_bitstring: str, best_energy: float, most_common_bitstring: str, most_common_bitstring_energy: float, distribution: dict, energies: dict, num_of_samples: int, average_energy: float, energy_std: float, result: Any)

Reference

class quantum_launcher.base.base.Result(best_bitstring: str, best_energy: float, most_common_bitstring: str, most_common_bitstring_energy: float, distribution: dict, energies: dict, num_of_samples: int, average_energy: float, energy_std: float, result: Any)

Bases: object

best_bitstring: str

best_energy: float

most_common_bitstring: str

most_common_bitstring_energy: float

distribution: dict

energies: dict

num_of_samples: int

average_energy: float

energy_std: float

result: Any

best()

most_common()

static from_distributions(bitstring_distribution: dict[str, float], energy_distribution: dict[str, float], result: Any = None) → Result

Constructs the Result object from Dictionary with bitstring to num of occurrences, dictionary mapping bitstring to energy and optional result (rest)

class quantum_launcher.base.base.Backend(name: str, parameters: list | None = None)

Bases: object

Abstract class representing a backend for quantum computing.

name

The name of the backend.

Type:

str

path

The path to the backend (optional).

Type:

str | None

parameters

A list of parameters for the backend (optional).

Type:

list

set_logger(logger: Logger)

class quantum_launcher.base.base.Problem(instance: Any, instance_name: str = 'unnamed')

Bases: ABC

Abstract class for defining Problems.

variant

The variant of the problem. The default variant is “Optimization”.

Type:

str

path

The path to the problem.

Type:

str | None

name

The name of the problem.

Type:

str

instance_name

The name of the instance.

Type:

str

instance

An instance of the problem.

Type:

any

classmethod from_file(path: str) → P

static from_preset(instance_name: str, **kwargs)

read_result(exp, log_path)

Reads a result from a file.

Parameters:

• exp – The experiment.

• log_path – The path to the log file.

Returns:

The result.

analyze_result(result)

Analyzes the result.

Parameters:

result – The result.

class quantum_launcher.base.base.Algorithm(**alg_kwargs)

Bases: ABC

Abstract class for Algorithms.

name

The name of the algorithm, derived from the class name in lowercase.

Type:

str

path

The path to the algorithm, if applicable.

Type:

str | None

parameters

A list of parameters for the algorithm.

Type:

list

alg_kwargs

Additional keyword arguments for the algorithm.

Type:

dict

Abstract methods:

__init__(self, **alg_kwargs): Initializes the Algorithm object. _get_path(self) -> str: Returns the common path for the algorithm. run(self, problem: Problem, backend: Backend): Runs the algorithm on a specific problem using a backend.

parse_result_to_json(o: object) → dict

Parses results so that they can be saved as a JSON file.

Parameters:

o (object) – The result object to be parsed.

Returns:

The parsed result as a dictionary.

Return type:

dict

abstractmethod run(problem: Problem, backend: Backend, formatter: Callable | None = None) → Result

Runs the algorithm on a specific problem using a backend.

Parameters:

• problem (Problem) – The problem to be solved.

• backend (Backend) – The backend to be used for execution.