qiskit.optimization.applications.ising.common

common module

Functions

get_gset_result(x)	Get graph solution in Gset format from binary string.
parse_gset_format(filename)	Read graph in Gset format from file.
random_graph(n[, weight_range, edge_prob, …])	Generate random Erdos-Renyi graph.
random_number_list(n[, weight_range, …])	Generate a set of positive integers within the given range.
read_numbers_from_file(filename)	Read numbers from a file
sample_most_likely(state_vector)	Compute the most likely binary string from state vector.

get_gset_result(x)

Get graph solution in Gset format from binary string.

매개변수

x (numpy.ndarray) – binary string as numpy array.

반환값

graph solution in Gset format.

반환 형식

Dict[int, int]

parse_gset_format(filename)

Read graph in Gset format from file.

매개변수

filename (str) – name of the file.

반환값

adjacency matrix as a 2D numpy array.

반환 형식

numpy.ndarray

random_graph(n, weight_range=10, edge_prob=0.3, negative_weight=True, savefile=None, seed=None)

Generate random Erdos-Renyi graph.

매개변수

• n (int) – number of nodes.

• weight_range (int) – weights will be smaller than this value, in absolute value. range: [1, weight_range).

• edge_prob (float) – probability of edge appearing.

• negative_weight (bool) – allow to have edge with negative weights

• savefile (str or None) – name of file where to save graph.

• seed (int or None) – random seed - if None, will not initialize.

반환값

adjacency matrix (with weights).

반환 형식

numpy.ndarray

random_number_list(n, weight_range=100, savefile=None, seed=None)

Generate a set of positive integers within the given range.

매개변수

• n (int) – size of the set of numbers.

• weight_range (int) – maximum absolute value of the numbers.

• savefile (str or None) – write numbers to this file.

• seed (Union(int,None)) – random seed - if None, will not initialize.

반환값

the list of integer numbers.

반환 형식

numpy.ndarray

read_numbers_from_file(filename)

Read numbers from a file

매개변수

filename (str) – name of the file.

반환값

list of numbers as a numpy.ndarray.

반환 형식

numpy.ndarray

sample_most_likely(state_vector)

Compute the most likely binary string from state vector. :param state_vector: state vector or counts. :type state_vector: numpy.ndarray or dict

반환값

binary string as numpy.ndarray of ints.

반환 형식

numpy.ndarray