Maxcut¶

class Maxcut(graph)[source]¶

Optimization application for the “max-cut” [1] problem based on a NetworkX graph.

References

Parameters:: graph (nx.Graph | np.ndarray | list) – A graph representing a problem. It can be specified directly as a NetworkX graph, or as an array or list format suitable to build out a NetworkX graph.

Attributes

graph¶

Getter of the graph

Methods

draw(result=None, pos=None)¶

Draw a graph with the result. When the result is None, draw an original graph without colors.

Parameters:

result (OptimizationResult | np.ndarray | None) – The calculated result for the problem
pos (dict[int, np.ndarray] | None) – The positions of nodes

static get_gset_result(x)[source]¶

Get graph solution in Gset format from binary string.

interpret(result)[source]¶

Interpret a result as two lists of node indices

Parameters:: result (OptimizationResult | np.ndarray) – The calculated result of the problem
Returns:: Two lists of node indices correspond to two node sets for the Max-cut
Return type:: list[list[int]]

static parse_gset_format(filename)[source]¶

Read graph in Gset format from file.

static sample_most_likely(state_vector)¶

Compute the most likely binary string from state vector.

Parameters:: state_vector (QuasiDistribution | Statevector | np.ndarray | dict) – state vector or counts or quasi-probabilities.
Returns:: binary string as numpy.ndarray of ints.
Raises:: ValueError – if state_vector is not QuasiDistribution, Statevector, np.ndarray, or dict.
Return type:: np.ndarray

to_quadratic_program()[source]¶

Convert a Max-cut problem instance into a QuadraticProgram