qlauncher.problems.optimization.tsp

qlauncher.problems.optimization.tsp#

Summary#

Classes:

TSP

Traveling Salesman Problem (TSP) definition.

Reference#

class qlauncher.problems.optimization.tsp.TSP(instance: Graph, instance_name: str = 'unnamed')[source]#

Bases: Problem

Traveling Salesman Problem (TSP) definition.

property setup: dict#
visualize(solution: SamplingMinimumEigensolverResult | str | list[int] | None = None) → None[source]#
static from_preset(instance_name: Literal['default'] = 'default', **kwargs) → TSP[source]#

Generate TSP instance from a preset name.

Parameters:

  • instance_name (str) – Name of the preset instance

  • quadratic (bool, optional) – Whether to use quadratic constraints. Defaults to False

Returns:

TSP instance

Return type:

TSP

static generate_tsp_instance(num_vertices: int, min_distance: float = 1.0, max_distance: float = 10.0, **kwargs) → TSP[source]#

Generate a random TSP instance.

Parameters:

  • num_vertices (int) – Number of vertices in the graph

  • min_distance (float, optional) – Minimum distance between vertices. Defaults to 1.0

  • max_distance (float, optional) – Maximum distance between vertices. Defaults to 10.0

  • quadratic (bool, optional) – Whether to use quadratic constraints. Defaults to False

Returns:

TSP instance

Return type:

TSP

to_hamiltonian(constraints_weight: int = 5, costs_weight: int = 1, return_to_start: bool = True, onehot: Literal['exact', 'quadratic'] = 'exact') → Hamiltonian[source]#

Creates a Hamiltonian for the TSP problem.

Parameters:

  • problem – TSP problem instance

  • quadratic – Whether to encode as a quadratic Hamiltonian

  • constraints_weight (int) – Weight of the constraints in the Hamiltonian

  • costs_weight (int) – Weight of the costs in the Hamiltonian

Returns:

Hamiltonian representing the TSP problem

Return type:

np.ndarray

to_qubo(constraints_weight: int = 5, costs_weight: int = 1, return_to_start: bool = True) → QUBO[source]#