quantum_launcher.problems.problem_formulations.hamiltonians.tsp

Summary

Functions:

make_connection_hamiltonian	Creates a Hamiltonian that represents the costs of picking each path.
make_non_collision_hamiltonian	Creates a Hamiltonian representing constraints for the TSP problem.
problem_to_hamiltonian	Creates a Hamiltonian for the TSP problem.

Reference

quantum_launcher.problems.problem_formulations.hamiltonians.tsp.make_non_collision_hamiltonian(node_count: int, quadratic=False)

Creates a Hamiltonian representing constraints for the TSP problem. (Each node visited only once, one node per timestep) Qubit mapping: [step1:[node1, node2,… noden]],[step2],…[stepn]

Parameters:

• node_count (int) – Number of nodes in the TSP problem

• quadratic – Whether to encode as a QUBO problem

Returns:

Hamiltonian representing the constraints

Return type:

np.ndarray

quantum_launcher.problems.problem_formulations.hamiltonians.tsp.make_connection_hamiltonian(edge_costs: ndarray, return_to_start: bool = True) → ndarray

Creates a Hamiltonian that represents the costs of picking each path.

Parameters:

tsp_matrix – Edge cost matrix of the TSP problem

Returns:

Optimal chain of nodes to visit

Return type:

np.ndarray

quantum_launcher.problems.problem_formulations.hamiltonians.tsp.problem_to_hamiltonian(problem: TSP, constraints_weight: int = 5, costs_weight: int = 1, return_to_start: bool = True, onehot: Literal['exact', 'quadratic'] = 'exact') → ndarray

Creates a Hamiltonian for the TSP problem.

Parameters:

• problem (TSP) – 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