qiskit.optimization.applications.ising.vehicle_routing¶

Converts vehicle routing instances into a list of Paulis, and provides some related routines (extracting a solution, checking its objective function value).

Functions

`get_operator`(instance, n, K)	Converts an instance of a vehicle routing problem into a list of Paulis.
`get_vehiclerouting_cost`(instance, n, K, x_sol)	Computes the cost of a solution to an instance of a vehicle routing problem.
`get_vehiclerouting_matrices`(instance, n, K)	Constructs auxiliary matrices from a vehicle routing instance,
`get_vehiclerouting_solution`(instance, n, K, …)	Tries to obtain a feasible solution (in vector form) of an instance

get_operator(instance, n, K)[source]¶

Converts an instance of a vehicle routing problem into a list of Paulis.

Parameters

instance (ndarray) – a customers-to-customers distance matrix.
n (int) – the number of customers.
K (int) – the number of vehicles available.

Return type

WeightedPauliOperator

Returns

operator for the Hamiltonian.

get_vehiclerouting_cost(instance, n, K, x_sol)[source]¶

Computes the cost of a solution to an instance of a vehicle routing problem.

Parameters

instance (ndarray) – a customers-to-customers distance matrix.
n (int) – the number of customers.
K (int) – the number of vehicles available.
x_sol (ndarray) – a solution, i.e., a path, in its binary representation.

Return type

float

Returns

objective function value.

get_vehiclerouting_matrices(instance, n, K)[source]¶

Constructs auxiliary matrices from a vehicle routing instance,: which represent the encoding into a binary quadratic program. This is used in the construction of the qubit ops and computation of the solution cost.

Parameters

instance (ndarray) – a customers-to-customers distance matrix.
n (int) – the number of customers.
K (int) – the number of vehicles available.

Return type

Tuple[ndarray, ndarray, float]

Returns

a matrix defining the interactions between variables. a matrix defining the contribution from the individual variables. the constant offset.

get_vehiclerouting_solution(instance, n, K, result)[source]¶

Tries to obtain a feasible solution (in vector form) of an instance: of vehicle routing from the results dictionary.

Parameters

instance (ndarray) – a customers-to-customers distance matrix.
n (int) – the number of customers.
K (int) – the number of vehicles available.
result (MinimumEigensolverResult) – a result obtained by QAOA.run or VQE.run.

Return type

List[int]

Returns

a solution, i.e., a path, in its binary representation.

#TODO: support statevector simulation, results should be a statevector or counts format, not: a result from algorithm run