  In this paper, we describe the usage of the Quantum Approximate Optimization
Algorithm (QAOA), which is a quantum-classical heuristic, to solve a
combinatorial optimization and integer programming task known as Vehicle
Routing Problem (VRP). We outline the Ising formulation for VRP and present a
detailed procedure to solve VRP by minimizing its simulated Ising Hamiltonian
using the IBM Qiskit platform. Here, we attempt to find solutions for the VRP
problems: (4,2), (5,2), and (5,3), where each (n, k) represents a VRP problem
with n locations and k vehicles. We find that the performance of QAOA is not
just dependent upon the classical optimizer used, the number of steps p in
which an adiabatic path is realized, or the way parameters are initialized, but
also on the problem instance itself.
