Optimization of the Hamiltonian Formulation for the Capacitated Vehicle Routing Problem

TL;DRAbstract

Logistics is a promising industry that has proliferated over the years. With growing\noperation sizes, the challenges in the supply chain have become more complex. Even\nminor improvements over the existing delivery routes can help companies significantly\ncut expenses. Current classical computers cannot optimally solve today’s real-world\napplications, which created a commercial interest in quantum computing technologies\nthat are potentially more capable than classical methods for these challenges. This\nMaster’s Thesis studies whether this interest is justified by solving Capacitated Vehicle\nRouting Problem (CVRP) with Quantum Approximate Optimization Algorithm (QAOA),\na quantum computing algorithm designed to solve combinatorial optimization problems.\nThe aim is to optimize the Hamiltonian formulation of CVRP, whose ground state\nencodes the optimal solution. This paper finds QAOA with its Ansatz variant in a\ncolumn generation setting as a viable solution that scales well with

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