Tackling Traveling Salesman Problems with a Cluster-Based Quantum-Classical Optimization
Saweed Hussain, Lutfiana Sausan, Abdur Rahman +2 more
A hybrid quantum-classical framework is exploited to solve the Traveling Salesman Problem (TSP) based on a Quantum Approximate Optimization Algorithm (QAOA). To overcome the computational limitation of the Grover’s Search Algorithm (GSA) stemming from path encoding and arithmetic subroutines, a clustering-based strategy is employed to decompose a 16-city TSP into four 4-city subproblems, each of which can be solved with a 16-qubit budget. Intra-cluster solutions are optimized with QAOA, and are classically stitched to construct the global tour. Our results clearly validate the effectiveness of the hierarchical design of the algorithm, highlighting its feasibility for mid-scale quantum optimizations.
International Conference on Information and Communication Technology Convergence (ICTC)•Oct 14, 2025•0 citations