Quantum-Aided Multi-Objective Routing Optimization Using Back-Tracing-Aided Dynamic Programming

Pareto optimality is capable of striking the optimal tradeoff amongst the diverse conflicting quality-of-service requirements of routing in wireless multihop networks. However, this comes at the cost of increased complexity owing to searching through the extended multiobjective search-space. We will demonstrate that the powerful quantum-assisted dynamic programming optimization framework is capable of circumventing this problem. In this context, the so-called evolutionary quantum Pareto optimization (EQPO) algorithm has been proposed, which is capable of identifying most of the optimal routes at a near-polynomial complexity versus the number of nodes. As a benefit, we improve both the EQPO algorithms by introducing a back-tracing process. We also demonstrate that the improved algorithm, namely the back-tracing-aided EQPO algorithm, imposes a negligible complexity overhead, while substantially improving our performance metrics, namely the relative frequency of finding all Pareto-optimal solutions and the probability that the Pareto-optimal solutions are, indeed, a part of the optimal Pareto front.