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

Datasets for plotting the performance figures of the paper:Alanis, D, Botsinis, P, Babar, Z, Nguyen, HV, Chandra, D, Ng, S & Hanzo, L (2018), 'Quantum-aided multi-objective routing optimization using back-tracing-aided dynamic programming' IEEE Transactions on Vehicular Technology.
DOI: 10.1109/TVT.2018.2822626
Results may reproduced using GLE graphics. Abstract: Pareto optimality is capable of striking the optimal trade-off amongst the diverse conflicting QoS requirements of routing in wireless multihop networks. However, this comes at the cost of increased complexity owing to searching through the extended multi-objective 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 the EQPO algorithm by introducing a back-tracing process. We also demonstrate that the improved algorithm, namely the Back-Tracing-Aided EQPO~(BTA-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 part of the optimal Pareto front.