Dataset for the paper "Quantum-aided Multi-Objective Routing Optimization Using Back-Tracing-Aided Dynamic Programming".

Dimitrios Alanis, Panagiotis Botsinis, Zunaira Babar, Hung Viet Nguyen, Daryus Chandra,  Soon Xin Ng, Lajos Hanzo.

IEEE Access (accepted). 

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.

Acknowledgements: The financial support of the European Research Council under the Advanced Fellow Grant, that of the Royal Society’s Wolfson Research Merit Award and that of the Engineering and Physical Sciences Research Council under Grant EP/L018659/1 is gratefully acknowledged. The use of the IRIDIS High Performance Computing Facility at the University of Southampton is also acknowledged.

* Fig 1 of the paper is located at ./Fig-1/Fig1.eps and may be reproduced from ./Fig-1/Fig-1.gle. The respective dataset is located at ./Fig-1/datasets directory.
* Fig 3a of the paper is located at ./Fig-4/Fig-4a.eps and may be reproduced from ./Fig-3/Fig-3a.gle. The respective dataset is located at ./Fig-3/datasets/ directory.
* Fig 3b of the paper is located at ./Fig-4/Fig-4b.eps and may be reproduced from ./Fig-3/Fig-3b.gle. The respective dataset is located at ./Fig-3/datasets/ directory.
* Fig 4a of the paper is located at ./Fig-5/Fig-5a.eps and may be reproduced from ./Fig-4/Fig-4a.gle. The respective dataset is located at ./Fig-4/datasets/ directory.
* Fig 4b of the paper is located at ./Fig-5/Fig-5b.eps and may be reproduced from ./Fig-4/Fig-4b.gle. The respective dataset is located at ./Fig-4/datasets/ directory.
* Fig 4c of the paper is located at ./Fig-5/Fig-5c.eps and may be reproduced from ./Fig-4/Fig-4c.gle. The respective dataset is located at ./Fig-4/datasets/ directory.
* Fig 4d of the paper is located at ./Fig-5/Fig-5d.eps and may be reproduced from ./Fig-4/Fig-4d.gle. The respective dataset is located at ./Fig-4/datasets/ directory.
