Quantum-aided multi-objective routing optimization using back-tracing-aided dynamic programming

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 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.

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Identifiers

ORCID for Dimitrios Alanis: orcid.org/0000-0002-6654-1702

ORCID for Zunaira Babar: orcid.org/0000-0002-7498-4474

ORCID for Hung Viet Nguyen: orcid.org/0000-0001-6349-1044

ORCID for Daryus Chandra: orcid.org/0000-0003-2406-7229

ORCID for Soon Ng: orcid.org/0000-0002-0930-7194

ORCID for Lajos Hanzo: orcid.org/0000-0002-2636-5214

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