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

TL;DR

This paper introduces a quantum-assisted dynamic programming framework for multi-objective routing in wireless networks, significantly improving the efficiency and accuracy of finding Pareto-optimal solutions.

Contribution

It proposes the BTA-EQPO algorithm, enhancing the EQPO method with back-tracing to better identify Pareto-optimal routes with minimal added complexity.

Findings

BTA-EQPO increases the frequency of finding all Pareto solutions.

It improves the probability of solutions being on the true Pareto front.

The algorithm operates with near-polynomial complexity.

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