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The widespread use of mobile networking devices, such as smart phones and tablets, has substantially increased the number of nodes in the operational networks. These devices often suffer from the lack of power and bandwidth. Hence, we have to optimize their message routing for the sake of maximizing their capabilities. However, the optimal routing typically relies on a delicate balance of diverse and often conflicting objectives, such as the route's delay and power consumption. The network design also has to consider the nodes' user-centric social behavior. Hence, the employment of socially aware load balancing becomes imperative for avoiding the potential formation of bottlenecks in the network's packet-flow. In this paper, we propose a novel algorithm, referred to as the multi-objective decomposition quantum optimization (MODQO) algorithm, which exploits the quantum parallelism to its full potential by reducing the database correlations for performing multi-objective routing optimization, while at the same time balancing the teletraffic load among the nodes without imposing a substantial degradation on the network's delay and power consumption. Furthermore, we introduce a novel socially aware load balancing metric, namely, the normalized entropy of the normalized composite betweenness of the associated socially aware network, for striking a better tradeoff between the network's delay and power consumption. We analytically prove that the MODQO algorithm achieves the full-search based accuracy at a significantly reduced complexity, which is several orders of magnitude lower than that of the full search. Finally, we compare the MODQO algorithm to the classic non-dominated sort genetic algorithm II evolutionary algorithm and demonstrate that the MODQO succeeds in halving the network's average delay, while simultaneously reducing the network's average power consumption by 6 dB without increasing the computational complexity.

Routing in wireless multihop networks (WMHNs) relies on a delicate balance of diverse and often conflicting parameters, when aiming for maximizing the WMHN performance. Classified as a non-deterministic polynomial-time hard problem, routing in WMHNs requires sophisticated methods. As a benefit of observing numerous variables in parallel, quantum computing offers a promising range of algorithms for complexity reduction by exploiting the principle of quantum parallelism (QP), while achieving the optimum full-search-based performance. In fact, the so-called non-dominated quantum optimization (NDQO) algorithm has been proposed for addressing the multiobjective routing problem with the goal of achieving a near-optimal performance, while imposing a complexity of the order of O(N) and O(N√N) in the best and worst case scenarios, respectively. However, as the number of nodes in the WMHN increases, the total number of routes increases exponentially, making its employment infeasible despite the complexity reduction offered. Therefore, we propose a novel optimal quantum-assisted algorithm, namely, the non-dominated quantum iterative optimization (NDQIO) algorithm, which exploits the synergy between the hardware and the QP for the sake of achieving a further complexity reduction, which is on the order of O(√N) and O(N√N) in the best and worst case scenarios, respectively. In addition, we provide simulation results for demonstrating that our NDQIO algorithm achieves an average complexity reduction of almost an order of magnitude compared with the near-optimal NDQO algorithm, while having the same order of power consumption.