Communication networks are managed more and more by using artificial intelligence. Anomaly detection, network monitoring and user behaviour are areas where machine learning offers advantages over more traditional methods. However, computer power is increasingly becoming a limiting factor in machine learning tasks. The rise of quantum computers may be helpful here, especially where machine learning is one of the areas where quantum computers are expected to bring an advantage. This paper proposes and evaluates three approaches for using quantum machine learning for a specific task in mobile networks: indoor–outdoor detection. Where current quantum computers are still limited in scale, we show the potential the approaches have when larger systems become available.

Model‐based acoustic localisation estimates the locations of underwater objects by comparing sensor measurements with model predictions. To obtain high quality predictions, propagation models need to be run for a large set of environmental parameters. However, real‐time Model‐based acoustic localisation estimations using onboard computational resources are often limited. To address this, the authors propose a Quantum annealing (QA) algorithm for enhancing underwater acoustic localisation. A restricted Boltzmann machine (RBM) is trained to predict the probability distribution of underwater targets. Advantage of this approach is that part of the computation is moved to offline‐training. Moreover, the probability distribution can potentially be sampled efficiently using a quantum annealer possibly enabling real‐time accurate target estimations being made onboard.The RBM is applied to a simplified multi‐sensor horizontal localisation problem where a constant and linear acoustic propagation is assumed. Using simulated annealing the authors show that the RBM is able to learn probability distributions that resemble target locations. Preliminary results show that training and sampling the RBM can be done using QA hardware by D‐Wave Systems.However, there remains room for improvement especially in ranging predictions. Further research into possible benefits of QA RBMs is needed to provide theoretical and practical results of a speed‐up.