# Indoor-Outdoor Detection in Mobile Networks Using Quantum Machine Learning Approaches
## 5 Conclusions  
Communication networks are managed more and more by using artificial intelligence. Anomaly detection, network monitoring and user behaviour are areas where machine learning offer advantages over more traditional methods. However, increasingly, computing power is a limited factor in machine learning tasks. For mobile network operators, it is becoming more and more important to be able to classify the location of mobile devices, to enhance network performance and to offer location-based services. The rise of quantum computers might bring the next step needed here. In particular, machine learning is seen as a promising task for the first generation of quantum computers. In this paper, we proposed three machine learning approaches for indoor-outdoor classification. These techniques are expected to be useful on the near-term hardware.  
The first approach is a quantum variational classifier. This approach only uses a small number of qubits and is able to manage a larger number of data points. The expected gain here is not the calculation time. The compactness of the formulation will be of added value, which will lead to more efficient learning of a model with multiple features. Next, we compared a 2-qubit implementation to a 4-qubit implementation. We showed that the 4-qubit implementation, although having twice as many parameters that needed to be optimised, required fewer iterations before finding its loss plateau.  
The quantum distance-based classifier brings the complexity back to \(\mathcal{O}(1)\), independent of the number of features and the number of data points. However, the data  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**N** & **Solver** & **Energy** & **F1** & **Acc** & **Pr** & **Rc** \\ \hline
25 & SA & \(-4.70\) & \(1\) & \(1\) & \(1\) & \(1\) \\  & HQPU & \(-4.83\) & \(1\) & \(1\) & \(1\) & \(1\) \\  & QPU & \(14.79\) & \(1\) & \(1\) & \(1\) & \(1\) \\  & LocalSolver—QUBO & \(-3.26\) & & & & \\  & LocalSolver—original & \(-6.09\) & & & & \\ \hline
70 & SA & \(-5.59\) & \(0.95\) & \(0.96\) & \(1\) & \(0.9\) \\  & HQPU & \(-6.28\) & \(1\) & \(1\) & \(1\) & \(1\) \\  & QPU & \(270.04\) & \(0.95\) & \(0.96\) & \(1\) & \(0.9\) \\  & LocalSolver—QUBO & \(-5.34\) & & & & \\  & LocalSolver—original & \(-9.14\) & & & & \\ \hline
225 & SA & \(-6.65\) & \(1\) & \(1\) & \(1\) & \(1\) \\  & HQPU & \(-7.53\) & \(1\) & \(1\) & \(1\) & \(1\) \\  & LocalSolver—QUBO & \(-9.38\) & & & & \\  & LocalSolver—original & \(-14.68\) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Overview of results.  
management is a big problem at this moment. The number of qubits needed to store big data input and the time it takes to encode the data are still under scientific investigation.  
The quantum annealing-based SVM is the most straightforward implementation of a conventional approach. Using the Hybrid Solver gave us the possibility to solve bigger problems than in the case where a direct implementation on the chip was realised. The expectation here is that the solution will scale better than conventional approaches when the underlying hardware grows.  
For further research, we are interested in how well the methods perform on more messy, imbalanced datasets and in some indications of the performance on full-size data sets. Lastly, we are interested in the level of convergence of the quantum variational classifier for more generic data sets.  
Conceptualization, F.P., R.S.W. and I.C.; methodology, F.P.; software, R.S.W. and I.C.; validation, F.P.; writing--original draft preparation, F.P. and R.S.W.; writing--review and editing, I.C.; visualization, R.S.W.; supervision, F.P.; project administration, F.P.; funding acquisition, F.P.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
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Authors can confirm that all relevant data are included in the article.  
The authors declare no conflict of interest.