# Indoor-Outdoor Detection in Mobile Networks Using Quantum Machine Learning Approaches
## 1 Introduction  
Fast and accurate detecting of the physical location of a mobile device and its user is crucial for many new smart services in the current mobile networks. This information can be used by mobile operators to optimise their network to provide better (perceived) quality to their customers. It helps, for example, in detecting low-coverage areas and improving indoor coverage. Additionally, the functionality of location-based services can be enhanced when the operator can better predict the position of the mobile user. Machine learning is an important candidate to provide this information. Machine learning-based models then learn to classify the environment the mobile device is in. This could be a simple indoor or outdoor classification, however, a more complex classification is also possible where a distinction is made between, e.g., offices, shops, transportation and residential areas. The overview paper of [1] shows the huge variety in techniques: K-nearest neighbour, random forest, decision tree, logistic regression, support vector machine, naive Bayes and neural networks (NNs). If we look at the combination of the techniques in combination with the used data, we can give the following overview:  
* Random forest and AdaBoost classifiers that use mobile device sensor data to classify the environment [2];
* Support vector machine (SVM) and deep learning (DL) techniques used in combination with a hybrid semi-supervised learning system to identify the indoor-outdoor environment using large and real collected 3 rd Generation Partnership Project (3 GPP) signal measurements [3];
* Deep learning, based on radio signals, time-related features and mobility indicators for a more complex environment classification, with multiple environments in [4]. * Ensemble learning schemes [5];
* Semi-supervised learning algorithm [6];
* Ensemble model based on stacking and filtering the detection results with a hidden Markov model [7]. For all these machine learning approaches, computing power is very important. More computing power leads to better results. In addition, the amount of available data is growing, which requires even more computing power. The end of [PERSON]'s law for classical computing is approaching [8], however, a new paradigm is approaching rapidly: quantum computing. Quantum computers make use of quantum mechanical phenomena, such as superposition and entanglement, to perform operations on data. Superposition means that the system can be described by a linear combination of distinct quantum states. Entanglement means that measuring the state of a qubit is always correlated to the state of the qubit it is entangled with. The quantum devices that are expected in the near feature to realise the first step in this demand for computing power are the so-called NISQ devices: noisy intermediate-scale quantum, involving a limited number of qubits that are not yet error-corrected nor noise-free. Machine learning is a prominent candidate to be among the first applications on these NISQ devices [9; 10; 11; 12]. For a recent overview of quantum machine learning approaches, the reader is referred to [PERSON] et al. [13]. One of the main benefits of quantum computers is the potential improvement in computational speed. Depending on the type of problem and algorithm, quantum algorithms can have a polynomial or exponential speed-up compared to classical algorithms. [PERSON] et al. [14] indicate what the expected gain will be. For Bayesian inference [15], online perceptron [16] and reinforcement learning [9; 17], quadratic speedup is expected. An exponential speed-up is expected for principal component analysis (PCA) [18], SVM [19] and least square fitting [20], compared to their classical counterparts. There are, however, also other relevant benefits in the near future [10]. Quantum computers could possibly learn from less data, deal with more complex structures or could be better in coping with noisy data.