Three Quantum Machine Learning Approaches for Mobile User Indoor-Outdoor Detection

Abstract

There is a growing trend in using machine learning techniques for detecting environmental context in communication networks. Machine learning is one of the promising candidate areas where quantum computing can show a quantum advantage over their classical algorithmic counterpart on near term Noisy Intermediate-Scale Quantum (NISQ) devices. The goal of this paper is to give a practical overview of (supervised) quantum machine learning techniques to be used for indoor-outdoor detection. Due to the small number of qubits in current quantum hardware, real application is not yet feasible. Our work is intended to be a starting point for further explorations of quantum machine learning techniques for indoor-outdoor detection.