Print Email Facebook Twitter Three Quantum Machine Learning Approaches for Mobile User Indoor-Outdoor Detection Title Three Quantum Machine Learning Approaches for Mobile User Indoor-Outdoor Detection Author Phillipson, F. Wezeman, R.S Chiscop, I. Publication year 2021 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 indooroutdoor 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. Subject Quantum machine learningMobile devicesIndoor-outdoor detectionHybrid Quantum-classicalVariational Quantum classifierQuantum classificationQuantum SVM To reference this document use: http://resolver.tudelft.nl/uuid:6c351b48-ac22-4e0f-967e-1c8d4de6541c TNO identifier 953028 Publisher Wiley Source Machine Learning for networking Third International Conference, MLN 2020, Paris, France, November 24–26, 2020, 176-183 Document type conference paper Files To receive the publication files, please send an e-mail request to TNO Library.