Indoor–Outdoor Detection in Mobile Networks Using Quantum Machine Learning Approaches

Indoor–Outdoor Detection in Mobile Networks Using Quantum Machine Learning Approaches

Phillipson, F.
Wezeman, R.S.
Chiscop, I.

2021

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.

Quantum machine learning
Mobile devices
Idoor–outdoor detection
Hybrid quantum–classical
Variational quantum classifier
Quantum classification
Quantum SVM

http://resolver.tudelft.nl/uuid:440bf3c4-367d-4d4a-9e22-4123a0999f55

957914

Computers, 10 (10)

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