Projected Quantum Kernel for IoT Data Analysis

The use of quantum computing for machine learning is among the most exciting applications of quantum technologies. Researchers are developing quantum models inspired by classical ones to find some possible quantum advantages over classical approaches. Although such a clear quantum advantage has not yet been achieved, the need for rigorous methodologies to test quantum machine learning (QML) algorithms is an evident requirement. A major challenge in QML development and testing is the lack of datasets specifically designed for quantum algorithms. Existing datasets, often borrowed from classical machine learning, need modifications to be compatible with current noisy quantum hardware. In this work, we use a dataset produced by internet-of-things (IoT) devices in a form usable for quantum algorithms without the need for feature reductions. Among quantum-inspired machine learning algorithms, the Projected Quantum Kernel (PQK) stands out for its elegant solution of projecting the data encoded in Hilbert space into classical space. In this paper, we detail how a PQK approach can be used to develop a prediction model on IoT data using quantum machine learning.