Assessing Projected Quantum Kernels for the Classification of IoT Data

TL;DR

This paper evaluates the effectiveness of projected quantum kernels in classifying IoT data, demonstrating their comparable performance to classical methods using a specialized quantum encoding approach.

Contribution

It introduces a quantum data processing method compatible with IoT datasets, enabling direct quantum classification without feature reduction.

Findings

Projected Quantum Kernel performs comparably to classical methods.

Quantum encoding with shallow circuits is effective for IoT data.

The dataset used is directly compatible with quantum hardware.

Abstract

The use of quantum computing for machine learning is among the most promising applications of quantum technologies. Quantum models inspired by classical algorithms are developed to explore some possible advantages over classical approaches. A primary challenge in the development and testing of Quantum Machine Learning (QML) algorithms is the scarcity of datasets designed specifically for a quantum approach. Existing datasets, often borrowed from classical machine learning, need modifications to be compatible with current quantum hardware. In this work, we utilize a dataset generated by Internet-of-Things (IoT) devices in a format directly compatible with the proposed quantum data process, eliminating the need for feature reduction. Among quantum-inspired machine learning algorithms, the Projected Quantum Kernel (PQK) stands out for its elegant solution of projecting the data encoded in the Hilbert space into a classical space. For a prediction task concerning office room occupancy, we compare PQK with the standard Quantum Kernel (QK) and their classical counterparts to investigate how different feature maps affect the encoding of IoT data. Our findings show that the PQK demonstrates comparable effectiveness to classical methods when the proposed shallow circuit is used for quantum encoding.