Quantum Machine Learning for Energy-Efficient 5G-Enabled IoMT Healthcare Systems: Enhancing Data Security and Processing

TL;DR

This paper introduces quantum machine learning algorithms to improve energy efficiency and data security in 5G-enabled IoMT healthcare systems, demonstrating high accuracy and robustness against noise.

Contribution

The paper proposes and evaluates three novel QML algorithms specifically designed for IoMT data classification, achieving superior performance and resilience in noisy environments.

Findings

UU†-QNN achieves 100% accuracy on key datasets.

Quantum algorithms demonstrate robustness against noise.

Proposed methods enhance data security and reduce power consumption.

Abstract

Energy-efficient healthcare systems are becoming increasingly critical for Industry 5.0 as the Internet of Medical Things (IoMT) expands, particularly with the integration of 5G technology. 5G-enabled IoMT systems allow real-time data collection, high-speed communication, and enhanced connectivity between medical devices and healthcare providers. However, these systems face energy consumption and data security challenges, especially with the growing number of connected devices operating in Industry 5.0 environments with limited power resources. Quantum computing integrated with machine learning (ML) algorithms, forming quantum machine learning (QML), offers exponential improvements in computational speed and efficiency through principles such as superposition and entanglement. In this paper, we propose and evaluate three QML algorithms, which are UU{\dag}, variational UU{\dag}, and UU{\dag}-quantum neural networks (QNN) for classifying data from four different datasets: 5G-South Asia, Lumos5G 1.0, WUSTL EHMS 2020, and PS-IoT. Our comparative analysis, using various evaluation metrics, reveals that the UU{\dag}-QNN method not only outperforms the other algorithms in the 5G-South Asia and WUSTL EHMS 2020 datasets, achieving 100% accuracy, but also aligns with the human-centric goals of Industry 5.0 by allowing more efficient and secure healthcare data processing. Furthermore, the robustness of the proposed quantum algorithms is verified against several noisy channels by analyzing accuracy variations in response to each noise model parameter, which contributes to the resilience aspect of Industry 5.0. These results offer promising quantum solutions for 5G-enabled IoMT healthcare systems by optimizing data classification and reducing power consumption while maintaining high levels of security even in noisy environments.