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

Aug 5, 2026

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Energy-efficient healthcare systems are essential to Industry 5.0 as the Internet of Medical Things (IoMT) grows and 5G enables real-time medical data collection and high-speed connectivity. Yet power limits and expanding device counts intensify energy consumption and data security risks. This study integrates quantum computing with machine learning (quantum machine learning) to improve computational speed and efficiency via superposition and entanglement. Three QML algorithms are evaluated for classifying data from multiple datasets, with UU†-QNN achieving 100% accuracy and improved robustness under noisy channels, supporting secure and resilient IoMT processing.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Introduction

IoMT and Industry 5.0 in healthcare

Role of 5G in connectivity and data transfer

Energy and security challenges

Machine learning and quantum machine learning approach

Quantum Machine Learning for Energy-Efficient 5G-Enabled IoMT Healthcare Systems: Enhancing Data Security and Processing Muhammad Zeeshan Riaz, Bikash K. Behera, Shahid Mumtaz, Saif Al-Kuwari, and Ahmed Farouk 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†, variational UU†, and UU†-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†-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 humancentric 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. Index Terms—Industry 5.0, 5G Technology, Internet of Medical Things (IoMT), UU† Method, Variational UU† Method, Quantum Neural Network (QNN) Muhammad Zeeshan Riaz is with the College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China, e-mail: ([zeeshanriaz@email.szu.edu.cn](zeeshanriaz@email.szu.edu.cn) ) . B. K. Behera is with the Bikash’s Quantum (OPC) Pvt. Ltd. , Mohanpur, WB, 741246 India, e-mail: ([bikas.riki@gmail.com](bikas.riki@gmail.com)). Saif Al-Kuwari is with the Qatar Center for Quantum Computing, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar. e-mail: ([smalkuwari@hbku.edu.qa](smalkuwari@hbku.edu.qa)). Shahid Mumtaz is with Nottingham Trent University, Engineering Department, United Kingdom. e-mail: ([dr.shahid.mumtaz@ieee.org](dr.shahid.mumtaz@ieee.org)). A. Farouk is with the Qatar Center for Quantum Computing, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar and with the Department of Computer Science, Faculty of Computers and Artificial Intelligence, Hurghada University, Hurghada, Egypt. e-mail:([ahmedfarouk@ieee.org](ahmedfarouk@ieee.org)). I. INTRODUCTION THE Internet of Medical Things (IoMT) is a crucial application of Industry 5.0 in healthcare, connecting medical devices and patients to revolutionize healthcare delivery globally [1] . By establishing a robust infrastructure between medical software and hardware applications, IoMT facilitates seamless interaction between biosensor nodes [2] and mobile edge computing (MEC) [3], linking patients with doctors and sharing data through secure networks, thus minimizing hospital visits and reducing workload on healthcare departments. The development of 5G networks accelerates the evolution of the Internet of Things (IoT) and IoMT within the Industry 5.0 healthcare system, supporting technologies such as device-to-device (D2D) communication

FAQs

Why are energy-efficient IoMT systems important in Industry 5.0?

Industry 5.0 healthcare relies on IoMT for connected medical devices and real-time data, but limited power resources make energy-efficient processing critical as the number of connected devices grows.

How does the paper address data security in 5G-enabled IoMT?

It proposes quantum machine learning algorithms to improve secure and efficient classification and processing of healthcare data, aligning with Industry 5.0’s human-centric goals.

Which quantum machine learning algorithm performs best and under what conditions?

The UU†-QNN method outperforms other proposed algorithms by reaching 100% accuracy on the 5G-South Asia and WUSTL EHMS 2020 datasets, and it remains robust under noisy channel models by analyzing accuracy variations.

Aug 5, 2026

Upload by Eden