标题： 用于节能5G使能的IoMT医疗系统中的量子机器学习：增强数据安全性和处理能力 显示英文标题

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

摘要： 能源高效的医疗系统在工业5.0中变得越来越关键，随着医疗物联网（IoMT）的扩展，特别是5G技术的集成。5G使能的IoMT系统允许实时数据收集、高速通信以及医疗设备和医疗提供者之间的增强连接。然而，这些系统面临能耗和数据安全挑战，尤其是在工业5.0环境中，连接设备数量不断增加，而电源资源有限。与机器学习（ML）算法结合的量子计算，形成量子机器学习（QML），通过叠加和纠缠等原理，提供了计算速度和效率的指数级提升。在本文中，我们提出了并评估了三种QML算法，即UU{\dag }、变分UU{\dag }和UU{\dag }-量子神经网络（QNN），用于对四个不同数据集的数据进行分类：5G-南亚、Lumos5G 1.0、WUSTL EHMS 2020和PS-IoT。我们的比较分析使用各种评估指标，结果显示，UU{\dag }-QNN方法不仅在5G-南亚和WUSTL EHMS 2020数据集中优于其他算法，达到100%的准确率，而且通过允许更高效和安全的医疗数据处理，符合工业5.0的人本目标。此外，通过分析准确性变化对每个噪声模型参数的响应，验证了所提出的量子算法在多种噪声信道下的鲁棒性，这有助于工业5.0的弹性方面。这些结果通过优化数据分类和减少功耗，同时在嘈杂环境中保持高水平的安全性，为5G使能的IoMT医疗系统提供了有前景的量子解决方案。 显示英文摘要

摘要： 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.