In current research, secure wireless communication system with high data rate is obtained by quantum computing (QC). Main requirement of using wireless infrastructure is to reduce cost and transmit faster in network. To speed up the data rate of transfer, QC has been combined with machine learning algorithms. Then the large volume of data that are transferred, stored and processed in wireless systems leads high energy of the system. This article proposes a power minimisation approach with QC based approach called improved sequential parametric convex approximation (ISPCA) trained by deep learning algorithm called graph convolutional neural network (GCNN). The proposed approach has been used to minimise the power and enhance the energy efficiency of the wireless communication systems such as LTE/5G. Evaluated result shows that our proposed consumes only 44.82J energy for computing 1000 samples. Proposed technique outperforms existing by consuming much less energy.
In current research, secure wireless communication system with high data rate is obtained by quantum computing (QC). Main requirement of using wireless infrastructure is to reduce cost and transmit faster in network. To speed up the data rate of transfer, QC has been combined with machine learning algorithms. Then the large volume of data that are transferred, stored and processed in wireless systems leads high energy of the system. This article proposes a power minimisation approach with QC based approach called improved sequential parametric convex approximation (ISPCA) trained by deep learning algorithm called graph convolutional neural network (GCNN). The proposed approach has been used to minimise the power and enhance the energy efficiency of the wireless communication systems such as LTE/5G. Evaluated result shows that our proposed consumes only 44.82J energy for computing 1000 samples. Proposed technique outperforms existing by consuming much less energy.
In current research, secure wireless communication system with high data rate is obtained by quantum computing (QC). Main requirement of using wireless infrastructure is to reduce cost and transmit faster in network. To speed up the data rate of transfer, QC has been combined with machine learning algorithms. Then the large volume of data that are transferred, stored and processed in wireless systems leads high energy of the system. This article proposes a power minimisation approach with QC based approach called improved sequential parametric convex approximation (ISPCA) trained by deep learning algorithm called graph convolutional neural network (GCNN). The proposed approach has been used to minimise the power and enhance the energy efficiency of the wireless communication systems such as LTE/5G. Evaluated result shows that our proposed consumes only 44.82J energy for computing 1000 samples. Proposed technique outperforms existing by consuming much less energy.
A potential way to improve wireless communication security in the face of increasingly complex cyberthreats is to combine deep learning techniques with hybrid quantum computing. By combining the processing benefits of quantum algorithms with the pattern recognition powers of deep learning, this article tackles the crucial problems of protecting wireless networks, which are vulnerable to eavesdropping, spoofing, and data breaches. The research uses a hybrid framework in which deep learning models like convolutional neural networks (CNNs) identify and stop possible invasions in real time, while quantum key distribution (QKD) guarantees safe data transfer. Simulations and experimental validation in a 5G network environment show that the proposed system has a 40% reduction in latency and a 35% increase in intrusion detection accuracy compared to traditional security methods, like standard encryption techniques and rule-based intrusion detection systems that lack quantum cryptographic mechanisms and advanced AI-driven pattern recognition. The findings demonstrate that integrating quantum and AI-driven methods to build a more robust wireless communication infrastructure is feasible. The study concludes by highlighting the potential of hybrid quantum-AI systems as a critical first step in protecting next-generation networks.
With the emergence of the 5G and beyond, wireless networks have transformed from a simple communication medium to ubiquitous versatile platforms. This trend has enabled numerous device-free and non-contact applications. As computing power and machine learning algorithms continue to improve, deep learning techniques are increasingly used in wireless sensing applications. However, the limits of deep learning-centered wireless sensing approaches are still being explored. Concurrently, research in quantum computing is advancing rapidly, prompting researchers to explore the burgeoning field of quantum machine learning, which combines quantum computing and machine learning, for its boundless potential. In this article, we propose a general quantum machine learning framework for wireless sensing applications in the Artificial Internet of Things (AIoT). The proposed framework provides a systematic approach for designing deeply interpreted wireless sensing models based on quantum machine learning. We then present several representative applications and case studies, and conclude this article with a discussion of the challenges and future research directions in this exciting area.
The advent of deep-learning technology promises major leaps forward in addressing the ever-enduring problems of wireless resource control and optimization, and improving key network performances, such as energy efficiency, spectral efficiency, transmission latency, etc. Therefore, a common understanding for quantum deep-learning algorithms is that they exploit advantages of quantum hardware, enabling massive optimization speed ups, which cannot be achieved by using classical computer hardware. In this respect, this paper investigates the possibility of resolving the energy efficiency problem in wireless communications by developing a quantum neural network (QNN) algorithm of deep-learning that can be tested on a classical computer setting by using any popular numerical simulation tool, such as Python. The computed results show that our QNN algorithm can be indeed trainable and that it can lead to solution convergence during the training phase. We also show that the proposed QNN algorithm exhibits slightly faster convergence speed than its classical ANN counterpart, which was considered in our previous work. Finally, we conclude that our solution can accurately resolve the energy efficiency problem and that it can be extended to optimize other communications problems, such as the global optimal power control problem, with promising trainability and generalization ability.
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 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.
The significant rise in wireless communication, driven by sixth-generation (6G) networks, underscores the urgent need for innovative resource allocation methods to achieve efficiency, scalability, and reliability. This survey explores the integration of Quantum Deep Reinforcement Learning (QDRL) as a strategy to address the challenges posed by dynamic and complex wireless environments. By leveraging quantum computing features such as superposition and entanglement, QDRL enhances computational efficiency and mitigates the scalability limitations of classical and deep reinforcement learning (DRL) in high-dimensional optimization. The study reviews recent QDRL approaches for key resource allocation tasks, including power distribution, user scheduling, and channel assignment, and examines quantum algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) and Quantum Neural Networks (QNN) for improved decision-making in wireless systems. Furthermore, it emphasizes the connection between quantum computing and DRL, highlighting hybrid architectures that balance computational cost, energy efficiency, and scalability. Finally, the survey identifies research gaps and outlines future directions, paving the way for robust QDRL strategies to support next-generation wireless networks.