As wireless networks advance toward the Sixth Generation (6G), which will support highly heterogeneous scenarios and massive data traffic, conventional computing methods may struggle to meet the immense processing demands in a resource-efficient manner. This paper explores the potential of quantum computing (QC) to address these challenges, specifically by enhancing the efficiency of Maximum-Likelihood detection in Multiple-Input Multiple-Output (MIMO) Non-Orthogonal Multiple Access (NOMA) communication systems, an essential technology anticipated for 6G. The study proposes the use of the Quantum Approximate Optimization Algorithm (QAOA), a variational quantum algorithm known for providing quantum advantages in certain combinatorial optimization problems. While current quantum systems are not yet capable of managing millions of physical qubits or performing high-fidelity, long gate sequences, the results indicate that QAOA is a promising QC approach for radio signal processing tasks. This research provides valuable insights into the potential transformative impact of QC on future wireless networks. This sets the stage for discussions on practical implementation challenges, such as constrained problem sizes and sensitivity to noise, and opens pathways for future research aimed at fully harnessing the potential of QC for 6G and beyond.
As wireless networks advance toward the Sixth Generation (6G), which will support highly heterogeneous scenarios and massive data traffic, conventional computing methods may struggle to meet the immense processing demands in a resource-efficient manner. This paper explores the potential of quantum computing (QC) to address these challenges, specifically by enhancing the efficiency of Maximum-Likelihood detection in Multiple-Input Multiple-Output (MIMO) Non-Orthogonal Multiple Access (NOMA) communication systems, an essential technology anticipated for 6G. The study proposes the use of the Quantum Approximate Optimization Algorithm (QAOA), a variational quantum algorithm known for providing quantum advantages in certain combinatorial optimization problems. While current quantum systems are not yet capable of managing millions of physical qubits or performing high-fidelity, long gate sequences, the results indicate that QAOA is a promising QC approach for radio signal processing tasks. This research provides valuable insights into the potential transformative impact of QC on future wireless networks. This sets the stage for discussions on practical implementation challenges, such as constrained problem sizes and sensitivity to noise, and opens pathways for future research aimed at fully harnessing the potential of QC for 6G and beyond.
This paper introduces a pioneering method in the realm of signal processing for Massive Multiple-Input Multiple-Output (MIMO) systems, leveraging the synergistic potential of hybrid quantum-classical computing paradigms. The escalating demand for high data rate communications necessitates the advancement of MIMO technologies. However, the computational complexity associated with signal processing in such systems, especially in scenarios involving large array sizes, presents a significant challenge. This study proposes an innovative framework that integrates quantum computing algorithms with traditional classical methods to address this challenge. The quantum algorithms are specifically designed to expedite matrix operations and optimization tasks that are central to MIMO signal processing, such as channel estimation and precoding. A comparative analysis with purely classical approaches demonstrates a substantial improvement in computational efficiency and latency reduction, while maintaining, and in certain aspects enhancing, the accuracy of signal detection and system throughput. Furthermore, this hybrid approach offers scalability and adaptability to varying channel conditions and system configurations, a crucial feature for next-generation wireless communication systems. The theoretical foundation is complemented by simulation results, showcasing the feasibility and effectiveness of this approach in practical scenarios. This research not only paves the way for advanced signal processing techniques in Massive MIMO systems but also opens new horizons in the application of quantum computing in telecommunications.
In this research, the goal is to assess the performance and computational efficiency of the Quantum Approximate Optimization Algorithm for signal detection in MIMO-NOMA systems compared with classical detection methods. The deep learning detection framework supported by the Quantum Approximate Optimization Algorithm (QAOA). The Quantum Approximate Optimization Algorithm (QAOA) assistance in the deep learning-based detection system. For this group, the sample size of signal examples considered is 5,000, and the simulations are carried out based on various signal to noise ratios, antenna array, modulations, and channel estimation errors. The bit error rate, accuracy of detection, channel estimation error, complexity, and throughput of the system are evaluated. The output of the simulation showed that, for a given detection performance, the QAOA-assisted deep learning detection framework outperforms other detection methods in terms of 18% lower bit error rate, 14% higher detection accuracy, 12% lower channel estimation error, 10% lower computational complexity, and 16% higher throughput. The result of the experiment suggests that the proposed framework for signal detection using the assistance of the QAOA-based deep learning algorithm is capable of carrying out the detection of the multiple users' signal compared to other signal detection methods in terms of the mentioned criteria.
Abstract Sixth Generation (6G) communication networks are anticipated to advance the fifth generation technologies as well as introduce new revolutionary technologies to deliver aggressive data rates, end-to-end latency, reliability, energy efficiency, security, network capacity, and mobility requirements of the future innovative applications. To deliver these demands, ultra-fast and secure computing capability for processing a huge amount of data, application-related tasks, and resource management algorithms will be required. Many 6G applications will use advanced learning techniques that work on large data sets and their working can be made more efficient with the help of Quantum Computing (QC) instead of conventional classical computing. In this paper, we provide an overview of QC and 6G networks and highlight the benefit of integrating these two technologies. We briefly review the recent work and present a framework for QC-assisted 6G networks. We also discuss four use cases of QC in 6G networks, including quick task offloading, fast optimization algorithm results, efficient machine learning techniques, and improved security. Finally, we discuss several open challenges of using QC in the context of 6G network applications.
Sixth-generation wireless technology (6G) introduces a paradigm shift and fundamental transformation of digital wireless connectivity by converging pillars of softwarization, virtualization, and wireless networks. Terahertz (THz) communication technologies are predicted to become more significant in 6G applications as the requirement for bandwidth grows and wireless cell sizes shrink. As a result, 6G will be able to deal with and manage numerous devices and services requiring enhanced spectral throughput and efficiently work with high interference levels. This convergence highlights the increased threat surface of 6G networks and the potentially severe impacts of sophisticated cyber incidents. Furthermore, the heterogeneity of connected devices and provided services will generate a huge amount of data to be processed and managed efficiently. Quantum computing (QC) can efficiently solve several 6G computationally hard problems with a quadratic speedup and provide adaptive techniques for controlling the current and future significant security threats of the 6G network. This article will discuss the role of various QC components on 6G and explore the opportunities and challenges to achieving such transformation.
Massive multiple-input multiple-output (MIMO) systems are a key enabling technology for 5G-Advanced and emerging 6G networks, yet their performance is fundamentally constrained by high-dimensional, non-convex optimization problems arising from beamforming, power allocation, and user scheduling under imperfect channel state information (CSI).This paper proposes a hybrid quantum-classical optimization framework that formulates discrete and continuous massive MIMO resource allocation problems using Quadratic Unconstrained Binary Optimization (QUBO) and variational quantum algorithms.Discrete beam selection is addressed using quantum annealing, while continuous power control and hybrid beamforming are refined using variational quantum circuits within a classical feedback loop.Simulation results demonstrate improvements in aggregate spectral efficiency, runtime scalability, and robustness under imperfect CSI compared to conventional ZF and WMMSE baselines.The benefits of using the proposed solution include scalable runtime performance with respect to larger numbers of antennas; thus, it highlights the potential of optimizing quantum-assisted technologies for the future of wireless networks based on realistic assumptions regarding near-term NISQ-era performance.
Sixth generation (6G) wireless technology introduces a paradigm shift and fundamental transformation of digital wireless connectivity by converging pillars of softwarization, virtualization, and wireless networks. Terahertz (THz) communication technologies are predicted to become more significant in 6G applications as the requirement for bandwidth grows and wireless cell sizes shrink. As a result, 6G will be able to deal with and manage numerous devices and services requiring enhanced spectral throughput and efficiently work with high interference levels. This convergence highlights the increased threat surface of 6G networks and the potentially severe impacts of sophisticated cyber incidents. Furthermore, the heterogeneity of connected devices and provided services will generate a huge amount of data to be processed and managed efficiently. Quantum computing (QC) can efficiently solve several 6G computationally hard problems with a quadratic speedup and provide adaptive techniques for controlling the current and future significant security threats of the 6G network. This article discusses the role of various QC components in 6G and explores the opportunities and challenges of achieving such transformation.