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.
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.
Quantum computing is a technology that utilizes quantum mechanics principles such as superposition and entanglement to offer significant advantages over classical computing.With the development of sixth generation (6G) technology quantum computing's high data processing capacity, efficiency in optimization processes, and contribution to secure communication systems are becoming increasingly important.This paper examines the role of quantum computing in 6G networks.It discusses its potential contributions in areas such as network optimization, quantum machine learning, secure communication protocols, and fault-tolerant computing.The ultra-low latency, high data rate, and dynamic spectrum management requirements of 6G can be significantly improved by integrating quantum algorithms and hybrid quantum-classical systems.However, obstacles such as current limitations in quantum hardware, error correction requirements, and implementation challenges are also considered in detail.The paper outlines the potential solutions that quantum computing can provide to meet the performance demands of 6G, and discusses future research directions to further explore this promising technology intersection.
Quantum computing is envisaged as an evolving paradigm for solving computationally complex optimization problems with a large-number factorization and exhaustive search. Recently, there has been a proliferation growth of the size of multi-dimensional datasets, the input-output space dimensionality, and data structures. Hence, the conventional machine learning approaches in data training and processing face a huge limited computing capabilities to support the sixth-generation (6G) networks with highly dynamic applications and services. Under this regard, the fast developing quantum computing with machine learning (QML) for 6G networks is investigated. QML algorithms can significantly enhanced the processing efficiency and exponentially computational speed-up for effective quantum data representation and superposition framework, highly capable of guaranteeing high data storage and secured communications. We present the state-of-the-art in quantum computing and provide comprehensive overviews through machine learning approaches for their applications in the 6G networks. Furthermore, we introduce quantum-inspired machine learning applications for 6G networks, considering their enabling technologies and potential challenges. Finally, some dominating research issues and future research directions for the quantum-inspired machine learning in 6G networks are elaborated.
The upcoming fifth generation (5G) of wireless networks is expected to lay a foundation of intelligent networks with the provision of some isolated artificial intelligence (AI) operations. However, fully intelligent network orchestration and management for providing innovative services will only be realized in Beyond 5G (B5G) networks. To this end, we envisage that the sixth generation (6G) of wireless networks will be driven by on-demand self-reconfiguration to ensure a many-fold increase in the network performance and service types. The increasingly stringent performance requirements of emerging networks may finally trigger the deployment of some interesting new technologies, such as large intelligent surfaces, electromagnetic-orbital angular momentum, visible light communications, and cell-free communications, to name a few. Our vision for 6G is a massively connected complex network capable of rapidly responding to the users' service calls through real-time learning of the network state as described by the network edge (e.g., base-station locations and cache contents), air interface (e.g., radio spectrum and propagation channel), and the user-side (e.g., battery-life and locations). The multi-state, multi-dimensional nature of the network state, requiring the real-time knowledge, can be viewed as a quantum uncertainty problem. In this regard, the emerging paradigms of machine learning (ML), quantum computing (QC), and quantum ML (QML) and their synergies with communication networks can be considered as core 6G enablers. Considering these potentials, starting with the 5G target services and enabling technologies, we provide a comprehensive review of the related state of the art in the domains of ML (including deep learning), QC, and QML and identify their potential benefits, issues, and use cases for their applications in the B5G networks. Subsequently, we propose a novel QC-assisted and QML-based framework for 6G communication networks while articulating its challenges and potential enabling technologies at the network infrastructure, network edge, air interface, and user end. Finally, some promising future research directions for the quantum- and QML-assisted B5G networks are identified and discussed.
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.
The development of a quantum-based architecture for wireless systems is required to respond to the growing need for faster and more effective communication. Quantum computing offers a huge promise for improving wireless communication networks due to its ability to perform complex computations and simulations in a fraction of the time compared to classical computing. With a focus on Quantum 6G, this paper examines the foundations of quantum computing, the state of conventional wireless systems, and the potential for quantum-assisted wireless systems. This paper also analyzes future research areas and briefly discusses the newly developing topic of quantum machine learning in the context of 6G. It starts with reviewing Quantum 6G, covering facets like its effectiveness and security. It also explores the challenges and constraints of managing wireless resources with quantum assistance. This study seeks to explore these issues in order to advance knowledge of quantum 6G networks and encourage additional investigation into this exciting area.
Abstract In the technologically changing world, the demand for ultra‐reliable, faster, low power, and secure communication has significantly risen in recent years. Researchers have shown immense interest in emerging quantum computing (QC) due to its potentials of solving the computing complexity in the robust and efficient manner. It is envisioned that QC can act as critical enablers and strong catalysts to considerably reduce the computing complexities and boost the future of sixth generation (6G) and beyond communication systems in terms of their security. In this study, the fundamentals of QC, the evolution of quantum communication that encompasses a wide spectrum of technologies and applications and quantum key distribution, which is one of the most promising applications of quantum security, have been presented. Furthermore, various parameters and important techniques are also investigated to optimise the performance of 6G communication in terms of their security, computing, and communication efficiency. Towards the end, potential challenges that QC and quantum communication may face in 6G have been highlighted along with future directions.