Abstract: Fifth-generation (5G) heterogeneous networks (HetNets), with ultra-dense small-cell deployments, diverse radio access technologies, and high user mobility, are among the most rapidly evolving infrastructures today. Hence, effective handover management has evolved as an essential requirement for sustaining seamless connectivity, Quality of Service (QoS), and user experience. Conventional handover optimization techniques are generally limited because of high latency, excessive signaling overhead, frequent handover failures, the ping-pong effect, and poor decision-making in a highly dynamic network environment. Recent advancements in quantum computing have opened new opportunities to address these challenges by offering powerful computational and optimization techniques. This survey provides a comprehensive overview of quantum computing and quantuminspired algorithms for handover optimization in 5G HetNets. In particular, it discusses the principles and methodologies of prominent approaches, such as quantum annealing, the Grover search algorithm, Quantum Genetic Algorithms (QGA), Quantum Particle Swarm Optimization (QPSO), quantum- inspired evolutionary algorithms, and Quantum Machine Learning (QML). It further examines their potential to address multidimensional handover optimization problems involving signal quality, user mobility, network load balancing, throughput, latency, energy efficiency, handover failure rate, and QoS requirements. A comparative analysis of the existing studies is provided to evaluate algorithm performance, computational complexity, and scalability for practical deployment in real-world 5G environments. The survey also emphasizes the integration of quantum computing with Artificial Intelligence (AI) and Machine Learning (ML) frameworks for intelligent mobility management. Finally, it highlights some significant research challenges, implementation issues, hardware limitations, and future directions for exploiting quantum technologies in 5G/6G mobility management systems.
Abstract: Fifth-generation (5G) heterogeneous networks (HetNets), with ultra-dense small-cell deployments, diverse radio access technologies, and high user mobility, are among the most rapidly evolving infrastructures today. Hence, effective handover management has evolved as an essential requirement for sustaining seamless connectivity, Quality of Service (QoS), and user experience. Conventional handover optimization techniques are generally limited because of high latency, excessive signaling overhead, frequent handover failures, the ping-pong effect, and poor decision-making in a highly dynamic network environment. Recent advancements in quantum computing have opened new opportunities to address these challenges by offering powerful computational and optimization techniques. This survey provides a comprehensive overview of quantum computing and quantuminspired algorithms for handover optimization in 5G HetNets. In particular, it discusses the principles and methodologies of prominent approaches, such as quantum annealing, the Grover search algorithm, Quantum Genetic Algorithms (QGA), Quantum Particle Swarm Optimization (QPSO), quantum- inspired evolutionary algorithms, and Quantum Machine Learning (QML). It further examines their potential to address multidimensional handover optimization problems involving signal quality, user mobility, network load balancing, throughput, latency, energy efficiency, handover failure rate, and QoS requirements. A comparative analysis of the existing studies is provided to evaluate algorithm performance, computational complexity, and scalability for practical deployment in real-world 5G environments. The survey also emphasizes the integration of quantum computing with Artificial Intelligence (AI) and Machine Learning (ML) frameworks for intelligent mobility management. Finally, it highlights some significant research challenges, implementation issues, hardware limitations, and future directions for exploiting quantum technologies in 5G/6G mobility management systems.
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.
This work explores the integration of Quantum Machine Learning (QML) and Quantum-Inspired (QI) techniques for optimizing End-to-End (E2E) network services in telecommunication systems, particularly focusing on 5G networks and beyond. The application of QML and QI algorithms is investigated, comparing their performance with classical Machine Learning (ML) approaches. The present study employs a hybrid framework combining quantum and classical computing leveraging the strengths of QML and QI, without the penalty of quantum hardware availability. This is particularized for the optimization of the Quality of Experience (QoE) over cellular networks. The framework comprises an estimator for obtaining the expected QoE based on user metrics, service settings, and cell configuration, and an optimizer that uses the estimation to choose the best cell and service configuration. Although the approach is applicable to any QoE-based network management, its implementation is particularized for the optimization of network configurations for Cloud Gaming services. Then, it is evaluated via performance metrics such as accuracy, model loading and inference times for the estimator, time to solution and solution score for the optimizer. The results indicate that QML models achieve similar or superior accuracy to classical ML models for estimation, while decreasing inference and loading times. Furthermore, potential for better performance is observed for higher-dimensional data, highlighting promising results for higher complexity problems. Thus, the results demonstrate the promising potential of QML in advancing network optimization, although challenges related to data availability and integration complexities between quantum and classical ML are identified as future research lines.
As cellular networks evolve towards 5G and beyond, the increasing complexity demands advanced approaches for efficient management and network intelligence. While classical machine learning (ML) plays a vital role in network planning, optimization and resilience, it faces limitations in handling largescale data and dynamic environments. Quantum Technologies (QTs) offer a promising way forward in this area, leveraging quantum computing principles to potentially enhance processing power and scalability. Toward this end, the present article explores the application of the Quantum Machine Learning (QML) and Quantum-Inspired (QI) algorithms in the management of cellular networks. A novel framework for the integration of such approaches in 5G / 6G networks is developed with a special focus on Open RAN architectures. The proposed application of QTs is then assessed based on real cellular data and Quantum algorithms for three main areas of cellular management: network metrics estimation, classification of failures, and resource optimization, showing the capabilities of the approach. Finally, the main challenges and future R&D lines in the area are identified.
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.
The forthcoming sixth generation (6G) of wireless networks requires fundamental rethinking of network intelligence, driven by the transition toward ubiquitous cognitive networks and the unprecedented complexity beyond 5G. The optimization demands of 6G surpass the capabilities of classical heuristics and conventional Machine Learning (ML), which encounter significant limitations in addressing dimensionality challenges in ultra-massive multiple-input multiple-output/terahertz/reconfigurable intelligent surfaces-assisted systems, stringent sub-millisecond latency requirements, and severe energy bottlenecks at the edge. Motivated by these gaps, this review investigates Quantum Machine Learning (QML) as a transformative solution, merging quantum mechanics with data-driven intelligence. We propose a unified and forward-looking perspective on ML integration for 6G, bridging previously siloed research domains such as cross-layer optimization, semantic- and intent-driven communication, and quantum-inspired acceleration. Furthermore, this work systematically reviews quantum-enhanced optimization methods and analyzes QML’s role as an intelligence anchor, demonstrating its potential to provide context-aware, resilient, and sustainable network control across various layers. Ultimately, the paper outlines pathways for integrating QML to ensure timely, scalable, and secure decision-making in the volatile 6G landscape.
The emergence of sixth-generation (6G) communication networks is expected to revolutionize wireless systems by enabling ultra-high data rates, ultra-low latency, and massive connectivity. Efficient resource management is a critical challenge due to the dynamic and heterogeneous nature of 6G environments. Traditional optimization techniques are insufficient to handle the complexity of such networks, leading to the adoption of artificial intelligence (AI)-driven approaches. This paper presents a comprehensive review of AI techniques for efficient resource management in 6G networks, focusing on hybrid quantum duplet-convolutional neural network (CNN) models. These models integrate quantum computing with classical deep learning to enhance computational efficiency and optimization capability. The study analyzes recent literature, covering deep learning, reinforcement learning, federated learning, and quantum machine learning approaches. A comparative analysis highlights the advantages of hybrid quantum-CNN models in terms of scalability, adaptability, and performance. The paper also discusses emerging trends such as AI-native network design, edge intelligence, and quantum-enhanced optimization. Furthermore, key challenges including computational complexity, energy consumption, and quantum hardware limitations are examined. The findings suggest that hybrid quantum deep learning models are promising for addressing resource management challenges in future 6G networks.