The emergence of fifth-generation (5G) wireless technologies leads to enlarging network complexity as a result of massive data generation, exhaustive operating costs, time, energy, and the burdens of planning and management. Artificial intelligence (AI) has been demonstrated to have a vital role in improving data analytics and decision-making in massive 5G networks. On the other hand, Quantum Computing is an evolving technology for handling exponential expansion in the data dimensions and calculating linear algebra quicker and more proficiently than traditional computers implying reduced computational costs and energy consumption. The unification between these disciplines engenders the concept of “Quantum Intelligence", which is an innovative and quite promising field with the possibility of unbounded capabilities for the 5G network. Beyond centralized learning, our discussions extend to debate the potentials of quantum intelligence to improve the distributed (federated) learning scenarios over several quantum computers, aiming to drastically enhance computational efficiency and energy consumption. Multiple simulation experiments are performed to evaluate and compare the performance of quantum intelligence on classical and quantum datasets. Finally, this article outlines the major technical and research challenges and open problems for future research on quantum intelligence in 5G wireless networks.
The emergence of fifth-generation (5G) wireless technologies leads to enlarging network complexity as a result of massive data generation, exhaustive operating costs, time, energy, and the burdens of planning and management. Artificial intelligence (AI) has been demonstrated to have a vital role in improving data analytics and decision-making in massive 5G networks. On the other hand, Quantum Computing is an evolving technology for handling exponential expansion in the data dimensions and calculating linear algebra quicker and more proficiently than traditional computers implying reduced computational costs and energy consumption. The unification between these disciplines engenders the concept of “Quantum Intelligence", which is an innovative and quite promising field with the possibility of unbounded capabilities for the 5G network. Beyond centralized learning, our discussions extend to debate the potentials of quantum intelligence to improve the distributed (federated) learning scenarios over several quantum computers, aiming to drastically enhance computational efficiency and energy consumption. Multiple simulation experiments are performed to evaluate and compare the performance of quantum intelligence on classical and quantum datasets. Finally, this article outlines the major technical and research challenges and open problems for future research on quantum intelligence in 5G wireless networks.
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.
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.
Recently fifth generation (5G) and beyond applications are evolving, which demands more computational and complex data processing. Quantum computing and quantum learning algorithms are incorporated to enhance processing capabilities and data computation compared to conventional machine learning approaches. This study presents the significance of quantum computing and quantum machine learning models and their research challenges concerning 5G and beyond applications. The researchers focus on global coverage, enhanced spectrum support, increased energy and cost efficiency, high security, and dynamic intelligence, along with big data processing that demands complex data structures and algorithms.
Quantum computing and artificial intelligence (AI) are two rapidly evolving technologies poised to redefine the future of computation and decision-making.The two fields unite under the name Quantum Artificial Intelligence (QAI) or Quantum Intelligence, which enables a transformative approach to problem-solving through the fusion of AI mental functions with quantum computing capabilities.This paper provides a comprehensive review of the current landscape, highlighting the opportunities and challenges in integrating quantum computing with AI systems.The research investigates current developments in quantum machine learning, quantum neural networks, and hybrid quantum-classical systems, which show promise for achieving rapid performance improvements and enhanced scalability and data processing capabilities.At the same time, it identifies critical barriers, including hardware limitations, algorithmic complexity, data encoding issues, and the lack of standardization in frameworks.The review further explores future directions, including the role of explainable AI, error correction, and interdisciplinary research in achieving practical quantum-AI synergy.By analyzing both the technological potential and the practical constraints, this paper aims to outline a roadmap for advancing quantum intelligence as a transformative force in next-generation computational paradigms.
The field of Quantum Artificial Intelligence (Quantum AI) has emerged as a promising interdisciplinary research area where the computational power of quantum computing is merged with the learning and decision-making power of AI. The application of quantum principles such as superposition, entanglement and quantum interference to machine learning algorithms has created new possibilities for solving problems that may be difficult to solve by classical machines. In the last few years, advances in Noisy Intermediate-Scale Quantum (NISQ) devices have helped to speed advances in Quantum Machine Learning (QML) algorithms, Quantum Neural Networks (QNNs), Quantum Support Vector Machines (QSVMs), Quantum Convolutional Neural Networks (QCNNs) and hybrid learning algorithms. This review provides a comprehensive overview of the research in the field of Quantum AI from 2020 to 2026, covering the key concepts, novel architectures, algorithmic advancements, and practical applications. A critical assessment of the application of Quantum AI in fields such as healthcare, finance, cybersecurity, scientific computing and optimization problems is provided, and the benefits of quantum-enhanced learning models are discussed. In addition, common difficulties such as quantum hardware limitations, sensitivity to noise, encoding of data, scalability constraints and a lack of speed in training variational quantum circuits are discussed. The review also covers new avenues of research, including trustworthy quantum machine learning, explainable Quantum AI, quantum federated learning, and fault-tolerant quantum computing. The results suggest while practical large-scale deployment is currently limited by existing hardware capabilities, hybrid quantum-classical systems have great potential to rapidly move intelligent data processing and develop next-generation AI systems.
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.