The sixth generation (6G) of wireless networks is expected to surpass the limitations of 5G by enabling terabit-level data rates, sub-millisecond latency, and ultra-dense connectivity through the integration of space-air-land-sea infrastructures and AI-driven autonomous management. However, such advancements pose new challenges in computational scalability and security against quantum-enabled threats. Quantum Artificial Intelligence (QAI), combining quantum computing principles with AI, offers exponential computational efficiency, enhanced model capacity, and resilient cryptographic solutions. This paper reviews QAI-based protocols for 6G across three domains: security, focusing on Quantum Key Distribution (QKD) and post-quantum cryptography; computing, including quantumenhanced edge intelligence and hybrid optimization; and data processing, with emphasis on terahertz (THz) channel modeling, holographic content, and real-time decision systems. The convergence of 6G and QAI is shown to mark a paradigm shift toward secure, scalable, and intelligent communication infrastructures of the future.
The sixth generation (6G) of wireless networks is expected to surpass the limitations of 5G by enabling terabit-level data rates, sub-millisecond latency, and ultra-dense connectivity through the integration of space-air-land-sea infrastructures and AI-driven autonomous management. However, such advancements pose new challenges in computational scalability and security against quantum-enabled threats. Quantum Artificial Intelligence (QAI), combining quantum computing principles with AI, offers exponential computational efficiency, enhanced model capacity, and resilient cryptographic solutions. This paper reviews QAI-based protocols for 6G across three domains: security, focusing on Quantum Key Distribution (QKD) and post-quantum cryptography; computing, including quantumenhanced edge intelligence and hybrid optimization; and data processing, with emphasis on terahertz (THz) channel modeling, holographic content, and real-time decision systems. The convergence of 6G and QAI is shown to mark a paradigm shift toward secure, scalable, and intelligent communication infrastructures of the future.
The rise of 6G communication networks, powered by artificial intelligence (AI), promises a new era of connectivity with ultra-fast data speeds, intelligent applications, and seamless integration across a wide range of devices. However, the immense volume of sensitive data handled by these ultra-dense networks brings heightened concerns about privacy and security. Quantum cryptography, which leverages the principles of quantum mechanics, offers groundbreaking potential for securing communications; yet, the advent of quantum computing poses significant threats to traditional cryptographic systems. This challenge underscores the need for quantum-resilient cryptographic solutions. This review delves into the intersection of quantum-resilient cryptographic techniques and AI-driven 6G communication networks. It provides a detailed examination of existing quantum-resistant algorithms, evaluates their suitability in AI-enabled 6G systems, and highlights critical challenges and potential future directions. By addressing the pressing demand for secure and resilient communication frameworks, this study contributes to the development of 6G systems that can withstand the security threats posed by the quantum computing era.
The amalgamation of quantum computing and artificial intelligence (AI) is poised to redefine the capabilities of future 6G networks. This paper presents a structured, system-level analysis of Quantum-enabled AI (QAI) for 6G, organized around a unifying technical framework that progresses from foundations to algorithms, integration, security and deployment. Specifically, we ask: How can quantum technologies be systematically integrated into AI-native 6G networks in a secure, scalable and practically deployable manner? To answer this, we first establish foundational concepts in quantum computing and hybrid quantum-classical architectures as the enabling substrate. Next, we classify quantum machine learning (QML) algorithms by learning paradigm, quantum subroutine, hardware model and ML pipeline stage, directly mapping each category to specific 6G network functions. Building on this algorithmic layer, we examine how QAI integrates into 6G AI pipelines across training, inference, sensing and federated learning. Beyond taxonomy, we provide a comparative tradeoff analysis of representative QML methods, highlighting their suitability, latency implications, hardware readiness and limitations across practical 6G communication scenarios. We then analyze the security implications of this integration, addressing new attack vectors, adversarial threats, hardware vulnerabilities and post-quantum cryptographic requirements that arise specifically at the quantum-classical interface. Finally, we identify the architectural, algorithmic and regulatory barriers that must be addressed before deployment and synthesize lessons learned and strategic recommendations for researchers, developers and policymakers. This layered, interdependent structure, from hardware substrate through algorithmic capability to secure deployment, provides a coherent roadmap for advancing trustworthy QAI ecosystems in 6G.
As we move toward the rollout of 6G networks, we're stepping into an era of groundbreaking technological advancements. These next-generation networks promise ultra-low latency and seamless integration with artificial intelligence (AI) and the Internet of Things (IoT), unlocking a new level of connectivity and performance. However, with these powerful capabilities come significant security challenges. Traditional encryption methods may not be enough to protect the complex and highly sensitive data traveling through 6G networks. One of the most promising solutions lies in Quantum Communication, particularly Quantum Key Distribution (QKD)-a method that leverages the laws of quantum mechanics to enable highly secure encryption. This paper explores the emerging security risks in both terrestrial and satellite communication channels and reviews current research on integrating quantum communication into 6G infrastructure. Special attention is given to its potential within the metaverse, a rapidly evolving digital environment that demands robust and future-proof security frameworks. Key areas of focus include-Addressing security vulnerabilities in quantum communication, Enhancing infrastructure to support quantum technologies, Reducing error rates in quantum protocols. the paper aims to propose a flexible, forward-looking framework designed to ensure fast, secure communication for critical sectors such as finance, defense, and healthcare.
The rapid evolution of communication networks, driven by the expansion of heterogeneous environments such as 6G, Internet of Things (IoT), and edge computing, has exposed a critical research gap in the lack of unified frameworks that jointly address intelligent network control and quantum-resilient security. Existing networking protocols were originally designed under static configurations and classical security assumptions, making them increasingly inadequate for dynamic, large-scale, and intelligent infrastructures exposed to quantum-enabled threats. At the same time, the emergence of Quantum Computing (QC) introduces severe security risks, as widely used cryptographic mechanisms supporting protocols such as Transport Layer Security (TLS) and Internet Protocol Security (IPsec) become vulnerable to quantum attacks. This paper presents a structured survey based on a systematic classification of recent literature across AI-enabled networking, Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD), and protocol-level integration approaches. The review categorizes existing works into AI-driven network optimization, quantum-resilient security mechanisms, and hybrid AI–QRC architectural frameworks, enabling a unified comparison of design strategies and deployment models. Furthermore, this survey analyzes how Artificial Intelligence (AI) techniques enable autonomous network operations while Quantum-Resilient Cryptography (QRC) approaches provide post-quantum security guarantees, highlighting their complementary roles in next-generation network design. The study further synthesizes key findings for a tightly coupled AI–QRC co-design, which remains an open challenge due to mismatched assumptions in control latency, cryptographic overhead, and protocol adaptability. Additionally, this review emphasizes the need for integrated frameworks that combine intelligence and quantum security to develop adaptive, scalable, and resilient network protocols for the post-quantum era.
Modern connectivity has changed significantly as a result of wireless mobile networks’ utilization of continuous communication across an international infrastructure. Data protection and cyber attacks are two issues that continue to arise as mobile computing and wireless communication networks develop. As 5G networks give way to 6G networks, terahertz communication, artificial intelligence integration, and quantum technologies are introduced to provide high-speed data transfer and communications, along with improved security. In contrast, post-quantum cryptography (PQC) and quantum key distribution (QKD) are two remote methods for addressing the need to adjust cryptographic systems in the current infrastructures to fight impending quantum threats. The fundamental ideas, real-world uses, and ways that PQC and QKD operate together to provide strong security for next-generation networks are all examined in this chapter.
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.