Semantic communication (SC) with native artificial intelligence (AI) is a context-centric framework that intelligently extracts task-specific semantics from source data and efficiently regenerates the intended meaning at the destination. Hence, this computing-intensive methodology enables goal-oriented communication by maintaining a high semantic quality of service with a low requirement for data transfer. Recently, the emergence of big-AI foundation models, such as the generative pre-trained transformer and diffusion models with zero-shot task generalization and native cross-modal learning capabilities, has brought a paradigm shift in designing AI-native frameworks for wireless networks. However, deploying big AI in wireless networks involves inherent challenges, such as large training parameters and computing requirements. To address these challenges, we use sustainability techniques, such as pruning and fine-tuning, to create sustainable (lightweight) models from big AI, which can reduce the resource consumption and environmental impact in computation-heavy SC systems while preserving or enhancing the task performance. Moreover, classical communication networks lack quantum-safe communication security and data privacy. In this article, we prototype a sustainable big AI-native quantum anonymous SC system. In this framework, we leverage big-AI models for semantic retrieval processing, that is, semantic extraction and recovery, and employ quantum anonymous communication protocols to broadcast semantics. We detail the underlying functionalities, sustainable practices, and potential challenges of integrating big AI into a quantum anonymous semantic broadcast system. We also formulate case studies demonstrating the sustainability and reliability of the envisioned framework. This work provides a sustainable and quantum-safe semantic communication framework by integrating big AI and quantum anonymous communication.
Semantic communication (SC) with native artificial intelligence (AI) is a context-centric framework that intelligently extracts task-specific semantics from source data and efficiently regenerates the intended meaning at the destination. Hence, this computing-intensive methodology enables goal-oriented communication by maintaining a high semantic quality of service with a low requirement for data transfer. Recently, the emergence of big-AI foundation models, such as the generative pre-trained transformer and diffusion models with zero-shot task generalization and native cross-modal learning capabilities, has brought a paradigm shift in designing AI-native frameworks for wireless networks. However, deploying big AI in wireless networks involves inherent challenges, such as large training parameters and computing requirements. To address these challenges, we use sustainability techniques, such as pruning and fine-tuning, to create sustainable (lightweight) models from big AI, which can reduce the resource consumption and environmental impact in computation-heavy SC systems while preserving or enhancing the task performance. Moreover, classical communication networks lack quantum-safe communication security and data privacy. In this article, we prototype a sustainable big AI-native quantum anonymous SC system. In this framework, we leverage big-AI models for semantic retrieval processing, that is, semantic extraction and recovery, and employ quantum anonymous communication protocols to broadcast semantics. We detail the underlying functionalities, sustainable practices, and potential challenges of integrating big AI into a quantum anonymous semantic broadcast system. We also formulate case studies demonstrating the sustainability and reliability of the envisioned framework. This work provides a sustainable and quantum-safe semantic communication framework by integrating big AI and quantum anonymous communication.
The Metaverse is a connected virtual space that has recently emerged as a new frontier for human interaction and experiences. For ideal immersive Metaverse experiences, perpetual data traffic poses stringent requirements on computation latency, communication bandwidth, data privacy, and transmission delay. Semantic communication with machine learning provides a nascent paradigm shift from context-agnostic communication toward semantics-centric communication to address these underlying challenges simultaneously. However, classical limitations of this computing-intensive communication partially serve the purposes of semantic extraction, privacy protection, and data-driven processing for Metaverse applications. In this article, we investigate the potential benefits of incorporating quantum anonymous communication and variational quantum computing to develop quantum semantic communication that enables Metaverse users to interact with virtual environments more reliably and securely. Specifically, quantum embedding and quantum machine learning are employed to extract and encode semantic messages of classical data into quantum states on high-dimensional Hilbert spaces. These semantic quantum states are then anonymously teleported to the receiving party using the quantum teleportation protocol that exploits anonymous entanglement and broadcast. We detail this anonymous quantum semantic communication framework that fully capitalizes on unique quantum resources and properties for the Metaverse, including fundamental principles and deployment challenges.
The paradigm of semantic and goal-oriented communication offers a transition toward highly efficient AI-native connect-compute-actuate 6G networks. Unlike current content-blind communication and ML/AI training, interactions among intelligent agents capture essential information properties, facilitating practical support for understanding and effective goal attainment. However, its full potential is hindered by excessive energy consumption, wireless system resource consumption, and computational limitations. Graph-based knowledge and communication systems effectively represent, communicate, and process complex, causal, structural, and multidimensional semantic meanings. Operating in terms of semantic entities, relationships, contextual logic, and reasoning rules, these systems face challenges in transmitting graphs over classical wireless channels, given the associated resource costs. To overcome this, we explore integrating quantum technology into semantic communication, presenting a novel approach - quantum semantic communications. In our study, we focus on sharing semantic knowledge graphs through quantum mechanical principles, utilizing remote state preparation within quantum protocols. We evaluate the tradeoffs between semantic graphs and quantum channel costs to determine the feasibility of our protocol. Additionally, we explore the scalability of the quantum approach and demonstrate the computational gains achievable in polynomial time.
This paper explores integrating quantum technologies with semantic communications to address the challenges of transmitting knowledge graph embeddings over traditional communication channels. As communication systems evolve to facilitate knowledge sharing and processing among natural and artificial intelligent agents, they encounter issues such as inefficiency, inaccuracy, and high complexity. To address these challenges, we propose a novel approach called quantum semantic communications, which integrates quantum technologies into the emerging framework of semantic communications. The primary goal of this protocol is to transmit semantic features efficiently, minimizing resource consumption while ensuring robustness. Our approach involves extracting semantic data from knowledge graphs using pre-trained large language model encoder and graph neural network as semantic encoders to generate compact knowledge representations transmitted over quantum communication channels. To assess the effectiveness of our protocol, we analyze performance metrics such as quantum semantic fidelity and F1 score under various noise conditions, including depolarizing, amplitude damping, and dephasing. Our simulations show that the proposed quantum semantic communication approach remains robust even in a low signal-to-noise regime.
This paper explores the integration of Quantum Communication Networks (QCNs), semantic communication, and covert communication within the context of 6G and future wireless networks. Introducing a new Quantum Semantic Communications (QSC) framework that capitalizes on advancements in quantum machine learning and semantic representations, this framework dramatically enhances resource efficiency in QCN s. It does so by embedding only relevant classical data into compact, high-dimensional quantum states for transmission, achieving a potential resource reduction of 50-75% while boosting quantum semantic fidelity. The paper further examines Artificial Intelligence's (AI) transformative role in upgrading tra-ditional communication paradigms into more efficient semantic communication systems, utilizing deep learning and end-to-end methodologies to ensure precise conveyance and interpretation of meanings in transmitted information. Additionally, it explores incorporating covert communication strategies within systems supported by a Reconfigurable Intelligent Surface (STAR-RIS) and Non-Orthogonal Multiple Access (NOMA), emphasizing the enhanced security and stealth necessary for modern networks. By merging these sophisticated communication strategies, the paper anticipates a new era of telecommunications that significantly surpasses existing security, efficiency, and semantic accuracy capabilities, marking a progressive step towards future networks optimized for secure, efficient, and meaning-focused communication in the quantum and AI era.
The rapid advancement of Artificial Intelligence (AI) is reshaping industries and driving global innovation. However, the increasing complexity of AI models demands substantial data and computational resources, leading to significant energy consumption and environmental impact. This article explores the integration of quantum computing and end-to-end automation strategies in cloud-edge architectures. It proposes a hybrid quantum-classical AI framework that enhances training efficiency and reduces data and processing intensity by minimizing energy consumption. The framework leverages automated model orchestration, adaptive resource allocation, and intelligent data processing at the edge to improve system efficiency. In addition, it addresses ethical considerations, including privacy, fairness, and trustworthiness, to ensure alignment with human values. This approach significantly improves AI performance while fostering a sustainable and ethical AI ecosystem.
AI-native 6G networks are envisioned to tightly embed artificial intelligence (AI) into the wireless ecosystem, enabling real-time, personalized, and privacy-preserving intelligence at the edge. A foundational pillar of this vision is federated learning (FL), which allows distributed model training across devices without sharing raw data. However, implementing classical FL methods faces several bottlenecks in heterogeneous dynamic wireless networks, including limited device compute capacity, unreliable connectivity, intermittent communications, and vulnerability to model security and data privacy breaches. This article investigates the integration of quantum federated learning (QFL) into AI-native 6G networks, forming a transformative paradigm capable of overcoming these challenges. By leveraging quantum techniques across computing, communication, and cryptography within FL workflows, QFL offers new capabilities along three key dimensions: (i) edge intelligence, (ii) network optimization, and (iii) security and privacy, which are studied in this work. We further present a case study demonstrating that a QFL framework employing the quantum approximate optimization algorithm outperforms classical methods in model convergence. We conclude the paper by identifying practical challenges facing QFL deployment, such as quantum state fragility, incompatibility with classical protocols, and hardware constraints, and then outline key research directions toward its scalable real-world adoption.