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.
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 delves into the integration of quantum technologies with semantic communications, addressing challenges related to transmitting knowledge graphs over conventional wireless channels. As traditional communications evolve towards the sharing and elaboration of knowledge between intelligent agents, both natural and artificial, they encounter critical challenges in terms of efficiency, accuracy, and the associated high complexity. To address these challenges, we propose leveraging quantum mechanical principles to communicate conceptual graph-based semantic representations of data between networked parties. The primary goal of the protocol is to transmit essential semantic features accurately while minimizing resource consumption and maintaining resilience against depolarizing errors. These include representing the semantic messages efficiently, using a quantum data encoding method, and employing remote state preparation to transmit the messages. Our work evaluates both the success probability of decoding semantic messages and investigates scalability analysis. Our results reveal that quantum semantic communication is robust against a noisy environment, and the evaluation of scalability analysis shows a trade-off regarding the complexity of communicating conceptual graphs.
As the world outgrows the capabilities of 5G networks, the search for the next technical standard — 6G — accelerates, promising unprecedented connectivity, capability, and adaptability. However, optimizing network performance to simultaneously satisfy purported multiple key performance indicators — latency, energy efficiency, resilience, trustworthiness etc. — is a herculean struggle. Traditional Shannon-based communication models struggle to address and overcome the complex tradeoffs required in ultra-reliable, low-latency, and secure network architectures. It is no surprise, then, that emerging paradigms such as semantic communication, goal-oriented communication, and quantum communication are being integrated into telecommunication research. Unlike classical communication, semantic communication focuses on transmitting meaning more so than merely raw data; additionally, quantum semantic communication leverages quantum mechanics to further optimize information processing and security. In this paper, we conceptually investigate whether these theoretical technological advancements, when interpreted in the right context, can intrinsically enhance resilience in 6G networks.
Semantic communications prioritize transmitting meaningful information over raw data in communication systems. However, these systems face significant optimization challenges, particularly concerning resource efficiency and fidelity due to the costly and delicate nature of photonic resources. Quantum computing offers promising solutions to these challenges through its unique capabilities, such as superposition and entanglement, within high-dimensional Hilbert spaces. This survey reviews the integration of quantum computing into semantic communications, tracing developments from foundational concepts to current advancements. It explores how quantum embeddings and machine learning techniques enhance semantic representation and transmission, enabling the encoding and processing of only relevant information. This approach addresses issues of polysemy and contextual variations in large datasets more effectively than classical methods. Key topics include the utilization of quantum probability and cognition in semantic analysis, optimization of quantum protocols for faster information retrieval, and the role of variational quantum circuits in improving computation latency, communication bandwidth, data privacy, and transmission delays. It also examines the implications for future communication systems like next-generation networks. It highlights the shift towards intelligent computing-intensive architectures and the support for advanced applications such as extended reality and holographic communications. Potential pathways are identified to achieve more efficient, secure, and intelligent data transmission, aiming to provide researchers and practitioners with a thorough understanding of this emerging field, outlining open research challenges and future directions to inspire further exploration and innovation.
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.
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.