Quantum communication networks (QCNs) utilize quantum mechanics for secure information transmission, but the reliance on fragile and expensive photonic quantum resources renders QCN resource optimization challenging. Unlike prior QCN works that relied on blindly compressing direct quantum embeddings of classical data, this letter proposes a novelquantum semantic communications (QSC)framework exploiting advancements in quantum machine learning and quantum semantic representations to extracts and embed only the relevant information from classical data into minimal high-dimensional quantum states that are accurately communicated over quantum channels with quantum communication and semantic fidelity measures. Simulation results indicate that, compared to semantic-agnostic QCN schemes, the proposed framework achieves approximately 50-75% reduction in quantum communication resources needed, while achieving a higher quantum semantic fidelity.
Quantum communication networks (QCNs) utilize quantum mechanics for secure information transmission, but the reliance on fragile and expensive photonic quantum resources renders QCN resource optimization challenging. Unlike prior QCN works that relied on blindly compressing direct quantum embeddings of classical data, this letter proposes a novelquantum semantic communications (QSC)framework exploiting advancements in quantum machine learning and quantum semantic representations to extracts and embed only the relevant information from classical data into minimal high-dimensional quantum states that are accurately communicated over quantum channels with quantum communication and semantic fidelity measures. Simulation results indicate that, compared to semantic-agnostic QCN schemes, the proposed framework achieves approximately 50-75% reduction in quantum communication resources needed, while achieving a higher quantum semantic fidelity.
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 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.
Quantum-enabled semantic communication networks (QESCs) leverage quantum technologies to transmit data meaning efficiently, yet face challenges from costly resources and noise. This letter introduces Quantum Markov Blankets (QMBs) to QESCs, a novel framework to isolate essential quantum information for semantic transmission. We prove QMBs' validity using quantum conditional mutual information, showing that they shield semantic content from irrelevant subsystems. An implementation strategy optimises QMB detection, reducing resource use. Simulations suggest that QMB-based QESCs cut qubit consumption by 50\%-75\% while enhancing fidelity compared with non-optimised quantum semantic schemes. Unlike classical approaches, QMBs offer inherent security by limiting an eavesdropper's access to classical data outside the blanket. We outline future directions, including real-time QMB adaptation. This work bridges quantum information theory and semantic communication, advancing resource-efficient and secure quantum networks.
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.
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.
Quantum communications is a promising technology that will play a fundamental role in the design of future networks. In fact, significant efforts are being dedicated by both the quantum physics and the classical communications communities on developing new architectures, solutions, and practical implementations of quantum communication networks (QCNs). Although these efforts led to various advances in today's technologies, there still exists a non-trivial gap between the research efforts of the two communities on designing and optimizing the performance of QCNs. For instance, most prior works by the classical communications community ignore important quantum physics-based constraints when designing QCNs. For example, many existing works on entanglement distribution do not account for the decoherence of qubits inside quantum memories and, thus, their designs become impractical since they assume an infinite lifetime of quantum states. In this paper, we bring forth a novel analysis of the performance of QCNs in a physics-informed manner, by relying on the quantum physics principles that underly the different components of QCNs. The need of the physics-informed approach is then assessed and its fundamental role in designing practical QCNs is analyzed across various open research areas. Moreover, we identify novel physics-informed performance metrics and controls that enable QCNs to leverage the state-of-the-art advancements in quantum technologies to enhance their performance. Finally, we analyze multiple pressing challenges and open research directions in QCNs that must be treated using a physics-informed approach to lead practically viable results. Ultimately, this work attempts to bridge the gap between the classical communications and the quantum physics communities in the area of QCNs to foster the development of the future communication networks towards the quantum Internet.