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 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.
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.
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.
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.
The convergence of quantum computing and metaverse technologies offers transformative opportunities for secure, scalable, and intelligent communication infrastructures. As metaverse platforms become more immersive and decentralized, supporting real-time voice, video, and biometric data, classical cryptography and computation encounter limitations in privacy, scalability, and trust. This chapter explores how quantum computing and communication techniques can overcome these constraints by enabling unbreakable encryption, quantum-safe identity verification, and high-efficiency processing for multi-user virtual environments. Foundational principles of quantum computing and communication are reviewed, followed by an analysis of quantum algorithms' implications for traditional cryptography. Key areas include post-quantum cryptography, quantum key distribution, and quantum secure direct communication adapted to metaverse infrastructure. Special focus is given to avatar authentication, secure asset transfers, and quantum-enabled decentralized identity systems.
The burgeoning concept of the metaverse as an interconnected virtual space represents the forefront of the next-generation internet. Quantum teleportation, known for its prowess in ensuring secure and reliable communications, stands poised to revolutionize interactions within this immersive digital realm. In this context, we propose a comprehensive interaction protocol tailored for the metaverse environment. The designed protocol entails two fundamental components: first, the interaction between a user and their avatar, facilitated by a secure seven-qubit entangled state; and second, the interaction between two avatars, enabled through an efficient two-qubit entangled state. To fortify the protocol’s resilience, quantum key distribution (QKD) is employed for secure key sharing, while a trusted certificate authority serves as an essential entity for signature verification. Through rigorous safety and efficiency analysis, we demonstrate the protocol’s robustness and performance, ensuring adherence to fundamental security properties such as unforgeability, undeniability, verifiability and traceability. By seamlessly integrating quantum teleportation principles with the metaverse environment, our protocol not only enhances security but also unlocks novel avenues for interaction and exploration within this digital frontier.
Quantum-enabled learning is increasingly being explored for future communication and networking applications, including distributed sensing, Internet of Things (IoT), and distributed quantum computing. However, existing approaches often face challenges in scalable inference and efficient information processing. To address these limitations, this paper integrates quantum machine learning (QML) with quantum semantic communication (QSemCom) in an end-to-end learning framework. A classical dataset is first mapped to a low-dimensional feature representation and encoded by a variational quantum transmitter that learns task-relevant semantic features. The resulting features are transmitted through a quantum channel and processed by a receiver to perform a downstream classification task. The proposed framework is evaluated using the MNIST dataset and variational quantum neural networks (QNNs) under ideal and noisy quantum-channel conditions. First, a baseline model is trained over an ideal channel and evaluated under increasing depolarizing noise without retraining. A second set of experiments introduces a trainable receiver QNN and jointly optimizes the transmitter and receiver through end-to-end (E2E) training. The results demonstrate that jointly trained quantum semantic transceivers can adapt the transmitted representation to channel impairments and preserve task-relevant information, highlighting the potential of receiver-aware QML for robust inference in quantum semantic communication.