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.
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.
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 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 exponential growth of IoT devices in future 6G networks necessitates a paradigm shift from traditional data-centric communication to semantically meaningful, goaloriented communication models that optimize information relevance, efficiency, and intent-driven exchange. In this work, we propose a novel quantum semantic sensing model that integrates semantic communication technologies with quantum mechanical principles for goal-oriented tasks. By leveraging quantum interference and quantum teleportation, our model enables goal-driven communication over noisy quantum links. To validate our theoretical framework, we implement the full pipeline using the NetSquid quantum network simulator. Instead of transmitting full sensor observations, our model semantically fuses taskrelevant embeddings into low-dimensional quantum states, encodes them for teleportation, and verifies alignment with receiver goals through quantum interference. The protocol's performance is evaluated using two baseline strategies: (i) quantum teleportation fidelity, and (ii) semantic fidelity i.e., the degree of semantic equivalence between sender and receiver assessed via quantum interference. Our results show that semantic fidelity remains above 0.8 even when teleportation fidelity drops below 0.6, confirming the hypothesis that exact quantum state reconstruction is not required for effective goaloriented semantic inference.
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.
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.
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.