Semantic communication (SC) enables bandwidth-efficient coordination in multi-agent systems by transmitting meaning rather than raw bits. However, when agents employ heterogeneous sensing modalities and AI architectures, perfect bit-level transmission no longer guarantees mutual understanding. Although deep learning methods for semantic compression have advanced, the information-theoretic limits of semantic alignment under heterogeneity remain poorly understood. Notably, semantic ambiguity shares the same mathematical structure as quantum contextuality, as both arise from cohomological obstructions, motivating a quantum formulation of SC. In this paper, an information-theoretic framework for quantum semantic communication is proposed using sheaf cohomology. Multi-agent semantic networks are modeled as quantum sheaves, where agents meaning spaces are Hilbert spaces connected by quantum channels. The first sheaf cohomology group is shown to characterize irreducible semantic ambiguity, representing a fundamental obstruction to alignment that no local processing can resolve. The minimum communication rate required for semantic alignment is proven to scale with the logarithm of the dimension of the cohomological space, establishing a semantic analog of Shannon limits. For entanglement-assisted channels, the achievable capacity is shown to strictly exceed classical bounds, with each shared ebit reducing the required classical communication by one bit, providing a rigorous interpretation of shared context. Additionally, quantum contextuality is shown to reduce cohomological obstructions, and a duality between quantum discord and integrated semantic information is established, linking quantum correlations to irreducible semantic content. This framework provides rigorous foundations for quantum-enhanced semantic communication in autonomous multi-agent systems.
Semantic communication (SC) enables bandwidth-efficient coordination in multi-agent systems by transmitting meaning rather than raw bits. However, when agents employ heterogeneous sensing modalities and AI architectures, perfect bit-level transmission no longer guarantees mutual understanding. Although deep learning methods for semantic compression have advanced, the information-theoretic limits of semantic alignment under heterogeneity remain poorly understood. Notably, semantic ambiguity shares the same mathematical structure as quantum contextuality, as both arise from cohomological obstructions, motivating a quantum formulation of SC. In this paper, an information-theoretic framework for quantum semantic communication is proposed using sheaf cohomology. Multi-agent semantic networks are modeled as quantum sheaves, where agents meaning spaces are Hilbert spaces connected by quantum channels. The first sheaf cohomology group is shown to characterize irreducible semantic ambiguity, representing a fundamental obstruction to alignment that no local processing can resolve. The minimum communication rate required for semantic alignment is proven to scale with the logarithm of the dimension of the cohomological space, establishing a semantic analog of Shannon limits. For entanglement-assisted channels, the achievable capacity is shown to strictly exceed classical bounds, with each shared ebit reducing the required classical communication by one bit, providing a rigorous interpretation of shared context. Additionally, quantum contextuality is shown to reduce cohomological obstructions, and a duality between quantum discord and integrated semantic information is established, linking quantum correlations to irreducible semantic content. This framework provides rigorous foundations for quantum-enhanced semantic communication in autonomous multi-agent systems.
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.
Life of Research AbstractThis paper introduces a novel theoretical framework for understanding semantic processing in Large LanguageModels through the lens of sheaf theory and quantum contextuality. We propose that the semantic structure ofLLM-generated text can be rigorously modeled as a sheaf over the topological space of prompt contexts, wherelocal semantic sections may fail to glue into globally consistent interpretations. This mathematical formalizationreveals a deep structural parallel between LLM behavior and quantum mechanical systems exhibitingcontextuality, as characterized by the Kochen-Specker theorem. Our central thesis posits that hallucinationphenomena in LLMs arise fundamentally from cohomological obstructions to the existence of global semanticsections, analogous to how quantum systems exhibit measurement outcomes that cannot be explained by preexisting hidden variables. We develop a complete categorical framework using topos-theoretic methods, introduceformal definitions of semantic presheaves and their associated Grothendieck topologies, and demonstrate howCech cohomology groups can quantify the degree of semantic inconsistency in model outputs. This work bridgesabstract mathematics with practical AI interpretability, offering new diagnostic tools for understanding when andwhy language models produce incoherent or factually inconsistent outputs.
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 communication has reached a rate bottleneck, yet enhancing its throughput remains of profound significance. Here, we propose a scheme termed semantic quantum secure direct communication (QSDC), which is a novel paradigm integrating semantic communication with QSDC to realize highly efficient, task-oriented, and intrinsically secure information transmission. In semantic QSDC, meaningful semantic content is directly encoded into quantum states, whereby the fundamental laws of quantum physics guarantee inherent eavesdropping-detection capability and information-theoretic security. By harnessing semantic compression and artificial intelligence–assisted encoding strategies, this approach surpasses the intrinsic rate constraints of conventional QSDC. Simulation results show that semantic QSDC can surpass the Shannonian mutual information bound compared with conventional QSDC systems and demonstrate its potential to break through the linear rate-transmittance bound of quantum communication. These findings pave the way for high-rate secure communication.
Word Sense Disambiguation (WSD) is one of the unresolved issues in natural language processing because polysemous words are inherently ambiguous and require semantic interpretation based on the context. Transformerbased models have made considerable progress in contextual modelling still, they cannot address fine-grained sense distinctions, long-range dependencies, and sentences containing multiple ambiguous words interacting with each other. At the same time quantum-inspired methods also take advantage of superposition and interference to model ambiguity, although generally only at the word level, and do not do contextual propagation. This paper presents Q-EntangleFormer, a new hybrid quantum-classical Transformer based on a unified quantum multi-sense superposition encoding, contextual entanglement of words, and quantum-enhanced attention of deep contextual WSD. The unclear word is represented as a quantum superposition of the possible meanings, the dependence between the context and sense is represented by entanglement networks and the hybrid attention model can combine classical and quantum signals with a fusion gate. Empirical testing of standard WSD metrics indicates that QEntangleFormer has significantly better accuracy than both classical Transformer models and current quantum WSD approaches. The framework also shows that it is parameterefficiently scalable and that it is better understood through amplitude-based semantic reasoning. It therefore connects quantum representation theory and contextual semantic modelling and points to a new direction of quantum-augmented natural language understanding.
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.