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.

In the context of 6G architecture development, the concept of a softwarized (orchestration) continuum is a key pillar. Nevertheless, achieving complete softwarization of network functionalities, tasks, and operations presents inherent challenges, leading to critical trade-offs and limitations. This article explores a novel approach to address these issues by integrating quantum technologies and the Physical Layer Service Integration (PLSI) paradigm. Specifically, we propose the formulation and analysis of network synchronization as a quantum PLSI problem. Our study evaluates synchronization time offset in both conventional Precision Time Protocol (PTP) and quantum-based approaches within the network. We investigate the impact of various network conditions on the precision of PTP synchronization, ranging from nanoseconds under ideal circumstances to microseconds when utilizing virtual network devices. Further, we perform a simulation to generate frequency-entangled photon pairs to access nonlocal temporal correlations and calculate the time offsets. Our findings reveal that entanglement-based PLSI for network synchronization achieves precision at the picosecond level. These results emphasises the high precision achievable by interpreting the network synchronisation problem in the perspective of PLSI and not as a service of the softwarized continuum.

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 trade-offs 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.