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 novel quantum 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.

翻译：量子通信网络（QCNs）利用量子力学实现安全信息传输，但其对脆弱且昂贵的光子量子资源的依赖致使QCN资源优化极具挑战性。与先前依赖对经典数据直接量子嵌入进行盲目压缩的QCN研究不同，本文提出一种新型量子语义通信（QSC）框架。该框架借助量子机器学习与量子语义表征领域的进展，从经典数据中仅提取并嵌入相关信息至最小的高维量子态，这些量子态通过量子信道进行精确传输，并采用量子通信与语义保真度指标进行评估。仿真结果表明，与语义无关的QCN方案相比，所提框架在实现更高量子语义保真度的同时，可减少约50-75%的量子通信资源需求。