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的信息传输，但脆弱且昂贵的量子光子资源使得QCN的资源优化极具挑战性。不同于先前通过盲目压缩经典数据的直接量子嵌入方案的QCN研究，本文提出了一种新颖的量子语义通信（QSC）框架，该框架利用量子机器学习和量子语义表征的进展，仅从经典数据中提取和嵌入相关信息，将其映射为最小化的高维量子态，并通过采用量子通信与语义保真度度量准则的量子信道实现精确传输。仿真结果表明，相较于语义无关的QCN方案，所提框架在保持较高量子语义保真度的同时，可将所需量子通信资源降低约50-75%。