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arXiv 2205.02422cs.NIquant-ph
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面向资源高效的量子网络的量子语义通信

Quantum Semantic Communications for Resource-Efficient Quantum Networking

Mahdi Chehimi, Christina Chaccour, Christo Kurisummoottil Thomas, Walid Saad

AI总结:

本文提出一种基于量子机器学习的量子语义通信(QSC)框架,通过提取并嵌入相关信息来优化高维量子态传输,在减少约50%量子通信资源的同时实现了更高的语义保真度。

摘要:

量子通信网络(QCNs)利用量子力学实现安全的信息传输,但对脆弱的量子资源的需求使得QCN资源优化极具挑战性。与以往依赖直接压缩经典量子嵌入的QCN工作不同,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来提取并仅嵌入来自经典数据的相关信息,从而实现最小高维量子态的传输。仿真结果表明,在量子通信资源受限的情况下,所提框架在实现更高量子语义保真度的同时,所需的量子通信资源减少了约50%。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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