Munar-Vallespir, P., & Nötzel, J. (2024). Joint Communication and Sensing over the Lossy Bosonic Quantum Channel. arXiv preprint arXiv:2411.11604.
This paper investigates the tradeoffs inherent in performing simultaneous communication and sensing (JCAS) using quantum systems, focusing on a lossy bosonic channel model. The authors aim to characterize the achievable communication and detection rates and quantify the potential advantages of quantum approaches over classical counterparts.
The authors utilize a theoretical framework based on quantum information theory. They model a bidirectional lossy bosonic channel where the sender transmits coherent states and receives a weak backscattered signal for sensing. The communication rate is analyzed using the Holevo information and the capacity of the lossy bosonic channel. For detection performance, the authors employ the quantum Chernoff bound to determine the achievable discrimination exponent between different channel states.
The study highlights the potential of quantum systems for JCAS, particularly in scenarios with low photon numbers where quantum communication capacity significantly surpasses classical limits. However, it also suggests that the detection performance gains from using quantum measurements might be less substantial compared to optimized classical techniques.
This research contributes to the nascent field of quantum JCAS by providing a theoretical framework for analyzing communication-sensing tradeoffs in a practically relevant channel model. It offers insights into the potential benefits and limitations of quantum approaches for simultaneous communication and sensing tasks.
The paper focuses on a simplified channel model considering only loss. Future research should incorporate more realistic channel impairments like thermal noise, fiber nonlinearities, and turbulence. Additionally, exploring the practical implementation of the theoretically optimal quantum measurements used in the analysis remains an open challenge. Investigating JCAS with Gaussian input states and more general Gaussian channel models is another promising avenue for future work.
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