In modern wireless networks, radio channels serve a dual role. Whilst their primary function is to carry bits of information from a transmitter to a receiver, the intrinsic sensitivity of transmitted signals to the physical structure of the environment makes the channel a powerful source of knowledge about the world. In this paper, we consider an agent that learns about its environment using a quantum sensing probe, optimised using a quantum circuit, which interacts with the radio-frequency (RF) electromagnetic field. We use data obtained from a ray-tracer to train the quantum circuit and learning model and we provide extensive experiments under realistic conditions on a localisation task. We show that using quantum sensors to learn from radio signals can enable intelligent systems that require no channel measurements at deployment, remain sensitive to weak and obstructed RF signals, and can learn about the world despite operating with strictly less information than classical baselines.

翻译：在现代无线网络中，射频信道具有双重作用。其主要功能固然是在发射机与接收机之间传递信息比特，但传输信号对环境物理结构的内在敏感性，使得信道成为认知世界的强大知识来源。本文研究一种智能体，该智能体通过量子传感探针（利用量子电路优化）与射频电磁场相互作用来学习环境信息。我们使用射线追踪器获取的数据训练量子电路与学习模型，并在实际条件下针对定位任务开展了大量实验。研究表明，利用量子传感器从射频信号中学习，能够实现以下优势：部署时无需信道测量、对微弱及受阻射频信号保持敏感、在严格少于经典基线信息量的条件下仍能有效认知世界。