Recent work has shown the promise of applying deep learning to enhance software processing of radio frequency (RF) signals. In parallel, hardware developments with quantum RF sensors based on Rydberg atoms are breaking longstanding barriers in frequency range, resolution, and sensitivity. In this paper, we describe our implementations of quantum-ready machine learning approaches for RF signal classification. Our primary objective is latency: while deep learning offers a more powerful computational paradigm, it also traditionally incurs latency overheads that hinder wider scale deployment. Our work spans three axes. (1) A novel continuous wavelet transform (CWT) based recurrent neural network (RNN) architecture that enables flexible online classification of RF signals on-the-fly with reduced sampling time. (2) Low-latency inference techniques for both GPU and CPU that span over 100x reductions in inference time, enabling real-time operation with sub-millisecond inference. (3) Quantum-readiness validated through application of our models to physics-based simulation of Rydberg atom QRF sensors. Altogether, our work bridges towards next-generation RF sensors that use quantum technology to surpass previous physical limits, paired with latency-optimized AI/ML software that is suitable for real-time deployment.

翻译：近期研究表明，将深度学习应用于增强射频（RF）信号的软件处理具有广阔前景。与此同时，基于里德伯原子的量子射频传感器硬件发展正突破频率范围、分辨率和灵敏度方面的长期瓶颈。本文阐述了面向射频信号分类的量子就绪机器学习方法实现。我们的首要目标是降低延迟：尽管深度学习提供了更强大的计算范式，但传统上其产生的延迟开销阻碍了更广泛的规模化部署。研究工作沿三个方向展开：（1）提出一种基于连续小波变换（CWT）的循环神经网络（RNN）新架构，可在降低采样时间的前提下实现灵活的在线射频信号实时分类；（2）针对GPU和CPU的低延迟推理技术，实现超过100倍的推理时间缩减，支持亚毫秒级实时推理；（3）通过将模型应用于里德伯原子量子射频传感器的物理仿真验证其量子就绪性。综合而言，我们的工作为下一代射频传感器架起桥梁——这类传感器利用量子技术突破传统物理极限，并配以适用于实时部署的延迟优化AI/ML软件。