A model-driven deep learning framework is proposed for channel estimation in Rydberg atomic quantum receivers (RAQRs) based on the measurement of holographic snapshots. Specifically, we develop a Transformer-based unrolling architecture, termed URformer, to solve the non-linear biased phase retrieval problem, which is derived by unrolling a stabilized variant of the expectation-maximization Gerchberg-Saxton (EM-GS) algorithm. Each layer of the proposed URformer incorporates three trainable modules: 1) a learnable filter network that replaces the fixed Bessel kernel in the classic EM-GS algorithm; 2) a trainable gating mechanism that adaptively combines classic updates to ensure training stability; and 3) an efficient channel Transformer module that learns to correct residual errors by capturing non-local channel dependencies. Numerical results demonstrate that the proposed URformer significantly outperforms classic iterative algorithms and conventional black-box neural networks with less pilot overhead.

翻译：本文提出了一种基于全息快照测量的模型驱动深度学习框架，用于里德伯原子量子接收机(RAQR)的信道估计。具体而言，我们开发了一种基于Transformer的展开架构URformer，通过展开期望最大化-格施贝格-萨克斯顿(EM-GS)算法的稳定变体，解决非线性有偏相位恢复问题。所提出的URformer每一层包含三个可训练模块：1)可学习滤波器网络，用于替代经典EM-GS算法中的固定贝塞尔核；2)可训练门控机制，自适应组合经典更新以保证训练稳定性；3)高效信道Transformer模块，通过捕获非局部信道依赖关系学习校正残差误差。数值结果表明，所提出的URformer在导频开销更小的条件下，显著优于经典迭代算法和传统黑箱神经网络。