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.
@article{arxiv.2509.12586,
title = {Channel Estimation for Rydberg Atomic Quantum Receivers: Unrolled Phase Retrieval from Holographic Snapshots},
author = {Jian Xiao and Ji Wang and Ming Zeng and Hongbo Xu and Xingwang Li and Arumugam Nallanathan},
journal= {arXiv preprint arXiv:2509.12586},
year = {2026}
}