AI-Accelerated Qubit Readout at the Single-Photon Level for Scalable Atomic Quantum Processors
Abstract
Quantum state readout with minimal resources is crucial for scalable quantum information processing. As a leading platform, neutral atom arrays rely on atomic fluorescence imaging for qubit readout, requiring short exposure, low photon count schemes to mitigate heating and atom loss while enabling mid-circuit feedback. However, a fundamental challenge arises in the single-photon regime where severe overlap in state distributions causes conventional threshold discrimination to fail. Here, we report an AI-accelerated Bayesian inference method for fluorescence readout in neutral atom arrays. Our approach leverages Bayesian inference to achieve reliable state detection at the single-photon level under short exposure. Specifically, we introduce a weakly anchored Bayesian scheme that requires calibration of only one state, addressing asymmetric calibration challenges common across quantum platforms. Furthermore, acceleration is achieved via a permutation-invariant neural network, which yields a 100-fold speedup by compressing iterative inference into a single forward pass. The approach achieves relative readout fidelity above 99% and 98% for histogram overlaps of 61% and 72%, respectively, enabling reliable extraction of Rabi oscillations and Ramsey interference results unattainable with conventional threshold based methods. This framework supports scalable, real-time readout of large atom arrays and paves the way toward AI-enhanced quantum technology in computation and sensing.
Cite
@article{arxiv.2512.20919,
title = {AI-Accelerated Qubit Readout at the Single-Photon Level for Scalable Atomic Quantum Processors},
author = {Yaoting Zhou and Weisen Wang and Zhuangzhuang Tian and Bin Huang and Huancheng Chen and Donghao Li and Zhongxiao Xu and Li Chen and Heng Shen},
journal= {arXiv preprint arXiv:2512.20919},
year = {2025}
}