Neural-Network-Assisted Bayesian Qubit Readout at the Single-Photon Level for Scalable Atomic Quantum Processors

Quantum state readout with minimal resources is crucial for scalable quantum information processing. As a leading platform, neutral atom arrays rely on fluorescence readout, requiring short-exposure schemes to mitigate heating and atom loss. 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 a neural-network-assisted Bayesian inference method for fluorescence readout in neutral atom arrays. We introduce a weakly anchored Bayesian scheme that requires calibration of only one state, addressing asymmetric calibration challenges common across quantum platforms. Acceleration via a permutation-invariant neural network, yields a 100-fold speedup by compressing Bayesian 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.