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

This is the authors' abstract. We don't add key points for this paper.

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.