AI-Accelerated Qubit Readout at the Single-Photon Level for Scalable Atomic Quantum Processors

• URL: http://arxiv.org/abs/2512.20919v1
• Date: Wed, 24 Dec 2025 03:51:56 GMT
• Title: AI-Accelerated Qubit Readout at the Single-Photon Level for Scalable Atomic Quantum Processors
• Authors: Yaoting Zhou, Weisen Wang, Zhuangzhuang Tian, Bin Huang, Huancheng Chen, Donghao Li, Zhongxiao Xu, Li Chen, Heng Shen,
• Abstract summary: We report an AI-accelerated Bayesian inference method for fluorescence readout in neutral atom arrays.<n>Our approach leverages Bayesian inference to achieve reliable state detection at the single-photon level under short exposure.<n>This framework supports scalable, real-time readout of large atom arrays and paves the way toward AI-enhanced quantum technology in computation and sensing.
• Score: 16.821447184897142
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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