AI-Accelerated Qubit Readout at the Single-Photon Level for Scalable Atomic Quantum Processors
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917168718807040 |
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| author | Zhou, Yaoting Wang, Weisen Tian, Zhuangzhuang Huang, Bin Chen, Huancheng Li, Donghao Xu, Zhongxiao Chen, Li Shen, Heng |
| author_facet | Zhou, Yaoting Wang, Weisen Tian, Zhuangzhuang Huang, Bin Chen, Huancheng Li, Donghao Xu, Zhongxiao Chen, Li Shen, Heng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_20919 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-Accelerated Qubit Readout at the Single-Photon Level for Scalable Atomic Quantum Processors Zhou, Yaoting Wang, Weisen Tian, Zhuangzhuang Huang, Bin Chen, Huancheng Li, Donghao Xu, Zhongxiao Chen, Li Shen, Heng Quantum Physics Atomic Physics 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. |
| title | AI-Accelerated Qubit Readout at the Single-Photon Level for Scalable Atomic Quantum Processors |
| topic | Quantum Physics Atomic Physics |
| url | https://arxiv.org/abs/2512.20919 |