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

TL;DR

This paper introduces an AI-accelerated Bayesian inference method for neutral atom qubit readout, enabling high-fidelity, real-time detection at the single-photon level with significant speed improvements, crucial for scalable quantum computing.

Contribution

It presents a novel weakly anchored Bayesian scheme combined with neural network acceleration for reliable, fast, single-photon qubit readout in neutral atom arrays, addressing calibration and speed challenges.

Findings

Achieves over 99% readout fidelity at the single-photon level.

Provides a 100-fold speedup in inference time.

Enables extraction of quantum coherence phenomena like Rabi oscillations.

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.