发表机构
Institute for Advanced Study, Tsinghua University; Intelligent Quantum Inception Co., Ltd.; iFLYTEK Research（清华大学高等研究院; 智能量子初创有限公司; 科大讯飞研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出神经关联学习框架，利用时间无关驱动的里德伯原子阵列生成多体关联，实现超越标准量子极限的量子增强传感，无需制备纠缠态。
AI 中文摘要
量子增强传感通常依赖于制备特定的纠缠态。然而，由于难以设计稳健的制备协议并考虑现实噪声，在可扩展的多体系统中工程化这些态仍然具有挑战性。我们表明，在里德伯原子阵列中，通过本征的时间无关哈密顿量动力学演化一个简单的乘积态，能够生成复杂的多体关联，这些关联可能使量子增强参数估计达到海森堡极限（HL）。关键挑战随后转移到提取测量模式空间关联中编码的计量学信息。我们引入了一种神经关联学习框架，该框架将贝叶斯推断与在标定数据上训练的神经网络相结合。在两阶段标定-感知协议中操作，我们数值上证明该框架能有效提取多体关联，以饱和由经典费舍尔信息决定的克拉美-罗界。因此，它实现了超越标准量子极限（SQL）的量子增强灵敏度。此外，学习到的估计器对现实噪声具有鲁棒性，并且在缺乏稀有测量模式的情况下仍保持准确。我们的结果建立了一种硬件高效且可扩展的量子传感范式，适用于相互作用的多体系统，而无需工程化纠缠态。
英文摘要
Quantum-enhanced sensing typically relies on preparing specific entangled states. However, engineering these states in scalable many-body systems remains challenging due to the difficulty of designing robust preparation protocols and accounting for realistic noise. We show that evolving a simple product state under native time-independent Hamiltonian dynamics in Rydberg atom arrays generates complex many-body correlations that can potentially enable quantum-enhanced parameter estimation reaching the Heisenberg limit (HL). The key challenge is then shifted to extracting the metrological information encoded in spatial correlations of measurement patterns. We introduce a neural correlation learning framework that combines Bayesian inference with neural networks trained on calibration data. Operating in a two-stage calibration-and-sensing protocol, we numerically demonstrate that this framework effectively extracts many-body correlations to saturate the Cramér--Rao bound dictated by the classical Fisher information. Consequently, it achieves quantum-enhanced sensitivity surpassing the standard quantum limit (SQL). Furthermore, the learned estimator is robust to realistic noise, and remains accurate in the absence of rare measurement patterns. Our results establish a hardware-efficient and scalable paradigm for quantum sensing in interacting many-body systems without requiring engineered entangled states.
Comments11 pages, 6 figures
