Abstract
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.
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Cite this article
APA 7
Zhang, T., Nie, X., & Chen, L. (2026). Neural Correlation Learning for Quantum-Enhanced Sensing with Time-Independently Driven Rydberg Atom Arrays. https://omanscience.com/en/articles/neural-correlation-learning-for-quantum-enhanced-sensing-with-time-independently-driven-rydberg-atom-arrays
MLA 9
Zhang, Tao, et al. "Neural Correlation Learning for Quantum-Enhanced Sensing with Time-Independently Driven Rydberg Atom Arrays." https://omanscience.com/en/articles/neural-correlation-learning-for-quantum-enhanced-sensing-with-time-independently-driven-rydberg-atom-arrays.
Chicago (author–date)
Zhang, Tao, Xiaotian Nie, and Linghui Chen. 2026. "Neural Correlation Learning for Quantum-Enhanced Sensing with Time-Independently Driven Rydberg Atom Arrays." https://omanscience.com/en/articles/neural-correlation-learning-for-quantum-enhanced-sensing-with-time-independently-driven-rydberg-atom-arrays.
Harvard
Zhang, T., Nie, X. and Chen, L. (2026) 'Neural Correlation Learning for Quantum-Enhanced Sensing with Time-Independently Driven Rydberg Atom Arrays', Available at: https://omanscience.com/en/articles/neural-correlation-learning-for-quantum-enhanced-sensing-with-time-independently-driven-rydberg-atom-arrays.
Vancouver
Zhang T, Nie X, Chen L. Neural Correlation Learning for Quantum-Enhanced Sensing with Time-Independently Driven Rydberg Atom Arrays. https://omanscience.com/en/articles/neural-correlation-learning-for-quantum-enhanced-sensing-with-time-independently-driven-rydberg-atom-arrays
IEEE
T. Zhang, X. Nie, and L. Chen, "Neural Correlation Learning for Quantum-Enhanced Sensing with Time-Independently Driven Rydberg Atom Arrays," https://omanscience.com/en/articles/neural-correlation-learning-for-quantum-enhanced-sensing-with-time-independently-driven-rydberg-atom-arrays.