Quantum

Neural Correlation Learning for Quantum-Enhanced Sensing with Time-Independently Driven Rydberg Atom Arrays

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Briefings

This preprint from October 2026 proposes a neural correlation learning framework for quantum-enhanced sensing using Rydberg atom arrays. The study demonstrates that evolving a simple product state under native time-independent Hamiltonian dynamics generates complex many-body correlations, enabling sensitivity reaching the Heisenberg limit. A two-stage protocol combining Bayesian inference with neural networks successfully extracts these correlations to saturate the Cramér-Rao bound, achieving quantum-enhanced sensitivity beyond the standard quantum limit while remaining robust to realistic noise and rare measurement patterns.

Key points

• The study introduces a neural correlation learning framework that combines Bayesian inference with neural networks trained on calibration data to extract many-body correlations from Rydberg atom arrays.
• Numerical simulations show that evolving a simple product state under native time-independent Hamiltonian dynamics generates complex many-body correlations sufficient to saturate the Cramér-Rao bound and achieve quantum-enhanced sensitivity surpassing the standard quantum limit.

Context and limitations

The results are based on numerical simulations of a two-stage calibration-and-sensing protocol. The framework's performance in real hardware with realistic noise and rare measurement patterns remains unverified, and the specific parameters of the time-independent Hamiltonian dynamics are not detailed.

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