The dynamical squeezing phase transition in bilayer XXZ spin models is universal across lattice geometries and interlayer coupling rescalings, with a new sub-linear scaling for short-range interactions.
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Reinforcement learning stabilizes more than 4 dB of fixed-axis spin squeezing under continuous nonlinear Zeeman evolution in the f=21/2 manifold of 161Dy, yielding a single-atom sensitivity of 13.9 pT/sqrt(Hz) that is 3 dB beyond the standard quantum limit.
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Universal Spin Squeezing Dynamical Phase Transitions across Lattice Geometries, Dimensions, and Microscopic Couplings
The dynamical squeezing phase transition in bilayer XXZ spin models is universal across lattice geometries and interlayer coupling rescalings, with a new sub-linear scaling for short-range interactions.
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Learning Unified Control of Intrinsic Nonlinear Spin Dynamics in Atomic Qudits for Magnetometry
Reinforcement learning stabilizes more than 4 dB of fixed-axis spin squeezing under continuous nonlinear Zeeman evolution in the f=21/2 manifold of 161Dy, yielding a single-atom sensitivity of 13.9 pT/sqrt(Hz) that is 3 dB beyond the standard quantum limit.