Reinforcement Learning Controls Nonlinear Spin Dynamics in Atomic Qudits for Magnetometry

A new study on arXiv demonstrates that reinforcement learning can control intrinsic nonlinear spin dynamics in atomic qudits for magnetometry. The nonlinear Zeeman (NLZ) effect in low-field atomic magnetometry with multilevel atoms both generates internal spin squeezing and distorts measurement quadratures under fixed readout, limiting metrological gain. Researchers show that reinforcement learning transforms NLZ dynamics into a sustained resource by identifying a unified control policy using only experimentally accessible low-order spin moments. The approach is illustrated in the f=21/2 manifold of 16