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The paper uses reinforcement learning to turn a nonlinear Zeeman effect, which usually degrades readout in multilevel atomic magnetometers, into a source of usable spin squeezing. The reported results are a stabilized squeezing above 4 dB and a single-atom sensitivity about 3 dB beyond the standard quantum limit, with preparation overhead included. For readers, the main point is a concrete case of learned control applied to quantum sensing hardware, though the claims still rest on one atomic species and one experimental manifold.
Learning unified control of intrinsic nonlinear spin dynamics in atomic qudits for magnetometry
arXiv:2603.28421v3 Announce Type: replace-cross Abstract: Generating and preserving metrologically useful quantum states is a central challenge in quantum-enhanced metrology. In low-field atomic magnetometry with multilevel atoms, the nonlinear Zeeman (NLZ) effect is both a resource and a limitation. It can generate internal spin squeezing within a single atomic qudit, but under fixed readout it also rotates and distorts the measurement-relevant quadrature, limiting the usable metrological gain. The problem is further complicated by the time dependence of both the squeezing axis and the nonlinear evolution itself. Here we show that reinforcement learning can transform NLZ dynamics from a source of readout degradation into a sustained metrological resource. Using only experimentally accessible low-order spin moments, a trained agent identifies a unified control policy for this class of intrinsically nonlinear sensing dynamics. We illustrate the approach in the \(f=21/2\) manifold of \(^{161}\mathrm{Dy}\), where the learned policy rapidly prepares strongly squeezed internal states and stabilizes more than \(4\,\mathrm{dB}\) of fixed-axis spin squeezing under continuous NLZ evolution. Including state-preparation overhead, the learned protocol yields a single-atom magnetic-field sensitivity of \(13.9\,\mathrm{pT}/\sqrt{\mathrm{Hz}}\), approximately \(3\,\mathrm{dB}\) beyond the standard quantum limit. Our results establish learning-based control as an experimentally feasible route for converting unavoidable intrinsic nonlinear dynamics in multilevel atomic sensors into operational metrological advantage.
Abstract:Generating and preserving metrologically useful quantum states is a central challenge in quantum-enhanced metrology. In low-field atomic magnetometry with multilevel atoms, the nonlinear Zeeman (NLZ) effect is both a resource and a limitation. It can generate internal spin squeezing within a single atomic qudit, but under fixed readout it also rotates and distorts the measurement-relevant quadrature, limiting the usable metrological gain. The problem is further complicated by the time dependence of both the squeezing axis and the nonlinear evolution itself. Here we show that reinforcement learning can transform NLZ dynamics from a source of readout degradation into a sustained metrological resource. Using only experimentally accessible low-order spin moments, a trained agent identifies a unified control policy for this class of intrinsically nonlinear sensing dynamics. We illustrate the approach in the \(f=21/2\) manifold of \(^{161}\mathrm{Dy}\), where the learned policy rapidly prepares strongly squeezed internal states and stabilizes more than \(4\,\mathrm{dB}\) of fixed-axis spin squeezing under continuous NLZ evolution. Including state-preparation overhead, the learned protocol yields a single-atom magnetic-field sensitivity of \(13.9\,\mathrm{pT}/\sqrt{\mathrm{Hz}}\), approximately \(3\,\mathrm{dB}\) beyond the standard quantum limit. Our results establish learning-based control as an experimentally feasible route for converting unavoidable intrinsic nonlinear dynamics in multilevel atomic sensors into operational metrological advantage.
(6.5+3.5+4) pages, (4+4) figures
Quantum Physics (quant-ph); Artificial Intelligence (cs.AI)
arXiv:2603.28421 [quant-ph]
(or arXiv:2603.28421v3 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2603.28421 Focus to learn more arXiv-issued DOI via DataCite
https://doi.org/10.1103/j8yl-nn8x Focus to learn more DOI(s) linking to related resources
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