Researchers have developed a reinforcement learning approach to control intrinsic nonlinear spin dynamics in atomic qudits for magnetometry. This method transforms the nonlinear Zeeman effect, which can degrade measurements, into a resource for enhanced metrology. The learned policy rapidly prepares squeezed internal states and achieves a magnetic-field sensitivity of 13.9 pT/sqrt(Hz), surpassing the standard quantum limit by approximately 3 dB. AI
IMPACT Demonstrates a new application of reinforcement learning for scientific discovery in quantum metrology.
RANK_REASON Academic paper detailing a novel application of reinforcement learning in quantum physics. [lever_c_demoted from research: ic=1 ai=1.0]
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