A two-stage optimization method for wide-range single-electron quantum magnetic sensing - Nature

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AbstractQuantum magnetic sensing is a new paradigm for detecting ultra-weak magnetic fields. Its protocol design concerns the devise of optimal sensing parameters to estimate the underlying signals of interest (SoI). Existing studies mainly rely on adaptive algorithms based on black-box AI models or formula-driven principled searches. However, when the SoI spans a wide range and the quantum sensor has physical constraints, these methods may fail to converge efficiently or optimally, causing prolonged interrogation times and reduced accuracy. This work presents a new protocol using a two-stage optimization method, consisting of a Bayesian neural network to first narrow the SoI range and a federated reinforcement learning agent to subsequently calibrate the SoI.