A new sequential Bayesian reconstruction method based on NV-center quantum Hamiltonian learning reconstructs dominant structures in synthetic dynamic 2D magnetic fields with low RMSE but only partially identifies the shared coupling parameter.
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Bidirectional nonlinear optical tomography breaks the η1 η2 degeneracy via forward/backward pumping and joint optimization to yield unbiased individual coupling efficiencies and on-chip figures of merit.
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Sequential Spatiotemporal Magnetic-Field Reconstruction via Quantum Hamiltonian Learning with NV-Center Spin-1 Hamiltonians
A new sequential Bayesian reconstruction method based on NV-center quantum Hamiltonian learning reconstructs dominant structures in synthetic dynamic 2D magnetic fields with low RMSE but only partially identifies the shared coupling parameter.