Any temperature-dependent unitary driving on a thermalized quantum probe universally boosts its quantum Fisher information for thermometry above the static equilibrium value via a positive kernel of information currents.
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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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Shake before use: universal enhancement of quantum thermometry by unitary driving
Any temperature-dependent unitary driving on a thermalized quantum probe universally boosts its quantum Fisher information for thermometry above the static equilibrium value via a positive kernel of information currents.
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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.