Ultimate precision bounds for multiparameter Markovian noise metrology show average variance scaling as Ω(1/(T R²)) with Heisenberg scaling in dissipative channels R when using entangled probes and high-rank signal correlations, attainable via rapid prepare-and-measure protocols.
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Direct use of mechanical qubits from levitated particles for gravimetry achieves m^{-1/2} sensitivity scaling and 0.1 μGal/√Hz performance, outperforming traditional schemes by two orders of magnitude while reaching double standard quantum limits.
Krylov shadow tomography produces exponentially converging bounds on quantum Fisher information that exactly match the QFI for low-rank states and outperform existing polynomial lower bounds.
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Precision Limits of Multiparameter Markovian-Noise Metrology
Ultimate precision bounds for multiparameter Markovian noise metrology show average variance scaling as Ω(1/(T R²)) with Heisenberg scaling in dissipative channels R when using entangled probes and high-rank signal correlations, attainable via rapid prepare-and-measure protocols.
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Quantum gravimetry with mechanical qubits
Direct use of mechanical qubits from levitated particles for gravimetry achieves m^{-1/2} sensitivity scaling and 0.1 μGal/√Hz performance, outperforming traditional schemes by two orders of magnitude while reaching double standard quantum limits.
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Superiority of Krylov shadow tomography in estimating quantum Fisher information: From bounds to exactness
Krylov shadow tomography produces exponentially converging bounds on quantum Fisher information that exactly match the QFI for low-rank states and outperform existing polynomial lower bounds.