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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In Gaussian quantum networks for distributed phase sensing, tailored photon-number correlated states achieve perfect privacy and optimal precision, while fully symmetric Gaussian states reach asymptotic perfect privacy with near-optimal performance and quadratic scaling under local homodyne readout.
Integrating quantum catalysis, entanglement, and squeezing in a distributed quantum network yields better multiphase sensing precision than any two alone, approaching the Heisenberg limit, with partial catalysis outperforming global catalysis in both ideal and lossy cases.
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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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Privacy in Distributed Quantum Sensing with Gaussian Quantum Networks
In Gaussian quantum networks for distributed phase sensing, tailored photon-number correlated states achieve perfect privacy and optimal precision, while fully symmetric Gaussian states reach asymptotic perfect privacy with near-optimal performance and quadratic scaling under local homodyne readout.
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Quantum-enhanced distributed network sensing using multiple quantum resources
Integrating quantum catalysis, entanglement, and squeezing in a distributed quantum network yields better multiphase sensing precision than any two alone, approaching the Heisenberg limit, with partial catalysis outperforming global catalysis in both ideal and lossy cases.