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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Convex optimization formulations and an analytical symplectic trace expression are introduced to reconstruct physical Gaussian covariance matrices and witness genuine multipartite entanglement from experimental data.
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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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Revisiting Gaussian genuine entanglement witnesses with modern software
Convex optimization formulations and an analytical symplectic trace expression are introduced to reconstruct physical Gaussian covariance matrices and witness genuine multipartite entanglement from experimental data.