Numerical optimization identifies non-Gaussian quantum states that outperform Gaussian states for sensing under loss and phase noise, with up to 2.2 dB advantage persisting under homodyne detection.
Quantum Fisher Information: Variational principle and simple iterative algorithm for its efficient computation
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abstract
We derive a new variational principle for the quantum Fisher information leading to a simple iterative alternating algorithm, the convergence of which is proved. The case of a fixed measurement, i.e. the classical Fisher information, is also discussed.
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quant-ph 2verdicts
UNVERDICTED 2representative citing papers
An iterative semidefinite programming method maximizes quantum Fisher information over local Hamiltonians to optimize metrological performance of quantum states and solves related entanglement problems.
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Optimized Quantum States for Sensing in the Presence of Loss and Phase Noise
Numerical optimization identifies non-Gaussian quantum states that outperform Gaussian states for sensing under loss and phase noise, with up to 2.2 dB advantage persisting under homodyne detection.
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Iterative optimization in quantum metrology and entanglement theory using semidefinite programming
An iterative semidefinite programming method maximizes quantum Fisher information over local Hamiltonians to optimize metrological performance of quantum states and solves related entanglement problems.