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Non-Gaussian $P$-function and disturbance of operation in quantum estimation for noised coherent light (Statistical Inference and the Bioequivalence Problem)

Yoshiyuki Tsuda

Published Jul 1, 2001• 0 citations
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Non-Gaussian $P$-function and disturbance of operation in quantum estimation for noised coherent light (Statistical Inference and the Bioequivalence Problem)

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Non-Gaussian PPP-function and disturbance of operation in quantum estimation for noised coherent light (Statistical Inference and the Bioequivalence Problem)
Non-Gaussian PPP-function and disturbance of operation in quantum estimation for noised coherent light (Statistical Inference and the Bioequivalence Problem)
We analyze in details a conditional measurement scheme based on linear optical components, feed-forward and homodyne detection. The scheme may be used to achieve two different tasks. On the one hand it allows the extraction of information with minimum disturbance about a set of coherent states. On the other hand, it represents a nondemolitive measurement scheme for the annihilation operator, i.e. an indirect measurement of the Q-function. We investigate the information/disturbance trade-off for state inference and introduce the estimation/distortion trade-off to assess estimation of the Q-function. For coherent states chosen from a Gaussian set we evaluate both information/disturbance and estimation/distortion trade-offs and found that non universal protocols may be optimized in order to achieve better performances than universal ones. For Fock number states we prove that universal protocols do not exist and evaluate the estimation/distortion trade-off for a thermal distribution.
We present an optimal control-based strategy to enhance the estimation of impulse-like disturbances in continuously monitored linear classical and quantum systems by exploiting non-equilibrium states. Using optimal estimation techniques for linear Gaussian systems to collect information from the temporal vicinity of the disturbance, we cast the minimization of disturbance estimation uncertainty as a nonlinear optimal control problem over time-dependent system parameters. The resulting method dynamically shapes the estimation covariances through parametric modulation, maximizing information gain at a known impulse time. This differs fundamentally from conventional squeezing protocols using periodic modulation that effectively degrade inference of impulse-like disturbances. Applied to nanomechanical resonators and levitated nanoparticles, optimal parametric driving reduces estimation variance by up to a factor of two relative to steady-state operation