Optimal estimation of pure states with displaced-null measurements

• URL: http://arxiv.org/abs/2310.06767v1
• Date: Tue, 10 Oct 2023 16:46:24 GMT
• Title: Optimal estimation of pure states with displaced-null measurements
• Authors: Federico Girotti, Alfred Godley, M\u{a}d\u{a}lin Gu\c{t}\u{a}
• Abstract summary: We revisit the problem of estimating an unknown parameter of a pure quantum state. We investigate null-measurement' strategies in which the experimenter aims to measure in a basis that contains a vector close to the true system state.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We revisit the problem of estimating an unknown parameter of a pure quantum state, and investigate `null-measurement' strategies in which the experimenter aims to measure in a basis that contains a vector close to the true system state. Such strategies are known to approach the quantum Fisher information for models where the quantum Cram\'{e}r-Rao bound is achievable but a detailed adaptive strategy for achieving the bound in the multi-copy setting has been lacking. We first show that the following naive null-measurement implementation fails to attain even the standard estimation scaling: estimate the parameter on a small sub-sample, and apply the null-measurement corresponding to the estimated value on the rest of the systems. This is due to non-identifiability issues specific to null-measurements, which arise when the true and reference parameters are close to each other. To avoid this, we propose the alternative displaced-null measurement strategy in which the reference parameter is altered by a small amount which is sufficient to ensure parameter identifiability. We use this strategy to devise asymptotically optimal measurements for models where the quantum Cram\'{e}r-Rao bound is achievable. More generally, we extend the method to arbitrary multi-parameter models and prove the asymptotic achievability of the the Holevo bound. An important tool in our analysis is the theory of quantum local asymptotic normality which provides a clear intuition about the design of the proposed estimators, and shows that they have asymptotically normal distributions.

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