February 2022 Minimax nonparametric estimation of pure quantum states
Samriddha Lahiry, Michael Nussbaum
Author Affiliations +
Ann. Statist. 50(1): 430-459 (February 2022). DOI: 10.1214/21-AOS2115

Abstract

In classical statistics, Pinsker’s theorem provides an exact asymptotic minimax bound in nonparametric estimation, improving upon optimal rates of convergence results. We obtain a quantum version of the theorem by establishing asymptotic minimax results for estimation of the displacement vector in a quantum Gaussian white noise model, given by a sequence of shifted vacuum states. Analogous results are then obtained for estimation of a general pure state from an ensemble of identically prepared, independent quantum systems, using the recently established local asymptotic equivalence to the quantum Gaussian white noise model. Optimality holds with respect to the most fundamental distance measure for quantum states, that is, trace norm distance, and in a true quantum sense, allowing for all possible measurements. Adaptive estimators are also obtained for the above cases. As an application, we obtain asymptotic minimax adaptive estimators for Wigner functions of pure states.

Funding Statement

Supported in part by NSF Grants DMS-1407600 and DMS-1915884.

Citation

Download Citation

Samriddha Lahiry. Michael Nussbaum. "Minimax nonparametric estimation of pure quantum states." Ann. Statist. 50 (1) 430 - 459, February 2022. https://doi.org/10.1214/21-AOS2115

Information

Received: 1 June 2020; Revised: 1 April 2021; Published: February 2022
First available in Project Euclid: 16 February 2022

MathSciNet: MR4382023
zbMATH: 1486.62086
Digital Object Identifier: 10.1214/21-AOS2115

Subjects:
Primary: 62G05
Secondary: 62C20 , 62G20 , 81P50

Keywords: adaptive estimation , block Stein method , Hermite–Sobolev class , local asymptotic equivalence , Pinsker’s theorem , pure quantum state , quantum Bayes estimation , quantum Gaussian sequence model , trace norm distance , Wigner function

Rights: Copyright © 2022 Institute of Mathematical Statistics

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Vol.50 • No. 1 • February 2022
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