PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Minimax and adaptive estimation of the Wigner function in quantum homodyne tomography with noisy data
Cristina Butucea, Madalin Guta and Luis Artiles
Minimax and adaptive estimation of the Wigner function in quantum homodyne 2005.

Abstract

We estimate the quantum state of a light beam from results of quantum homodyne measurements performed on identically prepared quantum systems. The state is represented through the Wigner function, a density on $\mathbb{R}^2$ which may take negative values but must respect intrinsic positivity constraints imposed by quantum physics. The effect of the losses due to detection inefficiencies which are always present in a real experiment is the addition to the tomographic data of independent Gaussian noise. We construct a kernel estimator for the Wigner function and prove that it is minimax efficient for the pointwise risk over a class of infinitely differentiable functions. We construct adaptive estimators, i.e. which do not depend on the smoothness parameters, and prove that in some set-ups they attain the minimax rates for the corresponding smoothness class.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:1968
Deposited By:Luis Artiles Martinez
Deposited On:03 January 2006