Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Machine Learning Based Parameter Estimation of Gaussian Quantum States   We propose a machine learning framework for parameter estimation of single
mode Gaussian quantum states. Under a Bayesian framework, our approach
estimates parameters of suitable prior distributions from measured data. For
phase-space displacement and squeezing parameter estimation, this is achieved
by introducing Expectation-Maximization (EM) based algorithms, while for phase
parameter estimation an empirical Bayes method is applied. The estimated prior
distribution parameters along with the observed data are used for finding the
optimal Bayesian estimate of the unknown displacement, squeezing and phase
parameters. Our simulation results show that the proposed algorithms have
estimation performance that is very close to that of Genie Aided Bayesian
estimators, that assume perfect knowledge of the prior parameters. Our proposed
methods can be utilized by experimentalists to find the optimum Bayesian
estimate of parameters of Gaussian quantum states by using only the observed
measurements without requiring any knowledge about the prior distribution
parameters.

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The categories of this paper are: patt-sol
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The categories of this paper are: chao-dyn
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The categories of this paper are: dg-ga
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The categories of this paper are: bayes-an
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The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
16
The categories of this paper are: stat
6
The categories of this paper are: astro-ph
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The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin