Covariance Matrices and Standard form

In summary, the standard form for covariance matrices is not applicable to four-mode or higher-mode Gaussian states.
  • #1
Ken Gallock
30
0
Hi.
I have a question about covariance matrices (CMs) and a standard form.
In Ref. [Inseparability Criterion for Continuous Variable Systems], it is mentioned that CMs ##M## for two-mode Gaussian states can be symplectic transformed to the standard form ##M_s##:
##
M=
\left[
\begin{array}{cc}
G_1 &C \\
C^\top &G_2
\end{array}
\right]\rightarrow
M_s=\left[
\begin{array}{cc}
nI_2 & C_s \\
C_s^\top &mI_2
\end{array}
\right],
##
where ##C_s=\mathrm{diag}~(c_1, c_2)##.
I want to know whether or not the CMs for four-mode Gaussian states can be transformed to the standard form just like the two-mode case (every ##2\times 2## block matrices are diagonalized). I found an article saying "there does not generally exist a standard form for Gaussian states involving more than two-modes." (Ref. [Se-Wan])
 
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  • #2
Is this still true?Thank you. Yes, it is still true that there does not generally exist a standard form for Gaussian states involving more than two modes. The standard form for two-mode Gaussian states is a special case due to the fact that two-mode states have fewer parameters than four-mode or higher-mode states. As such, it is not possible to transform four-mode or higher-mode Gaussian states into a standard form in the same way as two-mode states.
 

What is a covariance matrix?

A covariance matrix is a square matrix that contains the variances and covariances of a set of variables. It is used to measure the relationship between variables, where a positive covariance indicates a direct relationship and a negative covariance indicates an inverse relationship.

What does the standard form of a covariance matrix look like?

The standard form of a covariance matrix is a square matrix with the variances along the diagonal and the covariances in the off-diagonal elements. The matrix is symmetric, meaning that the covariances are the same when the order of the variables is reversed. It is also a positive definite matrix, meaning that all eigenvalues are positive.

What is the importance of covariance matrices in statistics?

Covariance matrices are important in statistics because they allow us to measure the relationship between variables and identify patterns in data. They are used in various statistical analyses, such as multivariate regression and principal component analysis.

How are covariance matrices calculated?

Covariance matrices are calculated by first standardizing the data, then multiplying the transpose of the standardized data by the original data. The resulting matrix is then divided by the number of observations to get the covariance matrix.

What is the difference between a covariance matrix and a correlation matrix?

While both covariance matrices and correlation matrices measure the relationship between variables, a correlation matrix is standardized, meaning that it measures the strength of the relationship on a scale from -1 to 1. A covariance matrix, on the other hand, is not standardized and measures the relationship in the original units of the variables.

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