Why Is My Matrix Not Diagonal After Transformation?

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SUMMARY

The discussion centers on the transformation of the matrix ##\sigma_y## using a unitary matrix ##U## formed from its eigenvectors. The user incorrectly attempts to diagonalize ##\sigma_y## using the expression ##U \sigma_y U^{\dagger}##, which does not yield a diagonal matrix. The correct approach is to use the expression ##U^{\dagger} \sigma_y U##, which will result in a diagonal matrix, as the eigenvectors are orthonormal and the unitary transformation preserves the eigenvalue structure.

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LagrangeEuler
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Homework Statement


Form unitary matrix from eigen vectors of ##\sigma_y## and using that unitary matrix diagonalize ##\sigma_y##.
<br /> \sigma_y=<br /> \begin{bmatrix}<br /> 0 &amp; -i &amp; \\<br /> i &amp; 0 &amp; \\<br /> <br /> \end{bmatrix}[/B]

Homework Equations


Eigen vectors of ##\sigma_y## are
<br /> \vec{X}_1=\frac{1}{\sqrt{2}}<br /> \begin{bmatrix}<br /> 1 \\<br /> i \\<br /> <br /> \end{bmatrix}
and
<br /> \vec{X}_2=\frac{1}{\sqrt{2}}<br /> \begin{bmatrix}<br /> 1 \\<br /> -i \\<br /> <br /> \end{bmatrix}

The Attempt at a Solution


I am not sure, where I am making a mistake. Unitary matrix ##U## is defined by
<br /> U=\frac{1}{\sqrt{2}}<br /> \begin{bmatrix}<br /> 1 &amp; 1 &amp; \\<br /> i &amp; -i &amp; \\<br /> <br /> \end{bmatrix}
whereas
<br /> U^{\dagger}=\frac{1}{\sqrt{2}}<br /> \begin{bmatrix}<br /> 1 &amp; -i &amp; \\<br /> 1 &amp; i &amp; \\<br /> <br /> \end{bmatrix}.
And somehow

##U \sigma_y U^{\dagger}##
is not diagonal. Is there any explanation? Where I am making a mistake?



[/B]
 
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LagrangeEuler said:
##U \sigma_y U^{\dagger}##
is not diagonal. Is there any explanation? Where I am making a mistake?

If ##U## is formed from the eigenvectors of ##\sigma_y##, then ##U^\dagger \sigma_y U## will be diagonal, not ##U \sigma_y U^\dagger##

In general, let ##A## be a square matrix, and let ##u_1##, ##u_2## etc. be eigenvectors with eigenvalues ##\lambda_1##, ##\lambda_2##, etc. Then let ##U## be the matrix formed by sticking the eigenvectors together: ##U = \left( \begin{array}\\ u_1 & u_2 & ...\end{array} \right)##. Then ##A U## will be the matrix

##A U = \left( \begin{array}\\ \lambda_1 u_1 & \lambda_2 u_2 & ...\end{array} \right)##.

Then ##U^\dagger## will be the matrix ##U^\dagger = \left( \begin{array}\\ u_1^\dagger \\ u_2^\dagger \\ . \\ . \\. \end{array} \right)##

So since ##u_n^\dagger u_m = 0## or ##1##, depending on whether ##n=m##, you will have:

##U^\dagger A U## will be the matrix ##U^\dagger = \left( \begin{array}\\ \lambda_1 & 0 & ...\\ 0 & \lambda_2 & 0 & ... \\ 0 & 0 & \lambda_3 & ... \\ . \\. \end{array} \right)##
 

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