Hermitian matrices and unitary similarity transformations

In summary, a Hermitian matrix is a square matrix that is equal to its own conjugate transpose, with real numbers on the diagonal and complex conjugates above and below. It has properties such as real eigenvalues, orthogonal eigenvectors, and being diagonalizable. A unitary similarity transformation can be used on a Hermitian matrix to reveal its eigenvalues and eigenvectors. In quantum mechanics, Hermitian matrices represent physical observables and play a role in the Schrödinger equation. Non-Hermitian matrices cannot be similar to Hermitian matrices. In data analysis and statistics, Hermitian matrices are used in principal component analysis and the calculation of correlation matrices.
  • #1
ShayanJ
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I tried to prove that a hermitian matrix remains hermitian under a unitary similarity transformation.I just could do it to he point shown below.Any ideas?

[itex] [ ( U A U ^ {\dagger}) B ] ^ {\dagger} = B ^ {\dagger} (U A U ^ {\dagger}) ^ {\dagger} = B (U A^ {\dagger} U ^ {\dagger}) [/itex]

thanks
 
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  • #2
Hi Shyan! :smile:

What's B doing there? :confused:
 
  • #3
Isn't B the hermitian matrix which should remain hermitian?
 
  • #4
But A is the matrix which undergoes the similarity transformation.
 
  • #5


It is true that a Hermitian matrix remains Hermitian under a unitary similarity transformation. This is because unitary matrices preserve the Hermitian structure of a matrix, meaning that the Hermitian property is not affected by a unitary transformation.

To prove this, we can use the fact that a unitary matrix U satisfies U^{\dagger} = U^{-1}. Therefore, we can rewrite the expression as:

[(UAU^{\dagger})B]^{\dagger} = (U^{\dagger})^{\dagger}A^{\dagger}U^{\dagger}B^{\dagger} = UAU^{\dagger}B^{\dagger}

Since A is Hermitian, we know that A^{\dagger} = A. And since B is an arbitrary matrix, we can switch the order of multiplication without affecting the result. Therefore, we can rewrite the expression as:

UAU^{\dagger}B^{\dagger} = UAB^{\dagger} = UBA^{\dagger} = (UBA^{\dagger})^{\dagger}

This shows that the resulting matrix after the unitary similarity transformation is also Hermitian, thus proving that a Hermitian matrix remains Hermitian under a unitary similarity transformation.

I hope this helps and provides some further insight into the topic. If you have any other questions or concerns, please feel free to ask. Keep up the good work in exploring the properties of Hermitian matrices and unitary transformations!
 

Related to Hermitian matrices and unitary similarity transformations

What is a Hermitian matrix and what are its properties?

A Hermitian matrix is a square matrix that is equal to its own conjugate transpose. This means that the elements on the diagonal are real numbers, and the elements above and below the diagonal are complex conjugates of each other. Hermitian matrices have the following properties:

  • All eigenvalues are real numbers
  • Eigenvectors corresponding to different eigenvalues are orthogonal
  • The matrix is diagonalizable
  • The determinant is always a real number

What is a unitary similarity transformation and how is it related to Hermitian matrices?

A unitary similarity transformation is a transformation of a matrix using a unitary matrix. This transformation preserves the eigenvalues and eigenvectors of the original matrix. For Hermitian matrices, a unitary similarity transformation can be used to diagonalize the matrix, making it easier to work with and revealing its eigenvalues and eigenvectors.

How are Hermitian matrices used in quantum mechanics?

Hermitian matrices play a crucial role in quantum mechanics. In this field, physical observables are represented by Hermitian operators, and the eigenvalues of these operators correspond to the possible outcomes of a measurement. Additionally, the time evolution of quantum states is described by the Schrödinger equation, which involves Hermitian operators.

Can a non-Hermitian matrix be similar to a Hermitian matrix?

No, a non-Hermitian matrix cannot be similar to a Hermitian matrix. Similar matrices have the same eigenvalues, but non-Hermitian matrices can have complex eigenvalues, while Hermitian matrices have only real eigenvalues. Therefore, it is not possible for a non-Hermitian matrix to be similar to a Hermitian matrix.

How are Hermitian matrices used in data analysis and statistics?

Hermitian matrices are commonly used in data analysis and statistics, particularly in the area of principal component analysis (PCA). In PCA, a covariance matrix is constructed from a dataset, and this matrix must be Hermitian in order for the analysis to be valid. Additionally, Hermitian matrices are used in the calculation of correlation matrices, which are used in many statistical analyses.

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