Matrix Transformation: U^\dagger vs U

In summary, the conversation discusses various ways of transforming a matrix, specifically in the context of quadratic forms between boson operators in a Hamiltonian. The proper way of doing this is to find the eigenvectors of the original matrix and put them in a unitary matrix. The vector of boson operators is then transformed using the unitary matrix, which can be written as either U or U^\dagger. The choice depends on the context and definitions used.
  • #1
Niles
1,866
0
Hi guys

Ok, let's say I have a matrix given by

[tex]
M = \sum_{ij}M_{ij}a_i^\dagger a_j,
[/tex]

and I wish to transform it. Now in some books I have read they write the transformation as

[tex]
s = \sum_{j}U_{ij}a_j,
[/tex]

while in some notes I have read they write it as

[tex]
s = \sum_{j}U^\dagger_{ij}a_j.
[/tex]

What is the deal here? What is the proper way of doing this? :confused:

Kind regards,
Niles.
 
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  • #2


Suppose you have a Hamiltonian which can be represented as a quadratic form between boson operators:
[tex]
{\cal H} = \sum_{ij}a^\dagger_iH_{ij}a_j = \mathbf a^\dagger\mathsf H \mathbf a
[/tex]
where the matrix [tex]H_{ij}[/tex] is hermitian.
Suppose you find the eigenvectors of [tex]H_{ij}[/tex] and put them as normalised columns in a matrix [tex]U_{ij}[/tex], which is unitary.
Then a natural way to rewrite the hamiltonian is:
[tex]
{\cal H} = \mathbf a^\dagger\mathsf H \mathbf a =
\mathbf a^\dagger \mathsf U\mathsf U^\dagger\mathsf H \mathsf U\mathsf U^\dagger\mathbf a = \mathbf b^\dagger \mathsf D \mathbf b
[/tex]
where [tex]\mathsf D = \mathsf U^\dagger\mathsf H \mathsf U[/tex] is diagonal, and the vector of boson operators is transformed as [tex]\mathbf b = U^\dagger\mathbf a[/tex].

Of course, I could equally well have said put the eigenvectors as normalised columns of the unitary matrix [tex]U^\dagger[/tex], and it would all be the other way round!

By the way, your first line is confusing: you have two matrices there. M acts on fock space as an operator (and is infinite dimensional for bosons), but M_ij is only as large as the number of sites.
 
  • #3


peteratcam said:
Suppose you have a Hamiltonian which can be represented as a quadratic form between boson operators:
[tex]
{\cal H} = \sum_{ij}a^\dagger_iH_{ij}a_j = \mathbf a^\dagger\mathsf H \mathbf a
[/tex]
where the matrix [tex]H_{ij}[/tex] is hermitian.
Suppose you find the eigenvectors of [tex]H_{ij}[/tex] and put them as normalised columns in a matrix [tex]U_{ij}[/tex], which is unitary.
Then a natural way to rewrite the hamiltonian is:
[tex]
{\cal H} = \mathbf a^\dagger\mathsf H \mathbf a =
\mathbf a^\dagger \mathsf U\mathsf U^\dagger\mathsf H \mathsf U\mathsf U^\dagger\mathbf a = \mathbf b^\dagger \mathsf D \mathbf b
[/tex]
where [tex]\mathsf D = \mathsf U^\dagger\mathsf H \mathsf U[/tex] is diagonal, and the vector of boson operators is transformed as [tex]\mathbf b = U^\dagger\mathbf a[/tex].

Of course, I could equally well have said put the eigenvectors as normalised columns of the unitary matrix [tex]U^\dagger[/tex], and it would all be the other way round!

By the way, your first line is confusing: you have two matrices there. M acts on fock space as an operator (and is infinite dimensional for bosons), but M_ij is only as large as the number of sites.

Just to be absolutely clear: When you say "other way around", then you mean that we go from

[tex]
\mathbf b = U^\dagger\mathbf a
[/tex]

to

[tex]
\mathbf b = U\mathbf a
[/tex]

?
 
  • #4


Yes, [tex]U[/tex] is a unitary matrix, which implies that [tex]U^\dagger[/tex] is also unitary. Whether you attach a dagger doesn't really matter, as long as you are consistent with the definitions you choose.

In the context of diagonalising hamiltonians like my example, the way I have written it is probably more conventional. In another situations, the other way might be more suitable.

Check out active/passive transformations, it might help.
 

FAQ: Matrix Transformation: U^\dagger vs U

What is the difference between U^\dagger and U in matrix transformation?

U^\dagger, also known as the Hermitian conjugate or adjoint, is the conjugate transpose of the matrix U. This means that the elements of U^\dagger are the complex conjugates of the elements of U, and the rows and columns are swapped. This is different from U, which is simply the transpose of U with no complex conjugation.

Why is U^\dagger used in quantum mechanics for matrix transformations?

In quantum mechanics, the Hermitian conjugate is used because it preserves the important property of unitarity. A unitary matrix is one that has an inverse which is equal to its Hermitian conjugate, making it useful for representing quantum states and transformations without losing any information.

How do you calculate the Hermitian conjugate of a matrix?

To calculate the Hermitian conjugate of a matrix, you simply take the transpose of the matrix and then take the complex conjugate of each element. This can be represented mathematically as U^\dagger = (U^T)^* where * represents the complex conjugate.

What is the relationship between U^\dagger and the inverse of a matrix?

The inverse of a matrix is the matrix that, when multiplied by the original matrix, results in the identity matrix. In the case of a unitary matrix, the inverse is equal to the Hermitian conjugate. This means that U^\dagger = U^{-1}, making it a useful tool for finding the inverse of a unitary matrix.

Are there any other uses for U^\dagger besides in quantum mechanics?

Yes, the Hermitian conjugate is also used in other areas of mathematics and physics, such as in signal processing and statistics. It is also commonly used in linear algebra and functional analysis, as it is closely related to the concept of adjoint operators.

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