Unitary Matrix Property: |Uij|2 = UijU*ji

  • Thread starter Niles
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In summary, the conversation discusses the property of unitary matrices where U_ijU*ji = |U_ij|^2 and how this relates to complex conjugation. It is mentioned that this property is not always valid, as shown by the example of a rotation matrix. The conversation also touches on finding the Hermitian conjugate of a transformation and clarifies the correct way to perform matrix multiplication.
  • #1
Niles
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Hi guys

I have been sitting here for a while thinking of why it is that for a unitary matrix U we have that UijU*ji = |Uij|2. What property of unitary matrices is it that gives U this property?


Niles.
 
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  • #2
This is true of any matrix: [tex]U^*=\overline{U}^T[/tex]. So [tex]U^*_{ji}=\overline{U_{ij}}[/tex]. And of course, for any complex number z, we have [itex]z\overline{z}=|z|^2[/itex]
 
  • #3
By an asterix I meant complex conjugation, so [tex]
(U^\dagger )_{ij} = (U_{ji})^*
[/tex]. Is it still valid then?
 
Last edited:
  • #4
It is not valid in this case. Take for example the rotation matrix

cos(t) -sin(t)
sin(t) cos(t)

It is orthogonal, hence unitary. But for any sin(t) different from 0, we have [itex]U_{12}U^*_{21}=-\sin^2(t)\neq |\sin(t)|^2=|U_{12}|^2[/itex].
 
  • #5
Hmm, I have a problem then. I have a transformation

[tex]
\mathbf{m} = S\mathbf{a},
[/tex]

which has the components

[tex]
m_i = \sum_j S_{ij}a_j.
[/tex]

Now I want to find the Hermitian conjugate (I denote this by a dagger, and complex conjugation is denoted by an asterix), and we have

[tex]
\mathbf{m}^\dagger = \mathbf{a}^\dagger S^\dagger,
[/tex]

which has the components

[tex]
m^\dagger_i &= \sum_j a_j^\dagger (S^\dagger)_{ij} \\
&=\sum_j a_j^\dagger (S^*)_{ji}.
[/tex]

My teacher says the last step is wrong, but I cannot see why. Can you help me spot the error?
 
Last edited:
  • #6
Your mistake is when you say

[tex]m^\dagger_i &= \sum_j a_j^\dagger (S^\dagger)_{ij}[/tex]

According to the definition of matrix multiplication, correct is

[tex]m^\dagger_i &= \sum_j a_j^\dagger (S^\dagger)_{ji}[/tex]
 
  • #7
Ahh, I see it now. Of course the column has to be fixed, not the row. Thanks.
 

Related to Unitary Matrix Property: |Uij|2 = UijU*ji

1. What is a unitary matrix?

A unitary matrix is a square matrix whose conjugate transpose is equal to its inverse. In other words, for a unitary matrix U, U*U^H = I, where U^H represents the conjugate transpose of U and I is the identity matrix.

2. What is the significance of the unitary matrix property?

The unitary matrix property is significant because it guarantees that the matrix will preserve the lengths of vectors and the angles between them. This makes unitary matrices useful in a variety of applications, including quantum mechanics, signal processing, and linear algebra.

3. How is the unitary matrix property expressed mathematically?

The unitary matrix property is expressed as |Uij|2 = UijU*ji, where Uij represents the element in the ith row and jth column of the matrix U, and U*ji represents the element in the jth row and ith column of the conjugate transpose of U.

4. How can the unitary matrix property be verified?

The unitary matrix property can be verified by multiplying the matrix U with its conjugate transpose, U*U^H. If the result is equal to the identity matrix, then the matrix U satisfies the unitary matrix property.

5. What are some examples of unitary matrices?

Some examples of unitary matrices include rotation matrices, reflection matrices, and the Hadamard matrix. In quantum mechanics, unitary matrices are used to represent quantum gates, which are fundamental operations on quantum bits (qubits).

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