Unitary Matrix mutually orthonormal vectors

In summary, the conversation discusses how to demonstrate that the columns of a unitary matrix form a set of mutually orthonormal vectors. The suggested method is to form the vectors u_i and u_k from the i-th and j-th columns of U and use the relationship U^{\dagger}U=I. The conversation also includes a series of attempts at solving the problem, with the final step being to show that the inner products of the vectors are either 1 or 0 in order to prove that the original matrix is the identity.
  • #1
physics2000
13
0

Homework Statement



Demonstrate that the columns of a unitary matrix form a set of mutually orthonormal vectors.

Homework Equations



hint - form the vectors [tex]u_i = {U_{ji}}[/tex] and [tex]u_k={U_{jk}}[/tex] from the [tex]i^{th}[/tex] and [tex]j^{th}[/tex] columns of [tex]U[/tex] and make use of the relationship [tex]U^{\dagger}U=I[/tex]

The Attempt at a Solution



I thought my work was following the hint...but not sure...I know I need to end up with an identity matrix from the hint, in which it shows the 3 columns are (1,0,0) , (0,1,0), and (0,0,1), respectively...to show that I have a set of basis vectors in the unitary matrix...

[url=http://postimage.org/][PLAIN]http://s9.postimage.org/sw7ugplvz/photo_3.jpg[/url] upload[/PLAIN]
 
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  • #2
It looks like you've got it. The Kronecker delta symbol ## \delta_{jk} ## is 1 if j=k and 0 if j≠k. Doesn't that give you your identity matrix?
 
  • #3
thanks,

I'm just a little confused because It looks like I only have one vector.

Do I need to turn that vector into a column, and for column 1 let i=k=1 , and for column 2 let i=k=3 and for column 3 let i=k=3, that would give:
[url=http://postimage.org/][PLAIN]http://s7.postimage.org/fj2bek7rf/photo_4.jpg[/url] free photo hosting[/PLAIN]

but I'm just not sure if my previous math boiled down to that, I feel like I'm missing something in the proof
 
  • #4
thanks,

would this be the next step? I feel like my notation is wrong or I did something wrong in the process

[url=http://postimage.org/][PLAIN]http://s16.postimage.org/4f3mski91/photo_5.jpg[/url] upload pics[/PLAIN]
 
  • #5
physics2000 said:
thanks,

would this be the next step? I feel like my notation is wrong or I did something wrong in the process

Yes, it's wrong. You want to show that the inner products (your sums) are 1 or 0. That's doesn't show that the original matrix U is the identity. What does 'orthonormal' mean?
 

1. What is a unitary matrix?

A unitary matrix is a square matrix whose conjugate transpose is equal to its inverse. In other words, the product of a unitary matrix and its conjugate transpose is equal to the identity matrix. This means that the columns (or rows) of a unitary matrix are mutually orthonormal vectors.

2. What does it mean for vectors to be mutually orthonormal?

Mutually orthonormal vectors are a set of vectors that are both orthogonal (perpendicular) and normalized (have a magnitude of 1). This means that each vector in the set is perpendicular to all other vectors in the set, and each vector has a length of 1.

3. How do you determine if a matrix is unitary?

A matrix can be determined to be unitary by checking if its conjugate transpose is equal to its inverse. This can be done by multiplying the matrix by its conjugate transpose and seeing if the product is equal to the identity matrix.

4. What is the significance of a unitary matrix with mutually orthonormal vectors?

A unitary matrix with mutually orthonormal vectors has several important properties. It preserves the length of vectors, meaning that the magnitude of a vector multiplied by a unitary matrix is equal to the magnitude of the original vector. It also preserves angles between vectors, making it useful for geometric transformations. Additionally, unitary matrices are used in quantum mechanics to represent transformations of quantum states.

5. How are unitary matrices used in applications?

Unitary matrices are used in a variety of applications in mathematics, physics, and engineering. They are commonly used for rotations, reflections, and other geometric transformations. In quantum mechanics, they are used to describe the evolution of quantum states. They also have applications in signal processing, coding theory, and quantum computing.

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