Inner Product in this step of the working

In summary, the conversation discussed evaluating an inner product at step 3.8 and how the average value of an observable can be found using equation (3.4). The conversation also touched on the norm of energy eigenkets and the additional factor of ##\frac{1}{\omega}## in the expression. It was clarified that the energy eigenkets do have a norm of 1 and the use of hats in the norm is necessary as the x and p in the norm should also have hats. The conversation also discussed the importance of using hats in the operators when working with kets.
  • #1
unscientific
1,734
13
Hi guys, I'm not sure how to evaluate this inner product at step (3.8)

I know that:

##\hat {H} |\phi> = E |\phi>##

2cna71v.png


[tex] <E_n|\frac{\hat H}{\hbar \omega} + \frac{1}{2}|E_n> [/tex]

[tex] <E_n| \frac{\hat H}{\hbar \omega}|E_n> + <E_n|\frac{1}{2}|E_n> [/tex]

I also know that ##<\psi|\hat Q | \psi>## gives the average value of observable to ##\hat Q##. In this case, it's not ##\psi## but ##E_n##, does the same principle hold?
 
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  • #2
Use equation (3.4).
 
  • #3
Hi George, I can only conclude that you figured out where he picked his 2 questions from. :biggrin:.
 
  • #4
dextercioby said:
Hi George, I can only conclude that you figured out where he picked his 2 questions from. :biggrin:.

Okay, I got step 3.8. But For Step 3.9, I'm having a slight issue here:

[tex]E_n = <E_n|\hat H|E_n> = \frac{1}{2m}<E_n|(m\omega \hat x)^2 + \hat {p}^2|E_n>[/tex]
[tex] = \frac{1}{2m}(m^2\omega^2)<E_n|\hat x \hat x|E_n> + \frac{1}{2m}<E_n|\hat p \hat p|E_n> [/tex]

Using ##\hat x|\phi> = x|\phi>## and ##\hat p |\phi> = p|\phi>##,

[tex] = \frac{1}{2m}(m^2\omega^2)<E_n|\hat x x|E_n> + \frac{1}{2m}<E_n|\hat p p|E_n>[/tex]

Removing the last "hats" from ##\hat p## and ##\hat x## and Using orthogonality ##<E_n|E_n> = 1 ##:

[tex] = \frac{1}{2m}\left ( (m\omega x)^2 + p^2 \right ) [/tex]

Is it wrong to assume that the energy eigenkets have norm = 1? That would be strange because later we show that ##E_n = (n + \frac{1}{2})\hbar \omega##

And, why is there an additional factor of ##\frac{1}{\omega}## in the expression?
 
  • #5
Energy eigenkets for the harmonic oscillator do have norm =1.
 
  • #6
dextercioby said:
Energy eigenkets for the harmonic oscillator do have norm =1.

Ok, then I have no idea what's gone wrong with my working..
 
  • #7
The energy eigenket is not an eigenket of either x nor p. You need the ladder operators to evaluate the matrix elements.
 
  • #8
dextercioby said:
The energy eigenket is not an eigenket of either x nor p. You need the ladder operators to evaluate the matrix elements.

How did they get 3.9 then?
 
  • #9
<p E_n, p E_n> = ||p |E_n> ||^2 = <E_n, p^2 E_n>, because the eigenkets of energy are in the domain of both p and p^2, on which the 2 operators are essentially self-adjoint. p^2 is a positive operator, hence the inequality at the end.

The same goes for x.
 
  • #10
dextercioby said:
<p E_n, p E_n> = ||p |E_n> ||^2 = <E_n, p^2 E_n>, because the eigenkets of energy are in the domain of both p and p^2, on which the 2 operators are essentially self-adjoint. p^2 is a positive operator, hence the inequality at the end.

The same goes for x.

I just don't get how ##<E_n|\hat x \hat x|E_n> = |x|E_n>|^2## and ##<E_n|\hat p \hat p|E_n> = |p|E_n>|^2##
 
  • #11
hmm because in the same way you have in the complex numbers that:
[itex] z^{*} z = |z|^{2}[/itex]
In fact the ket can be interpreted as a vector on Hilber space, while the bra as its dual.
So in the case of this, you can write:

[itex] < E_{n}| \hat{x} \hat{x} |E_{n}>= (\hat{x} |E_{n}>)^{t} \hat{x} |E_{n}>[/itex]
using that x operator is self adjoint. The same goes for p... with the "t" I denoted the adjoint conjugate operation
 
  • #12
ChrisVer said:
hmm because in the same way you have in the complex numbers that:
[itex] z^{*} z = |z|^{2}[/itex]
In fact the ket can be interpreted as a vector on Hilber space, while the bra as its dual.
So in the case of this, you can write:

[itex] < E_{n}| \hat{x} \hat{x} |E_{n}>= (\hat{x} |E_{n}>)^{t} \hat{x} |E_{n}>

That is true, but ##\hat x## and ##\hat p## are operators.. so the x and p in the norm should have hats?

##<E_n|\hat x \hat x|E_n> = |\hat x|E_n>|^2## and ##<E_n|\hat p \hat p|E_n> = |\hat p|E_n>|^2##
 
  • #13
they do have hats... the person who wrote the things in the image you posted, doesn't use hats so much...

You can get a feeling there must be hats because otherwise there would be no reason to use the eigenvectors in kets... (he'd get 1)
 
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  • #14
This clears things up a little, thanks!
 

Related to Inner Product in this step of the working

What is an inner product?

An inner product is a mathematical operation that takes two vectors and produces a scalar value. It is often used in linear algebra to measure the angle between two vectors or to find the length of a vector.

How is an inner product calculated?

The most common way to calculate an inner product is by taking the dot product between two vectors. This involves multiplying the corresponding components of the two vectors and then summing the results. Other methods, such as using matrices or complex numbers, can also be used depending on the specific application.

What is the significance of the inner product in science?

The inner product has many important applications in science, particularly in physics and engineering. It is used to calculate work and energy in mechanics, determine the similarity between two signals in signal processing, and even in quantum mechanics to describe the state of a system.

How is the inner product related to orthogonality?

Two vectors are considered orthogonal if their inner product is equal to zero. This means that the vectors are perpendicular to each other in n-dimensional space. Orthogonality is an important concept in linear algebra and is used in various scientific fields.

Can the inner product be extended to other mathematical structures?

Yes, the concept of an inner product can be extended to other mathematical structures, such as functions and matrices. For example, the inner product of two functions is defined as the integral of the product of the two functions over a certain interval. Similarly, the inner product of two matrices involves taking the dot product between the rows and columns of the two matrices.

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