Fourier transform as (continuous) change of basis

In summary, the conversation discusses the use of the Fourier transform to switch between coordinate representation and momentum representation. The speaker's concern is why it is necessary to change basis when performing a linear transformation in a Hilbert space with global sets of basis available. The conversation also touches on the relationship between the noncommuting of x and p (Heisenberg's Uncertainty Principle) and the Hilbert space's topological structure. The purpose of changing basis is to simplify calculations, as the momentum basis is an eigen-basis and makes it easier to calculate time-evolution. It is also mentioned that in the general case, changing basis is not always necessary if one knows the operator in the original basis.
  • #1
TrickyDicky
3,507
27
Trying not to get too confused with this but I'm not clear about switching from coordinate representation to momentum representation and back by changing basis thru the Fourier transform.
My concern is: why do we need to change basis? One would naively think that being in a Hilbert space where global sets of basis are available one shouldn't be required to change basis when performing a linear transformation.

I guess this is related to the noncommuting of x and p (HUP), and the Hilbert infinite -dimensional space topological structure but how exactly?
 
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  • #2
I am not completely sure if i understand the question correctly.

One likes to change the basis from the position to the momentum basis, because the momentum basis is an eigen-basis ie the plane waves are eigenstates of propagation and it is thus easy to calculate their time-evolution.

So we change basis, propagate, and change back.

Edit: In the general case you do not have to change the basis in order to apply any operator, provided you know what the operator looks like in the basis you start with.
 
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1. What is a Fourier transform?

A Fourier transform is a mathematical operation that decomposes a function into its constituent frequencies. It is used to analyze the frequency components of a signal or function.

2. How does the Fourier transform work?

The Fourier transform works by representing a function as a sum of sinusoidal functions with different frequencies, amplitudes, and phases. The Fourier transform then converts this representation into a frequency-domain representation, which shows the amount of each frequency present in the original function.

3. What is the purpose of using Fourier transform as a change of basis?

The purpose of using Fourier transform as a change of basis is to simplify the representation of a function. By transforming a function into its frequency-domain representation, we can better understand its frequency components and analyze it more easily.

4. Is Fourier transform only applicable to continuous functions?

No, Fourier transform can also be applied to discrete functions, such as digital signals. However, the continuous Fourier transform is used for continuous functions, while the discrete Fourier transform is used for discrete functions.

5. What are some practical applications of Fourier transform?

Fourier transform has various practical applications, such as signal processing, image processing, data compression, and solving differential equations. It is also used in fields like audio and video processing, astronomy, engineering, and physics.

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