Representing |psi> with Continuous Eigenvectors: An Example

In summary, state vectors can be represented by column vectors or functions depending on the type of basis vectors used. In the case of continuous eigenvalues, such as position eigenvectors, a state vector is represented by a function. For example, the state vector |psi> can be represented in a space spanned by position eigenvectors as C_n = <x_n|psi>, where C_n is the coefficient and x_n is the spectral value (or eigenvalue) of the position eigenvector |x_n>.
  • #1
jinksys
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Homework Statement


for discrete basis vectors {{e_n}}, a state vector |psi> is represented by a column vector, with elements being psi_n = <e_n|psi>. When basis vectors correspond to those with continuous eigenvalues, vectors are represented by functions. Give such an example of a state vector |psi> being represented in a space spanned by position eigenvectors {|x>}.


Homework Equations




The Attempt at a Solution



C_n = <x_n | psi>

I'm really taking a shot in the dark with this one. I'm using Griffith's, so I assume my solutions lies somewhere in chapter 3.
 
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So...[itex] |\psi\rangle [/itex] is a ket which is part of the space spanned by eigenvectors of X, the latter being kets satisfying [itex] X|x\rangle = x |x\rangle [/itex], where the x is a real number, the spectral value of X.

Soo... [itex] |\psi\rangle = \mbox{(something)} ~ |x\rangle [/itex]

What's (something) equal to ?
 
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When you say spectral value, is that the same as the eigenvalue?
 
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A generalization of eigenvalue. But you can think of it in a simplified way as an eigenvalue.
 
  • #5


Great job! Your attempt is correct. In this case, C_n would represent the coefficient of the state vector |psi> in the basis vector |x_n>. This means that to represent |psi> in terms of the continuous basis vectors {|x>}, we would need to find the coefficients C_n for each basis vector. These coefficients could be found by taking the inner product <x_n|psi>. In this way, we can expand the state vector |psi> in terms of the continuous basis vectors, similar to how we would expand it in terms of discrete basis vectors.
 

FAQ: Representing |psi> with Continuous Eigenvectors: An Example

What is the significance of representing |psi> with continuous eigenvectors?

Continuous eigenvectors allow for a more precise representation of a quantum system, as they can describe the system at any point in time rather than only at specific discrete points.

Can you provide an example of representing |psi> with continuous eigenvectors?

One example is representing the position of a particle in a one-dimensional quantum system using a continuous eigenvector, where the eigenvector represents the probability amplitude of finding the particle at a specific position.

How does representing |psi> with continuous eigenvectors differ from using discrete eigenvectors?

With discrete eigenvectors, the system is only described at certain points in time or space, whereas continuous eigenvectors allow for a more precise and continuous description of the system.

Are there any limitations to representing |psi> with continuous eigenvectors?

One limitation is that continuous eigenvectors can only be used for systems that are described by continuous variables, such as position or time. They cannot be used for systems with discrete variables, such as spin.

How does representing |psi> with continuous eigenvectors relate to the Heisenberg uncertainty principle?

Continuous eigenvectors provide a more precise description of a quantum system, allowing for a more accurate calculation of quantities such as position and momentum. This can help to reduce the uncertainty in these variables, as described by the Heisenberg uncertainty principle.

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