State vectors and Eigenvalues?

In summary: Statements such as these are eigenvalue problems. In these cases, by definition, ##\Psi## must be an eigenvector of the operator. The time-dependent Schroedinger equation is NOT an eigenvalue problem:$$H\left|\Psi\right>=i\hbar\frac{\partial}{\partial t}\left|\Psi\right>$$And so ACTUAL wave functions do NOT have to be eigen-vectors of any operator. We just use the eigenvalue problems to help us solve the full problem.
  • #1
kq6up
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If I define a state ket in the traditional way, Say:

$$|\Psi \rangle =\sum _{ i }^{ }{ a_{ i }|\varphi _{ i }\rangle \quad } $$

Where $$a_i$$ is the probability amplitude.

How does:

$$\hat {H } |\Psi \rangle =E|\Psi \rangle $$ if the states of $$\Psi$$ could possibly represent states that have different energy levels, and one would not be able to factor the energy eigenvalue out of the summation. I know I am missing something big here. Could someone point it out?

Edit: Does $$ \hat {H} $$ collapse psi to one state phi, and so render only energy eigenvalue of the one phi that remains?

Thanks,
Chris Maness
 
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  • #2
kq6up said:
If I define a state ket in the traditional way, Say:

$$|\Psi \rangle =\sum _{ i }^{ }{ a_{ i }|\varphi _{ i }\rangle \quad } $$

Where $$a_i$$ is the probability amplitude.

How does:

$$\hat {H } |\Psi \rangle =E|\Psi \rangle $$ if the states of $$|\Psi \rangle$$ could possibly represent states that have different energy levels, and one would not be able to factor the energy eigenvalue out of the summation. I know I am missing something big here. Could someone point it out?

Thanks,
Chris Maness

When you write ##\hat {H } |\Psi \rangle =E|\Psi \rangle ##, ##|\Psi \rangle## cannot represent a state with more than one energy. Each possible ##|\Psi \rangle## that is a solution to that equation is an energy eigenstate with a definite energy.

That equation has many solutions, each corresponding to a different energy. An arbitrary state will be a superposition of energy eigenstates with different energies.

The equation ##\hat {H } |\Psi \rangle =E|\Psi \rangle ## is the time-independent Schroedinger equation or the the energy eigenstate equation. It is only an intermediate step in the solution of the full time-dependent Schroedinger equation.
 
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  • #3
Ok, so $$ \Psi $$ can't be a superposition anymore.

Does this work for a generalized statement?

$$ \hat { B } \hat { A } |\Psi \rangle =ab|\Psi \rangle $$ Where if, psi is not collapsed, it is at least degenerate, and where A & B commute.

Thanks,
Chris Maness
 
  • #4
kq6up said:
Ok, so $$ \Psi $$ can't be a superposition anymore.

Does this work for a generalized statement?

$$ \hat { B } \hat { A } |\Psi \rangle =ab|\Psi \rangle $$ Where if, psi is not collapsed, it is at least degenerate, and where A & B commute.

Thanks,
Chris Maness

Statements such as these are eigenvalue problems. In these cases, by definition, ##\Psi## must be an eigenvector of the operator.

The time-dependent Schroedinger equation is NOT an eigenvalue problem:

$$H\left|\Psi\right>=i\hbar\frac{\partial}{\partial t}\left|\Psi\right>$$

And so ACTUAL wave functions do NOT have to be eigen-vectors of any operator. We just use the eigenvalue problems to help us solve the full problem.
 
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Likes 1 person
  • #5
It's the phis that have the different energy levels!
 
  • #6
kq6up said:
Ok, so $$ \Psi $$ can't be a superposition anymore.

Does this work for a generalized statement?

$$ \hat { B } \hat { A } |\Psi \rangle =ab|\Psi \rangle $$ Where if, psi is not collapsed, it is at least degenerate, and where A & B commute.

Thanks,
Chris Maness

Yes, in the example you give, if ##|\Psi \rangle## is an eigenstate of A and an eigenstate of B, then it is an eigenstate of AB or BA. This does not mean that ##|\Psi \rangle## is not a superposition. It is a superposition of eigenstates of other operators that do not commute with A or B.

http://en.wikipedia.org/wiki/Complete_set_of_commuting_observables
http://www2.ph.ed.ac.uk/~ldeldebb/docs/QM/lect2.pdf (Section 2.7)
 
Last edited:

Related to State vectors and Eigenvalues?

1. What are state vectors and eigenvalues?

State vectors and eigenvalues are concepts in linear algebra that are used to describe and analyze systems with multiple degrees of freedom. A state vector is a vector that represents the state of a system at a particular moment in time, while an eigenvalue is a scalar value that represents a property of the system. Both state vectors and eigenvalues are used to understand the behavior and evolution of a system.

2. How are state vectors and eigenvalues related?

State vectors and eigenvalues are related through the mathematical concept of eigenvectors and eigenvalues. An eigenvector is a special type of vector that, when multiplied by a specific matrix, results in a scalar multiple of itself. This scalar multiple is the eigenvalue. In other words, the eigenvector is the state vector and the eigenvalue is the corresponding scalar value.

3. What is the significance of eigenvalues in state vectors?

Eigenvalues are significant in state vectors because they represent the inherent properties and behavior of a system. They can indicate stability or instability, convergence or divergence, and other important characteristics of the system. By understanding the eigenvalues of a system, scientists can make predictions about its future behavior.

4. How are state vectors and eigenvalues used in real-world applications?

State vectors and eigenvalues are used in a variety of real-world applications, including physics, engineering, and computer science. They are commonly used in quantum mechanics to describe the state of a quantum system, in control theory to analyze the stability of systems, and in data analysis to identify patterns and relationships in large datasets.

5. Are state vectors and eigenvalues always applicable to every system?

No, state vectors and eigenvalues are not always applicable to every system. They are most commonly used in linear systems, meaning systems that can be described using linear equations. Nonlinear systems, such as chaotic systems, may not have well-defined state vectors and eigenvalues. Additionally, some systems may be too complex to be accurately described using state vectors and eigenvalues.

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