QM Orthogonality: Separate & Independent Eigenvalues?

In summary: How could one then say, from a practical point-of-view, that the eigenvalues are separate or distinct, even though the eigenfunctions themselves remain orthogonal?Thanks in advance for your input.
  • #1
LarryS
Gold Member
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In non-relativistic QM, given a Hilbert Space with a Hermitian operator A and a generic wave function
Ψ. The operator A has an orthogonal eigenbasis, {ai}.

I have often read that the orthogonality of such eigenfunctions is an indication of the separateness or distinctiveness of the associated eigenvalues, i.e. that orthogonality in QM means separate and independent.

But what if the probability distribution for Ψ is peaked at one value causing most of the eigenvalues to be clustered in a very narrow range? How could one then say, from a practical point-of-view, that the eigenvalues are separate or distinct, even though the eigenfunctions themselves remain orthogonal?

Thanks in advance
 
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  • #2
referframe said:
I have often read

Can you give some specific references?
 
  • #3
referframe said:
...what if the probability distribution for Ψ is peaked at one value causing most of the eigenvalues to be clustered in a very narrow range? How could one then say, from a practical point-of-view, that the eigenvalues are separate or distinct, even though the eigenfunctions themselves remain orthogonal?

Thanks in advance

The eigenvalues of an observable define the set of outcomes that such a function can have. For example, the position observable [itex]\hat{x}[/itex] has a continuous spectrum of eigenvalues spanning all the real numbers.

The eigenvalues and eigenstates of an observable depend on the observable, and are completely indpendent of the quantum state [itex]|\psi\rangle[/itex] or the quantum wavefunction [itex]\langle x|\psi\rangle = \psi(x)[/itex].

The eigenstates associated to different eigenvalues of an observable are orthogonal. What this means from a practical standpoint is that if a particle is prepared in an eigenstate [itex]|a_{i}\rangle[/itex] of observable [itex]\hat{A}=\sum_{i}a_{i} |a_{i}\rangle\langle a_{i}|[/itex], the probability that measuring [itex]\hat{A}[/itex] will give any other eigenvalue is zero, since the inner product [itex]\langle a_{i}|a_{j}\rangle = 0[/itex] if [itex]i\neq j[/itex], and the probability goes as the magnitude square of this inner product.
 
  • #4
referframe said:
I have often read that the orthogonality of such eigenfunctions is an indication of the separateness or distinctiveness of the associated eigenvalues, i.e. that orthogonality in QM means separate and independent.

I have never read that.

referframe said:
But what if the probability distribution for Ψ is peaked at one value causing most of the eigenvalues to be clustered in a very narrow range?

The eigenvalues and eigenvectors of an operator have nothing to do with the particular state of a system - or any particular vector - they are properties of the operator itself.
 
  • #5
referframe said:
In non-relativistic QM, given a Hilbert Space with a Hermitian operator A and a generic wave function
Ψ. The operator A has an orthogonal eigenbasis, {ai}.

I have often read that the orthogonality of such eigenfunctions is an indication of the separateness or distinctiveness of the associated eigenvalues, i.e. that orthogonality in QM means separate and independent.

For Hermitian operators, eigenfunctions for distinct eigenvalues have to be orthogonal.

Easy proof:Suppose ##A|\psi_1\rangle = \lambda_1 |\psi_1\rangle## and ##A|\psi_2\rangle = \lambda_2|\psi_2\rangle##. Then on the one hand,

##\langle \psi_2|A|\psi_1 \rangle = \langle \psi_2|(A|\psi_1\rangle) = \lambda_1 \langle \psi_2|\psi_1 \rangle##

On the other hand,

##\langle \psi_2|A|\psi_1 \rangle = (\langle \psi_2|A)|\psi_1\rangle = (A |\psi_2\rangle)^\dagger |\psi_1\rangle = (\lambda_2 |\psi_2\rangle)^\dagger |\psi_1\rangle = \lambda_2 \langle \psi_2|\psi_1\rangle##

So those two have to be equal: ##\lambda_1 \langle \psi_2|\psi_1\rangle = \lambda_2 \langle \psi_2|\psi_1\rangle##. That's only possible if ##\lambda_1 = \lambda_2## or ##\langle \psi_2|\psi_1\rangle = 0##
 
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  • #6
stevendaryl said:
Suppose A|ψ1⟩=λ1A|ψ1⟩=λ1A|\psi_1\rangle = \lambda_1 and A|ψ2⟩=λ2A|ψ2⟩=λ2A|\psi_2\rangle = \lambda_2.

I think this was meant to read ##A|\psi_1\rangle=\lambda_1 |\psi_1\rangle## and ##A|\psi_2\rangle=\lambda_2 |\psi_2\rangle## (may be a problem with my viewer).

Translating to plain English, I concur that orthogonality of states with different eigenvalues for the same Hermitian operators does mean "separate and distinctive"; if the state is ##|\psi_1\rangle##, then the state is not ##|\psi_2\rangle##; if the eigenvalue is ##\lambda_1##, then it is not ##\lambda_2##.

This crucially depends on the operator being Hermitian. A famous non-Hermitian operator is the annihilation operator a operating on a coherent state of the harmonic oscillator (or formally equivalent the field operator a operating on coherent states of the electromagnetic field). Coherent states are the closest approach to classical analog of these systems and satisfy the eigenvalue equation ##a|\alpha\rangle=\alpha|\alpha\rangle##. But a is not Hermitian, the eigenvalues ##\alpha## are complex rather than real, and the states ##|\alpha\rangle## are not orthogonal - rather they form an over-complete basis. A good article can be found here https://en.wikipedia.org/wiki/Coherent_states.
 
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  • #8
PeterDonis said:
Can you give some specific references?

I listed 3 references in my reply to PeroK above.
 
  • #9
referframe said:
I listed 3 references in my reply to PeroK below.

Yes, I see them, but I don't see how they support what you said in the OP. The references say that eigenvectors are mutually orthogonal. They don't say the eigenvalues are "separate and distinct" in the way you appear to be using that concept in the OP--for example, they don't say the eigenvalues have to have any minimum "spacing" (difference) between them. In fact, your third reference specifically talks about the case of degeneracy, where multiple orthogonal eigenvectors have the same eigenvalue.

So it looks to me like your OP is based on a misconception.
 
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  • #10
It is a misconception to assume that in general orthogonal eigenstates of an observable have distinct eigenvalues. Frequently they do but there may be degeneracies. Even the one-dimensional free particle has a degenerate energy spectrum, since eigenstates of momentum ##\hat p## with respective eigenvalues p and -p have the same energy p2/2m, but are nevertheless orthogonal eigenstates of the energy observable ##\hat p^2/2m##. Here, however, the spectrum of ##\hat p## is non-degenerate. Due to the density of the real number line, momentum eigenstates ##|p_1\rangle## and ##|p_2\rangle## can have eigenvalues p1 and p2 that are arbitrarily close together, but as long as p1p2, the eigenstates are orthogonal, i.e., ##\langle p_1|p_2\rangle = 0##.
 

1. What is QM Orthogonality?

QM Orthogonality refers to the concept of orthogonal matrices in quantum mechanics. An orthogonal matrix is a square matrix whose columns and rows are orthogonal unit vectors. In other words, the dot product of any two columns or rows is equal to 0, and the length of each column or row is equal to 1.

2. What are separate and independent eigenvalues?

Separate eigenvalues refer to the eigenvalues of a matrix that do not share any common factors. In other words, they are distinct from each other. Independent eigenvalues refer to the eigenvalues of a matrix that are not dependent on each other, meaning they can be varied independently without affecting each other.

3. How are separate and independent eigenvalues related to QM Orthogonality?

In QM Orthogonality, separate and independent eigenvalues play a crucial role in determining the behavior of quantum systems. They allow us to represent a system as a linear combination of independent eigenstates, which simplifies the mathematical analysis and makes it easier to solve complex problems.

4. Why is QM Orthogonality important in quantum mechanics?

QM Orthogonality is important because it allows us to describe and analyze quantum systems using the principles of linear algebra. It also provides a mathematical framework for understanding the behavior of particles at the quantum level and has many practical applications in fields such as quantum computing and quantum cryptography.

5. How is QM Orthogonality used in real-world applications?

QM Orthogonality is used in many real-world applications, particularly in fields such as physics, chemistry, and engineering. For example, it is used in quantum computing algorithms to manipulate and control qubits, which are the fundamental units of quantum information. It is also used in quantum cryptography to ensure the security of communication channels by encoding and decoding information using QM Orthogonality principles.

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