Linearly indep. annihilation operator

Therefore, we have shown that the only solution to the equation is a=b=0, and thus \hat{a}\varphi_{n} and \hat{a}\widetilde{\varphi}_{n} are linearly independent.In summary, we can prove that \hat{a}\varphi_{n} and \hat{a}\widetilde{\varphi}_{n} are linearly independent by showing that the only solution to the equation a\hat{a}\varphi_{n}+b\hat{a}\widetilde{\varphi}_{n}=0 is a=b=0, which can be done by using the fact that \hat{a}\varphi
  • #1
yakattack
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Homework Statement


If [tex]\varphi[/tex] and [tex]\widetilde{ \varphi }[/tex] are linearly independent and
[tex]\hat{N}[/tex][tex]\varphi[/tex]=n[tex]\varphi[/tex] and
[tex]\hat{N}[/tex][tex]\widetilde{\varphi}[/tex]=n[tex]\widetilde{\varphi}[/tex], with [tex]n\geq 1[/tex]
[tex]\ \text{Prove that } \hat{a}\varphi_{n} \text{ and } \hat{a}\widetilde{\varphi}_{n} \text{ are also linearly independent.}[/tex]

Homework Equations


[tex]\ \text{I have to use the fact that if } \hat{N}\varphi=\nu\varphi \text{ then } \nu\geq0 \text{ and } \nu=0 \text { iff } \hat{a}\varphi=0
\hat{N}=\hat{a^{*}}\hat{a}
\hat{a} \text { is the annihilation operator.}[/tex]

The Attempt at a Solution


If i suppose they aren't linearly independent then i can show that this means that
[tex]\hat{a}[/tex][tex]\varphi[/tex][tex]_{n}[/tex] and the same with tildas are linearly dependent. But does this show that the converse is true?
 
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  • #2




Thank you for your post. To prove that \hat{a}\varphi_{n} and \hat{a}\widetilde{\varphi}_{n} are linearly independent, we can use the definition of linear independence. This means that we need to show that the only solution to the equation a\hat{a}\varphi_{n}+b\hat{a}\widetilde{\varphi}_{n}=0 is a=b=0.

First, let's consider the case where a=0 and b=0. In this case, we have 0\hat{a}\varphi_{n}+0\hat{a}\widetilde{\varphi}_{n}=0, which is true. Therefore, we have shown that a=b=0 is a solution to the equation.

Now, let's consider the case where a\neq 0 and b\neq 0. From the given information, we know that \hat{N}\varphi=n\varphi and \hat{N}\widetilde{\varphi}=n\widetilde{\varphi}. This means that \hat{N}\hat{a}\varphi=\hat{a}\hat{N}\varphi=\hat{a}(n\varphi)=n(\hat{a}\varphi) and similarly for \hat{a}\widetilde{\varphi}. This shows that \hat{a}\varphi_{n} and \hat{a}\widetilde{\varphi}_{n} are eigenstates of \hat{N} with eigenvalues n.

Now, suppose that a\hat{a}\varphi_{n}+b\hat{a}\widetilde{\varphi}_{n}=0. This means that \hat{a}(a\varphi_{n}+b\widetilde{\varphi}_{n})=0. Since \hat{a}\varphi_{n} and \hat{a}\widetilde{\varphi}_{n} are eigenstates of \hat{N}, this implies that a\varphi_{n}+b\widetilde{\varphi}_{n} is also an eigenstate of \hat{N} with eigenvalue n. However, since \varphi and \widetilde{\varphi} are linearly independent, this implies that
 

FAQ: Linearly indep. annihilation operator

What is a linearly independent annihilation operator?

A linearly independent annihilation operator is a mathematical operator that acts on a vector space and "annihilates" or reduces the dimensionality of the space by removing linearly dependent vectors. It is often used in linear algebra to simplify calculations and solve systems of equations.

How does a linearly independent annihilation operator work?

A linearly independent annihilation operator works by identifying and removing linearly dependent vectors from a given vector space. It does this by applying a set of rules and operations to the vectors in the space, ultimately reducing the dimensionality of the space and making it easier to work with.

What are some real-world applications of linearly independent annihilation operators?

Linearly independent annihilation operators have many practical applications in fields such as engineering, physics, and computer science. For example, they are used in signal processing to remove redundant or unimportant data, in quantum mechanics to simplify calculations, and in machine learning to identify and eliminate correlated features.

Can a linearly independent annihilation operator be used on any vector space?

No, a linearly independent annihilation operator can only be applied to vector spaces that have a finite number of dimensions. It also requires the vectors in the space to be linearly independent, meaning they cannot be expressed as a linear combination of other vectors in the space.

How does a linearly independent annihilation operator differ from a linearly dependent annihilation operator?

A linearly independent annihilation operator removes linearly dependent vectors from a vector space, while a linearly dependent annihilation operator removes linearly independent vectors. This means that a linearly dependent annihilation operator can only be used on vector spaces with linearly dependent vectors, while a linearly independent annihilation operator can only be used on vector spaces with linearly independent vectors.

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