Where Does the Equation for Normalization and Expectation Come From?

In summary: Well, the simplest case is when there are only a finite number of states. For example, an electron's state, if you ignore the spatial part, is a two-state system: It can be spin-up or spin-down. So if we use matrices to represent the states, then we can have:|\psi_1\rangle = \left( \begin{array}\\ 1 \\ 0 \end{array} \right) which represents spin-up|\psi_2\rangle = \left( \begin{array}\\ 0 \\ 1 \end{array} \right) which represents spin-downAn arbitrary state |f\rangle is a combination:|f\
  • #1
gfd43tg
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Hello,

I am very confused how this is true? Where does this come from??
$$<f| \hat{Q}f> = (\sum_{n}a_{n}^{*} |\psi_{n}>)(\sum_{m}a_{m} \hat{Q} | \psi_{m}>)$$

thanks
 
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  • #2
Maylis said:
Hello,

I am very confused how this is true? Where does this come from??
$$<f| \hat{Q}f> = (\sum_{n}a_{n}^{*} |\psi_{n}>)(\sum_{m}a_{m} \hat{Q} | \psi_{m}>)$$

thanks
I think your formula is slightly wrong. If you have a complete set of orthonormal basis states [itex]|\psi_m\rangle[/itex], then you can write an arbitrary state [itex]|f\rangle[/itex] as a linear combination of basis states:

[itex]|f\rangle = \sum_m a_m |\psi_m\rangle[/itex]

That being the case, you take the adjoint of both sides:

[itex]|f\rangle^\dagger = \langle f | = \sum_m a_m^* \langle \psi_m|[/itex]

So as to not mix up the indices, let's relabel [itex] m[/itex] by [itex]n[/itex] in this expression:

[itex]|f\rangle^\dagger = \langle f | = \sum_n a_n^* \langle \psi_n|[/itex]

Then it follows automatically that

[itex]\langle f|\hat{O}|f\rangle = (\sum_n a_n^* \langle \psi_n|)\ \hat{O}\ ( \sum_m a_m |\psi_m\rangle)[/itex]

which can also be written as:
[itex]\langle f|\hat{O}|f\rangle = (\sum_n a_n |\psi\rangle)^\dagger\ \hat{O}\ ( \sum_m a_m |\psi_m\rangle)[/itex]

Maybe you don't know what [itex]\langle f|[/itex] means? Well, you know that for ordinary wave functions, you define [itex]\langle f|g\rangle[/itex] to be the integral:

[itex]\langle f|g\rangle = \int dx f^*(x) g(x)[/itex]

Then you can define [itex]\langle f|[/itex] as that operator that acts on [itex]|g\rangle[/itex] to produce [itex]\langle f|g\rangle[/itex]
 
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Ok, I was definitely unaware that ##|f \rangle^{ \dagger} = \langle f|##. No wonder I was so confused over the definition of a hermition conjugate, how somehow moving the operator to the bra somehow made it a complex conjugate.You are right, I am very shaky about ##\langle f|##. I understand the ket is a vector, but what is the bra? I know you say its an operator that acts on the vector g to produce the inner product, but that leaves me feeling icky inside. As in no intuition at all.
 
  • #4
Maylis said:
Ok, I was definitely unaware that ##|f \rangle^{ \dagger} = \langle f|##. No wonder I was so confused over the definition of a hermition conjugate, how somehow moving the operator to the bra somehow made it a complex conjugate.You are right, I am very shaky about ##\langle f|##. I understand the ket is a vector, but what is the bra? I know you say its an operator that acts on the vector g to produce the inner product, but that leaves me feeling icky inside. As in no intuition at all.

Well, the simplest case is when there are only a finite number of states. For example, an electron's state, if you ignore the spatial part, is a two-state system: It can be spin-up or spin-down. So if we use matrices to represent the states, then we can have:

[itex]|\psi_1\rangle = \left( \begin{array}\\ 1 \\ 0 \end{array} \right)[/itex] which represents spin-up

[itex]|\psi_2\rangle = \left( \begin{array}\\ 0 \\ 1 \end{array} \right)[/itex] which represents spin-down

An arbitrary state [itex]|f\rangle[/itex] is a combination:

[itex]|f\rangle = a_1 |\psi_1\rangle + a_2 |\psi_2\rangle = \left( \begin{array}\\ a_1 \\ a_2 \end{array} \right)[/itex]

Then [itex]|f\rangle^\dagger[/itex] is just [itex] \left( \begin{array}\\ a_1^* & a_2^* \end{array} \right)[/itex]. You compute [itex]|g\rangle^\dagger |f\rangle[/itex] by matrix multiplication: If [itex]|g\rangle[/itex] is [itex] \left( \begin{array}\\ b_1 \\ b_2 \end{array} \right)[/itex], then

[itex]|g\rangle^\dagger |f\rangle = \left( \begin{array}\\ b_1^* & b_2^* \end{array} \right)\ \left( \begin{array}\\ a_1 \\ a_2 \end{array} \right) = b_1^* a_1 + b_2^* a_2[/itex]

To generalize to an infinite, but countable, number of states,
[itex]|g\rangle^\dagger |f\rangle = \sum_i b_i^* a_i[/itex]

To generalize to a continuous number of states,

[itex]|g\rangle^\dagger |f\rangle = \int dx\ g(x)^* f(x)[/itex]
 
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Thanks, that clears things up a lot.
 

FAQ: Where Does the Equation for Normalization and Expectation Come From?

What is normalization and why is it important in data analysis?

Normalization is the process of transforming data to a common scale or range in order to remove any biases or inconsistencies between different variables. It is important in data analysis because it allows for fair comparison between variables and prevents any one variable from dominating the analysis.

What are some common methods of normalization?

Some common methods of normalization include min-max normalization, z-score normalization, and decimal scaling. These methods all involve rescaling the data to a range between 0 and 1, or a mean of 0 and a standard deviation of 1.

What is the difference between normalization and standardization?

Normalization and standardization are both methods of data transformation, but they have different goals and approaches. Normalization focuses on rescaling data to a common range, while standardization focuses on transforming data to have a mean of 0 and a standard deviation of 1.

How does normalization affect the interpretation of data?

Normalization can have a significant impact on the interpretation of data. It can help to remove any biases or discrepancies caused by different units or scales, and allows for more accurate comparison between variables. It also ensures that no single variable dominates the analysis.

What is the role of expectation in data analysis?

Expectation, also known as the mean or average, is a measure of central tendency that represents the "typical" value in a set of data. It is an important statistical concept in data analysis as it allows for summarization and comparison of data, and can also be used to make predictions about future data points.

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