Expectation value of following function

In summary, the expectation value of a system is the value that is most likely to be measured, and the standard deviation measures the spread of data around the mean value. The standard deviation for the momentum of a harmonic oscillator is calculated as the square root of the difference between the expectation values of p squared and p squared. In one experiment, it is not possible to measure both the expectation values of x and p, but in multiple experiments, the Heisenberg uncertainty principle will be satisfied, showing the relationship between the spread of values for x and p. The HUP is a statement about the probability distributions underlying the measured values, and can only be tested approximately with a finite number of trials.
  • #1
Kruger
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I need to find the momentum expectation value of the function in the attached picture. It is the function of the harmonic oscillator (first excited state). :confused:

I know that the expectation value is the value that we measure with the highest probability if we measure the system. But what the hell does the standard deviation d(p)=<p^2>-<p>^2 mean for the harmonic oscillator? Does it mean that if we make 1000 times the same experiment and measure with every experiment the momentum that we result in this d(p) if we always measure p?

And another question is can we measure in one experiment <p> and <x> (the two expectation values) exactly at the same time? I think no, because the uncertainty principle would not be sadisfied.
 

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  • #2
There's an error there:

[tex]\langle\hat{p}_{x}\rangle_{\psi (x)}=-i\hbar\int_{\mathbb{R}} \psi^{*}(x) \frac{d\psi (x)}{dx} \ dx [/tex]

Daniel.
 
  • #3
Hello Kruger,

standard deviation is the spread around your mean value. Say you measure your momentum 1000 times.

From the data you collected you can calculate the mean value <p>. In addition, you can also calculate the standard deviation often denoted as [itex]\sigma[/itex].

What does [itex]<p> \pm \sigma[/itex] mean?
In case of a normal distribution 68% of your data, that is 680 data points will lie within [itex]<p> \pm \sigma[/itex].

Or to give you a better feeling, say you have a friend who asks you about your measurements. He wants to know, what momentum he will measure if he conducted the same experiment. Then you can tell him: Well, the mean value is <p>, but you will measure with 68% probability a value within [itex]<p> \pm \sigma[/itex].

The standard deviation moreover tells you how "spread" your values will be around your mean value, the greater [itex]\sigma[/itex] the greater your spread.


By typing "Standard deviation" into google, I found the following websites:
http://en.wikipedia.org/wiki/Standard_deviation
http://www.robertniles.com/stats/stdev.shtml
 
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  • #4
Kruger said:
But what the hell does the standard deviation d(p)=<p^2>-<p>^2 mean for the harmonic oscillator?

Correction: you need to take the square root:

[tex]\Delta p = \sqrt {<p^2> - <p>^2} [/tex]

Does it mean that if we make 1000 times the same experiment and measure with every experiment the momentum that we result in this d(p) if we always measure p?

That is approximately correct. Think of the [itex]\Delta p[/itex] that you can calculate from the wave function as an "ideal" or "exact" value. The [itex]\Delta p[/itex] that you calculate from actual experimental measurements is an approximation, which improves as the number of data points increases.

And another question is can we measure in one experiment <p> and <x> (the two expectation values) exactly at the same time?

In one experiment (trial), you cannot meaningfully measure the expectation value of either x or p because you have only one data point to work with.

If you repeat the experiment many many times (identically prepared each time, or course), then measure both x and p each time, you can then estimate [itex]<x>[/itex] and [itex]<p>[/itex] from your data, also [itex]<x^2>[/itex] and [itex]<p^2>[/itex]. Now you can calculate [itex]\Delta p[/itex] as described above, and [itex]\Delta x[/itex] similarly. You will find that those two quantities always satisfy Heisenberg's uncertainty relation, in the limit of an infinite number of trials. If your measured values of p lie within a small range, then the values of x will spread out over a large range, and vice versa. (For some suitable definition of "small" and "large", of course.) That's the meaning of the Heisenberg uncertainty principle.

Note that the HUP is a statement about the probability distributions that underlie the measured values of x and p. In a finite number of trials, you can test the HUP only approximately, and there is a chance that your particular data may actually appear to violate the HUP! As the number of trials becomes larger this becomes less likely to happen.
 

What is the expectation value of a function?

The expectation value of a function is the average value that would be obtained if the function were measured repeatedly on a large number of samples.

How is the expectation value of a function calculated?

The expectation value of a function is calculated by taking the integral of the function multiplied by the probability density function over the entire range of values.

Why is the expectation value of a function important?

The expectation value of a function is important because it represents the most probable outcome when the function is measured, and can also give insights into the overall behavior of the function.

Can the expectation value of a function be negative?

Yes, the expectation value of a function can be negative if the function itself has negative values and the probability density function is also negative in some regions.

Is the expectation value of a function always a real number?

Yes, the expectation value of a function is always a real number as it represents a measurable quantity. However, it can take on complex values in quantum mechanics when dealing with wave functions.

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