Why Is the Expected Value a Linear Operator in Quantum Mechanics?

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In summary: The variance of A is then given by##Var(A) = \alpha \langle \phi | A | \phi \rangle + \beta \langle \phi | B | \phi \rangle##
  • #1
KastorPhys
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In a Quantum Mechanics Class, my tutor had shown me that
the variance Δa2

Δa2 = < ( a - < a > )2 >
= < a2 - 2 a < a > + < a >2 >
= < a2 > - 2 < a >< a > + < a >2
= < a2 > - 2 < a >2 + < a >2
= < a2 > - < a >2

and my questions are

i) why, in step 3,

< x - y + z > will become < x > - < y > + < z > ?

ii)why, also in step 3, the middle term,

< -2 a < a > > will finally become -2 < a >2 ?

Is it mean that < a < a > > = < a > < a > = < a >2 ?? but why?

Thanks!
 
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  • #2
KastorPhys said:
In a Quantum Mechanics Class, my tutor had shown me that
the variance Δa2

Δa2 = < ( a - < a > )2 >
= < a2 - 2 a < a > + < a >2 >
= < a2 > - 2 < a >< a > + < a >2
= < a2 > - 2 < a >2 + < a >2
= < a2 > - < a >2

and my questions are

i) why, in step 3,

< x - y + z > will become < x > - < y > + < z > ?

ii)why, also in step 3, the middle term,

< -2 a < a > > will finally become -2 < a >2 ?

Is it mean that < a < a > > = < a > < a > = < a >2 ?? but why?

Thanks!
I'm not familiar with your notation. One definition of the variance of a random variable X (from Wikipedia - see https://en.wikipedia.org/wiki/Variance) is:
##Var(X) = E[(X - \mu)^2]##
Here E[X] means the expected value of X.

The right side of the equation above can be expanded to yield
##Var(X) = E[(X - E[X])^2] \\
= E[X^2 - 2XE[X] + (E[X])^2] \\
= E[X^2 - 2E[X] E[X] +(E[X])^2] \\
= E[X^2] - (E[X])^2##
 
  • #3
In all three questions the answer is the expected value is a linear operator. For an observable ##A## the expected value is defined as
##\langle A \rangle _{\phi} := \langle \phi | \, A \,| \phi \rangle##. Suppose that ##A## and ##B## are observables (mathematically they are operators on some Hilbert space), and let ##\alpha## and ##\beta## be complex numbers. Then
##\langle \alpha A + \beta B \rangle_\phi = \langle \phi | \alpha A + \beta B | \phi \rangle = \langle \phi | \alpha A | \phi \rangle + \langle \phi | \beta B | \phi \rangle =\alpha \langle \phi | A | \phi \rangle + \beta \langle \phi | B | \phi \rangle = \alpha \langle A \rangle_\phi + \beta \langle A \rangle_\phi##
 

FAQ: Why Is the Expected Value a Linear Operator in Quantum Mechanics?

What is "Explanation of Variance"?

Explanation of variance is a statistical concept used to measure the variability or spread of data points from the mean or expected value. It helps in understanding the relationship between the independent and dependent variables in a dataset.

What is the importance of "Explanation of Variance"?

Explanation of variance is essential in understanding the underlying factors that contribute to the variability in a dataset. It helps in identifying the significant variables and their impact on the outcome, which is crucial in making informed decisions and drawing meaningful conclusions.

How is "Explanation of Variance" calculated?

Explanation of variance is calculated by subtracting the predicted value from the actual value and then squaring the difference. This value is then summed up for all data points and divided by the total number of data points to get the average. This average is the measure of the variance in the dataset.

What is the difference between "Total Variance" and "Explained Variance"?

Total variance is the sum of the squared differences between each data point and the mean, while explained variance is the sum of the squared differences between the predicted value and the actual value. Total variance measures the overall variability in the dataset, while explained variance measures the variability that can be attributed to the independent variables.

How is "Explanation of Variance" interpreted?

Explanation of variance is interpreted as a percentage, and it represents the proportion of variability in the data that can be explained by the independent variables. A higher value of explained variance indicates a strong relationship between the variables, while a lower value suggests a weak relationship.

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