Product rule of derivative of expectation values

In summary, the conversation discusses using Ehrenfest's theorem and commutation relations to show that the equation ##\frac{d}{dt}\langle \vec{r}\cdot \vec{p} \rangle = \langle 2T-\vec{r}\cdot \nabla V \rangle## is mathematically sound. It is mentioned that the product rule cannot be applied easily and that Ehrenfest's theorem only applies to self-adjoint operators. The use of Ehrenfest's theorem and commutation relations is shown to be a standard method for making non-hermitian operators self-adjoint.
  • #1
jonnaraev
3
0
Hello, first post here.

I am preparing for my Introductory Quantum Mechanics course, and in the exam questions, we are asked to use Ehrenfest's theorem to show that
[tex] \frac{d}{dt}\langle \vec{r}\cdot \vec{p} \rangle = \langle 2T-\vec{r}\cdot \nabla V \rangle [/tex]

Now, from other results:
[tex] \frac{d}{dt}\langle \vec{r} \rangle = \frac{1}{m} \langle \vec{p} \rangle [/tex]
[tex] \frac{d}{dt}\langle \vec{p} \rangle = \langle -\nabla V \rangle [/tex]

it can be solved using a product rule of differentiation:
[tex] \frac{d}{dt}\langle \vec{r}\cdot \vec{p} \rangle =\frac{d}{dt}\langle \vec{r} \rangle \cdot \vec{p} + \vec{r} \cdot \frac{d}{dt}\langle \vec{p} \rangle [/tex]
But I severely doubt this is the right way, or even mathematically sound. So my question is, is the above equation sound, or should I be calculating the commutators with the Hamiltonian, as per Ehrenfest's?
 
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  • #2
That equation's fine, as long as you keep the operators in their original order, which you did.
 
  • #3
Thanks. Google failed me, so I couldn't find any documentation. Happy to know my instincts weren't all off.
 
  • #4
But I severely doubt this is the right way, or even mathematically sound. So my question is, is the above equation sound, or should I be calculating the commutators with the Hamiltonian, as per Ehrenfest's?
I think the formula that follows the above words is not correct. On the left-hand side, there is a number, while on the right-hand side, there is an operator (##\mathbf{p} = -i\hbar \nabla##).

Your intuition is right, you can derive the virial theorem by using the time-dependent Schroedinger equation and the commutation relations. Here is how:$$
\frac{d}{dt}\langle \mathbf r\cdot\mathbf p\rangle = \int \dot \psi^* \mathbf r\cdot\mathbf p \psi + \psi^* \mathbf r\cdot\mathbf p \dot \psi\,dV
$$

Now use Schroedinger's equation

$$
\dot \psi = \frac{1}{i\hbar} {H} \psi
$$

to get rid of time derivatives. The integrand contains the commutator

$$
[\mathbf r\cdot\mathbf p, H].
$$

Replace ##H## by ##T+V## and use the commutation relations for position and momentum operator and the relation
$$
[A, BC] = B[A,C] + [A,B]C.
$$
After some manipulations, you should get ##\langle 2T - \mathbf r\cdot \nabla V\rangle##.
 
  • #5
You cannot use the product rule so easily, because the expectation value of a product is not the product of the expectation value. However, Ehrenfest's theorem tells you that
[tex]\frac{\mathrm{d}}{\mathrm{d} t} \langle A \rangle=\left \langle \frac{\mathrm{D} \hat{A}}{\mathrm{D} t} \right \rangle.[/tex]
For a not explicitly time-dependent operator the covariant time derivative is given by
[tex]\frac{\mathrm{D} \hat{A}}{\mathrm{D} t}=\frac{1}{\mathrm{i} \hbar} [\hat{A},\hat{H}].[/tex]
Since, mathematically the commutator is a derivation, here the product rule holds.
[tex]\frac{\mathrm{D} \hat{\vec{r}} \cdot \hat{\vec{p}}}{\mathrm{D} t} = \frac{\mathrm{D}\hat{\vec{r}}}{\mathrm{D} t} \cdot \hat{\vec{p}} + \hat{\vec{r}} \cdot \frac{\mathrm{D}\hat{\vec{p}}}{\mathrm{D} t}.[/tex]
Now, for a usual non-relativistic Hamiltonian,
[tex]\hat{H} = \frac{\hat{\vec{p}}^2}{2m} + V(\hat{\vec{r}}),[/tex]
you get
[tex]\frac{\mathrm{D} \hat{\vec{r}}}{\mathrm{D} t}=\frac{\hat{\vec{p}}}{m}, \quad \frac{\mathrm{D} \hat{\vec{p}}}{\mathrm{D} t} = -\vec{\nabla} V(\hat{\vec{r}}).[/tex]
Using
[tex]\hat{T}=\frac{\hat{\vec{p}}^2}{2m}[/tex]
for the kinetic energy, this gives the virial theorem.

Note, however, that this makes only sense as a property of physical observables, when you apply this to the self-adjoint operator,
[tex]\frac{1}{2}(\hat{\vec{r}} \cdot \hat{\vec{p}}+\hat{\vec{p}} \cdot \hat{\vec{r}}).[/tex]
 
  • #6
The problem is mathematically ill-posed, since Ehrenfest's theorem is formulated in terms of self-adjoint operators, while the operator [itex] \mathbb{r}\cdot\mathbb{p} [/itex] is not even symmetric, hence, if the equality is correct, it can't be proven using Ehrenfest's theorem.
 
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  • #7
Thanks for the answers. I ended up using Ehrenfest's theorem, and straight-forward calculation of the commutator with the Hamiltonian.
@dextercioby: So you're saying Ehrenfest doesn't even apply here? Is it only true in general for self-adjoint operators? Do you have an example of a case where there is a clear discrepancy between the answer from Ehrenfest and the correct answer?

EDIT: I see now vanhees mentioned the same at the end of his post. Is this a standard method for making non-hermitian operators self-adjoint?
 

Related to Product rule of derivative of expectation values

What is the product rule of derivative of expectation values?

The product rule of derivative of expectation values is a mathematical formula used to find the derivative of a product of two functions. It states that the derivative of the product of two functions is equal to the first function times the derivative of the second function, plus the second function times the derivative of the first function.

What is the importance of the product rule of derivative of expectation values in scientific research?

The product rule of derivative of expectation values is important in scientific research because it allows scientists to calculate the rate of change of a quantity that is a product of two variables. This is useful in many fields, such as physics, biology, and economics, where variables are often interdependent and their rates of change need to be calculated.

How is the product rule of derivative of expectation values applied in real-life scenarios?

The product rule of derivative of expectation values is applied in many real-life scenarios, such as calculating the rate of change of a chemical reaction, predicting the growth rate of a population, and determining the optimal price for a product in economics. It is also used in engineering to optimize designs and in finance to calculate the expected return on investments.

What are some common mistakes made when using the product rule of derivative of expectation values?

Some common mistakes made when using the product rule of derivative of expectation values include forgetting to apply the rule when finding the derivative of a product of functions, incorrectly applying the derivative to only one of the functions, and not simplifying the resulting expression. It is important to carefully follow the steps of the product rule to avoid these errors.

How can the product rule of derivative of expectation values be extended to more than two functions?

The product rule of derivative of expectation values can be extended to more than two functions by using the generalized product rule. This rule states that the derivative of the product of n functions is equal to the sum of the product of all possible combinations of n-1 functions, each multiplied by the derivative of the remaining function. This can be useful in situations where the quantity of interest is a product of multiple variables.

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