Physics Asked on January 6, 2021
So I’ve become rusty in Quantum Mechanics. What is $langle x_1 |V(hat x)| x_2 rangle$? Where $V$ is the potential and $|x rangle$ is the postion eigenket?
$$ langle x_1 |hat V(hat x)| x_2 rangle = ? $$
Is it with $V(x_1) delta(x_1 -x_2)$ but since the position operator can also act on a bra is it $V(x_2) delta(x_1 -x_2)$ or some combination of $x_1$, $x_2$ like $V(x_1,x_2) delta(x_1 -x_2)$? If it does not make any difference can you prove so? ( I was doing a calculation where it seemed to make a difference).
Thanks
The following is a snippet of what I was doing.
Let the Hamiltonian $hat H$ of the particle be:
begin{equation}
hat H = hat T + hat V
end{equation}
where $hat T$ is the kinetic energy and $hat V$ is the potential energy. Now, to find the velocity:
begin{equation}
hat v = frac{-i}{hbar}[hat H , hat x] = frac{-i}{hbar} [ hat T , hat x] = hat T'(hat p)
end{equation}
where $hat T'(hat p)$ is the velocity which is a function of momentum. Now, if we further assume $T'(p)$ is of degree $1$ and find the acceleration $hat a$:
begin{equation}
hat a = frac{-i}{hbar} [ hat H , hat T'(hat p) ] =frac{-i}{hbar} [hat V,hat T'( hat p )] = hat a (hat x)
end{equation}
We know that acceleration must be a function of $hat x$ since we have already assumed $T'(p)$ is of degree $1$ and find the acceleration $hat a$.
Hence,
begin{equation}
[hat a, hat x] = 0
end{equation}
Now, multiplying $| x rangle$ on the $hat a$ equation:
begin{equation}
langle x ‘ | hat a | x rangle = langle x ‘ | [hat V,hat T'( hat p )] | x rangle
end{equation}
Using the eigenvalue equation with eigenvalue $a$ (which represents the acceleration at a position $| x rangle $ ):
begin{equation}
a langle x ‘ | x rangle= a delta( x’ – x) = frac{-i}{hbar} langle x ‘ | (hat V hat T'( hat p ) – hat T'( hat p ) hat V) | x rangle = frac{-i}{hbar} (V(x’) – V(x)) langle x’ | hat T'( hat p ) | x rangle
end{equation}
Dividing both:
begin{equation}
frac{i hbar a}{(V(x’) – V(x)) } delta( x’ – x) = langle x’ | hat T'( hat p ) | x rangle
end{equation}
Let us consider $langle x’ | hat T'( hat p ) | psi rangle$:
begin{equation}
langle x’ | hat T'( hat p ) | psi rangle = int_{-infty}^infty langle x’ | hat T'( hat p ) | x rangle langle x| psi rangle dx = int_{- infty}^infty frac{i hbar a}{(V(x’) – V(x)) } delta( x’ – x) psi(x)dx
end{equation}
Or:
begin{equation}
langle x’ | hat v | psi rangle = int_{- infty}^infty frac{i hbar a}{(V(x’) – V(x)) } delta( x’ – x) psi(x) dx
end{equation}
Due to the quantization condition of position and momentum:
begin{equation}
frac{- i hbar}{m }frac{partial }{ partial x’} psi (x’) = int_{- infty}^infty frac{ a i hbar}{(V(x’) – V(x)) } delta( x’ – x) psi(x) dx
end{equation}
OR:
begin{equation}
frac{partial }{ partial x’} psi (x’) = int_{- infty}^infty frac{ – m a }{(V(x’) – V(x)) } delta( x’ – x) psi(x) dx
end{equation}
Let us now try $V(x) = – G frac{m M}{x}$ (point particle potential):
begin{equation}
frac{partial }{ partial x ‘} psi (x’) = int_{- infty}^infty frac{- a}{(-frac{GM}{x’} + frac{GM}{x}) } delta( x’ – x) psi(x) dx
end{equation}
Simplifying:
begin{equation}
GM frac{partial }{ partial x ‘} psi (x’) = int_{- infty}^infty frac{ a x x’}{(x’ – x) } delta( x’ – x) psi(x) dx
end{equation}
Again Taylor expanding around $psi (x)$ around $x’$:
begin{equation}
GM frac{partial }{ partial x ‘} psi (x’) = int_{- infty}^infty frac{ a x x’}{(x’ – x) } delta( x’ – x) (psi(x’) + (x-x’) partial_{x’} psi(x’) + dots) dx
end{equation}
Plugging in the ansatz $a = – frac{GM}{x x’}$ we get:
begin{equation}
GM frac{partial }{ partial x ‘} psi (x’) = int_{- infty}^infty – frac{ GM}{(x’ – x) } delta( x’ – x) (psi(x’) + (x-x’) partial_{x’} psi(x’) + dots) dx
end{equation}
Note the term $int_{- infty}^infty frac{ GM}{(x’ – x) } delta( x’ – x) psi(x’) $ goes to $0$ since is antisymmetric.
begin{equation}
GM frac{partial }{ partial x ‘} psi (x’) = GM frac{partial }{ partial x ‘} psi (x’)
end{equation}
Hence, we get a consist solution which agrees with classical calculations.
Note: $a = – GM{x^{-2}}$ or $a = – GM{x’^{-2}}$ both will give wrong answers.
You can think of it in the sense of distributions, and then Philip's answer applies, in the sense that, as distributions
$$ f(x)delta(x-y)=f(y)delta(x-y)$$
but more intuitively, you can also think of $delta(x-y)$ as having support only on $x=y$ meaning that it is equal to $0$ for all $xneq y$ (this is not mathematically rigorous, but it works on an intuitive level), so the only point where $V(x_1)delta(x_1-x_2)$ does not vanish is $x_1=x_2$, so you can put either of them in the potential. In fact you can think of
$$ V(x_1)delta(x_1-x_2)=V(x_2)delta(x_1-x_2)$$
as the continuous equivalent of
$$ delta_{ij} V_j=delta_{ij}V_i$$
where $delta_{ij}=1$ if $i=j$ and $0$ otherwise and $V$ is a vector. It doesn't matter whether you take $V_i$ or $V_j$, since in all cases where the above expression is not $0=0$, $i=j$.
Correct answer by user2723984 on January 6, 2021
I'm not very good at the rigorous math, but the way I think about it is that "functions" like the Dirac Delta are actually distributions, meaning that they only make sense inside of an integral of the form:
$$int_{a}^b text{d}x,,f(x)delta(x),$$
where $f(x)$ is a well-behaved (smooth, with a compact support over the interval, etc.) test function.
The quantity that you are talking about is also a distribution (it's just the product of the delta-function with the potential) and so it too must be defined in a similar way. However, it's pretty easy to show that:
$$int_{a}^b text{d}x_1,,f(x_1) V(x_1)delta(x_1 -x_2) = int_{a}^btext{d}x_2,, f(x_2) V(x_2)delta(x_1-x_2),$$
using a simple substitution of variables. As a result, both your answers are essentially equal, in the sense that their distributions are equal.
As to your example, there are many assumptions there that might be justified, but which I don't quite understand. However, there are some steps that I am uncomfortable with: for example, consider the step where you divide by $V(x')-V(x)$. The term on the left hand side is proportional to: $$frac{delta(x'-x)}{V(x')-V(x)},$$ and I suspect there are many problems with this. For starters, you will be dividing by zero when $V(x')=V(x)$, and more importantly, you cannot multiply distributions by singular functions, as the result would not be a well defined distribution.
Answered by Philip on January 6, 2021
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