Expectation value of an operator to the power of n

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The discussion focuses on proving the relationship <A^n> = <A>^n, which appears intuitive but requires rigorous justification. The initial proof attempt shows that both sides equal λ^n under the assumption that the state |Ψ⟩ is an eigenstate of operator A. However, it is noted that this equality holds only for eigenstates, as counterexamples exist, such as the ground state of a particle in a box where <x> = 0 but <x^2> > <x>^2. The conversation suggests using the Binomial Theorem to derive a more general expression for <A^n> - <A>^n, highlighting that the variance is not necessarily zero for n = 2. Overall, the need for a broader proof is emphasized, particularly for non-eigenstate scenarios.
patric44
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Homework Statement
prove that : <A^n>=<A>^n
Relevant Equations
<A^n>=<A>^n
hi all
how do I prove that
$$
<A^{n}>=<A>^{n}
$$
It seems intuitive but how do I rigorously prove it, My attempt was like , the LHS can be written as:
$$
\bra{\Psi}\hat{A}.\hat{A}.\hat{A}...\ket{\Psi}=\lambda^{n} \bra{\Psi}\ket{\Psi}=\lambda^{n}\delta_{ii}=\lambda^{n}
$$
and the RHS equal:
$$
<A>^{n}=[\bra{\Psi}A\ket{\Psi}]^{n}=\lambda^{n}[\bra{\Psi}\ket{\Psi}]^{n}=\lambda^{n}[\delta_{ii}]^{n}=\lambda^{n}
$$
Is my proof rigurus enough or there are other formal proof for that
 
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A counter example. For ground state of a partricle in a box [-a,a],
<x>=0 but &lt;x^2&gt; \ \ &gt;\ \ &lt;x&gt;^2=0

Your proof seems to be all right only when ##\Psi## is an eigenstate of A with eigenvalue ##\lambda##.
 
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patric44 said:
Homework Statement: prove that : <A^n>=<A>^n
Note that for ##n = 2##, the variance is not necessarily zero:
$$\sigma^2(A) =\langle A^2 \rangle - \langle A \rangle^2 \ne 0$$In general, you can derive an expression for ##\langle A^n \rangle - \langle A \rangle^n## by starting with:
$$\langle[A - \langle A \rangle]^n \rangle$$And expanding using the Binomial Theorem.
 
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So is there some elegant way to do this or am I just supposed to follow my nose and sub the Taylor expansions for terms in the two boost matrices under the assumption ##v,w\ll 1##, then do three ugly matrix multiplications and get some horrifying kludge for ##R## and show that the product of ##R## and its transpose is the identity matrix with det(R)=1? Without loss of generality I made ##\mathbf{v}## point along the x-axis and since ##\mathbf{v}\cdot\mathbf{w} = 0## I set ##w_1 = 0## to...

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