Choosing the eigenvector stochastically

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In summary, the equation ##\hat{O}|\psi\rangle \rightarrow \alpha_n|\mathbf{e}_n\rangle## is central in the quantum theory formalism and allows for the estimation of ##p_n\equiv |\alpha_n|^2## from observed frequencies of outcomes. In the case of 2 eigenstates, a stochastic evolution operator ##\hat{D}## can be defined, which selects which eigenstate to grow and shrink with proportions ##p## and ##1-p## respectively. However, for more than 2 dimensions, the form of ##\hat{D}## becomes more complex and it is uncertain how to extend this formalism to more than 3 eigenstates.
  • #1
Mentz114
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The equation ##\hat{O}|\psi\rangle \rightarrow \alpha_n|\mathbf{e}_n\rangle## where ##|\mathbf{e}_n\rangle## is an eigenvector of the operator and ##\alpha_n## is its eigenvalue, is central in the QT formalism. This is as much as we can get from quantum theory but an ideal instrument should enable us to estimate the ##p_n\equiv |\alpha_n|^2## from the observed frequencies of different outcomes.

When there are 2 eigenstates it seems straightforward to define a stochastic evolution operator thus ##\hat{D}=diag( e^{\lambda(p_0-s)t}, e^{-\lambda(p_0-s)t})##
where ##s## is a random variate (nearly) uniformly distributed in ##(0,1)##. ##\hat{D}## selects which eigenstate to grow and which to shrink with the proportions ##p## and ##1-p##. ##\hat{D}## evolves with time to having only one non-null eigenvector.
##\hat{D}## has to be considered part of the apparatus and the acquisition of ##s## could be from a random phase or from another special interaction perhaps with an internal state.

With more than 2 dimensions ##\hat{D} ## has the form

\begin{align}
\hat{D} &= \left[ \begin{array}{cccc}
e^{\lambda(p_0-s)t} & 0 & 0 & 0 \\\
0 & e^{-\lambda(p_0-s)(p_0+p_1-s)t} & 0 & 0 \\\
0 & 0 & e^{-\lambda(p_0+p_1-s)(p_0+p_1+p_2-s)t} & 0 \\\
0 & 0 & 0 & e^{\lambda(s-(p_0+p_2+p_3))t}
\end{array} \right]
\end{align}For any ##s## only one exponent is positive.
 
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  • #2
All other terms get exponentially small.It is not clear to me how to extend this formalism to more than 3 eigenstates.
 

1. What is an eigenvector and why is it important in stochastic processes?

An eigenvector is a vector that, when multiplied by a square matrix, results in a scaled version of itself. In stochastic processes, eigenvectors are important because they represent the directions along which the system evolves over time. They also help us understand the stability and behavior of the system.

2. How do you choose the eigenvector stochastically?

To choose the eigenvector stochastically, we use a random selection process. This involves randomly selecting a starting point and then iteratively multiplying it by the matrix until it converges to an eigenvector. This method is useful when the matrix is large and the exact eigenvector cannot be calculated.

3. What are the benefits of choosing the eigenvector stochastically?

Choosing the eigenvector stochastically can save time and computational resources, especially when dealing with large matrices. It also allows us to approximate the eigenvector without having to calculate it exactly, which may not always be possible.

4. Are there any limitations to choosing the eigenvector stochastically?

Yes, there are limitations to choosing the eigenvector stochastically. This method may not always converge to the exact eigenvector and may only provide an approximation. It also requires a random starting point, which may not always result in a good approximation.

5. How can we ensure the accuracy of the eigenvector chosen stochastically?

To ensure the accuracy of the eigenvector chosen stochastically, we can run the process multiple times with different starting points and check for consistency. We can also compare the results to the exact eigenvector, if it is known, to assess the accuracy of the approximation.

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