What is the mathematical intuition behind operator embedding?

In summary, the embedding of quantum operators between the conjugate wave function and the non-conjugated wave function is a necessary requirement for the norm of the wavefunction to be real. This is because to get from a complex number to a real number, the complex conjugate must be multiplied. It is not necessary to apply the operator to both the wave function and its conjugate because the equivalent of this is already embedded in the operator. This may seem counterintuitive, but it is the accepted way of formulating quantum observables.
  • #1
jshrager
Gold Member
24
1
Can someone explain to me the mathematical intuition that motivates the embedding of quantum operators between the conjugate wave function and the (non-conjugated) wave function? That is, we write: [itex]\Psi[/itex][itex]^{*}[/itex][itex]\hat{H}[/itex][itex]\Psi[/itex], that is: [itex]\Psi[/itex][itex]^{*}[/itex]([itex]\hat{H}[/itex][itex]\Psi[/itex]), so that [itex]\hat{H}[/itex] operates on [itex]\Psi[/itex] (not [itex]\Psi[/itex][itex]^{*}[/itex]) and THEN this result is multiplied by [itex]\Psi[/itex][itex]^{*}[/itex]. Okay, fine, but WHY? What is the mathematical intuition behind this way of formulating quantum observables? What intuitively does it do to multiply the conjugate of [itex]\Psi[/itex] (i.e., [itex]\Psi[/itex][itex]^{*}[/itex]) by a modified (i.e., operated-upon) [itex]\Psi[/itex]?
 
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  • #2
It's not intuition, it's a necessity from the fact that the norm of a wavefunction must be real, so that to get from a complex number to a real number, you need the complex conjugate.
 
  • #3
Okay, but then why don't we apply the operator to both the wave function and it's conjugate? That is, why isn't it necessary to do this: ([itex]\hat{H}[/itex][itex]\Psi^{*}[/itex])([itex]\hat{H}[/itex][itex]\Psi[/itex])? That would seem to make more sense. What amazes me is that we don't seem to have to turn ourselves inside out to take account of NOT operating on both the wave function and its conjugate, but it still works. This could only be true (to me...perhaps stupidly) if the equivalent of what I have just written is already embedded in the operator. No? (Yes, I know that it works the way we have learned to do it and that that should be enough for me...and it is. I'm just mathematically curious!)
 

Related to What is the mathematical intuition behind operator embedding?

What is operator embedding?

Operator embedding is a mathematical technique used in machine learning and natural language processing to represent and encode operators, such as mathematical operations or linguistic rules, as vectors in a high-dimensional space. This allows for more efficient and accurate processing of these operators.

What is the purpose of operator embedding?

The purpose of operator embedding is to enable computers to better understand and process mathematical operations or linguistic rules in a more intuitive and efficient manner. It allows for these operators to be represented as vectors in a high-dimensional space, which can then be used in various machine learning algorithms.

How is operator embedding different from word embedding?

Operator embedding is similar to word embedding, in that they both represent entities as vectors in a high-dimensional space. However, operator embedding focuses specifically on operators, such as mathematical operations or linguistic rules, while word embedding focuses on words or phrases.

What is the mathematical intuition behind operator embedding?

The mathematical intuition behind operator embedding lies in the idea of representing operators as vectors in a high-dimensional space. This allows for the operators to be compared and combined using mathematical operations, similar to how numbers are manipulated in traditional mathematical equations.

How is operator embedding used in real-world applications?

Operator embedding has various real-world applications, such as in natural language processing, where it can be used to represent linguistic rules and improve the performance of language models. It is also used in machine learning tasks, such as in reinforcement learning, where it can be used to represent actions and improve the learning process.

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