Algorithm for Gradient Descent Disentangler
Posted: 22 May 2020, 12:45
Hi! I was checking the documents for the Gradient Descent Disentangler, and I had some concepts that I cannot understand.
The document says \(dS = {\partial S(Utheta,n)} / {\partial U} \)
The point I cannot understand is how dS is given by:
Furthermore, is applying
Usually, some variable w is updated by \({w_{new} = w - \eta dS/dw}\), but in our case, we need to keep U unitary, so we are applying unitary exponentials (if dS is anti-hermitian).
Is it a correct approach?
The document says \(dS = {\partial S(Utheta,n)} / {\partial U} \)
R[i] is given by \( {\partial S(Y,n)} / {\partial Y_i}\)The point I cannot understand is how dS is given by:
Python: Select all
| .----X--R--Z----.
| | | | |
| | q0 q1 |
| | |
| | q0* q1* |
| | | | |
| .----X*-Y--Z*---.
exp(- tdS) a heuristic way to update U?Usually, some variable w is updated by \({w_{new} = w - \eta dS/dw}\), but in our case, we need to keep U unitary, so we are applying unitary exponentials (if dS is anti-hermitian).
Is it a correct approach?