Shuhul24

Can I use `tfq.layers.ControlledPQC` as a variable and then apply `tape.gradient` and `optimizer.apply_gradient`?

Cool. Will take this into consideration and try to implement it in an another way. Thanks a lot for the replies!

I have got one doubt that is kinda creating a problem. Say that I have built a circuit on 2 qubits, which are, `cirq.GridQubit(1,0)` and `cirq.GridQubit(1, 1)`. Consider that the...

Thanks for the reply! Actually in my case, I have got 2 circuits attached in one `ControlledPQC` inside `tf.keras.layers.Layer` and out of those 2 circuits, one circuit is as `ControlledPQC`,...

Thanks for the reply! One last thing. Will the parameters of H1 circuit change simultaneously if I apply gradients on `value` and `energy` both? Or will it cause some sort...

Thanks! But how can I extract the parameters of just H1 circuit, because by using `get_weights()` I am getting the parameters of H1 and H2 circuit combined. Also, the combined...

How I can update the parameters of just H2?

Thanks for the reply! So I want to clear out what you just wrote above. So when you doing the `tape.gradient` with repsect to `l.trainable_variables[1]`, you are basically evaluating gradient...

I wanted to know that is there any difference in the circuit `h1` in `self.h1h2obs` and `self.h1obs`? I mean, since both of these circuits are working in two different `tfq.layers.ControlledPQC`,...

Ok, got it! Can you please suggest a way to evaluate the probability of each state from a `tfq.layers.ControlledPQC`? I mean like in above example, I am using 2 qubit...