Hi @CatalinaAlbornoz,

Thanks for the answer. However, I think it got confusing as if I was asking about entanglement during the encoding, which i am not. let me just rephrase my query:

So I have this image (greyscale) classification task. For instance, the input feature size is 8, I use `pennylane's`

built-in `amplitude and angle embedding`

to encode features. Now for the ansatz I use two different parameterized quantum circuits.

- only contains one single-qubit parameterized unitaries per qubit
- contains one single-qubit parameterized unitaries per qubit as well as CNOT on neighboring qubits.

As far as I understand, the encoding part is not trained. So when I encode the data using `amplitude embedding`

the second ansatz (no entanglement) yields better performance (in terms of accuracy), whereas with angle embedding, the second ansatz (with entanglement) achieves better accuracy. This lead to a conclusion that with `amplitude embedding`

including entanglement in `quantum ansatz`

(after the embedding) seems like not useful (infact degrades the performance) than ansatz 1 which contains no entanglement. On the other hand, the inclusion of entanglement does play a positive role when using `angle embedding`

(higher accuracy).

Hope it will now help you better understand my query.

Thanks for the great help.