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
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.