Graph

[mickahell]

Graphing data

• Prepare graph to show the data input
• Prepare the graph to show the data output
• Graph error

Link to #19 and #23

[mickahell]

Can be a good idea of representation to use QuTiP and Matplotlib scatter points : Ressource : https://nbviewer.jupyter.org/github/qutip/qutip-notebooks/blob/master/examples/bloch-sphere-animation.ipynb

[muttley2k]

A concrete goal of the project can be this:

Compare 2a with 2b (see here) that is, check if data re-uploading yields better classification. (2b is the type of parameterized circuits that we saw many times during the challenge exercises, with no data re-uploading.)

But there is a caveat:

With 2a, the parameters are not hidden from the classifier, so we are essentially classifying classical input (the parameters), not quantum input. So 2a should be formulated differently, in a way that the classifier is not given access to the parameters. For example:

2a-alternative) Let's have M identical devices (as we cannot just clone M times the output of a single device). Then, a physical event makes them output the very same output. So we can structure our classifier circuit to make use of M identical device outputs.