11.2 Hybrid architectures in PennyLane

In this section, we are going to use PennyLane to implement and train a couple of hybrid QNNs in order to solve some classification problems. Firstly, we will tackle a binary classification problem, just to better understand how hybrid QNNs work in a familiar setting. Then, we will take one step further and do the same for a multi-class classification problem.

Before we get to the problems, though, let us set things up.

11.2.1 Setting things up

As on previous occasions, we shall begin by importing NumPy and TensorFlow and setting a seed for both packages — all to ensure the reproducibility of our results:

import numpy as np 
 
import tensorflow as tf 
 
 
 
seed = 1234 
 
np.random.seed(seed) 
 
tf.random.set_seed(seed)

Now we can also import some useful functions from scikit-learn. We’ve already used them extensively — they need no introduction!

from sklearn.metrics import accuracy_score 
 
from sklearn.model_selection...