keras_layer_torch.py

import tensorflow as tf

tf.keras.backend.set_floatx('float64')

layer_1 = tf.keras.layers.Dense(2)
layer_2 = tf.keras.layers.Dense(2, activation="softmax")

model = tf.keras.Sequential([layer_1, layer_2])
model.compile(loss="mae")

import matplotlib.pyplot as plt
import numpy as np
from sklearn.datasets import make_moons

# Set random seeds
np.random.seed(42)
tf.random.set_seed(42)

X, y = make_moons(n_samples=200, noise=0.1)
y_hot = tf.keras.utils.to_categorical(y, num_classes=2)  # one-hot encoded labels

c = ["#1f77b4" if y_ == 0 else "#ff7f0e" for y_ in y]  # colours for each class
plt.axis("off")
plt.scatter(X[:, 0], X[:, 1], c=c)
plt.show()

import pennylane as qml

n_qubits = 2
dev = qml.device("default.qubit", wires=n_qubits)

@qml.qnode(dev)
def qnode(inputs, weights):
    qml.AngleEmbedding(inputs, wires=range(n_qubits))
    qml.BasicEntanglerLayers(weights, wires=range(n_qubits))
    return [qml.expval(qml.PauliZ(wires=i)) for i in range(n_qubits)]

n_layers = 6
weight_shapes = {"weights": (n_layers, n_qubits)}

qlayer = qml.qnn.KerasLayer(qnode, weight_shapes, output_dim=n_qubits)