made some changes

Author: manuelpaeza

graph.py

@@ -5,9 +5,6 @@
 from qiskit import *
 
 from qiskit.tools.visualization import plot_histogram
-from qiskit_aqua import Operator, run_algorithm, get_algorithm_instance
-from qiskit_aqua.input import get_input_instance
-from qiskit_aqua.translators.ising import maxcut, tsp
 
 from qiskit import QuantumCircuit, 
 
@@ -20,4 +17,6 @@
 G.add_edges_from([(0, 1), (1, 2)])
 nx.draw(G, with_labels=True, alpha=0.8, node_size=500)
 
+nqubits = 7
 
+qc = 

qml.py

@@ -0,0 +1,42 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from IPython.display import clear_output
+from qiskit import QuantumCircuit
+from qiskit.algorithms.optimizers import COBYLA, L_BFGS_B
+from qiskit.circuit import Parameter
+from qiskit.circuit.library import RealAmplitudes, ZZFeatureMap
+from qiskit.utils import algorithm_globals
+from qiskit_machine_learning.algorithms.classifiers import (
+    VQC, NeuralNetworkClassifier)
+from qiskit_machine_learning.algorithms.regressors import (
+    VQR, NeuralNetworkRegressor)
+from qiskit_machine_learning.neural_networks import EstimatorQNN, SamplerQNN
+
+algorithm_globals.random_seed = 42
+
+num_inputs = 2
+num_samples = 20
+
+X = 2 * np.random.rand(num_samples, num_inputs) - 1
+y = np.random.choice([0, 1, 2], 100) 
+y_one_hot = np.zeros((num_samples, 3))
+
+for i in range(num_samples):
+    y_one_hot[i, y[i]] = 1
+
+for x, y_target in zip(X, y):
+    if y_target == 1:
+        plt.plot(x[0], x[1], "bo")
+    else:
+        plt.plot(x[0], x[1], "go")
+plt.plot([-1, 0, 1], [1, 0, -1], "--", color="black")
+plt.show()
+
+# construct QNN
+qc = QuantumCircuit(2)
+feature_map = ZZFeatureMap(2)
+ansatz = RealAmplitudes(2)
+qc.compose(feature_map, inplace=True)
+qc.compose(ansatz, inplace=True)
+qc.draw(output="mpl")
+