```
n_qubits =8
dev = qml.device("default.qubit", wires=n_qubits)
@qml.qnode(dev)
def circuit(weights, x=None):
for i in range(2):
AmplitudeEmbedding(x, wires = range(n_qubits), normalize=True)
StronglyEntanglingLayers(weights[i], wires = range(n_qubits))
return qml.expval(qml.PauliZ(0))
```

I want to repeat the circuit. But it gives an error DeviceError: Operation StatePrep cannot be used after other Operations have already been applied on a default.qubit.autograd device. Please help.

although if i use MottonenStatePreparation in place of amplitude. It works and slower

Anyway, How can I use amplitude or any other efficient way

…

Also, when i use MottonenStatePreparation

the loss starts becoming Nan in between model training

```
dev = qml.device("default.qubit", wires=n_qubits)
@qml.qnode(dev, diff_method="adjoint")
def circuit(weights, x=None):
norms = np.linalg.norm(x, keepdims=True)
x = x / norms
for i in range(blocks):
if i % 2 == 0:
#AmplitudeEmbedding(x, wires = range(n_qubits), normalize=True)
qml.MottonenStatePreparation(x, wires = range(n_qubits) )
StronglyEntanglingLayers(weights[i], wires = range(n_qubits))
return qml.expval(qml.PauliZ(0))
blocks=3
layers=2
weights = [np.random.random(size=(layers, n_qubits, 3), requires_grad=True) for i in range(blocks)]
num_samples = 100
num_features = 256
random_array = np.random.rand(num_samples, num_features)
# Generate random labels for the samples
labels = np.random.randint(2, size=num_samples) # Assuming binary labels
# Divide the data into train and test sets with an 80:20 ratio
X_train, X_test, y_train, y_test = train_test_split(random_array, labels, test_size=0.2, random_state=42)
y_train=y_train*2-1
y_test=y_test*2-1
for it in range(5):
weights, bias, _, _ = opt.step(cost, weights, bias, X_train, y_train)
# Compute the accuracy
predictions = [np.sign(variational_classifier(weights, bias, x)) for x in X_train]
#print(predictions)
acc = accuracy(y_train, predictions)
if acc > abest:
wbest = weights
bbest = bias
abest = acc
print('New best')
print(
"Iter: {:5d} | Training Cost: {:0.7f} | TR Accuracy: {:0.7f} ".format(
it + 1, cost(weights, bias, X_train, y_train), acc
)
)
```

I used identity block also at the beginning. but still Nan occurs when i use MottonenStatePreparation. Can you please check. Really need this to work either with amplitude or stateprep or Mottenstate. I am using Pennylane 0.33.1