Hello! I encountered a somewhat peculiar issue while using Keras to write code. Specifically, when attempting to create a simple circuit similar to a QCNN, I am trying to control the output of two circuit qubits using a Toffoli gate onto four additional qubits. Here is an example of my code:
import pennylane as qml
from pennylane import numpy as np
import logging
import tensorflow as tf
from pennylane.templates.embeddings import AmplitudeEmbedding
logging.getLogger('tensorflow').disabled = True
dev = qml.device("default.qubit", wires=8)
def kernel(params, wires): # 4 params
qml.RY(params[0], wires=wires[0])
qml.RY(params[1], wires=wires[1])
qml.CNOT(wires=[wires[1], wires[0]])
qml.RY(params[2], wires=wires[0])
qml.RY(params[3], wires=wires[1])
qml.CNOT(wires=[wires[0], wires[1]])
def pooling(params, wires): #2 params
qml.CRZ(params[0], wires=[wires[0], wires[1]])
qml.PauliX(wires=wires[0])
qml.CRX(params[1], wires=[wires[0], wires[1]])
@qml.qnode(dev, interface="tf")
def ancillary_qcnn_circuit(inputs, weights):
kernel_size, pooling_size = 4, 2
AmplitudeEmbedding(features=inputs, wires=range(4), normalize=True)
kernel(weights[:kernel_size], wires=[0, 1])
kernel(weights[:kernel_size], wires=[2, 3])
kernel(weights[:kernel_size], wires=[3, 0])
kernel(weights[:kernel_size], wires=[1, 2])
pooling(weights[kernel_size:kernel_size +
pooling_size], wires=[1, 0])
pooling(weights[kernel_size:kernel_size +
pooling_size], wires=[3, 2])
kernel(weights[kernel_size+pooling_size:kernel_size*2+pooling_size], wires=[0, 2])
# comment out any one of the four Toffoli gates
qml.Toffoli(wires=[0, 2, 4])
qml.Toffoli(wires=[0, 2, 5])
qml.Toffoli(wires=[0, 2, 6])
qml.Toffoli(wires=[0, 2, 7])
return [qml.expval(qml.PauliZ(i)) for i in range(4, 8)]
x_train = np.random.rand(100, 16)
y_train = np.random.rand(100, 4)
qcnnlayer = qml.qnn.KerasLayer(ancillary_qcnn_circuit, {
"weights": 10}, output_dim=4)
model = tf.keras.Sequential([qcnnlayer])
opt = tf.keras.optimizers.Adam(learning_rate=0.002)
model.compile(opt, loss=tf.keras.losses.MeanSquaredError())
model.fit(x_train, y_train, epochs=50, batch_size=50)
I have encountered the following errors:
tensorflow.python.framework.errors_impl.InvalidArgumentError: Exception encountered when calling layer 'keras_layer' (type KerasLayer).
{{function_node __wrapped__MatMul_device_/job:localhost/replica:0/task:0/device:CPU:0}} Matrix size-incompatible: In[0]: [4,4], In[1]: [100,128] [Op:MatMul]
Call arguments received by layer 'keras_layer' (type KerasLayer):
• inputs=tf.Tensor(shape=(50, 16), dtype=float32)
I’m not sure what errors are occurring in the code mentioned above. I have noticed that it seems to run fine if the input does not include a batch dimension, like:
input = np.random.rand(16)
# input = np.random.rand(20,16) would get an error
weights = np.random.rand(10)
ancillary_qcnn_circuit(input, weights)
Additionally, what further confuses me is that when I comment out any of the four Toffoli gates (or more) in the code, it runs.
My version is 0.34.0.dev0. （for I wanna use batch mode AmplitudeEmbedding in TensorFlow)
Can you verify this too? If so, can you reduce your code down as much as possible while still maintaining the error message? That would help us try to figure out what’s going on 
