Hello,
I am facing a rather complex scenario in an attempt to build a QCNN with nested elements.
Akin to classical CNN’s where we can have a torch nn.Conv2d repeatedly inside an nn.module, my code is as follows:
Assume I have a simple circuit as follows:
@qml.qnode(dev4)
def CONVCircuit(inputs, weights):
print("Shapes: inputs={}, params_arr={}, q_bits={}, q_depth={}".format(inputs.shape, weights, n_qubits, n_layers))
qml.templates.AmplitudeEmbedding(inputs, wires=[i for i in range(n_qubits)], normalize=True, pad_with=4)
qml.templates.StronglyEntanglingLayers(weights, wires=range(n_qubits))
exp_vals = [qml.expval(qml.PauliZ(position)) for position in range(n_qubits)]
return (exp_vals) #type=<class 'list'>,shape=(4,)
This is the quantum eqvivalent of of the torch Conv2d layer which uses the circuit above:
class Q2DCNN(nn.Module):
def __init__(self,circ,patch_size,n_qubits):
super(Q2DCNN, self).__init__()
self.patch_size=patch_size
self.n_qubits=n_qubits
self.weight_shapes = {"weights": (n_layers, n_qubits,3)}
self.pqc = circ
self.qlayer = qml.qnn.TorchLayer(self.pqc, self.weight_shapes)
def forward(self, x):
# print("Q2DCNN start shape:",x.shape) # torch.Size([16, 16, 4])
x=self.process_image(x)
x=x.permute(0, 3, 1, 2)
# print("Q2DCNN final shape:",x.shape) # torch.Size([16, 16, 4])
return x
Note that I declared the weights (the heart of the problem here) correctly inside q2dcnn.
Now my real QCNN is using the Q2DCNN several times, like so:
class QNN(torch.nn.Module):
def __init__(self, circ,patch_size,n_qubits,n_classes=2):
super(QNN, self).__init__()
# weight_shapes = {"weights": (n_layers, n_qubits)}
# self.pqc = circ
self.patch_size=patch_size
self.n_qubits=n_qubits
self.n_classes=n_classes
self.qconv2d1=Q2DCNN(circ,patch_size,n_qubits) # torch.Size([1, 1, 64, 64]) input
self.qconv2d2=Q2DCNN(circ,patch_size,n_qubits) # from H/ to H / 16
self.weight_shapes = {"weights": (n_layers, n_qubits,3)}
self.qlayer2d1 = qml.qnn.TorchLayer(self.qconv2d1, self.weight_shapes)
self.qlayer2d2 = qml.qnn.TorchLayer(self.qconv2d2, self.weight_shapes)
Of course this line: self.qlayer2d1 = qml.qnn.TorchLayer(self.qconv2d1, self.weight_shapes) throws an exception since TorchLayer is not expecting a torch layer (in my case Q2DCNN) but rather a circuit. BUT, how else can i associate a weight vector INSIDE the QNN(torch.nn.Module) to each of my self.qconv2d1=Q2DCNN(circ,patch_size,n_qubits) # torch.Size([1, 1, 64, 64]) input layers?
If on the other hand I delete these lines (which dont work anyway …):
self.qlayer2d1 = qml.qnn.TorchLayer(self.qconv2d1, self.weight_shapes)
self.qlayer2d2 = qml.qnn.TorchLayer(self.qconv2d2, self.weight_shapes)
Then the following exception is thrown:
Cell In[3], line 189, in QNN.forward(self, x)
186 def forward(self, x):
187 # print("QNN start shape:",x.shape)
188 # x=torch.tanh(x) * np.pi / 2.0
--> 189 x = self.qconv2d1(x)
190 # print("QNN first conv, before stacking:",x.shape) # torch.Size([batch, n_qubits, 16, 16])
192 f_maps = [] # List to store the feature maps
...
140 f"Weights tensor must have second dimension of length {len(wires)}; got {shape[1]}"
141 )
143 if shape[2] != 3:
How else custom torch.nn layers are supposed to be integrated into another torch.nn module?
Thanks.
!