Hello!
I want ask how the params.npy variable were obtained? It would be great if you provide the codes for my better understanding.
!
It would help to have your full code (copy-pastable to that I can run it on my end, i.e. not a screenshot) that reproduces that error so I can help better!
That said, it looks like qnodes is a list full of other QNodes. (more specifically here). You can do something like this to fix it:
results = [q(params) for q in qnodes]
Let me know if that helps!
I have tried the answers that have been given, the results failed. This is my code. I hope you can help me to solve this problem.
n_wires = 4
dev0 = qml.device("rigetti.qvm", device="4q-qvm")
dev1 = qml.device("qiskit.aer", wires=4)
devs = [dev0, dev1]
def circuit0(params, x=None):
for i in range(n_wires):
qml.RX(x[i % n_features], wires=i)
qml.Rot(*params[1, 0, i], wires=i)
qml.CZ(wires=[1, 0])
qml.CZ(wires=[1, 2])
qml.CZ(wires=[3, 0])
for i in range(n_wires):
qml.Rot(*params[1, 1, i], wires=i)
return qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliZ(1))
def circuit1(params, x=None):
for i in range(n_wires):
qml.RX(x[i % n_features], wires=i)
qml.Rot(*params[0, 0, i], wires=i)
qml.CZ(wires=[0, 1])
qml.CZ(wires=[1, 2])
qml.CZ(wires=[1, 3])
for i in range(n_wires):
qml.Rot(*params[0, 1, i], wires=i)
return qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliZ(1))
qnodes = [
qml.QNode(circuit0, dev0, interface="torch"),
qml.QNode(circuit1, dev1, interface="torch"),
]
import tensorflow as tf
n_classes = 2 #label
n_layers = 2
# the first index is for the two models
params = torch.tensor(np.random.random((2, n_layers, n_wires, 3)), requires_grad=True)
iteration = 1
def softmax_ensemble(params, x_point=None):
results = qnodes(params, x=x_point)
softmax = torch.nn.functional.softmax(results, dim=1)
choice = torch.where(softmax == torch.max(softmax))[0][0]
return softmax[choice]
def cost(params, y_point, x_point=None):
return torch.sum(torch.abs(softmax_ensemble(params, x_point=x_point) - y_point))
y_soft = torch.tensor(tf.one_hot(y_train, n_classes).numpy(), requires_grad=True)
opt = torch.optim.Adam([params], lr = 0.1) #learning rate
for x_point, y_point in zip(x_train, y_soft):
opt.zero_grad()
c = cost(params, y_point=y_point, x_point=x_point)
c.backward()
opt.step()
if iteration % 10 == 0 and iteration > 0:
print("Iteration : ", iteration)
iteration += 1
this is the output
Hey @nauvan,
It looks like you’re not using what I suggested in your code example you provided 
. Can you give that a try and see if that solves things?
It’s difficult to help without having the full code for me to run. If you could provide that, that would be a huge help!
That said, it looks like circuit is a list. Printing things before the error happens is a good way to diagnose and debug your code. Here’s a good article on tips for debugging: How to Debug Your Code
Hey @nauvan,
If you can narrow your issue down to something in PennyLane that you don’t have a great grasp on, I can certainly help you out 
. But we don’t have the resources to debug code from step 1. This is a great chance for you to do some debugging! It’s an essential skill that anyone who wants to program should have to a degree.
I tried but couldn’t, now the code is in error. I need to solve that for my course. What should I do? Because this is difficult for me.
Hey @nauvan!
We can’t do your homework for you. This is all a part of learning — sometimes it’s difficult! Being able to debug your code is an important skill to practice.