I am trying to use Pytorch to perform a gradient descent optimization.

I have successfully achieved this using the simple cost function:

```
def cost(params, theta):
return 1 - circuit(params, theta)[0]
```

where circuit is a standard qnode taking an array ‘params’ to be optimized, and theta, a fixed constant.

The difficulty I am having is with this more complex cost function:

```
def C_fsVFF(params, n_eig, theta):
total_cost = 1
test_params = params
for k in range(n_eig):
for j in range(3):
test_params[len(params)-1-j] = (params[len(params)-1-j] * (k + 1))
total_cost = total_cost - circuit(test_params, (k + 1) * theta)[0] / n_eig
return total_cost
```

When I try to optimize this cost function the same way, I get the error

`leaf variable has been moved into the graph interior`

Please could someone advise me how to get around this?

I am attempting to optimise this with something like

```
best_cost = 1
new_params = Variable(torch.tensor([1.0]*19), requires_grad=True)
steps = 10
n_eig = 1
theta = Variable(torch.tensor(1), requires_grad=False)
def closure():
opt.zero_grad()
loss = C_fsVFF(new_params, n_eig, theta)
loss.backward()
return loss
opt = torch.optim.SGD([new_params], lr = 0.1)
for i in range(steps):
new_cost = C_fsVFF(new_params, n_eig, theta)
print(new_cost)
opt.step(closure)
print("Optimized rotation angles: {}".format(new_params))
print("Lowest Cost Found: {}".format(new_cost))
```

Thank you