Hello,
I’ve implemented a pytorch model which takes a device when created.
I’m defining the qnode in the forward.
class mymodel(nn.Module):
def __init__(self,device, n_wires, n_features):
super(mymodel, self).__init__()
self.device = device
self.n_wires =n_wires
self.n_features= n_features
self.CE = torch.nn.Tanh()
self.wires_to_encode = list(range(0,n_wires))
self.QC_optvar = torch.nn.Parameter(torch.randn((1, 3, n_wires), requires_grad =True))
def forward(self, x):
x = self.CE(x)
@qml.qnode(self.device, interface='torch')
def circuit(x,QC_optvar):
embedding.AmplitudeEmbedding(x, self.wires_to_encode)
QC_layparam = QC_optvar[0,:,:]
stronglayer(QC_layparam,r=1, n_wires = self.n_wires)
return qml.expval.PauliZ(0)
out = 0.5*(1+(circuit(x,self.QC_optvar)))
out = out.unsqueeze(0)
return torch.cat((out,1-out,))#0.5*(1+(circuit(x,QE_param,self.QC_optvar)))
def num_flat_features(self, x):
size = x.size()[1:] # all dimensions except the batch dimension
num_features = 1
for s in size:
num_features *= s
print('numflat',num_features)
return num_features
When the device is simply the dev = qml.device('default.qubit', wires=2) it is working just fine.
When I’m changing it to dev = qml.device('default.qubit', wires=2, shots = 10) I get the following error :
ValueError Traceback (most recent call last)
<ipython-input-57-35e78050c922> in <module>
11 sample = train_dataset[j]
12 X, label = sample['X'], sample['label']
---> 13 output = model(X)
~\.conda\envs\penny\lib\site-packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs)
487 result = self._slow_forward(*input, **kwargs)
488 else:
--> 489 result = self.forward(*input, **kwargs)
490 for hook in self._forward_hooks.values():
491 hook_result = hook(self, input, result)
<ipython-input-55-cce0fb3f986b> in forward(self, x)
36
37 #QC
---> 38 out = 0.5*(1+(circuit(x,self.QC_optvar)))
39 out = out.unsqueeze(0)
40 # print(type(out),out,out.shape)
~\.conda\envs\penny\lib\site-packages\pennylane\interfaces\torch.py in custom_apply(*args, **kwargs)
388 # [keyword_values[k] for k in sorted(keyword_positions, key=keyword_positions.get)]
389
--> 390 return _TorchQNode.apply(keyword_values, *args)
391
392 return custom_apply
~\.conda\envs\penny\lib\site-packages\pennylane\interfaces\torch.py in forward(ctx, input_kwargs, *input_)
305
306 # evaluate the QNode
--> 307 res = qnode(*ctx.args, **ctx.kwargs)
308
309 if not isinstance(res, np.ndarray):
~\.conda\envs\penny\lib\site-packages\pennylane\qnode.py in __call__(self, *args, **kwargs)
511 # pylint: disable=no-member
512 args = autograd.builtins.tuple(args) # prevents autograd boxed arguments from going through to evaluate
--> 513 return self.evaluate(args, **kwargs) # args as one tuple
514
515 @ae.primitive
~\.conda\envs\penny\lib\site-packages\autograd\tracer.py in f_wrapped(*args, **kwargs)
46 return new_box(ans, trace, node)
47 else:
---> 48 return f_raw(*args, **kwargs)
49 f_wrapped.fun = f_raw
50 f_wrapped._is_autograd_primitive = True
~\.conda\envs\penny\lib\site-packages\pennylane\qnode.py in evaluate(self, args, **kwargs)
584 check_op(op)
585
--> 586 ret = self.device.execute(self.queue, self.ev)
587 return self.output_type(ret)
588
~\.conda\envs\penny\lib\site-packages\pennylane\_device.py in execute(self, queue, observables)
214 for obs in observables:
215 if obs.return_type == "expectation":
--> 216 results.append(self.expval(obs.name, obs.wires, obs.parameters))
217 elif obs.return_type == "variance":
218 results.append(self.var(obs.name, obs.wires, obs.parameters))
~\.conda\envs\penny\lib\site-packages\pennylane\plugins\default_qubit.py in expval(self, observable, wires, par)
366 a, P = spectral_decomposition_qubit(A)
367 p0 = self.ev(P[0], wires) # probability of measuring a[0]
--> 368 n0 = np.random.binomial(self.shots, p0)
369 ev = (n0*a[0] +(self.shots-n0)*a[1]) / self.shots
370
mtrand.pyx in mtrand.RandomState.binomial()
ValueError: p > 1
It looks like a bug, or maybe I have done something wrong…
