I used the MottonenStatePreparation function when running my own build of NN, and the following problem occurred:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/tmp/ipykernel_13608/2497313299.py in <module>
----> 1 his_acc_val, his_acc_train, his_cost, f1_train, f1_test, roc_auc_train, roc_auc_test = TrainQKSAN(params_init)
2
3 print(his_acc_val, his_acc_train, his_cost, f1_train, f1_test, roc_auc_train, roc_auc_test)
/tmp/ipykernel_13608/3138901598.py in TrainQKSAN(params_init)
65 Y_train_batch = Y_train[batch_index]
66
---> 67 weights, _, _ = opt.step(cost, weights, feats_train_batch, Y_train_batch)
68
69 # Compute predictions on train and validation set
/opt/conda/lib/python3.9/site-packages/pennylane/optimize/gradient_descent.py in step(self, objective_fn, grad_fn, *args, **kwargs)
86 """
87
---> 88 g, _ = self.compute_grad(objective_fn, args, kwargs, grad_fn=grad_fn)
89 new_args = self.apply_grad(g, args)
90
/opt/conda/lib/python3.9/site-packages/pennylane/optimize/nesterov_momentum.py in compute_grad(self, objective_fn, args, kwargs, grad_fn)
69
70 g = get_gradient(objective_fn) if grad_fn is None else grad_fn
---> 71 grad = g(*shifted_args, **kwargs)
72 forward = getattr(g, "forward", None)
73
/opt/conda/lib/python3.9/site-packages/pennylane/_grad.py in __call__(self, *args, **kwargs)
118 return ()
119
--> 120 grad_value, ans = grad_fn(*args, **kwargs) # pylint: disable=not-callable
121 self._forward = ans
122
/opt/conda/lib/python3.9/site-packages/autograd/wrap_util.py in nary_f(*args, **kwargs)
18 else:
19 x = tuple(args[i] for i in argnum)
---> 20 return unary_operator(unary_f, x, *nary_op_args, **nary_op_kwargs)
21 return nary_f
22 return nary_operator
/opt/conda/lib/python3.9/site-packages/pennylane/_grad.py in _grad_with_forward(fun, x)
136 difference being that it returns both the gradient *and* the forward pass
137 value."""
--> 138 vjp, ans = _make_vjp(fun, x)
139
140 if not vspace(ans).size == 1:
/opt/conda/lib/python3.9/site-packages/autograd/core.py in make_vjp(fun, x)
8 def make_vjp(fun, x):
9 start_node = VJPNode.new_root()
---> 10 end_value, end_node = trace(start_node, fun, x)
11 if end_node is None:
12 def vjp(g): return vspace(x).zeros()
/opt/conda/lib/python3.9/site-packages/autograd/tracer.py in trace(start_node, fun, x)
8 with trace_stack.new_trace() as t:
9 start_box = new_box(x, t, start_node)
---> 10 end_box = fun(start_box)
11 if isbox(end_box) and end_box._trace == start_box._trace:
12 return end_box._value, end_box._node
/opt/conda/lib/python3.9/site-packages/autograd/wrap_util.py in unary_f(x)
13 else:
14 subargs = subvals(args, zip(argnum, x))
---> 15 return fun(*subargs, **kwargs)
16 if isinstance(argnum, int):
17 x = args[argnum]
/tmp/ipykernel_13608/3138901598.py in cost(params, features, labels)
42
43 def cost(params, features, labels):
---> 44 predictions = [variational_classifier(params, feat) for feat in features]
45 return square_loss(labels, predictions)
46
/tmp/ipykernel_13608/3138901598.py in <listcomp>(.0)
42
43 def cost(params, features, labels):
---> 44 predictions = [variational_classifier(params, feat) for feat in features]
45 return square_loss(labels, predictions)
46
/tmp/ipykernel_13608/3138901598.py in variational_classifier(params, feat)
38
39 def variational_classifier(params, feat):
---> 40 return QKSAN(feat, params)
41
42
/opt/conda/lib/python3.9/site-packages/pennylane/qnode.py in __call__(self, *args, **kwargs)
972
973 # construct the tape
--> 974 self.construct(args, kwargs)
975
976 cache = self.execute_kwargs.get("cache", False)
/opt/conda/lib/python3.9/site-packages/pennylane/qnode.py in construct(self, args, kwargs)
870 self.interface = qml.math.get_interface(*args, *list(kwargs.values()))
871
--> 872 self._tape = make_qscript(self.func, shots)(*args, **kwargs)
873 self._qfunc_output = self.tape._qfunc_output
874
/opt/conda/lib/python3.9/site-packages/pennylane/tape/qscript.py in wrapper(*args, **kwargs)
1529 def wrapper(*args, **kwargs):
1530 with AnnotatedQueue() as q:
-> 1531 result = fn(*args, **kwargs)
1532
1533 qscript = QuantumScript.from_queue(q, shots)
/tmp/ipykernel_13608/3138901598.py in QKSAN(feat, params)
23 Ansatz(params[0: num_para], 0, wire)
24 qml.adjoint(Ansatz)(params[num_para: 2 * num_para], 0, wire)
---> 25 qml.adjoint(DataEncoding)(x2, 0, wire)
26
27 DataEncoding(x2, wire, (2 * wire))
/opt/conda/lib/python3.9/site-packages/pennylane/ops/op_math/adjoint.py in wrapper(*args, **kwargs)
129 @wraps(fn)
130 def wrapper(*args, **kwargs):
--> 131 qscript = make_qscript(fn)(*args, **kwargs)
132 if lazy:
133 adjoint_ops = [Adjoint(op) for op in reversed(qscript.operations)]
/opt/conda/lib/python3.9/site-packages/pennylane/tape/qscript.py in wrapper(*args, **kwargs)
1529 def wrapper(*args, **kwargs):
1530 with AnnotatedQueue() as q:
-> 1531 result = fn(*args, **kwargs)
1532
1533 qscript = QuantumScript.from_queue(q, shots)
/tmp/ipykernel_13608/3138901598.py in DataEncoding(x, wires_start, wires_end)
2 def DataEncoding(x, wires_start, wires_end):
3 wires = np.arange(wires_start, wires_end).tolist()
----> 4 qml.MottonenStatePreparation(state_vector=x, wires=wires)
5 #qml.AmplitudeEmbedding(features=x, wires=wires, pad_with=0., normalize=True)
6
/opt/conda/lib/python3.9/site-packages/pennylane/templates/state_preparations/mottonen.py in __init__(self, state_vector, wires, id)
312 norm = qml.math.sum(qml.math.abs(state) ** 2)
313 if not qml.math.allclose(norm, 1.0, atol=1e-3):
--> 314 raise ValueError(
315 f"State vectors have to be of norm 1.0, vector {i} has norm {norm}"
316 )
ValueError: State vectors have to be of norm 1.0, vector 0 has norm Autograd ArrayBox with value 1.002073287428885
But strangely enough the first iteration gets the result:
