Greetings!
I am trying to run a time series through Pennylane based upon an example - code below.
- is 100% self-contained — someone can copy-paste exactly what is here and run it to
reproduce the behaviour you are observing - includes comments
# Put code here
import pennylane as qml
from pennylane import numpy as np
from pennylane.optimize import AdamOptimizer
dev = qml.device("strawberryfields.fock", wires=1, cutoff_dim=10)
def layer(v):
# Matrix multiplication of input layer
qml.Rotation(v[0], wires=0)
qml.Squeezing(v[1], 0.0, wires=0)
qml.Rotation(v[2], wires=0)
# Bias
qml.Displacement(v[3], 0.0, wires=0)
# Element-wise nonlinear transformation
qml.Kerr(v[4], wires=0)
@qml.qnode(dev)
def quantum_neural_net(var, x):
# Encode input x into quantum state
qml.Displacement(x, 0.0, wires=0)
# "layer" subcircuits
for v in var:
layer(v)
return qml.expval(qml.QuadX(0))
def square_loss(labels, predictions):
loss = 0
for l, p in zip(labels, predictions):
loss = loss + (l - p) ** 2
loss = loss / len(labels)
return loss
def cost(var, features, labels):
preds = [quantum_neural_net(var, x) for x in features]
return square_loss(labels, preds)
np.random.seed(0)
num_layers = 4
var_init = 0.05 * np.random.randn(num_layers, 5, requires_grad=True)
print(var_init)
opt = AdamOptimizer(0.01, beta1=0.9, beta2=0.999)
var = var_init
for it in range(5):
(var, _, _), _cost = opt.step_and_cost(cost, var, X, Y)
print("Iter: {:5d} | Cost: {:0.7f} ".format(it, _cost))
If you want help with diagnosing an error, please put the full error message below:
# Put full error message here
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[125], line 5
3 var = var_init
4 for it in range(5):
----> 5 (var, _, _), _cost = opt.step_and_cost(cost, var, X, Y)
6 print("Iter: {:5d} | Cost: {:0.7f} ".format(it, _cost))
File ~\AppData\Local\anaconda3\Lib\site-packages\pennylane\optimize\gradient_descent.py:59, in GradientDescentOptimizer.step_and_cost(self, objective_fn, grad_fn, *args, **kwargs)
39 def step_and_cost(self, objective_fn, *args, grad_fn=None, **kwargs):
40 """Update trainable arguments with one step of the optimizer and return the corresponding
41 objective function value prior to the step.
42
(...)
56 If single arg is provided, list [array] is replaced by array.
57 """
---> 59 g, forward = self.compute_grad(objective_fn, args, kwargs, grad_fn=grad_fn)
60 new_args = self.apply_grad(g, args)
62 if forward is None:
File ~\AppData\Local\anaconda3\Lib\site-packages\pennylane\optimize\gradient_descent.py:117, in GradientDescentOptimizer.compute_grad(objective_fn, args, kwargs, grad_fn)
99 r"""Compute gradient of the objective function at the given point and return it along with
100 the objective function forward pass (if available).
101
(...)
114 will not be evaluted and instead ``None`` will be returned.
115 """
116 g = get_gradient(objective_fn) if grad_fn is None else grad_fn
--> 117 grad = g(*args, **kwargs)
118 forward = getattr(g, "forward", None)
120 num_trainable_args = sum(getattr(arg, "requires_grad", False) for arg in args)
File ~\AppData\Local\anaconda3\Lib\site-packages\pennylane\_grad.py:115, in grad.__call__(self, *args, **kwargs)
112 self._forward = self._fun(*args, **kwargs)
113 return ()
--> 115 grad_value, ans = grad_fn(*args, **kwargs)
116 self._forward = ans
118 return grad_value
File ~\AppData\Local\anaconda3\Lib\site-packages\autograd\wrap_util.py:20, in unary_to_nary.<locals>.nary_operator.<locals>.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)
File ~\AppData\Local\anaconda3\Lib\site-packages\pennylane\_grad.py:133, in grad._grad_with_forward(fun, x)
127 @staticmethod
128 @unary_to_nary
129 def _grad_with_forward(fun, x):
130 """This function is a replica of ``autograd.grad``, with the only
131 difference being that it returns both the gradient *and* the forward pass
132 value."""
--> 133 vjp, ans = _make_vjp(fun, x)
135 if not vspace(ans).size == 1:
136 raise TypeError(
137 "Grad only applies to real scalar-output functions. "
138 "Try jacobian, elementwise_grad or holomorphic_grad."
139 )
File ~\AppData\Local\anaconda3\Lib\site-packages\autograd\core.py:10, 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()
File ~\AppData\Local\anaconda3\Lib\site-packages\autograd\tracer.py:10, 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
File ~\AppData\Local\anaconda3\Lib\site-packages\autograd\wrap_util.py:15, in unary_to_nary.<locals>.nary_operator.<locals>.nary_f.<locals>.unary_f(x)
13 else:
14 subargs = subvals(args, zip(argnum, x))
---> 15 return fun(*subargs, **kwargs)
Cell In[114], line 2, in cost(var, features, labels)
1 def cost(var, features, labels):
----> 2 preds = [quantum_neural_net(var, x) for x in features]
3 return square_loss(labels, preds)
Cell In[114], line 2, in <listcomp>(.0)
1 def cost(var, features, labels):
----> 2 preds = [quantum_neural_net(var, x) for x in features]
3 return square_loss(labels, preds)
File ~\AppData\Local\anaconda3\Lib\site-packages\pennylane\qnode.py:842, in QNode.__call__(self, *args, **kwargs)
839 set_shots(self._original_device, override_shots)(self._update_gradient_fn)()
841 # construct the tape
--> 842 self.construct(args, kwargs)
844 cache = self.execute_kwargs.get("cache", False)
845 using_custom_cache = (
846 hasattr(cache, "__getitem__")
847 and hasattr(cache, "__setitem__")
848 and hasattr(cache, "__delitem__")
849 )
File ~\AppData\Local\anaconda3\Lib\site-packages\pennylane\qnode.py:751, in QNode.construct(self, args, kwargs)
748 if old_interface == "auto":
749 self.interface = qml.math.get_interface(*args, *list(kwargs.values()))
--> 751 self._tape = make_qscript(self.func)(*args, **kwargs)
752 self._qfunc_output = self.tape._qfunc_output
754 params = self.tape.get_parameters(trainable_only=False)
File ~\AppData\Local\anaconda3\Lib\site-packages\pennylane\tape\qscript.py:1371, in make_qscript.<locals>.wrapper(*args, **kwargs)
1369 def wrapper(*args, **kwargs):
1370 with AnnotatedQueue() as q:
-> 1371 result = fn(*args, **kwargs)
1373 qscript = QuantumScript.from_queue(q)
1374 qscript._qfunc_output = result
Cell In[112], line 10, in quantum_neural_net(var, x)
7 for v in var:
8 layer(v)
---> 10 return qml.expval(qml.QuadX(0))
File ~\AppData\Local\anaconda3\Lib\site-packages\pennylane\__init__.py:358, in __getattr__(name)
355 import pennylane.grouping as grouping # pylint:disable=import-outside-toplevel,consider-using-from-import
357 return grouping
--> 358 raise AttributeError(f"Module {__name__} has no attribute {name}")
AttributeError: Module pennylane has no attribute QuadX
And, finally, make sure to include the versions of your packages. Specifically, show us the output of qml.about().
Name: PennyLane
Version: 0.33.1
Summary: PennyLane is a Python quantum machine learning library by Xanadu Inc.
Home-page: GitHub - PennyLaneAI/pennylane: PennyLane is a cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural network.
Author:
Author-email:
License: Apache License 2.0
Location: C:\Users\gz129d\AppData\Local\anaconda3\Lib\site-packages
Requires: appdirs, autograd, autoray, cachetools, networkx, numpy, pennylane-lightning, requests, rustworkx, scipy, semantic-version, toml, typing-extensions
Required-by: PennyLane-Lightning, PennyLane-SF
Platform info: Windows-10-10.0.19045-SP0
Python version: 3.11.5
Numpy version: 1.23.5
Scipy version: 1.11.1
Installed devices:
- default.gaussian (PennyLane-0.29.1)
- default.mixed (PennyLane-0.29.1)
- default.qubit (PennyLane-0.29.1)
- default.qubit.autograd (PennyLane-0.29.1)
- default.qubit.jax (PennyLane-0.29.1)
- default.qubit.tf (PennyLane-0.29.1)
- default.qubit.torch (PennyLane-0.29.1)
- default.qutrit (PennyLane-0.29.1)
- null.qubit (PennyLane-0.29.1)
- lightning.qubit (PennyLane-Lightning-0.30.0)
- strawberryfields.fock (PennyLane-SF-0.29.1)
- strawberryfields.gaussian (PennyLane-SF-0.29.1)
- strawberryfields.gbs (PennyLane-SF-0.29.1)
- strawberryfields.remote (PennyLane-SF-0.29.1)
- strawberryfields.tf (PennyLane-SF-0.29.1)
I have tried PL 0.29 as PL SF but encountered the same error: Module pennylane has no attribute Quadx. PL 0.33 leads to the same error.
May I please request any feedback regarding this Quadx attribute?
Added Note: Strawberry Fields indicate it needs PL < 0.30 - same result though with 0.29.
Thanks
. Indeed, 