Hi @isaacdevlugt,
I made some progress using Training with QNG optimizer on circuit with data argument and Accelerating VQEs with quantum natural gradient.
I change my code to:
import pennylane as qml
from pennylane import numpy as np
from sklearn import datasets
data = datasets.make_classification(n_samples=1000, n_features=52, n_classes=2)
num_qubits = 8
dev = qml.device("default.qubit", wires=num_qubits)
def ansatz_3(params, X, depth=2):
'''
'''
#print(params)
#amplitudes(X_batch, num_qubits=num_qubits)
qml.AmplitudeEmbedding(features=X, pad_with=0.,wires=range(num_qubits),normalize=True)
step = 0
for _ in range(depth):
for i in range(num_qubits):
qml.RY(params[i+step], wires=i)
for i in range(num_qubits-1):
qml.CNOT([i,i+1])
for i in range(num_qubits):
qml.RY(params[i+step], wires=i)
step += num_qubits
coeffs = [1 for i in range(num_qubits)]
obs = [qml.PauliZ(i) for i in range(num_qubits)]
H = qml.Hamiltonian(coeffs, obs)
@qml.qnode(dev, interface="autograd")
def cost_fn(params, X):
ansatz_3(params, X)
return qml.expval(H)
init_params = np.random.random(40)
max_iterations = 500
step_size = 0.5
conv_tol = 1e-06
opt = qml.QNGOptimizer(step_size, lam=0.001, approx="block-diag")
depth = 2
batch_size = 5*depth #5
X_ = data[0]#np.concatenate([X_new, X_transform.to_numpy()], axis=1) #data_res.to_numpy() X_new
Y_ = data[1] #y
Y_ = Y_ * 2 - 1
params = init_params
prev_energy = cost_fn(params, X_[:batch_size])
qngd_cost = []
for n in range(max_iterations):
# Update the weights by one optimizer step
batch_index = np.random.randint(0, len(X_), (batch_size,))
X_batch = np.array(X_[batch_index], requires_grad=False)
Y_batch = Y_[batch_index]
params, prev_energy = opt.step_and_cost(cost_fn, params, X_batch)
qngd_cost.append(prev_energy)
energy = cost_fn(params, X_batch)
conv = np.abs(energy - prev_energy)
if n % 4 == 0:
print(
"Iteration = {:}, Energy = {:.8f} Ha".format(n, energy)
)
if conv <= conv_tol:
break
But I got this new error:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[6], line 28
24 X_batch = np.array(X_[batch_index], requires_grad=False)
25 Y_batch = Y_[batch_index]
---> 28 params, prev_energy = opt.step_and_cost(cost_fn, params, X_batch)
29 qngd_cost.append(prev_energy)
31 energy = cost_fn(params, X_batch)
File ~/PinQ2_py/lib/python3.9/site-packages/pennylane/optimize/qng.py:192, in QNGOptimizer.step_and_cost(self, qnode, grad_fn, recompute_tensor, metric_tensor_fn, *args, **kwargs)
189 if metric_tensor_fn is None:
190 metric_tensor_fn = qml.metric_tensor(qnode, approx=self.approx)
--> 192 _metric_tensor = metric_tensor_fn(*args, **kwargs)
193 # Reshape metric tensor to be square
194 shape = qml.math.shape(_metric_tensor)
File ~/PinQ2_py/lib/python3.9/site-packages/pennylane/workflow/qnode.py:1040, in QNode.__call__(self, *args, **kwargs)
1035 if hybrid:
1036 argnums = full_transform_program[-1]._kwargs.pop(
1037 "argnums", None
1038 ) # pylint: disable=protected-access
-> 1040 full_transform_program._set_all_classical_jacobians(
1041 self, args, kwargs, argnums
1042 ) # pylint: disable=protected-access
1043 full_transform_program._set_all_argnums(
1044 self, args, kwargs, argnums
1045 ) # pylint: disable=protected-access
1047 # pylint: disable=unexpected-keyword-arg
File ~/PinQ2_py/lib/python3.9/site-packages/pennylane/transforms/core/transform_program.py:420, in TransformProgram._set_all_classical_jacobians(self, qnode, args, kwargs, argnums)
416 raise qml.QuantumFunctionError(
417 "argnum does not work with the Jax interface. You should use argnums instead."
418 )
419 sub_program = TransformProgram(self[0:index])
--> 420 classical_jacobian = jacobian(
421 classical_preprocessing, sub_program, argnums, *args, **kwargs
422 )
423 qnode.construct(args, kwargs)
424 tapes, _ = sub_program((qnode.tape,))
File ~/PinQ2_py/lib/python3.9/site-packages/pennylane/transforms/core/transform_program.py:376, in TransformProgram._set_all_classical_jacobians.<locals>.jacobian(classical_function, program, argnums, *args, **kwargs)
373 classical_function = partial(classical_function, program)
375 if qnode.interface == "autograd":
--> 376 jac = qml.jacobian(classical_function, argnum=argnums)(*args, **kwargs)
378 if qnode.interface == "tf":
379 import tensorflow as tf # pylint: disable=import-outside-toplevel
File ~/PinQ2_py/lib/python3.9/site-packages/pennylane/_grad.py:455, in jacobian.<locals>._jacobian_function(*args, **kwargs)
449 if not _argnum:
450 warnings.warn(
451 "Attempted to differentiate a function with no trainable parameters. "
452 "If this is unintended, please add trainable parameters via the "
453 "'requires_grad' attribute or 'argnum' keyword."
454 )
--> 455 jac = tuple(_jacobian(func, arg)(*args, **kwargs) for arg in _argnum)
457 return jac[0] if unpack else jac
File ~/PinQ2_py/lib/python3.9/site-packages/pennylane/_grad.py:455, in <genexpr>(.0)
449 if not _argnum:
450 warnings.warn(
451 "Attempted to differentiate a function with no trainable parameters. "
452 "If this is unintended, please add trainable parameters via the "
453 "'requires_grad' attribute or 'argnum' keyword."
454 )
--> 455 jac = tuple(_jacobian(func, arg)(*args, **kwargs) for arg in _argnum)
457 return jac[0] if unpack else jac
File ~/PinQ2_py/lib/python3.9/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 ~/PinQ2_py/lib/python3.9/site-packages/autograd/differential_operators.py:60, in jacobian(fun, x)
50 @unary_to_nary
51 def jacobian(fun, x):
52 """
53 Returns a function which computes the Jacobian of `fun` with respect to
54 positional argument number `argnum`, which must be a scalar or array. Unlike
(...)
58 (out1, out2, ...) then the Jacobian has shape (out1, out2, ..., in1, in2, ...).
59 """
---> 60 vjp, ans = _make_vjp(fun, x)
61 ans_vspace = vspace(ans)
62 jacobian_shape = ans_vspace.shape + vspace(x).shape
File ~/PinQ2_py/lib/python3.9/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 ~/PinQ2_py/lib/python3.9/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 ~/PinQ2_py/lib/python3.9/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)
File ~/PinQ2_py/lib/python3.9/site-packages/pennylane/transforms/core/transform_program.py:355, in TransformProgram._set_all_classical_jacobians.<locals>.classical_preprocessing(program, *args, **kwargs)
353 qnode.construct(args, kwargs)
354 tape = qnode.qtape
--> 355 tapes, _ = program((tape,))
356 res = tuple(qml.math.stack(tape.get_parameters(trainable_only=True)) for tape in tapes)
357 if len(tapes) == 1:
File ~/PinQ2_py/lib/python3.9/site-packages/pennylane/transforms/core/transform_program.py:477, in TransformProgram.__call__(self, tapes)
475 if self._argnums is not None and self._argnums[i] is not None:
476 tape.trainable_params = self._argnums[i][j]
--> 477 new_tapes, fn = transform(tape, *targs, **tkwargs)
478 execution_tapes.extend(new_tapes)
480 fns.append(fn)
File ~/PinQ2_py/lib/python3.9/site-packages/pennylane/gradients/metric_tensor.py:82, in _expand_metric_tensor(tape, argnum, approx, allow_nonunitary, aux_wire, device_wires)
80 if not allow_nonunitary and approx is None:
81 return [qml.transforms.expand_nonunitary_gen(tape)], lambda x: x[0]
---> 82 return [qml.transforms.expand_multipar(tape)], lambda x: x[0]
File ~/PinQ2_py/lib/python3.9/site-packages/pennylane/transforms/tape_expand.py:102, in create_expand_fn.<locals>.expand_fn(tape, depth, **kwargs)
100 tape = tape.expand(depth=depth)
101 elif not all(stop_at(op) for op in tape.operations):
--> 102 tape = tape.expand(depth=depth, stop_at=stop_at)
103 else:
104 return tape
File ~/PinQ2_py/lib/python3.9/site-packages/pennylane/tape/qscript.py:912, in QuantumScript.expand(self, depth, stop_at, expand_measurements)
867 def expand(self, depth=1, stop_at=None, expand_measurements=False):
868 """Expand all operations to a specific depth.
869
870 Args:
(...)
910 RY(0.2, wires=['a'])]
911 """
--> 912 new_script = qml.tape.tape.expand_tape(
913 self, depth=depth, stop_at=stop_at, expand_measurements=expand_measurements
914 )
915 new_script._update()
916 return new_script
File ~/PinQ2_py/lib/python3.9/site-packages/pennylane/tape/tape.py:213, in expand_tape(tape, depth, stop_at, expand_measurements)
210 continue
212 # recursively expand out the newly created tape
--> 213 expanded_tape = expand_tape(obj, stop_at=stop_at, depth=depth - 1)
215 new_ops.extend(expanded_tape.operations)
216 new_measurements.extend(expanded_tape.measurements)
File ~/PinQ2_py/lib/python3.9/site-packages/pennylane/tape/tape.py:213, in expand_tape(tape, depth, stop_at, expand_measurements)
210 continue
212 # recursively expand out the newly created tape
--> 213 expanded_tape = expand_tape(obj, stop_at=stop_at, depth=depth - 1)
215 new_ops.extend(expanded_tape.operations)
216 new_measurements.extend(expanded_tape.measurements)
File ~/PinQ2_py/lib/python3.9/site-packages/pennylane/tape/tape.py:198, in expand_tape(tape, depth, stop_at, expand_measurements)
196 if obj.has_decomposition:
197 with QueuingManager.stop_recording():
--> 198 obj = QuantumScript(obj.decomposition(), _update=False)
199 else:
200 new_queue.append(obj)
File ~/PinQ2_py/lib/python3.9/site-packages/pennylane/operation.py:1292, in Operator.decomposition(self)
1280 def decomposition(self):
1281 r"""Representation of the operator as a product of other operators.
1282
1283 .. math:: O = O_1 O_2 \dots O_n
(...)
1290 list[Operator]: decomposition of the operator
1291 """
-> 1292 return self.compute_decomposition(
1293 *self.parameters, wires=self.wires, **self.hyperparameters
1294 )
File ~/PinQ2_py/lib/python3.9/site-packages/pennylane/templates/state_preparations/mottonen.py:346, in MottonenStatePreparation.compute_decomposition(state_vector, wires)
321 r"""Representation of the operator as a product of other operators.
322
323 .. math:: O = O_1 O_2 \dots O_n.
(...)
343 CNOT(wires=['a', 'b'])]
344 """
345 if len(qml.math.shape(state_vector)) > 1:
--> 346 raise ValueError(
347 "Broadcasting with MottonenStatePreparation is not supported. Please use the "
348 "qml.transforms.broadcast_expand transform to use broadcasting with "
349 "MottonenStatePreparation."
350 )
352 a = qml.math.abs(state_vector)
353 omega = qml.math.angle(state_vector)
ValueError: Broadcasting with MottonenStatePreparation is not supported. Please use the qml.transforms.broadcast_expand transform to use broadcasting with MottonenStatePreparation.
Looking at the forum, the error seems to be due to the fact the qml.MottonenStatePreparation has no broadcasting implemented: Parameter broadcast bug
But how to fix it? Because I don’t use qml.MottonenStatePrepration or is it used in qml.AmplitudeEmbedding?
Update:
When I changed AmplitudeEmbedding to AngleEmbedding and reduced the dataset with 8 variables to match 8 qubits, the error of broadcast disappeared, but I got a new one 
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[38], line 29
25 X_batch = np.array(X_[batch_index], requires_grad=False)
26 Y_batch = Y_[batch_index]
---> 29 params, prev_energy = opt.step_and_cost(cost_fn, params, X_batch)
30 qngd_cost.append(prev_energy)
32 energy = cost_fn(params, X_batch)
File ~/PinQ2_py/lib/python3.9/site-packages/pennylane/optimize/qng.py:192, in QNGOptimizer.step_and_cost(self, qnode, grad_fn, recompute_tensor, metric_tensor_fn, *args, **kwargs)
189 if metric_tensor_fn is None:
190 metric_tensor_fn = qml.metric_tensor(qnode, approx=self.approx)
--> 192 _metric_tensor = metric_tensor_fn(*args, **kwargs)
193 # Reshape metric tensor to be square
194 shape = qml.math.shape(_metric_tensor)
File ~/PinQ2_py/lib/python3.9/site-packages/pennylane/workflow/qnode.py:1048, in QNode.__call__(self, *args, **kwargs)
1043 full_transform_program._set_all_argnums(
1044 self, args, kwargs, argnums
1045 ) # pylint: disable=protected-access
1047 # pylint: disable=unexpected-keyword-arg
-> 1048 res = qml.execute(
1049 (self._tape,),
1050 device=self.device,
1051 gradient_fn=self.gradient_fn,
1052 interface=self.interface,
1053 transform_program=full_transform_program,
1054 config=config,
1055 gradient_kwargs=self.gradient_kwargs,
1056 override_shots=override_shots,
1057 **self.execute_kwargs,
1058 )
1060 res = res[0]
1062 # convert result to the interface in case the qfunc has no parameters
File ~/PinQ2_py/lib/python3.9/site-packages/pennylane/workflow/execution.py:685, in execute(tapes, device, gradient_fn, interface, transform_program, config, grad_on_execution, gradient_kwargs, cache, cachesize, max_diff, override_shots, expand_fn, max_expansion, device_batch_transform, device_vjp)
683 if no_interface_boundary_required:
684 results = inner_execute(tapes)
--> 685 return post_processing(results)
687 _grad_on_execution = False
689 if (
690 device_vjp
691 and "lightning" in getattr(device, "short_name", "")
692 and interface in jpc_interfaces
693 ):
File ~/PinQ2_py/lib/python3.9/site-packages/pennylane/transforms/core/transform_program.py:86, in _apply_postprocessing_stack(results, postprocessing_stack)
63 """Applies the postprocessing and cotransform postprocessing functions in a Last-In-First-Out LIFO manner.
64
65 Args:
(...)
83
84 """
85 for postprocessing in reversed(postprocessing_stack):
---> 86 results = postprocessing(results)
87 return results
File ~/PinQ2_py/lib/python3.9/site-packages/pennylane/transforms/core/transform_program.py:56, in _batch_postprocessing(results, individual_fns, slices)
30 def _batch_postprocessing(
31 results: ResultBatch, individual_fns: List[PostProcessingFn], slices: List[slice]
32 ) -> ResultBatch:
33 """Broadcast individual post processing functions onto their respective tapes.
34
35 Args:
(...)
54
55 """
---> 56 return tuple(fn(results[sl]) for fn, sl in zip(individual_fns, slices))
File ~/PinQ2_py/lib/python3.9/site-packages/pennylane/transforms/core/transform_program.py:56, in <genexpr>(.0)
30 def _batch_postprocessing(
31 results: ResultBatch, individual_fns: List[PostProcessingFn], slices: List[slice]
32 ) -> ResultBatch:
33 """Broadcast individual post processing functions onto their respective tapes.
34
35 Args:
(...)
54
55 """
---> 56 return tuple(fn(results[sl]) for fn, sl in zip(individual_fns, slices))
File ~/PinQ2_py/lib/python3.9/site-packages/pennylane/gradients/metric_tensor.py:504, in _metric_tensor_cov_matrix.<locals>.processing_fn(probs)
502 scale = qml.math.convert_like(qml.math.outer(coeffs, coeffs), p)
503 scale = qml.math.cast_like(scale, p)
--> 504 g = scale * qml.math.cov_matrix(p, obs, wires=tape.wires, diag_approx=diag_approx)
505 for i, in_argnum in enumerate(params_in_argnum):
506 # fill in rows and columns of zeros where a parameter was not in argnum
507 if not in_argnum:
File ~/PinQ2_py/lib/python3.9/site-packages/pennylane/math/quantum.py:102, in cov_matrix(prob, obs, wires, diag_approx)
100 eigvals = cast(o.eigvals(), dtype=float64)
101 w = o.wires.labels if wires is None else wires.indices(o.wires)
--> 102 p = marginal_prob(prob, w)
104 res = dot(eigvals**2, p) - (dot(eigvals, p)) ** 2
105 variances.append(res)
File ~/PinQ2_py/lib/python3.9/site-packages/pennylane/math/quantum.py:169, in marginal_prob(prob, axis)
166 return prob
168 inactive_wires = tuple(set(range(num_wires)) - set(axis))
--> 169 prob = np.reshape(prob, [2] * num_wires)
170 prob = np.sum(prob, axis=inactive_wires)
171 return np.flatten(prob)
File ~/PinQ2_py/lib/python3.9/site-packages/autoray/autoray.py:79, in do(fn, like, *args, **kwargs)
30 """Do function named ``fn`` on ``(*args, **kwargs)``, peforming single
31 dispatch to retrieve ``fn`` based on whichever library defines the class of
32 the ``args[0]``, or the ``like`` keyword argument if specified.
(...)
76 <tf.Tensor: id=91, shape=(3, 3), dtype=float32>
77 """
78 backend = choose_backend(fn, *args, like=like, **kwargs)
---> 79 return get_lib_fn(backend, fn)(*args, **kwargs)
File <__array_function__ internals>:180, in reshape(*args, **kwargs)
File ~/PinQ2_py/lib/python3.9/site-packages/numpy/core/fromnumeric.py:298, in reshape(a, newshape, order)
198 @array_function_dispatch(_reshape_dispatcher)
199 def reshape(a, newshape, order='C'):
200 """
201 Gives a new shape to an array without changing its data.
202
(...)
296 [5, 6]])
297 """
--> 298 return _wrapfunc(a, 'reshape', newshape, order=order)
File ~/PinQ2_py/lib/python3.9/site-packages/numpy/core/fromnumeric.py:57, in _wrapfunc(obj, method, *args, **kwds)
54 return _wrapit(obj, method, *args, **kwds)
56 try:
---> 57 return bound(*args, **kwds)
58 except TypeError:
59 # A TypeError occurs if the object does have such a method in its
60 # class, but its signature is not identical to that of NumPy's. This
(...)
64 # Call _wrapit from within the except clause to ensure a potential
65 # exception has a traceback chain.
66 return _wrapit(obj, method, *args, **kwds)
ValueError: cannot reshape array of size 2560 into shape (2,2,2,2,2,2,2,2,2,2,2)
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