# Error mitigation with Amplitude Embedding **URL:** \ **Category:** PennyLane Help\ **Created:** [January 16, 2024, 2:27pm UTC](https://discuss.pennylane.ai/t/error-mitigation-with-amplitude-embedding/3906 "2024-01-16T14:27:54Z")\ **Posts on this page:** 8\ **Page:** 1
**Author:** ![Laia\_Domingo](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/laia_domingo/32/2212_2.png) [@Laia\_Domingo](https://discuss.pennylane.ai/u/Laia_Domingo)\ **Post date:** [January 16, 2024, 2:27pm UTC](https://discuss.pennylane.ai/t/error-mitigation-with-amplitude-embedding/3906/1 "2024-01-16T14:27:54Z")
Hello! I’m getting an error when using the `qml.transforms.mitigate_with_zne` transform with a circuit including AmplitudeEmbedding. The forward pass already rises an error. This error disappears if we replace the AmplitudeEmbedding layer by another layer, such as AngleEmbedding. I need to encode a 16-feature vector in a quantum circuit, so that AmplitudeEmbeding is very convinient to reduce the number of qubits. Is there a way to use error mitigation with AmplitudeEmmbedding in Pennylane? Here’s my code: ```auto import pennylane as qml from pennylane import numpy as qnp from pennylane.transforms import richardson_extrapolate, fold_global # Hyperparameters of the circui nqbits=4 depth=1 # Device definition dev_ideal = qml.device('default.mixed', wires=nqbits) dev_mixed = qml.transforms.insert( qml.DepolarizingChannel, 0.05)(dev_ideal) # Adding noise @qml.transforms.mitigate_with_zne([1, 2, 3], fold_global, richardson_extrapolate) # Adding error mitigation @qnode(dev_mixed) def mitigated_qnode(inputs, weights): qml.AmplitudeEmbedding(features=inputs, wires=range(nqbits),normalize=True) qml.templates.StronglyEntanglingLayers(weights, wires=range(nqbits)) return qml.expval(qml.PauliZ(0)) # Running the circuit with random inputs/weights inputs = qnp.random.normal(2, 4,(1, 2**nqbits), requires_grad=False) weights = qnp.random.uniform(-1,1,(depth, nqbits,3)) mitigated_qnode(inputs, weights) ``` And here’s the error I get: ```auto --------------------------------------------------------------------------- ValueError Traceback (most recent call last) Cell In[92], line 23 21 inputs = qnp.random.normal(2, 4,(1, 2**nqbits), requires_grad=False) 22 weights = qnp.random.uniform(-1,1,(depth, nqbits,3)) ---> 23 mitigated_qnode(inputs, weights) File c:\Users\laiad\anaconda3\envs\Quantum3\Lib\site-packages\pennylane\transforms\batch_transform.py:300, in batch_transform.default_qnode_wrapper.._wrapper(*args, **kwargs) 297 qnode.interface = qml.math.get_interface(*args, *list(kwargs.values())) 299 qnode.construct(args, kwargs) --> 300 tapes, processing_fn = self.construct(qnode.qtape, *targs, **tkwargs) 302 interface = qnode.interface 303 execute_kwargs = getattr(qnode, "execute_kwargs", {}).copy() File c:\Users\laiad\anaconda3\envs\Quantum3\Lib\site-packages\pennylane\transforms\batch_transform.py:431, in batch_transform.construct(self, tape, *targs, **tkwargs) 429 if argnums is not None: 430 tape.trainable_params = argnums --> 431 tapes, processing_fn = self.transform_fn(tape, *targs, **tkwargs) 433 if processing_fn is None: 435 def processing_fn(x): File c:\Users\laiad\anaconda3\envs\Quantum3\Lib\site-packages\pennylane\transforms\mitigate.py:517, in mitigate_with_zne(circuit, scale_factors, folding, extrapolate, folding_kwargs, extrapolate_kwargs, reps_per_factor) 514 tape = circuit.expand(stop_at=lambda op: not isinstance(op, QuantumScript)) 515 script_removed = QuantumScript(tape._ops) --> 517 tapes = [ 518 [folding(script_removed, s, **folding_kwargs) for _ in range(reps_per_factor)] 519 for s in scale_factors 520 ] 522 tapes = [tape_ for tapes_ in tapes for tape_ in tapes_] # flattens nested list 523 out_tapes = [QuantumScript(tape_.operations, tape.measurements, tape._prep) for tape_ in tapes] File c:\Users\laiad\anaconda3\envs\Quantum3\Lib\site-packages\pennylane\transforms\mitigate.py:518, in (.0) 514 tape = circuit.expand(stop_at=lambda op: not isinstance(op, QuantumScript)) 515 script_removed = QuantumScript(tape._ops) 517 tapes = [ --> 518 [folding(script_removed, s, **folding_kwargs) for _ in range(reps_per_factor)] 519 for s in scale_factors 520 ] 522 tapes = [tape_ for tapes_ in tapes for tape_ in tapes_] # flattens nested list 523 out_tapes = [QuantumScript(tape_.operations, tape.measurements, tape._prep) for tape_ in tapes] File c:\Users\laiad\anaconda3\envs\Quantum3\Lib\site-packages\pennylane\transforms\mitigate.py:518, in (.0) 514 tape = circuit.expand(stop_at=lambda op: not isinstance(op, QuantumScript)) 515 script_removed = QuantumScript(tape._ops) 517 tapes = [ --> 518 [folding(script_removed, s, **folding_kwargs) for _ in range(reps_per_factor)] 519 for s in scale_factors 520 ] 522 tapes = [tape_ for tapes_ in tapes for tape_ in tapes_] # flattens nested list 523 out_tapes = [QuantumScript(tape_.operations, tape.measurements, tape._prep) for tape_ in tapes] File c:\Users\laiad\anaconda3\envs\Quantum3\Lib\site-packages\pennylane\transforms\mitigate.py:251, in fold_global_tape(circuit, scale_factor) 248 for meas in circuit.measurements: 249 apply(meas) --> 251 return QuantumScript.from_queue(new_circuit_q) File c:\Users\laiad\anaconda3\envs\Quantum3\Lib\site-packages\pennylane\tape\qscript.py:1331, in QuantumScript.from_queue(cls, queue) 1328 @classmethod 1329 def from_queue(cls, queue): 1330 """Construct a QuantumScript from an AnnotatedQueue.""" -> 1331 return cls(*process_queue(queue)) File c:\Users\laiad\anaconda3\envs\Quantum3\Lib\site-packages\pennylane\queuing.py:525, in process_queue(queue) 523 current_list = obj._queue_category 524 elif list_order[obj._queue_category] < list_order[current_list]: --> 525 raise ValueError( 526 f"{obj._queue_category[1:]} operation {obj} must occur prior " 527 f"to {current_list[1:]}. Please place earlier in the queue." 528 ) 529 lists[obj._queue_category].append(obj) 531 return lists["_ops"], lists["_measurements"], lists["_prep"] ValueError: prep operation Adjoint(QubitStateVector(tensor([[-0.38310708+0.j, 0.14985063+0.j, 0.08886766+0.j, 0.14269864+0.j, 0.34669516+0.j, -0.53808762+0.j, -0.01665238+0.j, 0.1849713 +0.j, 0.00796543+0.j, -0.22028881+0.j, 0.32889664+0.j, 0.18664527+0.j, -0.11318396+0.j, 0.19576172+0.j, 0.31751402+0.j, 0.12139773+0.j]], requires_grad=False), wires=[0, 1, 2, 3])) must occur prior to ops. Please place earlier in the queue. ``` Thanks for the help! Here is the output of `qml.about()`. ```auto Summary: PennyLane is a Python quantum machine learning library by Xanadu Inc. Home-page: https://github.com/XanaduAI/pennylane Author: Author-email: License: Apache License 2.0 Location: c:\Users\laiad\anaconda3\envs\Quantum3\Lib\site-packages Requires: appdirs, autograd, autoray, cachetools, networkx, numpy, pennylane-lightning, requests, rustworkx, scipy, semantic-version, toml Required-by: PennyLane-Lightning, PennyLane-qiskit Platform info: Windows-10-10.0.19045-SP0 Python version: 3.11.2 Numpy version: 1.23.5 Scipy version: 1.10.1 Installed devices: - qiskit.aer (PennyLane-qiskit-0.34.0) - qiskit.basicaer (PennyLane-qiskit-0.34.0) - qiskit.ibmq (PennyLane-qiskit-0.34.0) - qiskit.ibmq.circuit_runner (PennyLane-qiskit-0.34.0) - qiskit.ibmq.sampler (PennyLane-qiskit-0.34.0) - qiskit.remote (PennyLane-qiskit-0.34.0) - default.gaussian (PennyLane-0.30.0) - default.mixed (PennyLane-0.30.0) - default.qubit (PennyLane-0.30.0) - default.qubit.autograd (PennyLane-0.30.0) - default.qubit.jax (PennyLane-0.30.0) - default.qubit.tf (PennyLane-0.30.0) - default.qubit.torch (PennyLane-0.30.0) - default.qutrit (PennyLane-0.30.0) - null.qubit (PennyLane-0.30.0) - lightning.qubit (PennyLane-Lightning-0.31.0) ``` ---
**Author:** ![Alex](https://avatars.discourse-cdn.com/v4/letter/a/db5fbb/32.png) [@Alex](https://discuss.pennylane.ai/u/Alex)\ **Post date:** [January 17, 2024, 8:14pm UTC](https://discuss.pennylane.ai/t/error-mitigation-with-amplitude-embedding/3906/2 "2024-01-17T20:14:16Z")
Hello, state preparation must occur before other operation (such as the operations performed by mitigation). qml.transforms.mitigate\_with\_zne transform is trying to apply certain mitigations that are not compatible with the order of operations in the circuit containing AmplitudeEmbedding. Try this: ```auto import pennylane as qml from pennylane import numpy as qnp from pennylane.transforms import richardson_extrapolate, fold_global from functools import partial # Define a new device class by inheriting from the existing device class class MyMixedDevice(qml.devices.DefaultMixed): def __init__ (self, noise_strength, wires, **kwargs): super(). __init__ (wires=wires, **kwargs) self.noise_strength = noise_strength def apply(self, operations, **kwargs): # Apply the DepolarizingChannel noise to each operation for operation in operations: if isinstance(operation, qml.operation.Observable): self.noise_strength = operation.parameters[0] if operation.name == "DepolarizingChannel" else self.noise_strength if isinstance(operation, qml.operation.Expectation): operation.return_type = qml.operation.Expectation return super().apply(operations, **kwargs) # Hyperparameters of the circuit nqbits = 4 depth = 1 # Device definition dev_ideal = qml.device('default.mixed', wires=nqbits) # Adding noise with DepolarizingChannel on each wire dev_mixed = MyMixedDevice(0.05, wires=range(nqbits)) def state_preparation(inputs): qml.AmplitudeEmbedding(features=inputs, wires=range(nqbits), normalize=True) # Applying the transform with functools.partial mitigated_qnode = partial(qml.transforms.mitigate_with_zne, scale_factors=[1, 2, 3], folding=fold_global, extrapolate=richardson_extrapolate) @mitigated_qnode @qml.qnode(dev_mixed) def mitigated_qnode(inputs, weights): state_preparation(inputs) qml.templates.StronglyEntanglingLayers(weights, wires=range(nqbits)) return qml.expval(qml.PauliZ(0)) # Running the circuit with random inputs/weights inputs = qnp.random.normal(2, 4, (1, 2 ** nqbits), requires_grad=False) weights = qnp.random.uniform(-1, 1, (depth, nqbits, 3)) result = mitigated_qnode(inputs, weights) print(result) ``` ---
**Author:** ![Laia\_Domingo](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/laia_domingo/32/2212_2.png) [@Laia\_Domingo](https://discuss.pennylane.ai/u/Laia_Domingo)\ **Post date:** [January 18, 2024, 11:57am UTC](https://discuss.pennylane.ai/t/error-mitigation-with-amplitude-embedding/3906/3 "2024-01-18T11:57:32Z")
Hi Alex, thank you for your help! I copy pasted your code but I’m still getting the same error message: `--------------------------------------------------------------------------- ValueError Traceback (most recent call last) Cell In[1], [line 47](vscode-notebook-cell:?execution_count=1&line=47) [45](vscode-notebook-cell:?execution_count=1&line=45) inputs = qnp.random.normal(2, 4, (1, 2 **nqbits), requires_grad=False) [46](vscode-notebook-cell:?execution_count=1&line=46) weights = qnp.random.uniform(-1, 1, (depth, nqbits, 3)) ---> [47](vscode-notebook-cell:?execution_count=1&line=47) result = mitigated_qnode(inputs, weights) [48](vscode-notebook-cell:?execution_count=1&line=48) print(result) File [c:\Users\laiad\anaconda3\envs\Quantum3\Lib\site-packages\pennylane\transforms\batch_transform.py:300](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/batch_transform.py:300), in batch_transform.default_qnode_wrapper.._wrapper(*args,**kwargs) [297](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/batch_transform.py:297) qnode.interface = qml.math.get_interface(*args, *list(kwargs.values())) [299](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/batch_transform.py:299) qnode.construct(args, kwargs) --> [300](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/batch_transform.py:300) tapes, processing_fn = self.construct(qnode.qtape, *targs, **tkwargs) [302](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/batch_transform.py:302) interface = qnode.interface [303](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/batch_transform.py:303) execute_kwargs = getattr(qnode, "execute_kwargs", {}).copy() File [c:\Users\laiad\anaconda3\envs\Quantum3\Lib\site-packages\pennylane\transforms\batch_transform.py:431](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/batch_transform.py:431), in batch_transform.construct(self, tape, *targs,**tkwargs) [429](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/batch_transform.py:429) if argnums is not None: [430](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/batch_transform.py:430) tape.trainable_params = argnums --> [431](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/batch_transform.py:431) tapes, processing_fn = self.transform_fn(tape, *targs, **tkwargs) [433](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/batch_transform.py:433) if processing_fn is None: [435](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/batch_transform.py:435) def processing_fn(x): File [c:\Users\laiad\anaconda3\envs\Quantum3\Lib\site-packages\pennylane\transforms\mitigate.py:517](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/mitigate.py:517), in mitigate_with_zne(circuit, scale_factors, folding, extrapolate, folding_kwargs, extrapolate_kwargs, reps_per_factor) [514](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/mitigate.py:514) tape = circuit.expand(stop_at=lambda op: not isinstance(op, QuantumScript)) [515](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/mitigate.py:515) script_removed = QuantumScript(tape._ops) --> [517](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/mitigate.py:517) tapes = [[518](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/mitigate.py:518) [folding(script_removed, s,**folding_kwargs) for _ in range(reps_per_factor)] [519](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/mitigate.py:519) for s in scale_factors [520](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/mitigate.py:520) ] [522](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/mitigate.py:522) tapes = [tape_ for tapes_ in tapes for tape_ in tapes_] # flattens nested list [523](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/mitigate.py:523) out_tapes = [QuantumScript(tape_.operations, tape.measurements, tape._prep) for tape_ in tapes] File [c:\Users\laiad\anaconda3\envs\Quantum3\Lib\site-packages\pennylane\transforms\mitigate.py:518](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/mitigate.py:518), in (.0) [514](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/mitigate.py:514) tape = circuit.expand(stop_at=lambda op: not isinstance(op, QuantumScript)) [515](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/mitigate.py:515) script_removed = QuantumScript(tape._ops) [517](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/mitigate.py:517) tapes = [--> [518](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/mitigate.py:518) [folding(script_removed, s, **folding_kwargs) for _ in range(reps_per_factor)] [519](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/mitigate.py:519) for s in scale_factors [520](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/mitigate.py:520) ] [522](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/mitigate.py:522) tapes = [tape_ for tapes_ in tapes for tape_ in tapes_] # flattens nested list [523](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/mitigate.py:523) out_tapes = [QuantumScript(tape_.operations, tape.measurements, tape._prep) for tape_ in tapes] File [c:\Users\laiad\anaconda3\envs\Quantum3\Lib\site-packages\pennylane\transforms\mitigate.py:518](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/mitigate.py:518), in (.0) [514](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/mitigate.py:514) tape = circuit.expand(stop_at=lambda op: not isinstance(op, QuantumScript)) [515](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/mitigate.py:515) script_removed = QuantumScript(tape._ops) [517](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/mitigate.py:517) tapes = [--> [518](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/mitigate.py:518) [folding(script_removed, s,**folding_kwargs) for _ in range(reps_per_factor)] [519](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/mitigate.py:519) for s in scale_factors [520](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/mitigate.py:520) ] [522](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/mitigate.py:522) tapes = [tape_ for tapes_ in tapes for tape_ in tapes_] # flattens nested list [523](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/mitigate.py:523) out_tapes = [QuantumScript(tape_.operations, tape.measurements, tape._prep) for tape_ in tapes] File [c:\Users\laiad\anaconda3\envs\Quantum3\Lib\site-packages\pennylane\transforms\mitigate.py:251](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/mitigate.py:251), in fold_global_tape(circuit, scale_factor) [248](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/mitigate.py:248) for meas in circuit.measurements: [249](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/mitigate.py:249) apply(meas) --> [251](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/transforms/mitigate.py:251) return QuantumScript.from_queue(new_circuit_q) File [c:\Users\laiad\anaconda3\envs\Quantum3\Lib\site-packages\pennylane\tape\qscript.py:1331](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/tape/qscript.py:1331), in QuantumScript.from_queue(cls, queue) [1328](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/tape/qscript.py:1328) @classmethod [1329](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/tape/qscript.py:1329) def from_queue(cls, queue): [1330](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/tape/qscript.py:1330) """Construct a QuantumScript from an AnnotatedQueue.""" -> [1331](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/tape/qscript.py:1331) return cls(*process_queue(queue)) File [c:\Users\laiad\anaconda3\envs\Quantum3\Lib\site-packages\pennylane\queuing.py:525](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/queuing.py:525), in process_queue(queue) [523](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/queuing.py:523) current_list = obj._queue_category [524](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/queuing.py:524) elif list_order[obj._queue_category] < list_order[current_list]: --> [525](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/queuing.py:525) raise ValueError( [526](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/queuing.py:526) f"{obj._queue_category[1:]} operation {obj} must occur prior " [527](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/queuing.py:527) f"to {current_list[1:]}. Please place earlier in the queue." [528](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/queuing.py:528) ) [529](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/queuing.py:529) lists[obj._queue_category].append(obj) [531](file:///C:/Users/laiad/anaconda3/envs/Quantum3/Lib/site-packages/pennylane/queuing.py:531) return lists["_ops"], lists["_measurements"], lists["_prep"] ValueError: prep operation Adjoint(QubitStateVector(tensor([[0.04942569+0.j, 0.24269701+0.j, 0.34812557+0.j, 0.17978282+0.j, 0.1126188 +0.j, 0.33736393+0.j, -0.41157208+0.j, 0.3897833 +0.j, -0.1007921 +0.j, -0.13128589+0.j, -0.26301813+0.j, -0.05402194+0.j, -0.1528719 +0.j, 0.18562809+0.j, 0.30692938+0.j, 0.29291089+0.j]], requires_grad=False), wires=[0, 1, 2, 3])) must occur prior to ops. Please place earlier in the queue.` Is your example working for you? Maybe we have different Pennylane versions? ---
**Author:** ![Alex](https://avatars.discourse-cdn.com/v4/letter/a/db5fbb/32.png) [@Alex](https://discuss.pennylane.ai/u/Alex)\ **Post date:** [January 18, 2024, 2:16pm UTC](https://discuss.pennylane.ai/t/error-mitigation-with-amplitude-embedding/3906/4 "2024-01-18T14:16:38Z")
I’m sorry then. It worked for me, I installed pennylane in a google colab notebook. I would probably get a lot of errors if I run locally, but this sample worked for me in colab. ---
**Author:** ![Laia\_Domingo](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/laia_domingo/32/2212_2.png) [@Laia\_Domingo](https://discuss.pennylane.ai/u/Laia_Domingo)\ **Post date:** [January 18, 2024, 2:23pm UTC](https://discuss.pennylane.ai/t/error-mitigation-with-amplitude-embedding/3906/5 "2024-01-18T14:23:03Z")
Oh, no worries! Thank you so much for the idea! Could you provide the output of `qml.about()`in Google Colab? ---
**Author:** ![Laia\_Domingo](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/laia_domingo/32/2212_2.png) [@Laia\_Domingo](https://discuss.pennylane.ai/u/Laia_Domingo)\ **Post date:** [January 18, 2024, 4:41pm UTC](https://discuss.pennylane.ai/t/error-mitigation-with-amplitude-embedding/3906/6 "2024-01-18T16:41:22Z")
It turns out that this issue is solved in Pennylane’s version 0.34, so now both codes work! Thank you @Alex for your help! Now I have an issue when creating a keras layer, but I’ll open another discussion topic! ---
**Author:** ![Alex](https://avatars.discourse-cdn.com/v4/letter/a/db5fbb/32.png) [@Alex](https://discuss.pennylane.ai/u/Alex)\ **Post date:** [January 18, 2024, 5:48pm UTC](https://discuss.pennylane.ai/t/error-mitigation-with-amplitude-embedding/3906/7 "2024-01-18T17:48:20Z")
Ok, that is good to know. You’re welcome! ---
**Author:** ![isaacdevlugt](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/isaacdevlugt/32/1159_2.png) [@isaacdevlugt](https://discuss.pennylane.ai/u/isaacdevlugt)\ **Post date:** [January 18, 2024, 10:50pm UTC](https://discuss.pennylane.ai/t/error-mitigation-with-amplitude-embedding/3906/8 "2024-01-18T22:50:04Z")
Hey @Laia_Domingo! Welcome to the forum 🙂