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:
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:
---------------------------------------------------------------------------
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.<locals>._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 <listcomp>(.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 <listcomp>(.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().
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)
