Bugfix: Error when uploading some circuits to TorchQuantum #208
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
… (resulting in a set initial_layout but a null final_layout)
|
Hi Vivekyy, Thank you for your contributions! Merged! |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Fixes bug with importing transpiled circuits which are mapped trivially (resulting in a set initial_layout but a null final_layout). This will come up often on circuits that are transpiled with optimization_level=0, though this can happen on any level of optimization if the trivial layout happens to be optimal.
Code to replicate the bug:
`
from torchquantum.plugins import qiskit2tq
from qiskit.circuit.library import RealAmplitudes
from qiskit.compiler import transpile
from qiskit.providers.fake_provider import FakeGuadalupe
backend = FakeGuadalupe()
ansatz = RealAmplitudes(2, reps=1)
ansatz = transpile(ansatz, backend, optimization_level=0)
params = [0]*len(ansatz.parameters)
ansatz = ansatz.bind_parameters(params)
tq_layer = qiskit2tq(ansatz)
`