-
Notifications
You must be signed in to change notification settings - Fork 586
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Do not cast state to complex128 #5547
Conversation
Hello. You may have forgotten to update the changelog!
|
…chPulseGradErrors::test_nontrainable_batched_tape to reduce flakyness.
Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## master #5547 +/- ##
==========================================
- Coverage 99.69% 99.68% -0.01%
==========================================
Files 410 410
Lines 38230 37943 -287
==========================================
- Hits 38113 37825 -288
- Misses 117 118 +1 ☔ View full report in Codecov by Sentry. |
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Looks good! One open question, otherwise ready to approve.
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
🙌
### Before submitting Please complete the following checklist when submitting a PR: - [x] All new features must include a unit test. If you've fixed a bug or added code that should be tested, add a test to the test directory! - [x] All new functions and code must be clearly commented and documented. If you do make documentation changes, make sure that the docs build and render correctly by running `make docs`. - [x] Ensure that the test suite passes, by running `make test`. - [x] Add a new entry to the `doc/releases/changelog-dev.md` file, summarizing the change, and including a link back to the PR. - [x] The PennyLane source code conforms to [PEP8 standards](https://www.python.org/dev/peps/pep-0008/). We check all of our code against [Pylint](https://www.pylint.org/). To lint modified files, simply `pip install pylint`, and then run `pylint pennylane/path/to/file.py`. When all the above are checked, delete everything above the dashed line and fill in the pull request template. ------------------------------------------------------------------------------------------------------------ **Context:** The changes made in #5547 led to a performance regression in Lightning because a superfluous state vector copy is made when adding `0.j`, which was introduced to cast the output to complex. **Description of the Change:** Use `qml.math.cast` instead. **Benefits:** Avoid unnecessary sum of `0.j` **Possible Drawbacks:** **Related GitHub Issues:** [sc-65127]
Before submitting
Please complete the following checklist when submitting a PR:
All new features must include a unit test.
If you've fixed a bug or added code that should be tested, add a test to the
test directory!
All new functions and code must be clearly commented and documented.
If you do make documentation changes, make sure that the docs build and
render correctly by running
make docs
.Ensure that the test suite passes, by running
make test
.Add a new entry to the
doc/releases/changelog-dev.md
file, summarizing thechange, and including a link back to the PR.
The PennyLane source code conforms to
PEP8 standards.
We check all of our code against Pylint.
To lint modified files, simply
pip install pylint
, and thenrun
pylint pennylane/path/to/file.py
.When all the above are checked, delete everything above the dashed
line and fill in the pull request template.
Context:
The LQ new device API does not preserve the
dtype
of measurement results,The issue comes from
measurementprocess.process_state
as this method changes the specifieddtype
to the defaultcomplex128
.Description of the Change:
Modify
StateMP
andDensityMatrixMP
avoiding explicitly casting tocomplex128
, relying on the various frameworks casting rules, by adding0.0j
. This does not work in TensorFlow for which the current behaviour is preserved.Benefits:
Possible Drawbacks:
Related GitHub Issues:
[sc-60855]