Program capture sharp bits page for v0.41 #7062
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isaacdevlugt
changed the title
andrijapau
reviewed
Mar 10, 2025
Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## v0.41.0-rc0 #7062 +/- ##
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isaacdevlugt
commented
Mar 20, 2025
josh146
requested changes
Mar 21, 2025
Co-authored-by: Josh Izaac <josh146@gmail.com>
isaacdevlugt
commented
Mar 21, 2025
**Context:** To use the `default.tensor` device in PennyLane, we must
import the `quimb` Python package.
The strategy adopted for this was the same previously used for the
`default.clifford` device, which depends on the `stim` Python package.
That is, when the user imports `pennylane`, the package is imported if
installed on the system, otherwise it is not imported. An error is
raised if the user explicitly tries to instantiate the device without
the package installed.
However, `quimb` frequently raises a warning message when it is imported
if `kahypar` is not installed. This seems to be a long-standing issue
with the `contengra` package, and causes the following unpleasant
situation: if a user has `quimb` installed on the system (along with
some other packages) and imports `pennylane`, the following warning
message is raised:
```
UserWarning: Couldn't import `kahypar` - skipping from default hyper optimizer and using basic `labels` method instead.
```
**Description of the Change:** We no longer import the `DefaultTensor`
class in `pennylane/devices/__init__.py`.
**Benefits:** Now, the warning message is only raised if the user
explicitly instantiates the device, which makes more sense and is less
ambiguous:
```
qml.device("default.tensor", wires=2)
UserWarning: Couldn't import `kahypar` - skipping from default hyper optimizer and using basic `labels` method instead.
warnings.warn(
```
Furthermore, this also speeds up the pennylane import process.
**Benefits:** Warnings are no longer raised when importing pennylane if
`cotengra` is installed along with other packages.
**Possible Drawbacks:** The documentation of the `DefaultTensor` device,
as listed in the 'next generation devices', is slightly different:
**Related GitHub Issues:** #6893
[sc-83272]
PietropaoloFrisoni
approved these changes
Apr 7, 2025
**Context:** The recent paper [Rise of conditionally clean ancillae for optimizing quantum circuits](https://arxiv.org/abs/2407.17966) presents an improved decomposition of MCX gates using conditionally clean ancilla qubits. The proposed method achieves the following trade-offs in terms of Toffoli gate count and circuit depth: - 2n − 3 Toffoli and O(n) depth using 1 clean ancilla - 2n − 3 Toffoli and O(log(n)) depth using 2 clean ancilla - 4n − 8 Toffoli and O(n) depth using 1 dirty ancilla - 4n - 8 Toffoli and O(log(n)) depth using 2 dirty ancilla This approach is currently optimal regarding Toffoli count (for 1 and 2 ancillae) and significantly improves circuit depth. **Description of the Change:** Added `_decompose_mcx_with_two_workers` and `_decompose_mcx_with_one_worker_kg24` decompositions. **Benefits:** Extend support to: - `len(work_wires) == 2`, enabling a more efficient decomposition - Improved decomposition for `len(work_wires) == 1`, reducing circuit depth. **Related GitHub Issues:** Implements #6997 --------- Co-authored-by: Jay Soni <jbsoni@uwaterloo.ca> Co-authored-by: Jay Soni <jay@xanadu.ai> Co-authored-by: Astral Cai <astralcai@gmail.com>
andrijapau
approved these changes
Apr 7, 2025
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Amazing! Great work @isaacdevlugt. If anything comes up during QA we can always modify this document. 😄
isaacdevlugt
commented
Apr 7, 2025
isaacdevlugt
commented
Apr 7, 2025
isaacdevlugt
commented
Apr 7, 2025
isaacdevlugt
commented
Apr 7, 2025
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Context: Documentation for program capture (sc-84388)
Description of the Change: Adds a sharp bits page for program capture features (or lack thereof!)
Benefits: Explains the caveats, nuances, and edge cases associated with using this very new feature in PennyLane
Possible Drawbacks: Hopefully none :)
Related GitHub Issues: