[WIP] Get site_packages path with pybind11 API in C++ backend #701
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## master #701 +/- ##
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- Coverage 95.11% 95.06% -0.06%
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* Remove CPhase; Tidy up NDPermuter, gh scripts, and doc * Add release_notes to docs * Auto update version from '0.37.0-dev0' to '0.37.0-dev2' * Update changelog * Auto update version from '0.37.0-dev2' to '0.37.0-dev3' * trigger ci * Revert change in config.h * Update * Update * Update * Auto update version from '0.37.0-dev3' to '0.37.0-dev4' * Fix _version * Auto update version from '0.37.0-dev6' to '0.37.0-dev7' * Update format * trigger ci --------- Co-authored-by: ringo-but-quantum <[email protected]>
* Update LQ's mcm workflow. * Auto update version from '0.37.0-dev6' to '0.37.0-dev7' * Update changelog * trigger ci * trigger ci * Fix paths-ignore for python tests. * Update .github/CHANGELOG.md * Auto update version from '0.37.0-dev7' to '0.37.0-dev8' --------- Co-authored-by: ringo-but-quantum <[email protected]>
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------------------------------------------------------------------------------------------------------------
**Context:**
Add `aarch64` wheels recipes for `"lightning.gpu"`.
**Description of the Change:**
**Benefits:**
Users can pip install LGPU on ARM boxes.
**Possible Drawbacks:**
**Related GitHub Issues:**
[sc-60048]
[sc-60750]
---------
Co-authored-by: ringo-but-quantum <[email protected]>
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------------------------------------------------------------------------------------------------------------
**Context:**
**Description of the Change:**
**Benefits:**
**Possible Drawbacks:**
**Related GitHub Issues:**
---------
Co-authored-by: ringo-but-quantum <[email protected]>
…legacy device for MPI LGPU (#864) ### 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 [`tests`](../tests) 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 `.github/CHANGELOG.md` file, summarizing the change, and including a link back to the PR. - [x] Ensure that code is properly formatted by running `make format`. When all the above are checked, delete everything above the dashed line and fill in the pull request template. ------------------------------------------------------------------------------------------------------------ **Context:** - PR PennyLaneAI/pennylane#6021 removed code duplication for `BasisEmbedding` and `BasisState`. As the result `BasisState` no longer decomposes to `BasisStatePreparation`. This PR updates Python unit tests to support this generalization of basis state preparation. - PR PennyLaneAI/pennylane#6046 added a facade wrapper class for "legacy" devices. This PR is a follow up to PR #839 updating Multi-GPU LGPU device and tests. **Description of the Change:** **Benefits:** **Possible Drawbacks:** **Related GitHub Issues:** --------- Co-authored-by: ringo-but-quantum <[email protected]>
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------------------------------------------------------------------------------------------------------------
**Context:**
[SC-71278]
Add `qml.StatePrep()` and `qml.QubitStateVector()` support to
`lightning.tensor`. Note that `svd` decomposition is conduct by `numpy`
in the python layer based on the fact that `svd` operation with `numpy`
on cpu is faster than `cutensornet` on `A100` GPU if the `bond
dimension` is less than `256`. For more info, please visit
[here](https://developer.nvidia.com/cuquantum-sdk).
**Description of the Change:**
**Benefits:**
**Possible Drawbacks:**
**Related GitHub Issues:**
---------
Co-authored-by: ringo-but-quantum <[email protected]>
Co-authored-by: Vincent Michaud-Rioux <[email protected]>
Co-authored-by: Ali Asadi <[email protected]>
Co-authored-by: Lee James O'Riordan <[email protected]>
Co-authored-by: Josh Izaac <[email protected]>
Co-authored-by: Lee J. O'Riordan <[email protected]>
Co-authored-by: Pietropaolo Frisoni <[email protected]>
Co-authored-by: erick-xanadu <[email protected]>
Co-authored-by: Astral Cai <[email protected]>
Co-authored-by: Amintor Dusko <[email protected]>
Co-authored-by: Shiro-Raven <[email protected]>
Co-authored-by: albi3ro <[email protected]>
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------------------------------------------------------------------------------------------------------------
**Context:**
PR PennyLaneAI/pennylane#6042 adds support for
multi-controlled zyz.
For Lightning, this means that custom decompositions to special zyz with
multiple controlled wires will be supported.
**Description of the Change:**
- Add tests for adjoint jacobian of the controlled gate with multiple
controlled_wires.
**Benefits:**
**Possible Drawbacks:**
**Related GitHub Issues:**
[sc-67711]
---------
Co-authored-by: ringo-but-quantum <[email protected]>
**Context:** The kokkos simulator needs to override the default implementation for setting the state and basis state. **Description of the Change:** Override the default implementation for setting the state and basis state. --------- Co-authored-by: ringo-but-quantum <[email protected]> Co-authored-by: Vincent Michaud-Rioux <[email protected]> Co-authored-by: Vincent Michaud-Rioux <[email protected]> Co-authored-by: Amintor Dusko <[email protected]> Co-authored-by: Ali Asadi <[email protected]>
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------------------------------------------------------------------------------------------------------------
**Context:** Pybind11's 2.12 release is the first version to officially
support Numpy 2.0. This PR updates Lightning's pybind11 version to
ensure compatibility to/from numpy arrays.
**Description of the Change:** Updates Pybind11 version to the latest
(2.13.5)
**Benefits:** Ensures direct compatibility with numpy 2.x
**Possible Drawbacks:** None.
**Related GitHub Issues:**
---------
Co-authored-by: ringo-but-quantum <[email protected]>
**Context:** Tooling Team has requested that all CI checks for draft PRs across all PennyLane repos are to be disabled to free up GH runner resources. [SC Story](https://app.shortcut.com/xanaduai/story/66346/disable-ci-for-pl-draft-prs). **Description of the Change:** Conditions for checking whether a PR is in draft state have been added to existing workflows. **Benefits:** Reduces GH runner usage. **Possible Drawbacks:** Some development steps are deferred to PRs in "ready for review" state only. **Related GitHub Issues:** N/A ### Verification: Created this PR as draft, only lightweight CI checks use GitHub runners (formatting, changelog reminder). When marked as "ready for review" the CI checks ran (excluding wheel builds and multi-gpu runners). When labels added for wheel builds and multi-gpu runners all CI checks ran.  --------- Co-authored-by: ringo-but-quantum <[email protected]> Co-authored-by: Lee James O'Riordan <[email protected]>
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------------------------------------------------------------------------------------------------------------
**Context:**
**Description of the Change:**
**Benefits:**
**Possible Drawbacks:**
**Related GitHub Issues:**
---------
Co-authored-by: ringo-but-quantum <[email protected]>
**Context:** We have several occurrences of missing std namespace. **Description of the Change:** Add std namespace to usual suspects. **Benefits:** **Possible Drawbacks:** **Related GitHub Issues:** [sc-71935] --------- Co-authored-by: ringo-but-quantum <[email protected]> Co-authored-by: Vincent Michaud-Rioux <[email protected]> Co-authored-by: Luis Alfredo Nuñez Meneses <[email protected]>
**Context:** Update compatibility cron jobs for v0.38.0 release. **Description of the Change:** **Benefits:** **Possible Drawbacks:** **Related GitHub Issues:** --------- Co-authored-by: ringo-but-quantum <[email protected]>
…ation folder (#874) **Context:** `configure_pyproject_toml.py` assumes it is being invoked from the root directory of Lightning. But the reality is that it can be invoked from anywhere else. **Description of the Change:** Calculate safely the root folder by traversing back the folder structure starting from the current file location. **Benefits:** The file can be invoked from any place. --------- Co-authored-by: ringo-but-quantum <[email protected]>
### Before submitting
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------------------------------------------------------------------------------------------------------------
**Context:**
[SC-72517]
Current implementation of `applyOperation` avoid the overhead of a
`cutensornetStateUpdateTensorOperator` call and the creation of a new
`DataBuffer` object. A new `gate_ids_` private data is added for the
quick generation of a new key, which does not exist in the `gate_cache`.
**Description of the Change:**
**Benefits:**
**Possible Drawbacks:**
**Related GitHub Issues:**
---------
Co-authored-by: ringo-but-quantum <[email protected]>
Co-authored-by: Luis Alfredo Nuñez Meneses <[email protected]>
updated changelog and _version.py --------- Co-authored-by: vincentmr <[email protected]> Co-authored-by: Amintor Dusko <[email protected]> Co-authored-by: Vincent Michaud-Rioux <[email protected]> Co-authored-by: Lee James O'Riordan <[email protected]> Co-authored-by: paul0403 <[email protected]> Co-authored-by: ringo-but-quantum <[email protected]> Co-authored-by: Ali Asadi <[email protected]> Co-authored-by: Raul Torres <[email protected]>
### Before submitting
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------------------------------------------------------------------------------------------------------------
**Context:**
Following the design of *Lightning Qubit* uses the New Device API end to
end, we can now migrate `lightning.kokkos` device to the new device API.
**Description of the Change:**
Migration of Lightning Kokkos to integrate the New device API. The list
of main features changes are
- Add the `state_vector`, `measurements`, and `adjoint-jacobian` classes
for `lightning.kokkos`
- Add the `simulate`, `jacobian`, `simulate_and_jacobian`, `vjp`, and
`simulate_and_vjp` `methods to lighting.kokkos`
- Update unit/integration tests for the new device API to work with
`lightning.kokkos`
- Check the full support for sampling in full parity with Lightning
Qubit
- Replace the old device API for Lightning Kokkos.
**Benefits:**
Full integration of **Lightning kokkos** with the new device API.
**Possible Drawbacks:**
**Related GitHub Issues:**
[sc-59207], [sc-68825]
---------
Co-authored-by: ringo-but-quantum <[email protected]>
Co-authored-by: Amintor Dusko <[email protected]>
Co-authored-by: Vincent Michaud-Rioux <[email protected]>
Co-authored-by: Ali Asadi <[email protected]>
**Context:** GitHub Actions need to be updated. **Description of the Change:** GH detected a [high-severity](GHSA-cxww-7g56-2vh6) vulnerability, with the download artifact action. **Benefits:** **Possible Drawbacks:** **Related GitHub Issues:** [sc-72698] --------- Co-authored-by: ringo-but-quantum <[email protected]>
…okkos (#878) **Context:** Catalyst recently fixed a bug where recycling a device when a previous execution involves stateprep causes a crash: PennyLaneAI/catalyst#1047 The fix is made to `lightning.qubit`, and thus we need to fix it for `lightning.kokkos` here as well. **Description of the Change:** In `LightningKokkosSimulator`, `SetState` and `SetBasisState` now correctly query the DevQubits from the SimQubits in its qubit_manger's map. **Benefits:** We can now have multiple qnode functions involving stateprep in a workflow **Possible Drawbacks:** **Related GitHub Issues:** PennyLaneAI/catalyst#1044 --------- Co-authored-by: paul0403 <[email protected]> Co-authored-by: ringo-but-quantum <[email protected]> Co-authored-by: Ali Asadi <[email protected]>
Please complete the following checklist when submitting a PR:
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------------------------------------------------------------------------------------------------------------
**Context:**
Parallelizing over observables can accelerate adjoint Jacobian
calculations' backward pass. This PR revisits our implementation for
L-Qubit and L-GPU which are the two devices that support it. Certain
observables like Hamiltonian, PauliSentence, and LinearCombination can
be split into many observables, enabling the distribution of the cost of
expectation value computation. This strategy is initiated by the
serializer which partitions the observables if `split_obs` is not
`False`. The serializer proceeds to a complete partitioning, meaning a
1000-PauliWord PauliSentence is partitioned into a 1000 PauliWords. We
note in passing that L-Qubit does not split observables since it does
not pass a `split_obs` value to `_process_jacobian_tape`. This is
wasteful because we end up with either of two situations:
- The Jacobian is computed N processes (threads, devices, etc.) at a
time which results in a lot of duplicate computation (forward/backward
passes are repeated and the results combined);
- The Jacobian is parallelized over all observables, each of which
requires a state vector copy which increases the memory requirements by
as much.
We explore chunking instead of full partitioning for
LinearCombination-like objects, meaning a 1000-PauliWord PauliSentence
is partitioned into four 250-PauliWords PauliSentences if we parallelize
over 4 processes.
**Description of the Change:**
Modify the serializer to chunk LinearCombination-like objects if
`self.split_obs` is truthy.
Correctly route `_batch_obs` such that L-Qubit splits observables.
Enhance/adapt tests.
**Analysis:**
**Lightning-Qubit**
`applyObservable` is a bottleneck for somewhat large linear combinations
(say 100s or 1000s of terms). Chunking isn't helpful for a circuit like
```
@qml.qnode(dev, diff_method="adjoint")
def c(weights):
qml.templates.AllSinglesDoubles(weights, wires, hf_state, singles, doubles)
return qml.expval(ham)
```
because L-Qubit's `applyObservable` method is parallelized over terms
for a single `Hamiltonian` observable. Chunking in this case is
counter-productive because it requires extra state vectors, extra
backward passes, etc.
For a circuit like however
```
@qml.qnode(dev, diff_method="adjoint")
def c(weights):
qml.templates.AllSinglesDoubles(weights, wires, hf_state, singles, doubles)
return np.array([qml.expval(ham), qml.expval(qml.PauliZ(0))])
```
`applyObservable` is parallelized over observables, which only scales up
to 2 threads, and with poor load-balance. In this case, it is better to
split the observable, which is what the current changes do.
| mol | master-serial | master-batched | chunk-serial | chunk-batched |
| --- | ------------- | -------------- | ------------ | ------------- |
| CH4 | 1.793e+01 | 1.330e+01 | 1.819e+01 | 8.040e+00 |
| Li2 | 5.333e+01 | 3.354e+01 | 5.289e+01 | 1.839e+01 |
| CO | 9.817e+01 | 5.945e+01 | 9.619e+01 | 2.559e+01 |
| H10 | 1.220e+02 | 7.317e+01 | 1.182e+02 | 3.305e+01 |
So for this circuit the current PR yields speeds-up ranging from 1.5x to
>2x by using obs-batching + chunking (compared with the previous
obs-batching).
**Lightning-GPU**
Lightning-GPU splits the observables as soon as `batch_obs` is true. The
current code splits a Hamiltonian into all its individual terms, which
is quite inefficient and induces a lot of redundant backward passes.
This is visible benchmarking the circuit
```
@qml.qnode(dev, diff_method="adjoint")
def c(weights):
qml.templates.AllSinglesDoubles(weights, wires, hf_state, singles, doubles)
return qml.expval(ham)
```
| mol | master-serial | master-batched | chunk-serial | chunk-batched |
| --- | ------------- | -------------- | ------------ | ------------- |
| CH4 | 1.463e+01 | forever | 5.583e+00 | 3.405e+00 |
| Li2 | 1.201e+01 | forever | 5.284e+00 | 2.658e+00 |
| CO | 2.357e+01 | forever | 4.716e+00 | 4.577e+00 |
| H10 | 2.992e+01 | forever | 5.476e+00 | 5.469e+00 |
| HCN | 8.622e+01 | forever | 3.144e+01 | 2.452e+01 |
The batched L-GPU runs are using 2 x A100 GPUs on ISAIC. The speed-ups
for batched versus serial are OK, but most important is the optimization
of `Hamiltonian::applyInPlace` which brings about nice speed-ups between
master and this PR.
**Related GitHub Issues:**
---------
Co-authored-by: ringo-but-quantum <[email protected]>
Co-authored-by: Amintor Dusko <[email protected]>
Co-authored-by: AmintorDusko <[email protected]>
… stable installation of LQ & LK (#890) ### 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 [`tests`](../tests) 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 `.github/CHANGELOG.md` file, summarizing the change, and including a link back to the PR. - [ ] Ensure that code is properly formatted by running `make format`. When all the above are checked, delete everything above the dashed line and fill in the pull request template. ------------------------------------------------------------------------------------------------------------ **Context:** **Description of the Change:** **Benefits:** **Possible Drawbacks:** **Related GitHub Issues:** --------- Co-authored-by: ringo-but-quantum <[email protected]> Co-authored-by: Amintor Dusko <[email protected]>
### Before submitting
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------------------------------------------------------------------------------------------------------------
**Context:**
Add controlled gate support to `lightning.tensor`. This could improve
the performance of `lightning.tensor` by avoiding full gate matrix build
for controlled gates. However, as of v24.08, cutensornet only supports
controlled gates with only 1 wire target. [SC-72522]
**Description of the Change:**
**Benefits:**
**Possible Drawbacks:**
**Related GitHub Issues:**
---------
Co-authored-by: ringo-but-quantum <[email protected]>
Co-authored-by: Vincent Michaud-Rioux <[email protected]>
Co-authored-by: Ali Asadi <[email protected]>
### Before submitting
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------------------------------------------------------------------------------------------------------------
**Context:**
**Description of the Change:**
**Benefits:**
**Possible Drawbacks:**
**Related GitHub Issues:**
### Before submitting
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------------------------------------------------------------------------------------------------------------
**Context:** This PR removes support for Python 3.9 and migrates to 3.10
as the oldest supported version.
**Description of the Change:**
**Benefits:**
**Possible Drawbacks:**
**Related GitHub Issues:**
---------
Co-authored-by: ringo-but-quantum <[email protected]>
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[SC-53754]
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