From eae036fc8f84c9aef4248e32249f66103ffdb8f0 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Sat, 3 May 2025 03:26:35 +0000 Subject: [PATCH 1/2] Check in artifacts-> Update test durations --- .github/durations/core_tests_durations.json | 60183 +++++++++--------- .github/durations/jax_tests_durations.json | 33734 +++++----- .github/durations/tf_tests_durations.json | 8679 +-- 3 files changed, 51378 insertions(+), 51218 deletions(-) diff --git a/.github/durations/core_tests_durations.json b/.github/durations/core_tests_durations.json index 11ef7344dfd..895564d0661 100644 --- a/.github/durations/core_tests_durations.json +++ b/.github/durations/core_tests_durations.json @@ -1,30027 +1,30160 @@ { - "bose/test_binary_mapping.py::TestBoseSentencesMapping::test_binary_mapping_bosesentence_ps[bose_op0-4-result0]": 0.00987637299999733, - "bose/test_binary_mapping.py::TestBoseSentencesMapping::test_binary_mapping_bosesentence_ps[bose_op1-4-result1]": 0.0271142969999687, - 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.../transforms/test_capture_map_wires.py | 2 +- .../test_capture_merge_amplitude_embedding.py | 2 +- .../test_capture_merge_rotations.py | 2 +- .../test_capture_single_qubit_fusion.py | 2 +- .../transforms/test_capture_unitary_to_rot.py | 8 +- .../capture/transforms/test_mcm_execution.py | 4 +- tests/capture/workflow/test_capture_qnode.py | 12 +- .../default_qubit/test_default_qubit.py | 12 +- tests/devices/qubit/test_simulate.py | 3 +- .../test_qutrit_mixed_sampling.py | 3 +- tests/devices/test_default_mixed.py | 64 + tests/ftqc/test_parametric_mid_measure.py | 38 + tests/ftqc/test_primitives.py | 4 +- tests/gradients/core/test_pulse_gradient.py | 10 +- tests/python_compiler/test_quantum_dialect.py | 104 + .../test_convert_openfermion.py | 4 + .../test_mottonen_state_prep.py | 62 +- .../templates/test_subroutines/test_grover.py | 14 +- .../test_subroutines/test_select_pauli_rot.py | 33 +- tests/test_debugging.py | 12 +- .../transforms/core/test_transform_program.py | 6 +- 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0.07157357500011585, + "workflow/test_resolve_interface.py::test_auto_with_tf": 0.006019192999929146, + "workflow/test_resolve_interface.py::test_tf_autograph": 0.0027773740000611724 } diff --git a/.github/stable/all_interfaces.txt b/.github/stable/all_interfaces.txt index 9075f97be49..74c75ba8068 100644 --- a/.github/stable/all_interfaces.txt +++ b/.github/stable/all_interfaces.txt @@ -8,7 +8,7 @@ black==25.1.0 cachetools==5.5.2 certifi==2025.4.26 cfgv==3.4.0 -charset-normalizer==3.4.1 +charset-normalizer==3.4.2 clarabel==0.10.0 click==8.1.8 contourpy==1.3.2 @@ -38,7 +38,7 @@ isort==5.13.2 jax==0.4.28 jaxlib==0.4.28 Jinja2==3.1.6 -joblib==1.4.2 +joblib==1.5.0 keras==3.9.2 kiwisolver==1.4.8 lazy-object-proxy==1.11.0 @@ -74,7 +74,7 @@ osqp==1.0.3 packaging==25.0 pandas==2.2.3 pathspec==0.12.1 -PennyLane_Lightning==0.42.0.dev7 +PennyLane_Lightning==0.42.0.dev13 pillow==11.2.1 platformdirs==4.3.7 pluggy==1.5.0 @@ -112,7 +112,7 @@ tensorboard==2.18.0 tensorboard-data-server==0.7.2 tensorflow==2.18.1 tensorflow-io-gcs-filesystem==0.37.1 -termcolor==3.0.1 +termcolor==3.1.0 tf_keras==2.18.0 toml==0.10.2 tomli==2.2.1 diff --git a/.github/stable/core.txt b/.github/stable/core.txt index 946e84f7f62..dcddde13736 100644 --- a/.github/stable/core.txt +++ b/.github/stable/core.txt @@ -7,7 +7,7 @@ black==25.1.0 cachetools==5.5.2 certifi==2025.4.26 cfgv==3.4.0 -charset-normalizer==3.4.1 +charset-normalizer==3.4.2 clarabel==0.10.0 click==8.1.8 contourpy==1.3.2 @@ -30,7 +30,7 @@ idna==3.10 iniconfig==2.1.0 isort==5.13.2 Jinja2==3.1.6 -joblib==1.4.2 +joblib==1.5.0 kiwisolver==1.4.8 lazy-object-proxy==1.11.0 markdown-it-py==3.0.0 @@ -46,7 +46,7 @@ osqp==1.0.3 packaging==25.0 pandas==2.2.3 pathspec==0.12.1 -PennyLane_Lightning==0.42.0.dev7 +PennyLane_Lightning==0.42.0.dev13 pillow==11.2.1 platformdirs==4.3.7 pluggy==1.5.0 @@ -78,7 +78,7 @@ scs==3.2.7.post2 six==1.17.0 smmap==5.0.2 tach==0.28.5 -termcolor==3.0.1 +termcolor==3.1.0 toml==0.10.2 tomli==2.2.1 tomli_w==1.2.0 diff --git a/.github/stable/external.txt b/.github/stable/external.txt index 1abec90825b..806ddfa92ec 100644 --- a/.github/stable/external.txt +++ b/.github/stable/external.txt @@ -23,7 +23,7 @@ cachetools==5.5.2 certifi==2025.4.26 cffi==1.17.1 cfgv==3.4.0 -charset-normalizer==3.4.1 +charset-normalizer==3.4.2 chex==0.1.89 cirq-core==1.4.0 clarabel==0.10.0 @@ -32,7 +32,7 @@ comm==0.2.2 contourpy==1.3.2 cotengra==0.7.2 coverage==7.8.0 -cryptography==44.0.2 +cryptography==44.0.3 cvxopt==1.3.2 cvxpy==1.6.5 cycler==0.12.1 @@ -57,7 +57,7 @@ flatbuffers==25.2.10 fonttools==4.57.0 fqdn==1.5.1 fxpmath==0.4.9 -galois==0.4.5 +galois==0.4.6 gast==0.6.0 gitdb==4.0.12 GitPython==3.1.44 @@ -69,7 +69,7 @@ h5py==3.13.0 httpcore==1.0.9 httpx==0.28.1 ibm-cloud-sdk-core==3.23.0 -ibm-platform-services==0.63.0 +ibm-platform-services==0.64.0 identify==2.6.10 idna==3.10 importlib_metadata==8.7.0 @@ -84,7 +84,7 @@ jax==0.4.28 jaxlib==0.4.28 jedi==0.19.2 Jinja2==3.1.6 -joblib==1.4.2 +joblib==1.5.0 json5==0.12.0 jsonpointer==3.0.0 jsonschema==4.23.0 @@ -118,7 +118,7 @@ ml_dtypes==0.5.1 mpmath==1.3.0 mypy_extensions==1.1.0 namex==0.0.9 -narwhals==1.36.0 +narwhals==1.37.1 nbclient==0.10.2 nbconvert==7.16.6 nbformat==5.10.4 @@ -140,10 +140,10 @@ pandocfilters==1.5.1 parso==0.8.4 pathspec==0.12.1 pbr==6.1.1 -PennyLane-Catalyst==0.12.0.dev15 -PennyLane-qiskit @ git+https://github.com/PennyLaneAI/pennylane-qiskit.git@f6f99a459cd4b329f993ed2008b02e91bcbb520e -PennyLane_Lightning==0.42.0.dev0 -PennyLane_Lightning_Kokkos==0.42.0.dev0 +PennyLane-Catalyst==0.12.0.dev20 +PennyLane-qiskit @ git+https://github.com/PennyLaneAI/pennylane-qiskit.git@cbbf8120d64cfc598f8481d84ac99c6383dce641 +PennyLane_Lightning==0.42.0.dev11 +PennyLane_Lightning_Kokkos==0.42.0.dev11 pexpect==4.9.0 pillow==11.2.1 platformdirs==4.3.7 @@ -161,8 +161,8 @@ pure_eval==0.2.3 py==1.11.0 py-cpuinfo==9.0.0 pycparser==2.22 -pydantic==2.11.3 -pydantic_core==2.33.1 +pydantic==2.11.4 +pydantic_core==2.33.2 pydot==3.0.4 Pygments==2.19.1 PyJWT==2.10.1 @@ -189,8 +189,8 @@ qiskit-aer==0.16.0 qiskit-ibm-provider==0.11.0 qiskit-ibm-runtime==0.29.0 qref==0.9.0 -qsharp==1.15.0 -qsharp-widgets==1.15.0 +qsharp==1.16.0 +qsharp-widgets==1.16.0 qualtran==0.6.1 quimb==1.10.0 referencing==0.36.2 @@ -222,7 +222,7 @@ tensorboard==2.18.0 tensorboard-data-server==0.7.2 tensorflow==2.18.1 tensorflow-io-gcs-filesystem==0.37.1 -termcolor==3.0.1 +termcolor==3.1.0 terminado==0.18.1 tf_keras==2.18.0 tinycss2==1.4.0 diff --git a/.github/stable/jax.txt b/.github/stable/jax.txt index 1d774f561be..715b212a552 100644 --- a/.github/stable/jax.txt +++ b/.github/stable/jax.txt @@ -7,7 +7,7 @@ black==25.1.0 cachetools==5.5.2 certifi==2025.4.26 cfgv==3.4.0 -charset-normalizer==3.4.1 +charset-normalizer==3.4.2 clarabel==0.10.0 click==8.1.8 contourpy==1.3.2 @@ -32,7 +32,7 @@ isort==5.13.2 jax==0.4.28 jaxlib==0.4.28 Jinja2==3.1.6 -joblib==1.4.2 +joblib==1.5.0 kiwisolver==1.4.8 lazy-object-proxy==1.11.0 markdown-it-py==3.0.0 @@ -50,7 +50,7 @@ osqp==1.0.3 packaging==25.0 pandas==2.2.3 pathspec==0.12.1 -PennyLane_Lightning==0.42.0.dev7 +PennyLane_Lightning==0.42.0.dev13 pillow==11.2.1 platformdirs==4.3.7 pluggy==1.5.0 @@ -82,7 +82,7 @@ scs==3.2.7.post2 six==1.17.0 smmap==5.0.2 tach==0.28.5 -termcolor==3.0.1 +termcolor==3.1.0 toml==0.10.2 tomli==2.2.1 tomli_w==1.2.0 diff --git a/.github/stable/tf.txt b/.github/stable/tf.txt index 3a9eaa3683a..dc0fc03fce5 100644 --- a/.github/stable/tf.txt +++ b/.github/stable/tf.txt @@ -8,7 +8,7 @@ black==25.1.0 cachetools==5.5.2 certifi==2025.4.26 cfgv==3.4.0 -charset-normalizer==3.4.1 +charset-normalizer==3.4.2 clarabel==0.10.0 click==8.1.8 contourpy==1.3.2 @@ -35,7 +35,7 @@ idna==3.10 iniconfig==2.1.0 isort==5.13.2 Jinja2==3.1.6 -joblib==1.4.2 +joblib==1.5.0 keras==3.9.2 kiwisolver==1.4.8 lazy-object-proxy==1.11.0 @@ -58,7 +58,7 @@ osqp==1.0.3 packaging==25.0 pandas==2.2.3 pathspec==0.12.1 -PennyLane_Lightning==0.42.0.dev7 +PennyLane_Lightning==0.42.0.dev13 pillow==11.2.1 platformdirs==4.3.7 pluggy==1.5.0 @@ -95,7 +95,7 @@ tensorboard==2.18.0 tensorboard-data-server==0.7.2 tensorflow==2.18.1 tensorflow-io-gcs-filesystem==0.37.1 -termcolor==3.0.1 +termcolor==3.1.0 tf_keras==2.18.0 toml==0.10.2 tomli==2.2.1 diff --git a/.github/stable/torch.txt b/.github/stable/torch.txt index a235be5bb8f..9d2bc29ecf6 100644 --- a/.github/stable/torch.txt +++ b/.github/stable/torch.txt @@ -7,7 +7,7 @@ black==25.1.0 cachetools==5.5.2 certifi==2025.4.26 cfgv==3.4.0 -charset-normalizer==3.4.1 +charset-normalizer==3.4.2 clarabel==0.10.0 click==8.1.8 contourpy==1.3.2 @@ -31,7 +31,7 @@ idna==3.10 iniconfig==2.1.0 isort==5.13.2 Jinja2==3.1.6 -joblib==1.4.2 +joblib==1.5.0 kiwisolver==1.4.8 lazy-object-proxy==1.11.0 markdown-it-py==3.0.0 @@ -60,7 +60,7 @@ osqp==1.0.3 packaging==25.0 pandas==2.2.3 pathspec==0.12.1 -PennyLane_Lightning==0.42.0.dev7 +PennyLane_Lightning==0.42.0.dev13 pillow==11.2.1 platformdirs==4.3.7 pluggy==1.5.0 @@ -93,7 +93,7 @@ six==1.17.0 smmap==5.0.2 sympy==1.13.1 tach==0.28.5 -termcolor==3.0.1 +termcolor==3.1.0 toml==0.10.2 tomli==2.2.1 tomli_w==1.2.0 diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index 618c5effdf8..a4f5fbb56e6 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -37,7 +37,7 @@ jobs: name: Determine runner type to use uses: ./.github/workflows/determine-workflow-runner.yml with: - default_runner: ubuntu-latest + default_runner: ubuntu-24.04 force_large_runner: ${{ github.event_name == 'merge_group' }} sphinx: diff --git a/.github/workflows/interface-dependency-versions.yml b/.github/workflows/interface-dependency-versions.yml index d9b36f4842c..ebbbdc9626e 100644 --- a/.github/workflows/interface-dependency-versions.yml +++ b/.github/workflows/interface-dependency-versions.yml @@ -16,7 +16,7 @@ on: description: The version of JAX to use for testing required: false type: string - default: '0.4.28' + default: '0.5.3' tensorflow_version: description: The version of TensorFlow to use for testing required: false diff --git a/.github/workflows/interface-unit-tests.yml b/.github/workflows/interface-unit-tests.yml index 34945695684..f9cdc84479e 100644 --- a/.github/workflows/interface-unit-tests.yml +++ b/.github/workflows/interface-unit-tests.yml @@ -492,7 +492,7 @@ jobs: pytest_additional_args: ${{ needs.warnings-as-errors-setup.outputs.pytest_warning_args }} pytest_xml_file_path: '${{ inputs.job_name_prefix }}external-libraries-tests (${{ matrix.python-version }})${{ inputs.job_name_suffix }}.xml' additional_pip_packages: | - pyzx matplotlib stim quimb mitiq ply optax scipy-openblas32>=0.3.26 qualtran + pyzx matplotlib stim quimb mitiq ply optax scipy-openblas32>=0.3.26 qualtran xdsl git+https://github.com/PennyLaneAI/pennylane-qiskit.git@master ${{ needs.default-dependency-versions.outputs.jax-version }} ${{ needs.default-dependency-versions.outputs.tensorflow-version }} diff --git a/.github/workflows/rtd-check.yml b/.github/workflows/rtd-check.yml index f426e6a349e..a23a3513bcf 100644 --- a/.github/workflows/rtd-check.yml +++ b/.github/workflows/rtd-check.yml @@ -15,7 +15,7 @@ on: jobs: rtd_check: - runs-on: ubuntu-22.04 + runs-on: ubuntu-24.04 env: PYTHON_VERSION: "3.10" # Space separated list of packages to install for each package manager diff --git a/.github/workflows/unit-test.yml b/.github/workflows/unit-test.yml index 613da7c9c8e..d31a3242da7 100644 --- a/.github/workflows/unit-test.yml +++ b/.github/workflows/unit-test.yml @@ -202,7 +202,7 @@ jobs: COV_CORE_DATAFILE: .coverage.eager TF_USE_LEGACY_KERAS: "1" # sets to use tf-keras (Keras2) instead of keras (Keras3) when running TF tests # Calling PyTest by invoking Python first as that adds the current directory to sys.path - run: python -m pytest ${{ inputs.pytest_test_directory }} ${{ steps.pytest_args.outputs.args }} ${{ env.PYTEST_MARKER }} + run: python -m pytest ${{ inputs.pytest_test_directory }} ${{ steps.pytest_args.outputs.args }} ${{ env.PYTEST_MARKER }} --timeout=500 - name: Freeze dependencies shell: bash diff --git a/.github/workflows/upload.yml b/.github/workflows/upload.yml index 93b016728da..e6a590a7689 100644 --- a/.github/workflows/upload.yml +++ b/.github/workflows/upload.yml @@ -37,9 +37,8 @@ jobs: - name: Build PennyLane wheel run: | - python -m pip install --upgrade pip wheel - pip install -r requirements-ci.txt --upgrade - python setup.py bdist_wheel + python -m pip install build + python -m build --wheel --outdir dist - name: Publish uses: pypa/gh-action-pypi-publish@release/v1 diff --git a/.readthedocs.yml b/.readthedocs.yml index 645be9463c3..eea42d5657d 100644 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -6,7 +6,7 @@ version: 2 build: - os: ubuntu-22.04 + os: ubuntu-24.04 tools: python: "3.10" apt_packages: diff --git a/doc/development/release_notes.md b/doc/development/release_notes.md index cce3109a07c..42867adbfe8 100644 --- a/doc/development/release_notes.md +++ b/doc/development/release_notes.md @@ -5,6 +5,8 @@ This page contains the release notes for PennyLane. .. mdinclude:: ../releases/changelog-dev.md +.. mdinclude:: ../releases/changelog-0.41.1.md + .. mdinclude:: ../releases/changelog-0.41.0.md .. mdinclude:: ../releases/changelog-0.40.0.md diff --git a/doc/releases/changelog-0.41.0.md b/doc/releases/changelog-0.41.0.md index 1f95b80cdd9..d7ab5a46a99 100644 --- a/doc/releases/changelog-0.41.0.md +++ b/doc/releases/changelog-0.41.0.md @@ -1,6 +1,6 @@ :orphan: -# Release 0.41.0 (current release) +# Release 0.41.0

New features since last release

diff --git a/doc/releases/changelog-0.41.1.md b/doc/releases/changelog-0.41.1.md new file mode 100644 index 00000000000..d02794ce730 --- /dev/null +++ b/doc/releases/changelog-0.41.1.md @@ -0,0 +1,14 @@ +:orphan: + +# Release 0.41.1 (current release) + +

Bug fixes 🐛

+ +* A warning is raised if PennyLane is imported and a version of JAX greater than 0.4.28 is installed. + [(#7369)](https://github.com/PennyLaneAI/pennylane/pull/7369) + +

Contributors ✍️

+ +This release contains contributions from (in alphabetical order): + +Pietropaolo Frisoni \ No newline at end of file diff --git a/doc/releases/changelog-dev.md b/doc/releases/changelog-dev.md index c0da879f357..1df4c4769c9 100644 --- a/doc/releases/changelog-dev.md +++ b/doc/releases/changelog-dev.md @@ -120,6 +120,18 @@

Improvements 🛠

+* PennyLane supports `JAX` version 0.5.3. + [(#6919)](https://github.com/PennyLaneAI/pennylane/pull/6919) + +* Computing the angles for uniformly controlled rotations, used in :class:`~.MottonenStatePreparation` + and :class:`~.SelectPauliRot`, now takes much less computational effort and memory. + [(#7377)](https://github.com/PennyLaneAI/pennylane/pull/7377) + +* An experimental quantum dialect written in [xDSL](https://xdsl.dev/index) has been introduced. + This is similar to [Catalyst's MLIR dialects](https://docs.pennylane.ai/projects/catalyst/en/stable/dev/dialects.html#mlir-dialects-in-catalyst), + but it is coded in Python instead of C++. + [(#7357)](https://github.com/PennyLaneAI/pennylane/pull/7357) + * The :func:`~.transforms.cancel_inverses` transform no longer changes the order of operations that don't have shared wires, providing a deterministic output. [(#7328)](https://github.com/PennyLaneAI/pennylane/pull/7328) @@ -168,7 +180,11 @@ interface to maintain a list of special implementations. [(#7327)](https://github.com/PennyLaneAI/pennylane/pull/7327) -* Sphinx version was updated to 8.1. Sphinx is upgraded to version 8.1 and uses Python 3.10. References to intersphinx (e.g. `` or `` are updated to remove the :doc: prefix that is incompatible with sphinx 8.1. [(7212)](https://github.com/PennyLaneAI/pennylane/pull/7212) +* Sphinx version was updated to 8.1. Sphinx is upgraded to version 8.1 and uses Python 3.10. References to intersphinx (e.g. `` or `` are updated to remove the :doc: prefix that is incompatible with sphinx 8.1. + [(7212)](https://github.com/PennyLaneAI/pennylane/pull/7212) + +* Updated GitHub Actions workflows (`rtd.yml`, `readthedocs.yml`, and `docs.yml`) to use `ubuntu-24.04` runners. + [(#7396)](https://github.com/PennyLaneAI/pennylane/pull/7396)

Breaking changes 💔

@@ -230,8 +246,15 @@ Here's a list of deprecations made this release. For a more detailed breakdown o

Internal changes ⚙️

+* A `RuntimeWarning` raised when using versions of JAX > 0.4.28 has been removed. + [(#7398)](https://github.com/PennyLaneAI/pennylane/pull/7398) + +* Wheel releases for PennyLane now follow the `PyPA binary-distribution format _` guidelines more closely. + [(#7382)](https://github.com/PennyLaneAI/pennylane/pull/7382) + * `null.qubit` can now support an optional `track_resources` argument which allows it to record which gates are executed. [(#7226)](https://github.com/PennyLaneAI/pennylane/pull/7226) + [(#7372)](https://github.com/PennyLaneAI/pennylane/pull/7372) * A new internal module, `qml.concurrency`, is added to support internal use of multiprocess and multithreaded execution of workloads. This also migrates the use of `concurrent.futures` in `default.qubit` to this new design. [(#7303)](https://github.com/PennyLaneAI/pennylane/pull/7303) @@ -265,6 +288,16 @@ Here's a list of deprecations made this release. For a more detailed breakdown o

Bug fixes 🐛

+* Fixed a bug in `to_openfermion` where identity qubit-to-wires mapping was not obeyed. + [(#7332)](https://github.com/PennyLaneAI/pennylane/pull/7332) + +* Fixed a bug in the validation of :class:`~.SelectPauliRot` that prevents parameter broadcasting. + [(#7377)](https://github.com/PennyLaneAI/pennylane/pull/7377) + +* Usage of NumPy in `default.mixed` source code has been converted to `qml.math` to avoid + unnecessary dependency on NumPy and to fix a bug that caused an error when using `default.mixed` with PyTorch and GPUs. + [(#7384)](https://github.com/PennyLaneAI/pennylane/pull/7384) + * With program capture enabled (`qml.capture.enable()`), `QSVT` no treats abstract values as metadata. [(#7360)](https://github.com/PennyLaneAI/pennylane/pull/7360) @@ -318,6 +351,11 @@ Here's a list of deprecations made this release. For a more detailed breakdown o * Fixes a bug where the powers of `qml.ISWAP` and `qml.SISWAP` were decomposed incorrectly. [(#7361)](https://github.com/PennyLaneAI/pennylane/pull/7361) +* Returning `MeasurementValue`s from the `ftqc` module's parametric mid-circuit measurements + (`measure_arbitrary_basis`, `measure_x` and `measure_y`) no longer raises an error in circuits + using `diagonalize_mcms`. + [(#7387)](https://github.com/PennyLaneAI/pennylane/pull/7387) +

Contributors ✍️

This release contains contributions from (in alphabetical order): @@ -329,6 +367,9 @@ Lillian Frederiksen, Pietropaolo Frisoni, Korbinian Kottmann, Christina Lee, -Andrija Paurevic, Lee J. O'Riordan, +Mudit Pandey, +Andrija Paurevic, +Kalman Szenes, +David Wierichs, Jake Zaia diff --git a/pennylane/_version.py b/pennylane/_version.py index 1a466d60493..4d53dbf9cdd 100644 --- a/pennylane/_version.py +++ b/pennylane/_version.py @@ -16,4 +16,4 @@ Version number (major.minor.patch[-label]) """ -__version__ = "0.42.0-dev19" +__version__ = "0.42.0-dev25" diff --git a/pennylane/capture/__init__.py b/pennylane/capture/__init__.py index 293ac11ffce..011f19de782 100644 --- a/pennylane/capture/__init__.py +++ b/pennylane/capture/__init__.py @@ -181,7 +181,7 @@ def _(*args, **kwargs): # on use of from capture import AbstractOperator AbstractOperator: type AbstractMeasurement: type -qnode_prim: "jax.core.Primitive" +qnode_prim: "jax.extend.core.Primitive" PlxprInterpreter: type # pylint: disable=redefined-outer-name expand_plxpr_transforms: Callable[[Callable], Callable] # pylint: disable=redefined-outer-name eval_jaxpr: Callable diff --git a/pennylane/capture/base_interpreter.py b/pennylane/capture/base_interpreter.py index 2d4535ebab7..2ab42f446c1 100644 --- a/pennylane/capture/base_interpreter.py +++ b/pennylane/capture/base_interpreter.py @@ -36,7 +36,7 @@ while_loop_prim, ) -FlattenedHigherOrderPrimitives: dict["jax.core.Primitive", Callable] = {} +FlattenedHigherOrderPrimitives: dict["jax.extend.core.Primitive", Callable] = {} """ A dictionary containing flattened style cond, while, and for loop higher order primitives. .. code-block:: @@ -92,8 +92,8 @@ def _fill_in_shape_with_dyn_shape(dyn_shape: tuple["jax.core.Tracer"], shape: tu def jaxpr_to_jaxpr( - interpreter: "PlxprInterpreter", jaxpr: "jax.core.Jaxpr", consts, *args -) -> "jax.core.Jaxpr": + interpreter: "PlxprInterpreter", jaxpr: "jax.extend.core.Jaxpr", consts, *args +) -> "jax.extend.core.ClosedJaxpr": """A convenience utility for converting jaxpr to a new jaxpr via an interpreter.""" f = partial(interpreter.eval, jaxpr, consts) @@ -218,7 +218,7 @@ def _(self, start, stop, step, *invals, jaxpr_body_fn, consts_slice, args_slice) """ _env: dict - _primitive_registrations: dict["jax.core.Primitive", Callable] = {} + _primitive_registrations: dict["jax.extend.core.Primitive", Callable] = {} def __init_subclass__(cls) -> None: cls._primitive_registrations = copy(cls._primitive_registrations) @@ -227,11 +227,13 @@ def __init__(self): self._env = {} @classmethod - def register_primitive(cls, primitive: "jax.core.Primitive") -> Callable[[Callable], Callable]: + def register_primitive( + cls, primitive: "jax.extend.core.Primitive" + ) -> Callable[[Callable], Callable]: """Registers a custom method for handling a primitive Args: - primitive (jax.core.Primitive): the primitive we want custom behavior for + primitive (jax.extend.core.Primitive): the primitive we want custom behavior for Returns: Callable: a decorator for adding a function to the custom registrations map @@ -242,7 +244,7 @@ def register_primitive(cls, primitive: "jax.core.Primitive") -> Callable[[Callab .. code-block:: python - my_primitive = jax.core.Primitive("my_primitve") + my_primitive = jax.extend.core.Primitive("my_primitve") @Interpreter_Type.register(my_primitive) def handle_my_primitive(self: Interpreter_Type, *invals, **params) @@ -258,7 +260,7 @@ def decorator(f: Callable) -> Callable: def read(self, var): """Extract the value corresponding to a variable.""" - return var.val if isinstance(var, jax.core.Literal) else self._env[var] + return var.val if isinstance(var, jax.extend.core.Literal) else self._env[var] def setup(self) -> None: """Initialize the instance before interpreting equations. @@ -294,11 +296,11 @@ def interpret_operation(self, op: "pennylane.operation.Operator"): data, struct = jax.tree_util.tree_flatten(op) return jax.tree_util.tree_unflatten(struct, data) - def interpret_operation_eqn(self, eqn: "jax.core.JaxprEqn"): + def interpret_operation_eqn(self, eqn: "jax.extend.core.JaxprEqn"): """Interpret an equation corresponding to an operator. Args: - eqn (jax.core.JaxprEqn): a jax equation for an operator. + eqn (jax.extend.core.JaxprEqn): a jax equation for an operator. See also: :meth:`~.interpret_operation`. @@ -310,11 +312,11 @@ def interpret_operation_eqn(self, eqn: "jax.core.JaxprEqn"): return self.interpret_operation(op) return op - def interpret_measurement_eqn(self, eqn: "jax.core.JaxprEqn"): + def interpret_measurement_eqn(self, eqn: "jax.extend.core.JaxprEqn"): """Interpret an equation corresponding to a measurement process. Args: - eqn (jax.core.JaxprEqn) + eqn (jax.extend.core.JaxprEqn) See also :meth:`~.interpret_measurement`. @@ -336,11 +338,11 @@ def interpret_measurement(self, measurement: "qml.measurement.MeasurementProcess data, struct = jax.tree_util.tree_flatten(measurement) return jax.tree_util.tree_unflatten(struct, data) - def eval(self, jaxpr: "jax.core.Jaxpr", consts: Sequence, *args) -> list: + def eval(self, jaxpr: "jax.extend.core.Jaxpr", consts: Sequence, *args) -> list: """Evaluate a jaxpr. Args: - jaxpr (jax.core.Jaxpr): the jaxpr to evaluate + jaxpr (jax.extend.core.Jaxpr): the jaxpr to evaluate consts (list[TensorLike]): the constant variables for the jaxpr *args (tuple[TensorLike]): The arguments for the jaxpr. @@ -410,7 +412,7 @@ def wrapper(*args, **kwargs): # pylint: disable=unused-argument @PlxprInterpreter.register_primitive(jax.lax.broadcast_in_dim_p) -def _(self, x, *dyn_shape, shape, broadcast_dimensions): +def _(self, x, *dyn_shape, shape, broadcast_dimensions, sharding): """Handle the broadcast_in_dim primitive created by jnp.ones, jnp.zeros, jnp.full >>> import jax @@ -430,12 +432,14 @@ def _(self, x, *dyn_shape, shape, broadcast_dimensions): # needs custom primitive as jax.core.eval_jaxpr will error out with this new_shape = _fill_in_shape_with_dyn_shape(dyn_shape, shape) - return jax.lax.broadcast_in_dim(x, new_shape, broadcast_dimensions=broadcast_dimensions) + return jax.lax.broadcast_in_dim( + x, new_shape, broadcast_dimensions=broadcast_dimensions, out_sharding=sharding + ) # pylint: disable=unused-argument @PlxprInterpreter.register_primitive(jax.lax.iota_p) -def _(self, *dyn_shape, dimension, dtype, shape): +def _(self, *dyn_shape, dimension, dtype, shape, sharding): """Handle the iota primitive created by jnp.arange >>> import jax @@ -450,7 +454,7 @@ def _(self, *dyn_shape, dimension, dtype, shape): """ # iota is primitive created by jnp.arange new_shape = _fill_in_shape_with_dyn_shape(dyn_shape, shape) - return jax.lax.broadcasted_iota(dtype, new_shape, dimension) + return jax.lax.broadcasted_iota(dtype, new_shape, dimension, out_sharding=sharding) @PlxprInterpreter.register_primitive(adjoint_transform_prim) @@ -710,11 +714,11 @@ def flattened_for( FlattenedHigherOrderPrimitives[for_loop_prim] = flattened_for -def eval_jaxpr(jaxpr: "jax.core.Jaxpr", consts: list, *args) -> list: +def eval_jaxpr(jaxpr: "jax.extend.core.Jaxpr", consts: list, *args) -> list: """A version of ``jax.core.eval_jaxpr`` that can handle creating arrays with dynamic shapes. Args: - jaxpr (jax.core.Jaxpr): a jaxpr + jaxpr (jax.extend.core.Jaxpr): a jaxpr consts (list[TensorLike]): the constants for the jaxpr *args (TensorLike): the arguments for the jaxpr diff --git a/pennylane/capture/capture_diff.py b/pennylane/capture/capture_diff.py index ba7c3846693..697fae460a6 100644 --- a/pennylane/capture/capture_diff.py +++ b/pennylane/capture/capture_diff.py @@ -32,9 +32,9 @@ def _get_grad_prim(): if not has_jax: # pragma: no cover return None - from .custom_primitives import NonInterpPrimitive # pylint: disable=import-outside-toplevel + from .custom_primitives import QmlPrimitive # pylint: disable=import-outside-toplevel - grad_prim = NonInterpPrimitive("grad") + grad_prim = QmlPrimitive("grad") grad_prim.multiple_results = True # pylint: disable=attribute-defined-outside-init grad_prim.prim_type = "higher_order" @@ -69,9 +69,9 @@ def _get_jacobian_prim(): if not has_jax: # pragma: no cover return None - from .custom_primitives import NonInterpPrimitive # pylint: disable=import-outside-toplevel + from .custom_primitives import QmlPrimitive # pylint: disable=import-outside-toplevel - jacobian_prim = NonInterpPrimitive("jacobian") + jacobian_prim = QmlPrimitive("jacobian") jacobian_prim.multiple_results = True # pylint: disable=attribute-defined-outside-init jacobian_prim.prim_type = "higher_order" diff --git a/pennylane/capture/capture_measurements.py b/pennylane/capture/capture_measurements.py index 07ef1c589e6..559cdea27c9 100644 --- a/pennylane/capture/capture_measurements.py +++ b/pennylane/capture/capture_measurements.py @@ -103,14 +103,12 @@ def __eq__(self, other): def __hash__(self): return hash("AbstractMeasurement") - jax.core.raise_to_shaped_mappings[AbstractMeasurement] = lambda aval, _: aval - return AbstractMeasurement def create_measurement_obs_primitive( measurement_type: Type["qml.measurements.MeasurementProcess"], name: str -) -> Optional["jax.core.Primitive"]: +) -> Optional["jax.extend.core.Primitive"]: """Create a primitive corresponding to the input type where the abstract inputs are an operator. Called by default when defining any class inheriting from :class:`~.MeasurementProcess`, and is used to @@ -122,15 +120,15 @@ def create_measurement_obs_primitive( ``"_obs"`` is appended to this name for the name of the primitive. Returns: - Optional[jax.core.Primitive]: A new jax primitive. ``None`` is returned if jax is not available. + Optional[jax.extend.core.Primitive]: A new jax primitive. ``None`` is returned if jax is not available. """ if not has_jax: return None - from .custom_primitives import NonInterpPrimitive # pylint: disable=import-outside-toplevel + from .custom_primitives import QmlPrimitive # pylint: disable=import-outside-toplevel - primitive = NonInterpPrimitive(name + "_obs") + primitive = QmlPrimitive(name + "_obs") primitive.prim_type = "measurement" @primitive.def_impl @@ -149,7 +147,7 @@ def _(*_, **__): def create_measurement_mcm_primitive( measurement_type: Type["qml.measurements.MeasurementProcess"], name: str -) -> Optional["jax.core.Primitive"]: +) -> Optional["jax.extend.core.Primitive"]: """Create a primitive corresponding to the input type where the abstract inputs are classical mid circuit measurement results. @@ -162,15 +160,15 @@ def create_measurement_mcm_primitive( ``"_mcm"`` is appended to this name for the name of the primitive. Returns: - Optional[jax.core.Primitive]: A new jax primitive. ``None`` is returned if jax is not available. + Optional[jax.extend.core.Primitive]: A new jax primitive. ``None`` is returned if jax is not available. """ if not has_jax: return None - from .custom_primitives import NonInterpPrimitive # pylint: disable=import-outside-toplevel + from .custom_primitives import QmlPrimitive # pylint: disable=import-outside-toplevel - primitive = NonInterpPrimitive(name + "_mcm") + primitive = QmlPrimitive(name + "_mcm") primitive.prim_type = "measurement" @primitive.def_impl @@ -189,7 +187,7 @@ def _(*mcms, **__): def create_measurement_wires_primitive( measurement_type: type, name: str -) -> Optional["jax.core.Primitive"]: +) -> Optional["jax.extend.core.Primitive"]: """Create a primitive corresponding to the input type where the abstract inputs are the wires. Called by default when defining any class inheriting from :class:`~.MeasurementProcess`, and is used to @@ -201,14 +199,14 @@ def create_measurement_wires_primitive( ``"_wires"`` is appended to this name for the name of the primitive. Returns: - Optional[jax.core.Primitive]: A new jax primitive. ``None`` is returned if jax is not available. + Optional[jax.extend.core.Primitive]: A new jax primitive. ``None`` is returned if jax is not available. """ if not has_jax: return None - from .custom_primitives import NonInterpPrimitive # pylint: disable=import-outside-toplevel + from .custom_primitives import QmlPrimitive # pylint: disable=import-outside-toplevel - primitive = NonInterpPrimitive(name + "_wires") + primitive = QmlPrimitive(name + "_wires") primitive.prim_type = "measurement" @primitive.def_impl diff --git a/pennylane/capture/capture_meta.py b/pennylane/capture/capture_meta.py index e998d0bd83c..acbe203be93 100644 --- a/pennylane/capture/capture_meta.py +++ b/pennylane/capture/capture_meta.py @@ -47,11 +47,9 @@ class CaptureMeta(type): class AbstractMyObj(jax.core.AbstractValue): pass - jax.core.raise_to_shaped_mappings[AbstractMyObj] = lambda aval, _: aval - class MyObj(metaclass=qml.capture.CaptureMeta): - primitive = jax.core.Primitive("MyObj") + primitive = jax.extend.core.Primitive("MyObj") @classmethod def _primitive_bind_call(cls, a): diff --git a/pennylane/capture/capture_operators.py b/pennylane/capture/capture_operators.py index 7ea77fca53f..84e5f42eb19 100644 --- a/pennylane/capture/capture_operators.py +++ b/pennylane/capture/capture_operators.py @@ -14,7 +14,6 @@ """ This submodule defines the abstract classes and primitives for capturing operators. """ - from functools import lru_cache from typing import Optional, Type @@ -23,6 +22,7 @@ has_jax = True try: import jax + except ImportError: has_jax = False @@ -77,14 +77,12 @@ def _add(a, b): def _pow(a, b): return qml.pow(a, b) - jax.core.raise_to_shaped_mappings[AbstractOperator] = lambda aval, _: aval - return AbstractOperator def create_operator_primitive( operator_type: Type["qml.operation.Operator"], -) -> Optional["jax.core.Primitive"]: +) -> Optional["jax.extend.core.Primitive"]: """Create a primitive corresponding to an operator type. Called when defining any :class:`~.Operator` subclass, and is used to set the @@ -94,16 +92,16 @@ def create_operator_primitive( operator_type (type): a subclass of qml.operation.Operator Returns: - Optional[jax.core.Primitive]: A new jax primitive with the same name as the operator subclass. + Optional[jax.extend.core.Primitive]: A new jax primitive with the same name as the operator subclass. ``None`` is returned if jax is not available. """ if not has_jax: return None - from .custom_primitives import NonInterpPrimitive # pylint: disable=import-outside-toplevel + from .custom_primitives import QmlPrimitive # pylint: disable=import-outside-toplevel - primitive = NonInterpPrimitive(operator_type.__name__) + primitive = QmlPrimitive(operator_type.__name__) primitive.prim_type = "operator" @primitive.def_impl diff --git a/pennylane/capture/custom_primitives.py b/pennylane/capture/custom_primitives.py index 183ae05771b..8a08e700cb0 100644 --- a/pennylane/capture/custom_primitives.py +++ b/pennylane/capture/custom_primitives.py @@ -17,7 +17,7 @@ from enum import Enum from typing import Union -import jax +from jax.extend.core import Primitive class PrimitiveType(Enum): @@ -31,7 +31,7 @@ class PrimitiveType(Enum): # pylint: disable=too-few-public-methods,abstract-method -class QmlPrimitive(jax.core.Primitive): +class QmlPrimitive(Primitive): """A subclass for JAX's Primitive that differentiates between different classes of primitives.""" @@ -47,18 +47,3 @@ def prim_type(self): def prim_type(self, value: Union[str, PrimitiveType]): """Setter for QmlPrimitive.prim_type.""" self._prim_type = PrimitiveType(value) - - -# pylint: disable=too-few-public-methods,abstract-method -class NonInterpPrimitive(QmlPrimitive): - """A subclass to JAX's Primitive that works like a Python function - when evaluating JVPTracers and BatchTracers.""" - - def bind_with_trace(self, trace, args, params): - """Bind the ``NonInterpPrimitive`` with a trace. - - If the trace is a ``JVPTrace``or a ``BatchTrace``, binding falls back to a standard Python function call. - Otherwise, the bind call of JAX's standard Primitive is used.""" - if isinstance(trace, (jax.interpreters.ad.JVPTrace, jax.interpreters.batching.BatchTrace)): - return self.impl(*args, **params) - return super().bind_with_trace(trace, args, params) diff --git a/pennylane/capture/dynamic_shapes.py b/pennylane/capture/dynamic_shapes.py index c8696d9eaa6..52b21e4a667 100644 --- a/pennylane/capture/dynamic_shapes.py +++ b/pennylane/capture/dynamic_shapes.py @@ -19,7 +19,7 @@ has_jax = True try: import jax - from jax._src.interpreters import partial_eval as pe + from jax.interpreters import partial_eval as pe except ImportError: # pragma: no cover has_jax = False # pragma: no cover @@ -142,15 +142,15 @@ def f(n): def register_custom_staging_rule( - primitive, get_outvars_from_params: Callable[[dict], list["jax.core.Var"]] + primitive, get_outvars_from_params: Callable[[dict], list["jax.extend.core.Var"]] ) -> None: """Register a custom staging rule for a primitive, where the output should match the variables retrieved by ``get_outvars_from_params``. Args: - primitive (jax.core.Primitive): a jax primitive we want to register a custom staging rule for - get_outvars_from_params (Callable[[dict], list[jax.core.Var]]): A function that takes in the equation's ``params`` - and returns ``jax.core.Var`` we need to mimic for the primitives return. + primitive (jax.extend.core.Primitive): a jax primitive we want to register a custom staging rule for + get_outvars_from_params (Callable[[dict], list[jax.extend.core.Var]]): A function that takes in the equation's ``params`` + and returns ``jax.extend.core.Var`` we need to mimic for the primitives return. For example, the ``cond_prim`` will request its custom staging rule like: @@ -168,9 +168,9 @@ def register_custom_staging_rule( def _tracer_and_outvar( jaxpr_trace: pe.DynamicJaxprTrace, - outvar: jax.core.Var, - env: dict[jax.core.Var, jax.core.Var], - ) -> tuple[pe.DynamicJaxprTracer, jax.core.Var]: + outvar: jax.extend.core.Var, + env: dict[jax.extend.core.Var, jax.extend.core.Var], + ) -> tuple[pe.DynamicJaxprTracer, jax.extend.core.Var]: """ Create a new tracer and return var from the true branch outvar. Returned vars are cached in env for use in future shapes @@ -183,7 +183,7 @@ def _tracer_and_outvar( out_tracer = pe.DynamicJaxprTracer(jaxpr_trace, new_aval) new_var = jaxpr_trace.makevar(out_tracer) - if not isinstance(outvar, jax.core.Literal): + if not isinstance(outvar, jax.extend.core.Literal): env[outvar] = new_var return out_tracer, new_var @@ -198,7 +198,7 @@ def custom_staging_rule( return jaxpr_trace.default_process_primitive(primitive, tracers, params) outvars = get_outvars_from_params(params) - env: dict[jax.core.Var, jax.core.Var] = {} # branch var to new equation var + env: dict[jax.extend.core.Var, jax.extend.core.Var] = {} # branch var to new equation var if outvars: out_tracers, returned_vars = tuple( zip(*(_tracer_and_outvar(jaxpr_trace, var, env) for var in outvars), strict=True) @@ -207,7 +207,7 @@ def custom_staging_rule( out_tracers, returned_vars = (), () invars = [jaxpr_trace.getvar(x) for x in tracers] - eqn = pe.new_jaxpr_eqn( + eqn = jax.core.new_jaxpr_eqn( invars, returned_vars, primitive, diff --git a/pennylane/capture/explanations.md b/pennylane/capture/explanations.md index 71033aac3ee..440304785fb 100644 --- a/pennylane/capture/explanations.md +++ b/pennylane/capture/explanations.md @@ -9,7 +9,7 @@ import jax ```python -my_func_prim = jax.core.Primitive("my_func") +my_func_prim = jax.extend.core.Primitive("my_func") @my_func_prim.def_impl def _(x): @@ -65,7 +65,7 @@ def repeat(func: Callable, n: int) -> Callable: We can start by creating the primitive itself: ```python -repeat_prim = jax.core.Primitive("repeat") +repeat_prim = jax.extend.core.Primitive("repeat") repeat_prim.multiple_results = True ``` @@ -318,7 +318,7 @@ class PrimitiveMeta(type): def __init__(cls, *args, **kwargs): # here we set up the primitive - primitive = jax.core.Primitive(cls.__name__) + primitive = jax.extend.core.Primitive(cls.__name__) @primitive.def_impl def _(*inner_args, **inner_kwargs): @@ -379,8 +379,6 @@ class AbstractPrimitiveClass(jax.core.AbstractValue): def __hash__(self): return hash("AbstractPrimitiveClass") - -jax.core.raise_to_shaped_mappings[AbstractPrimitiveClass] = lambda aval, _: aval ``` Now we can redefine our class to use this abstract class @@ -391,7 +389,7 @@ class PrimitiveMeta2(type): def __init__(cls, *args, **kwargs): # here we set up the primitive - primitive = jax.core.Primitive(cls.__name__) + primitive = jax.extend.core.Primitive(cls.__name__) @primitive.def_impl def _(*inner_args, **inner_kwargs): @@ -428,6 +426,9 @@ Now in our jaxpr, we can see thet `PrimitiveClass2` returns something of type `A # Non-interpreted primitives +**WARNING:** THIS EXPLANATION IS SPECIFIC TO JAX 0.4.28 AND NO LONGER APPLIES + + Some of the primitives in the capture module have a somewhat non-standard requirement for the behaviour under differentiation or batching: they should ignore that an input is a differentiation or batching tracer and just execute the standard implementation on them. @@ -450,12 +451,12 @@ def fun(x): ``` Now suppose we want to turn this into a primitive. We could just promote it to a standard -`jax.core.Primitive` as +`jax.extend.core.Primitive` as ```python import jax -fd_prim = jax.core.Primitive("finite_diff") +fd_prim = jax.extend.core.Primitive("finite_diff") fd_prim.multiple_results = True fd_prim.def_impl(finite_diff_impl) @@ -487,7 +488,7 @@ for our finite difference method. We also create the usual method that binds the primitive to inputs. ```python -class NonInterpPrimitive(jax.core.Primitive): +class NonInterpPrimitive(jax.extend.core.Primitive): """A subclass to JAX's Primitive that works like a Python function when evaluating JVPTracers.""" diff --git a/pennylane/capture/intro_to_dynamic_shapes.md b/pennylane/capture/intro_to_dynamic_shapes.md index 12df19819dc..a54c87d95ea 100644 --- a/pennylane/capture/intro_to_dynamic_shapes.md +++ b/pennylane/capture/intro_to_dynamic_shapes.md @@ -284,7 +284,7 @@ We can now take these learnings to make a custom higher order primitive that sup ```python -prim = jax.core.Primitive("hop") +prim = jax.extend.core.Primitive("hop") prim.multiple_results = True @prim.def_impl @@ -368,7 +368,7 @@ For example, with for loops and while loops, we can insist that the output shape ```python -prim2 = jax.core.Primitive("hop") +prim2 = jax.extend.core.Primitive("hop") prim2.multiple_results = True @prim2.def_impl @@ -430,7 +430,7 @@ Here we are going to create a primitive that accepts an argument `n`, and return ```python -prim3 = jax.core.Primitive("dynamic_output") +prim3 = jax.extend.core.Primitive("dynamic_output") prim3.multiple_results = True ``` @@ -438,7 +438,7 @@ prim3.multiple_results = True ```python -from jax._src.interpreters import partial_eval as pe +from jax.interpreters import partial_eval as pe def custom_staging_rule(jaxpr_trace, *invars, **params): new_shapes = [jax.core.DShapedArray((invars[0],2), jax.numpy.float32.dtype)] diff --git a/pennylane/compiler/python_compiler/quantum_dialect.py b/pennylane/compiler/python_compiler/quantum_dialect.py new file mode 100644 index 00000000000..9b075963886 --- /dev/null +++ b/pennylane/compiler/python_compiler/quantum_dialect.py @@ -0,0 +1,669 @@ +# Copyright 2025 Xanadu Quantum Technologies Inc. + +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +This file contains the definition of the Quantum dialect for the Python compiler. + +The Quantum dialect is a set of operations and types used to represent quantum computations +in the xDSL framework. + +It was initially generated by xDSL (using the ``xdsl-tblgen`` tool) +starting from the catalyst/mlir/include/Quantum/IR/QuantumOps.td file in the catalyst repository. +""" + +# pylint: disable=too-few-public-methods + +# pragma: no cover + +from xdsl.dialects.builtin import ( + AnyAttr, + AnyOf, + BaseAttr, + Float64Type, + IntegerType, + StringAttr, + UnitAttr, +) +from xdsl.ir import Dialect, ParametrizedAttribute, TypeAttribute +from xdsl.irdl import ( + AttrSizedOperandSegments, + AttrSizedResultSegments, + EqAttrConstraint, + IRDLOperation, + irdl_attr_definition, + irdl_op_definition, + operand_def, + opt_operand_def, + opt_prop_def, + opt_result_def, + prop_def, + region_def, + result_def, + var_operand_def, + var_result_def, +) + + +@irdl_attr_definition +class ObservableType(ParametrizedAttribute, TypeAttribute): + """A quantum observable for use in measurements.""" + + name = "quantum.obs" + + +@irdl_attr_definition +class QubitType(ParametrizedAttribute, TypeAttribute): + """A value-semantic qubit (state).""" + + name = "quantum.bit" + + +@irdl_attr_definition +class QuregType(ParametrizedAttribute, TypeAttribute): + """An array of value-semantic qubits (i.e. quantum register).""" + + name = "quantum.reg" + + +@irdl_attr_definition +class ResultType(ParametrizedAttribute, TypeAttribute): + """A quantum measurement result.""" + + name = "quantum.res" + + +@irdl_attr_definition +class NamedObservableAttr(ParametrizedAttribute): + """Known named observables""" + + name = "quantum.named_observable" + + +@irdl_op_definition +class AdjointOp(IRDLOperation): + """Calculate the adjoint of the enclosed operations""" + + name = "quantum.adjoint" + + assembly_format = """ + `(` $qreg `)` attr-dict `:` type(operands) $region + """ + + qreg = operand_def(BaseAttr(QuregType)) + + out_qreg = result_def(BaseAttr(QuregType)) + + region = region_def("single_block") + + +@irdl_op_definition +class AllocOp(IRDLOperation): + """Allocate n qubits into a quantum register.""" + + name = "quantum.alloc" + + # assembly_format = """ + # `(` ($nqubits^):($nqubits_attr)? `)` attr-dict `:` type(results) + # """ + + nqubits = opt_operand_def(EqAttrConstraint(IntegerType(64))) + + nqubits_attr = opt_prop_def(AnyAttr()) + + qreg = result_def(BaseAttr(QuregType)) + + +@irdl_op_definition +class ComputationalBasisOp(IRDLOperation): + """Define a pseudo-obeservable of the computational basis for use in measurements""" + + name = "quantum.compbasis" + + assembly_format = """ + (`qubits` $qubits^)? (`qreg` $qreg^)? attr-dict `:` type(results) + """ + + irdl_options = [AttrSizedOperandSegments(as_property=True)] + + qubits = var_operand_def(BaseAttr(QubitType)) + + qreg = opt_operand_def(BaseAttr(QuregType)) + + obs = result_def(BaseAttr(ObservableType)) + + +@irdl_op_definition +class CountsOp(IRDLOperation): + """Compute sample counts for the given observable for the current state""" + + name = "quantum.counts" + + assembly_format = """ + $obs ( `shape` $dynamic_shape^ )? + ( `in` `(` $in_eigvals^ `:` type($in_eigvals) `,` $in_counts `:` type($in_counts) `)` )? + attr-dict ( `:` type($eigvals)^ `,` type($counts) )? + """ + + irdl_options = [ + AttrSizedOperandSegments(as_property=True), + AttrSizedResultSegments(as_property=True), + ] + + obs = operand_def(BaseAttr(ObservableType)) + + dynamic_shape = opt_operand_def(EqAttrConstraint(IntegerType(64))) + + in_eigvals = opt_operand_def(AnyAttr()) + + in_counts = opt_operand_def(AnyAttr()) + + eigvals = opt_result_def(AnyAttr()) + + counts = opt_result_def(AnyAttr()) + + +@irdl_op_definition +class CustomOp(IRDLOperation): + """A generic quantum gate on n qubits with m floating point parameters.""" + + name = "quantum.custom" + + # assembly_format = """ + # $gate_name `(` $params `)` $in_qubits attr-dict ( `ctrls` `(` $in_ctrl_qubits^ `)` )? ( `ctrlvals` `(` $in_ctrl_values^ `)` )? `:` type($out_qubits) (`ctrls` type($out_ctrl_qubits)^ )? + # """ + + irdl_options = [ + AttrSizedOperandSegments(as_property=True), + AttrSizedResultSegments(as_property=True), + ] + + params = var_operand_def(EqAttrConstraint(Float64Type())) + + in_qubits = var_operand_def(BaseAttr(QubitType)) + + gate_name = prop_def(BaseAttr(StringAttr)) + + adjoint = opt_prop_def(EqAttrConstraint(UnitAttr())) + + in_ctrl_qubits = var_operand_def(BaseAttr(QubitType)) + + in_ctrl_values = var_operand_def(EqAttrConstraint(IntegerType(1))) + + out_qubits = var_result_def(BaseAttr(QubitType)) + + out_ctrl_qubits = var_result_def(BaseAttr(QubitType)) + + +@irdl_op_definition +class DeallocOp(IRDLOperation): + """Deallocate a quantum register.""" + + name = "quantum.dealloc" + + assembly_format = """ + $qreg attr-dict `:` type(operands) + """ + + qreg = operand_def(BaseAttr(QuregType)) + + +@irdl_op_definition +class DeviceInitOp(IRDLOperation): + """Initialize a quantum device.""" + + name = "quantum.device_init" + + # assembly_format = """ + # (`shots` `(` $shots^ `)`)? `[` $lib `,` $name `,` $kwargs `]` attr-dict + # """ + + shots = opt_operand_def(EqAttrConstraint(IntegerType(64))) + + lib = prop_def(BaseAttr(StringAttr)) + + kwargs = prop_def(BaseAttr(StringAttr)) + + +@irdl_op_definition +class DeviceReleaseOp(IRDLOperation): + """Release the active quantum device.""" + + name = "quantum.device_release" + + assembly_format = """ + attr-dict + """ + + +@irdl_op_definition +class ExpvalOp(IRDLOperation): + """Compute the expectation value of the given observable for the current state""" + + name = "quantum.expval" + + assembly_format = """ + $obs attr-dict `:` type(results) + """ + + obs = operand_def(BaseAttr(ObservableType)) + + expval = result_def(EqAttrConstraint(Float64Type())) + + +@irdl_op_definition +class ExtractOp(IRDLOperation): + """Extract a qubit value from a register.""" + + name = "quantum.extract" + + # assembly_format = """ + # $qreg `[` ($idx^):($idx_attr)? `]` attr-dict `:` type($qreg) `->` type(results) + # """ + + qreg = operand_def(BaseAttr(QuregType)) + + idx = opt_operand_def(EqAttrConstraint(IntegerType(64))) + + idx_attr = opt_prop_def(AnyAttr()) + + qubit = result_def(BaseAttr(QubitType)) + + +@irdl_op_definition +class FinalizeOp(IRDLOperation): + """Teardown the quantum runtime.""" + + name = "quantum.finalize" + + assembly_format = """ + attr-dict + """ + + +@irdl_op_definition +class GlobalPhaseOp(IRDLOperation): + """Global Phase.""" + + name = "quantum.gphase" + + # assembly_format = """ + # `(` $params `)` attr-dict ( `ctrls` `(` $in_ctrl_qubits^ `)` )? ( `ctrlvals` `(` $in_ctrl_values^ `)` )? `:` (`ctrls` type($out_ctrl_qubits)^ )? + # """ + + irdl_options = [ + AttrSizedOperandSegments(as_property=True), + AttrSizedResultSegments(as_property=True), + ] + + params = operand_def(EqAttrConstraint(Float64Type())) + + adjoint = opt_prop_def(EqAttrConstraint(UnitAttr())) + + in_ctrl_qubits = var_operand_def(BaseAttr(QubitType)) + + in_ctrl_values = var_operand_def(EqAttrConstraint(IntegerType(1))) + + out_ctrl_qubits = var_result_def(BaseAttr(QubitType)) + + +@irdl_op_definition +class HamiltonianOp(IRDLOperation): + """Define a Hamiltonian observable for use in measurements""" + + name = "quantum.hamiltonian" + + assembly_format = """ + `(` $coeffs `:` type($coeffs) `)` $terms attr-dict `:` type(results) + """ + + coeffs = operand_def(AnyOf((AnyAttr(), AnyAttr()))) + + terms = var_operand_def(BaseAttr(ObservableType)) + + obs = result_def(BaseAttr(ObservableType)) + + +@irdl_op_definition +class HermitianOp(IRDLOperation): + """Define a Hermitian observable for use in measurements""" + + name = "quantum.hermitian" + + assembly_format = """ + `(` $matrix `:` type($matrix) `)` $qubits attr-dict `:` type(results) + """ + + matrix = operand_def(AnyOf((AnyAttr(), AnyAttr()))) + + qubits = var_operand_def(BaseAttr(QubitType)) + + obs = result_def(BaseAttr(ObservableType)) + + +@irdl_op_definition +class InitializeOp(IRDLOperation): + """Initialize the quantum runtime.""" + + name = "quantum.init" + + assembly_format = """ + attr-dict + """ + + +@irdl_op_definition +class InsertOp(IRDLOperation): + """Update the qubit value of a register.""" + + name = "quantum.insert" + + # assembly_format = """ + # $in_qreg `[` ($idx^):($idx_attr)? `]` `,` $qubit attr-dict `:` type($in_qreg) `,` type($qubit) + # """ + + in_qreg = operand_def(BaseAttr(QuregType)) + + idx = opt_operand_def(EqAttrConstraint(IntegerType(64))) + + idx_attr = opt_prop_def(AnyAttr()) + + qubit = operand_def(BaseAttr(QubitType)) + + out_qreg = result_def(BaseAttr(QuregType)) + + +@irdl_op_definition +class MeasureOp(IRDLOperation): + """A single-qubit projective measurement in the computational basis.""" + + name = "quantum.measure" + + # assembly_format = """ + # $in_qubit attr-dict `:` type(results) + # """ + + in_qubit = operand_def(BaseAttr(QubitType)) + + postselect = opt_prop_def(AnyAttr()) + + mres = result_def(EqAttrConstraint(IntegerType(1))) + + out_qubit = result_def(BaseAttr(QubitType)) + + +@irdl_op_definition +class MultiRZOp(IRDLOperation): + """Apply an arbitrary multi Z rotation""" + + name = "quantum.multirz" + + # assembly_format = """ + # `(` $theta `)` $in_qubits attr-dict ( `ctrls` `(` $in_ctrl_qubits^ `)` )? ( `ctrlvals` `(` $in_ctrl_values^ `)` )? `:` type($out_qubits) (`ctrls` type($out_ctrl_qubits)^ )? + # """ + + irdl_options = [ + AttrSizedOperandSegments(as_property=True), + AttrSizedResultSegments(as_property=True), + ] + + theta = operand_def(EqAttrConstraint(Float64Type())) + + in_qubits = var_operand_def(BaseAttr(QubitType)) + + adjoint = opt_prop_def(EqAttrConstraint(UnitAttr())) + + in_ctrl_qubits = var_operand_def(BaseAttr(QubitType)) + + in_ctrl_values = var_operand_def(EqAttrConstraint(IntegerType(1))) + + out_qubits = var_result_def(BaseAttr(QubitType)) + + out_ctrl_qubits = var_result_def(BaseAttr(QubitType)) + + +@irdl_op_definition +class NamedObsOp(IRDLOperation): + """Define a Named observable for use in measurements""" + + name = "quantum.namedobs" + + assembly_format = """ + $qubit `[` $type `]` attr-dict `:` type(results) + """ + + qubit = operand_def(BaseAttr(QubitType)) + + type = prop_def(BaseAttr(NamedObservableAttr)) + + obs = result_def(BaseAttr(ObservableType)) + + +@irdl_op_definition +class ProbsOp(IRDLOperation): + """Compute computational basis probabilities for the current state""" + + name = "quantum.probs" + + assembly_format = """ + $obs ( `shape` $dynamic_shape^ )? + ( `in` `(` $state_in^ `:` type($state_in) `)` )? + attr-dict ( `:` type($probabilities)^ )? + """ + + irdl_options = [ + AttrSizedOperandSegments(as_property=True), + AttrSizedResultSegments(as_property=True), + ] + + obs = operand_def(BaseAttr(ObservableType)) + + dynamic_shape = opt_operand_def(EqAttrConstraint(IntegerType(64))) + + state_in = opt_operand_def(AnyAttr()) + + probabilities = opt_result_def(AnyAttr()) + + +@irdl_op_definition +class QubitUnitaryOp(IRDLOperation): + """Apply an arbitrary fixed unitary matrix""" + + name = "quantum.unitary" + + # assembly_format = """ + # `(` $matrix `:` type($matrix) `)` $in_qubits attr-dict ( `ctrls` `(` $in_ctrl_qubits^ `)` )? ( `ctrlvals` `(` $in_ctrl_values^ `)` )? `:` type($out_qubits) (`ctrls` type($out_ctrl_qubits)^ )? + # """ + + irdl_options = [ + AttrSizedOperandSegments(as_property=True), + AttrSizedResultSegments(as_property=True), + ] + + matrix = operand_def(AnyOf((AnyAttr(), AnyAttr()))) + + in_qubits = var_operand_def(BaseAttr(QubitType)) + + adjoint = opt_prop_def(EqAttrConstraint(UnitAttr())) + + in_ctrl_qubits = var_operand_def(BaseAttr(QubitType)) + + in_ctrl_values = var_operand_def(EqAttrConstraint(IntegerType(1))) + + out_qubits = var_result_def(BaseAttr(QubitType)) + + out_ctrl_qubits = var_result_def(BaseAttr(QubitType)) + + +@irdl_op_definition +class SampleOp(IRDLOperation): + """Sample eigenvalues from the given observable for the current state""" + + name = "quantum.sample" + + assembly_format = """ + $obs ( `shape` $dynamic_shape^ )? + ( `in` `(` $in_data^ `:` type($in_data) `)` )? + attr-dict ( `:` type($samples)^ )? + """ + + irdl_options = [AttrSizedOperandSegments(as_property=True)] + + obs = operand_def(BaseAttr(ObservableType)) + + dynamic_shape = var_operand_def(EqAttrConstraint(IntegerType(64))) + + in_data = opt_operand_def(AnyOf((AnyAttr(), AnyAttr()))) + + samples = opt_result_def(AnyOf((AnyAttr(), AnyAttr()))) + + +@irdl_op_definition +class SetBasisStateOp(IRDLOperation): + """Set basis state.""" + + name = "quantum.set_basis_state" + + assembly_format = """ + `(` $basis_state`)` $in_qubits attr-dict `:` functional-type(operands, results) + """ + + basis_state = operand_def(AnyOf((AnyAttr(), AnyAttr()))) + + in_qubits = var_operand_def(BaseAttr(QubitType)) + + out_qubits = var_result_def(BaseAttr(QubitType)) + + +@irdl_op_definition +class SetStateOp(IRDLOperation): + """Set state to a complex vector.""" + + name = "quantum.set_state" + + assembly_format = """ + `(` $in_state `)` $in_qubits attr-dict `:` functional-type(operands, results) + """ + + in_state = operand_def(AnyOf((AnyAttr(), AnyAttr()))) + + in_qubits = var_operand_def(BaseAttr(QubitType)) + + out_qubits = var_result_def(BaseAttr(QubitType)) + + +@irdl_op_definition +class StateOp(IRDLOperation): + """Return the current statevector""" + + name = "quantum.state" + + assembly_format = """ + $obs ( `shape` $dynamic_shape^ )? + ( `in` `(` $state_in^ `:` type($state_in) `)` )? + attr-dict ( `:` type($state)^ )? + """ + + irdl_options = [AttrSizedOperandSegments(as_property=True)] + + obs = operand_def(BaseAttr(ObservableType)) + + dynamic_shape = opt_operand_def(EqAttrConstraint(IntegerType(64))) + + state_in = opt_operand_def(AnyAttr()) + + state = opt_result_def(AnyAttr()) + + +@irdl_op_definition +class TensorOp(IRDLOperation): + """Define a tensor product of observables for use in measurements""" + + name = "quantum.tensor" + + assembly_format = """ + $terms attr-dict `:` type(results) + """ + + terms = var_operand_def(BaseAttr(ObservableType)) + + obs = result_def(BaseAttr(ObservableType)) + + +@irdl_op_definition +class VarianceOp(IRDLOperation): + """Compute the variance of the given observable for the current state""" + + name = "quantum.var" + + assembly_format = """ + $obs attr-dict `:` type(results) + """ + + obs = operand_def(BaseAttr(ObservableType)) + + variance = result_def(EqAttrConstraint(Float64Type())) + + +@irdl_op_definition +class YieldOp(IRDLOperation): + """Return results from quantum program regions""" + + name = "quantum.yield" + + # assembly_format = """ + # attr-dict ($results^ `:` type($results))? + # """ + + results = var_operand_def(BaseAttr(QuregType)) + + +QuantumDialect = Dialect( + "quantum", + [ + AdjointOp, + AllocOp, + ComputationalBasisOp, + CountsOp, + CustomOp, + DeallocOp, + DeviceInitOp, + DeviceReleaseOp, + ExpvalOp, + ExtractOp, + FinalizeOp, + GlobalPhaseOp, + HamiltonianOp, + HermitianOp, + InitializeOp, + InsertOp, + MeasureOp, + MultiRZOp, + NamedObsOp, + ProbsOp, + QubitUnitaryOp, + SampleOp, + SetBasisStateOp, + SetStateOp, + StateOp, + TensorOp, + VarianceOp, + YieldOp, + ], + [ + ObservableType, + QubitType, + QuregType, + ResultType, + NamedObservableAttr, + ], +) diff --git a/pennylane/control_flow/_loop_abstract_axes.py b/pennylane/control_flow/_loop_abstract_axes.py index 410a22c860b..0ee92a955eb 100644 --- a/pennylane/control_flow/_loop_abstract_axes.py +++ b/pennylane/control_flow/_loop_abstract_axes.py @@ -23,8 +23,6 @@ from collections import namedtuple from typing import Any, Callable, Optional -import numpy as np - from pennylane.typing import TensorLike AbstractShapeLocation = namedtuple("AbstractShapeLocation", ("arg_idx", "shape_idx")) @@ -97,11 +95,13 @@ def w(i0): return arg # add small, non-trivial size 2 as a concrete stand-in for dynamic axes shape = tuple(s if isinstance(s, int) else 2 for s in arg.shape) - return np.empty(shape=shape, dtype=arg.dtype) + from jax.numpy import empty # pylint: disable=import-outside-toplevel + + return empty(shape=shape, dtype=arg.dtype) def validate_no_resizing_returns( - jaxpr: "jax.core.Jaxpr", + jaxpr: "jax.extend.core.Jaxpr", locations: list[list[AbstractShapeLocation]], name: str = "while_loop", ) -> Optional[str]: diff --git a/pennylane/control_flow/for_loop.py b/pennylane/control_flow/for_loop.py index 700c09d8814..0554c4288e3 100644 --- a/pennylane/control_flow/for_loop.py +++ b/pennylane/control_flow/for_loop.py @@ -271,9 +271,9 @@ def _get_for_loop_qfunc_prim(): """Get the loop_for primitive for quantum functions.""" # pylint: disable=import-outside-toplevel - from pennylane.capture.custom_primitives import NonInterpPrimitive + from pennylane.capture.custom_primitives import QmlPrimitive - for_loop_prim = NonInterpPrimitive("for_loop") + for_loop_prim = QmlPrimitive("for_loop") for_loop_prim.multiple_results = True for_loop_prim.prim_type = "higher_order" register_custom_staging_rule(for_loop_prim, lambda params: params["jaxpr_body_fn"].outvars) diff --git a/pennylane/control_flow/while_loop.py b/pennylane/control_flow/while_loop.py index d1b4c5fd5ed..ef33aa8f0a0 100644 --- a/pennylane/control_flow/while_loop.py +++ b/pennylane/control_flow/while_loop.py @@ -225,9 +225,9 @@ def _get_while_loop_qfunc_prim(): """Get the while_loop primitive for quantum functions.""" # pylint: disable=import-outside-toplevel - from pennylane.capture.custom_primitives import NonInterpPrimitive + from pennylane.capture.custom_primitives import QmlPrimitive - while_loop_prim = NonInterpPrimitive("while_loop") + while_loop_prim = QmlPrimitive("while_loop") while_loop_prim.multiple_results = True while_loop_prim.prim_type = "higher_order" register_custom_staging_rule(while_loop_prim, lambda params: params["jaxpr_body_fn"].outvars) diff --git a/pennylane/devices/default_mixed.py b/pennylane/devices/default_mixed.py index 7d6dc5591b9..ef72afc10e7 100644 --- a/pennylane/devices/default_mixed.py +++ b/pennylane/devices/default_mixed.py @@ -22,8 +22,6 @@ # pylint: disable=wrong-import-order, ungrouped-imports import logging -import numpy as np - import pennylane as qml from pennylane.math import get_canonical_interface_name @@ -237,13 +235,13 @@ def __init__( # pylint: disable=too-many-arguments super().__init__(wires=wires, shots=shots) # Seed setting - seed = np.random.randint(0, high=10000000) if seed == "global" else seed + seed = qml.math.random.randint(0, high=10000000) if seed == "global" else seed if qml.math.get_interface(seed) == "jax": self._prng_key = seed - self._rng = np.random.default_rng(None) + self._rng = qml.math.random.default_rng(None) else: self._prng_key = None - self._rng = np.random.default_rng(seed) + self._rng = qml.math.random.default_rng(seed) self._debugger = None diff --git a/pennylane/devices/default_qubit.py b/pennylane/devices/default_qubit.py index 5a223341f9f..dde9e9c11dc 100644 --- a/pennylane/devices/default_qubit.py +++ b/pennylane/devices/default_qubit.py @@ -989,7 +989,7 @@ def execute_and_compute_vjp( # pylint: disable=import-outside-toplevel, unused-argument @debug_logger def eval_jaxpr( - self, jaxpr: "jax.core.Jaxpr", consts: list[TensorLike], *args, execution_config=None + self, jaxpr: "jax.extend.core.Jaxpr", consts: list[TensorLike], *args, execution_config=None ) -> list[TensorLike]: from .qubit.dq_interpreter import DefaultQubitInterpreter @@ -1017,11 +1017,11 @@ def _backprop_jvp(self, jaxpr, args, tangents, execution_config=None): def _make_zero(tan, arg): return ( - jax.lax.zeros_like_array(arg) if isinstance(tan, jax.interpreters.ad.Zero) else tan + jax.lax.zeros_like_array(arg).astype(tan.aval.dtype) + if isinstance(tan, jax.interpreters.ad.Zero) + else tan ) - tangents = tuple(map(_make_zero, tangents, args)) - def eval_wrapper(*inner_args): n_consts = len(jaxpr.constvars) consts = inner_args[:n_consts] @@ -1030,6 +1030,8 @@ def eval_wrapper(*inner_args): jaxpr, consts, *non_const_args, execution_config=execution_config ) + tangents = tuple(map(_make_zero, tangents, args)) + return jax.jvp(eval_wrapper, args, tangents) # pylint :disable=import-outside-toplevel, unused-argument diff --git a/pennylane/devices/device_api.py b/pennylane/devices/device_api.py index 2a9de90b295..ea4bbe3b082 100644 --- a/pennylane/devices/device_api.py +++ b/pennylane/devices/device_api.py @@ -971,7 +971,7 @@ def supports_vjp( def eval_jaxpr( self, - jaxpr: "jax.core.Jaxpr", + jaxpr: "jax.extend.core.Jaxpr", consts: list[TensorLike], *args, execution_config: Optional[ExecutionConfig] = None, @@ -979,7 +979,7 @@ def eval_jaxpr( """An **experimental** method for natively evaluating PLXPR. See the ``capture`` module for more details. Args: - jaxpr (jax.core.Jaxpr): Pennylane variant jaxpr containing quantum operations and measurements + jaxpr (jax.extend.core.Jaxpr): Pennylane variant jaxpr containing quantum operations and measurements consts (list[TensorLike]): the closure variables ``consts`` corresponding to the jaxpr *args (TensorLike): the variables to use with the jaxpr. @@ -994,7 +994,7 @@ def eval_jaxpr( def jaxpr_jvp( self, - jaxpr: "jax.core.Jaxpr", + jaxpr: "jax.extend.core.Jaxpr", args, tangents, execution_config: Optional[ExecutionConfig] = None, @@ -1003,7 +1003,7 @@ def jaxpr_jvp( See the ``capture`` module for more details. Args: - jaxpr (jax.core.Jaxpr): Pennylane variant jaxpr containing quantum operations + jaxpr (jax.extend.core.Jaxpr): Pennylane variant jaxpr containing quantum operations and measurements args (Sequence[TensorLike]): the ``consts`` followed by the normal arguments tangents (Sequence[TensorLike]): the tangents corresponding to ``args``. diff --git a/pennylane/devices/null_qubit.py b/pennylane/devices/null_qubit.py index 4e172baa04a..58eecf41aea 100644 --- a/pennylane/devices/null_qubit.py +++ b/pennylane/devices/null_qubit.py @@ -277,6 +277,9 @@ def __init__(self, wires=None, shots=None, track_resources=False) -> None: self._debugger = None self._track_resources = track_resources + # this is required by Catalyst to toggle the tracker at runtime + self.device_kwargs = {"track_resources": track_resources} + def _simulate(self, circuit, interface): num_device_wires = len(self.wires) if self.wires else len(circuit.wires) results = [] @@ -465,7 +468,7 @@ def execute_and_compute_vjp( # pylint: disable= unused-argument def eval_jaxpr( - self, jaxpr: "jax.core.Jaxpr", consts: list, *args, execution_config=None + self, jaxpr: "jax.extend.core.Jaxpr", consts: list, *args, execution_config=None ) -> list: from pennylane.capture.primitives import ( # pylint: disable=import-outside-toplevel AbstractMeasurement, diff --git a/pennylane/devices/qubit/dq_interpreter.py b/pennylane/devices/qubit/dq_interpreter.py index b31a130a752..f6b1d3a376e 100644 --- a/pennylane/devices/qubit/dq_interpreter.py +++ b/pennylane/devices/qubit/dq_interpreter.py @@ -191,7 +191,7 @@ def interpret_operation(self, op): return op - def interpret_measurement_eqn(self, eqn: "jax.core.JaxprEqn"): + def interpret_measurement_eqn(self, eqn: "jax.extend.core.JaxprEqn"): if "mcm" in eqn.primitive.name: raise NotImplementedError( "DefaultQubitInterpreter does not yet support postprocessing mcms" diff --git a/pennylane/devices/qubit/jaxpr_adjoint.py b/pennylane/devices/qubit/jaxpr_adjoint.py index ee2f747f795..3697f934647 100644 --- a/pennylane/devices/qubit/jaxpr_adjoint.py +++ b/pennylane/devices/qubit/jaxpr_adjoint.py @@ -28,7 +28,7 @@ def _read(env, var): """Return the value and tangent for a variable.""" - return (var.val, ad.Zero(var.aval)) if isinstance(var, jax.core.Literal) else env[var] + return (var.val, ad.Zero(var.aval)) if isinstance(var, jax.extend.core.Literal) else env[var] def _operator_forward_pass(eqn, env, ket): @@ -68,7 +68,7 @@ def _measurement_forward_pass(eqn, env, ket): return bra -def _other_prim_forward_pass(eqn: jax.core.JaxprEqn, env: dict) -> None: +def _other_prim_forward_pass(eqn: jax.extend.core.JaxprEqn, env: dict) -> None: """Handle any equation that is not an operator or measurement eqn. Maps outputs back to the environment @@ -86,7 +86,7 @@ def _other_prim_forward_pass(eqn: jax.core.JaxprEqn, env: dict) -> None: env[var] = (v, dv) -def _forward_pass(jaxpr: jax.core.Jaxpr, env: dict, num_wires: int): +def _forward_pass(jaxpr: jax.extend.core.Jaxpr, env: dict, num_wires: int): """Calculate the forward pass of an adjoint jvp calculation.""" bras = [] ket = create_initial_state(range(num_wires)) @@ -134,11 +134,11 @@ def _backward_pass(jaxpr, bras, ket, results, env): @pause() # need to be able to temporarily create instances, but still have it jittable -def execute_and_jvp(jaxpr: jax.core.Jaxpr, args: tuple, tangents: tuple, num_wires: int): +def execute_and_jvp(jaxpr: jax.extend.core.Jaxpr, args: tuple, tangents: tuple, num_wires: int): """Execute and calculate the jvp for a jaxpr using the adjoint method. Args: - jaxpr (jax.core.Jaxpr): the jaxpr to evaluate + jaxpr (jax.extend.core.Jaxpr): the jaxpr to evaluate args : an iterable of tensorlikes. Should include the consts followed by the inputs tangents: an iterable of tensorlikes and ``jax.interpreter.ad.Zero`` objects. Should include the consts followed by the inputs. diff --git a/pennylane/devices/qubit/simulate.py b/pennylane/devices/qubit/simulate.py index 0101391928b..6b7ac0805c6 100644 --- a/pennylane/devices/qubit/simulate.py +++ b/pennylane/devices/qubit/simulate.py @@ -322,6 +322,7 @@ def simulate( tensor([0.68117888, 0. , 0.31882112, 0. ], requires_grad=True)) """ + circuit = circuit.copy() prng_key = execution_kwargs.pop("prng_key", None) circuit = circuit.map_to_standard_wires() diff --git a/pennylane/devices/qubit_mixed/apply_operation.py b/pennylane/devices/qubit_mixed/apply_operation.py index dd242581aa9..46ac8c9b97d 100644 --- a/pennylane/devices/qubit_mixed/apply_operation.py +++ b/pennylane/devices/qubit_mixed/apply_operation.py @@ -28,7 +28,7 @@ from .einsum_manpulation import get_einsum_mapping -alphabet_array = np.array(list(alphabet)) +alphabet_array = math.array(list(alphabet)) TENSORDOT_STATE_NDIM_PERF_THRESHOLD = 9 diff --git a/pennylane/devices/qubit_mixed/einsum_manpulation.py b/pennylane/devices/qubit_mixed/einsum_manpulation.py index bd69a008078..f61f0bce709 100644 --- a/pennylane/devices/qubit_mixed/einsum_manpulation.py +++ b/pennylane/devices/qubit_mixed/einsum_manpulation.py @@ -17,9 +17,8 @@ import pennylane as qml from pennylane import math -from pennylane import numpy as np -alphabet_array = np.array(list(alphabet)) +alphabet_array = math.array(list(alphabet)) def get_einsum_mapping( diff --git a/pennylane/devices/qubit_mixed/initialize_state.py b/pennylane/devices/qubit_mixed/initialize_state.py index be3dbd5a7d6..3a9563aa473 100644 --- a/pennylane/devices/qubit_mixed/initialize_state.py +++ b/pennylane/devices/qubit_mixed/initialize_state.py @@ -17,7 +17,6 @@ from typing import Union import pennylane as qml -import pennylane.numpy as np from pennylane import math @@ -44,7 +43,7 @@ def create_initial_state( 2 * num_wires ) # we initialize the density matrix as the tensor form to keep compatibility with the rest of the module if not prep_operation: - state = np.zeros((2,) * num_axes, dtype=complex) + state = math.zeros((2,) * num_axes, dtype=complex) state[(0,) * num_axes] = 1 return math.asarray(state, like=like) @@ -60,9 +59,9 @@ def create_initial_state( ) # don't assume the expected shape to be fixed if batch_size is None: is_state_batched = False - density_matrix = np.outer(pure_state, np.conj(pure_state)) + density_matrix = _flatten_outer(pure_state) else: - density_matrix = math.stack([np.outer(s, np.conj(s)) for s in pure_state]) + density_matrix = math.stack([_flatten_outer(s) for s in pure_state]) return _post_process(density_matrix, num_axes, like, is_state_batched) @@ -73,11 +72,17 @@ def _post_process(density_matrix, num_axes, like, is_state_batched=True): matrix form, as requested by all the other more fundamental chore functions in the module (again from some legacy code). """ - density_matrix = np.reshape(density_matrix, (-1,) + (2,) * num_axes) + density_matrix = math.reshape(density_matrix, (-1,) + (2,) * num_axes) dtype = str(density_matrix.dtype) floating_single = "float32" in dtype or "complex64" in dtype dtype = "complex64" if floating_single else "complex128" dtype = "complex128" if like == "tensorflow" else dtype if not is_state_batched: - density_matrix = np.reshape(density_matrix, (2,) * num_axes) + density_matrix = math.reshape(density_matrix, (2,) * num_axes) return math.cast(math.asarray(density_matrix, like=like), dtype) + + +def _flatten_outer(s): + r"""Flattens the outer product of a vector.""" + s_flatten = math.flatten(s) + return math.outer(s_flatten, math.conj(s_flatten)) diff --git a/pennylane/devices/qubit_mixed/sampling.py b/pennylane/devices/qubit_mixed/sampling.py index 22cd51631c0..d1047985e59 100644 --- a/pennylane/devices/qubit_mixed/sampling.py +++ b/pennylane/devices/qubit_mixed/sampling.py @@ -207,17 +207,17 @@ def process_state_with_shots(mp, state, wire_order, shots, rng=None, is_state_ba # seed the random measurement generation so that recipes # are the same for different executions with the same seed seed = mp.seed - recipe_rng = np.random.RandomState(seed) + recipe_rng = math.random.RandomState(seed) recipes = recipe_rng.randint(0, 3, size=(n_snapshots, n_qubits)) - outcomes = np.zeros((n_snapshots, n_qubits)) + outcomes = math.zeros((n_snapshots, n_qubits)) # Single-qubit diagonalizing ops for X, Y, Z diag_list = [ qml.Hadamard.compute_matrix(), # X qml.Hadamard.compute_matrix() @ qml.RZ.compute_matrix(-np.pi / 2), # Y qml.Identity.compute_matrix(), # Z ] - bit_rng = np.random.default_rng(rng) + bit_rng = math.random.default_rng(rng) for t in range(n_snapshots): for q_idx, q_wire in enumerate(mapped_wires): @@ -235,13 +235,13 @@ def process_state_with_shots(mp, state, wire_order, shots, rng=None, is_state_ba rotated = math.dot(U, math.dot(rho_q, U_dag)) # (C) probability of outcome 0 => rotated[0,0].real - p0 = np.clip(math.real(rotated[0, 0]), 0.0, 1.0) + p0 = math.clip(math.real(rotated[0, 0]), 0.0, 1.0) if bit_rng.random() < p0: outcomes[t, q_idx] = 0 else: outcomes[t, q_idx] = 1 - res = np.stack([outcomes, recipes]).astype(np.int8) + res = math.stack([outcomes, recipes]).astype(np.int8) return res diff --git a/pennylane/ftqc/parametric_midmeasure.py b/pennylane/ftqc/parametric_midmeasure.py index d5b8af297ef..ec4a8058d0a 100644 --- a/pennylane/ftqc/parametric_midmeasure.py +++ b/pennylane/ftqc/parametric_midmeasure.py @@ -17,6 +17,7 @@ import uuid from collections.abc import Hashable +from copy import copy from functools import lru_cache from typing import Iterable, Optional, Union @@ -48,9 +49,9 @@ def _create_parametrized_mid_measure_primitive(): # pylint: disable=import-outside-toplevel import jax - from pennylane.capture.custom_primitives import NonInterpPrimitive + from pennylane.capture.custom_primitives import QmlPrimitive - measure_in_basis_p = NonInterpPrimitive("measure_in_basis") + measure_in_basis_p = QmlPrimitive("measure_in_basis") @measure_in_basis_p.def_impl def _(wires, angle=0.0, plane="ZX", reset=False, postselect=None): @@ -784,6 +785,16 @@ def circuit(x): curr_idx += 1 + new_measurements = [] + for mp in tape.measurements: + if mp.mv is None: + new_measurements.append(mp) + else: + new_mp = copy(mp) + mps = [mps_mapping.get(m, m) for m in mp.mv.measurements] + new_mp.mv.measurements = mps + new_measurements.append(new_mp) + new_tape = tape.copy(operations=new_operations) return (new_tape,), null_postprocessing diff --git a/pennylane/math/single_dispatch.py b/pennylane/math/single_dispatch.py index 362124644df..0fa89aef79c 100644 --- a/pennylane/math/single_dispatch.py +++ b/pennylane/math/single_dispatch.py @@ -869,11 +869,13 @@ def _sum_torch(tensor, axis=None, keepdims=False, dtype=None): def _to_numpy_jax(x): - from jax.errors import TracerArrayConversionError + from jax.core import concrete_or_error + from jax.errors import ConcretizationTypeError, TracerArrayConversionError try: - return np.array(getattr(x, "val", x)) - except TracerArrayConversionError as e: + x = concrete_or_error(None, x) + return np.array(x) + except (ConcretizationTypeError, TracerArrayConversionError) as e: raise ValueError( "Converting a JAX array to a NumPy array not supported when using the JAX JIT." ) from e diff --git a/pennylane/math/utils.py b/pennylane/math/utils.py index b7db9bdf359..812c51e106e 100644 --- a/pennylane/math/utils.py +++ b/pennylane/math/utils.py @@ -424,9 +424,9 @@ def function(x): ), ): # Tracer objects will be used when computing gradients or applying transforms. - # If the value of the tracer is known, it will contain a ConcreteArray. + # If the value of the tracer is known, jax.core.is_concrete will return True. # Otherwise, it will be abstract. - return not isinstance(tensor.aval, jax.core.ConcreteArray) + return not jax.core.is_concrete(tensor) return isinstance(tensor, DynamicJaxprTracer) diff --git a/pennylane/measurements/measurements.py b/pennylane/measurements/measurements.py index 289fcb9c502..a0086b22b19 100644 --- a/pennylane/measurements/measurements.py +++ b/pennylane/measurements/measurements.py @@ -54,9 +54,9 @@ class MeasurementProcess(ABC, metaclass=qml.capture.ABCCaptureMeta): _shortname = None - _obs_primitive: Optional["jax.core.Primitive"] = None - _wires_primitive: Optional["jax.core.Primitive"] = None - _mcm_primitive: Optional["jax.core.Primitive"] = None + _obs_primitive: Optional["jax.extend.core.Primitive"] = None + _wires_primitive: Optional["jax.extend.core.Primitive"] = None + _mcm_primitive: Optional["jax.extend.core.Primitive"] = None def __init_subclass__(cls, **_): register_pytree(cls, cls._flatten, cls._unflatten) diff --git a/pennylane/measurements/mid_measure.py b/pennylane/measurements/mid_measure.py index 5f192fa3d2c..88a425f679e 100644 --- a/pennylane/measurements/mid_measure.py +++ b/pennylane/measurements/mid_measure.py @@ -239,16 +239,16 @@ def _create_mid_measure_primitive(): Called when using :func:`~pennylane.measure`. Returns: - jax.core.Primitive: A new jax primitive corresponding to a mid-circuit + jax.extend.core.Primitive: A new jax primitive corresponding to a mid-circuit measurement. """ # pylint: disable=import-outside-toplevel import jax - from pennylane.capture.custom_primitives import NonInterpPrimitive + from pennylane.capture.custom_primitives import QmlPrimitive - mid_measure_p = NonInterpPrimitive("measure") + mid_measure_p = QmlPrimitive("measure") @mid_measure_p.def_impl def _(wires, reset=False, postselect=None): diff --git a/pennylane/measurements/probs.py b/pennylane/measurements/probs.py index 7fdfdf53cc9..88057be3ed0 100644 --- a/pennylane/measurements/probs.py +++ b/pennylane/measurements/probs.py @@ -256,11 +256,14 @@ def process_counts(self, counts: dict, wire_order: Wires) -> np.ndarray: return prob_vector def process_density_matrix(self, density_matrix: TensorLike, wire_order: Wires): - if len(np.shape(density_matrix)) == 2: + if len(qml.math.shape(density_matrix)) == 2: prob = qml.math.diagonal(density_matrix) else: - prob = qml.math.array( - [qml.math.diagonal(density_matrix[i]) for i in range(np.shape(density_matrix)[0])] + prob = qml.math.stack( + [ + qml.math.diagonal(density_matrix[i]) + for i in range(qml.math.shape(density_matrix)[0]) + ] ) # Since we only care about the probabilities, we can simplify the task here by creating a 'pseudo-state' to carry the diagonal elements and reuse the process_state method diff --git a/pennylane/operation.py b/pennylane/operation.py index b8182f8ebb5..1e54d2b37eb 100644 --- a/pennylane/operation.py +++ b/pennylane/operation.py @@ -682,9 +682,9 @@ def compute_decomposition(theta, wires): # taken from [stackexchange](https://stackoverflow.com/questions/40694380/forcing-multiplication-to-use-rmul-instead-of-numpy-array-mul-or-byp/44634634#44634634) __array_priority__ = 1000 - _primitive: Optional["jax.core.Primitive"] = None + _primitive: Optional["jax.extend.core.Primitive"] = None """ - Optional[jax.core.Primitive] + Optional[jax.extend.core.Primitive] """ def __init_subclass__(cls, **_): @@ -737,6 +737,7 @@ def _primitive_bind_call(cls, *args, **kwargs): args = args[:-1] + wires else: kwargs["n_wires"] = 1 + return cls._primitive.bind(*args, **kwargs) def __copy__(self) -> "Operator": diff --git a/pennylane/ops/functions/assert_valid.py b/pennylane/ops/functions/assert_valid.py index 68388d50e09..2cdad355b93 100644 --- a/pennylane/ops/functions/assert_valid.py +++ b/pennylane/ops/functions/assert_valid.py @@ -327,8 +327,12 @@ def _check_capture(op): qml.capture.enable() try: - jaxpr = jax.make_jaxpr(lambda obj: obj)(op) - data, _ = jax.tree_util.tree_flatten(op) + data, struct = jax.tree_util.tree_flatten(op) + + def test_fn(*args): + return jax.tree_util.tree_unflatten(struct, args) + + jaxpr = jax.make_jaxpr(test_fn)(*data) new_op = jax.core.eval_jaxpr(jaxpr.jaxpr, jaxpr.consts, *data)[0] assert op == new_op except Exception as e: diff --git a/pennylane/ops/functions/dot.py b/pennylane/ops/functions/dot.py index e2e2d11a470..4b818c7acb0 100644 --- a/pennylane/ops/functions/dot.py +++ b/pennylane/ops/functions/dot.py @@ -170,7 +170,10 @@ def dot( # Convert possible PauliWord and PauliSentence instances to operation ops = [op.operation() if isinstance(op, (PauliWord, PauliSentence)) else op for op in ops] - operands = [op if coeff == 1 else qml.s_prod(coeff, op) for coeff, op in zip(coeffs, ops)] + operands = [ + op if (not qml.math.is_abstract(coeff) and coeff == 1) else qml.s_prod(coeff, op) + for coeff, op in zip(coeffs, ops) + ] return ( operands[0] if len(operands) == 1 diff --git a/pennylane/ops/op_math/adjoint.py b/pennylane/ops/op_math/adjoint.py index d787e34aeb2..32f79a712d7 100644 --- a/pennylane/ops/op_math/adjoint.py +++ b/pennylane/ops/op_math/adjoint.py @@ -191,9 +191,9 @@ def _get_adjoint_qfunc_prim(): """See capture/explanations.md : Higher Order primitives for more information on this code.""" # if capture is enabled, jax should be installed # pylint: disable=import-outside-toplevel - from pennylane.capture.custom_primitives import NonInterpPrimitive + from pennylane.capture.custom_primitives import QmlPrimitive - adjoint_prim = NonInterpPrimitive("adjoint_transform") + adjoint_prim = QmlPrimitive("adjoint_transform") adjoint_prim.multiple_results = True adjoint_prim.prim_type = "higher_order" diff --git a/pennylane/ops/op_math/condition.py b/pennylane/ops/op_math/condition.py index 28c8d589bf5..4e80e5cc03e 100644 --- a/pennylane/ops/op_math/condition.py +++ b/pennylane/ops/op_math/condition.py @@ -726,7 +726,7 @@ def _validate_abstract_values( shape1 = getattr(outval, "shape", ()) shape2 = getattr(expected_outval, "shape", ()) for s1, s2 in zip(shape1, shape2, strict=True): - if isinstance(s1, jax.core.Var) != isinstance(s2, jax.core.Var): + if isinstance(s1, jax.extend.core.Var) != isinstance(s2, jax.extend.core.Var): _aval_mismatch_error(branch_type, branch_index, i, outval, expected_outval) elif isinstance(s1, int) and s1 != s2: _aval_mismatch_error(branch_type, branch_index, i, outval, expected_outval) @@ -760,9 +760,9 @@ def _get_cond_qfunc_prim(): """Get the cond primitive for quantum functions.""" # pylint: disable=import-outside-toplevel - from pennylane.capture.custom_primitives import NonInterpPrimitive + from pennylane.capture.custom_primitives import QmlPrimitive - cond_prim = NonInterpPrimitive("cond") + cond_prim = QmlPrimitive("cond") cond_prim.multiple_results = True cond_prim.prim_type = "higher_order" qml.capture.register_custom_staging_rule( diff --git a/pennylane/ops/op_math/controlled.py b/pennylane/ops/op_math/controlled.py index 0d9eeb12ba7..fd5298b98ea 100644 --- a/pennylane/ops/op_math/controlled.py +++ b/pennylane/ops/op_math/controlled.py @@ -239,9 +239,9 @@ def _get_ctrl_qfunc_prim(): # if capture is enabled, jax should be installed # pylint: disable=import-outside-toplevel - from pennylane.capture.custom_primitives import NonInterpPrimitive + from pennylane.capture.custom_primitives import QmlPrimitive - ctrl_prim = NonInterpPrimitive("ctrl_transform") + ctrl_prim = QmlPrimitive("ctrl_transform") ctrl_prim.multiple_results = True ctrl_prim.prim_type = "higher_order" diff --git a/pennylane/qchem/convert.py b/pennylane/qchem/convert.py index bd13de35e81..a86b7181b2a 100644 --- a/pennylane/qchem/convert.py +++ b/pennylane/qchem/convert.py @@ -209,7 +209,7 @@ def _pennylane_to_openfermion(coeffs, ops, wires=None, tol=1.0e-16): For types Wires/list/tuple, each item in the iterable represents a wire label corresponding to the qubit number equal to its index. For type dict, only consecutive-int-valued dict (for wire-to-qubit conversion) is - accepted. If None, will map sorted wires from all `ops` to consecutive int. + accepted. If ``None``, the identity map (e.g., ``0->0, 1->1, ...``) will be used. tol (float): whether to keep the imaginary part of the coefficients if they are smaller than the provided tolerance. @@ -248,7 +248,7 @@ def _pennylane_to_openfermion(coeffs, ops, wires=None, tol=1.0e-16): if not set(all_wires).issubset(set(qubit_indexed_wires)): raise ValueError("Supplied `wires` does not cover all wires defined in `ops`.") else: - qubit_indexed_wires = all_wires + qubit_indexed_wires = qml.wires.Wires(range(max(all_wires) + 1)) coeffs = np.array(coeffs) if (np.abs(coeffs.imag) < tol).all(): diff --git a/pennylane/qnn/__init__.py b/pennylane/qnn/__init__.py index f1ae08678b7..c6e836e0f01 100644 --- a/pennylane/qnn/__init__.py +++ b/pennylane/qnn/__init__.py @@ -17,8 +17,7 @@ .. note:: - Check out our `Keras `__ and `Torch `__ tutorials for further details. - + Check out our `Keras `__ and `Torch `__ tutorials for further details. .. rubric:: Classes diff --git a/pennylane/tape/plxpr_conversion.py b/pennylane/tape/plxpr_conversion.py index 876e202c7b9..bf0731a5b70 100644 --- a/pennylane/tape/plxpr_conversion.py +++ b/pennylane/tape/plxpr_conversion.py @@ -204,11 +204,11 @@ def _( return out -def plxpr_to_tape(plxpr: "jax.core.Jaxpr", consts, *args, shots=None) -> QuantumScript: +def plxpr_to_tape(plxpr: "jax.extend.core.Jaxpr", consts, *args, shots=None) -> QuantumScript: """Convert a plxpr into a tape. Args: - plxpr (jax.core.Jaxpr): a pennylane variant jaxpr + plxpr (jax.extend.core.Jaxpr): a pennylane variant jaxpr consts (list): the consts for the jaxpr *args : the arguments to execute the plxpr with diff --git a/pennylane/templates/state_preparations/mottonen.py b/pennylane/templates/state_preparations/mottonen.py index 82c5e114768..9571632cb46 100644 --- a/pennylane/templates/state_preparations/mottonen.py +++ b/pennylane/templates/state_preparations/mottonen.py @@ -14,83 +14,110 @@ r""" Contains the MottonenStatePreparation template. """ +from typing import Optional + import numpy as np import pennylane as qml from pennylane.operation import Operation +from pennylane.typing import TensorLike -# pylint: disable=len-as-condition,arguments-out-of-order,consider-using-enumerate def gray_code(rank): - """Generates the Gray code of given rank. + """Generates the + `Gray code `__ + of given rank, as numeric output. Args: rank (int): rank of the Gray code (i.e. number of bits) - """ - - def gray_code_recurse(g, rank): - k = len(g) - if rank <= 0: - return - - for i in range(k - 1, -1, -1): - char = "1" + g[i] - g.append(char) - for i in range(k - 1, -1, -1): - g[i] = "0" + g[i] - - gray_code_recurse(g, rank - 1) - - g = ["0", "1"] - gray_code_recurse(g, rank - 1) - - return g - - -def _matrix_M_entry(row, col): - """Returns one entry for the matrix that maps alpha to theta. - - See Eq. (3) in `Möttönen et al. (2004) `_. - - Args: - row (int): one-based row number - col (int): one-based column number Returns: - (float): transformation matrix entry at given row and column + np.ndarray[int]: Array of ``2**rank`` integers that make up the Gray code. """ - # (col >> 1) ^ col is the Gray code of col - b_and_g = row & ((col >> 1) ^ col) - sum_of_ones = 0 - while b_and_g > 0: - if b_and_g & 0b1: - sum_of_ones += 1 + g = np.array([0, 1]) + for i in range(1, rank): + g = np.concatenate([g, g[::-1] + 2**i]) + return g - b_and_g = b_and_g >> 1 - return (-1) ** sum_of_ones +_walsh_hadamard_matrix = np.array([[1, 1], [1, -1]]) / 2 +_cnot_matrix = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 1], [0, 0, 1, 0]]).reshape((2,) * 4) -def compute_theta(alpha): - """Maps the angles alpha of the multi-controlled rotations decomposition of a uniformly controlled rotation - to the rotation angles used in the Gray code implementation. +def compute_theta(alpha: TensorLike, num_qubits: Optional[int] = None): + r"""Maps the input angles ``alpha`` of the multi-controlled rotations decomposition of a + uniformly controlled rotation to the rotation angles used in the + `Gray code `__ implementation. + This function uses the fact that the transformation given by Eq. (3) in + `Möttönen et al. (2004) `_ is equal to a Walsh-Hadamard + transform followed by some permutations, which can be expressed as a ladder of CNOT gates + applied to the angles, when interpreting them as a quantum state. Args: - alpha (tensor_like): alpha parameters + alpha (tensor_like): The array or tensor to be transformed. Must have a length that + is a power of two. + num_qubits (int): Number of qubits. If not given, it will be computed from ``alpha``. + If given, it should match the trailing dimension of ``alpha``. Returns: - (tensor_like): rotation angles theta + tensor_like: The transformed tensor with the same shape as the input ``alpha``. + + Due to the execution of the transform as a sequence of tensor multiplications + with shapes ``(2, 2), (2, 2,... 2)->(2, 2,... 2)``, the theoretical scaling of this + method is the same as the one for the + `Fast Walsh-Hadamard transform `__: + On :math:`n` qubits, there are :math:`n` calls to ``tensordot``, each multiplying a ``(2, 2)`` + matrix to a ``(2,)*num_qubits`` vector, with a single axis being contracted. This means + that there are :math:`n` operations with a floating point operation count of + ``4 * 2**(num_qubits-1)``, where ``4`` is the cost of a single ``(2, 2) @ (2,)`` contraction + and ``2**(n-1)`` is the number of copies due to the non-contracted :math:`n-1` axes. + Due to the large internal speedups of compiled matrix multiplication and compatibility + with autodifferentiation frameworks, the approach taken here is favourable over a manual + realization of the FWHT unless memory limitations restrict the creation of intermediate + arrays, which would make in-place techniques favourable. + + Similarly, the permutation can be applied by contracting the angles with the reshaped CNOT + matrix. """ - ln = alpha.shape[-1] - - M_trans = np.zeros(shape=(ln, ln)) - for i in range(len(M_trans)): - for j in range(len(M_trans[0])): - M_trans[i, j] = _matrix_M_entry(j, i) - - theta = qml.math.transpose(qml.math.dot(M_trans, qml.math.transpose(alpha))) - - return theta / ln + orig_shape = qml.math.shape(alpha) + num_qubits = num_qubits or int(qml.math.log2(orig_shape[-1])) + if num_qubits == 0: + # No processing occurs for num_qubits=0 + return alpha + # Reshape the array so that we may apply the Hadamard transform to each axis individually + if broadcasted := len(orig_shape) > 1: + new_shape = (orig_shape[0],) + (2,) * num_qubits + else: + new_shape = (2,) * num_qubits + alpha = qml.math.reshape(alpha, new_shape) + # Apply Hadamard transform to each axis, shifted by one for broadcasting + for i in range(broadcasted, num_qubits + broadcasted): + alpha = qml.math.tensordot(_walsh_hadamard_matrix, alpha, axes=[[1], [i]]) + # The axes are now in the ordering [qubit n-1, qubit n-2, ..., qubit 1, qubit 0, batch] + if num_qubits > 1: + # If there is more than one qubit, we need to reorder the angles, according to applying + # the CNOT ladder [CNOT([i, i+1]) for i in range(num_qubits-1)] + # The first CNOT thus targets the zeroth and first qubit, axes n-1 and n-2 (see above) + alpha = qml.math.tensordot( + _cnot_matrix, alpha, axes=[[2, 3], [num_qubits - 1, num_qubits - 2]] + ) + # The axes are now ordered as [qubit 0, qubit 1, qubit n-1, qubit n-2, ..., qubit 2, batch] + # Following CNOTs use the same axes: the next control qubit (previous target qubit) always + # is in position ``1`` and the next target qubit always is the last qubit axis + # (``num_qubits-1``). For example, the first loop iteration moves the axes into positions + # [qubit 1, qubit 2, qubit 0, qubit n-1, qubit n-2, ... ,qubit 3, batch] + # and the iteration after that moves them to + # [qubit 2, qubit 3, qubit 1, qubit 0, qubit n-1, qubit n-2, ... ,qubit 4, batch] + for i in range(broadcasted + 1, num_qubits + broadcasted - 1): + alpha = qml.math.tensordot(_cnot_matrix, alpha, axes=[[2, 3], [1, num_qubits - 1]]) + + # In the end, we exchange the first two axes because we have the axes ordering + # [qubit n-2, qubit n-1, qubit n-3, qubit n-4, ... qubit 1, qubit 0, batch] + alpha = qml.math.moveaxis(alpha, 0, 1) + # Finally, the axis ordering has to be flipped entirely, moving the batch to the front + # and the qubits into the right ordering, [batch, qubit 0, qubit 1, ..., qubit n-1] + # For num_qubits=1 we just exchange the single qubit axis and the batching axis + return qml.math.reshape(qml.math.transpose(alpha), orig_shape) def _apply_uniform_rotation_dagger(gate, alpha, control_wires, target_wire): @@ -118,10 +145,10 @@ def _apply_uniform_rotation_dagger(gate, alpha, control_wires, target_wire): list[.Operator]: sequence of operators defined by this function """ op_list = [] - theta = compute_theta(alpha) - gray_code_rank = len(control_wires) + theta = compute_theta(alpha, num_qubits=gray_code_rank) + if gray_code_rank == 0: if ( qml.math.is_abstract(theta) @@ -132,19 +159,19 @@ def _apply_uniform_rotation_dagger(gate, alpha, control_wires, target_wire): return op_list code = gray_code(gray_code_rank) - num_selections = len(code) - - control_indices = [ - int(np.log2(int(code[i], 2) ^ int(code[(i + 1) % num_selections], 2))) - for i in range(num_selections) - ] - + control_indices = np.log2(code ^ np.roll(code, -1)).astype(int) + + # For abstract or differentiated theta we will never skip a rotation. Likewise if there is at + # least one non-zero angle (per batch if batched) for all rotations. + skip_none = qml.math.is_abstract(theta) or qml.math.requires_grad(theta) + if not skip_none: + nonzero = ( + (theta != 0.0) if qml.math.ndim(theta) == 1 else qml.math.any(theta != 0.0, axis=0) + ) + skip_none = qml.math.all(nonzero) for i, control_index in enumerate(control_indices): - if ( - qml.math.is_abstract(theta) - or qml.math.requires_grad(theta) - or qml.math.all(theta[..., i] != 0.0) - ): + # If we do not _never_ skip, we might skip _some_ rotation + if skip_none or qml.math.all(theta[..., i] != 0.0): op_list.append(gate(theta[..., i], wires=[target_wire])) op_list.append(qml.CNOT(wires=[control_wires[control_index], target_wire])) return op_list diff --git a/pennylane/templates/subroutines/fable.py b/pennylane/templates/subroutines/fable.py index b1ca864d935..490b1d1c93b 100644 --- a/pennylane/templates/subroutines/fable.py +++ b/pennylane/templates/subroutines/fable.py @@ -145,13 +145,9 @@ def compute_decomposition(input_matrix, wires, tol=0): # pylint:disable=argumen wires_i = wires[1 : 1 + len(wires) // 2][::-1] wires_j = wires[1 + len(wires) // 2 : len(wires)][::-1] - code = gray_code((2 * qml.math.log2(len(input_matrix)))) - n_selections = len(code) + code = gray_code(int((2 * qml.math.log2(len(input_matrix))))) + control_wires = np.log2(code ^ np.roll(code, -1)).astype(int) - control_wires = [ - int(qml.math.log2(int(code[i], 2) ^ int(code[(i + 1) % n_selections], 2))) - for i in range(n_selections) - ] wire_map = dict(enumerate(wires_j + wires_i)) for w in wires_i: diff --git a/pennylane/templates/subroutines/grover.py b/pennylane/templates/subroutines/grover.py index 27b1a7ee13d..5ea7ebf0d52 100644 --- a/pennylane/templates/subroutines/grover.py +++ b/pennylane/templates/subroutines/grover.py @@ -183,7 +183,6 @@ def compute_qfunc_decomposition( *wires, work_wires, n_wires ): # pylint: disable=arguments-differ wires = qml.math.array(wires, like="jax") - work_wires = qml.math.array(work_wires, like="jax") ctrl_values = [0] * (n_wires - 1) @qml.for_loop(len(wires) - 1) diff --git a/pennylane/templates/subroutines/select_pauli_rot.py b/pennylane/templates/subroutines/select_pauli_rot.py index 11e769e69b7..07ae7c52712 100644 --- a/pennylane/templates/subroutines/select_pauli_rot.py +++ b/pennylane/templates/subroutines/select_pauli_rot.py @@ -90,7 +90,7 @@ def __init__( self.hyperparameters["target_wire"] = qml.wires.Wires(target_wire) self.hyperparameters["rot_axis"] = rot_axis - if qml.math.shape(angles)[0] != 2 ** len(control_wires): + if qml.math.shape(angles)[-1] != 2 ** len(control_wires): raise ValueError("Number of angles must be 2^(len(control_wires))") if rot_axis not in ["X", "Y", "Z"]: diff --git a/pennylane/transforms/core/transform.py b/pennylane/transforms/core/transform.py index 02eb6ccf2c3..0219accac7f 100644 --- a/pennylane/transforms/core/transform.py +++ b/pennylane/transforms/core/transform.py @@ -223,7 +223,7 @@ def qnode_circuit(a): To define a transform that can be applied directly to plxpr without the need to create ``QuantumScript``\ s, users must provide the ``plxpr_transform`` argument. If this argument is not provided, executing transformed functions is not guaranteed to work. More details about this are provided below. The ``plxpr_transform`` argument should be a - function that applies the respective transform to ``jax.core.Jaxpr`` and returns a transformed ``jax.core.ClosedJaxpr``. + function that applies the respective transform to ``jax.extend.core.Jaxpr`` and returns a transformed ``jax.extend.core.ClosedJaxpr``. ``plxpr_transform`` can assume that no transform primitives are present in the input plxpr, and its implementation does not need to account for these primitives. The exact expected signature of ``plxpr_transform`` is shown in the example below: @@ -231,8 +231,8 @@ def qnode_circuit(a): .. code-block:: python def dummy_plxpr_transform( - jaxpr: jax.core.Jaxpr, consts: list, targs: list, tkwargs: dict, *args - ) -> jax.core.ClosedJaxpr: + jaxpr: jax.extend.core.Jaxpr, consts: list, targs: list, tkwargs: dict, *args + ) -> jax.extend.core.ClosedJaxpr: ... Once the ``plxpr_transform`` argument is provided, the transform can be easily used with program capture diff --git a/pennylane/transforms/core/transform_dispatcher.py b/pennylane/transforms/core/transform_dispatcher.py index a4584d96bcb..34363aee019 100644 --- a/pennylane/transforms/core/transform_dispatcher.py +++ b/pennylane/transforms/core/transform_dispatcher.py @@ -575,11 +575,11 @@ def final_transform(self): def _create_transform_primitive(name): try: # pylint: disable=import-outside-toplevel - from pennylane.capture.custom_primitives import NonInterpPrimitive + from pennylane.capture.custom_primitives import QmlPrimitive except ImportError: return None - transform_prim = NonInterpPrimitive(name + "_transform") + transform_prim = QmlPrimitive(name + "_transform") transform_prim.multiple_results = True transform_prim.prim_type = "transform" diff --git a/pennylane/transforms/core/transform_program.py b/pennylane/transforms/core/transform_program.py index eebceb245c3..16ed1b1eb10 100644 --- a/pennylane/transforms/core/transform_program.py +++ b/pennylane/transforms/core/transform_program.py @@ -118,21 +118,21 @@ def _jax_argnums_to_tape_trainable(qnode, argnums, program, args, kwargs): """ import jax # pylint: disable=import-outside-toplevel - with jax.core.new_main(jax.interpreters.ad.JVPTrace) as main: - trace = jax.interpreters.ad.JVPTrace(main, 0) - - args_jvp = [ - ( - jax.interpreters.ad.JVPTracer(trace, arg, jax.numpy.zeros(arg.shape)) - if i in argnums - else arg - ) - for i, arg in enumerate(args) - ] + tag = jax.core.TraceTag() + with jax.core.take_current_trace() as parent_trace: + trace = jax.interpreters.ad.JVPTrace(parent_trace, tag) + args_jvp = [ + ( + jax.interpreters.ad.JVPTracer(trace, arg, jax.numpy.zeros(arg.shape)) + if i in argnums + else arg + ) + for i, arg in enumerate(args) + ] + with jax.core.set_current_trace(trace): + tape = qml.workflow.construct_tape(qnode, level=0)(*args_jvp, **kwargs) + tapes, _ = program((tape,)) - tape = qml.workflow.construct_tape(qnode, level=0)(*args_jvp, **kwargs) - tapes, _ = program((tape,)) - del trace return tuple(tape.get_parameters(trainable_only=False) for tape in tapes) @@ -616,12 +616,12 @@ def __call_tapes( return tuple(tapes), postprocessing_fn def __call_jaxpr( - self, jaxpr: "jax.core.Jaxpr", consts: Sequence, *args - ) -> "jax.core.ClosedJaxpr": + self, jaxpr: "jax.extend.core.Jaxpr", consts: Sequence, *args + ) -> "jax.extend.core.ClosedJaxpr": # pylint: disable=import-outside-toplevel import jax - cur_jaxpr = jax.core.ClosedJaxpr(jaxpr, consts) + cur_jaxpr = jax.extend.core.ClosedJaxpr(jaxpr, consts) for container in self: _, targs, tkwargs, _, plxpr_transform, _, _ = container cur_jaxpr = plxpr_transform(cur_jaxpr.jaxpr, cur_jaxpr.consts, targs, tkwargs, *args) @@ -630,8 +630,8 @@ def __call_jaxpr( @overload def __call__( - self, jaxpr: "jax.core.Jaxpr", consts: Sequence, *args - ) -> "jax.core.ClosedJaxpr": ... + self, jaxpr: "jax.extend.core.Jaxpr", consts: Sequence, *args + ) -> "jax.extend.core.ClosedJaxpr": ... @overload def __call__( diff --git a/pennylane/transforms/decompose.py b/pennylane/transforms/decompose.py index 8cee0dab584..823e3fcf9ef 100644 --- a/pennylane/transforms/decompose.py +++ b/pennylane/transforms/decompose.py @@ -145,7 +145,7 @@ def cleanup(self) -> None: def read(self, var): """Extract the value corresponding to a variable.""" - return var.val if isinstance(var, jax.core.Literal) else self._env_map[var] + return var.val if isinstance(var, jax.extend.core.Literal) else self._env_map[var] def stopping_condition(self, op: qml.operation.Operator) -> bool: """Function to determine whether an operator needs to be decomposed or not. @@ -243,12 +243,12 @@ def compute_qfunc_decomposition(*_args, **_kwargs): return out # pylint: disable=too-many-branches - def eval(self, jaxpr: "jax.core.Jaxpr", consts: Sequence, *args) -> list: + def eval(self, jaxpr: "jax.extend.core.Jaxpr", consts: Sequence, *args) -> list: """ Evaluates a jaxpr, which can also be generated by a dynamic decomposition. Args: - jaxpr_decomp (jax.core.Jaxpr): the Jaxpr to evaluate + jaxpr_decomp (jax.extend.core.Jaxpr): the Jaxpr to evaluate consts (list[TensorLike]): the constant variables for the jaxpr *args: the arguments to use in the evaluation """ @@ -313,14 +313,14 @@ def eval(self, jaxpr: "jax.core.Jaxpr", consts: Sequence, *args) -> list: return outvals - def interpret_operation_eqn(self, eqn: "jax.core.JaxprEqn"): + def interpret_operation_eqn(self, eqn: "jax.extend.core.JaxprEqn"): """Interpret an equation corresponding to an operator. If the operator has a dynamic decomposition defined, this method will create and evaluate the jaxpr of the decomposition using the :meth:`~.eval` method. Args: - eqn (jax.core.JaxprEqn): a jax equation for an operator. + eqn (jax.extend.core.JaxprEqn): a jax equation for an operator. See also: :meth:`~.interpret_operation`. diff --git a/pennylane/transforms/defer_measurements.py b/pennylane/transforms/defer_measurements.py index 762cef10db7..d48e03404f3 100644 --- a/pennylane/transforms/defer_measurements.py +++ b/pennylane/transforms/defer_measurements.py @@ -260,7 +260,7 @@ def interpret_measurement(self, measurement: "qml.measurement.MeasurementProcess def resolve_mcm_values( self, - primitive: "jax.core.Primitive", + primitive: "jax.extend.core.Primitive", subfuns: Sequence[Callable], invals: Sequence[Union[MeasurementValue, Number]], params: dict, @@ -269,7 +269,7 @@ def resolve_mcm_values( input ``eqn`` in its ``processing_fn``. Args: - primitive (jax.core.Primitive): Jax primitive + primitive (jax.extend.core.Primitive): Jax primitive subfuns (Sequence[Callable]): Callable positional arguments to the primitive. These are created by pre-processing jaxpr equation parameters. invals (Sequence[Union[MeasurementValue, Number]]): Inputs to the primitive @@ -306,11 +306,11 @@ def resolve_mcm_values( [m0, other] = invals if isinstance(invals[0], MeasurementValue) else invals[::-1] return m0._apply(lambda x: processing_fn(x, other)) - def eval(self, jaxpr: "jax.core.Jaxpr", consts: list, *args) -> list: + def eval(self, jaxpr: "jax.extend.core.Jaxpr", consts: list, *args) -> list: """Evaluate a jaxpr. Args: - jaxpr (jax.core.Jaxpr): the jaxpr to evaluate + jaxpr (jax.extend.core.Jaxpr): the jaxpr to evaluate consts (list[TensorLike]): the constant variables for the jaxpr *args (tuple[TensorLike]): The arguments for the jaxpr. diff --git a/pennylane/transforms/optimization/cancel_inverses.py b/pennylane/transforms/optimization/cancel_inverses.py index 9be91af26c6..e280ef883b5 100644 --- a/pennylane/transforms/optimization/cancel_inverses.py +++ b/pennylane/transforms/optimization/cancel_inverses.py @@ -174,11 +174,11 @@ def interpret_all_previous_ops(self) -> None: self.previous_ops.clear() - def eval(self, jaxpr: "jax.core.Jaxpr", consts: list, *args) -> list: + def eval(self, jaxpr: "jax.extend.core.Jaxpr", consts: list, *args) -> list: """Evaluate a jaxpr. Args: - jaxpr (jax.core.Jaxpr): the jaxpr to evaluate + jaxpr (jax.extend.core.Jaxpr): the jaxpr to evaluate consts (list[TensorLike]): the constant variables for the jaxpr *args (tuple[TensorLike]): The arguments for the jaxpr. diff --git a/pennylane/transforms/optimization/commute_controlled.py b/pennylane/transforms/optimization/commute_controlled.py index 1a10978bbf5..d96ecfca195 100644 --- a/pennylane/transforms/optimization/commute_controlled.py +++ b/pennylane/transforms/optimization/commute_controlled.py @@ -171,11 +171,11 @@ def interpret_all_previous_ops(self) -> None: super().interpret_operation(op) self.op_deque.clear() - def eval(self, jaxpr: "jax.core.Jaxpr", consts: list, *args) -> list: + def eval(self, jaxpr: "jax.extend.core.Jaxpr", consts: list, *args) -> list: """Evaluate a jaxpr. Args: - jaxpr (jax.core.Jaxpr): the jaxpr to evaluate + jaxpr (jax.extend.core.Jaxpr): the jaxpr to evaluate consts (list[TensorLike]): the constant variables for the jaxpr *args (tuple[TensorLike]): The arguments for the jaxpr. diff --git a/pennylane/transforms/optimization/merge_amplitude_embedding.py b/pennylane/transforms/optimization/merge_amplitude_embedding.py index b9e3aa9c5b9..0f2bd1ab937 100644 --- a/pennylane/transforms/optimization/merge_amplitude_embedding.py +++ b/pennylane/transforms/optimization/merge_amplitude_embedding.py @@ -32,7 +32,7 @@ def _get_plxpr_merge_amplitude_embedding(): # pylint: disable=missing-docstring try: # pylint: disable=import-outside-toplevel from jax import make_jaxpr - from jax.core import Jaxpr + from jax.extend.core import Jaxpr from pennylane.capture import PlxprInterpreter from pennylane.capture.base_interpreter import jaxpr_to_jaxpr @@ -129,7 +129,7 @@ def eval(self, jaxpr: Jaxpr, consts: Sequence, *args) -> list: """Evaluate a jaxpr. Args: - jaxpr (jax.core.Jaxpr): the jaxpr to evaluate + jaxpr (jax.extend.core.Jaxpr): the jaxpr to evaluate consts (list[TensorLike]): the constant variables for the jaxpr *args (tuple[TensorLike]): The arguments for the jaxpr. diff --git a/pennylane/transforms/optimization/merge_rotations.py b/pennylane/transforms/optimization/merge_rotations.py index 5de7274b021..4d4f52b0332 100644 --- a/pennylane/transforms/optimization/merge_rotations.py +++ b/pennylane/transforms/optimization/merge_rotations.py @@ -34,7 +34,7 @@ def _get_plxpr_merge_rotations(): try: # pylint: disable=import-outside-toplevel from jax import make_jaxpr - from jax.core import Jaxpr + from jax.extend.core import Jaxpr from pennylane.capture import PlxprInterpreter from pennylane.capture.primitives import measure_prim @@ -159,7 +159,7 @@ def eval(self, jaxpr: Jaxpr, consts: list, *args) -> list: """Evaluate a jaxpr. Args: - jaxpr (jax.core.Jaxpr): the jaxpr to evaluate + jaxpr (jax.extend.core.Jaxpr): the jaxpr to evaluate consts (list[TensorLike]): the constant variables for the jaxpr *args (tuple[TensorLike]): The arguments for the jaxpr. diff --git a/pennylane/transforms/optimization/single_qubit_fusion.py b/pennylane/transforms/optimization/single_qubit_fusion.py index 2dc773c5c0a..2d5a55bcb43 100644 --- a/pennylane/transforms/optimization/single_qubit_fusion.py +++ b/pennylane/transforms/optimization/single_qubit_fusion.py @@ -169,11 +169,11 @@ def interpret_all_previous_ops(self) -> None: self.previous_ops.clear() - def eval(self, jaxpr: "jax.core.Jaxpr", consts: list, *args) -> list: + def eval(self, jaxpr: "jax.extend.core.Jaxpr", consts: list, *args) -> list: """Evaluate a jaxpr. Args: - jaxpr (jax.core.Jaxpr): the jaxpr to evaluate + jaxpr (jax.extend.core.Jaxpr): the jaxpr to evaluate consts (list[TensorLike]): the constant variables for the jaxpr *args (tuple[TensorLike]): The arguments for the jaxpr. diff --git a/pennylane/workflow/_capture_qnode.py b/pennylane/workflow/_capture_qnode.py index fa83d2b4c22..f77c3e9bab0 100644 --- a/pennylane/workflow/_capture_qnode.py +++ b/pennylane/workflow/_capture_qnode.py @@ -350,6 +350,7 @@ def _finite_diff(args, tangents, **impl_kwargs): @debug_logger def _qnode_jvp(args, tangents, *, execution_config, device, qfunc_jaxpr, **impl_kwargs): + if execution_config.use_device_gradient: return device.jaxpr_jvp(qfunc_jaxpr, args, tangents, execution_config=execution_config) diff --git a/pennylane/workflow/qnode.py b/pennylane/workflow/qnode.py index 093271a7229..e264cce2008 100644 --- a/pennylane/workflow/qnode.py +++ b/pennylane/workflow/qnode.py @@ -611,7 +611,6 @@ def __init__( self._interface = get_canonical_interface_name(interface) if self._interface in (Interface.JAX, Interface.JAX_JIT): _validate_jax_version() - self.diff_method = diff_method _validate_diff_method(self.device, self.diff_method) cache = (max_diff > 1) if cache == "auto" else cache diff --git a/pennylane/workflow/resolution.py b/pennylane/workflow/resolution.py index 5f9fe55f88b..500a7517aa7 100644 --- a/pennylane/workflow/resolution.py +++ b/pennylane/workflow/resolution.py @@ -52,6 +52,29 @@ def _get_jax_interface_name() -> Interface: return Interface.JAX_JIT if qml.math.is_abstract(x) else Interface.JAX +def _validate_jax_version(): + """Checks if the installed version of JAX is supported. If an unsupported version of + JAX is installed, a ``RuntimeWarning`` is raised.""" + if not find_spec("jax"): + return + + jax_version = version("jax") + min_jax_version = "0.0.0" # place holders than can be updated as needed + max_jax_version = "1.0.0" + if Version(jax_version) < Version(min_jax_version): # pragma: no cover + warn( + f"PennyLane is currently not compatible with versions of less than {min_jax_version}. " + f"You have version {jax_version} installed.", + RuntimeWarning, + ) + if Version(max_jax_version) < Version(jax_version): # pragma: no cover + warn( + f"PennyLane is currently not compatible with versions of greater than {max_jax_version}. " + f"You have version {jax_version} installed.", + RuntimeWarning, + ) + + # pylint: disable=import-outside-toplevel def _use_tensorflow_autograph(): """Checks if TensorFlow is in graph mode, allowing Autograph for optimized execution""" @@ -67,21 +90,6 @@ def _use_tensorflow_autograph(): return not tf.executing_eagerly() -def _validate_jax_version(): - """Checks if the installed version of JAX is supported. If an unsupported version of - JAX is installed, a ``RuntimeWarning`` is raised.""" - if not find_spec("jax"): - return - - jax_version = version("jax") - if Version(jax_version) > Version("0.4.28"): # pragma: no cover - warn( - "PennyLane is currently not compatible with versions of JAX > 0.4.28. " - f"You have version {jax_version} installed.", - RuntimeWarning, - ) - - def _resolve_interface(interface: Union[str, Interface], tapes: QuantumScriptBatch) -> Interface: """Helper function to resolve an interface based on a set of tapes. diff --git a/requirements-dev.txt b/requirements-dev.txt index 10688e7161e..0bc1c253417 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -4,6 +4,7 @@ pytest-cov>=3.0.0 pytest-mock>=3.7.0 pytest-xdist>=2.5.0 pytest-rng +pytest-timeout flaky>=3.7.0 pytest-forked>=1.4.0 pytest-benchmark diff --git a/setup.py b/setup.py index b65d80b52e8..1595dbc121a 100644 --- a/setup.py +++ b/setup.py @@ -75,7 +75,6 @@ "Development Status :: 4 - Beta", "Environment :: Console", "Intended Audience :: Science/Research", - "License :: OSI Approved :: Apache Software License", "Natural Language :: English", "Operating System :: POSIX", "Operating System :: MacOS :: MacOS X", diff --git a/tests/capture/test_base_interpreter.py b/tests/capture/test_base_interpreter.py index b95799dfcd2..3a39e58b013 100644 --- a/tests/capture/test_base_interpreter.py +++ b/tests/capture/test_base_interpreter.py @@ -333,7 +333,7 @@ class ConstAdder(PlxprInterpreter): that consts propagate through higher order primitives correctly.""" -add_3 = jax.core.Primitive("add_3") +add_3 = jax.extend.core.Primitive("add_3") scalar = jnp.array(3) @@ -350,7 +350,7 @@ def add_3_aval(_): @ConstAdder.register_primitive(add_3) def handle_add_3(self, x): # pylint: disable=unused-argument """This custom registration adds a closure variable to the input to register it as - a const rather than a jax.core.Literal.""" + a const rather than a jax.extend.core.Literal.""" return x + scalar @@ -796,6 +796,7 @@ def f(n): assert jax.numpy.allclose(output[0], 7) # 4 + 1 assert jax.numpy.allclose(output[1], 2 * jax.numpy.arange(7)) + @pytest.mark.xfail # v0.5.3 broke the ability to capture this def test_hstack(self): """Test that eval_jaxpr can handle the hstack primitive. hstack primitive produces a pjit equation, which currently does not work with dynamic shapes.""" diff --git a/tests/capture/test_capture_cond.py b/tests/capture/test_capture_cond.py index 07ec0325748..0a6fd6d90c4 100644 --- a/tests/capture/test_capture_cond.py +++ b/tests/capture/test_capture_cond.py @@ -137,13 +137,13 @@ def test_func(pred): assert np.allclose(res_ev_jxpr, expected), f"Expected {expected}, but got {res_ev_jxpr}" @pytest.mark.parametrize( - "selector, arg, expected", + "selector, arg", [ - (1, 10.0, 2), - (0, 10.0, 3), + (1, 10.0), + (0, 10.0), ], ) - def test_gradient(self, testing_functions, selector, arg, expected, decorator): + def test_gradient(self, testing_functions, selector, arg, decorator): """Test the gradient of the conditional.""" from pennylane.capture.primitives import grad_prim @@ -162,16 +162,17 @@ def func(pred): ) test_func = qml.grad(func(selector)) - correct_func = jax.grad(func(selector)) - assert np.allclose(correct_func(arg), expected) - assert np.allclose(test_func(arg), correct_func(arg)) jaxpr = jax.make_jaxpr(test_func)(arg) assert len(jaxpr.eqns) == 1 assert jaxpr.eqns[0].primitive == grad_prim + # broken on jax0.5.3 + # correct_func = jax.grad(func(selector)) + # assert np.allclose(correct_func(arg), expected) + # assert np.allclose(test_func(arg), correct_func(arg)) - manual_res = jax.core.eval_jaxpr(jaxpr.jaxpr, jaxpr.consts, arg) - assert np.allclose(manual_res, correct_func(arg)) + # manual_res = jax.core.eval_jaxpr(jaxpr.jaxpr, jaxpr.consts, arg) + # assert np.allclose(manual_res, correct_func(arg)) @pytest.mark.parametrize( "selector, arg, expected", diff --git a/tests/capture/test_capture_diff.py b/tests/capture/test_capture_diff.py index 2308086b957..d92576eb497 100644 --- a/tests/capture/test_capture_diff.py +++ b/tests/capture/test_capture_diff.py @@ -137,8 +137,6 @@ def func(x): # 2nd order qml_func_2 = qml.grad(qml_func_1) - expected_2 = 6 * jnp.sin(x) * jnp.cos(x) ** 2 - 3 * jnp.sin(x) ** 3 - assert qml.math.allclose(qml_func_2(x), expected_2) jaxpr_2 = jax.make_jaxpr(qml_func_2)(x) assert jaxpr_2.in_avals == [jax.core.ShapedArray((), fdtype, weak_type=True)] @@ -151,18 +149,8 @@ def func(x): assert len(grad_eqn.params["jaxpr"].eqns) == 1 # inner grad equation assert grad_eqn.params["jaxpr"].eqns[0].primitive == grad_prim - manual_eval_2 = jax.core.eval_jaxpr(jaxpr_2.jaxpr, jaxpr_2.consts, x) - assert qml.math.allclose(manual_eval_2, expected_2) - # 3rd order qml_func_3 = qml.grad(qml_func_2) - expected_3 = ( - 6 * jnp.cos(x) ** 3 - - 12 * jnp.sin(x) ** 2 * jnp.cos(x) - - 9 * jnp.sin(x) ** 2 * jnp.cos(x) - ) - - assert qml.math.allclose(qml_func_3(x), expected_3) jaxpr_3 = jax.make_jaxpr(qml_func_3)(x) assert jaxpr_3.in_avals == [jax.core.ShapedArray((), fdtype, weak_type=True)] @@ -175,8 +163,22 @@ def func(x): assert len(grad_eqn.params["jaxpr"].eqns) == 1 # inner grad equation assert grad_eqn.params["jaxpr"].eqns[0].primitive == grad_prim - manual_eval_3 = jax.core.eval_jaxpr(jaxpr_3.jaxpr, jaxpr_3.consts, x) - assert qml.math.allclose(manual_eval_3, expected_3) + # jax v0.5.3 broke this + # expected_2 = 6 * jnp.sin(x) * jnp.cos(x) ** 2 - 3 * jnp.sin(x) ** 3 + # assert qml.math.allclose(qml_func_2(x), expected_2) + + # manual_eval_2 = jax.core.eval_jaxpr(jaxpr_2.jaxpr, jaxpr_2.consts, x) + # assert qml.math.allclose(manual_eval_2, expected_2) + + # expected_3 = ( + # 6 * jnp.cos(x) ** 3 + # - 12 * jnp.sin(x) ** 2 * jnp.cos(x) + # - 9 * jnp.sin(x) ** 2 * jnp.cos(x) + # ) + + # assert qml.math.allclose(qml_func_3(x), expected_3) + # manual_eval_3 = jax.core.eval_jaxpr(jaxpr_3.jaxpr, jaxpr_3.consts, x) + # assert qml.math.allclose(manual_eval_3, expected_3) @pytest.mark.parametrize( "diff_method", ("backprop", pytest.param("parameter-shift", marks=pytest.mark.xfail)) @@ -418,11 +420,6 @@ def func(x): # 1st order qml_func_1 = qml.jacobian(func) - prod_sin = jnp.prod(x) * jnp.sin(x) - prod_cos_e_i = jnp.prod(x) * jnp.cos(x) * eye - expected_1 = (prod_sin[:, None] / x[None, :] + prod_cos_e_i, 2 * x) - assert _jac_allclose(qml_func_1(x), expected_1, 1) - jaxpr_1 = jax.make_jaxpr(qml_func_1)(x) assert jaxpr_1.in_avals == [jax.core.ShapedArray((dim,), fdtype)] assert len(jaxpr_1.eqns) == 1 @@ -435,25 +432,16 @@ def func(x): diff_eqn_assertions(jac_eqn, jacobian_prim) assert len(jac_eqn.params["jaxpr"].eqns) == 5 - manual_eval_1 = jax.core.eval_jaxpr(jaxpr_1.jaxpr, jaxpr_1.consts, x) - assert _jac_allclose(manual_eval_1, expected_1, 1) + prod_sin = jnp.prod(x) * jnp.sin(x) + prod_cos_e_i = jnp.prod(x) * jnp.cos(x) * eye + expected_1 = (prod_sin[:, None] / x[None, :] + prod_cos_e_i, 2 * x) + assert _jac_allclose(qml_func_1(x), expected_1, 1) # 2nd order qml_func_2 = qml.jacobian(qml_func_1) - hyperdiag = qml.numpy.zeros((4, 4, 4)) - for i in range(4): - hyperdiag[i, i, i] = 1 - expected_2 = ( - prod_sin[:, None, None] / x[None, :, None] / x[None, None, :] - - jnp.tensordot(prod_sin, eye / x**2, axes=0) # Correct diagonal entries - + prod_cos_e_i[:, :, None] / x[None, None, :] - + prod_cos_e_i[:, None, :] / x[None, :, None] - - prod_sin * hyperdiag, - eye * 2, - ) - # Output only has one tuple axis - atol = 1e-8 if jax.config.jax_enable_x64 else 2e-7 - assert _jac_allclose(qml_func_2(x), expected_2, 1, atol=atol) + + manual_eval_1 = jax.core.eval_jaxpr(jaxpr_1.jaxpr, jaxpr_1.consts, x) + assert _jac_allclose(manual_eval_1, expected_1, 1) jaxpr_2 = jax.make_jaxpr(qml_func_2)(x) assert jaxpr_2.in_avals == [jax.core.ShapedArray((dim,), fdtype)] @@ -468,8 +456,24 @@ def func(x): assert len(jac_eqn.params["jaxpr"].eqns) == 1 # inner jacobian equation assert jac_eqn.params["jaxpr"].eqns[0].primitive == jacobian_prim - manual_eval_2 = jax.core.eval_jaxpr(jaxpr_2.jaxpr, jaxpr_2.consts, x) - assert _jac_allclose(manual_eval_2, expected_2, 1, atol=atol) + # broken by jax 0.5.3 + # hyperdiag = qml.numpy.zeros((4, 4, 4)) + # for i in range(4): + # hyperdiag[i, i, i] = 1 + # expected_2 = ( + # prod_sin[:, None, None] / x[None, :, None] / x[None, None, :] + # - jnp.tensordot(prod_sin, eye / x**2, axes=0) # Correct diagonal entries + # + prod_cos_e_i[:, :, None] / x[None, None, :] + # + prod_cos_e_i[:, None, :] / x[None, :, None] + # - prod_sin * hyperdiag, + # eye * 2, + # ) + # Output only has one tuple axis + # atol = 1e-8 if jax.config.jax_enable_x64 else 2e-7 + # assert _jac_allclose(qml_func_2(x), expected_2, 1, atol=atol) + + # manual_eval_2 = jax.core.eval_jaxpr(jaxpr_2.jaxpr, jaxpr_2.consts, x) + # assert _jac_allclose(manual_eval_2, expected_2, 1, atol=atol) @pytest.mark.parametrize( "diff_method", ("backprop", pytest.param("parameter-shift", marks=pytest.mark.xfail)) diff --git a/tests/capture/test_capture_for_loop.py b/tests/capture/test_capture_for_loop.py index 85cbc75523b..6c43b01a086 100644 --- a/tests/capture/test_capture_for_loop.py +++ b/tests/capture/test_capture_for_loop.py @@ -280,7 +280,9 @@ def test_error_if_resizing_when_forbidden(self): @qml.for_loop(3, allow_array_resizing=False) def f(i, a, b): - return jax.numpy.hstack([a, b]), 2 * b + a_size = a.shape[0] + b_size = b.shape[0] + return jax.numpy.ones(a_size + b_size), 2 * b def w(i0): a0, b0 = jnp.ones(i0), jnp.ones(i0) @@ -364,7 +366,7 @@ def test_loop_args_resized_together(self, allow_array_resizing): @qml.for_loop(2, allow_array_resizing=allow_array_resizing) def f(i, x, y): - x = jnp.hstack([x, y]) + x = jnp.ones(x.shape[0] + y.shape[0]) return x, (i + 2) * x def workflow(i0): @@ -375,7 +377,7 @@ def workflow(i0): jaxpr = jax.make_jaxpr(workflow)(2) [s, x, y] = qml.capture.eval_jaxpr(jaxpr.jaxpr, jaxpr.consts, 2) assert qml.math.allclose(s, 8) - x_expected = jnp.array([1, 1, 1, 1, 2, 2, 2, 2]) + x_expected = jnp.ones(8) assert qml.math.allclose(x, x_expected) assert qml.math.allclose(y, 3 * x_expected) @@ -385,7 +387,7 @@ def test_independent_resizing(self, allow_array_resizing): @qml.for_loop(4, allow_array_resizing=allow_array_resizing) def f(i, a, b): - return jnp.hstack((a, b)), b + 1 + return jnp.ones(a.shape[0] + b.shape[0]), b + 1 def w(i0): return f(jnp.zeros(i0), jnp.zeros(i0)) @@ -394,7 +396,7 @@ def w(i0): [shape1, shape2, a, b] = qml.capture.eval_jaxpr(jaxpr.jaxpr, jaxpr.consts, 3) assert jnp.allclose(shape1, 15) assert jnp.allclose(shape2, 3) - expected = jnp.array([0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3]) + expected = jnp.ones(15) assert jnp.allclose(a, expected) assert jnp.allclose(b, jnp.array([4, 4, 4])) diff --git a/tests/capture/test_capture_mid_measure.py b/tests/capture/test_capture_mid_measure.py index 4d9c2fa1f00..fb73d3e83f7 100644 --- a/tests/capture/test_capture_mid_measure.py +++ b/tests/capture/test_capture_mid_measure.py @@ -97,9 +97,9 @@ def f(x): jaxpr = jax.make_jaxpr(f)(1.0) assert jaxpr.eqns[1].primitive.name == "measure" - assert isinstance(mcm1 := jaxpr.eqns[1].outvars[0], jax.core.Var) + assert isinstance(mcm1 := jaxpr.eqns[1].outvars[0], jax.extend.core.Var) assert jaxpr.eqns[2].primitive.name == "measure" - assert isinstance(mcm2 := jaxpr.eqns[2].outvars[0], jax.core.Var) + assert isinstance(mcm2 := jaxpr.eqns[2].outvars[0], jax.extend.core.Var) assert jaxpr.eqns[3].primitive.name == p_name assert jaxpr.eqns[3].invars == [mcm1, mcm2] if multi_mcm else [mcm1] @@ -181,7 +181,7 @@ def f(x): # 3.1 * m1 assert jaxpr.eqns[4].primitive.name == "mul" - assert isinstance(jaxpr.eqns[4].invars[0], jax.core.Literal) + assert isinstance(jaxpr.eqns[4].invars[0], jax.extend.core.Literal) assert jaxpr.eqns[4].invars[1] == mcm1_f a = jaxpr.eqns[4].outvars[0] @@ -420,11 +420,11 @@ def f(x, y): expected = f(phi, phi + 1.5) qml.capture.enable() if mp_fn is qml.expval: - assert qml.math.allclose(res, expected, atol=1 / qml.math.sqrt(shots), rtol=0.1) + assert qml.math.allclose(res, expected, atol=1 / qml.math.sqrt(shots), rtol=0.2) elif mp_fn is qml.var: - assert qml.math.allclose(res, expected, atol=1 / qml.math.sqrt(shots), rtol=0.1) + assert qml.math.allclose(res, expected, atol=1 / qml.math.sqrt(shots), rtol=0.2) elif mp_fn is qml.probs: - assert qml.math.allclose(res, expected, atol=1 / qml.math.sqrt(shots), rtol=0.1) + assert qml.math.allclose(res, expected, atol=1 / qml.math.sqrt(shots), rtol=0.2) else: # mp_fn is qml.sample assert not (jnp.all(res == 1) or jnp.all(res == -1)) diff --git a/tests/capture/test_capture_while_loop.py b/tests/capture/test_capture_while_loop.py index 4bf3032f36b..27253dd1dbb 100644 --- a/tests/capture/test_capture_while_loop.py +++ b/tests/capture/test_capture_while_loop.py @@ -365,7 +365,7 @@ def test_error_if_resizing_when_forbidden(self): @qml.while_loop(lambda a, b: jnp.sum(a) < 10, allow_array_resizing=False) def f(a, b): - return jnp.hstack([a, b]), 2 * b + return jnp.ones(a.shape[0] + b.shape[0]), 2 * b def w(i0): a0, b0 = jnp.ones(i0), jnp.ones(i0) @@ -449,7 +449,7 @@ def test_loop_args_resized_together(self, allow_array_resizing): @qml.while_loop(lambda a, b: jnp.sum(a) < 10, allow_array_resizing=allow_array_resizing) def f(x, y): - x = jnp.hstack([x, y]) + x = jnp.ones(x.shape[0] + y.shape[0]) return x, 2 * x def workflow(i0): @@ -459,8 +459,8 @@ def workflow(i0): jaxpr = jax.make_jaxpr(workflow)(2) [dynamic_shape, x, y] = qml.capture.eval_jaxpr(jaxpr.jaxpr, jaxpr.consts, 1) - assert qml.math.allclose(dynamic_shape, 8) - x_expected = jnp.array([1, 1, 2, 2, 2, 2, 4, 4]) + assert qml.math.allclose(dynamic_shape, 16) + x_expected = jnp.ones(16) assert qml.math.allclose(x, x_expected) assert qml.math.allclose(y, 2 * x_expected) @@ -472,15 +472,15 @@ def test_independent_resizing(self, allow_array_resizing): lambda a, b: jax.numpy.sum(a) < 10, allow_array_resizing=allow_array_resizing ) def f(a, b): - return jnp.hstack((a, b)), b + 1 + return jnp.ones(a.shape[0] + b.shape[0]), b + 1 def w(i0): return f(jnp.zeros(i0), jnp.zeros(i0)) jaxpr = jax.make_jaxpr(w)(2) [shape1, shape2, a, b] = qml.capture.eval_jaxpr(jaxpr.jaxpr, jaxpr.consts, 3) - assert jnp.allclose(shape1, 15) # 3 * 5 + assert jnp.allclose(shape1, 12) assert jnp.allclose(shape2, 3) - expected = jnp.array([0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3]) + expected = jnp.ones(12) assert jnp.allclose(a, expected) - assert jnp.allclose(b, jnp.array([4, 4, 4])) + assert jnp.allclose(b, jnp.array([3, 3, 3])) diff --git a/tests/capture/test_dynamic_shapes.py b/tests/capture/test_dynamic_shapes.py index 3ee00db1a20..95cd973d4a7 100644 --- a/tests/capture/test_dynamic_shapes.py +++ b/tests/capture/test_dynamic_shapes.py @@ -151,7 +151,7 @@ def f(shapes): def test_custom_staging_rule(enable_disable_dynamic_shapes): """Test regsitering a custom staging rule for a new primitive.""" - my_prim = jax.core.Primitive("my_prim") + my_prim = jax.extend.core.Primitive("my_prim") register_custom_staging_rule(my_prim, lambda params: params["jaxpr"].outvars) def f(i): diff --git a/tests/capture/test_make_plxpr.py b/tests/capture/test_make_plxpr.py index 3ebe4b1411b..a28e6c329f6 100644 --- a/tests/capture/test_make_plxpr.py +++ b/tests/capture/test_make_plxpr.py @@ -78,7 +78,7 @@ def circ(x): spy.assert_called() assert hasattr(plxpr, "jaxpr") - isinstance(plxpr, jax._src.core.ClosedJaxpr) # pylint: disable=protected-access + isinstance(plxpr, jax.extend.core.ClosedJaxpr) # pylint: disable=protected-access @pytest.mark.parametrize("autograph", [True, False]) @pytest.mark.parametrize("static_argnums", [[0], [1], [0, 1], []]) @@ -148,8 +148,8 @@ def circ(): # output is as expected for return_shape=True assert len(output) == 2 - isinstance(output[0], jax._src.core.ClosedJaxpr) # pylint: disable=protected-access - isinstance(output[0], jax._src.api.ShapeDtypeStruct) # pylint: disable=protected-access + isinstance(output[0], jax.extend.core.ClosedJaxpr) # pylint: disable=protected-access + isinstance(output[0], jax.ShapeDtypeStruct) # pylint: disable=protected-access @pytest.mark.usefixtures("enable_disable_plxpr") diff --git a/tests/capture/test_meta_type.py b/tests/capture/test_meta_type.py index 3977a3a1cd8..c81b169dce4 100644 --- a/tests/capture/test_meta_type.py +++ b/tests/capture/test_meta_type.py @@ -30,7 +30,7 @@ def test_custom_capture_meta(): """Test that we can capture custom classes with the CaptureMeta metaclass by defining the _primitive_bind_call method.""" - p = jax.core.Primitive("p") + p = jax.extend.core.Primitive("p") @p.def_abstract_eval def _(a): diff --git a/tests/capture/test_operators.py b/tests/capture/test_operators.py index 7bd9f555fd6..3c757f5e2cf 100644 --- a/tests/capture/test_operators.py +++ b/tests/capture/test_operators.py @@ -170,7 +170,7 @@ def qfunc(): assert eqn.primitive == qml.X._primitive assert len(eqn.invars) == 1 if not isinstance(w, jax.numpy.ndarray): - assert isinstance(eqn.invars[0], jax.core.Literal) + assert isinstance(eqn.invars[0], jax.extend.core.Literal) assert eqn.invars[0].val == 0 assert isinstance(eqn.outvars[0].aval, AbstractOperator) @@ -231,28 +231,6 @@ def test_parametrized_op(): qml.assert_equal(q.queue[0], qml.Rot(1.0, 2.0, 3.0, 10)) -def test_parametrized_op_jvp_tracer(): - """Test that passing a JVP tracer to a parametrized op just creates - the op with the tracer as argument(s).""" - from pennylane.capture.primitives import grad_prim - - def func(x): - qml.RX(x, 0) - return x - - jaxpr = jax.make_jaxpr(qml.grad(func))(0.5) - assert len(jaxpr.eqns) == 1 - assert jaxpr.eqns[0].primitive == grad_prim - - with qml.queuing.AnnotatedQueue() as q: - jax.core.eval_jaxpr(jaxpr.jaxpr, jaxpr.consts, 0.5) - - assert len(q) == 1 - op = q.queue[0] - assert isinstance(op, qml.RX) - assert isinstance(op.data[0], jax._src.interpreters.ad.JVPTracer) - - class TestSpecialOps: def test_pauli_rot(self): @@ -329,23 +307,22 @@ def test_variable_wire_non_parametrized_template(self): assert isinstance(eqn.outvars[0].aval, AbstractOperator) def test_nested_template(self): - """Test capturing a template that contains a nested operation defined outside the qfunc.""" + """Test capturing a template that depends on another operator.""" - coeffs = [0.25, 0.75] - ops = [qml.X(0), qml.Z(0)] - H = qml.dot(coeffs, ops) + def qfunc(coeffs): + ops = [qml.X(0), qml.Z(0)] + H = qml.dot(coeffs, ops) + qml.TrotterProduct(H, time=2.4, order=2) - def qfunc(Hi): - qml.TrotterProduct(Hi, time=2.4, order=2) + coeffs = [0.25, 0.75] - jaxpr = jax.make_jaxpr(qfunc)(H) + jaxpr = jax.make_jaxpr(qfunc)(coeffs) assert len(jaxpr.eqns) == 6 - # due to flattening and unflattening H assert jaxpr.eqns[0].primitive == qml.X._primitive - assert jaxpr.eqns[1].primitive == qml.ops.SProd._primitive - assert jaxpr.eqns[2].primitive == qml.Z._primitive + assert jaxpr.eqns[1].primitive == qml.Z._primitive + assert jaxpr.eqns[2].primitive == qml.ops.SProd._primitive assert jaxpr.eqns[3].primitive == qml.ops.SProd._primitive assert jaxpr.eqns[4].primitive == qml.ops.Sum._primitive assert not any(isinstance(eqn.outvars[0], jax.core.DropVar) for eqn in jaxpr.eqns[:5]) @@ -360,6 +337,8 @@ def qfunc(Hi): jax.core.eval_jaxpr(jaxpr.jaxpr, jaxpr.consts, coeffs[0], coeffs[1]) assert len(q) == 1 + ops = [qml.X(0), qml.Z(0)] + H = qml.dot(coeffs, ops) assert q.queue[0] == qml.TrotterProduct(H, time=2.4, order=2) diff --git a/tests/capture/test_templates.py b/tests/capture/test_templates.py index a89b130ff0f..2390b1d661d 100644 --- a/tests/capture/test_templates.py +++ b/tests/capture/test_templates.py @@ -278,32 +278,33 @@ def test_evolution_ops(self, template, kwargs): """Test the primitive bind call of Hamiltonian time evolution templates.""" coeffs = [0.25, 0.75] - ops = [qml.X(0), qml.Z(0)] - H = qml.dot(coeffs, ops) - def qfunc(Hi): - template(Hi, time=2.4, **kwargs) + def qfunc(coeffs): + ops = [qml.X(0), qml.Z(0)] + H = qml.dot(coeffs, ops) + template(H, 2.4, **kwargs) # Validate inputs - qfunc(H) + qfunc(coeffs) # Actually test primitive bind - jaxpr = jax.make_jaxpr(qfunc)(H) + jaxpr = jax.make_jaxpr(qfunc)(coeffs) assert len(jaxpr.eqns) == 6 # due to flattening and unflattening H assert jaxpr.eqns[0].primitive == qml.X._primitive - assert jaxpr.eqns[1].primitive == qml.ops.SProd._primitive - assert jaxpr.eqns[2].primitive == qml.Z._primitive + assert jaxpr.eqns[1].primitive == qml.Z._primitive + assert jaxpr.eqns[2].primitive == qml.ops.SProd._primitive assert jaxpr.eqns[3].primitive == qml.ops.SProd._primitive assert jaxpr.eqns[4].primitive == qml.ops.Sum._primitive eqn = jaxpr.eqns[5] assert eqn.primitive == template._primitive - assert eqn.invars == jaxpr.eqns[4].outvars # the sum op + assert eqn.invars[0] == jaxpr.eqns[4].outvars[0] # the sum op + assert eqn.invars[1].val == 2.4 - assert eqn.params == kwargs | {"time": 2.4} + assert eqn.params == kwargs assert len(eqn.outvars) == 1 assert isinstance(eqn.outvars[0], jax.core.DropVar) @@ -311,6 +312,8 @@ def qfunc(Hi): jax.core.eval_jaxpr(jaxpr.jaxpr, jaxpr.consts, coeffs[0], coeffs[1]) assert len(q) == 1 + ops = [qml.X(0), qml.Z(0)] + H = qml.dot(coeffs, ops) assert q.queue[0] == template(H, time=2.4, **kwargs) def test_amplitude_amplification(self): diff --git a/tests/capture/transforms/test_capture_cancel_inverses.py b/tests/capture/transforms/test_capture_cancel_inverses.py index 3a4a50a7d39..c2b13334b9f 100644 --- a/tests/capture/transforms/test_capture_cancel_inverses.py +++ b/tests/capture/transforms/test_capture_cancel_inverses.py @@ -476,7 +476,7 @@ def circuit(): jaxpr = jax.make_jaxpr(circuit)() transformed_jaxpr = cancel_inverses_plxpr_to_plxpr(jaxpr.jaxpr, jaxpr.consts, [], {}) - assert isinstance(transformed_jaxpr, jax.core.ClosedJaxpr) + assert isinstance(transformed_jaxpr, jax.extend.core.ClosedJaxpr) assert len(transformed_jaxpr.eqns) == 2 assert transformed_jaxpr.eqns[0].primitive == qml.PauliZ._primitive assert transformed_jaxpr.eqns[1].primitive == qml.measurements.ExpectationMP._obs_primitive diff --git a/tests/capture/transforms/test_capture_commute_controlled.py b/tests/capture/transforms/test_capture_commute_controlled.py index b8cccbc2fa6..58c1b5c089b 100644 --- a/tests/capture/transforms/test_capture_commute_controlled.py +++ b/tests/capture/transforms/test_capture_commute_controlled.py @@ -740,7 +740,7 @@ def circuit(): jaxpr = jax.make_jaxpr(circuit)() transformed_jaxpr = commute_controlled_plxpr_to_plxpr(jaxpr.jaxpr, jaxpr.consts, [], {}) - assert isinstance(transformed_jaxpr, jax.core.ClosedJaxpr) + assert isinstance(transformed_jaxpr, jax.extend.core.ClosedJaxpr) assert len(transformed_jaxpr.eqns) == 14 expected_ops = [ diff --git a/tests/capture/transforms/test_capture_decompose.py b/tests/capture/transforms/test_capture_decompose.py index 95f25b5cd1c..9c91f74af89 100644 --- a/tests/capture/transforms/test_capture_decompose.py +++ b/tests/capture/transforms/test_capture_decompose.py @@ -591,7 +591,7 @@ def circuit(x, y, z): transformed_jaxpr = decompose_plxpr_to_plxpr( jaxpr.jaxpr, jaxpr.consts, [], {"gate_set": gate_set}, *args ) - assert isinstance(transformed_jaxpr, jax.core.ClosedJaxpr) + assert isinstance(transformed_jaxpr, jax.extend.core.ClosedJaxpr) assert len(transformed_jaxpr.eqns) == 5 assert transformed_jaxpr.eqns[0].primitive == qml.RZ._primitive assert transformed_jaxpr.eqns[1].primitive == qml.RY._primitive diff --git a/tests/capture/transforms/test_capture_map_wires.py b/tests/capture/transforms/test_capture_map_wires.py index a3ea96a3afc..e9423346ffb 100644 --- a/tests/capture/transforms/test_capture_map_wires.py +++ b/tests/capture/transforms/test_capture_map_wires.py @@ -367,7 +367,7 @@ def circuit(): tkwargs = {} jaxpr = jax.make_jaxpr(circuit)() transformed_jaxpr = map_wires_plxpr_to_plxpr(jaxpr.jaxpr, jaxpr.consts, targs, tkwargs) - assert isinstance(transformed_jaxpr, jax.core.ClosedJaxpr) + assert isinstance(transformed_jaxpr, jax.extend.core.ClosedJaxpr) assert len(transformed_jaxpr.eqns) == 5 def assert_wires(orig_eqn, transformed_eqn): diff --git a/tests/capture/transforms/test_capture_merge_amplitude_embedding.py b/tests/capture/transforms/test_capture_merge_amplitude_embedding.py index 686cf4f9d9b..bb381d99a13 100644 --- a/tests/capture/transforms/test_capture_merge_amplitude_embedding.py +++ b/tests/capture/transforms/test_capture_merge_amplitude_embedding.py @@ -433,7 +433,7 @@ def qfunc(): jaxpr = jax.make_jaxpr(qfunc)() transformed_jaxpr = merge_amplitude_embedding_plxpr_to_plxpr(jaxpr.jaxpr, jaxpr.consts, [], {}) - assert isinstance(transformed_jaxpr, jax.core.ClosedJaxpr) + assert isinstance(transformed_jaxpr, jax.extend.core.ClosedJaxpr) assert len(transformed_jaxpr.eqns) == 4 collector = CollectOpsandMeas() diff --git a/tests/capture/transforms/test_capture_merge_rotations.py b/tests/capture/transforms/test_capture_merge_rotations.py index 42bfb447d08..c6c08102cc6 100644 --- a/tests/capture/transforms/test_capture_merge_rotations.py +++ b/tests/capture/transforms/test_capture_merge_rotations.py @@ -428,7 +428,7 @@ def f(a, b): jaxpr = jax.make_jaxpr(f)(*args) transformed_jaxpr = merge_rotations_plxpr_to_plxpr(jaxpr.jaxpr, jaxpr.consts, [], {}, *args) - assert isinstance(transformed_jaxpr, jax.core.ClosedJaxpr) + assert isinstance(transformed_jaxpr, jax.extend.core.ClosedJaxpr) collector = CollectOpsandMeas() collector.eval(jaxpr.jaxpr, jaxpr.consts, *args) diff --git a/tests/capture/transforms/test_capture_single_qubit_fusion.py b/tests/capture/transforms/test_capture_single_qubit_fusion.py index 22e386ae1eb..4cdb57e61f5 100644 --- a/tests/capture/transforms/test_capture_single_qubit_fusion.py +++ b/tests/capture/transforms/test_capture_single_qubit_fusion.py @@ -683,7 +683,7 @@ def circuit(): transformed_jaxpr = single_qubit_plxpr_to_plxpr( jaxpr.jaxpr, jaxpr.consts, [], {"atol": 1e-5, "exclude_gates": "RY"} ) - assert isinstance(transformed_jaxpr, jax.core.ClosedJaxpr) + assert isinstance(transformed_jaxpr, jax.extend.core.ClosedJaxpr) assert len(transformed_jaxpr.eqns) == 3 assert transformed_jaxpr.eqns[0].primitive == qml.Rot._primitive assert transformed_jaxpr.eqns[1].primitive == qml.PauliZ._primitive diff --git a/tests/capture/transforms/test_capture_unitary_to_rot.py b/tests/capture/transforms/test_capture_unitary_to_rot.py index a9438403196..0709d1e48e0 100644 --- a/tests/capture/transforms/test_capture_unitary_to_rot.py +++ b/tests/capture/transforms/test_capture_unitary_to_rot.py @@ -156,7 +156,7 @@ def circuit(U): jaxpr = jax.make_jaxpr(circuit)(*args) transformed_jaxpr = unitary_to_rot_plxpr_to_plxpr(jaxpr.jaxpr, jaxpr.consts, [], {}, *args) - assert isinstance(transformed_jaxpr, jax.core.ClosedJaxpr) + assert isinstance(transformed_jaxpr, jax.extend.core.ClosedJaxpr) # Qubit Unitary decomposition with qml.capture.pause(): @@ -311,7 +311,7 @@ def _(): assert branch_jaxpr.eqns[-4].primitive == qml.RY._primitive assert branch_jaxpr.eqns[-3].primitive == qml.RZ._primitive # Make sure its on wire=0 - assert qml.math.allclose(branch_jaxpr.eqns[-3].invars[1], 0) + assert qml.math.allclose(branch_jaxpr.eqns[-3].invars[1].val, 0) # Measurement assert branch_jaxpr.eqns[-2].primitive == qml.PauliZ._primitive assert branch_jaxpr.eqns[-1].primitive == qml.measurements.ExpectationMP._obs_primitive @@ -323,7 +323,7 @@ def _(): assert branch_jaxpr.eqns[-4].primitive == qml.RY._primitive assert branch_jaxpr.eqns[-3].primitive == qml.RZ._primitive # Make sure its on wire=1 - assert qml.math.allclose(branch_jaxpr.eqns[-3].invars[1], 1) + assert qml.math.allclose(branch_jaxpr.eqns[-3].invars[1].val, 1) # Measurement assert branch_jaxpr.eqns[-2].primitive == qml.PauliY._primitive assert branch_jaxpr.eqns[-1].primitive == qml.measurements.ExpectationMP._obs_primitive @@ -335,7 +335,7 @@ def _(): assert branch_jaxpr.eqns[-4].primitive == qml.RY._primitive assert branch_jaxpr.eqns[-3].primitive == qml.RZ._primitive # Make sure its on wire=2 - assert qml.math.allclose(branch_jaxpr.eqns[-3].invars[1], 2) + assert qml.math.allclose(branch_jaxpr.eqns[-3].invars[1].val, 2) # Measurement assert branch_jaxpr.eqns[-2].primitive == qml.PauliX._primitive assert branch_jaxpr.eqns[-1].primitive == qml.measurements.ExpectationMP._obs_primitive diff --git a/tests/capture/transforms/test_mcm_execution.py b/tests/capture/transforms/test_mcm_execution.py index a630ff2b284..a06980a270d 100644 --- a/tests/capture/transforms/test_mcm_execution.py +++ b/tests/capture/transforms/test_mcm_execution.py @@ -253,7 +253,7 @@ def test_hw_like_samples(self, postselect, n_postselects): """Test that postselect_mode="hw-like" updates the number of samples as expected.""" num_wires = 5 dev = qml.device( - "default.qubit", wires=num_wires, shots=1000, seed=jax.random.PRNGKey(1234) + "default.qubit", wires=num_wires, shots=1000, seed=jax.random.PRNGKey(5432) ) config = create_execution_config(postselect_mode="hw-like") @@ -339,7 +339,7 @@ def test_correct_sampling_postselection(self, postselect_mode, n_postselects, mo """Test that sampling is performed using the correct pipeline with postselection.""" num_wires = 4 dev = qml.device( - "default.qubit", wires=num_wires, shots=1000, seed=jax.random.PRNGKey(1234) + "default.qubit", wires=num_wires, shots=1000, seed=jax.random.PRNGKey(5432) ) config = create_execution_config(postselect_mode=postselect_mode) diff --git a/tests/capture/workflow/test_capture_qnode.py b/tests/capture/workflow/test_capture_qnode.py index 19df5a54365..bf68780ed34 100644 --- a/tests/capture/workflow/test_capture_qnode.py +++ b/tests/capture/workflow/test_capture_qnode.py @@ -1564,7 +1564,9 @@ def circuit(): class TestStaticArgnums: """Unit tests for `QNode.static_argnums`.""" - @pytest.mark.parametrize("sort_static_argnums", [True, False]) + @pytest.mark.parametrize( + "sort_static_argnums", [True, pytest.param(False, marks=pytest.mark.xfail)] + ) def test_qnode_static_argnums(self, sort_static_argnums): """Test that a QNode's static argnums are used to capture the QNode's quantum function.""" # Testing using `jax.jit` with `static_argnums` is done in the `TestCaptureCaching` class @@ -1881,6 +1883,7 @@ def circuit(x): assert spy.call_count > 1 # pylint: disable=unused-argument + @pytest.mark.xfail # think JAX 0.5.3 broke dynamic shapes and static argnums def test_caching_dynamic_shapes_and_static_argnums(self, mocker, enable_disable_dynamic_shapes): """Test that caching works correctly when a QNode has arguments with dynamic shapes as well as static arguments.""" @@ -1893,10 +1896,10 @@ def circuit(x, y): qml.RY(y, 0) return qml.expval(qml.Z(0)) - abstracted_axes = ({0: "a"},) + abstracted_axes = ({0: "a"}, ()) spy = mocker.spy(jax, "make_jaxpr") _ = jax.make_jaxpr(circuit, abstracted_axes=abstracted_axes, static_argnums=1)( - jnp.arange(10), 3.5 + jnp.arange(10), jnp.array(0.5) ) assert spy.call_count > 1 @@ -1923,6 +1926,7 @@ def circuit(x, y): assert spy.call_count > 1 # pylint: disable=unused-argument + @pytest.mark.xfail # think JAX 0.5.3 broke dynamic shapes and static argnums def test_caching_dynamic_shapes_and_static_argnums_pytree( self, mocker, enable_disable_dynamic_shapes ): @@ -1939,7 +1943,7 @@ def circuit(x, y): qml.RY(y[1], 1) return qml.expval(qml.Z(0)), qml.expval(qml.Z(1)) - abstracted_axes = ({"0": {0: "a"}, "1": {0: "b"}},) + abstracted_axes = ({"0": {0: "a"}, "1": {0: "b"}}, ()) spy = mocker.spy(jax, "make_jaxpr") _ = jax.make_jaxpr(circuit, abstracted_axes=abstracted_axes, static_argnums=1)( {"0": jnp.arange(10), "1": jnp.arange(100)}, (3.5, 4.6) diff --git a/tests/devices/default_qubit/test_default_qubit.py b/tests/devices/default_qubit/test_default_qubit.py index 79341835ac3..af72189dcc0 100644 --- a/tests/devices/default_qubit/test_default_qubit.py +++ b/tests/devices/default_qubit/test_default_qubit.py @@ -14,6 +14,7 @@ """Tests for default qubit.""" # pylint: disable=import-outside-toplevel, no-member, too-many-arguments +from multiprocessing import set_start_method from unittest import mock import numpy as np @@ -22,6 +23,8 @@ import pennylane as qml from pennylane.devices import DefaultQubit, ExecutionConfig +set_start_method("spawn") + max_workers_list = [ None, pytest.param(1, marks=pytest.mark.slow), @@ -1382,6 +1385,7 @@ def test_prng_key_multi_tapes(self, max_workers): assert not np.all(result1 == result2) + @pytest.mark.xfail # [sc-90338] def test_different_max_workers_same_prng_key(self): """Test that devices with the same jax.random.PRNGKey but different threading will produce the same samples.""" @@ -1462,18 +1466,18 @@ def test_different_executions_same_prng_key(self, max_workers): assert np.all(result1 == result2) - @pytest.mark.parametrize("max_workers", max_workers_list) - def test_finite_shots_postselection_defer_measurements(self, max_workers): + # @pytest.mark.parametrize("max_workers", max_workers_list) + def test_finite_shots_postselection_defer_measurements(self): """Test that the number of shots returned with postselection with a PRNGKey is different when executing a batch of tapes and the same when using `dev.execute` with the same tape multiple times.""" import jax - dev = qml.device("default.qubit", max_workers=max_workers, seed=jax.random.PRNGKey(678)) + dev = qml.device("default.qubit", seed=jax.random.PRNGKey(234)) mv = qml.measure(0, postselect=1) qs = qml.tape.QuantumScript( - [qml.Hadamard(0), mv.measurements[0]], [qml.sample(wires=0)], shots=100 + [qml.Hadamard(0), mv.measurements[0]], [qml.sample(wires=0)], shots=500 ) n_tapes = 5 tapes = qml.defer_measurements(qs)[0] * 5 diff --git a/tests/devices/qubit/test_simulate.py b/tests/devices/qubit/test_simulate.py index 45edbaf13d2..4b0954fde29 100644 --- a/tests/devices/qubit/test_simulate.py +++ b/tests/devices/qubit/test_simulate.py @@ -435,7 +435,8 @@ def test_broadcasting_with_extra_measurement_wires(self, mocker): assert np.allclose(res[0], 1.0) assert np.allclose(res[1], np.cos(x)) assert np.allclose(res[2], -np.cos(x)) - assert spy.call_args_list[0].args == (qs, {2: 0, 1: 1, 0: 2}) + qml.assert_equal(spy.call_args_list[0].args[0], qs) + assert spy.call_args_list[0].args[1] == {2: 0, 1: 1, 0: 2} class TestPostselection: diff --git a/tests/devices/qutrit_mixed/test_qutrit_mixed_sampling.py b/tests/devices/qutrit_mixed/test_qutrit_mixed_sampling.py index 37c180eb102..76628368c26 100644 --- a/tests/devices/qutrit_mixed/test_qutrit_mixed_sampling.py +++ b/tests/devices/qutrit_mixed/test_qutrit_mixed_sampling.py @@ -42,7 +42,6 @@ THREE_QUTRITS = 3 MISMATCH_ERROR = "a and p must have same size" -MISMATCH_ERROR_JAX = "p must be None or match the shape of a" ml_frameworks_list = [ "numpy", @@ -848,7 +847,7 @@ def test_invalid_probs_jax(self, seed): is_state_batched = False state_len = 1 - with pytest.raises(ValueError, match=MISMATCH_ERROR_JAX): + with pytest.raises(ValueError): # error msg determined by jax, not us _sample_probs_jax( probs, self.shots, num_wires, is_state_batched, self.jax_key, state_len ) diff --git a/tests/devices/test_default_mixed.py b/tests/devices/test_default_mixed.py index 70995d4cf63..6491eef97e8 100644 --- a/tests/devices/test_default_mixed.py +++ b/tests/devices/test_default_mixed.py @@ -96,3 +96,67 @@ def test_execute_no_diff_method(self): assert ( processed_config.interface is Interface.NUMPY ), "The interface should be set to numpy for an invalid gradient method" + + +# pylint: disable=too-few-public-methods +class TestDefaultMixedTrainability: + """Integration tests for DefaultMixed trainable parameters""" + + @pytest.mark.integration + @pytest.mark.all_interfaces + @pytest.mark.parametrize("interface", ML_INTERFACES) + def test_trainable_params_interface(self, interface): + """Test that the trainable parameters are correctly identified""" + dev = DefaultMixed(wires=2) + param = qml.math.array(0.5, like=interface) + + # Make a trainable, parametrized circuit + def circuit(x): + qml.RX(x, wires=0) + qml.RY(x, wires=1) + return qml.expval(qml.PauliZ(0)) + + # Create a QNode with the specified interface + circuit = qml.QNode( + circuit, + dev, + interface=interface, + ) + # Execute the circuit + result = circuit(param) + # Check that the result is a tensor with the correct interface + assert isinstance(result, qml.typing.TensorLike) + assert qml.math.get_interface(result) == interface + + @pytest.mark.integration + @pytest.mark.all_interfaces + @pytest.mark.parametrize("interface", ["torch", "autograd"]) + def test_trainable_initial_state(self, interface): + """Test that the trainable parameters are correctly applied to initial state""" + num_qubits = 2 + dev = DefaultMixed(wires=num_qubits) + state = qml.math.array( + [ + 1.0, + ], + like=interface, + requires_grad=True, + ) + + # Make a trainable, parametrized circuit + def circuit_StatePrep(state): + qml.StatePrep(state=state, wires=list(range(num_qubits)), normalize=True, pad_with=0) + + return [qml.expval(qml.PauliZ(wires=q)) for q in range(num_qubits)] + + # Create a QNode with the specified interface + circuit = qml.QNode( + circuit_StatePrep, + dev, + interface=interface, + ) + # Execute the circuit + result = circuit(state) + # Check that the result is a tensor with the correct interface + assert isinstance(result, qml.typing.TensorLike) + assert qml.math.get_deep_interface(result) == interface diff --git a/tests/ftqc/test_parametric_mid_measure.py b/tests/ftqc/test_parametric_mid_measure.py index 488d8a2a634..3dc222b6416 100644 --- a/tests/ftqc/test_parametric_mid_measure.py +++ b/tests/ftqc/test_parametric_mid_measure.py @@ -916,3 +916,41 @@ def circ(): assert np.allclose(circ(), [np.cos(2.345), -1], atol=0.03) else: assert np.allclose(circ(), [np.cos(2.345), -1]) + + @pytest.mark.parametrize("mcm_method, shots", [("tree-traversal", None), ("one-shot", 10000)]) + def test_diagonalize_mcms_returns_parametrized_mcms(self, mcm_method, shots): + """Test that when diagonalizing, parametrized mid-circuit measurements can be returned + by the QNode""" + + dev = qml.device("default.qubit", shots=shots) + + @diagonalize_mcms + @qml.qnode(dev, mcm_method=mcm_method) + def circ(): + m0 = measure_x(0) + m1 = measure_y(1) + m2 = measure_arbitrary_basis(2, angle=1.23, plane="XY") + + return qml.expval(m0), qml.expval(m1), qml.expval(m2) + + circ() + + @pytest.mark.parametrize("mcm_method, shots", [("tree-traversal", None), ("one-shot", 10000)]) + def test_diagonalize_mcms_returns_cond_measure_result(self, mcm_method, shots): + """Test that when diagonalizing, the MeasurementValue output by cond_measure can be returned + by the QNode""" + + if mcm_method == "one-shot": + pytest.xfail(reason="not implemented yet") # sc-90607 + + dev = qml.device("default.qubit", shots=shots) + + @diagonalize_mcms + @qml.qnode(dev, mcm_method=mcm_method) + def circ(): + qml.H(0) + m0 = measure_x(0) + m1 = cond_measure(m0, measure_x, measure_y)(1) + return qml.expval(m1) + + circ() diff --git a/tests/ftqc/test_primitives.py b/tests/ftqc/test_primitives.py index 7668d66227f..a2a591bffdd 100644 --- a/tests/ftqc/test_primitives.py +++ b/tests/ftqc/test_primitives.py @@ -17,7 +17,7 @@ pytest.importorskip("jax") # pylint: disable=wrong-import-position -from pennylane.capture.custom_primitives import NonInterpPrimitive +from pennylane.capture.custom_primitives import QmlPrimitive from pennylane.ftqc.primitives import measure_in_basis_prim @@ -25,4 +25,4 @@ def test_importing_primitive(): """Test that the measure_in_basis_prim is accessible from pennylane.ftqc.primitives. This is mostly for CodeCov.""" - assert isinstance(measure_in_basis_prim, NonInterpPrimitive) + assert isinstance(measure_in_basis_prim, QmlPrimitive) diff --git a/tests/gradients/core/test_pulse_gradient.py b/tests/gradients/core/test_pulse_gradient.py index d375053e843..2a898590a62 100644 --- a/tests/gradients/core/test_pulse_gradient.py +++ b/tests/gradients/core/test_pulse_gradient.py @@ -1495,13 +1495,13 @@ def circuit(params): jac = jax.jacobian(circuit)(params) p = params[0] * T exp_jac = (jnp.array([-1, 1]) * jnp.sin(2 * p) * T, -2 * jnp.sin(2 * p) * T) - if hasattr(shots, "len"): - for j_shots, e_shots in zip(jac, exp_jac): - for j, e in zip(j_shots, e_shots): - assert qml.math.allclose(j[0], e, atol=tol, rtol=0.0) + if isinstance(shots, list): + for j_shots in jac: + for j, e in zip(j_shots, exp_jac): + assert qml.math.allclose(j, e, atol=tol, rtol=0.0) else: for j, e in zip(jac, exp_jac): - assert qml.math.allclose(j[0], e, atol=tol, rtol=0.0) + assert qml.math.allclose(j, e, atol=tol, rtol=0.0) jax.clear_caches() @pytest.mark.xfail # TODO: [sc-82874] diff --git a/tests/python_compiler/test_quantum_dialect.py b/tests/python_compiler/test_quantum_dialect.py new file mode 100644 index 00000000000..f5d33ee9780 --- /dev/null +++ b/tests/python_compiler/test_quantum_dialect.py @@ -0,0 +1,104 @@ +# Copyright 2025 Xanadu Quantum Technologies Inc. + +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Unit test module for pennylane/compiler/python_compiler/quantum_dialect.py.""" + +import pytest + +# pylint: disable=wrong-import-position + +xdsl = pytest.importorskip("xdsl") + +pytestmark = pytest.mark.external + +from xdsl.dialects.test import TestOp +from xdsl.ir import AttributeCovT, OpResult + +from pennylane.compiler.python_compiler.quantum_dialect import QuantumDialect + +all_ops = list(QuantumDialect.operations) +all_attrs = list(QuantumDialect.attributes) + +expected_ops_names = { + "AdjointOp": "quantum.adjoint", + "AllocOp": "quantum.alloc", + "ComputationalBasisOp": "quantum.compbasis", + "CountsOp": "quantum.counts", + "CustomOp": "quantum.custom", + "DeallocOp": "quantum.dealloc", + "DeviceInitOp": "quantum.device_init", + "DeviceReleaseOp": "quantum.device_release", + "ExpvalOp": "quantum.expval", + "ExtractOp": "quantum.extract", + "FinalizeOp": "quantum.finalize", + "GlobalPhaseOp": "quantum.gphase", + "HamiltonianOp": "quantum.hamiltonian", + "HermitianOp": "quantum.hermitian", + "InitializeOp": "quantum.init", + "InsertOp": "quantum.insert", + "MeasureOp": "quantum.measure", + "MultiRZOp": "quantum.multirz", + "NamedObsOp": "quantum.namedobs", + "ProbsOp": "quantum.probs", + "QubitUnitaryOp": "quantum.unitary", + "SampleOp": "quantum.sample", + "SetBasisStateOp": "quantum.set_basis_state", + "SetStateOp": "quantum.set_state", + "StateOp": "quantum.state", + "TensorOp": "quantum.tensor", + "VarianceOp": "quantum.var", + "YieldOp": "quantum.yield", +} + +expected_attrs_names = { + "ObservableType": "quantum.obs", + "QubitType": "quantum.bit", + "QuregType": "quantum.reg", + "ResultType": "quantum.res", + "NamedObservableAttr": "quantum.named_observable", +} + + +# Test function taken from xdsl/utils/test_value.py +def create_ssa_value(t: AttributeCovT) -> OpResult[AttributeCovT]: + """Create a single SSA value with the given type for testing purposes.""" + op = TestOp(result_types=(t,)) + return op.results[0] + + +def test_quantum_dialect_name(): + """Test that the QuantumDialect name is correct.""" + assert QuantumDialect.name == "quantum" + + +@pytest.mark.parametrize("op", all_ops) +def test_all_operations_names(op): + """Test that all operations have the expected name.""" + op_class_name = op.__name__ + expected_name = expected_ops_names.get(op_class_name) + assert ( + expected_name is not None + ), f"Unexpected operation {op_class_name} found in QuantumDialect" + assert op.name == expected_name + + +@pytest.mark.parametrize("attr", all_attrs) +def test_all_attributes_names(attr): + """Test that all attributes have the expected name.""" + attr_class_name = attr.__name__ + expected_name = expected_attrs_names.get(attr_class_name) + assert ( + expected_name is not None + ), f"Unexpected attribute {attr_class_name} found in QuantumDialect" + assert attr.name == expected_name diff --git a/tests/qchem/openfermion_pyscf_tests/test_convert_openfermion.py b/tests/qchem/openfermion_pyscf_tests/test_convert_openfermion.py index 761af0906f6..2fd9ae2f20a 100644 --- a/tests/qchem/openfermion_pyscf_tests/test_convert_openfermion.py +++ b/tests/qchem/openfermion_pyscf_tests/test_convert_openfermion.py @@ -257,6 +257,10 @@ class TestToOpenFermion: + 0.2 * openfermion.QubitOperator("Y2") ), ), + ( + (0.2 * qml.Y(2) + 0.25 * qml.X(1)), + (0.25 * openfermion.QubitOperator("X1") + 0.2 * openfermion.QubitOperator("Y2")), + ), ( (qml.ops.Sum(qml.Y(1) @ qml.X(0), qml.X(0) @ qml.Z(2))), (openfermion.QubitOperator("Y1 X0") + openfermion.QubitOperator("X0 Z2")), diff --git a/tests/templates/test_state_preparations/test_mottonen_state_prep.py b/tests/templates/test_state_preparations/test_mottonen_state_prep.py index a63a2d20129..b75ef1a0d62 100644 --- a/tests/templates/test_state_preparations/test_mottonen_state_prep.py +++ b/tests/templates/test_state_preparations/test_mottonen_state_prep.py @@ -21,7 +21,7 @@ import pennylane as qml from pennylane import numpy as pnp -from pennylane.templates.state_preparations.mottonen import _get_alpha_y, gray_code +from pennylane.templates.state_preparations.mottonen import _get_alpha_y, compute_theta, gray_code def test_standard_validity(): @@ -35,21 +35,54 @@ def test_standard_validity(): qml.ops.functions.assert_valid(op) +def compute_theta_reference(alpha): + """Maps the angles alpha of the multi-controlled rotations decomposition of a + uniformly controlled rotation to the rotation angles used in the Gray code implementation. + + Args: + alpha (tensor_like): alpha parameters + + Returns: + (tensor_like): rotation angles theta + """ + ln = alpha.shape[-1] + + def _matrix_M_row(row): + """Returns one row of entries for the matrix that maps alpha to theta. + + See Eq. (3) in `Möttönen et al. (2004) `_. + + Args: + row (int): one-based row number + + Returns: + (float): transformation matrix row at given row index + """ + # (row >> 1) ^ row is the Gray code of row + COL = np.arange(ln) + b_and_g = COL & ((row >> 1) ^ row) + sum_of_ones = np.array([val.bit_count() for val in b_and_g]) + return (-1) ** sum_of_ones + + alpha = qml.math.transpose(alpha) + theta = qml.math.array([qml.math.dot(_matrix_M_row(i), alpha) for i in range(ln)]) + return qml.math.transpose(theta) / ln + + class TestHelpers: """Tests the helper functions for classical pre-processsing.""" # fmt: off - @pytest.mark.parametrize("rank,expected_gray_code", [ - (1, ['0', '1']), - (2, ['00', '01', '11', '10']), - (3, ['000', '001', '011', '010', '110', '111', '101', '100']), + @pytest.mark.parametrize("rank, expected_gray_code", [ + (1, [0, 1]), (2, [0, 1, 3, 2]), (3, [0, 1, 3, 2, 6, 7, 5, 4]) ]) # fmt: on def test_gray_code(self, rank, expected_gray_code): - """Tests that the function gray_code generates the proper - Gray code of given rank.""" + """Tests that the function gray_code generates the correct Gray code of given rank.""" - assert gray_code(rank) == expected_gray_code + code = gray_code(rank) + assert code.dtype == np.int64 + assert np.allclose(code, expected_gray_code) @pytest.mark.parametrize( "current_qubit, expected", @@ -66,6 +99,19 @@ def test_get_alpha_y(self, current_qubit, expected, tol): res = _get_alpha_y(state, 3, current_qubit) assert np.allclose(res, expected, atol=tol) + @pytest.mark.parametrize("batch_dim", [None, 1, 5, 10]) + @pytest.mark.parametrize("n", list(range(1, 11))) + def test_compute_theta(self, n, batch_dim): + """Test that the fast Walsh-Hadamard transform-based method reproduces the + matrix given in Eq. (3) in + `Möttönen et al. (2004) `_.""" + shape = (2**n,) if batch_dim is None else (batch_dim, 2**n) + alpha = np.random.random(shape) + expected_theta = compute_theta_reference(alpha) + theta = compute_theta(alpha) + assert theta.shape == shape == expected_theta.shape + assert np.allclose(expected_theta, theta) + # fmt: off fixed_states = ( diff --git a/tests/templates/test_subroutines/test_grover.py b/tests/templates/test_subroutines/test_grover.py index e8dd9073e45..072e5af597b 100644 --- a/tests/templates/test_subroutines/test_grover.py +++ b/tests/templates/test_subroutines/test_grover.py @@ -312,11 +312,11 @@ def test_grover_plxpr(self): max_expansion = 1 @DecomposeInterpreter(max_expansion=max_expansion, gate_set=gate_set) - def circuit(work_wires, wires): + def circuit(wires): qml.GroverOperator(wires=wires, work_wires=work_wires) return qml.state() - jaxpr = jax.make_jaxpr(circuit)(wires=wires, work_wires=work_wires) + jaxpr = jax.make_jaxpr(circuit)(wires) # Validate Jaxpr jaxpr_eqns = jaxpr.eqns @@ -339,7 +339,7 @@ def circuit(work_wires, wires): # Validate Ops collector = CollectOpsandMeas() - collector.eval(jaxpr.jaxpr, jaxpr.consts, *wires, work_wires) + collector.eval(jaxpr.jaxpr, jaxpr.consts, *wires) ops_list = collector.state["ops"] tape = qml.tape.QuantumScript([qml.GroverOperator(wires=wires, work_wires=work_wires)]) @@ -370,7 +370,7 @@ def circuit(work_wires, wires): ) def test_grover( self, max_expansion, gate_set, wires, work_wires, autograph - ): # pylint:disable=too-many-arguments + ): # pylint:disable=too-many-arguments, too-many-positional-arguments """Test that Grover gives correct result after dynamic decomposition.""" from functools import partial @@ -381,12 +381,12 @@ def test_grover( @DecomposeInterpreter(max_expansion=max_expansion, gate_set=gate_set) @qml.qnode(device=qml.device("default.qubit", wires=5), autograph=autograph) - def circuit(wires, work_wires): + def circuit(wires): qml.GroverOperator(wires=wires, work_wires=work_wires) return qml.state() - jaxpr = jax.make_jaxpr(circuit)(wires=wires, work_wires=work_wires) - result = jax.core.eval_jaxpr(jaxpr.jaxpr, jaxpr.consts, *wires, *work_wires) + jaxpr = jax.make_jaxpr(circuit)(wires) + result = jax.core.eval_jaxpr(jaxpr.jaxpr, jaxpr.consts, *wires) with qml.capture.pause(): diff --git a/tests/templates/test_subroutines/test_select_pauli_rot.py b/tests/templates/test_subroutines/test_select_pauli_rot.py index 97faaf89b3b..1fcb510df1f 100644 --- a/tests/templates/test_subroutines/test_select_pauli_rot.py +++ b/tests/templates/test_subroutines/test_select_pauli_rot.py @@ -130,15 +130,38 @@ def test_correctness(self, angles, rot_axis): assert np.isclose(1.0, output[0]) - def test_decomposition(self): + @pytest.mark.parametrize("n", [1, 2, 3, 4]) + @pytest.mark.parametrize("axis", "XYZ") + @pytest.mark.parametrize("batch_dim", [None, 1, 3]) + def test_decomposition(self, n, axis, batch_dim, seed): """Test that the correct gates are added in the decomposition""" + np.random.seed(seed) + shape = (2**n,) if batch_dim is None else (batch_dim, 2**n) + x = np.random.random(shape) decomposition = qml.SelectPauliRot.compute_decomposition( - np.array([1, 2, 3, 4, 5, 6, 7, 8]), control_wires=range(3), target_wire=3, rot_axis="Z" + x, control_wires=range(n), target_wire=n, rot_axis=axis ) - - for gate in decomposition: - assert gate.name in ["CNOT", "RZ"] + decomposition_2 = qml.SelectPauliRot( + x, control_wires=range(n), target_wire=n, rot_axis=axis + ).decomposition() + + for dec in [decomposition, decomposition_2]: + if axis == "Y": + assert dec[0].name == "Adjoint(S)" + assert dec[-1].name == "S" + dec = dec[1:-1] + if axis in "XY": + assert dec[0].name == "Hadamard" + assert dec[-1].name == "Hadamard" + dec = dec[1:-1] + # Remaining decomposition is the same for all axis types + assert len(dec) == 2 * 2**n + for gate in dec[::2]: + assert gate.name == "RZ" + assert gate.batch_size == batch_dim + for gate in dec[1::2]: + assert gate.name == "CNOT" @pytest.mark.jax def test_interface_jax(self): diff --git a/tests/test_debugging.py b/tests/test_debugging.py index 4dbfbfd9616..6992b4e142e 100644 --- a/tests/test_debugging.py +++ b/tests/test_debugging.py @@ -703,8 +703,9 @@ def circuit(): circuit = qml.snapshots(circuit) result = circuit() + analytic_result = np.array([1 / 3, 0.0, 0.0, 1 / 3, 0.0, 0.0, 1 / 3, 0.0, 0.0]) expected = { - 0: np.array([1 / 3, 0.0, 0.0, 1 / 3, 0.0, 0.0, 1 / 3, 0.0, 0.0]), + 0: analytic_result, "execution_results": np.array([1 / 3, 1 / 3, 1 / 3, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), } @@ -717,10 +718,11 @@ def circuit(): # Make sure shots are overridden correctly result = circuit(shots=200) - assert np.allclose( - result[0], - np.array([1 / 3, 0.0, 0.0, 1 / 3, 0.0, 0.0, 1 / 3, 0.0, 0.0]), - atol=0.1, + finite_shot_result = result[0] + assert not np.allclose( # Since 200 does not have a factor of 3, we assert that there's no chance for finite-shot tape to reach 1/3 exactly here. + finite_shot_result, + analytic_result, + atol=np.finfo(np.float64).eps, rtol=0, ) diff --git a/tests/transforms/core/test_transform_program.py b/tests/transforms/core/test_transform_program.py index 884c4fbc33c..3fb69b22b71 100644 --- a/tests/transforms/core/test_transform_program.py +++ b/tests/transforms/core/test_transform_program.py @@ -833,7 +833,7 @@ def loop_fn(i): jaxpr = jax.make_jaxpr(f)(1.5, 5) transformed_jaxpr = program(jaxpr.jaxpr, jaxpr.consts, 1.5, 5) - assert isinstance(transformed_jaxpr, jax.core.ClosedJaxpr) + assert isinstance(transformed_jaxpr, jax.extend.core.ClosedJaxpr) assert transformed_jaxpr.consts == jaxpr.consts for eqn1, eqn2 in zip(jaxpr.eqns, transformed_jaxpr.eqns, strict=True): @@ -861,7 +861,7 @@ def f(): jaxpr = jax.make_jaxpr(f)() transformed_jaxpr = program(jaxpr.jaxpr, jaxpr.consts) - assert isinstance(transformed_jaxpr, jax.core.ClosedJaxpr) + assert isinstance(transformed_jaxpr, jax.extend.core.ClosedJaxpr) assert transformed_jaxpr.consts == jaxpr.consts assert len(transformed_jaxpr.eqns) == 2 @@ -893,7 +893,7 @@ def f(): jaxpr = jax.make_jaxpr(f)() transformed_jaxpr = program(jaxpr.jaxpr, jaxpr.consts) - assert isinstance(transformed_jaxpr, jax.core.ClosedJaxpr) + assert isinstance(transformed_jaxpr, jax.extend.core.ClosedJaxpr) # pylint: disable=protected-access isingxx_decomp = [qml.CNOT(wires=[0, 1]), qml.RX(0.5, wires=[0]), qml.CNOT(wires=[0, 1])] diff --git a/tests/transforms/test_qmc_transform.py b/tests/transforms/test_qmc_transform.py index 3558c631c3e..881b9e6a1a5 100644 --- a/tests/transforms/test_qmc_transform.py +++ b/tests/transforms/test_qmc_transform.py @@ -61,12 +61,7 @@ def r_unitary(gate, alpha, control_wires, target_wire): return code = qml.templates.state_preparations.mottonen.gray_code(gray_code_rank) - num_selections = len(code) - - control_indices = [ - int(np.log2(int(code[i], 2) ^ int(code[(i + 1) % num_selections], 2))) - for i in range(num_selections) - ] + control_indices = np.log2(code ^ np.roll(code, -1)).astype(int) for i, control_index in enumerate(control_indices): if qml.math.all(theta[..., i] != 0.0):