Merge branch 'master' into lk-control

.github/CHANGELOG.md

@@ -2,6 +2,9 @@
 
 ### New features since last release
 
+* Add `mid-circuit measurements` support to `lightning.gpu`'s single-GPU backend.
+  [(#931)](https://github.com/PennyLaneAI/pennylane-lightning/pull/931)
+
 * Add Matrix Product Operator (MPO) for all gates support to `lightning.tensor`. Note current C++ implementation only works for MPO sites data provided by users.
   [(#859)](https://github.com/PennyLaneAI/pennylane-lightning/pull/859)
 
@@ -21,8 +24,14 @@
 * Lightning-Kokkos migrated to the new device API.
   [(#810)](https://github.com/PennyLaneAI/pennylane-lightning/pull/810)
 
+* Lightning-GPU migrated to the new device API.
+  [(#853)](https://github.com/PennyLaneAI/pennylane-lightning/pull/853)
+
 ### Breaking changes
 
+* Deprecate `initSV()` and add `resetStateVector()` to `lightning.gpu`.
+  [(#933)](https://github.com/PennyLaneAI/pennylane-lightning/pull/933)
+
 * Deprecate PI gates implementation.
   [(#925)](https://github.com/PennyLaneAI/pennylane-lightning/pull/925)
 
@@ -37,6 +46,21 @@
 
 ### Improvements
 
+* Optimize the cartesian product to reduce the amount of memory necessary to set the StatePrep with LightningTensor. 
+  [(#943)](https://github.com/PennyLaneAI/pennylane-lightning/pull/943)
+
+* The `prob` data return `lightning.gpu` C++ layer is aligned with other state-vector backends and `lightning.gpu` supports out-of-order `qml.prob`.
+    [(#941)](https://github.com/PennyLaneAI/pennylane-lightning/pull/941)
+
+* Add `setStateVector(state, wire)` support to the `lightning.gpu` C++ layer.
+  [(#930)](https://github.com/PennyLaneAI/pennylane-lightning/pull/930)
+
+* Add zero-state initialization to both `StateVectorCudaManaged` and `StateVectorCudaMPI` constructors to remove the `reset_state` in the python layer ctor and refactor `setBasisState(state, wires)` in the C++ layer.
+  [(#933)](https://github.com/PennyLaneAI/pennylane-lightning/pull/933)
+
+* The `generate_samples` methods of lightning.{qubit/kokkos} can now take in a seed number to make the generated samples deterministic. This can be useful when, among other things, fixing flaky tests in CI.
+  [(#927)](https://github.com/PennyLaneAI/pennylane-lightning/pull/927)
+
 * Always decompose `qml.QFT` in Lightning.
   [(#924)](https://github.com/PennyLaneAI/pennylane-lightning/pull/924)
 
@@ -95,6 +119,15 @@
 
 ### Bug fixes
 
+* Fix missing `liblightning_kokkos_catalyst.so` in Lightning-Kokkos editable installation.
+  [(#945)](https://github.com/PennyLaneAI/pennylane-lightning/pull/945)
+
+* Add concept restriction to ensure `ConstMult` inline function only hit with arithmetic-values times complex values. Fixes build failures with the test suite when enabling OpenMP, and disabling BLAS and Python under clang.
+  [(#936)](https://github.com/PennyLaneAI/pennylane-lightning/pull/936)
+
+* Bug fix for `applyMatrix` in `lightning.tensor`. Matrix operator data is not stored in the `cuGateCache` object to support `TensorProd` obs with multiple `Hermitian` obs.
+  [(#932)](https://github.com/PennyLaneAI/pennylane-lightning/pull/932)
+
 * Bug fix for `_pauli_word` of `QuantumScriptSerializer`. `_pauli_word` can process `PauliWord` object: `I`.
   [(#919)](https://github.com/PennyLaneAI/pennylane-lightning/pull/919)
 
@@ -105,7 +138,7 @@
 
 This release contains contributions from (in alphabetical order):
 
-Ali Asadi, Amintor Dusko, Luis Alfredo Nuñez Meneses, Vincent Michaud-Rioux, Lee J. O'Riordan, Mudit Pandey, Shuli Shu
+Ali Asadi, Amintor Dusko, Luis Alfredo Nuñez Meneses, Vincent Michaud-Rioux, Lee J. O'Riordan, Mudit Pandey, Shuli Shu, Haochen Paul Wang
 
 ---
 

.github/workflows/wheel_linux_aarch64.yml

@@ -123,8 +123,13 @@ jobs:
           mkdir Kokkos
           cp -rf ${{ github.workspace }}/Kokkos_install/${{ matrix.exec_model }}/* Kokkos/
 
+      - name: Install Python 3.10
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.10'
+
       - name: Install dependencies
-        run: python -m pip install cibuildwheel~=2.20.0 tomlkit
+        run: python3.10 -m pip install cibuildwheel~=2.20.0 tomlkit
 
       - name: Configure pyproject.toml file
         run: PL_BACKEND="${{ matrix.pl_backend }}" python scripts/configure_pyproject_toml.py

.github/workflows/wheel_linux_aarch64_cuda.yml

@@ -48,8 +48,13 @@ jobs:
       - name: Checkout PennyLane-Lightning
         uses: actions/checkout@v4
 
+      - name: Install Python 3.10
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.10'
+
       - name: Install cibuildwheel
-        run: python -m pip install cibuildwheel~=2.20.0 tomlkit
+        run: python3.10 -m pip install cibuildwheel~=2.20.0 tomlkit
 
       - name: Configure pyproject.toml file
         run: PL_BACKEND="${{ matrix.pl_backend }}" python scripts/configure_pyproject_toml.py

.github/workflows/wheel_noarch.yml

@@ -50,7 +50,6 @@ jobs:
         if: ${{ matrix.pl_backend == 'lightning_qubit'}}
         uses: actions/checkout@v4
 
-
       - uses: actions/setup-python@v5
         if: ${{ matrix.pl_backend == 'lightning_qubit'}}
         with:

MANIFEST.in

@@ -3,7 +3,7 @@ include cmake/*
 include requirements.txt
 include .github/CHANGELOG.md
 include pennylane_lightning/lightning_qubit/lightning_qubit.toml
-include pennylane_lightning/lightning_qpu/lightning_gpu.toml
+include pennylane_lightning/lightning_gpu/lightning_gpu.toml
 include pennylane_lightning/lightning_kokkos/lightning_kokkos.toml
 include pennylane_lightning/core/_version.py
 graft pennylane_lightning/core/src/

Makefile

@@ -35,9 +35,11 @@ help:
 	@echo "  test-cpp [verbose=1]     to run the C++ test suite (requires CMake)"
 	@echo "                           use with 'verbose=1' for building with verbose flag"
 	@echo "  test-cpp [target=?]      to run a specific C++ test target (requires CMake)."
+	@echo "  test-cpp-mpi [backend=?] to run the C++ test suite with MPI (requires CMake and MPI)"
+	@echo "                           Default: lightning_gpu"
 	@echo "  test-python [device=?]   to run the Python test suite"
 	@echo "                           Default: lightning.qubit"
-	@echo "  wheel [backend=?]        to configure and build Python wheels
+	@echo "  wheel [backend=?]        to configure and build Python wheels"
 	@echo "                           Default: lightning_qubit"
 	@echo "  coverage [device=?]      to generate a coverage report for python interface"
 	@echo "                           Default: lightning.qubit"
@@ -98,7 +100,7 @@ coverage-cpp:
 	lcov --directory . -b ../pennylane_lightning/core/src/ --capture --output-file coverage.info; \
 	genhtml coverage.info --output-directory out
 
-.PHONY: test-python test-builtin test-suite test-cpp
+.PHONY: test-python test-builtin test-suite test-cpp test-cpp-mpi
 test-python: test-builtin test-suite
 
 test-builtin:
@@ -124,6 +126,27 @@ else
 	cmake --build ./BuildTests $(VERBOSE) --target test
 endif
 
+test-cpp-mpi:
+	rm -rf ./BuildTests
+	cmake -BBuildTests -G Ninja \
+		  -DCMAKE_BUILD_TYPE=Debug \
+		  -DBUILD_TESTS=ON \
+		  -DENABLE_WARNINGS=ON \
+		  -DPL_BACKEND=lightning_gpu \
+		  -DENABLE_MPI=ON \
+		  $(OPTIONS)
+ifdef target
+	cmake --build ./BuildTests $(VERBOSE) --target $(target)
+	mpirun -np 2 ./BuildTests/$(target)
+else
+	cmake --build ./BuildTests $(VERBOSE)
+	for file in ./BuildTests/*_test_runner_mpi; do \
+		echo "Running $$file"; \
+		mpirun -np 2 $$file ; \
+	done
+endif
+
+
 .PHONY: format format-cpp format-python
 format: format-cpp format-python
 

doc/lightning_gpu/device.rst

@@ -11,9 +11,9 @@ A ``lightning.gpu`` device can be loaded using:
     import pennylane as qml
     dev = qml.device("lightning.gpu", wires=2)
 
-If the NVIDIA cuQuantum libraries are available, the above device will allow all operations to be performed on a CUDA capable GPU of generation SM 7.0 (Volta) and greater. If the libraries are not correctly installed, or available on path, the device will fall-back to ``lightning.qubit`` and perform all simulation on the CPU.
+If the NVIDIA cuQuantum libraries are available, the above device will allow all operations to be performed on a CUDA capable GPU of generation SM 7.0 (Volta) and greater. If the libraries are not correctly installed, or available on path, the device will raise an error.
 
-The ``lightning.gpu`` device also directly supports quantum circuit gradients using the adjoint differentiation method. This can be enabled at the PennyLane QNode level with:
+The ``lightning.gpu`` device supports quantum circuit gradients using the adjoint differentiation method by default. This can be enabled at the PennyLane QNode level with:
 
 .. code-block:: python
 
@@ -281,3 +281,6 @@ To enable the memory-optimized adjoint method with MPI support, ``batch_obs`` sh
     dev = qml.device('lightning.gpu', wires= n_wires, mpi=True, batch_obs=True)
 
 For the adjoint method, each MPI process will provide the overall simulation results.
+
+.. note::
+    The observable ``Projector``` does not have support with the multi-GPU backend.

mpitests/conftest.py

@@ -98,6 +98,13 @@ def get_device():
 # Device specification
 if device_name == "lightning.gpu":
     from pennylane_lightning.lightning_gpu import LightningGPU as LightningDevice
+    from pennylane_lightning.lightning_gpu._measurements import (
+        LightningGPUMeasurements as LightningMeasurements,
+    )
+    from pennylane_lightning.lightning_gpu._state_vector import (
+        LightningGPUStateVector as LightningStateVector,
+    )
+
 else:
     raise qml.DeviceError(f"The MPI tests do not apply to the {device_name} device.")