Merge branch 'master' into sc-73711-onboard-to-large-runners

.github/workflows/install_deps/action.yml

@@ -15,23 +15,23 @@ inputs:
   jax_version:
     description: The version of JAX to install for any job that requires JAX
     required: false
-    default: 0.4.23
+    default: '0.4.23'
   install_tensorflow:
     description: Indicate if TensorFlow should be installed or not
     required: false
     default: 'true'
   tensorflow_version:
     description: The version of TensorFlow to install for any job that requires TensorFlow
     required: false
-    default: 2.16.0
+    default: '2.16.0'
   install_pytorch:
     description: Indicate if PyTorch should be installed or not
     required: false
     default: 'true'
   pytorch_version:
     description: The version of PyTorch to install for any job that requires PyTorch
     required: false
-    default: 2.3.0
+    default: '2.3.0'
   install_pennylane_lightning_master:
     description: Indicate if PennyLane-Lightning should be installed from the master branch
     required: false

.github/workflows/interface-unit-tests.yml

@@ -400,8 +400,7 @@ jobs:
       # catalyst requires the latest version of pennylane that is about to be released.
       # Installing catalyst after pennylane to make sure that the latest catalyst is used.
       install_catalyst_nightly: true
-      # using lightning master does not work for the tests with external libraries
-      install_pennylane_lightning_master: false
+      install_pennylane_lightning_master: true
       pytest_coverage_flags: ${{ inputs.pytest_coverage_flags }}
       pytest_markers: external
       additional_pip_packages: pyzx matplotlib stim quimb mitiq pennylane-qiskit ply

.gitignore

@@ -27,3 +27,5 @@ config.toml
 qml_debug.log
 datasets/*
 .benchmarks/*
+*.h5
+*.hdf5

Makefile

@@ -60,12 +60,10 @@ clean-docs:
 
 test:
 	$(PYTHON) $(TESTRUNNER)
-	$(PYTHON) $(PLUGIN_TESTRUNNER) --device=default.qubit.autograd
 
 coverage:
 	@echo "Generating coverage report..."
 	$(PYTHON) $(TESTRUNNER) $(COVERAGE)
-	$(PYTHON) $(PLUGIN_TESTRUNNER) --device=default.qubit.autograd $(COVERAGE) --cov-append
 
 .PHONY:format
 format:

doc/development/deprecations.rst

@@ -9,24 +9,19 @@ deprecations are listed below.
 Pending deprecations
 --------------------
 
+* ``Device``, ``QubitDevice``, and ``QutritDevice`` will no longer be imported top level in v0.40.  They instead
+  we be available as ``qml.devices.LegacyDevice``, ``qml.devices.QubitDevice``, and ``qml.devices.QutritDevice``
+  respectively.
+
+  - Deprecated top level access in v0.39
+  - Top level access removed in v0.40
+
 * `QNode.gradient_fn` is deprecated. Please use `QNode.diff_method` instead. `QNode.get_gradient_fn` can also be used to
   process the diff method.
 
   - Deprecated in v0.39
   - Will be removed in v0.40
 
-* All of the legacy devices (any with the name ``default.qubit.{autograd,torch,tf,jax,legacy}``) are deprecated. Use ``default.qubit`` instead,
-  as it supports backpropagation for the many backends the legacy devices support.
-
-  - Deprecated in v0.38
-  - Will be removed in v0.39
-
-* The logic for internally switching a device for a different backpropagation
-  compatible device is now deprecated, as it was in place for the deprecated ``default.qubit.legacy``.
-
-  - Deprecated in v0.38
-  - Will be removed in v0.39  
-
 * The ``decomp_depth`` argument in ``qml.device`` is deprecated. 
 
   - Deprecated in v0.38
@@ -88,6 +83,18 @@ Other deprecations
 Completed deprecation cycles
 ----------------------------
 
+* All of the legacy devices (any with the name ``default.qubit.{autograd,torch,tf,jax,legacy}``) are removed. Use ``default.qubit`` instead,
+  as it supports backpropagation for the many backends the legacy devices support.
+
+  - Deprecated in v0.38
+  - Removed in v0.39
+
+* The logic for internally switching a device for a different backpropagation
+  compatible device is removed, as it was in place for removed ``default.qubit.legacy``.
+
+  - Deprecated in v0.38
+  - Removed in v0.39
+
 * `Operator.expand` is now removed. Use `qml.tape.QuantumScript(op.deocomposition())` instead.
 
   - Deprecated in v0.38

doc/releases/changelog-dev.md

@@ -6,28 +6,28 @@
 
 <h3>Improvements 🛠</h3>
 
+* PennyLane is now compatible with NumPy 2.0.
+   [(#6061)](https://github.com/PennyLaneAI/pennylane/pull/6061)
+
 * `qml.qchem.excitations` now optionally returns fermionic operators.
-   [(#6171)](https://github.com/PennyLaneAI/pennylane/pull/6171)
+  [(#6171)](https://github.com/PennyLaneAI/pennylane/pull/6171)
 
 * The `diagonalize_measurements` transform now uses a more efficient method of diagonalization 
   when possible, based on the `pauli_rep` of the relevant observables.
   [#6113](https://github.com/PennyLaneAI/pennylane/pull/6113/)
 
-<h4>Capturing and representing hybrid programs</h4>
-
-* Differentiation of hybrid programs via `qml.grad` can now be captured into plxpr.
-  When evaluating a captured `qml.grad` instruction, it will dispatch to `jax.grad`,
-  which differs from the Autograd implementation of `qml.grad` itself.
-  [(#6120)](https://github.com/PennyLaneAI/pennylane/pull/6120)
+* The `Hermitian` operator now has a `compute_sparse_matrix` implementation.
+  [(#6225)](https://github.com/PennyLaneAI/pennylane/pull/6225)
 
 <h4>Capturing and representing hybrid programs</h4>
 
 * Differentiation of hybrid programs via `qml.grad` and `qml.jacobian` can now be captured
   into plxpr. When evaluating a captured `qml.grad` (`qml.jacobian`) instruction, it will
   dispatch to `jax.grad` (`jax.jacobian`), which differs from the Autograd implementation
-  without capture.
+  without capture. Pytree inputs and outputs are supported.
   [(#6120)](https://github.com/PennyLaneAI/pennylane/pull/6120)
   [(#6127)](https://github.com/PennyLaneAI/pennylane/pull/6127)
+  [(#6134)](https://github.com/PennyLaneAI/pennylane/pull/6134)
 
 * Improve unit testing for capturing of nested control flows.
   [(#6111)](https://github.com/PennyLaneAI/pennylane/pull/6111)
@@ -59,10 +59,12 @@
 * Remove support for Python 3.9.
   [(#6223)](https://github.com/PennyLaneAI/pennylane/pull/6223)
 
-* `DefaultQubitTF`, `DefaultQubitTorch`, and `DefaultQubitJax` are removed. Please use `default.qubit` for all interfaces.
+* `DefaultQubitTF`, `DefaultQubitTorch`, `DefaultQubitJax`, and `DefaultQubitAutograd` are removed.
+  Please use `default.qubit` for all interfaces.
   [(#6207)](https://github.com/PennyLaneAI/pennylane/pull/6207)
   [(#6208)](https://github.com/PennyLaneAI/pennylane/pull/6208)
   [(#6209)](https://github.com/PennyLaneAI/pennylane/pull/6209)
+  [(#6210)](https://github.com/PennyLaneAI/pennylane/pull/6210)
 
 * `expand_fn`, `max_expansion`, `override_shots`, and `device_batch_transform` are removed from the
   signature of `qml.execute`.
@@ -81,8 +83,14 @@
 * `Operator.expand` is now removed. Use `qml.tape.QuantumScript(op.deocomposition())` instead.
   [(#6227)](https://github.com/PennyLaneAI/pennylane/pull/6227)
 
+
 <h3>Deprecations 👋</h3>
 
+* `Device`, `QubitDevice`, and `QutritDevice` will no longer be accessible via top-level import in v0.40.
+  They will still be accessible as `qml.devices.LegacyDevice`, `qml.devices.QubitDevice`, and `qml.devices.QutritDevice`
+  respectively.
+  [(#6238)](https://github.com/PennyLaneAI/pennylane/pull/6238/)
+
 * `QNode.gradient_fn` is deprecated. Please use `QNode.diff_method` and `QNode.get_gradient_fn` instead.
   [(#6244)](https://github.com/PennyLaneAI/pennylane/pull/6244)
 
@@ -108,13 +116,22 @@
 * The ``qml.FABLE`` template now returns the correct value when JIT is enabled.
   [(#6263)](https://github.com/PennyLaneAI/pennylane/pull/6263)
 
-* <h3>Contributors ✍️</h3>
+* Fixes a bug where a circuit using the `autograd` interface sometimes returns nested values that are not of the `autograd` interface.
+  [(#6225)](https://github.com/PennyLaneAI/pennylane/pull/6225)
+
+* Fixes a bug where a simple circuit with no parameters or only builtin/numpy arrays as parameters returns autograd tensors.
+  [(#6225)](https://github.com/PennyLaneAI/pennylane/pull/6225)
+
+<h3>Contributors ✍️</h3>
 
 This release contains contributions from (in alphabetical order):
 
 Guillermo Alonso,
 Utkarsh Azad,
+Astral Cai,
 Lillian M. A. Frederiksen,
+Pietropaolo Frisoni,
+Emiliano Godinez,
 Christina Lee,
 William Maxwell,
 Lee J. O'Riordan,

pennylane/__init__.py

@@ -16,8 +16,6 @@
 PennyLane can be directly imported.
 """
 
-import numpy as _np
-
 
 from pennylane.boolean_fn import BooleanFn
 import pennylane.numpy
@@ -180,13 +178,30 @@ def __getattr__(name):
     if name == "plugin_devices":
         return pennylane.devices.device_constructor.plugin_devices
 
+    from warnings import warn  # pylint: disable=import-outside-toplevel
+
     if name == "QubitDevice":
+        warn(
+            "QubitDevice will no longer be accessible top level. Please access "
+            " the class as pennylane.devices.QubitDevice",
+            PennyLaneDeprecationWarning,
+        )
         return pennylane.devices._qubit_device.QubitDevice  # pylint:disable=protected-access
 
     if name == "QutritDevice":
+        warn(
+            "QutritDevice will no longer be accessible top level. Please access "
+            " the class as pennylane.devices.QutritDevice",
+            PennyLaneDeprecationWarning,
+        )
         return pennylane.devices._qutrit_device.QutritDevice  # pylint:disable=protected-access
 
     if name == "Device":
+        warn(
+            "Device will no longer be accessible top level. Please access "
+            " the class as pennylane.devices.LegacyDevice",
+            PennyLaneDeprecationWarning,
+        )
         return pennylane.devices._legacy_device.Device  # pylint:disable=protected-access
 
     raise AttributeError(f"module 'pennylane' has no attribute '{name}'")

pennylane/_grad.py

@@ -25,6 +25,7 @@
 
 from pennylane.capture import enabled
 from pennylane.capture.capture_diff import _get_grad_prim, _get_jacobian_prim
+from pennylane.capture.flatfn import FlatFn
 from pennylane.compiler import compiler
 from pennylane.compiler.compiler import CompileError
 
@@ -33,18 +34,53 @@
 
 def _capture_diff(func, argnum=None, diff_prim=None, method=None, h=None):
     """Capture-compatible gradient computation."""
-    import jax  # pylint: disable=import-outside-toplevel
+    # pylint: disable=import-outside-toplevel
+    import jax
+    from jax.tree_util import tree_flatten, tree_leaves, tree_unflatten, treedef_tuple
 
-    if isinstance(argnum, int):
-        argnum = [argnum]
     if argnum is None:
-        argnum = [0]
+        argnum = 0
+    if argnum_is_int := isinstance(argnum, int):
+        argnum = [argnum]
 
     @wraps(func)
     def new_func(*args, **kwargs):
-        jaxpr = jax.make_jaxpr(partial(func, **kwargs))(*args)
-        prim_kwargs = {"argnum": argnum, "jaxpr": jaxpr.jaxpr, "n_consts": len(jaxpr.consts)}
-        return diff_prim.bind(*jaxpr.consts, *args, **prim_kwargs, method=method, h=h)
+        flat_args, in_trees = zip(*(tree_flatten(arg) for arg in args))
+        full_in_tree = treedef_tuple(in_trees)
+
+        # Create a new input tree that only takes inputs marked by argnum into account
+        trainable_in_trees = (in_tree for i, in_tree in enumerate(in_trees) if i in argnum)
+        # If an integer was provided as argnum, unpack the arguments axis of the derivatives
+        if argnum_is_int:
+            trainable_in_tree = list(trainable_in_trees)[0]
+        else:
+            trainable_in_tree = treedef_tuple(trainable_in_trees)
+
+        # Create argnum for the flat list of input arrays. For each flattened argument,
+        # add a list of flat argnums if the argument is trainable and an empty list otherwise.
+        start = 0
+        flat_argnum_gen = (
+            (
+                list(range(start, (start := start + len(flat_arg))))
+                if i in argnum
+                else list(range((start := start + len(flat_arg)), start))
+            )
+            for i, flat_arg in enumerate(flat_args)
+        )
+        flat_argnum = sum(flat_argnum_gen, start=[])
+
+        # Create fully flattened function (flat inputs & outputs)
+        flat_fn = FlatFn(partial(func, **kwargs) if kwargs else func, full_in_tree)
+        flat_args = sum(flat_args, start=[])
+        jaxpr = jax.make_jaxpr(flat_fn)(*flat_args)
+        prim_kwargs = {"argnum": flat_argnum, "jaxpr": jaxpr.jaxpr, "n_consts": len(jaxpr.consts)}
+        out_flat = diff_prim.bind(*jaxpr.consts, *flat_args, **prim_kwargs, method=method, h=h)
+        # flatten once more to go from 2D derivative structure (outputs, args) to flat structure
+        out_flat = tree_leaves(out_flat)
+        assert flat_fn.out_tree is not None, "out_tree should be set after executing flat_fn"
+        # The derivative output tree is the composition of output tree and trainable input trees
+        combined_tree = flat_fn.out_tree.compose(trainable_in_tree)
+        return tree_unflatten(combined_tree, out_flat)
 
     return new_func
 

pennylane/_version.py

@@ -16,4 +16,4 @@
 Version number (major.minor.patch[-label])
 """
 
-__version__ = "0.39.0-dev14"
+__version__ = "0.39.0-dev15"

pennylane/capture/__init__.py

@@ -34,6 +34,7 @@
     ~create_measurement_wires_primitive
     ~create_measurement_mcm_primitive
     ~qnode_call
+    ~FlatFn
 
 
 The ``primitives`` submodule offers easy access to objects with jax dependencies such as
@@ -154,6 +155,7 @@ def _(*args, **kwargs):
     create_measurement_mcm_primitive,
 )
 from .capture_qnode import qnode_call
+from .flatfn import FlatFn
 
 # by defining this here, we avoid
 # E0611: No name 'AbstractOperator' in module 'pennylane.capture' (no-name-in-module)
@@ -196,4 +198,5 @@ def __getattr__(key):
     "AbstractOperator",
     "AbstractMeasurement",
     "qnode_prim",
+    "FlatFn",
 )

pennylane/capture/capture_diff.py

@@ -103,7 +103,7 @@ def _(*args, argnum, jaxpr, n_consts, method, h):
         def func(*inner_args):
             return jax.core.eval_jaxpr(jaxpr, consts, *inner_args)
 
-        return jax.jacobian(func, argnums=argnum)(*args)
+        return jax.tree_util.tree_leaves(jax.jacobian(func, argnums=argnum)(*args))
 
     # pylint: disable=unused-argument
     @jacobian_prim.def_abstract_eval

pennylane/capture/capture_qnode.py

@@ -82,8 +82,12 @@ def _(*args, qnode, shots, device, qnode_kwargs, qfunc_jaxpr, n_consts):
         mps = qfunc_jaxpr.outvars
         return _get_shapes_for(*mps, shots=shots, num_device_wires=len(device.wires))
 
-    def _qnode_jvp(*args_and_tangents, **impl_kwargs):
-        return jax.jvp(partial(qnode_prim.impl, **impl_kwargs), *args_and_tangents)
+    def make_zero(tan, arg):
+        return jax.lax.zeros_like_array(arg) if isinstance(tan, ad.Zero) else tan
+
+    def _qnode_jvp(args, tangents, **impl_kwargs):
+        tangents = tuple(map(make_zero, tangents, args))
+        return jax.jvp(partial(qnode_prim.impl, **impl_kwargs), args, tangents)
 
     ad.primitive_jvps[qnode_prim] = _qnode_jvp
 
@@ -174,7 +178,7 @@ def f(x):
     qnode_kwargs = {"diff_method": qnode.diff_method, **execute_kwargs, **mcm_config}
     qnode_prim = _get_qnode_prim()
 
-    flat_args, _ = jax.tree_util.tree_flatten(args)
+    flat_args = jax.tree_util.tree_leaves(args)
     res = qnode_prim.bind(
         *qfunc_jaxpr.consts,
         *flat_args,

pennylane/capture/explanations.md

@@ -159,12 +159,11 @@ You can also see the const variable `a` as argument `e:i32[]` to the inner neste
 ### Pytree handling
 
 Evaluating a jaxpr requires accepting and returning a flat list of tensor-like inputs and outputs.
-list of tensor-like outputs.  These long lists can be hard to manage and are very
-restrictive on the allowed functions, but we can take advantage of pytrees to allow handling
-arbitrary functions.
+These long lists can be hard to manage and are very restrictive on the allowed functions, but we
+can take advantage of pytrees to allow handling arbitrary functions.
 
 To start, we import the `FlatFn` helper. This class converts a function to one that caches
-the resulting result pytree into `flat_fn.out_tree` when executed. This can be used to repack the
+the result pytree into `flat_fn.out_tree` when executed. This can be used to repack the
 results into the correct shape. It also returns flattened results. This does not particularly
 matter for program capture, as we will only be producing jaxpr from the function, not calling
 it directly.