Daily rc sync to master 2022-06-17 (#2743)

* Update phase decomp test (#2697) * Update interfaces.rst (#2698) * Allow templates to be decomposed (#2704) * Allow templates to be decomposed * Add TODO to clean * Update changelog * Fix pylint error Co-authored-by: Edward Jiang <edward.jiang@resident.xanadu.ai> * Deprecate `qml.ExpvalCost` (#2571) * Add UserWarning to ExpvalCost and catch said warning in all tests containing ExpvalCost * added warning in doc string * added to the changelog in 0.23.1 * Update pennylane/vqe/vqe.py Co-authored-by: Josh Izaac <josh146@gmail.com> * Update pennylane/vqe/vqe.py Co-authored-by: Josh Izaac <josh146@gmail.com> * Update pennylane/vqe/vqe.py Co-authored-by: Josh Izaac <josh146@gmail.com> * Update pennylane/vqe/vqe.py Co-authored-by: Josh Izaac <josh146@gmail.com> * Update tests/optimize/test_qng.py Co-authored-by: Josh Izaac <josh146@gmail.com> * Update tests/qchem/of_tests/test_convert.py Co-authored-by: Josh Izaac <josh146@gmail.com> * Update tests/test_qaoa.py Co-authored-by: Josh Izaac <josh146@gmail.com> * Update tests/test_vqe.py Co-authored-by: Josh Izaac <josh146@gmail.com> * Update tests/transforms/test_adjoint_metric_tensor.py Co-authored-by: Josh Izaac <josh146@gmail.com> * Update tests/optimize/test_optimize_shot_adapative.py Co-authored-by: Josh Izaac <josh146@gmail.com> * overlooked to catch the new warning in test_dipole_of * switched to the right changelog Co-authored-by: Qottmann <qottmann@LT0290.ad.icfo.net> Co-authored-by: Josh Izaac <josh146@gmail.com> Co-authored-by: Romain Moyard <rmoyard@gmail.com> * Update JAX jit forward mode forward evaluation (#2700) * update logic * update logic * test case * test assertion Co-authored-by: Romain Moyard <rmoyard@gmail.com> * Improve ising gates documentation (#2711) * Update doc * Update * Update pennylane/ops/qubit/parametric_ops.py Co-authored-by: antalszava <antalszava@gmail.com> * Apply suggestions from code review * CHange * Typo Co-authored-by: antalszava <antalszava@gmail.com> * Support classical fisher gradients when using Autograd (#2688) * Support classical fisher gradients when using Autograd * Update doc/releases/changelog-dev.md * added test for jax and autograd * black * added xfail tests * added comment in doc string about torch and tf not diffable Co-authored-by: Qottmann <korbinian.kottmann@gmail.com> Co-authored-by: Romain Moyard <rmoyard@gmail.com> * Support classical Fisher gradients when using TF and torch (#2710) * Support classical fisher gradients when using Autograd * Update doc/releases/changelog-dev.md * added test for jax and autograd * black * added xfail tests * added comment in doc string about torch and tf not diffable * Fix differentiability for tensorflow and torch * Remove warning * Fix TF not iterable error * Trigger CI * added example in doc-string about diffability * close-block becomes code-block lol * Update pennylane/qinfo/transforms.py Co-authored-by: Josh Izaac <josh146@gmail.com> * Change pytest mark Co-authored-by: Josh Izaac <josh146@gmail.com> Co-authored-by: Qottmann <korbinian.kottmann@gmail.com> Co-authored-by: Romain Moyard <rmoyard@gmail.com> * Remove `hardware` argument in `qml.qinfo.quantum_fisher` (#2695) * removed hardware argument, adapted doc string * updated tests * black * Update pennylane/qinfo/transforms.py Co-authored-by: Josh Izaac <josh146@gmail.com> * Update pennylane/qinfo/transforms.py Co-authored-by: Josh Izaac <josh146@gmail.com> * add default.qubit condition and change doc string Co-authored-by: Josh Izaac <josh146@gmail.com> Co-authored-by: Romain Moyard <rmoyard@gmail.com> * Add `qinfo` measurements in supported configurations docs (#2712) * Add documentation for new qinfo measurements * Add docs for finite diff * Fix JAX tests * Change param shift to red * Move entropy param shift tests Co-authored-by: Romain Moyard <rmoyard@gmail.com> * Use access_state (#2719) Co-authored-by: Romain Moyard <rmoyard@gmail.com> * Update docs v0.24 (#2724) * private func has no indices arg * update the circuit name in the example * reduced_dm doc update * correct Hamiltonian creation for classical Fisher * Delete exactly one space from reduced_dm docstring to correct rendering * Update outputting quantum_fisher example * Move summary to start (#2727) * added weights initialization example (#2735) * Wires not updated for a hamiltonian with in-place addition (#2738) * fixed hamiltonian wire bug * changelog * Cleanup docs (#2736) * Documentation changes for `batch_partial` (#2737) * Update docs * Add double backticks * Add other backtick :xd: Co-authored-by: Christina Lee <christina@xanadu.ai> * exclude files from pr Co-authored-by: antalszava <antalszava@gmail.com> Co-authored-by: Edward Jiang <34989448+eddddddy@users.noreply.github.com> Co-authored-by: Edward Jiang <edward.jiang@resident.xanadu.ai> Co-authored-by: Korbinian Kottmann <43949391+Qottmann@users.noreply.github.com> Co-authored-by: Qottmann <qottmann@LT0290.ad.icfo.net> Co-authored-by: Josh Izaac <josh146@gmail.com> Co-authored-by: Romain Moyard <rmoyard@gmail.com> Co-authored-by: Qottmann <korbinian.kottmann@gmail.com> Co-authored-by: Jay Soni <jbsoni@uwaterloo.ca> Co-authored-by: Christina Lee <christina@xanadu.ai> Co-authored-by: GitHub Actions Bot <> Co-authored-by: David Ittah <dime10@users.noreply.github.com>
PennyLaneAI · Jun 17, 2022 · 9c61cc7 · 9c61cc7
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diff --git a/doc/code/qml_operation.rst b/doc/code/qml_operation.rst
@@ -36,5 +36,6 @@ new :class:`~pennylane.ops.qubit.attributes.Attribute` objects.
     ~ops.qubit.attributes.diagonal_in_z_basis
     ~ops.qubit.attributes.has_unitary_generator
     ~ops.qubit.attributes.self_inverses
+    ~ops.qubit.attributes.supports_broadcasting
     ~ops.qubit.attributes.symmetric_over_all_wires
     ~ops.qubit.attributes.symmetric_over_control_wires
diff --git a/pennylane/math/quantum.py b/pennylane/math/quantum.py
@@ -501,11 +501,11 @@ def _compute_vn_entropy(density_matrix, base=None):
     **Example**
 
     >>> x = [[1/2, 0], [0, 1/2]]
-    >>> _compute_vn_entropy(x, indices=[0])
+    >>> _compute_vn_entropy(x)
     0.6931472
 
     >>> x = [[1/2, 0], [0, 1/2]]
-    >>> _compute_vn_entropy(x, indices=[0], base=2)
+    >>> _compute_vn_entropy(x, base=2)
     1.0
 
     """

diff --git a/pennylane/measurements.py b/pennylane/measurements.py
@@ -870,7 +870,7 @@ def vn_entropy(wires, log_base=None):
         dev = qml.device("default.qubit", wires=2)
 
         @qml.qnode(dev)
-        def circuit(x):
+        def circuit_entropy(x):
             qml.IsingXX(x, wires=[0, 1])
             return qml.vn_entropy(wires=[0])
 

diff --git a/pennylane/ops/qubit/hamiltonian.py b/pennylane/ops/qubit/hamiltonian.py
@@ -396,6 +396,7 @@ def simplify(self):
 
         self._coeffs = qml.math.stack(new_coeffs) if new_coeffs else []
         self._ops = new_ops
+        self._wires = qml.wires.Wires.all_wires([op.wires for op in self.ops], sort=True)
         # reset grouping, since the indices refer to the old observables and coefficients
         self._grouping_indices = None
 

diff --git a/pennylane/qinfo/transforms.py b/pennylane/qinfo/transforms.py
@@ -21,27 +21,28 @@
 
 
 def reduced_dm(qnode, wires):
-    """Compute the reduced density matrix from a :class:`~.QNode` returning :func:`~.state`.
+    """Compute the reduced density matrix from a :class:`~.QNode` returning
+    :func:`~.state`.
 
-     Args:
-         qnode (QNode): A :class:`~.QNode` returning :func:`~.state`.
-         wires (Sequence(int)): List of wires in the considered subsystem.
+    Args:
+        qnode (QNode): A :class:`~.QNode` returning :func:`~.state`.
+        wires (Sequence(int)): List of wires in the considered subsystem.
 
-     Returns:
-         func: Function which wraps the QNode and accepts the same arguments. When called, this function will
-            return the density matrix.
+    Returns:
+        func: Function which wraps the QNode and accepts the same arguments. When called, this function will
+        return the density matrix.
 
-     **Example**
+    **Example**
 
-     .. code-block:: python
+    .. code-block:: python
 
-         import numpy as np
+        import numpy as np
 
-         dev = qml.device("default.qubit", wires=2)
-         @qml.qnode(dev)
-         def circuit(x):
-           qml.IsingXX(x, wires=[0,1])
-           return qml.state()
+        dev = qml.device("default.qubit", wires=2)
+        @qml.qnode(dev)
+        def circuit(x):
+          qml.IsingXX(x, wires=[0,1])
+          return qml.state()
 
     >>> reduced_dm(circuit, wires=[0])(np.pi/2)
      [[0.5+0.j 0.+0.j]
@@ -332,7 +333,7 @@ def circ(x, y):
 
     .. code-block:: python
 
-        H = qml.Hamiltonian(coeffs = [0.5, 0.5], ops = [qml.PauliZ(0), qml.PauliZ(1)])
+        H = qml.Hamiltonian(coeffs=[0.5, 0.5], observables=[qml.PauliZ(0), qml.PauliZ(1)])
 
         @qml.qnode(dev)
         def circ(params):
@@ -478,13 +479,15 @@ def circ(params):
     The natural gradient is then simply the QFIM multiplied by the gradient:
 
     >>> grad = qml.grad(circ)(params)
-    [ 0.59422561, -0.02615095, -0.05146226]
-
+    >>> grad
+    array([ 0.59422561, -0.02615095, -0.05146226])
     >>> qfim = qml.qinfo.quantum_fisher(circ)(params)
-    np.diag([1., 1., 0.77517241])
-
-    >>> q_nat_grad = qfim @ grad
-    [ 0.59422561 -0.02615095 -0.03989212]
+    >>> qfim
+    tensor([[1.        , 0.        , 0.        ],
+            [0.        , 1.        , 0.        ],
+            [0.        , 0.        , 0.77517241]], requires_grad=True)
+    >>> qfim @ grad
+    tensor([ 0.59422561, -0.02615095, -0.03989212], requires_grad=True)
 
     When using real hardware or finite shots, ``quantum_fisher`` is internally calling :func:`~.pennylane.metric_tensor`.
     To obtain the full QFIM, we need an auxilary wire to perform the Hadamard test.

diff --git a/pennylane/qnn/torch.py b/pennylane/qnn/torch.py
@@ -104,7 +104,10 @@ def qnode(inputs, weights_0, weight_1):
 
         **Initializing weights**
 
-        The optional ``init_method`` argument of :class:`~.TorchLayer` allows for the initialization
+        If ``init_method`` is not specified, weights are randomly initialized from the uniform
+        distribution on the interval :math:`[0, 2 \pi]`.
+
+        Alternative a): The optional ``init_method`` argument of :class:`~.TorchLayer` allows for the initialization
         method of the QNode weights to be specified. The function passed to the argument must be
         from the `torch.nn.init <https://pytorch.org/docs/stable/nn.init.html>`__ module. For
         example, weights can be randomly initialized from the normal distribution by passing:
@@ -113,8 +116,39 @@ def qnode(inputs, weights_0, weight_1):
 
             init_method = torch.nn.init.normal_
 
-        If ``init_method`` is not specified, weights are randomly initialized from the uniform
-        distribution on the interval :math:`[0, 2 \pi]`.
+        Alternative b): Two dictionaries ``weight_shapes`` and ``init_method`` are passed, whose ``keys`` match the ``args`` of the qnode.
+
+        .. code-block::
+
+            @qml.qnode(dev)
+            def qnode(inputs, weights_0, weights_1, weights_2, weight_3, weight_4):
+                qml.templates.AngleEmbedding(inputs, wires=range(n_qubits))
+                qml.templates.StronglyEntanglingLayers(weights_0, wires=range(n_qubits))
+                qml.templates.BasicEntanglerLayers(weights_1, wires=range(n_qubits))
+                qml.Rot(*weights_2, wires=0)
+                qml.RY(weight_3, wires=1)
+                qml.RZ(weight_4, wires=1)
+                qml.CNOT(wires=[0, 1])
+                return qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliZ(1))
+
+
+            weight_shapes = {
+                "weights_0": (3, n_qubits, 3),
+                "weights_1": (3, n_qubits),
+                "weights_2": 3,
+                "weight_3": 1,
+                "weight_4": (1,),
+            }
+
+            init_method = {
+                "weights_0": torch.nn.init.normal_,
+                "weights_1": torch.nn.init.uniform,
+                "weights_2": torch.tensor([1., 2., 3.]),
+                "weight_3": torch.tensor(1.),  # scalar when shape is not an iterable and is <= 1
+                "weight_4": torch.tensor([1.]),
+            }
+
+            qlayer = qml.qnn.TorchLayer(qnode, weight_shapes=weight_shapes, init_method=init_method)
 
         **Full code example**
 

diff --git a/pennylane/tape/tape.py b/pennylane/tape/tape.py
@@ -1293,7 +1293,7 @@ def batch_size(self):
         .. seealso:: :attr:`~.Operator.batch_size` for details.
 
         Returns:
-            int: The batch size of the quantum tape.
+            int or None: The batch size of the quantum tape if present, else ``None``.
         """
         return self._batch_size
 

diff --git a/pennylane/transforms/batch_partial.py b/pennylane/transforms/batch_partial.py
@@ -41,22 +41,23 @@ def batch_partial(qnode, all_operations=False, preprocess=None, **partial_kwargs
     """
     Create a batched partial callable object from the QNode specified.
 
-    This transform provides functionality akin to `functools.partial` and
+    This transform provides functionality akin to ``functools.partial`` and
     allows batching the arguments used for calling the batched partial object.
 
     Args:
         qnode (pennylane.QNode): QNode to pre-supply arguments to
         all_operations (bool): If ``True``, a batch dimension will be added to *all* operations
             in the QNode, rather than just trainable QNode parameters.
+        preprocess (dict): If provided, maps every QNode argument name to a preprocessing
+            function. When the returned partial function is called, the arguments are
+            first passed to the preprocessing functions, and the return values are
+            passed to the QNode.
         partial_kwargs (dict): pre-supplied arguments to pass to the QNode.
 
     Returns:
         func: Function which wraps the QNode and accepts the same arguments minus the
-        pre-supplied arguments provided, and behaves the same as the QNode called with
-        both the pre-supplied arguments and the other arguments passed to this wrapper
-        function. However, the first dimension of each argument of the wrapper function
-        will be treated as a batch dimension. The function output will also contain
-        an initial batch dimension.
+        pre-supplied arguments provided. The first dimension of each argument of the
+        wrapper function will be treated as a batch dimension.
 
     **Example**
 
@@ -101,7 +102,7 @@ def circuit(x, y):
 
     >>> x = np.array(0.1)
     >>> y_fn = lambda y0: y0 * 0.2 + 0.3
-    >>> batched_lambda_circuit = qml.batch_partial(circuit, x=x, y=y_fn)
+    >>> batched_lambda_circuit = qml.batch_partial(circuit, x=x, preprocess={"y": y_fn})
 
     The wrapped function ``batched_lambda_circuit`` also expects arguments to
     have an initial batch dimension:

diff --git a/pennylane/transforms/broadcast_expand.py b/pennylane/transforms/broadcast_expand.py
@@ -57,7 +57,7 @@ def broadcast_expand(tape):
     with broadcasting axis of length ``3`` passed to ``qml.RX``:
 
     >>> x = pnp.array([0.2, 0.6, 1.0], requires_grad=True)
-    >>> qml.draw(expanded_circuit)(x)
+    >>> print(qml.draw(expanded_circuit)(x))
     0: ──RX(0.20)─┤  <Z>
     0: ──RX(0.60)─┤  <Z>
     0: ──RX(1.00)─┤  <Z>
@@ -71,13 +71,11 @@ def broadcast_expand(tape):
     We also can call the transform manually on a tape:
 
     >>> with qml.tape.QuantumTape() as tape:
-    >>>     qml.RX(np.array([0.2, 0.6, 1.0], requires_grad=True), wires=0)
+    >>>     qml.RX(pnp.array([0.2, 0.6, 1.0], requires_grad=True), wires=0)
     >>>     qml.expval(qml.PauliZ(0))
     >>> tapes, fn = qml.transforms.broadcast_expand(tape)
     >>> tapes
-    [<QuantumTape: wires=[0], params=1>,
-     <QuantumTape: wires=[0], params=1>,
-     <QuantumTape: wires=[0], params=1>]
+    [<QuantumTape: wires=[0], params=1>, <QuantumTape: wires=[0], params=1>, <QuantumTape: wires=[0], params=1>]
     >>> fn(qml.execute(tapes, qml.device("default.qubit", wires=1), None))
     array([0.98006658, 0.82533561, 0.54030231])
     """

diff --git a/pennylane/vqe/vqe.py b/pennylane/vqe/vqe.py
@@ -25,6 +25,10 @@
 
 class ExpvalCost:
     """
+    Create a cost function that gives the expectation value of an input Hamiltonian.
+
+    This cost function is useful for a range of problems including VQE and QAOA.
+
     .. warning::
         ``ExpvalCost`` is deprecated. Instead, it is recommended to simply
         pass Hamiltonians to the :func:`~.expval` function inside QNodes.
@@ -38,10 +42,6 @@ def ansatz(params):
 
         In order to optimize the Hamiltonian evaluation taking into account commuting terms, use the ``grouping_type`` keyword in :class:`~.Hamiltonian`.
 
-    Create a cost function that gives the expectation value of an input Hamiltonian.
-
-    This cost function is useful for a range of problems including VQE and QAOA.
-
     Args:
         ansatz (callable): The ansatz for the circuit before the final measurement step.
             Note that the ansatz **must** have the following signature:

diff --git a/tests/ops/qubit/test_hamiltonian.py b/tests/ops/qubit/test_hamiltonian.py
@@ -266,6 +266,12 @@
             np.array([qml.PauliX(0), qml.PauliZ(1), qml.PauliX(2), qml.PauliX(1)]),
         ),
     ),
+    # Case where the 1st hamiltonian doesn't contain all wires
+    (
+        qml.Hamiltonian([1.23, -3.45], [qml.PauliX(0), qml.PauliY(1)]),
+        qml.Hamiltonian([6.78], [qml.PauliZ(2)]),
+        qml.Hamiltonian([1.23, -3.45, 6.78], [qml.PauliX(0), qml.PauliY(1), qml.PauliZ(2)]),
+    ),
 ]
 
 add_zero_hamiltonians = [
@@ -361,6 +367,12 @@
             np.array([qml.PauliX(0), qml.PauliZ(1), qml.PauliX(2), qml.PauliX(1)]),
         ),
     ),
+    # Case where the 1st hamiltonian doesn't contain all wires
+    (
+        qml.Hamiltonian([1.23, -3.45], [qml.PauliX(0), qml.PauliY(1)]),
+        qml.Hamiltonian([6.78], [qml.PauliZ(2)]),
+        qml.Hamiltonian([1.23, -3.45, -6.78], [qml.PauliX(0), qml.PauliY(1), qml.PauliZ(2)]),
+    ),
 ]
 
 mul_hamiltonians = [
@@ -808,6 +820,7 @@ def test_hamiltonian_iadd(self, H1, H2, H):
         """Tests that Hamiltonians are added inline correctly"""
         H1 += H2
         assert H.compare(H1)
+        assert H.wires == H1.wires
 
     @pytest.mark.parametrize(("H1", "H2"), iadd_zero_hamiltonians)
     def test_hamiltonian_iadd_zero(self, H1, H2):
@@ -830,6 +843,7 @@ def test_hamiltonian_isub(self, H1, H2, H):
         """Tests that Hamiltonians are subtracted inline correctly"""
         H1 -= H2
         assert H.compare(H1)
+        assert H.wires == H1.wires
 
     def test_arithmetic_errors(self):
         """Tests that the arithmetic operations thrown the correct errors"""