V0.15.1 bugfix (#1268)

* allow pyscf>=1.7.2 for QChem (#1254)

(cherry picked from commit 34ada2c)

* merge

* merge

* Fixes two bugs in the parameter-shift Hessian (#1260)

* Fixes two bugs in the parameter-shift Hessian

* fix

* added additional test

(cherry picked from commit beaf0a6)

* merge

* changelog

* Apply suggestions from code review

Co-authored-by: Maria Schuld <mariaschuld@gmail.com>

Co-authored-by: antalszava <antalszava@gmail.com>
Co-authored-by: Maria Schuld <mariaschuld@gmail.com>

.github/CHANGELOG.md

@@ -1,3 +1,38 @@
+# Release 0.15.1 (current release)
+
+<h3>Bug fixes</h3>
+
+* Fixes two bugs in the parameter-shift Hessian.
+  [(#1260)](https://github.com/PennyLaneAI/pennylane/pull/1260)
+
+  - Fixes a bug where having an unused parameter in the Autograd interface
+    would result in an indexing error during backpropagation.
+
+  - The parameter-shift Hessian only supports the two-term parameter-shift
+    rule currently, so raises an error if asked to differentiate
+    any unsupported gates (such as the controlled rotation gates).
+
+* A bug which resulted in `qml.adjoint()` and `qml.inv()` failing to work with
+  templates has been fixed.
+  [(#1243)](https://github.com/PennyLaneAI/pennylane/pull/1243)
+
+* Deprecation warning instances in PennyLane have been changed to `UserWarning`,
+  to account for recent changes to how Python warnings are filtered in
+  [PEP565](https://www.python.org/dev/peps/pep-0565/).
+  [(#1211)](https://github.com/PennyLaneAI/pennylane/pull/1211)
+
+<h3>Documentation</h3>
+
+* Updated the order of the parameters to the `GaussianState` operation to match
+  the way that the PennyLane-SF plugin uses them.
+  [(#1255)](https://github.com/PennyLaneAI/pennylane/pull/1255)
+
+<h3>Contributors</h3>
+
+This release contains contributions from (in alphabetical order):
+
+Josh Izaac, Maria Schuld, Antal Száva.
+
 # Release 0.15.0 (current release)
 
 <h3>New features since last release</h3>
@@ -622,7 +657,7 @@ fully differentiable.
   of shots is set explicitly. 
 
 * If creating a QNode from a quantum function with an argument named `shots`,
-  a `DeprecationWarning` is raised, warning the user that this is a reserved
+  a `UserWarning` is raised, warning the user that this is a reserved
   argument to change the number of shots on a per-call basis.
   [(#1075)](https://github.com/PennyLaneAI/pennylane/pull/1075)
 

pennylane/_qubit_device.py

@@ -475,7 +475,7 @@ def sample_basis_states(self, number_of_states, state_probability):
                 "The number of shots has to be explicitly set on the device "
                 "when using sample-based measurements. Since no shots are specified, "
                 "a default of 1000 shots is used.",
-                DeprecationWarning,
+                UserWarning,
             )
 
         shots = self.shots or 1000

pennylane/_version.py

@@ -16,4 +16,4 @@
 Version number (major.minor.patch[-label])
 """
 
-__version__ = "0.15.0"
+__version__ = "0.15.1"

pennylane/beta/devices/default_tensor.py

@@ -100,7 +100,7 @@ class DefaultTensor(Device):
     name = "PennyLane TensorNetwork simulator plugin"
     short_name = "default.tensor"
     pennylane_requires = "0.15"
-    version = "0.15.0"
+    version = "0.15.1"
     author = "Xanadu Inc."
 
     _operation_map = {

pennylane/devices/default_gaussian.py

@@ -432,7 +432,7 @@ def thermal_state(nbar, hbar=2.0):
     return state
 
 
-def gaussian_state(mu, cov, hbar=2.0):
+def gaussian_state(cov, mu, hbar=2.0):
     r"""Returns a Gaussian state.
 
     This is simply a bare wrapper function,
@@ -444,17 +444,19 @@ def gaussian_state(mu, cov, hbar=2.0):
     ordering.
 
     Args:
-        mu (array): vector means. Must be length-:math:`2N`,
-            where N is the number of modes
         cov (array): covariance matrix. Must be dimension :math:`2N\times 2N`,
             where N is the number of modes
+        mu (array): vector means. Must be length-:math:`2N`,
+            where N is the number of modes
         hbar (float): (default 2) the value of :math:`\hbar` in the commutation
             relation :math:`[\x,\p]=i\hbar`
 
     Returns:
-        tuple: the mean and covariance matrix of the Gaussian state
+        tuple: the mean and the covariance matrix of the Gaussian state
     """
     # pylint: disable=unused-argument
+
+    # Note: the internal order of mu and cov is different to the one used in Strawberry Fields
     return mu, cov
 
 
@@ -648,7 +650,7 @@ class DefaultGaussian(Device):
     name = "Default Gaussian PennyLane plugin"
     short_name = "default.gaussian"
     pennylane_requires = "0.15"
-    version = "0.15.0"
+    version = "0.15.1"
     author = "Xanadu Inc."
 
     _operation_map = {

pennylane/devices/default_mixed.py

@@ -51,7 +51,7 @@ class DefaultMixed(QubitDevice):
     name = "Default mixed-state qubit PennyLane plugin"
     short_name = "default.mixed"
     pennylane_requires = "0.15"
-    version = "0.15.0"
+    version = "0.15.1"
     author = "Xanadu Inc."
 
     operations = {

pennylane/devices/default_qubit.py

@@ -85,7 +85,7 @@ class DefaultQubit(QubitDevice):
     name = "Default qubit PennyLane plugin"
     short_name = "default.qubit"
     pennylane_requires = "0.15"
-    version = "0.15.0"
+    version = "0.15.1"
     author = "Xanadu Inc."
 
     operations = {

pennylane/devices/default_qubit_jax.py

@@ -247,8 +247,8 @@ def sample_basis_states(self, number_of_states, state_probability):
                 "The number of shots has to be explicitly set on the jax device "
                 "when using sample-based measurements. Since no shots are specified, "
                 "a default of 1000 shots is used.\n"
-                "This warning will replaced with an error in a future release.",
-                DeprecationWarning,
+                "This warning will be replaced with an error in a future release.",
+                UserWarning,
             )
 
         shots = self.shots or 1000

pennylane/devices/tests/conftest.py

@@ -67,7 +67,7 @@ def skip_if():
     """Fixture to skip tests."""
 
     def _skip_if(dev, capabilities):
-        """ Skip test if device has any of the given capabilities. """
+        """Skip test if device has any of the given capabilities."""
 
         dev_capabilities = dev.capabilities()
         for capability, value in capabilities.items():

pennylane/devices/tests/test_properties.py

@@ -39,7 +39,7 @@
 
 
 def qfunc_no_input():
-    """Model agnostic quantum function """
+    """Model agnostic quantum function"""
     return qml.expval(qml.Identity(wires=0))
 
 

pennylane/interfaces/autograd.py

@@ -261,7 +261,8 @@ def hessian_product(ddy):
                     if dy.size > 1:
                         vhp = dy @ ddy @ hessian @ dy.T
                     else:
-                        vhp = np.squeeze(ddy @ hessian)
+                        vhp = ddy @ hessian
+                        vhp = vhp.flatten()
 
                     return vhp
 

pennylane/ops/cv.py

@@ -710,7 +710,7 @@ class ThermalState(CVOperation):
 
 
 class GaussianState(CVOperation):
-    r"""pennylane.GaussianState(r, V, wires)
+    r"""pennylane.GaussianState(V, r, wires)
     Prepare subsystems in a given Gaussian state.
 
     **Details:**
@@ -720,9 +720,9 @@ class GaussianState(CVOperation):
     * Gradient recipe: None
 
     Args:
+        V (array): the :math:`2N\times 2N` (real and positive definite) covariance matrix
         r (array): a length :math:`2N` vector of means, of the
             form :math:`(\x_0,\dots,\x_{N-1},\p_0,\dots,\p_{N-1})`
-        V (array): the :math:`2N\times 2N` (real and positive definite) covariance matrix
     """
     num_wires = AnyWires
     num_params = 2

pennylane/qnode.py

@@ -157,7 +157,7 @@ def __init__(
                 "Detected 'shots' as an argument to the given quantum function. "
                 "The 'shots' argument name is reserved for overriding the number of shots "
                 "taken by the device. Its use outside of this context should be avoided.",
-                DeprecationWarning,
+                UserWarning,
             )
             self._qfunc_uses_shots_arg = True
         else:

pennylane/tape/jacobian_tape.py

@@ -685,6 +685,36 @@ def hessian(self, device, params=None, **options):
             # parameters. Simply return an empty Hessian.
             return np.zeros((len(params), len(params)), dtype=float)
 
+        # The parameter-shift Hessian implementation currently only supports
+        # the two-term parameter-shift rule. Raise an error for unsupported operations.
+        supported_ops = (
+            "RX",
+            "RY",
+            "RZ",
+            "Rot",
+            "PhaseShift",
+            "ControlledPhaseShift",
+            "MultiRZ",
+            "PauliRot",
+            "U1",
+            "U2",
+            "U3",
+            "SingleExcitationMinus",
+            "SingleExcitationPlus",
+            "DoubleExcitationMinus",
+            "DoubleExcitationPlus",
+        )
+
+        for idx, info in self._par_info.items():
+            op = info["op"]
+
+            if idx in self.trainable_params and op.name not in supported_ops:
+                raise ValueError(
+                    f"The operation {op.name} is currently not supported for the "
+                    f"parameter-shift Hessian.\nPlease decompose the operation in your "
+                    f"QNode by replacing it with '{op.__str__().replace('(', '.decomposition(')}'"
+                )
+
         # some gradient methods need the device or the device wires
         options["device"] = device
         options["dev_wires"] = device.wires

pennylane/tape/tape.py

@@ -648,11 +648,40 @@ def inv(self):
         self.trainable_params = {parameter_mapping[i] for i in self.trainable_params}
         self._par_info = {parameter_mapping[k]: v for k, v in self._par_info.items()}
 
-        for op in self._ops:
-            op.inverse = not op.inverse
+        for idx, op in enumerate(self._ops):
+            try:
+                self._ops[idx] = op.adjoint()
+            except NotImplementedError:
+                op.inverse = not op.inverse
 
         self._ops = list(reversed(self._ops))
 
+    def adjoint(self):
+        """Create a tape that is the adjoint of this one.
+
+        Adjointed tapes are the conjugated and transposed version of the
+        original tapes. Adjointed ops are equivalent to the inverted operation for unitary
+        gates.
+
+        Returns:
+            ~.QuantumTape: the adjointed tape
+        """
+        new_tape = self.copy(copy_operations=True)
+        qml.transforms.invisible(new_tape.inv)()
+
+        # the current implementation of the adjoint
+        # transform requires that the returned inverted object
+        # is automatically queued.
+        QuantumTape._lock.acquire()
+        try:
+            QueuingContext.append(new_tape)
+        except Exception as _:
+            QuantumTape._lock.release()
+            raise
+        QuantumTape._lock.release()
+
+        return new_tape
+
     # ========================================================
     # Parameter handling
     # ========================================================

pennylane/templates/embeddings/amplitude.py

@@ -131,7 +131,7 @@ def __init__(self, features, wires, pad_with=None, normalize=False, pad=None, do
         if pad is not None:
             warnings.warn(
                 "The pad argument will be replaced by the pad_with option in future versions of PennyLane.",
-                PendingDeprecationWarning,
+                UserWarning,
             )
             if pad_with is None:
                 pad_with = pad
@@ -143,6 +143,11 @@ def __init__(self, features, wires, pad_with=None, normalize=False, pad=None, do
         features = self._preprocess(features, wires, pad_with, normalize)
         super().__init__(features, wires=wires, do_queue=do_queue)
 
+    def adjoint(self):  # pylint: disable=arguments-differ
+        return qml.adjoint(qml.templates.MottonenStatePreparation)(
+            self.parameters[0], wires=self.wires
+        )
+
     def expand(self):
 
         with qml.tape.QuantumTape() as tape:

pennylane/templates/layers/particle_conserving_u1.py

@@ -266,13 +266,14 @@ def expand(self):
 
         with qml.tape.QuantumTape() as tape:
 
-            qml.BasisState(self.init_state, wires=self.wires)
+            qml.templates.BasisEmbedding(self.init_state, wires=self.wires)
 
             for l in range(self.n_layers):
                 for i, wires_ in enumerate(nm_wires):
                     u1_ex_gate(
                         self.parameters[0][l, i, 0], self.parameters[0][l, i, 1], wires=wires_
                     )
+
         return tape
 
     @staticmethod

pennylane/templates/layers/particle_conserving_u2.py

@@ -183,7 +183,7 @@ def expand(self):
 
         with qml.tape.QuantumTape() as tape:
 
-            qml.BasisState(self.init_state, wires=self.wires)
+            qml.templates.BasisEmbedding(self.init_state, wires=self.wires)
 
             for l in range(self.n_layers):