(non)Differentiable CV parameter-shift gradient transform (#1486)

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.github/CHANGELOG.md

@@ -75,10 +75,14 @@
 * A new gradients module `qml.gradients` has been added, which provides
   differentiable quantum gradient transforms.
   [(#1476)](https://github.com/PennyLaneAI/pennylane/pull/1476)
+  [(#1479)](https://github.com/PennyLaneAI/pennylane/pull/1479)
+  [(#1486)](https://github.com/PennyLaneAI/pennylane/pull/1486)
 
   Available quantum gradient transforms include:
 
   - `qml.gradients.finite_diff`
+  - `qml.gradients.param_shift`
+  - `qml.gradients.param_shift_cv`
 
   For example,
 

pennylane/gradients/__init__.py

@@ -16,6 +16,8 @@
 
 from . import finite_difference
 from . import parameter_shift
+from . import parameter_shift_cv
 
 from .finite_difference import finite_diff, finite_diff_coeffs, generate_shifted_tapes
 from .parameter_shift import param_shift
+from .parameter_shift_cv import param_shift_cv

pennylane/gradients/finite_difference.py

@@ -161,7 +161,7 @@ def generate_shifted_tapes(tape, idx, shifts, multipliers=None):
         ``idx`` shifted by consecutive values of ``shift``. The length
         of the returned list of tapes will match the length of ``shifts``.
     """
-    params = tape.get_parameters()
+    params = list(tape.get_parameters())
     tapes = []
 
     for i, s in enumerate(shifts):
@@ -300,7 +300,7 @@ def processing_fn(results):
         # In the future, we might want to change this so that only tuples
         # of arrays are returned.
         for i, g in enumerate(grads):
-            g = qml.math.convert_like(g, res[0])
+            g = qml.math.convert_like(g, results[0])
             if hasattr(g, "dtype") and g.dtype is np.dtype("object"):
                 grads[i] = qml.math.hstack(g)