Add warning message to Gradients and training documentation about `Co…

…mplexWarnings` (#6543)

**Context:**
- This PR addresses a recurring ComplexWarning issue that appears when
using differentiable workflows involving both complex and float types,
particularly during backpropagation. Users have reported this issue (see
an example
[here](https://discuss.pennylane.ai/t/casting-of-complex-to-float-when-using-phaseshift-in-circuit-for-qml/7439)),
and [internal
discussions](https://xanaduhq.slack.com/archives/C0743CNS9E3/p1730329623028289)
have concluded that while the warning is generally harmless, it can
still be confusing for users.

**Description of the Change:**
- A warning note has been added to the Gradients and training page in
the documentation. The note explains the origin of the ComplexWarning,
assures users that it is non-critical in this context, and provides a
code snippet to suppress the warning.

**Benefits:**
- Clarifies to users that the ComplexWarning does not indicate a
calculation error, which can help avoid unnecessary troubleshooting.
- Offers an easy method to suppress the warning

**Possible Drawbacks:**
- Suppressing warnings could potentially hide other, less common issues
involving complex numbers in more advanced workflows
- Users may overlook the warning's informational value if they suppress
it without fully understanding the implications

**Related GitHub Issues:**

---------

Co-authored-by: Mudit Pandey <[email protected]>

doc/introduction/interfaces.rst

@@ -34,6 +34,17 @@ a :class:`QNode <pennylane.QNode>`, e.g.,
     If no interface is specified, PennyLane will automatically determine the interface based on provided arguments and keyword arguments.
     See ``qml.workflow.SUPPORTED_INTERFACES`` for a list of all accepted interface strings.
 
+.. warning::
+
+    ``ComplexWarning`` messages may appear when running differentiable workflows involving both complex and float types, particularly    
+    with certain interfaces. These warnings are common in backpropagation due to the nature of complex casting and do not 
+    indicate an error in computation. If desired, you can suppress these warnings by adding the following code:
+
+    .. code-block:: python
+
+        import warnings
+        warnings.filterwarnings("ignore", category=np.ComplexWarning)
+
 This will allow native numerical objects of the specified library (NumPy arrays, JAX arrays, Torch Tensors,
 or TensorFlow Tensors) to be passed as parameters to the quantum circuit. It also makes
 the gradients of the quantum circuit accessible to the classical library, enabling the

doc/releases/changelog-dev.md

@@ -52,6 +52,9 @@
 
 <h3>Documentation 📝</h3>
 
+* Add a warning message to Gradients and training documentation about ComplexWarnings
+  [(#6543)](https://github.com/PennyLaneAI/pennylane/pull/6543)
+
 <h3>Bug fixes 🐛</h3>
 
 <h3>Contributors ✍️</h3>
@@ -60,5 +63,7 @@ This release contains contributions from (in alphabetical order):
 
 Shiwen An
 Astral Cai,
-Pietropaolo Frisoni,
-Andrija Paurevic
+Andrija Paurevic,
+Justin Pickering
+Pietropaolo Frisoni
+