diff --git a/demonstrations/ensemble_multi_qpu.metadata.json b/demonstrations/ensemble_multi_qpu.metadata.json
index a53870d745..2b09303751 100644
--- a/demonstrations/ensemble_multi_qpu.metadata.json
+++ b/demonstrations/ensemble_multi_qpu.metadata.json
@@ -6,7 +6,7 @@
         }
     ],
     "dateOfPublication": "2020-02-14T00:00:00+00:00",
-    "dateOfLastModification": "2024-03-04T00:00:00+00:00",
+    "dateOfLastModification": "2024-07-05T00:00:00+00:00",
     "categories": [
         "Quantum Machine Learning"
     ],
diff --git a/demonstrations/ensemble_multi_qpu.py b/demonstrations/ensemble_multi_qpu.py
index d062bb3cc3..d55885fcc1 100644
--- a/demonstrations/ensemble_multi_qpu.py
+++ b/demonstrations/ensemble_multi_qpu.py
@@ -17,6 +17,12 @@
 This tutorial outlines how two QPUs can be combined in parallel to help solve a machine learning
 classification problem.
 
+.. warning::
+    This demo does not work with the latest version of Qiskit or the Pennylane-Qiskit plugin.
+    It is compatible with ``qiskit==0.46`` and ``pennylane-qiskit==0.35.1``. Older versions of
+    Qiskit and the Pennylane-Qiskit plugin should not be installed in environments with an
+    existing installation of Qiskit 1.0 or above.
+
 We use the ``rigetti.qvm`` device to simulate one QPU and the ``qiskit.aer`` device to
 simulate another. Each QPU makes an independent prediction, and an ensemble model is
 formed by choosing the prediction of the most confident QPU. The iris dataset is used in this