diff --git a/demonstrations_v2/tutorial_stochastic_parameter_shift/demo.py b/demonstrations_v2/tutorial_stochastic_parameter_shift/demo.py
index 9447e2203b..e456d4899d 100644
--- a/demonstrations_v2/tutorial_stochastic_parameter_shift/demo.py
+++ b/demonstrations_v2/tutorial_stochastic_parameter_shift/demo.py
@@ -53,7 +53,7 @@
 The Parameter-Shift Rule
 ------------------------
 
-In the quantum case, the expectation value of a circuit with respect to an measurement operator
+In the quantum case, the expectation value of a circuit with respect to a measurement operator
 :math:`\hat{C}` depends smoothly on the the circuit's gate parameters :math:`\theta.` We can write this
 expectation value as :math:`\langle \hat{C}(\theta)\rangle.` This means that the derivatives
 :math:`\nabla_\theta \langle \hat{C} \rangle` exist and gradient descent can be used.
diff --git a/demonstrations_v2/tutorial_stochastic_parameter_shift/metadata.json b/demonstrations_v2/tutorial_stochastic_parameter_shift/metadata.json
index e1058082ab..80a0e103fd 100644
--- a/demonstrations_v2/tutorial_stochastic_parameter_shift/metadata.json
+++ b/demonstrations_v2/tutorial_stochastic_parameter_shift/metadata.json
@@ -8,7 +8,7 @@
     "executable_stable": true,
     "executable_latest": true,
     "dateOfPublication": "2020-05-25T00:00:00+00:00",
-    "dateOfLastModification": "2026-01-14T15:48:14+00:00",
+    "dateOfLastModification": "2026-04-09T16:53:00+00:00",
     "categories": [
         "Optimization"
     ],
@@ -73,4 +73,4 @@
             "weight": 1.0
         }
     ]
-}
\ No newline at end of file
+}