diff --git a/docs/tutorials/quantum-kernel-training.ipynb b/docs/tutorials/quantum-kernel-training.ipynb
index aa861e8eb05..4883c3f25c4 100644
--- a/docs/tutorials/quantum-kernel-training.ipynb
+++ b/docs/tutorials/quantum-kernel-training.ipynb
@@ -229,7 +229,7 @@
     "\n",
     "Use the `Sampler` primitive from Qiskit Runtime to reconstruct a quasi-probability distribution of states yielded from sampling the circuit. For the task of generating a kernel matrix, we are particularly interested in the probability of measuring the |0> state.\n",
     "\n",
-    "Since we optimized the circuit for the backend in Step 2, we can avoid doing transpilation on the Runtime server by setting `skip_transpilation=True` and passing the optimized circuit to the `Sampler`. For this demo, we will run on a QPU with `qiskit-ibm-runtime` primitives. To run on `qiskit` statevector-based primitives, replace the block of code using Qiskit IBM® Runtime primitives with the commented block."
+    "For this demo, we will run on a QPU with `qiskit-ibm-runtime` primitives. To run on `qiskit` statevector-based primitives, replace the block of code using Qiskit IBM® Runtime primitives with the commented block."
    ]
   },
   {
diff --git a/docs/tutorials/spin-chain-vqe.ipynb b/docs/tutorials/spin-chain-vqe.ipynb
index 01de08d28bd..9cc37a60143 100644
--- a/docs/tutorials/spin-chain-vqe.ipynb
+++ b/docs/tutorials/spin-chain-vqe.ipynb
@@ -239,7 +239,7 @@
     "\n",
     "Minimize the estimated ground state energy of the system by optimizing the circuit parameters. Use the `Estimator` primitive from Qiskit Runtime to evaluate the cost function during optimization.\n",
     "\n",
-    "Since we optimized the circuit for the backend in Step 2, we can avoid doing transpilation on the Runtime server by setting `skip_transpilation=True` and passing the optimized circuit. For this demo, we will run on a QPU using `qiskit-ibm-runtime` primitives. To run with `qiskit` statevector-based primitives, replace the block of code using Qiskit IBM Runtime primitives with the commented block."
+    "For this demo, we will run on a QPU using `qiskit-ibm-runtime` primitives. To run with `qiskit` statevector-based primitives, replace the block of code using Qiskit IBM Runtime primitives with the commented block."
    ]
   },
   {
diff --git a/learning/courses/quantum-machine-learning/quantum-kernel-methods.ipynb b/learning/courses/quantum-machine-learning/quantum-kernel-methods.ipynb
index bb8f1cc6346..8dd55576730 100644
--- a/learning/courses/quantum-machine-learning/quantum-kernel-methods.ipynb
+++ b/learning/courses/quantum-machine-learning/quantum-kernel-methods.ipynb
@@ -63,8 +63,6 @@
     "\n",
     "Use the `Sampler` primitive from Qiskit Runtime to reconstruct a probability distribution of states yielded from sampling the circuit. Note that you may see this referred to as a \"quasi-probability distribution\", a term which is applicable where noise is an issue and when extra steps are introduced, such as in error mitigation. In such cases, the sum of all probabilities may not exactly equal 1; hence \"quasi-probability\".\n",
     "\n",
-    "Since we optimized the circuit for the backend in Step 2, we can avoid doing transpilation on the Runtime server by setting `skip_transpilation=True` and passing the optimized circuit to the `Sampler`.\n",
-    "\n",
     "### Step 4: Post-process, return result in classical format\n",
     "\n",
     "*   Input: Probability distribution\n",