diff --git a/docs/tutorials/projected-quantum-kernels.ipynb b/docs/tutorials/projected-quantum-kernels.ipynb
index 9a8ca77a0b7..9123575580b 100644
--- a/docs/tutorials/projected-quantum-kernels.ipynb
+++ b/docs/tutorials/projected-quantum-kernels.ipynb
@@ -23,6 +23,21 @@
     "\n",
     "*Usage estimate: 80 minutes on a Heron r3 processor (NOTE: This is an estimate only. Your runtime might vary.)*\n",
     "\n",
+    "## Learning outcomes\n",
+    "\n",
+    "After going through this tutorial, users should understand:\n",
+    "- How projected quantum kernels (PQK) work and when they offer a potential quantum advantage.\n",
+    "- How to run a PQK on hardware using a real-world dataset.\n",
+    "\n",
+    "## Prerequisites\n",
+    "\n",
+    "We suggest that users are familiar with the following topics before going through this tutorial:\n",
+    "- [quantum kernels](https://quantum.cloud.ibm.com/learning/en/courses/quantum-machine-learning/quantum-kernel-methods) from the quantum machine learning course on the IBM Quantum Learning Platform\n",
+    "\n",
+    "## Background\n",
+    "\n",
+    "*Usage estimate: 80 minutes on a Heron r3 processor (NOTE: This is an estimate only. Your runtime might vary.)*\n",
+    "\n",
     "In this tutorial, we demonstrate how to run a [projected quantum kernel](https://www.nature.com/articles/s41467-021-22539-9) (PQK) with Qiskit on a real-world biological dataset, based on the paper [Enhanced Prediction of CAR T-Cell Cytotoxicity with Quantum-Kernel Methods](https://arxiv.org/abs/2507.22710) [[1]](#references).\n",
     "\n",
     "PQK is a method used in quantum machine learning (QML) to encode classical data into a quantum feature space and project them back into the classical domain, by using quantum computers to enhance feature selection. It involves encoding classical data into quantum states using a quantum circuit, typically through a process called feature mapping, where the data is transformed into a high-dimensional Hilbert space. The \"projected\" aspect refers to extracting classical information from the quantum states, by measuring specific observables, to construct a kernel matrix that can be used in classical kernel-based algorithms like support vector machines. This approach leverages the computational advantages of quantum systems to potentially achieve better performance on certain tasks compared to classical methods.\n",
@@ -1461,6 +1476,18 @@
     "print(f\"Quantum model complexity is {s_q:.4f}\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "32606150-5820-44f3-a669-920712792350",
+   "metadata": {},
+   "source": [
+    "## Next steps\n",
+    "\n",
+    "If you found this work interesting, you might be interested in the following material:\n",
+    "- In-depth [quantum machine learning course](https://quantum.cloud.ibm.com/learning/en/courses/quantum-machine-learning) from IBM Quantum Learning Platform\n",
+    "- [Quantum kernel training](https://quantum.cloud.ibm.com/docs/en/tutorials/quantum-kernel-training) tutorial"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "f082899c-b763-4df0-a81c-5efb3ca43451",