diff --git a/docs/guides/multiverse-computing-singularity.ipynb b/docs/guides/multiverse-computing-singularity.ipynb
index 402c08437ad..80e4181ad1a 100644
--- a/docs/guides/multiverse-computing-singularity.ipynb
+++ b/docs/guides/multiverse-computing-singularity.ipynb
@@ -1,21 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "613c4d93",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# This cell is hidden from users\n",
-    "# It stops the linter complaining about undefined variables\n",
-    "# ruff: noqa: F821"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "dde95705",
@@ -97,38 +81,75 @@
    "source": [
     "## Action-based approach\n",
     "\n",
-    "The function uses an action-based approach. You can think of it as a virtual environment where you use actions to perform tasks or change its state. Currently, it offers the following actions: [list](#1-list), [create](#2-create), [delete](#3-delete), [fit](#4-fit), [predict](#5-predict), [fit_predict](#6-fit-predict), and [create_fit_predict](#7-create-fit-predict). The general structure is as follows:"
+    "The function uses an action-based approach. You can think of it as a virtual environment where you use actions to perform tasks or change its state. Currently, it offers the following actions: [list](#1-list), [create](#2-create), [delete](#3-delete), [fit](#4-fit), [predict](#5-predict), [fit_predict](#6-fit-predict), and [create_fit_predict](#7-create-fit-predict). The following example demonstrates the `create_fit_predict` action."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "537c436e",
+   "execution_count": 1,
+   "id": "9d7d2edb",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Job status:  QUEUED\n",
+      "Action result status:  ok\n",
+      "Action result message:  Classifier created, fitted, and predicted.\n",
+      "Predictions (first five results):  [0, 1, 0, 0, 0]\n",
+      "Probabilities (first five results):  [[0.7975136940667589, 0.20248630593324138], [0.08459026150938889, 0.9154097384906114], [0.8043402543461567, 0.19565974565384334], [0.7975136940667589, 0.20248630593324138], [0.7975136940667589, 0.20248630593324138]]\n"
+     ]
+    }
+   ],
    "source": [
+    "# Import QiskitFunctionsCatalog to load the\n",
+    "# \"Singularity Machine Learning - Classification\" function by Multiverse Computing\n",
+    "from qiskit_ibm_catalog import QiskitFunctionsCatalog\n",
+    "\n",
+    "# Import the make_moons and the train_test_split functions from scikit-learn\n",
+    "# to create a synthetic dataset and split it into training and test datasets\n",
+    "from sklearn.datasets import make_moons\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# authentication\n",
+    "# If you have not previously saved your credentials, follow instructions at\n",
+    "# https://docs.quantum.ibm.com/guides/functions\n",
+    "# to authenticate with your API token.\n",
+    "catalog = QiskitFunctionsCatalog()\n",
+    "\n",
+    "# load \"Singularity Machine Learning - Classification\" function by Multiverse Computing\n",
+    "singularity = catalog.load(\"multiverse/singularity\")\n",
+    "\n",
+    "# generate the synthetic dataset\n",
+    "X, y = make_moons(n_samples=1000)\n",
+    "\n",
+    "# split the data into training and test datasets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
+    "\n",
     "job = singularity.run(\n",
-    "    action=\"action_name\",\n",
-    "    options={},  # use this parameter to provide action options\n",
-    "    **action_params,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "201173e9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# job status and result\n",
+    "    action=\"create_fit_predict\",\n",
+    "    num_learners=10,\n",
+    "    regularization=0.01,\n",
+    "    optimizer_options={\"simulator\": True},\n",
+    "    X_train=X_train,\n",
+    "    y_train=y_train,\n",
+    "    X_test=X_test,\n",
+    "    options={\"save\": False},\n",
+    ")\n",
+    "\n",
+    "# get job status and result\n",
     "status = job.status()\n",
     "result = job.result()\n",
     "\n",
     "print(\"Job status: \", status)\n",
-    "print(\"Status: \", result[\"status\"])\n",
-    "print(\"Message: \", result[\"message\"])\n",
-    "print(\"Data: \", result[\"data\"])"
+    "print(\"Action result status: \", result[\"status\"])\n",
+    "print(\"Action result message: \", result[\"message\"])\n",
+    "print(\"Predictions (first five results): \", result[\"data\"][\"predictions\"][:5])\n",
+    "print(\n",
+    "    \"Probabilities (first five results): \",\n",
+    "    result[\"data\"][\"probabilities\"][:5],\n",
+    ")"
    ]
   },
   {
@@ -158,7 +179,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3e0fba1f",
+   "id": "b0a52919",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -254,14 +275,16 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "32ce65ce",
+   "id": "6d370303",
    "metadata": {},
    "outputs": [],
    "source": [
     "job = singularity.run(\n",
     "    action=\"create\",\n",
-    "    name=\"classifier_name\",\n",
-    "    **classifier_params,\n",
+    "    name=\"classifier_name\",  # specify your custom name for the classifier here\n",
+    "    num_learners=10,\n",
+    "    regularization=0.01,\n",
+    "    optimizer_options={\"simulator\": True},\n",
     ")"
    ]
   },
@@ -300,13 +323,13 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "01d12732",
+   "id": "715cd0ed",
    "metadata": {},
    "outputs": [],
    "source": [
     "job = singularity.run(\n",
     "    action=\"delete\",\n",
-    "    name=\"classifier_name\",\n",
+    "    name=\"classifier_name\",  # specify the name of the classifier to delete here\n",
     ")"
    ]
   },
@@ -346,7 +369,7 @@
     "##### fit_params\n",
     "|     Name    |    Type     | Description |   Required  |\n",
     "|-------------|-------------|-------------|-------------|\n",
-    "| validation | tuple | The validation data and labels. | No |\n",
+    "| validation_data | tuple | The validation data and labels. | No |\n",
     "| pos_label | integer or string | The class label to be mapped to 1. | No |\n",
     "\n",
     "#### Usage"
@@ -355,16 +378,16 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "dd96bc17",
+   "id": "3c63d593",
    "metadata": {},
    "outputs": [],
    "source": [
     "job = singularity.run(\n",
     "    action=\"fit\",\n",
-    "    name=\"classifier_name\",\n",
+    "    name=\"classifier_name\",  # specify the name of the classifier to train here\n",
     "    X=X_train,  # or \"X_train.npy\" if you uploaded it in the shared data directory\n",
     "    y=y_train,  # or \"y_train.npy\" if you uploaded it in the shared data directory\n",
-    "    fit_params=fit_params,\n",
+    "    fit_params={},  # define the fit parameters here\n",
     ")"
    ]
   },
@@ -406,13 +429,13 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1665481b",
+   "id": "b41e72a6",
    "metadata": {},
    "outputs": [],
    "source": [
     "job = singularity.run(\n",
     "    action=\"predict\",\n",
-    "    name=\"classifier_name\",\n",
+    "    name=\"classifier_name\",  # specify the name of the classifier to use here\n",
     "    X=X_test,  # or \"X_test.npy\" if you uploaded it to the shared data directory\n",
     "    options={\n",
     "        \"out\": \"output.json\",\n",
@@ -466,17 +489,17 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "77a33082",
+   "id": "5e232c27",
    "metadata": {},
    "outputs": [],
    "source": [
     "job = singularity.run(\n",
     "    action=\"fit_predict\",\n",
-    "    name=\"classifier_name\",\n",
+    "    name=\"classifier_name\",  # specify the name of the classifier to use here\n",
     "    X_train=X_train,  # or \"X_train.npy\" if you uploaded it in the shared data directory\n",
     "    y_train=y_train,  # or \"y_train.npy\" if you uploaded it in the shared data directory\n",
     "    X_test=X_test,  # or \"X_test.npy\" if you uploaded it in the shared data directory\n",
-    "    fit_params=fit_params,\n",
+    "    fit_params={},  # define the fit parameters here\n",
     "    options={\n",
     "        \"out\": \"output.json\",\n",
     "    },\n",
@@ -532,22 +555,24 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1ef7e73f",
+   "id": "c43df07b",
    "metadata": {},
    "outputs": [],
    "source": [
     "job = singularity.run(\n",
     "    action=\"create_fit_predict\",\n",
-    "    name=\"classifier_name\",\n",
+    "    name=\"classifier_name\",  # specify your custom name for the classifier here\n",
+    "    num_learners=10,\n",
+    "    regularization=0.01,\n",
+    "    optimizer_options={\"simulator\": True},\n",
     "    X_train=X_train,  # or \"X_train.npy\" if you uploaded it in the shared data directory\n",
     "    y_train=y_train,  # or \"y_train.npy\" if you uploaded it in the shared data directory\n",
     "    X_test=X_test,  # or \"X_test.npy\" if you uploaded it in the shared data directory\n",
-    "    fit_params=fit_params,\n",
+    "    fit_params={},  # define the fit parameters here\n",
     "    options={\n",
     "        \"save\": True,\n",
     "        \"out\": \"output.json\",\n",
     "    },\n",
-    "    **classifier_params,\n",
     ")"
    ]
   },
@@ -597,7 +622,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "95a715d2",
    "metadata": {},
    "outputs": [],
@@ -641,21 +666,11 @@
    "id": "dba5a40e",
    "metadata": {},
    "source": [
-    "Before starting, you need to install [scikit-learn](https://scikit-learn.org/). Install it using the following command:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "eb718607",
-   "metadata": {
-    "vscode": {
-     "languageId": "shellscript"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "python3 -m pip install scikit-learn"
+    "Before starting, you need to install [scikit-learn](https://scikit-learn.org/). Install it using the following command:\n",
+    "\n",
+    "```sh\n",
+    "python3 -m pip install scikit-learn\n",
+    "```"
    ]
   },
   {
@@ -685,7 +700,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 7,
    "id": "731db300",
    "metadata": {},
    "outputs": [
@@ -693,17 +708,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Features: [[ 1.87473986  0.0154072 ]\n",
-      " [ 0.36087969  0.93261238]\n",
-      " [ 0.55043235  0.83487977]\n",
-      " [ 1.68029338 -0.23293991]\n",
-      " [ 0.99999822  0.00188533]\n",
-      " [ 1.81988231 -0.07253209]\n",
-      " [-0.00785547  0.99996915]\n",
-      " [-0.21419789  0.97679029]\n",
-      " [ 0.25920056 -0.17172628]\n",
-      " [ 0.93688334 -0.49800616]]\n",
-      "Targets: [1 0 0 1 0 1 0 0 1 1]\n"
+      "Features: [[ 6.27427223e-01 -4.28002977e-01]\n",
+      " [ 1.50566059e-01  9.88599950e-01]\n",
+      " [ 6.51703130e-01  7.58474146e-01]\n",
+      " [ 6.64219130e-04  4.63558306e-01]\n",
+      " [ 1.80634911e+00 -9.14398615e-02]\n",
+      " [ 5.57234689e-01  8.30355045e-01]\n",
+      " [ 1.98286424e+00  3.15669108e-01]\n",
+      " [ 7.38804205e-01 -4.65285842e-01]\n",
+      " [ 1.98768342e+00  3.43534500e-01]\n",
+      " [ 7.59101739e-02  1.17824656e-01]]\n",
+      "Targets: [1 0 0 1 1 0 1 1 1 1]\n"
      ]
     }
    ],
@@ -739,7 +754,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 8,
    "id": "7265965f",
    "metadata": {},
    "outputs": [
@@ -763,7 +778,7 @@
     "np.save(\"X_test.npy\", X_test)\n",
     "np.save(\"y_test.npy\", y_test)\n",
     "\n",
-    "# create tar files for the datasets (shared data directory only supports h5 and tar files)\n",
+    "# create tar files for the datasets\n",
     "make_tarfile(\"X_train.npy\", \"X_train.npy.tar\")\n",
     "make_tarfile(\"y_train.npy\", \"y_train.npy.tar\")\n",
     "make_tarfile(\"X_test.npy\", \"X_test.npy.tar\")\n",
@@ -789,7 +804,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 9,
    "id": "d56e1440",
    "metadata": {},
    "outputs": [
@@ -804,7 +819,7 @@
    "source": [
     "job = singularity.run(\n",
     "    action=\"create\",\n",
-    "    name=\"classifier_name\",\n",
+    "    name=\"my_classifier\",\n",
     "    num_learners=10,\n",
     "    learners_types=[\n",
     "        \"DecisionTreeClassifier\",\n",
@@ -825,7 +840,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 10,
    "id": "f5a54432",
    "metadata": {},
    "outputs": [
@@ -833,7 +848,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['X_test.npy.tar', 'X_train.npy.tar', 'y_test.npy.tar', 'y_train.npy.tar', 'classifier_name.pkl.tar']\n"
+      "{'status': 'ok', 'message': 'Classifiers listed.', 'data': {'classifiers': ['my_classifier']}}\n",
+      "['X_test.npy.tar', 'X_train.npy.tar', 'my_classifier.pkl.tar', 'y_test.npy.tar', 'y_train.npy.tar']\n"
      ]
     }
    ],
@@ -841,6 +857,8 @@
     "# list available classifiers using the list action\n",
     "job = singularity.run(action=\"list\")\n",
     "\n",
+    "print(job.result())\n",
+    "\n",
     "# you can also find your classifiers in the shared data directory with a *.pkl.tar extension\n",
     "print(catalog.files(singularity))"
    ]
@@ -855,7 +873,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 12,
    "id": "13699d25",
    "metadata": {},
    "outputs": [
@@ -870,7 +888,7 @@
    "source": [
     "job = singularity.run(\n",
     "    action=\"fit\",\n",
-    "    name=\"classifier_name\",\n",
+    "    name=\"my_classifier\",\n",
     "    X=\"X_train.npy\",  # you do not need to specify the tar extension\n",
     "    y=\"y_train.npy\",  # you do not need to specify the tar extension\n",
     ")\n",
@@ -888,7 +906,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 13,
    "id": "92f35d5a",
    "metadata": {},
    "outputs": [
@@ -896,26 +914,28 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'status': 'ok', 'message': 'Classifier predicted.', 'data': {'predictions': [1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 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[1.0, 0.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0], [1.0, 0.0]]\n"
+      "Action result status:  ok\n",
+      "Action result message:  Classifier predicted.\n",
+      "Predictions (first five results): [1, 0, 1, 1, 1]\n",
+      "Probabilities (first five results): [[0.0, 1.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0]]\n"
      ]
     }
    ],
    "source": [
     "job = singularity.run(\n",
     "    action=\"predict\",\n",
-    "    name=\"classifier_name\",\n",
+    "    name=\"my_classifier\",\n",
     "    X=\"X_test.npy\",  # you do not need to specify the tar extension\n",
     ")\n",
     "\n",
     "result = job.result()\n",
-    "predictions = result[\"data\"][\"predictions\"]\n",
-    "probabilities = result[\"data\"][\"probabilities\"]\n",
     "\n",
-    "print(result)\n",
-    "print(\"Predictions:\", predictions)\n",
-    "print(\"Probabilities:\", probabilities)"
+    "print(\"Action result status: \", result[\"status\"])\n",
+    "print(\"Action result message: \", result[\"message\"])\n",
+    "print(\"Predictions (first five results):\", result[\"data\"][\"predictions\"][:5])\n",
+    "print(\n",
+    "    \"Probabilities (first five results):\", result[\"data\"][\"probabilities\"][:5]\n",
+    ")"
    ]
   },
   {
@@ -928,7 +948,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 14,
    "id": "c4705154",
    "metadata": {},
    "outputs": [
@@ -943,11 +963,11 @@
    "source": [
     "job = singularity.run(\n",
     "    action=\"delete\",\n",
-    "    name=\"classifier_name\",\n",
+    "    name=\"my_classifier\",\n",
     ")\n",
     "\n",
     "# or you can delete from the shared data directory\n",
-    "# catalog.file_delete(\"classifier_name.pkl.tar\", singularity)\n",
+    "# catalog.file_delete(\"my_classifier.pkl.tar\", singularity)\n",
     "\n",
     "print(job.result())"
    ]
@@ -986,14 +1006,6 @@
     "catalog.file_delete(\"y_test.npy.tar\", singularity)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "bc2d9435",
-   "metadata": {},
-   "source": [
-    "---"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "5cc6f2b3",
@@ -1038,8 +1050,8 @@
     "\n",
     "<Admonition type=\"tip\" title=\"Recommendations\">\n",
     "\n",
-    "    - Request access to [Multiverse Computing's Singularity Machine Learning Classification function](https://quantum.ibm.com/functions).\n",
-    "    - Try the [Solve classification problems with Multiverse Computing's Singularity Machine Learning](https://learning.quantum.ibm.com/catalog/tutorials) tutorial.\n",
+    "- Request access to [Multiverse Computing's Singularity Machine Learning Classification function](https://quantum.ibm.com/functions).\n",
+    "- Try the [Solve classification problems with Multiverse Computing's Singularity Machine Learning](https://learning.quantum.ibm.com/catalog/tutorials) tutorial.\n",
     "\n",
     "</Admonition>"
    ]
diff --git a/scripts/config/notebook-testing.toml b/scripts/config/notebook-testing.toml
index 3d73c6c7f59..6fc669e6f3b 100644
--- a/scripts/config/notebook-testing.toml
+++ b/scripts/config/notebook-testing.toml
@@ -75,6 +75,7 @@ notebooks = [
     "docs/guides/primitives-examples.ipynb",
     "docs/guides/qiskit-addons-obp-get-started.ipynb",
     "docs/guides/qiskit-addons-sqd-get-started.ipynb",
+    "docs/guides/multiverse-computing-singularity.ipynb",
 ]
 
 # Only run the following notebooks in our fortnightly cron job on real hardware.
@@ -119,7 +120,6 @@ notebooks = [
 [groups.exclude]
 notebooks = [
     # This notebook contains undefined variables so can't run at all.
-    "docs/guides/multiverse-computing-singularity.ipynb",
     "docs/guides/function-template-hamiltonian-simulation.ipynb",
 
     # These notebooks are failing with auth errors and need investigating
diff --git a/scripts/nb-tester/requirements.txt b/scripts/nb-tester/requirements.txt
index 163b9777534..4cb99b087fc 100644
--- a/scripts/nb-tester/requirements.txt
+++ b/scripts/nb-tester/requirements.txt
@@ -13,5 +13,6 @@ qiskit-addon-aqc-tensor~=0.1.2
 qiskit-addon-obp~=0.1.0
 qiskit-addon-cutting~=0.9.0
 scipy~=1.15.2
+scikit-learn~=1.6.1
 pyscf~=2.8.0; sys.platform != 'win32'
 networkx~=3.4.2