diff --git a/scripts/nb-tester/notebooks.toml b/scripts/nb-tester/notebooks.toml
index faee00dd19b..84cd15d643d 100644
--- a/scripts/nb-tester/notebooks.toml
+++ b/scripts/nb-tester/notebooks.toml
@@ -40,18 +40,18 @@ notebooks_exclude = [
 
 # The following notebooks submit jobs that can be mocked with a simulator
 notebooks_that_submit_jobs = [
-    "tutorials/build-repetition-codes/build-repetition-codes.ipynb",
     "tutorials/chsh-inequality/chsh-inequality.ipynb",
     "tutorials/grovers-algorithm/grovers.ipynb",
     "tutorials/quantum-approximate-optimization-algorithm/qaoa.ipynb",
     "tutorials/variational-quantum-eigensolver/vqe.ipynb",
 ]
 
-# The following notebooks submit jobs that are too big to mock with a simulator
+# The following notebooks submit jobs that are too big to mock with a simulator (or use functions that aren't supported on sims)
 # A job is "too big" if a cell can't run in under 5 mins, or we run out of
 # memory on a reasonable device.
 notebooks_no_mock = [
     "docs/start/hello-world.ipynb",
     "tutorials/combine-error-mitigation-options-with-the-estimator-primitive/combine-error-mitigation-options-with-the-estimator-primitive.ipynb",
     "tutorials/repeat-until-success/repeat-until-success.ipynb",
+    "tutorials/build-repetition-codes/build-repetition-codes.ipynb",
 ]
diff --git a/tutorials/build-repetition-codes/build-repetition-codes.ipynb b/tutorials/build-repetition-codes/build-repetition-codes.ipynb
index b1919b33ffa..a954e68fed9 100644
--- a/tutorials/build-repetition-codes/build-repetition-codes.ipynb
+++ b/tutorials/build-repetition-codes/build-repetition-codes.ipynb
@@ -12,11 +12,28 @@
    "source": [
     "# Build repetition codes\n",
     "\n",
+    "*Estimated QPU usage: 2 seconds (tested on IBM Algiers)*\n",
+    "\n",
     "## Background\n",
     "\n",
-    "To enable real-time quantum error correction (QEC), we require the capability to dynamically control quantum program flow during execution so that quantum gates may be conditioned on measurement results. In this tutorial, we will run the bit-flip code, which is a very simple form of QEC. We will demonstrate a dynamic quantum circuit that can protect an encoded qubit from a single bit-flip error, and then evaluate the performance of the bit-flip code.\n",
+    "To enable real-time quantum error correction (QEC), you need to be able to dynamically control quantum program flow during execution so that quantum gates can be conditioned on measurement results. This tutorial runs the bit-flip code, which is a very simple form of QEC. It demonstrates a dynamic quantum circuit that can protect an encoded qubit from a single bit-flip error, and then evaluates the bit-flip code performance.\n",
+    "\n",
+    "You can exploit additional ancilla qubits and entanglement to measure *stabilizers* that do not transform encoded quantum information, while still informing you of some classes of errors that might have occurred. A quantum stabilizer code encodes $k$ logical qubits into $n$ physical qubits. Stabilizer codes critically focus on correcting a discrete error set with support from the Pauli group $\\Pi^n$.\n",
+    "\n",
+    "For more information about QEC, refer to [Quantum Error Correction for Beginners.](https://arxiv.org/abs/0905.2794)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "88672bd6",
+   "metadata": {},
+   "source": [
+    "## Requirements\n",
+    "\n",
+    "Before starting this tutorial, ensure that you have the following installed:\n",
     "\n",
-    "We can exploit additional ancilla qubits and entanglement to measure what are known as *stabilizers* that do not transform our encoded quantum information, while still informing us of some classes of errors that may have occurred. A quantum stabilizer code encodes $k$ logical qubits into $n$ physical qubits. Stabilizer codes critically focus on correcting a discrete error set with support from the Pauli group $\\Pi^n$. Assume the set of possible errors are $ \\epsilon \\subset \\Pi^n$. For example, in a bit-flip code with three qubits encoding the quantum state, we will have $\\epsilon = \\{IIX, IXI, XII\\}$. We can measure the stabilizers and observing their eigenvalues to dete"
+    "* Qiskit SDK 1.0 or later with visualization support (`pip install qiskit[visualization]`)\n",
+    "* Qiskit Runtime (`pip install qiskit-ibm-runtime`) 0.22 or later"
    ]
   },
   {
@@ -29,47 +46,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 16,
    "id": "1b9fd8ad",
    "metadata": {
     "slideshow": {
      "slide_type": "-"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      ">>> Connected to ibm_algiers.\n"
+     ]
+    }
+   ],
    "source": [
     "# Qiskit imports\n",
     "from qiskit import transpile, QuantumCircuit, QuantumRegister, ClassicalRegister\n",
     "from qiskit.result import marginal_counts\n",
     "\n",
-    "# Qiskit IBM Provider\n",
-    "from qiskit_ibm_provider import IBMProvider, least_busy\n",
+    "# Qiskit Runtime\n",
+    "from qiskit_ibm_runtime import QiskitRuntimeService, SamplerV2 as Sampler\n",
     "\n",
-    "import warnings\n",
-    "warnings.filterwarnings(\"ignore\")"
+    "service = QiskitRuntimeService()"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "bbd87434",
+   "cell_type": "markdown",
+   "id": "4d01e8d3",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<IBMBackend('ibm_kyiv')>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "provider = IBMProvider()\n",
-    "backend = least_busy(provider.backends(simulator=False, operational=True))\n",
-    "backend"
+    "## Step 1: Map classical inputs to a quantum problem"
    ]
   },
   {
@@ -77,22 +86,34 @@
    "id": "cdee0b18",
    "metadata": {},
    "source": [
-    "## Build a bit-flip stabilizer circuit\n",
+    "### Build a bit-flip stabilizer circuit\n",
     "\n",
-    "The bit-flip code is among the simplest examples of a stabilizer code. It can protect our state against a single bit-flip (X) error on any of the encoding qubits. If we consider the action of bit-flip error $X$ which maps $|0\\rangle \\rightarrow |1\\rangle$ and $|1\\rangle \\rightarrow |0\\rangle$ on any of our qubits, we have $\\epsilon = \\{E_0, E_1, E_2 \\} = \\{IIX, IXI, XII\\}$. The code requires five qubits: three are used to encode the protected state, and the remaining two are used as stabilizer measurement ancillas."
+    "The bit-flip code is among the simplest examples of a stabilizer code. It protects the state against a single bit-flip (X) error on any of the encoding qubits. Consider the action of bit-flip error $X$, which maps $|0\\rangle \\rightarrow |1\\rangle$ and $|1\\rangle \\rightarrow |0\\rangle$ on any of our qubits, then we have $\\epsilon = \\{E_0, E_1, E_2 \\} = \\{IIX, IXI, XII\\}$. The code requires five qubits: three are used to encode the protected state, and the remaining two are used as stabilizer measurement ancillas."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b588703a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_qubits = 5\n",
+    "backend = service.least_busy(operational=True, simulator=False, min_num_qubits=num_qubits)\n",
+    "backend"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 17,
    "id": "606dff18",
    "metadata": {},
    "outputs": [],
    "source": [
     "qreg_data = QuantumRegister(3)\n",
     "qreg_measure = QuantumRegister(2)\n",
-    "creg_data = ClassicalRegister(3)\n",
-    "creg_syndrome = ClassicalRegister(2)\n",
+    "creg_data = ClassicalRegister(3, name=\"data\")\n",
+    "creg_syndrome = ClassicalRegister(2, name=\"syndrome\")\n",
     "state_data = qreg_data[0]\n",
     "ancillas_data = qreg_data[1:]\n",
     "\n",
@@ -156,18 +177,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 18,
    "id": "dbe02949",
    "metadata": {},
    "outputs": [
     {
      "data": {
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       "text/plain": [
-       "<Figure size 2228.78x1287.61 with 1 Axes>"
+       "<Figure size 2317.92x1287.61 with 1 Axes>"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -190,6 +211,16 @@
     "circuit.draw(output=\"mpl\", style='iqp')"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "609c0c47",
+   "metadata": {},
+   "source": [
+    "## Step 2. Optimize the problem for quantum execution\n",
+    "\n",
+    "To reduce the total job execution time, Qiskit primitives only accept circuits and observables that conforms to the instructions and connectivity supported by the target system (referred to as instruction set architecture (ISA) circuits and observables).  [Learn more about transpilation.](https://docs.quantum.ibm.com/transpile)"
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -200,14 +231,12 @@
     }
    },
    "source": [
-    "## Optimize the circuit\n",
-    "\n",
-    "We will transpile the circuit to select the best qubit layout and routing."
+    "### Generate ISA circuits"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 19,
    "id": "67b55eef",
    "metadata": {
     "slideshow": {
@@ -217,43 +246,43 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<Figure size 2299.9x1287.61 with 1 Axes>"
+       "<Figure size 2317.92x1287.61 with 1 Axes>"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "transpiled_circuit = transpile(circuit, backend)\n",
-    "transpiled_circuit.draw(output=\"mpl\", style='iqp', idle_wires=False)"
+    "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n",
+    "\n",
+    "pm = generate_preset_pass_manager(backend=backend, optimization_level=1)\n",
+    "isa_circuit = pm.run(circuit)\n",
+    "\n",
+    "isa_circuit.draw('mpl', style='iqp', idle_wires=False)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 20,
    "id": "67acea4f",
    "metadata": {},
    "outputs": [],
    "source": [
     "no_correction_circuit = build_error_correction_sequence(apply_correction=False)\n",
-    "transpiled_no_correction_circuit = transpile(no_correction_circuit, backend)"
+    "\n",
+    "isa_no_correction_circuit = pm.run(no_correction_circuit)"
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
-   "id": "e4f569c5",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
+   "id": "bcd61a1f",
+   "metadata": {},
    "source": [
-    "## Execute the circuit in hardware"
+    "## Step 3: Execute using Qiskit Primitives"
    ]
   },
   {
@@ -261,23 +290,24 @@
    "id": "e68d10d2",
    "metadata": {},
    "source": [
-    "We'll run the version with correction applied, and one without correction"
+    "Run the version with correction applied and one without correction."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 21,
    "id": "d53319ba",
    "metadata": {},
    "outputs": [],
    "source": [
-    "job_no_correction = backend.run(transpiled_no_correction_circuit, shots=1000, dynamic=True)\n",
-    "result_no_correction = job_no_correction.result()"
+    "sampler_no_correction = Sampler(backend)\n",
+    "job_no_correction = sampler_no_correction.run([isa_no_correction_circuit], shots=1000)\n",
+    "result_no_correction = job_no_correction.result()[0]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 22,
    "id": "df7421d0",
    "metadata": {
     "slideshow": {
@@ -286,8 +316,53 @@
    },
    "outputs": [],
    "source": [
-    "job_with_correction = backend.run(transpiled_circuit, shots=1000, dynamic=True)\n",
-    "result_with_correction = job_with_correction.result()"
+    "sampler_with_correction = Sampler(backend)\n",
+    "job_with_correction = sampler_with_correction.run([isa_circuit], shots=1000)\n",
+    "result_with_correction = job_with_correction.result()[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "1cba37f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data (no correction):\n",
+      "{'101': 146, '111': 516, '011': 180, '000': 25, '110': 23, '100': 16, '001': 46, '010': 48}\n",
+      "Syndrome (no correction):\n",
+      "{'01': 206, '00': 736, '11': 20, '10': 38}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Data (no correction):\\n{result_no_correction.data.data.get_counts()}\")\n",
+    "print(f\"Syndrome (no correction):\\n{result_no_correction.data.syndrome.get_counts()}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "7b7697f2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data (corrected):\n",
+      "{'010': 18, '101': 139, '111': 540, '100': 18, '001': 47, '011': 169, '000': 18, '110': 51}\n",
+      "Syndrome (corrected):\n",
+      "{'11': 19, '00': 809, '10': 90, '01': 82}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Data (corrected):\\n{result_with_correction.data.data.get_counts()}\")\n",
+    "print(f\"Syndrome (corrected):\\n{result_with_correction.data.syndrome.get_counts()}\")"
    ]
   },
   {
@@ -295,12 +370,14 @@
    "id": "1b652319",
    "metadata": {},
    "source": [
-    "## Analyze the results"
+    "## Step 4: Post-process, return result in classical format\n",
+    "\n",
+    "You can see that the bit flip code detected and corrected many errors; resulting in fewer errors overall."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 25,
    "id": "fa59fb42",
    "metadata": {
     "slideshow": {
@@ -309,21 +386,18 @@
    },
    "outputs": [],
    "source": [
-    "data_indices = list(range(len(qreg_data)))\n",
-    "syndrome_indices = list(range(data_indices[-1] + 1, len(qreg_data) + len(qreg_measure)))\n",
-    "\n",
     "def decode_result(data_counts, syndrome_counts):\n",
     "    shots = sum(data_counts.values())\n",
     "    success_trials = data_counts.get('000', 0) + data_counts.get('111', 0)\n",
     "    failed_trials = shots - success_trials\n",
     "    error_correction_events = shots - syndrome_counts.get('00', 0)\n",
-    "    print(f\"Bit flip errors were detected/corrected on {error_correction_events}/{shots} trials\")\n",
-    "    print(f\"A final parity error was detected on {failed_trials}/{shots} trials\")"
+    "    print(f\"Bit flip errors were detected/corrected on {error_correction_events}/{shots} trials.\")\n",
+    "    print(f\"A final parity error was detected on {failed_trials}/{shots} trials.\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 26,
    "id": "5b1ff3a3",
    "metadata": {},
    "outputs": [
@@ -331,92 +405,48 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Completed bit code experiment data measurement counts {'000': 5, '001': 7, '011': 100, '100': 3, '110': 31, '111': 818, '010': 2, '101': 34}\n",
-      "Completed bit code experiment syndrome measurement counts {'00': 862, '10': 73, '11': 13, '01': 52}\n",
-      "Bit flip errors were detected/corrected on 138/1000 trials\n",
-      "A final parity error was detected on 177/1000 trials\n"
+      "Completed bit code experiment data measurement counts (without correction): {'101': 146, '111': 516, '011': 180, '000': 25, '110': 23, '100': 16, '001': 46, '010': 48}\n",
+      "Completed bit code experiment syndrome measurement counts (without correction): {'01': 206, '00': 736, '11': 20, '10': 38}\n",
+      "Bit flip errors were detected/corrected on 264/1000 trials.\n",
+      "A final parity error was detected on 459/1000 trials.\n"
      ]
     }
    ],
    "source": [
-    "non_corrected_marginalized_data_result = marginal_counts(result_no_correction, data_indices)\n",
-    "non_corrected_marginalized_syndrome_result = marginal_counts(result_no_correction, syndrome_indices)\n",
+    "# non-corrected marginalized results\n",
+    "data_result = result_no_correction.data.data.get_counts()\n",
+    "marginalized_syndrome_result = result_no_correction.data.syndrome.get_counts()\n",
     "\n",
-    "print(f'Completed bit code experiment data measurement counts {non_corrected_marginalized_data_result.get_counts(0)}')\n",
-    "print(f'Completed bit code experiment syndrome measurement counts {non_corrected_marginalized_syndrome_result.get_counts(0)}')\n",
-    "decode_result(non_corrected_marginalized_data_result.get_counts(0), non_corrected_marginalized_syndrome_result.get_counts(0))"
+    "print(f'Completed bit code experiment data measurement counts (no correction): {data_result}')\n",
+    "print(f'Completed bit code experiment syndrome measurement counts (no correction): {marginalized_syndrome_result}')\n",
+    "decode_result(data_result, marginalized_syndrome_result)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "50bff74a",
+   "execution_count": 27,
+   "id": "7f1c2d48",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Completed bit code experiment data measurement counts {'000': 6, '001': 1, '010': 7, '011': 19, '110': 29, '111': 886, '100': 2, '101': 50}\n",
-      "Completed bit code experiment syndrome measurement counts {'00': 902, '10': 37, '11': 8, '01': 53}\n",
-      "Bit flip errors were detected/corrected on 98/1000 trials\n",
-      "A final parity error was detected on 108/1000 trials\n"
+      "Completed bit code experiment data measurement counts (corrected): {'010': 18, '101': 139, '111': 540, '100': 18, '001': 47, '011': 169, '000': 18, '110': 51}\n",
+      "Completed bit code experiment syndrome measurement counts (corrected): {'11': 19, '00': 809, '10': 90, '01': 82}\n",
+      "Bit flip errors were detected/corrected on 191/1000 trials.\n",
+      "A final parity error was detected on 442/1000 trials.\n"
      ]
     }
    ],
    "source": [
-    "corrected_marginalized_data_result = marginal_counts(result_with_correction, data_indices)\n",
-    "corrected_marginalized_syndrome_result = marginal_counts(result_with_correction, syndrome_indices)\n",
-    "\n",
-    "print(f'Completed bit code experiment data measurement counts {corrected_marginalized_data_result.get_counts(0)}')\n",
-    "print(f'Completed bit code experiment syndrome measurement counts {corrected_marginalized_syndrome_result.get_counts(0)}')\n",
-    "decode_result(corrected_marginalized_data_result.get_counts(0), corrected_marginalized_syndrome_result.get_counts(0))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "7f1c2d48",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'0.9.0'"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import qiskit_ibm_provider\n",
-    "\n",
-    "qiskit_ibm_provider.version.get_version_info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "a6f30a15",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'1.0.0rc1'"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import qiskit\n",
+    "# corrected marginalized results\n",
+    "corrected_data_result = result_with_correction.data.data.get_counts()\n",
+    "corrected_syndrome_result = result_with_correction.data.syndrome.get_counts()\n",
     "\n",
-    "qiskit.version.get_version_info()"
+    "print(f'Completed bit code experiment data measurement counts (corrected): {corrected_data_result}')\n",
+    "print(f'Completed bit code experiment syndrome measurement counts (corrected): {corrected_syndrome_result}')\n",
+    "decode_result(corrected_data_result, corrected_syndrome_result)"
    ]
   }
  ],