diff --git a/docs/tutorials/calibrating_armonk.ipynb b/docs/tutorials/calibrating_armonk.ipynb
index db4e77529d..742412c9ee 100644
--- a/docs/tutorials/calibrating_armonk.ipynb
+++ b/docs/tutorials/calibrating_armonk.ipynb
@@ -183,7 +183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 16,
    "id": "fa22b8a4",
    "metadata": {},
    "outputs": [
@@ -221,68 +221,68 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>β</td>\n",
+       "      <td>σ</td>\n",
        "      <td>()</td>\n",
-       "      <td>sx</td>\n",
-       "      <td>0.000000e+00</td>\n",
+       "      <td>x</td>\n",
+       "      <td>8.000000e+01+0.000000e+00j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:21:13.496348+0200</td>\n",
+       "      <td>2021-10-28 10:27:44.953702+0200</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>σ</td>\n",
-       "      <td>()</td>\n",
-       "      <td>sx</td>\n",
-       "      <td>8.000000e+01</td>\n",
+       "      <td>qubit_lo_freq</td>\n",
+       "      <td>(0,)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>4.971589e+09+0.000000e+00j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:21:13.496341+0200</td>\n",
+       "      <td>2021-10-28 10:27:44.953446+0200</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>amp</td>\n",
+       "      <td>β</td>\n",
        "      <td>()</td>\n",
        "      <td>sx</td>\n",
-       "      <td>2.500000e-01</td>\n",
+       "      <td>0.000000e+00+0.000000e+00j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:21:13.496333+0200</td>\n",
+       "      <td>2021-10-28 10:27:44.953730+0200</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>duration</td>\n",
        "      <td>()</td>\n",
-       "      <td>sx</td>\n",
-       "      <td>3.200000e+02</td>\n",
+       "      <td>x</td>\n",
+       "      <td>3.200000e+02+0.000000e+00j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:21:13.496325+0200</td>\n",
+       "      <td>2021-10-28 10:27:44.953694+0200</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>qubit_lo_freq</td>\n",
-       "      <td>(0,)</td>\n",
-       "      <td>None</td>\n",
-       "      <td>4.971593e+09</td>\n",
+       "      <td>duration</td>\n",
+       "      <td>()</td>\n",
+       "      <td>sx</td>\n",
+       "      <td>3.200000e+02+0.000000e+00j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:21:13.495991+0200</td>\n",
+       "      <td>2021-10-28 10:27:44.953716+0200</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>meas_lo_freq</td>\n",
-       "      <td>(0,)</td>\n",
-       "      <td>None</td>\n",
-       "      <td>6.993371e+09</td>\n",
+       "      <td>amp</td>\n",
+       "      <td>()</td>\n",
+       "      <td>sx</td>\n",
+       "      <td>2.500000e-01+0.000000e+00j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:21:13.496017+0200</td>\n",
+       "      <td>2021-10-28 10:27:44.953709+0200</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -290,43 +290,65 @@
        "      <td>β</td>\n",
        "      <td>()</td>\n",
        "      <td>x</td>\n",
-       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00+0.000000e+00j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:21:13.496317+0200</td>\n",
+       "      <td>2021-10-28 10:27:44.953685+0200</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>σ</td>\n",
-       "      <td>()</td>\n",
+       "      <td>amp</td>\n",
+       "      <td>(0,)</td>\n",
        "      <td>x</td>\n",
-       "      <td>8.000000e+01</td>\n",
+       "      <td>8.578134e-01+0.000000e+00j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:21:13.496309+0200</td>\n",
-       "      <td>None</td>\n",
+       "      <td>2021-10-28 10:37:56.254000+0200</td>\n",
+       "      <td>aa8b9513-a1d8-48b5-82ed-2e3538860ad3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
-       "      <td>amp</td>\n",
-       "      <td>()</td>\n",
-       "      <td>x</td>\n",
-       "      <td>5.000000e-01</td>\n",
+       "      <td>meas_lo_freq</td>\n",
+       "      <td>(0,)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>6.993371e+09+0.000000e+00j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:21:13.496299+0200</td>\n",
+       "      <td>2021-10-28 10:27:44.953469+0200</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
-       "      <td>duration</td>\n",
+       "      <td>σ</td>\n",
+       "      <td>()</td>\n",
+       "      <td>sx</td>\n",
+       "      <td>8.000000e+01+0.000000e+00j</td>\n",
+       "      <td>default</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2021-10-28 10:27:44.953723+0200</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>amp</td>\n",
+       "      <td>(0,)</td>\n",
+       "      <td>sx</td>\n",
+       "      <td>4.289067e-01+0.000000e+00j</td>\n",
+       "      <td>default</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2021-10-28 10:37:56.254000+0200</td>\n",
+       "      <td>aa8b9513-a1d8-48b5-82ed-2e3538860ad3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>amp</td>\n",
        "      <td>()</td>\n",
        "      <td>x</td>\n",
-       "      <td>3.200000e+02</td>\n",
+       "      <td>5.000000e-01+0.000000e+00j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:21:13.496281+0200</td>\n",
+       "      <td>2021-10-28 10:27:44.953672+0200</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -334,32 +356,36 @@
        "</div>"
       ],
       "text/plain": [
-       "       parameter qubits schedule         value    group  valid  \\\n",
-       "0              β     ()       sx  0.000000e+00  default   True   \n",
-       "1              σ     ()       sx  8.000000e+01  default   True   \n",
-       "2            amp     ()       sx  2.500000e-01  default   True   \n",
-       "3       duration     ()       sx  3.200000e+02  default   True   \n",
-       "4  qubit_lo_freq   (0,)     None  4.971593e+09  default   True   \n",
-       "5   meas_lo_freq   (0,)     None  6.993371e+09  default   True   \n",
-       "6              β     ()        x  0.000000e+00  default   True   \n",
-       "7              σ     ()        x  8.000000e+01  default   True   \n",
-       "8            amp     ()        x  5.000000e-01  default   True   \n",
-       "9       duration     ()        x  3.200000e+02  default   True   \n",
+       "        parameter qubits schedule                       value    group  valid  \\\n",
+       "0               σ     ()        x  8.000000e+01+0.000000e+00j  default   True   \n",
+       "1   qubit_lo_freq   (0,)     None  4.971589e+09+0.000000e+00j  default   True   \n",
+       "2               β     ()       sx  0.000000e+00+0.000000e+00j  default   True   \n",
+       "3        duration     ()        x  3.200000e+02+0.000000e+00j  default   True   \n",
+       "4        duration     ()       sx  3.200000e+02+0.000000e+00j  default   True   \n",
+       "5             amp     ()       sx  2.500000e-01+0.000000e+00j  default   True   \n",
+       "6               β     ()        x  0.000000e+00+0.000000e+00j  default   True   \n",
+       "7             amp   (0,)        x  8.578134e-01+0.000000e+00j  default   True   \n",
+       "8    meas_lo_freq   (0,)     None  6.993371e+09+0.000000e+00j  default   True   \n",
+       "9               σ     ()       sx  8.000000e+01+0.000000e+00j  default   True   \n",
+       "10            amp   (0,)       sx  4.289067e-01+0.000000e+00j  default   True   \n",
+       "11            amp     ()        x  5.000000e-01+0.000000e+00j  default   True   \n",
        "\n",
-       "                         date_time exp_id  \n",
-       "0  2021-10-21 14:21:13.496348+0200   None  \n",
-       "1  2021-10-21 14:21:13.496341+0200   None  \n",
-       "2  2021-10-21 14:21:13.496333+0200   None  \n",
-       "3  2021-10-21 14:21:13.496325+0200   None  \n",
-       "4  2021-10-21 14:21:13.495991+0200   None  \n",
-       "5  2021-10-21 14:21:13.496017+0200   None  \n",
-       "6  2021-10-21 14:21:13.496317+0200   None  \n",
-       "7  2021-10-21 14:21:13.496309+0200   None  \n",
-       "8  2021-10-21 14:21:13.496299+0200   None  \n",
-       "9  2021-10-21 14:21:13.496281+0200   None  "
+       "                          date_time                                exp_id  \n",
+       "0   2021-10-28 10:27:44.953702+0200                                  None  \n",
+       "1   2021-10-28 10:27:44.953446+0200                                  None  \n",
+       "2   2021-10-28 10:27:44.953730+0200                                  None  \n",
+       "3   2021-10-28 10:27:44.953694+0200                                  None  \n",
+       "4   2021-10-28 10:27:44.953716+0200                                  None  \n",
+       "5   2021-10-28 10:27:44.953709+0200                                  None  \n",
+       "6   2021-10-28 10:27:44.953685+0200                                  None  \n",
+       "7   2021-10-28 10:37:56.254000+0200  aa8b9513-a1d8-48b5-82ed-2e3538860ad3  \n",
+       "8   2021-10-28 10:27:44.953469+0200                                  None  \n",
+       "9   2021-10-28 10:27:44.953723+0200                                  None  \n",
+       "10  2021-10-28 10:37:56.254000+0200  aa8b9513-a1d8-48b5-82ed-2e3538860ad3  \n",
+       "11  2021-10-28 10:27:44.953672+0200                                  None  "
       ]
      },
-     "execution_count": 9,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -379,13 +405,13 @@
    "source": [
     "freq01_estimate = backend.defaults().qubit_freq_est[qubit]\n",
     "frequencies = np.linspace(freq01_estimate -15e6, freq01_estimate + 15e6, 51)\n",
-    "spec = RoughFrequencyCal(qubit, cals, frequencies)\n",
+    "spec = RoughFrequencyCal(qubit, cals, frequencies, backend=backend)\n",
     "spec.set_experiment_options(amp=0.1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "91184061",
    "metadata": {},
    "outputs": [
@@ -396,30 +422,30 @@
        "<Figure size 392.294x144.48 with 1 Axes>"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "circuit = spec.circuits(backend)[0]\n",
+    "circuit = spec.circuits()[0]\n",
     "circuit.draw(output=\"mpl\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "32a49399",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 936x237.6 with 1 Axes>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -430,28 +456,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "1e24ce2a",
    "metadata": {},
    "outputs": [],
    "source": [
-    "spec_data = spec.run(backend).block_for_results()"
+    "spec_data = spec.run().block_for_results()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "e880af97",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 576x360 with 1 Axes>"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -462,7 +488,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "6e8e067c",
    "metadata": {},
    "outputs": [
@@ -472,8 +498,8 @@
      "text": [
       "DbAnalysisResultV1\n",
       "- name: f01\n",
-      "- value: 4971617198.1562395 ± 48877.278375288435 Hz\n",
-      "- χ²: 1.2265631671328414\n",
+      "- value: 4971657997.745945 ± 45050.505720374684 Hz\n",
+      "- χ²: 0.7650116404414524\n",
       "- quality: good\n",
       "- device_components: ['Q0']\n",
       "- verified: False\n"
@@ -494,7 +520,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "id": "6937956d",
    "metadata": {},
    "outputs": [
@@ -532,13 +558,13 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>meas_lo_freq</td>\n",
+       "      <td>qubit_lo_freq</td>\n",
        "      <td>(0,)</td>\n",
        "      <td>None</td>\n",
-       "      <td>6.993371e+09</td>\n",
+       "      <td>4.971589e+09</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:21:13.496017+0200</td>\n",
+       "      <td>2021-10-28 09:22:53.219031+0200</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -546,22 +572,22 @@
        "      <td>qubit_lo_freq</td>\n",
        "      <td>(0,)</td>\n",
        "      <td>None</td>\n",
-       "      <td>4.971593e+09</td>\n",
+       "      <td>4.971658e+09</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:21:13.495991+0200</td>\n",
-       "      <td>None</td>\n",
+       "      <td>2021-10-28 09:27:39.803000+0200</td>\n",
+       "      <td>595c08f2-ed79-4c25-8843-803c569b4e90</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>qubit_lo_freq</td>\n",
+       "      <td>meas_lo_freq</td>\n",
        "      <td>(0,)</td>\n",
        "      <td>None</td>\n",
-       "      <td>4.971617e+09</td>\n",
+       "      <td>6.993371e+09</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:26:42.953000+0200</td>\n",
-       "      <td>553c95bf-e578-4064-9b17-68a07b84a7f0</td>\n",
+       "      <td>2021-10-28 09:22:53.219055+0200</td>\n",
+       "      <td>None</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -569,17 +595,17 @@
       ],
       "text/plain": [
        "       parameter qubits schedule         value    group  valid  \\\n",
-       "0   meas_lo_freq   (0,)     None  6.993371e+09  default   True   \n",
-       "1  qubit_lo_freq   (0,)     None  4.971593e+09  default   True   \n",
-       "2  qubit_lo_freq   (0,)     None  4.971617e+09  default   True   \n",
+       "0  qubit_lo_freq   (0,)     None  4.971589e+09  default   True   \n",
+       "1  qubit_lo_freq   (0,)     None  4.971658e+09  default   True   \n",
+       "2   meas_lo_freq   (0,)     None  6.993371e+09  default   True   \n",
        "\n",
        "                         date_time                                exp_id  \n",
-       "0  2021-10-21 14:21:13.496017+0200                                  None  \n",
-       "1  2021-10-21 14:21:13.495991+0200                                  None  \n",
-       "2  2021-10-21 14:26:42.953000+0200  553c95bf-e578-4064-9b17-68a07b84a7f0  "
+       "0  2021-10-28 09:22:53.219031+0200                                  None  \n",
+       "1  2021-10-28 09:27:39.803000+0200  595c08f2-ed79-4c25-8843-803c569b4e90  \n",
+       "2  2021-10-28 09:22:53.219055+0200                                  None  "
       ]
      },
-     "execution_count": 16,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -603,58 +629,91 @@
    "source": [
     "## 2. Calibrating the pulse amplitudes with a Rabi experiment\n",
     "\n",
-    "In the Rabi experiment we apply a pulse at the frequency of the qubit and scan its amplitude to find the amplitude that creates a rotation of a desired angle."
+    "In the Rabi experiment we apply a pulse at the frequency of the qubit and scan its amplitude to find the amplitude that creates a rotation of a desired angle. We do this with the calibration experiment `RoughXSXAmplitudeCal`. This is a specialization of the `Rabi` experiment that will update the calibrations for both the `X` pulse and the `SX` pulse using a single experiment."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 8,
    "id": "ed4a5f77",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from qiskit_experiments.library.calibration import Rabi\n",
-    "from qiskit_experiments.calibration_management.update_library import Amplitude"
+    "from qiskit_experiments.library.calibration import RoughXSXAmplitudeCal"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 9,
    "id": "8227b8ba",
    "metadata": {},
    "outputs": [],
    "source": [
-    "rabi = Rabi(qubit)\n",
-    "rabi.set_experiment_options(\n",
-    "    amplitudes=np.linspace(-0.95, 0.95, 51), \n",
-    "    schedule=cals.get_schedule(\"x\", (qubit,), assign_params={\"amp\": Parameter(\"amp\")}),\n",
-    ")"
+    "rabi = RoughXSXAmplitudeCal(qubit, cals, backend=backend)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b425031",
+   "metadata": {},
+   "source": [
+    "The rough amplitude calibration is therefore a Rabi experiment in which each circuit contains a pulse with a gate. Different circuits correspond to pulses with different amplitudes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b82cf6dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 392.294x144.48 with 1 Axes>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rabi.circuits()[0].draw(\"mpl\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f8ecc750",
+   "metadata": {},
+   "source": [
+    "After the experiment completes the value of the amplitudes in the calibrations will automatically be updated. This behaviour can be controlled using the `auto_update` argument given to the calibration experiment at initialization."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 11,
    "id": "68d32b29",
    "metadata": {},
    "outputs": [],
    "source": [
-    "rabi_data = rabi.run(backend).block_for_results()"
+    "rabi_data = rabi.run().block_for_results()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 12,
    "id": "9eefc00c",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 576x360 with 1 Axes>"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -665,7 +724,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 13,
    "id": "444d829c",
    "metadata": {},
    "outputs": [
@@ -675,8 +734,8 @@
      "text": [
       "DbAnalysisResultV1\n",
       "- name: rabi_rate\n",
-      "- value: 0.5773601216615961 ± 0.00302334045912944\n",
-      "- χ²: 1.7757334563985634\n",
+      "- value: 0.5828773180419505 ± 0.0027186379727953116\n",
+      "- χ²: 1.6337913847682912\n",
       "- quality: good\n",
       "- device_components: ['Q0']\n",
       "- verified: False\n"
@@ -689,17 +748,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "f883b472",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Amplitude.update(cals, rabi_data, angles_schedules=[(np.pi, \"amp\", \"x\"), (np.pi/2, \"amp\", \"sx\")])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 17,
    "id": "7fa0e4b4",
    "metadata": {},
    "outputs": [
@@ -738,46 +787,46 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>amp</td>\n",
-       "      <td>()</td>\n",
-       "      <td>sx</td>\n",
-       "      <td>0.250000+0.000000j</td>\n",
+       "      <td>(0,)</td>\n",
+       "      <td>x</td>\n",
+       "      <td>0.857813+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:21:13.496333+0200</td>\n",
-       "      <td>None</td>\n",
+       "      <td>2021-10-28 10:37:56.254000+0200</td>\n",
+       "      <td>aa8b9513-a1d8-48b5-82ed-2e3538860ad3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>amp</td>\n",
-       "      <td>(0,)</td>\n",
+       "      <td>()</td>\n",
        "      <td>sx</td>\n",
-       "      <td>0.433005+0.000000j</td>\n",
+       "      <td>0.250000+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:39:49.487000+0200</td>\n",
-       "      <td>1b5c7f5c-2a93-4beb-a3cd-037e3f18c397</td>\n",
+       "      <td>2021-10-28 10:27:44.953709+0200</td>\n",
+       "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>amp</td>\n",
-       "      <td>()</td>\n",
-       "      <td>x</td>\n",
-       "      <td>0.500000+0.000000j</td>\n",
+       "      <td>(0,)</td>\n",
+       "      <td>sx</td>\n",
+       "      <td>0.428907+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:21:13.496299+0200</td>\n",
-       "      <td>None</td>\n",
+       "      <td>2021-10-28 10:37:56.254000+0200</td>\n",
+       "      <td>aa8b9513-a1d8-48b5-82ed-2e3538860ad3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>amp</td>\n",
-       "      <td>(0,)</td>\n",
+       "      <td>()</td>\n",
        "      <td>x</td>\n",
-       "      <td>0.866011+0.000000j</td>\n",
+       "      <td>0.500000+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:39:49.487000+0200</td>\n",
-       "      <td>1b5c7f5c-2a93-4beb-a3cd-037e3f18c397</td>\n",
+       "      <td>2021-10-28 10:27:44.953672+0200</td>\n",
+       "      <td>None</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -785,19 +834,19 @@
       ],
       "text/plain": [
        "  parameter qubits schedule               value    group  valid  \\\n",
-       "0       amp     ()       sx  0.250000+0.000000j  default   True   \n",
-       "1       amp   (0,)       sx  0.433005+0.000000j  default   True   \n",
-       "2       amp     ()        x  0.500000+0.000000j  default   True   \n",
-       "3       amp   (0,)        x  0.866011+0.000000j  default   True   \n",
+       "0       amp   (0,)        x  0.857813+0.000000j  default   True   \n",
+       "1       amp     ()       sx  0.250000+0.000000j  default   True   \n",
+       "2       amp   (0,)       sx  0.428907+0.000000j  default   True   \n",
+       "3       amp     ()        x  0.500000+0.000000j  default   True   \n",
        "\n",
        "                         date_time                                exp_id  \n",
-       "0  2021-10-21 14:21:13.496333+0200                                  None  \n",
-       "1  2021-10-21 14:39:49.487000+0200  1b5c7f5c-2a93-4beb-a3cd-037e3f18c397  \n",
-       "2  2021-10-21 14:21:13.496299+0200                                  None  \n",
-       "3  2021-10-21 14:39:49.487000+0200  1b5c7f5c-2a93-4beb-a3cd-037e3f18c397  "
+       "0  2021-10-28 10:37:56.254000+0200  aa8b9513-a1d8-48b5-82ed-2e3538860ad3  \n",
+       "1  2021-10-28 10:27:44.953709+0200                                  None  \n",
+       "2  2021-10-28 10:37:56.254000+0200  aa8b9513-a1d8-48b5-82ed-2e3538860ad3  \n",
+       "3  2021-10-28 10:27:44.953672+0200                                  None  "
       ]
      },
-     "execution_count": 23,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/qiskit_experiments/calibration_management/__init__.py b/qiskit_experiments/calibration_management/__init__.py
index 6a41faeb59..0a5f6ed86e 100644
--- a/qiskit_experiments/calibration_management/__init__.py
+++ b/qiskit_experiments/calibration_management/__init__.py
@@ -39,7 +39,6 @@
     BackendCalibrations
     Calibrations
     Frequency
-    Amplitude
     Drag
 
 Managing Calibration Data
@@ -150,4 +149,4 @@
 from .backend_calibrations import BackendCalibrations
 from .base_calibration_experiment import BaseCalibrationExperiment
 
-from .update_library import Frequency, Drag, Amplitude, FineDragUpdater
+from .update_library import Frequency, Drag, FineDragUpdater
diff --git a/qiskit_experiments/calibration_management/update_library.py b/qiskit_experiments/calibration_management/update_library.py
index 57a177aba8..c699d1e25b 100644
--- a/qiskit_experiments/calibration_management/update_library.py
+++ b/qiskit_experiments/calibration_management/update_library.py
@@ -14,7 +14,7 @@
 
 from abc import ABC
 from datetime import datetime, timezone
-from typing import List, Optional, Tuple, Union
+from typing import Optional, Union
 import numpy as np
 
 from qiskit.circuit import Parameter
@@ -245,56 +245,3 @@ def update(
         new_beta = old_beta + d_beta
 
         cls.add_parameter_value(calibrations, exp_data, new_beta, parameter, schedule, group)
-
-
-class Amplitude(BaseUpdater):
-    """Update pulse amplitudes."""
-
-    # pylint: disable=arguments-differ,unused-argument
-    @classmethod
-    def update(
-        cls,
-        calibrations: Calibrations,
-        exp_data: ExperimentData,
-        result_index: Optional[int] = -1,
-        group: str = "default",
-        angles_schedules: List[Tuple[float, str, Union[str, ScheduleBlock]]] = None,
-        **options,
-    ):
-        """Update the amplitude of pulses.
-
-        The value of the amplitude must be derived from the fit so the base method cannot be used.
-
-        Args:
-            calibrations: The calibrations to update.
-            exp_data: The experiment data from which to update.
-            result_index: The result index to use which defaults to -1.
-            group: The calibrations group to update. Defaults to "default."
-            angles_schedules: A list of tuples specifying which angle to update for which
-                pulse schedule. Each tuple is of the form: (angle, parameter_name,
-                schedule). Here, angle is the rotation angle for which to extract the amplitude,
-                parameter_name is the name of the parameter whose value is to be updated, and
-                schedule is the schedule or its name that contains the parameter.
-            options: Trailing options.
-
-        Raises:
-            CalibrationError: If the experiment is not of the supported type.
-        """
-        from qiskit_experiments.library.calibration.rabi import Rabi
-
-        if angles_schedules is None:
-            angles_schedules = [(np.pi, "amp", "xp")]
-
-        if isinstance(exp_data.experiment, Rabi):
-            rate = 2 * np.pi * BaseUpdater.get_value(exp_data, "rabi_rate", result_index)
-
-            for angle, param, schedule in angles_schedules:
-                qubits = exp_data.metadata["physical_qubits"]
-                prev_amp = calibrations.get_parameter_value(param, qubits, schedule, group=group)
-
-                value = np.round(angle / rate, decimals=8) * np.exp(1.0j * np.angle(prev_amp))
-
-                cls.add_parameter_value(calibrations, exp_data, value, param, schedule, group)
-
-        else:
-            raise CalibrationError(f"{cls.__name__} updates from {type(Rabi.__name__)}.")
diff --git a/qiskit_experiments/library/__init__.py b/qiskit_experiments/library/__init__.py
index a2e1bdc093..e721e8c4ed 100644
--- a/qiskit_experiments/library/__init__.py
+++ b/qiskit_experiments/library/__init__.py
@@ -66,6 +66,8 @@
     ~characterization.FineAmplitude
     ~characterization.FineXAmplitude
     ~characterization.FineSXAmplitude
+    ~characterization.Rabi
+    ~characterization.EFRabi
 
 .. _calibration:
 
@@ -87,12 +89,13 @@ class instance to manage parameters and pulse schedules.
     ~calibration.FineDrag
     ~calibration.FineXDrag
     ~calibration.FineSXDrag
-    ~calibration.Rabi
-    ~calibration.EFRabi
     ~calibration.FineAmplitudeCal
     ~calibration.FineXAmplitudeCal
     ~calibration.FineSXAmplitudeCal
     ~calibration.RamseyXY
+    ~calibration.RoughAmplitudeCal
+    ~calibration.RoughXSXAmplitudeCal
+    ~calibration.EFRoughXSXAmplitudeCal
 
 """
 from .calibration import (
@@ -100,8 +103,9 @@ class instance to manage parameters and pulse schedules.
     FineDrag,
     FineXDrag,
     FineSXDrag,
-    Rabi,
-    EFRabi,
+    RoughAmplitudeCal,
+    RoughXSXAmplitudeCal,
+    EFRoughXSXAmplitudeCal,
     FineAmplitudeCal,
     FineXAmplitudeCal,
     FineSXAmplitudeCal,
@@ -115,6 +119,8 @@ class instance to manage parameters and pulse schedules.
     EFSpectroscopy,
     CrossResonanceHamiltonian,
     EchoedCrossResonanceHamiltonian,
+    Rabi,
+    EFRabi,
     HalfAngle,
     FineAmplitude,
     FineXAmplitude,
diff --git a/qiskit_experiments/library/calibration/__init__.py b/qiskit_experiments/library/calibration/__init__.py
index 139c575207..fdc52b110a 100644
--- a/qiskit_experiments/library/calibration/__init__.py
+++ b/qiskit_experiments/library/calibration/__init__.py
@@ -44,11 +44,13 @@
     FineDrag
     FineXDrag
     FineSXDrag
-    Rabi
     FineAmplitudeCal
     FineXAmplitudeCal
     FineSXAmplitudeCal
     RamseyXY
+    RoughAmplitudeCal
+    RoughXSXAmplitudeCal
+    EFRoughXSXAmplitudeCal
 
 Calibration analysis
 ====================
@@ -70,8 +72,8 @@
 from .rough_frequency import RoughFrequencyCal
 from .drag import DragCal
 from .fine_drag import FineDrag, FineXDrag, FineSXDrag
+from .rough_amplitude_cal import RoughAmplitudeCal, RoughXSXAmplitudeCal, EFRoughXSXAmplitudeCal
 from .fine_amplitude import FineAmplitudeCal, FineXAmplitudeCal, FineSXAmplitudeCal
-from .rabi import Rabi, EFRabi
 from .ramsey_xy import RamseyXY
 
 from .analysis.drag_analysis import DragCalAnalysis
diff --git a/qiskit_experiments/library/calibration/fine_amplitude.py b/qiskit_experiments/library/calibration/fine_amplitude.py
index fe21079a31..6be253fd4a 100644
--- a/qiskit_experiments/library/calibration/fine_amplitude.py
+++ b/qiskit_experiments/library/calibration/fine_amplitude.py
@@ -57,7 +57,6 @@ def __init__(
             cal_parameter_name: The name of the parameter in the schedule to update.
             auto_update: Whether or not to automatically update the calibrations. By
                 default this variable is set to True.
-            on.
         """
         super().__init__(
             calibrations,
diff --git a/qiskit_experiments/library/calibration/rabi.py b/qiskit_experiments/library/calibration/rabi.py
deleted file mode 100644
index 9668381589..0000000000
--- a/qiskit_experiments/library/calibration/rabi.py
+++ /dev/null
@@ -1,309 +0,0 @@
-# This code is part of Qiskit.
-#
-# (C) Copyright IBM 2021.
-#
-# This code is licensed under the Apache License, Version 2.0. You may
-# obtain a copy of this license in the LICENSE.txt file in the root directory
-# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
-#
-# Any modifications or derivative works of this code must retain this
-# copyright notice, and modified files need to carry a notice indicating
-# that they have been altered from the originals.
-
-"""Rabi amplitude experiment."""
-
-from typing import List, Optional
-import numpy as np
-
-from qiskit import QuantumCircuit
-from qiskit.circuit import Gate, Parameter
-from qiskit.qobj.utils import MeasLevel
-from qiskit.providers import Backend
-import qiskit.pulse as pulse
-
-from qiskit_experiments.framework import BaseExperiment, Options, fix_class_docs
-from qiskit_experiments.curve_analysis import ParameterRepr, OscillationAnalysis
-from qiskit_experiments.exceptions import CalibrationError
-
-
-@fix_class_docs
-class Rabi(BaseExperiment):
-    """An experiment that scans the amplitude of a pulse to calibrate rotations between 0 and 1.
-
-    # section: overview
-
-        The circuits that are run have a custom rabi gate with the pulse schedule attached to it
-        through the calibrations. The circuits are of the form:
-
-        .. parsed-literal::
-
-                       ┌───────────┐ ░ ┌─┐
-                  q_0: ┤ Rabi(amp) ├─░─┤M├
-                       └───────────┘ ░ └╥┘
-            measure: 1/═════════════════╩═
-                                        0
-
-        If the user provides his own schedule for the Rabi then it must have one free parameter,
-        i.e. the amplitude that will be scanned, and a drive channel which matches the qubit.
-
-    # section: tutorial
-        :doc:`/tutorials/calibrating_armonk`
-
-        See also `Qiskit Textbook <https://qiskit.org/textbook/ch-quantum-hardware/\
-        calibrating-qubits-pulse.html>`_
-        for the pulse level programming of Rabi experiment.
-
-    """
-
-    __analysis_class__ = OscillationAnalysis
-    __rabi_gate_name__ = "Rabi"
-
-    @classmethod
-    def _default_run_options(cls) -> Options:
-        """Default option values for the experiment :meth:`run` method."""
-        options = super()._default_run_options()
-
-        options.meas_level = MeasLevel.KERNELED
-        options.meas_return = "single"
-
-        return options
-
-    @classmethod
-    def _default_experiment_options(cls) -> Options:
-        """Default values for the pulse if no schedule is given.
-
-        Users can set a schedule by doing
-
-        .. code-block::
-
-            rabi.set_experiment_options(schedule=rabi_schedule)
-
-        Experiment Options:
-            duration (int): The duration of the default Gaussian pulse.
-            sigma (float): The standard deviation of the default Gaussian pulse.
-            amplitudes (iterable): The list of amplitude values to scan.
-            schedule (ScheduleBlock): The schedule for the Rabi pulse that overrides the default.
-
-        """
-        options = super()._default_experiment_options()
-
-        options.duration = 160
-        options.sigma = 40
-        options.amplitudes = np.linspace(-0.95, 0.95, 51)
-        options.schedule = None
-
-        return options
-
-    @classmethod
-    def _default_analysis_options(cls) -> Options:
-        """Default analysis options."""
-        options = super()._default_analysis_options()
-        options.result_parameters = [ParameterRepr("freq", "rabi_rate")]
-        options.xlabel = "Amplitude"
-        options.ylabel = "Signal (arb. units)"
-        options.normalization = True
-
-        return options
-
-    def __init__(self, qubit: int, backend: Optional[Backend] = None):
-        """Initialize a Rabi experiment on the given qubit.
-
-        The parameters of the Gaussian Rabi pulse can be specified at run-time.
-        The rabi pulse has the following parameters:
-        - duration: The duration of the rabi pulse in samples, the default is 160 samples.
-        - sigma: The standard deviation of the pulse, the default is duration 40.
-        - amplitudes: The amplitude that are scanned in the experiment, default  is
-        np.linspace(-0.95, 0.95, 51)
-
-        Args:
-            qubit: The qubit on which to run the Rabi experiment.
-            backend: Optional, the backend to run the experiment on.
-        """
-        super().__init__([qubit], backend=backend)
-
-    def _template_circuit(self, amp_param) -> QuantumCircuit:
-        """Return the template quantum circuit."""
-        gate = Gate(name=self.__rabi_gate_name__, num_qubits=1, params=[amp_param])
-
-        circuit = QuantumCircuit(1)
-        circuit.append(gate, (0,))
-        circuit.measure_active()
-
-        return circuit
-
-    def _default_gate_schedule(self, backend: Optional[Backend] = None):
-        """Create the default schedule for the Rabi gate."""
-        amp = Parameter("amp")
-        with pulse.build(backend=backend, name="rabi") as default_schedule:
-            pulse.play(
-                pulse.Gaussian(
-                    duration=self.experiment_options.duration,
-                    amp=amp,
-                    sigma=self.experiment_options.sigma,
-                ),
-                pulse.DriveChannel(self.physical_qubits[0]),
-            )
-
-        return default_schedule
-
-    def circuits(self) -> List[QuantumCircuit]:
-        """Create the circuits for the Rabi experiment.
-
-        Returns:
-            A list of circuits with a rabi gate with an attached schedule. Each schedule
-            will have a different value of the scanned amplitude.
-
-        Raises:
-            CalibrationError:
-                - If the user-provided schedule does not contain a channel with an index
-                  that matches the qubit on which to run the Rabi experiment.
-                - If the user provided schedule has more than one free parameter.
-        """
-        schedule = self.experiment_options.get("schedule", None)
-
-        if schedule is None:
-            schedule = self._default_gate_schedule(backend=self.backend)
-        else:
-            if self.physical_qubits[0] not in set(ch.index for ch in schedule.channels):
-                raise CalibrationError(
-                    f"User provided schedule {schedule.name} does not contain a channel "
-                    "for the qubit on which to run Rabi."
-                )
-
-        if len(schedule.parameters) != 1:
-            raise CalibrationError("Schedule in Rabi must have exactly one free parameter.")
-
-        param = next(iter(schedule.parameters))
-
-        # Create template circuit
-        circuit = self._template_circuit(param)
-        circuit.add_calibration(
-            self.__rabi_gate_name__, (self.physical_qubits[0],), schedule, params=[param]
-        )
-
-        # Get backend dt
-        if self.backend is not None:
-            backend_dt = getattr(self.backend.configuration(), "dt", "n.a.")
-        else:
-            backend_dt = "n.a"
-
-        # Create the circuits to run
-        circs = []
-        for amp in self.experiment_options.amplitudes:
-            amp = np.round(amp, decimals=6)
-            assigned_circ = circuit.assign_parameters({param: amp}, inplace=False)
-            assigned_circ.metadata = {
-                "experiment_type": self._type,
-                "qubits": (self.physical_qubits[0],),
-                "xval": amp,
-                "unit": "arb. unit",
-                "amplitude": amp,
-                "schedule": str(schedule),
-                "dt": backend_dt,
-            }
-
-            circs.append(assigned_circ)
-
-        return circs
-
-
-class EFRabi(Rabi):
-    """An experiment that scans the amplitude of a pulse to calibrate rotations between 1 and 2.
-
-    # section: overview
-
-        This experiment is a subclass of the :class:`Rabi` experiment but takes place between
-        the first and second excited state. An initial X gate used to populate the first excited
-        state. The Rabi pulse is then applied on the 1 <-> 2 transition (sometimes also labeled
-        the e <-> f transition) which implies that frequency shift instructions are used. The
-        necessary frequency shift (typically the qubit anharmonicity) should be specified
-        through the experiment options.
-
-        The circuits are of the form:
-
-        .. parsed-literal::
-
-                       ┌───┐┌───────────┐ ░ ┌─┐
-                  q_0: ┤ X ├┤ Rabi(amp) ├─░─┤M├
-                       └───┘└───────────┘ ░ └╥┘
-            measure: 1/══════════════════════╩═
-                                             0
-
-    # section: example
-        Users can set a schedule by doing
-
-        .. code-block::
-
-            ef_rabi.set_experiment_options(schedule=rabi_schedule)
-
-    """
-
-    @classmethod
-    def _default_experiment_options(cls) -> Options:
-        """Default values for the pulse if no schedule is given.
-
-        Experiment Options:
-
-            frequency_shift (float): The frequency by which the 1 to 2 transition is
-                detuned from the 0 to 1 transition.
-        """
-        options = super()._default_experiment_options()
-        options.frequency_shift = None
-
-        return options
-
-    @classmethod
-    def _default_analysis_options(cls) -> Options:
-        """Default analysis options."""
-        options = super()._default_analysis_options()
-        options.result_parameters = [ParameterRepr("freq", "rabi_rate_12")]
-
-        return options
-
-    def _default_gate_schedule(self, backend: Optional[Backend] = None):
-        """Create the default schedule for the EFRabi gate with a frequency shift to the 1-2
-        transition."""
-
-        if self.experiment_options.frequency_shift is None:
-            try:
-                anharm, _ = backend.properties().qubit_property(self.physical_qubits[0])[
-                    "anharmonicity"
-                ]
-                self.set_experiment_options(frequency_shift=anharm)
-            except KeyError as key_err:
-                raise CalibrationError(
-                    f"The backend {backend} does not provide an anharmonicity for qubit "
-                    f"{self.physical_qubits[0]}. Use EFRabi.set_experiment_options(frequency_shift="
-                    f"anharmonicity) to manually set the correct frequency for the 1-2 transition."
-                ) from key_err
-            except AttributeError as att_err:
-                raise CalibrationError(
-                    "When creating the default schedule without passing a backend, the frequency needs "
-                    "to be set manually through EFRabi.set_experiment_options(frequency_shift=..)."
-                ) from att_err
-
-        amp = Parameter("amp")
-        with pulse.build(backend=backend, name=self.__rabi_gate_name__) as default_schedule:
-            with pulse.frequency_offset(
-                self.experiment_options.frequency_shift,
-                pulse.DriveChannel(self.physical_qubits[0]),
-            ):
-                pulse.play(
-                    pulse.Gaussian(
-                        duration=self.experiment_options.duration,
-                        amp=amp,
-                        sigma=self.experiment_options.sigma,
-                    ),
-                    pulse.DriveChannel(self.physical_qubits[0]),
-                )
-
-        return default_schedule
-
-    def _template_circuit(self, amp_param) -> QuantumCircuit:
-        """Return the template quantum circuit."""
-        circuit = QuantumCircuit(1)
-        circuit.x(0)
-        circuit.append(Gate(name=self.__rabi_gate_name__, num_qubits=1, params=[amp_param]), (0,))
-        circuit.measure_active()
-
-        return circuit
diff --git a/qiskit_experiments/library/calibration/rough_amplitude_cal.py b/qiskit_experiments/library/calibration/rough_amplitude_cal.py
new file mode 100644
index 0000000000..9538425431
--- /dev/null
+++ b/qiskit_experiments/library/calibration/rough_amplitude_cal.py
@@ -0,0 +1,294 @@
+# This code is part of Qiskit.
+#
+# (C) Copyright IBM 2021.
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+
+"""Rough amplitude calibration using Rabi."""
+
+from collections import namedtuple
+from typing import Iterable, List, Optional
+import numpy as np
+
+from qiskit import QuantumCircuit
+from qiskit.circuit import Parameter
+from qiskit.providers.backend import Backend
+
+from qiskit_experiments.framework import ExperimentData, Options, fix_class_docs
+from qiskit_experiments.calibration_management import BaseCalibrationExperiment, BackendCalibrations
+from qiskit_experiments.library.characterization import Rabi
+from qiskit_experiments.calibration_management.update_library import BaseUpdater
+from qiskit_experiments.curve_analysis import ParameterRepr
+
+AnglesSchedules = namedtuple(
+    "AnglesSchedules", ["target_angle", "parameter", "schedule", "previous_value"]
+)
+
+
+@fix_class_docs
+class RoughAmplitudeCal(BaseCalibrationExperiment, Rabi):
+    """A calibration version of the Rabi experiment.
+
+    # section: see_also
+        qiskit_experiments.library.characterization.rabi.Rabi
+    """
+
+    def __init__(
+        self,
+        qubit: int,
+        calibrations: BackendCalibrations,
+        schedule_name: str = "x",
+        amplitudes: Iterable[float] = None,
+        cal_parameter_name: Optional[str] = "amp",
+        target_angle: float = np.pi,
+        auto_update: bool = True,
+        group: str = "default",
+        backend: Optional[Backend] = None,
+    ):
+        r"""see class :class:`Rabi` for details.
+
+        Args:
+            qubit: The qubit for which to run the rough amplitude calibration.
+            calibrations: The calibrations instance with the schedules.
+            schedule_name: The name of the schedule to calibrate. Defaults to "x".
+            amplitudes: A list of amplitudes to scan. If None is given 51 amplitudes ranging
+                from -0.95 to 0.95 will be scanned.
+            cal_parameter_name: The name of the parameter in the schedule to update.
+            target_angle: The target angle of the gate to calibrate this will default to a
+                :math:`\pi`-pulse.
+            auto_update: Whether or not to automatically update the calibrations. By
+                default this variable is set to True.
+            group: The group of calibration parameters to use. The default value is "default".
+            backend: Optional, the backend to run the experiment on.
+        """
+        schedule = calibrations.get_schedule(
+            schedule_name, qubit, assign_params={cal_parameter_name: Parameter("amp")}, group=group
+        )
+
+        super().__init__(
+            calibrations,
+            qubit,
+            schedule=schedule,
+            amplitudes=amplitudes,
+            backend=backend,
+            schedule_name=schedule_name,
+            cal_parameter_name=cal_parameter_name,
+            auto_update=auto_update,
+        )
+
+        # Needed for subclasses that will drive other transitions than the 0<->1 transition.
+        self.set_transpile_options(inst_map=calibrations.default_inst_map)
+        self._analysis_param_name = "rabi_rate"
+
+        # Set the pulses to update.
+        prev_amp = calibrations.get_parameter_value(cal_parameter_name, qubit, schedule_name)
+        self.experiment_options.group = group
+        self.experiment_options.angles_schedules = [
+            AnglesSchedules(
+                target_angle=target_angle,
+                parameter=cal_parameter_name,
+                schedule=schedule_name,
+                previous_value=prev_amp,
+            )
+        ]
+
+    @classmethod
+    def _default_experiment_options(cls):
+        """Default values for the rough amplitude calibration experiment.
+
+        Experiment Options:
+            result_index (int): The index of the result from which to update the calibrations.
+            angles_schedules (list(float, str, str, float)): A list of parameter update information.
+                Each entry of the list is a tuple with four entries: the target angle of the
+                rotation, the name of the amplitude parameter to update, the name of the schedule
+                containing the amplitude parameter to update, and the previous value of the
+                amplitude parameter to update. This allows one experiment to update several
+                schedules, see for example :class:`RoughXSXAmplitudeCal`.
+            group (str): The calibration group to which the parameter belongs. This will default
+                to the value "default".
+        """
+        options = super()._default_experiment_options()
+
+        options.result_index = -1
+        options.angles_schedules = [
+            AnglesSchedules(target_angle=np.pi, parameter="amp", schedule="x", previous_value=None)
+        ]
+        options.group = "default"
+
+        return options
+
+    def _add_cal_metadata(self, circuits: List[QuantumCircuit]):
+        """Add metadata to the circuit to make the experiment data more self contained.
+
+        The following keys are added to each circuit's metadata:
+            angles_schedules: A list of parameter update information. Each entry of the list
+                is a tuple with four entries: the target angle of the rotation, the name of the
+                amplitude parameter to update, the name of the schedule containing the amplitude
+                parameter to update, and the previous value of the amplitude parameter to update.
+            cal_group: The calibration group to which the amplitude parameters belong.
+        """
+
+        param_values = []
+        for angle, param_name, schedule_name, _ in self.experiment_options.angles_schedules:
+            param_val = self._cals.get_parameter_value(
+                param_name,
+                self._physical_qubits,
+                schedule_name,
+                group=self.experiment_options.group,
+            )
+
+            param_values.append(
+                AnglesSchedules(
+                    target_angle=angle,
+                    parameter=param_name,
+                    schedule=schedule_name,
+                    previous_value=param_val,
+                )
+            )
+
+        for circuit in circuits:
+            circuit.metadata["angles_schedules"] = param_values
+            circuit.metadata["cal_group"] = self.experiment_options.group
+
+        return circuits
+
+    def update_calibrations(self, experiment_data: ExperimentData):
+        r"""Update the amplitude of one or several schedules.
+
+        The update rule extracts the rate of the oscillation from the fit to the cosine function.
+        Recall that the amplitude is the x-axis in the analysis of the :class:`Rabi` experiment.
+        The value of the amplitude is thus the desired rotation-angle divided by the rate of
+        the oscillation:
+
+        .. math::
+
+            A_i \to \frac{\theta_i}{\omega}
+
+        where :math:`\theta_i` is the desired rotation angle (e.g. :math:`\pi` and :math:`\pi/2`
+        for "x" and "sx" gates, respectively) and :math:`\omega` is the rate of the oscillation.
+
+        Args:
+            experiment_data: The experiment data from which to extract the measured Rabi oscillation
+            used to set the pulse amplitude.
+        """
+
+        data = experiment_data.data()
+
+        # No data -> no update
+        if len(data) > 0:
+            result_index = self.experiment_options.result_index
+            group = data[0]["metadata"]["cal_group"]
+
+            rate = (
+                2
+                * np.pi
+                * BaseUpdater.get_value(experiment_data, self._analysis_param_name, result_index)
+            )
+
+            for angle, param, schedule, prev_amp in data[0]["metadata"]["angles_schedules"]:
+
+                value = np.round(angle / rate, decimals=8) * np.exp(1.0j * np.angle(prev_amp))
+
+                BaseUpdater.add_parameter_value(
+                    self._cals, experiment_data, value, param, schedule, group
+                )
+
+
+@fix_class_docs
+class RoughXSXAmplitudeCal(RoughAmplitudeCal):
+    """A rough amplitude calibration of x and sx gates."""
+
+    def __init__(
+        self,
+        qubit: int,
+        calibrations: BackendCalibrations,
+        amplitudes: Iterable[float] = None,
+        backend: Optional[Backend] = None,
+    ):
+        """A rough amplitude calibration that updates both the sx and x pulses."""
+        super().__init__(
+            qubit,
+            calibrations,
+            schedule_name="x",
+            amplitudes=amplitudes,
+            backend=backend,
+            cal_parameter_name="amp",
+            target_angle=np.pi,
+        )
+
+        self.experiment_options.angles_schedules = [
+            AnglesSchedules(target_angle=np.pi, parameter="amp", schedule="x", previous_value=None),
+            AnglesSchedules(
+                target_angle=np.pi / 2, parameter="amp", schedule="sx", previous_value=None
+            ),
+        ]
+
+
+@fix_class_docs
+class EFRoughXSXAmplitudeCal(RoughAmplitudeCal):
+    """A rough amplitude calibration of x and sx gates on the 1<->2 transition."""
+
+    def __init__(
+        self,
+        qubit: int,
+        calibrations: BackendCalibrations,
+        amplitudes: Iterable[float] = None,
+        backend: Optional[Backend] = None,
+        ef_pulse_label: str = "12",
+    ):
+        r"""A rough amplitude calibration that updates both the sx and x pulses on 1<->2.
+
+        Args:
+            qubit: The index of the qubit (technically a qutrit) to run on.
+            calibrations: The calibrations instance that stores the pulse schedules.
+            amplitudes: The amplitudes to scan.
+            backend: Optional, the backend to run the experiment on.
+            ef_pulse_label: A label that is post-pended to "x" and "sx" to obtain the name
+                of the pulses that drive a :math:`\pi` and :math:`\pi/2` rotation on
+                the 1<->2 transition.
+        """
+        super().__init__(
+            qubit,
+            calibrations,
+            schedule_name="x" + ef_pulse_label,
+            amplitudes=amplitudes,
+            backend=backend,
+            cal_parameter_name="amp",
+            target_angle=np.pi,
+        )
+
+        self._analysis_param_name = "rabi_rate_12"
+        self.experiment_options.angles_schedules = [
+            AnglesSchedules(
+                target_angle=np.pi,
+                parameter="amp",
+                schedule="x" + ef_pulse_label,
+                previous_value=None,
+            ),
+            AnglesSchedules(
+                target_angle=np.pi / 2,
+                parameter="amp",
+                schedule="sx" + ef_pulse_label,
+                previous_value=None,
+            ),
+        ]
+
+    @classmethod
+    def _default_analysis_options(cls) -> Options:
+        """Default analysis options."""
+        options = super()._default_analysis_options()
+        options.result_parameters = [ParameterRepr("freq", "rabi_rate_12")]
+
+        return options
+
+    def _pre_circuit(self) -> QuantumCircuit:
+        """A circuit with operations to perform before the Rabi."""
+        circ = QuantumCircuit(1)
+        circ.x(0)
+        return circ
diff --git a/qiskit_experiments/library/characterization/__init__.py b/qiskit_experiments/library/characterization/__init__.py
index f4c23747d4..08f9460e48 100644
--- a/qiskit_experiments/library/characterization/__init__.py
+++ b/qiskit_experiments/library/characterization/__init__.py
@@ -28,6 +28,8 @@
     QubitSpectroscopy
     CrossResonanceHamiltonian
     EchoedCrossResonanceHamiltonian
+    Rabi
+    EFRabi
     HalfAngle
     FineAmplitude
     FineXAmplitude
@@ -55,5 +57,6 @@
 from .t2ramsey_analysis import T2RamseyAnalysis
 from .cr_hamiltonian import CrossResonanceHamiltonian, EchoedCrossResonanceHamiltonian
 from .cr_hamiltonian_analysis import CrossResonanceHamiltonianAnalysis
+from .rabi import Rabi, EFRabi
 from .half_angle import HalfAngle
 from .fine_amplitude import FineAmplitude, FineXAmplitude, FineSXAmplitude
diff --git a/qiskit_experiments/library/characterization/rabi.py b/qiskit_experiments/library/characterization/rabi.py
new file mode 100644
index 0000000000..761774ced7
--- /dev/null
+++ b/qiskit_experiments/library/characterization/rabi.py
@@ -0,0 +1,211 @@
+# This code is part of Qiskit.
+#
+# (C) Copyright IBM 2021.
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+
+"""Rabi amplitude experiment."""
+
+from typing import Iterable, List, Optional, Tuple
+import numpy as np
+
+from qiskit import QuantumCircuit
+from qiskit.circuit import Gate, Parameter
+from qiskit.qobj.utils import MeasLevel
+from qiskit.providers import Backend
+from qiskit.pulse import ScheduleBlock
+from qiskit.exceptions import QiskitError
+
+from qiskit_experiments.framework import BaseExperiment, Options, fix_class_docs
+from qiskit_experiments.curve_analysis import ParameterRepr, OscillationAnalysis
+
+
+@fix_class_docs
+class Rabi(BaseExperiment):
+    """An experiment that scans a pulse amplitude to calibrate rotations between 0 and 1.
+
+    # section: overview
+
+        The circuits have a custom rabi gate with the pulse schedule attached to it
+        through the calibrations. The circuits are of the form:
+
+        .. parsed-literal::
+
+                       ┌───────────┐ ░ ┌─┐
+                  q_0: ┤ Rabi(amp) ├─░─┤M├
+                       └───────────┘ ░ └╥┘
+            measure: 1/═════════════════╩═
+                                        0
+
+        The user provides his own schedule for the Rabi at initialization which must have one
+        free parameter, i.e. the amplitude to scan and a drive channel which matches the qubit.
+
+    # section: tutorial
+        :doc:`/tutorials/calibrating_armonk`
+
+        See also `Qiskit Textbook <https://qiskit.org/textbook/ch-quantum-hardware/\
+        calibrating-qubits-pulse.html>`_
+        for the pulse level programming of a Rabi experiment.
+
+    """
+
+    __analysis_class__ = OscillationAnalysis
+    __gate_name__ = "Rabi"
+
+    @classmethod
+    def _default_run_options(cls) -> Options:
+        """Default option values for the experiment :meth:`run` method."""
+        options = super()._default_run_options()
+
+        options.meas_level = MeasLevel.KERNELED
+        options.meas_return = "single"
+
+        return options
+
+    @classmethod
+    def _default_experiment_options(cls) -> Options:
+        """Default values for the pulse if no schedule is given.
+
+        Experiment Options:
+            amplitudes (iterable): The list of amplitude values to scan.
+            schedule (ScheduleBlock): The schedule for the Rabi pulse. This schedule must have
+                exactly one free parameter. The drive channel should match the qubit.
+
+        """
+        options = super()._default_experiment_options()
+
+        options.amplitudes = np.linspace(-0.95, 0.95, 51)
+        options.schedule = None
+
+        return options
+
+    @classmethod
+    def _default_analysis_options(cls) -> Options:
+        """Default analysis options."""
+        options = super()._default_analysis_options()
+        options.result_parameters = [ParameterRepr("freq", "rabi_rate")]
+        options.xlabel = "Amplitude"
+        options.ylabel = "Signal (arb. units)"
+        options.normalization = True
+
+        return options
+
+    def __init__(
+        self,
+        qubit: int,
+        schedule: ScheduleBlock,
+        amplitudes: Optional[Iterable[float]] = None,
+        backend: Optional[Backend] = None,
+    ):
+        """Initialize a Rabi experiment on the given qubit.
+
+        Args:
+            qubit: The qubit on which to run the Rabi experiment.
+            schedule: The schedule that will be used in the Rabi experiment. This schedule
+                should have one free parameter namely the amplitude.
+            amplitudes: The pulse amplitudes that one wishes to scan. If this variable is not
+                specified it will default to :code:`np.linspace(-0.95, 0.95, 51)`.
+            backend: Optional, the backend to run the experiment on.
+        """
+        super().__init__([qubit], backend=backend)
+
+        if amplitudes is not None:
+            self.experiment_options.amplitudes = amplitudes
+
+        self.experiment_options.schedule = schedule
+
+    def _pre_circuit(self) -> QuantumCircuit:
+        """A circuit with operations to perform before the Rabi."""
+        return QuantumCircuit(1)
+
+    def _template_circuit(self) -> Tuple[QuantumCircuit, Parameter]:
+        """Return the template quantum circuit."""
+        sched = self.experiment_options.schedule
+        param = next(iter(sched.parameters))
+
+        if len(sched.parameters) != 1:
+            raise QiskitError(
+                f"Schedule {sched} for {self.__class__.__name__} experiment must have "
+                f"exactly one free parameter, found {sched.parameters} parameters."
+            )
+
+        gate = Gate(name=self.__gate_name__, num_qubits=1, params=[param])
+
+        circuit = self._pre_circuit()
+        circuit.append(gate, (0,))
+        circuit.measure_active()
+        circuit.add_calibration(gate, self._physical_qubits, sched, params=[param])
+
+        return circuit, param
+
+    def circuits(self) -> List[QuantumCircuit]:
+        """Create the circuits for the Rabi experiment.
+
+        Returns:
+            A list of circuits with a rabi gate with an attached schedule. Each schedule
+            will have a different value of the scanned amplitude.
+        """
+
+        # Create template circuit
+        circuit, param = self._template_circuit()
+
+        # Create the circuits to run
+        circs = []
+        for amp in self.experiment_options.amplitudes:
+            amp = np.round(amp, decimals=6)
+            assigned_circ = circuit.assign_parameters({param: amp}, inplace=False)
+            assigned_circ.metadata = {
+                "experiment_type": self._type,
+                "qubits": self.physical_qubits,
+                "xval": amp,
+                "unit": "arb. unit",
+                "amplitude": amp,
+            }
+
+            circs.append(assigned_circ)
+
+        return circs
+
+
+@fix_class_docs
+class EFRabi(Rabi):
+    """An experiment that scans the amplitude of a pulse inducing rotations between 1 and 2.
+
+    # section: overview
+
+        This experiment is a subclass of the :class:`Rabi` experiment but takes place between
+        the first and second excited state. An initial X gate populates the first excited state.
+        The Rabi pulse is applied on the 1 <-> 2 transition (sometimes also labeled the e <-> f
+        transition). The necessary frequency shift (typically the qubit anharmonicity) is given
+        through the pulse schedule given at initialization. The schedule is then also stored in
+        the experiment options. The circuits are of the form:
+
+        .. parsed-literal::
+
+                       ┌───┐┌───────────┐ ░ ┌─┐
+                  q_0: ┤ X ├┤ Rabi(amp) ├─░─┤M├
+                       └───┘└───────────┘ ░ └╥┘
+            measure: 1/══════════════════════╩═
+                                             0
+
+    """
+
+    @classmethod
+    def _default_analysis_options(cls) -> Options:
+        """Default analysis options."""
+        options = super()._default_analysis_options()
+        options.result_parameters = [ParameterRepr("freq", "rabi_rate_12")]
+
+        return options
+
+    def _pre_circuit(self) -> QuantumCircuit:
+        """A circuit with operations to perform before the Rabi."""
+        circ = QuantumCircuit(1)
+        circ.x(0)
+        return circ
diff --git a/qiskit_experiments/test/mock_iq_backend.py b/qiskit_experiments/test/mock_iq_backend.py
index c142880a42..606f4d4dc2 100644
--- a/qiskit_experiments/test/mock_iq_backend.py
+++ b/qiskit_experiments/test/mock_iq_backend.py
@@ -156,6 +156,31 @@ def _compute_probability(self, circuit: QuantumCircuit) -> float:
         return np.sin(n_gates * self._error * (beta - self.ideal_beta)) ** 2
 
 
+class RabiBackend(MockIQBackend):
+    """A simple and primitive backend, to be run by the Rabi tests."""
+
+    def __init__(
+        self,
+        iq_cluster_centers: Tuple[float, float, float, float] = (1.0, 1.0, -1.0, -1.0),
+        iq_cluster_width: float = 1.0,
+        amplitude_to_angle: float = np.pi,
+    ):
+        """Initialize the rabi backend."""
+        self._amplitude_to_angle = amplitude_to_angle
+
+        super().__init__(iq_cluster_centers, iq_cluster_width)
+
+    @property
+    def rabi_rate(self) -> float:
+        """Returns the rabi rate."""
+        return self._amplitude_to_angle / np.pi
+
+    def _compute_probability(self, circuit: QuantumCircuit) -> float:
+        """Returns the probability based on the rotation angle and amplitude_to_angle."""
+        amp = next(iter(circuit.calibrations["Rabi"].keys()))[1][0]
+        return np.sin(self._amplitude_to_angle * amp) ** 2
+
+
 class MockFineAmp(MockIQBackend):
     """A mock backend for fine amplitude calibration."""
 
diff --git a/test/calibration/experiments/test_fine_amplitude.py b/test/calibration/experiments/test_fine_amplitude.py
index 55ec52f161..9b7f9b9387 100644
--- a/test/calibration/experiments/test_fine_amplitude.py
+++ b/test/calibration/experiments/test_fine_amplitude.py
@@ -200,7 +200,9 @@ def test_run_x_cal(self):
 
         amp_cal = FineXAmplitudeCal(0, self.cals, "x")
 
-        circs = transpile(amp_cal.circuits(), self.backend, **amp_cal.transpile_options.__dict__)
+        circs = transpile(
+            amp_cal.circuits(), self.backend, inst_map=amp_cal.transpile_options.inst_map
+        )
 
         with pulse.build(name="x") as expected_x:
             pulse.play(pulse.Drag(160, 0.5, 40, 0), pulse.DriveChannel(0))
@@ -216,7 +218,9 @@ def test_run_x_cal(self):
         d_theta = exp_data.analysis_results(1).value.value
         new_amp = init_amp * np.pi / (np.pi + d_theta)
 
-        circs = transpile(amp_cal.circuits(), self.backend, **amp_cal.transpile_options.__dict__)
+        circs = transpile(
+            amp_cal.circuits(), self.backend, inst_map=amp_cal.transpile_options.inst_map
+        )
 
         x_cal = circs[5].calibrations["x"][((0,), ())]
 
@@ -238,7 +242,9 @@ def test_run_sx_cal(self):
 
         amp_cal = FineSXAmplitudeCal(0, self.cals, "sx")
 
-        circs = transpile(amp_cal.circuits(), self.backend, **amp_cal.transpile_options.__dict__)
+        circs = transpile(
+            amp_cal.circuits(), self.backend, inst_map=amp_cal.transpile_options.inst_map
+        )
 
         with pulse.build(name="sx") as expected_sx:
             pulse.play(pulse.Drag(160, 0.25, 40, 0), pulse.DriveChannel(0))
@@ -250,7 +256,9 @@ def test_run_sx_cal(self):
         d_theta = exp_data.analysis_results(1).value.value
         new_amp = init_amp * (np.pi / 2) / (np.pi / 2 + d_theta)
 
-        circs = transpile(amp_cal.circuits(), self.backend, **amp_cal.transpile_options.__dict__)
+        circs = transpile(
+            amp_cal.circuits(), self.backend, inst_map=amp_cal.transpile_options.inst_map
+        )
 
         sx_cal = circs[5].calibrations["sx"][((0,), ())]
 
diff --git a/test/calibration/experiments/test_rabi.py b/test/calibration/experiments/test_rabi.py
index e3b06e37ac..c75a1203b7 100644
--- a/test/calibration/experiments/test_rabi.py
+++ b/test/calibration/experiments/test_rabi.py
@@ -12,7 +12,6 @@
 
 """Test Rabi amplitude Experiment class."""
 
-from typing import Tuple
 import numpy as np
 
 from qiskit import QuantumCircuit, transpile
@@ -29,36 +28,22 @@
 from qiskit_experiments.curve_analysis.standard_analysis.oscillation import OscillationAnalysis
 from qiskit_experiments.data_processing.data_processor import DataProcessor
 from qiskit_experiments.data_processing.nodes import Probability
-from qiskit_experiments.test.mock_iq_backend import MockIQBackend
+from qiskit_experiments.test.mock_iq_backend import RabiBackend
 
 
-class RabiBackend(MockIQBackend):
-    """A simple and primitive backend, to be run by the Rabi tests."""
-
-    def __init__(
-        self,
-        iq_cluster_centers: Tuple[float, float, float, float] = (1.0, 1.0, -1.0, -1.0),
-        iq_cluster_width: float = 1.0,
-        amplitude_to_angle: float = np.pi,
-    ):
-        """Initialize the rabi backend."""
-        self._amplitude_to_angle = amplitude_to_angle
+class TestRabiEndToEnd(QiskitTestCase):
+    """Test the rabi experiment."""
 
-        super().__init__(iq_cluster_centers, iq_cluster_width)
+    def setUp(self):
+        """Setup the tests."""
+        super().setUp()
 
-    @property
-    def rabi_rate(self) -> float:
-        """Returns the rabi rate."""
-        return self._amplitude_to_angle / np.pi
+        self.qubit = 1
 
-    def _compute_probability(self, circuit: QuantumCircuit) -> float:
-        """Returns the probability based on the rotation angle and amplitude_to_angle."""
-        amp = next(iter(circuit.calibrations["Rabi"].keys()))[1][0]
-        return np.sin(self._amplitude_to_angle * amp) ** 2
+        with pulse.build(name="x") as sched:
+            pulse.play(pulse.Drag(160, Parameter("amp"), 40, 0.4), pulse.DriveChannel(self.qubit))
 
-
-class TestRabiEndToEnd(QiskitTestCase):
-    """Test the rabi experiment."""
+        self.sched = sched
 
     def test_rabi_end_to_end(self):
         """Test the Rabi experiment end to end."""
@@ -66,7 +51,7 @@ def test_rabi_end_to_end(self):
         test_tol = 0.01
         backend = RabiBackend()
 
-        rabi = Rabi(1)
+        rabi = Rabi(self.qubit, self.sched)
         rabi.set_experiment_options(amplitudes=np.linspace(-0.95, 0.95, 21))
         expdata = rabi.run(backend)
         expdata.block_for_results()
@@ -77,7 +62,7 @@ def test_rabi_end_to_end(self):
 
         backend = RabiBackend(amplitude_to_angle=np.pi / 2)
 
-        rabi = Rabi(1)
+        rabi = Rabi(self.qubit, self.sched)
         rabi.set_experiment_options(amplitudes=np.linspace(-0.95, 0.95, 21))
         expdata = rabi.run(backend)
         expdata.block_for_results()
@@ -87,7 +72,7 @@ def test_rabi_end_to_end(self):
 
         backend = RabiBackend(amplitude_to_angle=2.5 * np.pi)
 
-        rabi = Rabi(1)
+        rabi = Rabi(self.qubit, self.sched)
         rabi.set_experiment_options(amplitudes=np.linspace(-0.95, 0.95, 101))
         expdata = rabi.run(backend)
         expdata.block_for_results()
@@ -100,7 +85,7 @@ def test_wrong_processor(self):
 
         backend = RabiBackend()
 
-        rabi = Rabi(1)
+        rabi = Rabi(self.qubit, self.sched)
 
         fail_key = "fail_key"
 
@@ -114,7 +99,7 @@ def test_wrong_processor(self):
 
     def test_experiment_config(self):
         """Test converting to and from config works"""
-        exp = Rabi(0)
+        exp = Rabi(0, self.sched)
         config = exp.config
         loaded_exp = Rabi.from_config(config)
         self.assertNotEqual(exp, loaded_exp)
@@ -124,18 +109,29 @@ def test_experiment_config(self):
 class TestEFRabi(QiskitTestCase):
     """Test the ef_rabi experiment."""
 
+    def setUp(self):
+        """Setup the tests."""
+        super().setUp()
+
+        self.qubit = 0
+
+        with pulse.build(name="x") as sched:
+            with pulse.frequency_offset(-300e6, pulse.DriveChannel(self.qubit)):
+                pulse.play(
+                    pulse.Drag(160, Parameter("amp"), 40, 0.4), pulse.DriveChannel(self.qubit)
+                )
+
+        self.sched = sched
+
     def test_ef_rabi_end_to_end(self):
         """Test the EFRabi experiment end to end."""
 
         test_tol = 0.01
         backend = RabiBackend()
-        qubit = 0
 
         # Note that the backend is not sophisticated enough to simulate an e-f
         # transition so we run the test with a tiny frequency shift, still driving the e-g transition.
-        freq_shift = 0.01
-        rabi = EFRabi(qubit)
-        rabi.set_experiment_options(frequency_shift=freq_shift)
+        rabi = EFRabi(self.qubit, self.sched)
         rabi.set_experiment_options(amplitudes=np.linspace(-0.95, 0.95, 21))
         expdata = rabi.run(backend)
         expdata.block_for_results()
@@ -147,9 +143,14 @@ def test_ef_rabi_end_to_end(self):
     def test_ef_rabi_circuit(self):
         """Test the EFRabi experiment end to end."""
         anharm = -330e6
-        rabi12 = EFRabi(2)
-        rabi12.set_experiment_options(amplitudes=[0.5], frequency_shift=anharm)
-        rabi12.backend = RabiBackend()
+
+        with pulse.build() as sched:
+            pulse.shift_frequency(anharm, pulse.DriveChannel(2))
+            pulse.play(pulse.Gaussian(160, Parameter("amp"), 40), pulse.DriveChannel(2))
+            pulse.shift_frequency(-anharm, pulse.DriveChannel(2))
+
+        rabi12 = EFRabi(2, sched)
+        rabi12.set_experiment_options(amplitudes=[0.5])
         circ = rabi12.circuits()[0]
 
         with pulse.build() as expected:
@@ -163,7 +164,7 @@ def test_ef_rabi_circuit(self):
 
     def test_experiment_config(self):
         """Test converting to and from config works"""
-        exp = EFRabi(0)
+        exp = EFRabi(0, self.sched)
         config = exp.config
         loaded_exp = EFRabi.from_config(config)
         self.assertNotEqual(exp, loaded_exp)
@@ -173,10 +174,19 @@ def test_experiment_config(self):
 class TestRabiCircuits(QiskitTestCase):
     """Test the circuits generated by the experiment and the options."""
 
+    def setUp(self):
+        """Setup tests."""
+        super().setUp()
+
+        with pulse.build() as sched:
+            pulse.play(pulse.Gaussian(160, Parameter("amp"), 40), pulse.DriveChannel(2))
+
+        self.sched = sched
+
     def test_default_schedule(self):
         """Test the default schedule."""
 
-        rabi = Rabi(2)
+        rabi = Rabi(2, self.sched)
         rabi.set_experiment_options(amplitudes=[0.5])
         rabi.backend = RabiBackend()
         circs = rabi.circuits()
@@ -195,7 +205,7 @@ def test_user_schedule(self):
             pulse.play(pulse.Drag(160, amp, 40, 10), pulse.DriveChannel(2))
             pulse.play(pulse.Drag(160, amp, 40, 10), pulse.DriveChannel(2))
 
-        rabi = Rabi(2)
+        rabi = Rabi(2, self.sched)
         rabi.set_experiment_options(schedule=my_schedule, amplitudes=[0.5])
         rabi.backend = RabiBackend()
         circs = rabi.circuits()
@@ -288,9 +298,10 @@ def test_calibrations(self):
 
         experiments = []
         for qubit in range(3):
-            rabi = Rabi(qubit)
-            rabi.set_experiment_options(amplitudes=[0.5])
-            experiments.append(rabi)
+            with pulse.build() as sched:
+                pulse.play(pulse.Gaussian(160, Parameter("amp"), 40), pulse.DriveChannel(qubit))
+
+            experiments.append(Rabi(qubit, sched, amplitudes=[0.5]))
 
         par_exp = ParallelExperiment(experiments)
         par_circ = par_exp.circuits()[0]
diff --git a/test/calibration/experiments/test_rough_amplitude.py b/test/calibration/experiments/test_rough_amplitude.py
new file mode 100644
index 0000000000..8bb3a58e90
--- /dev/null
+++ b/test/calibration/experiments/test_rough_amplitude.py
@@ -0,0 +1,142 @@
+# This code is part of Qiskit.
+#
+# (C) Copyright IBM 2021.
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+
+"""Test rough amplitude calibration experiment classes."""
+
+import numpy as np
+
+from qiskit import transpile
+import qiskit.pulse as pulse
+from qiskit.circuit import Parameter
+from qiskit.test import QiskitTestCase
+from qiskit.test.mock import FakeArmonk
+
+from qiskit_experiments.calibration_management.basis_gate_library import FixedFrequencyTransmon
+from qiskit_experiments.calibration_management import BackendCalibrations
+from qiskit_experiments.library import EFRoughXSXAmplitudeCal, RoughXSXAmplitudeCal
+from qiskit_experiments.test.mock_iq_backend import RabiBackend
+
+
+class TestRoughAmpCal(QiskitTestCase):
+    """A class to test the rough amplitude calibration experiments."""
+
+    def setUp(self):
+        """Setup the tests."""
+        super().setUp()
+        library = FixedFrequencyTransmon()
+
+        self.backend = FakeArmonk()
+        self.cals = BackendCalibrations(self.backend, library)
+
+    def test_circuits(self):
+        """Test the quantum circuits."""
+        test_amps = [-0.5, 0, 0.5]
+        rabi = RoughXSXAmplitudeCal(0, self.cals, amplitudes=test_amps)
+
+        circs = transpile(rabi.circuits(), self.backend, inst_map=rabi.transpile_options.inst_map)
+
+        for circ, amp in zip(circs, test_amps):
+            self.assertEqual(circ.count_ops()["Rabi"], 1)
+
+            d0 = pulse.DriveChannel(0)
+            with pulse.build(name="x") as expected_x:
+                pulse.play(pulse.Drag(160, amp, 40, 0), d0)
+
+            self.assertEqual(circ.calibrations["Rabi"][((0,), (amp,))], expected_x)
+
+    def test_update(self):
+        """Test that the calibrations update properly."""
+
+        self.assertTrue(np.allclose(self.cals.get_parameter_value("amp", 0, "x"), 0.5))
+        self.assertTrue(np.allclose(self.cals.get_parameter_value("amp", 0, "sx"), 0.25))
+
+        rabi_ef = RoughXSXAmplitudeCal(0, self.cals)
+        rabi_ef.run(RabiBackend(amplitude_to_angle=np.pi * 1.5)).block_for_results()
+
+        tol = 0.002
+        self.assertTrue(abs(self.cals.get_parameter_value("amp", 0, "x") - 0.333) < tol)
+        self.assertTrue(abs(self.cals.get_parameter_value("amp", 0, "sx") - 0.333 / 2) < tol)
+
+    def test_experiment_config(self):
+        """Test converting to and from config works"""
+        exp = RoughXSXAmplitudeCal(0, self.cals)
+        config = exp.config
+        loaded_exp = RoughXSXAmplitudeCal.from_config(config)
+        self.assertNotEqual(exp, loaded_exp)
+        self.assertEqual(config, loaded_exp.config)
+
+
+class TestSpecializations(QiskitTestCase):
+    """Test the specialized versions of the calibration."""
+
+    def setUp(self):
+        """Setup the tests"""
+        super().setUp()
+
+        library = FixedFrequencyTransmon()
+
+        self.backend = FakeArmonk()
+        self.cals = BackendCalibrations(self.backend, library)
+
+        # Add some pulses on the 1-2 transition.
+        d0 = pulse.DriveChannel(0)
+        with pulse.build(name="x12") as x12:
+            with pulse.frequency_offset(-300e6, d0):
+                pulse.play(pulse.Drag(160, Parameter("amp"), 40, 0.0), d0)
+
+        with pulse.build(name="sx12") as sx12:
+            with pulse.frequency_offset(-300e6, d0):
+                pulse.play(pulse.Drag(160, Parameter("amp"), 40, 0.0), d0)
+
+        self.cals.add_schedule(x12, 0)
+        self.cals.add_schedule(sx12, 0)
+        self.cals.add_parameter_value(0.4, "amp", 0, "x12")
+        self.cals.add_parameter_value(0.2, "amp", 0, "sx12")
+
+    def test_ef_circuits(self):
+        """Test that we get the expected circuits with calibrations for the EF experiment."""
+
+        test_amps = [-0.5, 0, 0.5]
+        rabi_ef = EFRoughXSXAmplitudeCal(0, self.cals, amplitudes=test_amps)
+
+        circs = transpile(
+            rabi_ef.circuits(), self.backend, inst_map=rabi_ef.transpile_options.inst_map
+        )
+
+        for circ, amp in zip(circs, test_amps):
+
+            self.assertEqual(circ.count_ops()["x"], 1)
+            self.assertEqual(circ.count_ops()["Rabi"], 1)
+
+            d0 = pulse.DriveChannel(0)
+            with pulse.build(name="x") as expected_x:
+                pulse.play(pulse.Drag(160, 0.5, 40, 0), d0)
+
+            with pulse.build(name="x12") as expected_x12:
+                with pulse.frequency_offset(-300e6, d0):
+                    pulse.play(pulse.Drag(160, amp, 40, 0), d0)
+
+            self.assertEqual(circ.calibrations["x"][((0,), ())], expected_x)
+            self.assertEqual(circ.calibrations["Rabi"][((0,), (amp,))], expected_x12)
+
+    def test_ef_update(self):
+        """Tes that we properly update the pulses on the 1<->2 transition."""
+
+        self.assertTrue(np.allclose(self.cals.get_parameter_value("amp", 0, "x12"), 0.4))
+        self.assertTrue(np.allclose(self.cals.get_parameter_value("amp", 0, "sx12"), 0.2))
+
+        rabi_ef = EFRoughXSXAmplitudeCal(0, self.cals)
+        rabi_ef.run(RabiBackend(amplitude_to_angle=np.pi * 1.5)).block_for_results()
+
+        tol = 0.002
+        self.assertTrue(abs(self.cals.get_parameter_value("amp", 0, "x12") - 0.333) < tol)
+        self.assertTrue(abs(self.cals.get_parameter_value("amp", 0, "sx12") - 0.333 / 2) < tol)
diff --git a/test/calibration/test_update_library.py b/test/calibration/test_update_library.py
index 94f97f6e58..c0c8fbca93 100644
--- a/test/calibration/test_update_library.py
+++ b/test/calibration/test_update_library.py
@@ -12,7 +12,6 @@
 
 """Test the calibration update library."""
 
-from test.calibration.experiments.test_rabi import RabiBackend
 from test.test_qubit_spectroscopy import SpectroscopyBackend
 import numpy as np
 
@@ -22,12 +21,10 @@
 import qiskit.pulse as pulse
 from qiskit.test.mock import FakeAthens
 
-from qiskit_experiments.library import Rabi, FineXDrag, DragCal, QubitSpectroscopy
+from qiskit_experiments.library import FineXDrag, DragCal, QubitSpectroscopy
 from qiskit_experiments.calibration_management.calibrations import Calibrations
-from qiskit_experiments.exceptions import CalibrationError
 from qiskit_experiments.calibration_management.update_library import (
     Frequency,
-    Amplitude,
     Drag,
     FineDragUpdater,
 )
@@ -61,43 +58,6 @@ def setUp(self):
         self.cals.add_parameter_value(0.2, "amp", self.qubit, "xp")
         self.cals.add_parameter_value(0.1, "amp", self.qubit, "x90p")
 
-    def test_amplitude(self):
-        """Test amplitude update from Rabi."""
-
-        rabi = Rabi(self.qubit)
-        rabi.set_experiment_options(amplitudes=np.linspace(-0.95, 0.95, 21))
-        exp_data = rabi.run(RabiBackend())
-        exp_data.block_for_results()
-
-        with self.assertRaises(CalibrationError):
-            self.cals.get_schedule("xp", qubits=0)
-
-        to_update = [(np.pi, "amp", "xp"), (np.pi / 2, "amp", self.x90p)]
-
-        self.assertEqual(len(self.cals.parameters_table()), 2)
-
-        Amplitude.update(self.cals, exp_data, angles_schedules=to_update)
-
-        with self.assertRaises(CalibrationError):
-            self.cals.get_schedule("xp", qubits=0)
-
-        self.assertEqual(len(self.cals.parameters_table()["data"]), 4)
-
-        # Now check the corresponding schedules
-        result = exp_data.analysis_results(1)
-        rate = 2 * np.pi * result.value.value
-        amp = np.round(np.pi / rate, decimals=8)
-        with pulse.build(name="xp") as expected:
-            pulse.play(pulse.Gaussian(160, amp, 40), pulse.DriveChannel(self.qubit))
-
-        self.assertEqual(self.cals.get_schedule("xp", qubits=self.qubit), expected)
-
-        amp = np.round(0.5 * np.pi / rate, decimals=8)
-        with pulse.build(name="xp") as expected:
-            pulse.play(pulse.Gaussian(160, amp, 40), pulse.DriveChannel(self.qubit))
-
-        self.assertEqual(self.cals.get_schedule("x90p", qubits=self.qubit), expected)
-
 
 class TestFrequencyUpdate(QiskitTestCase):
     """Test the frequency update function in the update library."""