diff --git a/docs/tutorials/calibrating_armonk.ipynb b/docs/tutorials/calibrating_armonk.ipynb
index 742412c9ee..dff2c44234 100644
--- a/docs/tutorials/calibrating_armonk.ipynb
+++ b/docs/tutorials/calibrating_armonk.ipynb
@@ -24,6 +24,7 @@
    "outputs": [],
    "source": [
     "import numpy as np\n",
+    "import pandas as pd\n",
     "\n",
     "import qiskit.pulse as pulse\n",
     "from qiskit.circuit import Parameter\n",
@@ -183,7 +184,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 9,
    "id": "fa22b8a4",
    "metadata": {},
    "outputs": [
@@ -221,57 +222,57 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>σ</td>\n",
-       "      <td>()</td>\n",
-       "      <td>x</td>\n",
-       "      <td>8.000000e+01+0.000000e+00j</td>\n",
+       "      <td>qubit_lo_freq</td>\n",
+       "      <td>(0,)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>4.971589e+09</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-28 10:27:44.953702+0200</td>\n",
+       "      <td>2021-11-03 11:43:19.418796+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\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>β</td>\n",
+       "      <td>()</td>\n",
+       "      <td>x</td>\n",
+       "      <td>0.000000e+00</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-28 10:27:44.953446+0200</td>\n",
+       "      <td>2021-11-03 11:43:19.419463+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>β</td>\n",
+       "      <td>duration</td>\n",
        "      <td>()</td>\n",
-       "      <td>sx</td>\n",
-       "      <td>0.000000e+00+0.000000e+00j</td>\n",
+       "      <td>x</td>\n",
+       "      <td>3.200000e+02</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-28 10:27:44.953730+0200</td>\n",
+       "      <td>2021-11-03 11:43:19.419439+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>duration</td>\n",
        "      <td>()</td>\n",
-       "      <td>x</td>\n",
-       "      <td>3.200000e+02+0.000000e+00j</td>\n",
+       "      <td>sx</td>\n",
+       "      <td>3.200000e+02</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-28 10:27:44.953694+0200</td>\n",
+       "      <td>2021-11-03 11:43:19.419569+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>duration</td>\n",
-       "      <td>()</td>\n",
-       "      <td>sx</td>\n",
-       "      <td>3.200000e+02+0.000000e+00j</td>\n",
+       "      <td>meas_lo_freq</td>\n",
+       "      <td>(0,)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>6.993371e+09</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-28 10:27:44.953716+0200</td>\n",
+       "      <td>2021-11-03 11:43:19.418870+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -279,43 +280,43 @@
        "      <td>amp</td>\n",
        "      <td>()</td>\n",
        "      <td>sx</td>\n",
-       "      <td>2.500000e-01+0.000000e+00j</td>\n",
+       "      <td>2.500000e-01</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-28 10:27:44.953709+0200</td>\n",
+       "      <td>2021-11-03 11:43:19.419501+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>β</td>\n",
        "      <td>()</td>\n",
-       "      <td>x</td>\n",
-       "      <td>0.000000e+00+0.000000e+00j</td>\n",
+       "      <td>sx</td>\n",
+       "      <td>0.000000e+00</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-28 10:27:44.953685+0200</td>\n",
+       "      <td>2021-11-03 11:43:19.419546+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>amp</td>\n",
-       "      <td>(0,)</td>\n",
+       "      <td>σ</td>\n",
+       "      <td>()</td>\n",
        "      <td>x</td>\n",
-       "      <td>8.578134e-01+0.000000e+00j</td>\n",
+       "      <td>8.000000e+01</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",
+       "      <td>2021-11-03 11:43:19.419410+0100</td>\n",
+       "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\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>amp</td>\n",
+       "      <td>()</td>\n",
+       "      <td>x</td>\n",
+       "      <td>5.000000e-01</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-28 10:27:44.953469+0200</td>\n",
+       "      <td>2021-11-03 11:43:19.419368+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -323,32 +324,10 @@
        "      <td>σ</td>\n",
        "      <td>()</td>\n",
        "      <td>sx</td>\n",
-       "      <td>8.000000e+01+0.000000e+00j</td>\n",
+       "      <td>8.000000e+01</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>5.000000e-01+0.000000e+00j</td>\n",
-       "      <td>default</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2021-10-28 10:27:44.953672+0200</td>\n",
+       "      <td>2021-11-03 11:43:19.419524+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -356,43 +335,37 @@
        "</div>"
       ],
       "text/plain": [
-       "        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",
+       "       parameter qubits schedule         value    group  valid  \\\n",
+       "0  qubit_lo_freq   (0,)     None  4.971589e+09  default   True   \n",
+       "1              β     ()        x  0.000000e+00  default   True   \n",
+       "2       duration     ()        x  3.200000e+02  default   True   \n",
+       "3       duration     ()       sx  3.200000e+02  default   True   \n",
+       "4   meas_lo_freq   (0,)     None  6.993371e+09  default   True   \n",
+       "5            amp     ()       sx  2.500000e-01  default   True   \n",
+       "6              β     ()       sx  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              σ     ()       sx  8.000000e+01  default   True   \n",
        "\n",
-       "                          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  "
+       "                         date_time exp_id  \n",
+       "0  2021-11-03 11:43:19.418796+0100   None  \n",
+       "1  2021-11-03 11:43:19.419463+0100   None  \n",
+       "2  2021-11-03 11:43:19.419439+0100   None  \n",
+       "3  2021-11-03 11:43:19.419569+0100   None  \n",
+       "4  2021-11-03 11:43:19.418870+0100   None  \n",
+       "5  2021-11-03 11:43:19.419501+0100   None  \n",
+       "6  2021-11-03 11:43:19.419546+0100   None  \n",
+       "7  2021-11-03 11:43:19.419410+0100   None  \n",
+       "8  2021-11-03 11:43:19.419368+0100   None  \n",
+       "9  2021-11-03 11:43:19.419524+0100   None  "
       ]
      },
-     "execution_count": 16,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "import pandas as pd\n",
-    "\n",
     "pd.DataFrame(**cals.parameters_table(qubit_list=[qubit, ()]))"
    ]
   },
@@ -965,7 +938,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 8,
    "id": "24256b82",
    "metadata": {},
    "outputs": [],
@@ -976,7 +949,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 9,
    "id": "80ca665c",
    "metadata": {},
    "outputs": [
@@ -1020,7 +993,7 @@
        "      <td>0.250000+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:40:51.746391+0200</td>\n",
+       "      <td>2021-11-03 12:05:42.885706+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1031,7 +1004,7 @@
        "      <td>0.250000+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:21:13.496333+0200</td>\n",
+       "      <td>2021-11-03 10:23:20.785453+0100</td>\n",
        "      <td></td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1039,11 +1012,11 @@
        "      <td>amp</td>\n",
        "      <td>(0,)</td>\n",
        "      <td>sx</td>\n",
-       "      <td>0.433005+0.000000j</td>\n",
+       "      <td>0.430256+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>2021-11-03 10:25:52.837000+0100</td>\n",
+       "      <td>d4d482d0-34c7-476a-870e-6984aa387f94</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -1053,7 +1026,7 @@
        "      <td>0.500000+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:40:51.746354+0200</td>\n",
+       "      <td>2021-11-03 12:05:42.885622+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1064,7 +1037,7 @@
        "      <td>0.500000+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:21:13.496299+0200</td>\n",
+       "      <td>2021-11-03 10:23:20.785369+0100</td>\n",
        "      <td></td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1072,11 +1045,11 @@
        "      <td>amp</td>\n",
        "      <td>(0,)</td>\n",
        "      <td>x</td>\n",
-       "      <td>0.866011+0.000000j</td>\n",
+       "      <td>0.860512+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>2021-11-03 10:25:52.837000+0100</td>\n",
+       "      <td>d4d482d0-34c7-476a-870e-6984aa387f94</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1086,21 +1059,21 @@
        "  parameter qubits schedule               value    group  valid  \\\n",
        "0       amp     ()       sx  0.250000+0.000000j  default   True   \n",
        "1       amp     ()       sx  0.250000+0.000000j  default   True   \n",
-       "2       amp   (0,)       sx  0.433005+0.000000j  default   True   \n",
+       "2       amp   (0,)       sx  0.430256+0.000000j  default   True   \n",
        "3       amp     ()        x  0.500000+0.000000j  default   True   \n",
        "4       amp     ()        x  0.500000+0.000000j  default   True   \n",
-       "5       amp   (0,)        x  0.866011+0.000000j  default   True   \n",
+       "5       amp   (0,)        x  0.860512+0.000000j  default   True   \n",
        "\n",
        "                         date_time                                exp_id  \n",
-       "0  2021-10-21 14:40:51.746391+0200                                  None  \n",
-       "1  2021-10-21 14:21:13.496333+0200                                        \n",
-       "2  2021-10-21 14:39:49.487000+0200  1b5c7f5c-2a93-4beb-a3cd-037e3f18c397  \n",
-       "3  2021-10-21 14:40:51.746354+0200                                  None  \n",
-       "4  2021-10-21 14:21:13.496299+0200                                        \n",
-       "5  2021-10-21 14:39:49.487000+0200  1b5c7f5c-2a93-4beb-a3cd-037e3f18c397  "
+       "0  2021-11-03 12:05:42.885706+0100                                  None  \n",
+       "1  2021-11-03 10:23:20.785453+0100                                        \n",
+       "2  2021-11-03 10:25:52.837000+0100  d4d482d0-34c7-476a-870e-6984aa387f94  \n",
+       "3  2021-11-03 12:05:42.885622+0100                                  None  \n",
+       "4  2021-11-03 10:23:20.785369+0100                                        \n",
+       "5  2021-11-03 10:25:52.837000+0100  d4d482d0-34c7-476a-870e-6984aa387f94  "
       ]
      },
-     "execution_count": 29,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1396,7 +1369,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 20,
    "id": "20ab91f2",
    "metadata": {},
    "outputs": [],
@@ -1406,17 +1379,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 23,
    "id": "bfb3124b",
    "metadata": {},
    "outputs": [],
    "source": [
-    "amp_x_cal = FineXAmplitudeCal(qubit, cals, schedule_name=\"x\")"
+    "amp_x_cal = FineXAmplitudeCal(qubit, cals, backend=backend, schedule_name=\"x\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 24,
    "id": "c6127e65",
    "metadata": {},
    "outputs": [
@@ -1427,39 +1400,39 @@
        "<Figure size 538.279x144.48 with 1 Axes>"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "amp_x_cal.circuits(backend)[5].draw(output=\"mpl\")"
+    "amp_x_cal.circuits()[5].draw(output=\"mpl\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 25,
    "id": "24067164",
    "metadata": {},
    "outputs": [],
    "source": [
-    "data_fine = amp_x_cal.run(backend).block_for_results()"
+    "data_fine = amp_x_cal.run().block_for_results()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 26,
    "id": "076bed0c",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 576x360 with 1 Axes>"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1695,7 +1668,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data_fine2 = amp_x_cal.run(backend).block_for_results()"
+    "data_fine2 = amp_x_cal.run().block_for_results()"
    ]
   },
   {
@@ -1764,7 +1737,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 10,
    "id": "50423105",
    "metadata": {},
    "outputs": [],
@@ -1774,60 +1747,60 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 11,
    "id": "a075eacb",
    "metadata": {},
    "outputs": [],
    "source": [
-    "amp_sx_cal = FineSXAmplitudeCal(qubit, cals, schedule_name=\"sx\")"
+    "amp_sx_cal = FineSXAmplitudeCal(qubit, cals, backend=backend, schedule_name=\"sx\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 12,
    "id": "3d38a13f",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 779.079x144.48 with 1 Axes>"
+       "<Figure size 658.679x144.48 with 1 Axes>"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "amp_sx_cal.circuits(backend)[5].draw(output=\"mpl\")"
+    "amp_sx_cal.circuits()[5].draw(output=\"mpl\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 13,
    "id": "6c00c3f5",
    "metadata": {},
    "outputs": [],
    "source": [
-    "data_fine_sx = amp_sx_cal.run(backend).block_for_results()"
+    "data_fine_sx = amp_sx_cal.run().block_for_results()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 14,
    "id": "0117cb2d",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 576x360 with 1 Axes>"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1838,7 +1811,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 15,
    "id": "5d5a1131",
    "metadata": {},
    "outputs": [
@@ -1847,17 +1820,18 @@
      "output_type": "stream",
      "text": [
       "DbAnalysisResultV1\n",
-      "- name: d_theta\n",
-      "- value: 0.04145980422899995 ± 0.0015231144619942427\n",
-      "- χ²: 1.0258195129532202\n",
+      "- name: @Parameters_FineAmplitudeAnalysis\n",
+      "- value: [0.80534284 0.03632038 0.49111484] ± [0.01305091 0.0011369  0.00350642]\n",
+      "- χ²: 1.4919645773726637\n",
       "- quality: good\n",
+      "- extra: <4 items>\n",
       "- device_components: ['Q0']\n",
       "- verified: False\n"
      ]
     }
    ],
    "source": [
-    "print(data_fine_sx.analysis_results(\"d_theta\"))"
+    "print(data_fine_sx.analysis_results(0))"
    ]
   },
   {
@@ -1870,7 +1844,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 16,
    "id": "ae984c47",
    "metadata": {},
    "outputs": [
@@ -1914,7 +1888,7 @@
        "      <td>0.250000+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:40:51.746391+0200</td>\n",
+       "      <td>2021-11-03 12:05:42.885706+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1925,7 +1899,7 @@
        "      <td>0.250000+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:21:13.496333+0200</td>\n",
+       "      <td>2021-11-03 10:23:20.785453+0100</td>\n",
        "      <td></td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1933,22 +1907,22 @@
        "      <td>amp</td>\n",
        "      <td>(0,)</td>\n",
        "      <td>sx</td>\n",
-       "      <td>0.433005+0.000000j</td>\n",
+       "      <td>0.430256+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>2021-11-03 10:25:52.837000+0100</td>\n",
+       "      <td>d4d482d0-34c7-476a-870e-6984aa387f94</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>amp</td>\n",
        "      <td>(0,)</td>\n",
        "      <td>sx</td>\n",
-       "      <td>0.421870+0.000000j</td>\n",
+       "      <td>0.420532+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:46:58.782000+0200</td>\n",
-       "      <td>ce7fdf7f-1243-44f8-92b3-2861f75b955e</td>\n",
+       "      <td>2021-11-03 12:06:25.685000+0100</td>\n",
+       "      <td>dd676b11-1014-470a-b6b9-67bc0da045ef</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -1958,7 +1932,7 @@
        "      <td>0.500000+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:40:51.746354+0200</td>\n",
+       "      <td>2021-11-03 12:05:42.885622+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1969,7 +1943,7 @@
        "      <td>0.500000+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:21:13.496299+0200</td>\n",
+       "      <td>2021-11-03 10:23:20.785369+0100</td>\n",
        "      <td></td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1977,33 +1951,11 @@
        "      <td>amp</td>\n",
        "      <td>(0,)</td>\n",
        "      <td>x</td>\n",
-       "      <td>0.866011+0.000000j</td>\n",
+       "      <td>0.860512+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",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>amp</td>\n",
-       "      <td>(0,)</td>\n",
-       "      <td>x</td>\n",
-       "      <td>0.896207+0.000000j</td>\n",
-       "      <td>default</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2021-10-21 14:42:13.806000+0200</td>\n",
-       "      <td>1507e843-ca7f-4670-a2a8-ad3bbc51fe0c</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>amp</td>\n",
-       "      <td>(0,)</td>\n",
-       "      <td>x</td>\n",
-       "      <td>0.902828+0.000000j</td>\n",
-       "      <td>default</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2021-10-21 14:44:44.903000+0200</td>\n",
-       "      <td>31ae56ba-bad3-4eed-9a80-7e54282318f7</td>\n",
+       "      <td>2021-11-03 10:25:52.837000+0100</td>\n",
+       "      <td>d4d482d0-34c7-476a-870e-6984aa387f94</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2013,27 +1965,23 @@
        "  parameter qubits schedule               value    group  valid  \\\n",
        "0       amp     ()       sx  0.250000+0.000000j  default   True   \n",
        "1       amp     ()       sx  0.250000+0.000000j  default   True   \n",
-       "2       amp   (0,)       sx  0.433005+0.000000j  default   True   \n",
-       "3       amp   (0,)       sx  0.421870+0.000000j  default   True   \n",
+       "2       amp   (0,)       sx  0.430256+0.000000j  default   True   \n",
+       "3       amp   (0,)       sx  0.420532+0.000000j  default   True   \n",
        "4       amp     ()        x  0.500000+0.000000j  default   True   \n",
        "5       amp     ()        x  0.500000+0.000000j  default   True   \n",
-       "6       amp   (0,)        x  0.866011+0.000000j  default   True   \n",
-       "7       amp   (0,)        x  0.896207+0.000000j  default   True   \n",
-       "8       amp   (0,)        x  0.902828+0.000000j  default   True   \n",
+       "6       amp   (0,)        x  0.860512+0.000000j  default   True   \n",
        "\n",
        "                         date_time                                exp_id  \n",
-       "0  2021-10-21 14:40:51.746391+0200                                  None  \n",
-       "1  2021-10-21 14:21:13.496333+0200                                        \n",
-       "2  2021-10-21 14:39:49.487000+0200  1b5c7f5c-2a93-4beb-a3cd-037e3f18c397  \n",
-       "3  2021-10-21 14:46:58.782000+0200  ce7fdf7f-1243-44f8-92b3-2861f75b955e  \n",
-       "4  2021-10-21 14:40:51.746354+0200                                  None  \n",
-       "5  2021-10-21 14:21:13.496299+0200                                        \n",
-       "6  2021-10-21 14:39:49.487000+0200  1b5c7f5c-2a93-4beb-a3cd-037e3f18c397  \n",
-       "7  2021-10-21 14:42:13.806000+0200  1507e843-ca7f-4670-a2a8-ad3bbc51fe0c  \n",
-       "8  2021-10-21 14:44:44.903000+0200  31ae56ba-bad3-4eed-9a80-7e54282318f7  "
+       "0  2021-11-03 12:05:42.885706+0100                                  None  \n",
+       "1  2021-11-03 10:23:20.785453+0100                                        \n",
+       "2  2021-11-03 10:25:52.837000+0100  d4d482d0-34c7-476a-870e-6984aa387f94  \n",
+       "3  2021-11-03 12:06:25.685000+0100  dd676b11-1014-470a-b6b9-67bc0da045ef  \n",
+       "4  2021-11-03 12:05:42.885622+0100                                  None  \n",
+       "5  2021-11-03 10:23:20.785369+0100                                        \n",
+       "6  2021-11-03 10:25:52.837000+0100  d4d482d0-34c7-476a-870e-6984aa387f94  "
       ]
      },
-     "execution_count": 48,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2044,17 +1992,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 17,
    "id": "f7cb5878",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "ScheduleBlock(Play(Drag(duration=320, amp=(0.421870407153443+0j), sigma=80, beta=0), DriveChannel(0)), name=\"sx\", transform=AlignLeft())"
+       "ScheduleBlock(Play(Drag(duration=320, amp=(0.420532092119502+0j), sigma=80, beta=0), DriveChannel(0)), name=\"sx\", transform=AlignLeft())"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2065,17 +2013,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 18,
    "id": "f45f6482",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "ScheduleBlock(Play(Drag(duration=320, amp=(0.902828399794558+0j), sigma=80, beta=0), DriveChannel(0)), name=\"x\", transform=AlignLeft())"
+       "ScheduleBlock(Play(Drag(duration=320, amp=(0.86051151+0j), sigma=80, beta=0), DriveChannel(0)), name=\"x\", transform=AlignLeft())"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2086,17 +2034,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 19,
    "id": "68f6e469",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "ScheduleBlock(Play(Drag(duration=320, amp=0.902828399794558j, sigma=80, beta=0), DriveChannel(0)), name=\"y\", transform=AlignLeft())"
+       "ScheduleBlock(Play(Drag(duration=320, amp=0.86051151j, sigma=80, beta=0), DriveChannel(0)), name=\"y\", transform=AlignLeft())"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/qiskit_experiments/curve_analysis/standard_analysis/error_amplification_analysis.py b/qiskit_experiments/curve_analysis/standard_analysis/error_amplification_analysis.py
index 00e2cd0ae5..19b9ef8634 100644
--- a/qiskit_experiments/curve_analysis/standard_analysis/error_amplification_analysis.py
+++ b/qiskit_experiments/curve_analysis/standard_analysis/error_amplification_analysis.py
@@ -119,6 +119,7 @@ def _default_options(cls):
         default_options.angle_per_gate = None
         default_options.phase_offset = 0.0
         default_options.max_good_angle_error = np.pi / 2
+        default_options.amp = 1.0
 
         return default_options
 
@@ -145,7 +146,7 @@ def _generate_fit_guesses(
 
         if "amp" in user_opt.p0:
             user_opt.p0.set_if_empty(amp=max_y - min_y)
-            user_opt.bounds.set_if_empty(amp=(-2 * max_abs_y, 2 * max_abs_y))
+            user_opt.bounds.set_if_empty(amp=(0, 2 * max_abs_y))
 
         # Base the initial guess on the intended angle_per_gate and phase offset.
         apg = self._get_option("angle_per_gate")
diff --git a/qiskit_experiments/library/calibration/analysis/__init__.py b/qiskit_experiments/library/calibration/analysis/__init__.py
index fe6c0a5e01..92091667e3 100644
--- a/qiskit_experiments/library/calibration/analysis/__init__.py
+++ b/qiskit_experiments/library/calibration/analysis/__init__.py
@@ -14,3 +14,5 @@
 
 from .drag_analysis import DragCalAnalysis
 from .fine_half_angle_analysis import FineHalfAngleAnalysis
+from .fine_x_amplitude_analysis import FineXAmplitudeAnalysis
+from .fine_amplitude_analysis import FineAmplitudeAnalysis
diff --git a/qiskit_experiments/library/calibration/analysis/fine_amplitude_analysis.py b/qiskit_experiments/library/calibration/analysis/fine_amplitude_analysis.py
index 7e94763b41..d41575e92b 100644
--- a/qiskit_experiments/library/calibration/analysis/fine_amplitude_analysis.py
+++ b/qiskit_experiments/library/calibration/analysis/fine_amplitude_analysis.py
@@ -29,5 +29,4 @@ class FineAmplitudeAnalysis(ErrorAmplificationAnalysis):
 
     # The intended angle per gat of the gate being calibrated, e.g. pi for a pi-pulse.
 
-    # TODO remove amp from fixed parameter.
-    __fixed_parameters__ = ["angle_per_gate", "phase_offset", "amp"]
+    __fixed_parameters__ = ["angle_per_gate", "phase_offset"]
diff --git a/qiskit_experiments/library/calibration/analysis/fine_x_amplitude_analysis.py b/qiskit_experiments/library/calibration/analysis/fine_x_amplitude_analysis.py
new file mode 100644
index 0000000000..364a49a105
--- /dev/null
+++ b/qiskit_experiments/library/calibration/analysis/fine_x_amplitude_analysis.py
@@ -0,0 +1,34 @@
+# 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.
+
+"""Fine Amplitude calibration analysis."""
+
+from qiskit_experiments.curve_analysis import ErrorAmplificationAnalysis
+
+
+class FineXAmplitudeAnalysis(ErrorAmplificationAnalysis):
+    r"""An analysis class for fine amplitude calibrations to define the fixed parameters.
+
+    # section: note
+
+        The following parameters are fixed.
+
+        * :math:`{\rm apg}` The angle per gate is set by the user, for example pi for a pi-pulse.
+        * :math:`{\rm phase\_offset}` The phase offset in the cosine oscillation, for example,
+          :math:`\pi/2` if a square-root of X gate is added before the repeated gates.
+        * :math:`{\rm amp}` The amplitude of the oscillation.
+    """
+
+    # The intended angle per gat of the gate being calibrated, e.g. pi for a pi-pulse.
+
+    # TODO remove amp from fixed parameter.
+    __fixed_parameters__ = ["angle_per_gate", "phase_offset", "amp"]
diff --git a/qiskit_experiments/library/calibration/fine_amplitude.py b/qiskit_experiments/library/calibration/fine_amplitude.py
index 6be253fd4a..98a97c1b4d 100644
--- a/qiskit_experiments/library/calibration/fine_amplitude.py
+++ b/qiskit_experiments/library/calibration/fine_amplitude.py
@@ -25,6 +25,7 @@
 from qiskit_experiments.library.characterization import FineAmplitude
 from qiskit_experiments.framework import ExperimentData, Options, fix_class_docs
 from qiskit_experiments.calibration_management.update_library import BaseUpdater
+from qiskit_experiments.library.calibration.analysis import FineXAmplitudeAnalysis
 
 
 @fix_class_docs
@@ -141,6 +142,10 @@ def update_calibrations(self, experiment_data: ExperimentData):
             target_angle = data[0]["metadata"]["target_angle"]
             prev_amp = data[0]["metadata"]["cal_param_value"]
 
+            # Protect against cases where the complex amplitude was converted to a list.
+            if isinstance(prev_amp, list) and len(prev_amp) == 2:
+                prev_amp = prev_amp[0] + 1.0j * prev_amp[1]
+
             d_theta = BaseUpdater.get_value(experiment_data, "d_theta", result_index)
 
             BaseUpdater.add_parameter_value(
@@ -157,6 +162,8 @@ def update_calibrations(self, experiment_data: ExperimentData):
 class FineXAmplitudeCal(FineAmplitudeCal):
     """A calibration experiment to calibrate the amplitude of the X schedule."""
 
+    __analysis_class__ = FineXAmplitudeAnalysis
+
     @classmethod
     def _default_experiment_options(cls) -> Options:
         r"""Default values for the fine amplitude experiment.
@@ -194,6 +201,7 @@ def _default_analysis_options(cls) -> Options:
         options = super()._default_analysis_options()
         options.angle_per_gate = np.pi
         options.phase_offset = np.pi / 2
+        options.amp = 1
 
         return options
 
@@ -221,7 +229,7 @@ def _default_experiment_options(cls) -> Options:
         options = super()._default_experiment_options()
         options.add_sx = False
         options.add_xp_circuit = False
-        options.repetitions = [1, 2, 3, 5, 7, 9, 11, 13, 15, 17, 21, 23, 25]
+        options.repetitions = [0, 1, 2, 3, 5, 7, 9, 11, 13, 15, 17, 21, 23, 25]
         options.target_angle = np.pi / 2
         return options
 
@@ -244,6 +252,6 @@ def _default_analysis_options(cls) -> Options:
         """Default analysis options."""
         options = super()._default_analysis_options()
         options.angle_per_gate = np.pi / 2
-        options.phase_offset = 0
+        options.phase_offset = np.pi
 
         return options
diff --git a/qiskit_experiments/library/characterization/fine_amplitude.py b/qiskit_experiments/library/characterization/fine_amplitude.py
index 160a6fbd90..a9e0e5c041 100644
--- a/qiskit_experiments/library/characterization/fine_amplitude.py
+++ b/qiskit_experiments/library/characterization/fine_amplitude.py
@@ -20,8 +20,9 @@
 from qiskit.circuit.library import XGate, SXGate
 from qiskit.providers.backend import Backend
 from qiskit_experiments.framework import BaseExperiment, Options, fix_class_docs
-from qiskit_experiments.library.calibration.analysis.fine_amplitude_analysis import (
+from qiskit_experiments.library.calibration.analysis import (
     FineAmplitudeAnalysis,
+    FineXAmplitudeAnalysis,
 )
 from qiskit_experiments.exceptions import CalibrationError
 
@@ -118,14 +119,6 @@ def _default_experiment_options(cls) -> Options:
 
         return options
 
-    @classmethod
-    def _default_analysis_options(cls) -> Options:
-        """Default analysis options."""
-        options = super()._default_analysis_options()
-        options.amp = 1.0
-
-        return options
-
     def __init__(self, qubit: int, gate: Gate, backend: Optional[Backend] = None):
         """Setup a fine amplitude experiment on the given qubit.
 
@@ -221,6 +214,8 @@ class FineXAmplitude(FineAmplitude):
         the appropriate values for the default options.
     """
 
+    __analysis_class__ = FineXAmplitudeAnalysis
+
     def __init__(self, qubit: int, backend: Optional[Backend] = None):
         """Initialize the experiment."""
         super().__init__(qubit, XGate(), backend=backend)
@@ -250,6 +245,7 @@ def _default_analysis_options(cls) -> Options:
         options = super()._default_analysis_options()
         options.angle_per_gate = np.pi
         options.phase_offset = np.pi / 2
+        options.amp = 1.0
 
         return options
 
@@ -287,7 +283,7 @@ def _default_experiment_options(cls) -> Options:
         options.gate = SXGate()
         options.add_sx = False
         options.add_xp_circuit = False
-        options.repetitions = [1, 2, 3, 5, 7, 9, 11, 13, 15, 17, 21, 23, 25]
+        options.repetitions = [0, 1, 2, 3, 5, 7, 9, 11, 13, 15, 17, 21, 23, 25]
 
         return options
 
diff --git a/test/calibration/experiments/test_fine_amplitude.py b/test/calibration/experiments/test_fine_amplitude.py
index 9b7f9b9387..692bd86a4a 100644
--- a/test/calibration/experiments/test_fine_amplitude.py
+++ b/test/calibration/experiments/test_fine_amplitude.py
@@ -114,7 +114,7 @@ def test_x90p(self):
 
         amp_cal = FineSXAmplitude(0)
 
-        expected = [1, 2, 3, 5, 7, 9, 11, 13, 15, 17, 21, 23, 25]
+        expected = [0, 1, 2, 3, 5, 7, 9, 11, 13, 15, 17, 21, 23, 25]
         for idx, circ in enumerate(amp_cal.circuits()):
             self.assertEqual(circ.count_ops().get("sx", 0), expected[idx])
 
@@ -141,7 +141,7 @@ def test_fine_sx_amp(self):
         self.assertFalse(exp.experiment_options.add_sx)
         self.assertFalse(exp.experiment_options.add_xp_circuit)
 
-        expected = [1, 2, 3, 5, 7, 9, 11, 13, 15, 17, 21, 23, 25]
+        expected = [0, 1, 2, 3, 5, 7, 9, 11, 13, 15, 17, 21, 23, 25]
         self.assertEqual(exp.experiment_options.repetitions, expected)
         self.assertEqual(exp.analysis_options.angle_per_gate, np.pi / 2)
         self.assertEqual(exp.analysis_options.phase_offset, np.pi)