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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "charged-grill",
+   "metadata": {},
+   "source": [
+    "# Calibrating single-qubit gates on Armonk\n",
+    "\n",
+    "In this tutorial we demonstrate how to calibrate single-qubit gates on Armonk using the Calibration framework in qiskit-experiments. We will run experiments to find the qubit frequency, calibrate the amplitude of DRAG pulses and chose the value of the DRAG parameter that minimizes leakage. The calibration framework requires the user to\n",
+    "\n",
+    "* setup an instance of `Calibrations` or `BackendCalibrations`,\n",
+    "* run calibration experiments which can be found either in `qiskit_experiments.library.calibration` or `qiskit_experiments.library.characterization`, and\n",
+    "* update the values of the parameters stored in the instance of `Calibrations` (or `BackendCalibrations`) using `Update` classes. \n",
+    "\n",
+    "You will see that the `Update` classes are not meant to be instantiated but provide an `update` class method to extract calibrated parameter values and add them to the calibrations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "czech-strength",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "import qiskit.pulse as pulse\n",
+    "from qiskit.circuit import Parameter\n",
+    "\n",
+    "from qiskit_experiments.calibration_management.backend_calibrations import BackendCalibrations\n",
+    "\n",
+    "from qiskit import IBMQ, schedule"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "oriental-league",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "IBMQ.load_account()\n",
+    "provider = IBMQ.get_provider(hub='ibm-q', group='open', project='main')\n",
+    "backend = provider.get_backend('ibmq_armonk')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "every-diploma",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qubit = 0  # The qubit we will work with"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "local-entry",
+   "metadata": {},
+   "source": [
+    "The two functions below show how to setup an instance of `BackendCalibrations`. To do this the user defines the template schedules to calibrate. These template schedules are fully parameterized, even the channel indices on which the pulses are played. Furthermore, the name of the parameter in the channel index must follow the convention laid out in the documentation of the calibration module. Note that the parameters in the channel indices are automatically mapped to the channel index when `get_schedule` is called. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "indie-dominican",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def setup_cals(backend) -> BackendCalibrations:\n",
+    "    \"\"\"A function to instantiate calibrations and add a couple of template schedules.\"\"\"\n",
+    "    cals = BackendCalibrations(backend)\n",
+    "\n",
+    "    dur = Parameter(\"dur\")\n",
+    "    amp = Parameter(\"amp\")\n",
+    "    sigma = Parameter(\"σ\")\n",
+    "    beta = Parameter(\"β\")\n",
+    "    drive = pulse.DriveChannel(Parameter(\"ch0\"))\n",
+    "\n",
+    "    # Define and add template schedules.\n",
+    "    with pulse.build(name=\"xp\") as xp:\n",
+    "        pulse.play(pulse.Drag(dur, amp, sigma, beta), drive)\n",
+    "\n",
+    "    with pulse.build(name=\"xm\") as xm:\n",
+    "        pulse.play(pulse.Drag(dur, -amp, sigma, beta), drive)\n",
+    "        \n",
+    "    with pulse.build(name=\"x90p\") as x90p:\n",
+    "        pulse.play(pulse.Drag(dur, Parameter(\"amp\"), sigma, Parameter(\"β\")), drive)\n",
+    "\n",
+    "    cals.add_schedule(xp)\n",
+    "    cals.add_schedule(xm)\n",
+    "    cals.add_schedule(x90p)\n",
+    "    \n",
+    "    return cals\n",
+    "\n",
+    "def add_parameter_guesses(cals: BackendCalibrations):\n",
+    "    \"\"\"Add guesses for the parameter values to the calibrations.\"\"\"\n",
+    "    for sched in [\"xp\", \"x90p\"]:\n",
+    "        cals.add_parameter_value(80, \"σ\", schedule=sched)\n",
+    "        cals.add_parameter_value(0.5, \"β\", schedule=sched)\n",
+    "        cals.add_parameter_value(320, \"dur\", schedule=sched)\n",
+    "        cals.add_parameter_value(0.5, \"amp\", schedule=sched)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "opened-ghost",
+   "metadata": {},
+   "source": [
+    "When setting up the calibrations we add three pulses: a $\\pi$-rotation, with a schedule named `xp`, a schedule `xm` identical to `xp` but with a nagative amplitude, and a $\\pi/2$-rotation, with a schedule named `x90p`. Here, we have linked the amplitude of the `xp` and `xm` pulses. Therefore, calibrating the parameters of `xp` will also calibrate the parameters of `xm`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "numerical-bradford",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cals = setup_cals(backend)\n",
+    "add_parameter_guesses(cals)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "stupid-investigation",
+   "metadata": {},
+   "source": [
+    "## 1. Finding qubits with spectroscopy\n",
+    "\n",
+    "Here, we are using a backend for which we already know the qubit frequency. We will therefore use the spectroscopy experiment to confirm that there is a resonance at the qubit frequency reported by the backend."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "located-roots",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit_experiments.library.characterization.qubit_spectroscopy import QubitSpectroscopy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "diagnostic-thailand",
+   "metadata": {},
+   "source": [
+    "We first show the contents of the calibrations for qubit 0. Note that the guess values that we added before apply to all qubits on the chip. We see this in the table below as an empty tuple `()` in the qubits column. Observe that the parameter values of `xm` do not appear in this table as they are given by the values of `xp`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "indirect-faculty",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "      <th>date_time</th>\n",
+       "      <th>valid</th>\n",
+       "      <th>exp_id</th>\n",
+       "      <th>group</th>\n",
+       "      <th>qubits</th>\n",
+       "      <th>parameter</th>\n",
+       "      <th>schedule</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.000000e-01</td>\n",
+       "      <td>2021-07-20 19:25:02.649323+0000</td>\n",
+       "      <td>True</td>\n",
+       "      <td>None</td>\n",
+       "      <td>default</td>\n",
+       "      <td>()</td>\n",
+       "      <td>β</td>\n",
+       "      <td>x90p</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3.200000e+02</td>\n",
+       "      <td>2021-07-20 19:25:02.649313+0000</td>\n",
+       "      <td>True</td>\n",
+       "      <td>None</td>\n",
+       "      <td>default</td>\n",
+       "      <td>()</td>\n",
+       "      <td>dur</td>\n",
+       "      <td>xp</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.971659e+09</td>\n",
+       "      <td>2021-07-20 19:25:02.648323+0000</td>\n",
+       "      <td>True</td>\n",
+       "      <td>None</td>\n",
+       "      <td>default</td>\n",
+       "      <td>(0,)</td>\n",
+       "      <td>qubit_lo_freq</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>5.000000e-01</td>\n",
+       "      <td>2021-07-20 19:25:02.649331+0000</td>\n",
+       "      <td>True</td>\n",
+       "      <td>None</td>\n",
+       "      <td>default</td>\n",
+       "      <td>()</td>\n",
+       "      <td>amp</td>\n",
+       "      <td>x90p</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>6.993371e+09</td>\n",
+       "      <td>2021-07-20 19:25:02.648345+0000</td>\n",
+       "      <td>True</td>\n",
+       "      <td>None</td>\n",
+       "      <td>default</td>\n",
+       "      <td>(0,)</td>\n",
+       "      <td>meas_lo_freq</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>8.000000e+01</td>\n",
+       "      <td>2021-07-20 19:25:02.649298+0000</td>\n",
+       "      <td>True</td>\n",
+       "      <td>None</td>\n",
+       "      <td>default</td>\n",
+       "      <td>()</td>\n",
+       "      <td>σ</td>\n",
+       "      <td>xp</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>3.200000e+02</td>\n",
+       "      <td>2021-07-20 19:25:02.649328+0000</td>\n",
+       "      <td>True</td>\n",
+       "      <td>None</td>\n",
+       "      <td>default</td>\n",
+       "      <td>()</td>\n",
+       "      <td>dur</td>\n",
+       "      <td>x90p</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>5.000000e-01</td>\n",
+       "      <td>2021-07-20 19:25:02.649308+0000</td>\n",
+       "      <td>True</td>\n",
+       "      <td>None</td>\n",
+       "      <td>default</td>\n",
+       "      <td>()</td>\n",
+       "      <td>β</td>\n",
+       "      <td>xp</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>5.000000e-01</td>\n",
+       "      <td>2021-07-20 19:25:02.649316+0000</td>\n",
+       "      <td>True</td>\n",
+       "      <td>None</td>\n",
+       "      <td>default</td>\n",
+       "      <td>()</td>\n",
+       "      <td>amp</td>\n",
+       "      <td>xp</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>8.000000e+01</td>\n",
+       "      <td>2021-07-20 19:25:02.649320+0000</td>\n",
+       "      <td>True</td>\n",
+       "      <td>None</td>\n",
+       "      <td>default</td>\n",
+       "      <td>()</td>\n",
+       "      <td>σ</td>\n",
+       "      <td>x90p</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          value                        date_time  valid exp_id    group  \\\n",
+       "0  5.000000e-01  2021-07-20 19:25:02.649323+0000   True   None  default   \n",
+       "1  3.200000e+02  2021-07-20 19:25:02.649313+0000   True   None  default   \n",
+       "2  4.971659e+09  2021-07-20 19:25:02.648323+0000   True   None  default   \n",
+       "3  5.000000e-01  2021-07-20 19:25:02.649331+0000   True   None  default   \n",
+       "4  6.993371e+09  2021-07-20 19:25:02.648345+0000   True   None  default   \n",
+       "5  8.000000e+01  2021-07-20 19:25:02.649298+0000   True   None  default   \n",
+       "6  3.200000e+02  2021-07-20 19:25:02.649328+0000   True   None  default   \n",
+       "7  5.000000e-01  2021-07-20 19:25:02.649308+0000   True   None  default   \n",
+       "8  5.000000e-01  2021-07-20 19:25:02.649316+0000   True   None  default   \n",
+       "9  8.000000e+01  2021-07-20 19:25:02.649320+0000   True   None  default   \n",
+       "\n",
+       "  qubits      parameter schedule  \n",
+       "0     ()              β     x90p  \n",
+       "1     ()            dur       xp  \n",
+       "2   (0,)  qubit_lo_freq     None  \n",
+       "3     ()            amp     x90p  \n",
+       "4   (0,)   meas_lo_freq     None  \n",
+       "5     ()              σ       xp  \n",
+       "6     ()            dur     x90p  \n",
+       "7     ()              β       xp  \n",
+       "8     ()            amp       xp  \n",
+       "9     ()              σ     x90p  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "pd.DataFrame(cals.parameters_table(qubit_list=[qubit, ()]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "quantitative-rates",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "freq01_estimate = backend.defaults().qubit_freq_est[qubit]\n",
+    "frequencies = np.linspace(freq01_estimate -15e6, freq01_estimate + 15e6, 51)\n",
+    "spec = QubitSpectroscopy(qubit, frequencies)\n",
+    "spec.set_experiment_options(amp=0.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "medical-instrument",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 452.494x144.48 with 1 Axes>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "circuit = spec.circuits(backend)[0]\n",
+    "circuit.draw(output=\"mpl\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "saved-relations",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x241.987 with 1 Axes>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "schedule(circuit, backend).draw()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "ready-packaging",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spec_data = spec.run(backend)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "nominated-orange",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spec_data.block_for_results()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "weird-buyer",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x360 with 1 Axes>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "spec_data.figure(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "inclusive-yacht",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---------------------------------------------------\n",
+      "Experiment: QubitSpectroscopy\n",
+      "Experiment ID: 98e1cf76-cd87-43b7-a42a-d830edde8832\n",
+      "Status: DONE\n",
+      "Backend: ibmq_armonk\n",
+      "Data: 51\n",
+      "Analysis Results: 1\n",
+      "Figures: 1\n",
+      "---------------------------------------------------\n",
+      "Last Analysis Result:\n",
+      "\n",
+      "Analysis Result: QubitSpectroscopy\n",
+      "Analysis Result ID: e8da1b69-b115-49f8-ab2a-3d7d03d7c146\n",
+      "Experiment ID: 98e1cf76-cd87-43b7-a42a-d830edde8832\n",
+      "Device Components: [<Qubit(Q0)>]\n",
+      "Quality: good\n",
+      "Verified: True\n",
+      "Result Data:<super: <class 'CurveAnalysisResultData'>, <CurveAnalysisResultData object>>\n",
+      "  - a: -0.997711616147317 ± 0.013412900337231153\n",
+      "  - sigma: 3182353.179077804 ± 47682.951132104696\n",
+      "  - freq: 4971726352.692174 ± 45118.837185988334\n",
+      "  - b: 0.9674999638307883 ± 0.0043760971999863415\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(spec_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "presidential-amplifier",
+   "metadata": {},
+   "source": [
+    "We now update the instance of `Calibrations` with the value of the frequency that we measured using the `Frequency.update` function. Note that for the remainder of this notebook we use the value of the qubit frequency in the backend as it is not yet possible to updated qubit frequencies with the circuit path."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "global-advocacy",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit_experiments.calibration_management.update_library import Frequency\n",
+    "\n",
+    "Frequency.update(cals, spec_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "naval-dialogue",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "      <th>date_time</th>\n",
+       "      <th>valid</th>\n",
+       "      <th>exp_id</th>\n",
+       "      <th>group</th>\n",
+       "      <th>qubits</th>\n",
+       "      <th>parameter</th>\n",
+       "      <th>schedule</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4.971659e+09</td>\n",
+       "      <td>2021-07-20 19:25:02.648323+0000</td>\n",
+       "      <td>True</td>\n",
+       "      <td>None</td>\n",
+       "      <td>default</td>\n",
+       "      <td>(0,)</td>\n",
+       "      <td>qubit_lo_freq</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.971726e+09</td>\n",
+       "      <td>2021-07-20 21:26:14.962000+0200</td>\n",
+       "      <td>True</td>\n",
+       "      <td>98e1cf76-cd87-43b7-a42a-d830edde8832</td>\n",
+       "      <td>default</td>\n",
+       "      <td>(0,)</td>\n",
+       "      <td>qubit_lo_freq</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6.993371e+09</td>\n",
+       "      <td>2021-07-20 19:25:02.648345+0000</td>\n",
+       "      <td>True</td>\n",
+       "      <td>None</td>\n",
+       "      <td>default</td>\n",
+       "      <td>(0,)</td>\n",
+       "      <td>meas_lo_freq</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          value                        date_time  valid  \\\n",
+       "0  4.971659e+09  2021-07-20 19:25:02.648323+0000   True   \n",
+       "1  4.971726e+09  2021-07-20 21:26:14.962000+0200   True   \n",
+       "2  6.993371e+09  2021-07-20 19:25:02.648345+0000   True   \n",
+       "\n",
+       "                                 exp_id    group qubits      parameter  \\\n",
+       "0                                  None  default   (0,)  qubit_lo_freq   \n",
+       "1  98e1cf76-cd87-43b7-a42a-d830edde8832  default   (0,)  qubit_lo_freq   \n",
+       "2                                  None  default   (0,)   meas_lo_freq   \n",
+       "\n",
+       "  schedule  \n",
+       "0     None  \n",
+       "1     None  \n",
+       "2     None  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(cals.parameters_table(qubit_list=[qubit]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adjusted-heritage",
+   "metadata": {},
+   "source": [
+    "As seen from the table above the measured frequency has been added to the calibrations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "minus-vitamin",
+   "metadata": {},
+   "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."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "considered-advocate",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit_experiments.library.calibration import Rabi\n",
+    "from qiskit_experiments.calibration_management.update_library import Amplitude"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "accurate-pursuit",
+   "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(\"xp\", (0,), assign_params={\"amp\": Parameter(\"amp\")}),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "powered-pacific",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rabi_data = rabi.run(backend)\n",
+    "rabi_data.block_for_results()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "biological-repository",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x360 with 1 Axes>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rabi_data.figure(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "outer-being",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---------------------------------------------------\n",
+      "Experiment: Rabi\n",
+      "Experiment ID: dedf23f2-98f6-4714-aaba-37806fa94f51\n",
+      "Status: DONE\n",
+      "Backend: ibmq_armonk\n",
+      "Data: 51\n",
+      "Analysis Results: 1\n",
+      "Figures: 1\n",
+      "---------------------------------------------------\n",
+      "Last Analysis Result:\n",
+      "\n",
+      "Analysis Result: Rabi\n",
+      "Analysis Result ID: 5949a598-68d0-45ac-b093-67b349f8dc1c\n",
+      "Experiment ID: dedf23f2-98f6-4714-aaba-37806fa94f51\n",
+      "Device Components: [<Qubit(Q0)>]\n",
+      "Quality: good\n",
+      "Verified: True\n",
+      "Result Data:<super: <class 'CurveAnalysisResultData'>, <CurveAnalysisResultData object>>\n",
+      "  - amp: 0.488796705224479 ± 0.002341718272038815\n",
+      "  - freq: 0.6347478478973844 ± 0.0022653212032643327\n",
+      "  - phase: 0.018155363229460934 ± 0.0067989940385690525\n",
+      "  - baseline: 0.49406829235869 ± 0.0020205738937396638\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(rabi_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "unavailable-james",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Amplitude.update(cals, rabi_data, angles_schedules=[(np.pi, \"amp\", \"xp\"), (np.pi/2, \"amp\", \"x90p\")])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "architectural-afternoon",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "      <th>date_time</th>\n",
+       "      <th>valid</th>\n",
+       "      <th>exp_id</th>\n",
+       "      <th>group</th>\n",
+       "      <th>qubits</th>\n",
+       "      <th>parameter</th>\n",
+       "      <th>schedule</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>2021-07-20 19:25:02.649331+0000</td>\n",
+       "      <td>True</td>\n",
+       "      <td>None</td>\n",
+       "      <td>default</td>\n",
+       "      <td>()</td>\n",
+       "      <td>amp</td>\n",
+       "      <td>x90p</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>2021-07-20 19:25:02.649316+0000</td>\n",
+       "      <td>True</td>\n",
+       "      <td>None</td>\n",
+       "      <td>default</td>\n",
+       "      <td>()</td>\n",
+       "      <td>amp</td>\n",
+       "      <td>xp</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.393857</td>\n",
+       "      <td>2021-07-20 21:27:25.663000+0200</td>\n",
+       "      <td>True</td>\n",
+       "      <td>dedf23f2-98f6-4714-aaba-37806fa94f51</td>\n",
+       "      <td>default</td>\n",
+       "      <td>(0,)</td>\n",
+       "      <td>amp</td>\n",
+       "      <td>x90p</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.787714</td>\n",
+       "      <td>2021-07-20 21:27:25.663000+0200</td>\n",
+       "      <td>True</td>\n",
+       "      <td>dedf23f2-98f6-4714-aaba-37806fa94f51</td>\n",
+       "      <td>default</td>\n",
+       "      <td>(0,)</td>\n",
+       "      <td>amp</td>\n",
+       "      <td>xp</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      value                        date_time  valid  \\\n",
+       "0  0.500000  2021-07-20 19:25:02.649331+0000   True   \n",
+       "1  0.500000  2021-07-20 19:25:02.649316+0000   True   \n",
+       "2  0.393857  2021-07-20 21:27:25.663000+0200   True   \n",
+       "3  0.787714  2021-07-20 21:27:25.663000+0200   True   \n",
+       "\n",
+       "                                 exp_id    group qubits parameter schedule  \n",
+       "0                                  None  default     ()       amp     x90p  \n",
+       "1                                  None  default     ()       amp       xp  \n",
+       "2  dedf23f2-98f6-4714-aaba-37806fa94f51  default   (0,)       amp     x90p  \n",
+       "3  dedf23f2-98f6-4714-aaba-37806fa94f51  default   (0,)       amp       xp  "
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(cals.parameters_table(qubit_list=[qubit, ()], parameters=\"amp\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "innovative-wealth",
+   "metadata": {},
+   "source": [
+    "The table above shows that we have now updated the amplitude of our $\\pi$-pulse from 0.5 to the value obtained in the most recent Rabi experiment. Importantly, since we linked the amplitudes of the `xp` and `xm` schedules we will see that the amplitude of the `xm` schedule has also been updated as seen when requesting schedules form the `Calibrations` instance. Furthermore, we used the result from the `Rabi` experiment to also update the value of the `x90p` pulse. This was achieved by specifying `(np.pi/2, \"amp\", \"x90p\")` when calling `update`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "intended-announcement",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ScheduleBlock(Play(Drag(duration=320, amp=(0.39385718+0j), sigma=80, beta=0.5), DriveChannel(0)), name=\"x90p\", transform=AlignLeft())"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cals.get_schedule(\"x90p\", 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "fresh-royal",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ScheduleBlock(Play(Drag(duration=320, amp=(0.78771437+0j), sigma=80, beta=0.5), DriveChannel(0)), name=\"xp\", transform=AlignLeft())"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cals.get_schedule(\"xp\", 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "divine-banks",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ScheduleBlock(Play(Drag(duration=320, amp=(-0.78771437+0j), sigma=80, beta=0.5), DriveChannel(0)), name=\"xm\", transform=AlignLeft())"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cals.get_schedule(\"xm\", 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "broke-accuracy",
+   "metadata": {},
+   "source": [
+    "## 3. Saving and loading calibrations\n",
+    "\n",
+    "The values of the calibrated parameters can be saved to a `.csv` file and reloaded at a later point in time.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "confident-millennium",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/daniel/Documents/IBM/qiskit/qiskit-experiments/qiskit_experiments/calibration_management/calibrations.py:937: UserWarning: Schedules are only saved in text format. They cannot be re-loaded.\n",
+      "  warnings.warn(\"Schedules are only saved in text format. They cannot be re-loaded.\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "cals.save(file_type=\"csv\", overwrite=True, file_prefix=\"Armonk\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "complete-transcription",
+   "metadata": {},
+   "source": [
+    "After saving the values of the parameters you may restart your kernel. If you do so, you will only need to run the following cell to recover the state of your calibrations. Since the schedules are currently not stored we need to call our `setup_cals` function to populate an instance of `Calibrations` with the template schedules. By contrast, the value of the parameters will be recovered from the file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "gorgeous-comparison",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cals = setup_cals(backend)\n",
+    "cals.load_parameter_values(file_name=\"Armonkparameter_values.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "spectacular-communication",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "      <th>date_time</th>\n",
+       "      <th>valid</th>\n",
+       "      <th>exp_id</th>\n",
+       "      <th>group</th>\n",
+       "      <th>qubits</th>\n",
+       "      <th>parameter</th>\n",
+       "      <th>schedule</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>2021-07-20 19:25:02.649331+0000</td>\n",
+       "      <td>True</td>\n",
+       "      <td></td>\n",
+       "      <td>default</td>\n",
+       "      <td>()</td>\n",
+       "      <td>amp</td>\n",
+       "      <td>x90p</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>2021-07-20 19:25:02.649316+0000</td>\n",
+       "      <td>True</td>\n",
+       "      <td></td>\n",
+       "      <td>default</td>\n",
+       "      <td>()</td>\n",
+       "      <td>amp</td>\n",
+       "      <td>xp</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.393857</td>\n",
+       "      <td>2021-07-20 21:27:25.663000+0200</td>\n",
+       "      <td>True</td>\n",
+       "      <td>dedf23f2-98f6-4714-aaba-37806fa94f51</td>\n",
+       "      <td>default</td>\n",
+       "      <td>(0,)</td>\n",
+       "      <td>amp</td>\n",
+       "      <td>x90p</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.787714</td>\n",
+       "      <td>2021-07-20 21:27:25.663000+0200</td>\n",
+       "      <td>True</td>\n",
+       "      <td>dedf23f2-98f6-4714-aaba-37806fa94f51</td>\n",
+       "      <td>default</td>\n",
+       "      <td>(0,)</td>\n",
+       "      <td>amp</td>\n",
+       "      <td>xp</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      value                        date_time  valid  \\\n",
+       "0  0.500000  2021-07-20 19:25:02.649331+0000   True   \n",
+       "1  0.500000  2021-07-20 19:25:02.649316+0000   True   \n",
+       "2  0.393857  2021-07-20 21:27:25.663000+0200   True   \n",
+       "3  0.787714  2021-07-20 21:27:25.663000+0200   True   \n",
+       "\n",
+       "                                 exp_id    group qubits parameter schedule  \n",
+       "0                                        default     ()       amp     x90p  \n",
+       "1                                        default     ()       amp       xp  \n",
+       "2  dedf23f2-98f6-4714-aaba-37806fa94f51  default   (0,)       amp     x90p  \n",
+       "3  dedf23f2-98f6-4714-aaba-37806fa94f51  default   (0,)       amp       xp  "
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(cals.parameters_table(qubit_list=[qubit, ()], parameters=\"amp\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "about-train",
+   "metadata": {},
+   "source": [
+    "## 4. Calibrating the value of the DRAG coefficient\n",
+    "\n",
+    "A Derivative Removal by Adiabatic Gate (DRAG) pulse is designed to minimize leakage\n",
+    "to a neighbouring transition. It is a standard pulse with an additional derivative\n",
+    "component. It is designed to reduce the frequency spectrum of a normal pulse near\n",
+    "the $|1\\rangle$ - $|2\\rangle$ transition, reducing the chance of leakage\n",
+    "to the $|2\\rangle$ state. The optimal value of the DRAG parameter is chosen to\n",
+    "minimize both leakage and phase errors resulting from the AC Stark shift.\n",
+    "The pulse envelope is $f(t) = \\Omega_x(t) + j \\beta \\frac{\\rm d}{{\\rm d }t} \\Omega_x(t)$.\n",
+    "Here, $\\Omega_x$ is the envelop of the in-phase component of the pulse and\n",
+    "$\\beta$ is the strength of the quadrature which we refer to as the DRAG\n",
+    "parameter and seek to calibrate in this experiment. \n",
+    "The DRAG calibration will run\n",
+    "several series of circuits. In a given circuit a Rp(β) - Rm(β) block is repeated\n",
+    "$N$ times. Here, Rp is a rotation with a positive angle and Rm is the same rotation\n",
+    "with a negative amplitude."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "respected-shift",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit_experiments.library.calibration.drag import DragCal\n",
+    "from qiskit_experiments.calibration_management.update_library import Drag"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "ambient-positive",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cal_drag = DragCal(qubit)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "finished-arizona",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1355.49x144.48 with 1 Axes>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cal_drag.set_experiment_options(\n",
+    "    rp=cals.get_schedule(\"xp\", 0, assign_params={\"β\": Parameter(\"β\")}),\n",
+    "    betas=np.linspace(-20, 20, 25),\n",
+    "    reps=[3, 5, 7]\n",
+    ")\n",
+    "\n",
+    "cal_drag.circuits(backend)[1].draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "increasing-spyware",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "drag_data = cal_drag.run(backend)\n",
+    "drag_data.block_for_results()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "robust-observer",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x360 with 1 Axes>"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "drag_data.figure(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "better-eight",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Drag.update(cals, drag_data, parameter=\"β\", schedule=\"xp\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "protected-restriction",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "      <th>date_time</th>\n",
+       "      <th>valid</th>\n",
+       "      <th>exp_id</th>\n",
+       "      <th>group</th>\n",
+       "      <th>qubits</th>\n",
+       "      <th>parameter</th>\n",
+       "      <th>schedule</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>2021-07-20 19:25:02.649323+0000</td>\n",
+       "      <td>True</td>\n",
+       "      <td></td>\n",
+       "      <td>default</td>\n",
+       "      <td>()</td>\n",
+       "      <td>β</td>\n",
+       "      <td>x90p</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>2021-07-20 19:25:02.649308+0000</td>\n",
+       "      <td>True</td>\n",
+       "      <td></td>\n",
+       "      <td>default</td>\n",
+       "      <td>()</td>\n",
+       "      <td>β</td>\n",
+       "      <td>xp</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.815154</td>\n",
+       "      <td>2021-07-20 22:19:03.010000+0200</td>\n",
+       "      <td>True</td>\n",
+       "      <td>fb95c5bf-d80a-48a4-b70c-1658b322ccde</td>\n",
+       "      <td>default</td>\n",
+       "      <td>(0,)</td>\n",
+       "      <td>β</td>\n",
+       "      <td>xp</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      value                        date_time  valid  \\\n",
+       "0  0.500000  2021-07-20 19:25:02.649323+0000   True   \n",
+       "1  0.500000  2021-07-20 19:25:02.649308+0000   True   \n",
+       "2 -0.815154  2021-07-20 22:19:03.010000+0200   True   \n",
+       "\n",
+       "                                 exp_id    group qubits parameter schedule  \n",
+       "0                                        default     ()         β     x90p  \n",
+       "1                                        default     ()         β       xp  \n",
+       "2  fb95c5bf-d80a-48a4-b70c-1658b322ccde  default   (0,)         β       xp  "
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(cals.parameters_table(qubit_list=[qubit, ()], parameters=\"β\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "accepting-oxford",
+   "metadata": {},
+   "source": [
+    "## 5. Fine amplitude calibration\n",
+    "\n",
+    "The `FineAmplitude` calibration experiment repeats $N$ times a gate with a pulse\n",
+    "to amplify the under or over-rotations in the gate to determine the optimal amplitude.\n",
+    "The circuits that are run have a custom gate with the pulse schedule attached to it\n",
+    "through the calibrations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "environmental-peace",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit_experiments.library.calibration.fine_amplitude import FineXAmplitude\n",
+    "from qiskit_experiments.calibration_management.update_library import Amplitude"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "proof-wrestling",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "amp_x_cal = FineXAmplitude(qubit)\n",
+    "amp_x_cal.set_experiment_options(schedule=cals.get_schedule(\"xp\", 0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "applied-mountain",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 538.279x144.48 with 1 Axes>"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "amp_x_cal.circuits(backend)[5].draw(output=\"mpl\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "possible-johnson",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_fine = amp_x_cal.run(backend)\n",
+    "data_fine.block_for_results()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "respected-piece",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x360 with 1 Axes>"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_fine.figure(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "royal-episode",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result = data_fine.analysis_results(-1).data()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "twelve-limitation",
+   "metadata": {},
+   "source": [
+    "The cell below shows how the amplitude is updated based on the error in the rotation angle measured by the `FineXAmplitude` experiment. Note that this calculation is automatically done by the `Amplitude.update` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "hundred-inspiration",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The ideal angle is 3.14 rad. We measured a deviation of -0.056 rad.\n",
+      "Thus, scale the 0.7877 pulse amplitude by 1.018 to obtain 0.80198.\n"
+     ]
+    }
+   ],
+   "source": [
+    "dtheta = result[\"popt\"][1]\n",
+    "target_angle = np.pi\n",
+    "scale = target_angle / (target_angle + dtheta)\n",
+    "pulse_amp = cals.get_parameter_value(\"amp\", 0, \"xp\")\n",
+    "print(f\"The ideal angle is {target_angle:.2f} rad. We measured a deviation of {dtheta:.3f} rad.\")\n",
+    "print(f\"Thus, scale the {pulse_amp:.4f} pulse amplitude by {scale:.3f} to obtain {pulse_amp*scale:.5f}.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "scientific-friend",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Amplitude.update(cals, data_fine, angles_schedules=[(target_angle, \"amp\", \"xp\")])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "harmful-transition",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "      <th>date_time</th>\n",
+       "      <th>valid</th>\n",
+       "      <th>exp_id</th>\n",
+       "      <th>group</th>\n",
+       "      <th>qubits</th>\n",
+       "      <th>parameter</th>\n",
+       "      <th>schedule</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>2021-07-20 19:25:02.649331+0000</td>\n",
+       "      <td>True</td>\n",
+       "      <td></td>\n",
+       "      <td>default</td>\n",
+       "      <td>()</td>\n",
+       "      <td>amp</td>\n",
+       "      <td>x90p</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>2021-07-20 19:25:02.649316+0000</td>\n",
+       "      <td>True</td>\n",
+       "      <td></td>\n",
+       "      <td>default</td>\n",
+       "      <td>()</td>\n",
+       "      <td>amp</td>\n",
+       "      <td>xp</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.393857</td>\n",
+       "      <td>2021-07-20 21:27:25.663000+0200</td>\n",
+       "      <td>True</td>\n",
+       "      <td>dedf23f2-98f6-4714-aaba-37806fa94f51</td>\n",
+       "      <td>default</td>\n",
+       "      <td>(0,)</td>\n",
+       "      <td>amp</td>\n",
+       "      <td>x90p</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.787714</td>\n",
+       "      <td>2021-07-20 21:27:25.663000+0200</td>\n",
+       "      <td>True</td>\n",
+       "      <td>dedf23f2-98f6-4714-aaba-37806fa94f51</td>\n",
+       "      <td>default</td>\n",
+       "      <td>(0,)</td>\n",
+       "      <td>amp</td>\n",
+       "      <td>xp</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.801978</td>\n",
+       "      <td>2021-07-20 22:21:40.070000+0200</td>\n",
+       "      <td>True</td>\n",
+       "      <td>d6a5c8a7-173a-4cd1-9453-5fafe1aab331</td>\n",
+       "      <td>default</td>\n",
+       "      <td>(0,)</td>\n",
+       "      <td>amp</td>\n",
+       "      <td>xp</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      value                        date_time  valid  \\\n",
+       "0  0.500000  2021-07-20 19:25:02.649331+0000   True   \n",
+       "1  0.500000  2021-07-20 19:25:02.649316+0000   True   \n",
+       "2  0.393857  2021-07-20 21:27:25.663000+0200   True   \n",
+       "3  0.787714  2021-07-20 21:27:25.663000+0200   True   \n",
+       "4  0.801978  2021-07-20 22:21:40.070000+0200   True   \n",
+       "\n",
+       "                                 exp_id    group qubits parameter schedule  \n",
+       "0                                        default     ()       amp     x90p  \n",
+       "1                                        default     ()       amp       xp  \n",
+       "2  dedf23f2-98f6-4714-aaba-37806fa94f51  default   (0,)       amp     x90p  \n",
+       "3  dedf23f2-98f6-4714-aaba-37806fa94f51  default   (0,)       amp       xp  \n",
+       "4  d6a5c8a7-173a-4cd1-9453-5fafe1aab331  default   (0,)       amp       xp  "
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(cals.parameters_table(qubit_list=[qubit, ()], parameters=\"amp\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "miniature-commission",
+   "metadata": {},
+   "source": [
+    "To check that we have managed to reduce the error in the rotation angle we will run the fine amplitude calibration experiment once again."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "comparable-feeding",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "amp_x_cal.set_experiment_options(schedule=cals.get_schedule(\"xp\", 0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "canadian-jamaica",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_fine2 = amp_x_cal.run(backend)\n",
+    "data_fine2.block_for_results()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "geographic-paradise",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x360 with 1 Axes>"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_fine2.figure(0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "addressed-project",
+   "metadata": {},
+   "source": [
+    "As can be seen from the data above and the analysis result below we have managed to reduce the error in the rotation angle ${\\rm d}\\theta$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "royal-found",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'analysis_type': 'FineAmplitudeAnalysis',\n",
+       " 'popt': array([-0.80247685, -0.00554933,  0.4864584 ]),\n",
+       " 'popt_keys': ['amp', 'd_theta', 'baseline'],\n",
+       " 'popt_err': array([0.02131984, 0.00123576, 0.00403482]),\n",
+       " 'pcov': array([[ 4.54535634e-04, -3.73966148e-06,  3.25586390e-05],\n",
+       "        [-3.73966148e-06,  1.52711350e-06, -5.22463483e-07],\n",
+       "        [ 3.25586390e-05, -5.22463483e-07,  1.62797864e-05]]),\n",
+       " 'reduced_chisq': 1.4103179033516524,\n",
+       " 'dof': 13,\n",
+       " 'xrange': [0.0, 14.0],\n",
+       " 'success': True,\n",
+       " 'quality': 'good'}"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_fine2.analysis_results(-1).data()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bright-edgar",
+   "metadata": {},
+   "source": [
+    "### Fine amplitude calibration of the $\\pi/2$ rotation\n",
+    "\n",
+    "We now wish to calibrate the amplitude of the $\\pi/2$ rotation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "seeing-words",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit_experiments.library.calibration.fine_amplitude import FineSXAmplitude"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "durable-charm",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "amp_sx_cal = FineSXAmplitude(qubit)\n",
+    "amp_sx_cal.set_experiment_options(schedule=cals.get_schedule(\"x90p\", 0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "selected-malta",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1320.88x144.48 with 1 Axes>"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "amp_sx_cal.circuits(backend)[5].draw(output=\"mpl\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "pleasant-modeling",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_fine_sx = amp_sx_cal.run(backend)\n",
+    "data_fine_sx.block_for_results()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "upper-recorder",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x360 with 1 Axes>"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_fine_sx.figure(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "excited-depth",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Amplitude.update(cals, data_fine_sx, angles_schedules=[(np.pi/2, \"amp\", \"x90p\")])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "excellent-mother",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "      <th>date_time</th>\n",
+       "      <th>valid</th>\n",
+       "      <th>exp_id</th>\n",
+       "      <th>group</th>\n",
+       "      <th>qubits</th>\n",
+       "      <th>parameter</th>\n",
+       "      <th>schedule</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>2021-07-20 19:25:02.649331+0000</td>\n",
+       "      <td>True</td>\n",
+       "      <td></td>\n",
+       "      <td>default</td>\n",
+       "      <td>()</td>\n",
+       "      <td>amp</td>\n",
+       "      <td>x90p</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>2021-07-20 19:25:02.649316+0000</td>\n",
+       "      <td>True</td>\n",
+       "      <td></td>\n",
+       "      <td>default</td>\n",
+       "      <td>()</td>\n",
+       "      <td>amp</td>\n",
+       "      <td>xp</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.393857</td>\n",
+       "      <td>2021-07-20 21:27:25.663000+0200</td>\n",
+       "      <td>True</td>\n",
+       "      <td>dedf23f2-98f6-4714-aaba-37806fa94f51</td>\n",
+       "      <td>default</td>\n",
+       "      <td>(0,)</td>\n",
+       "      <td>amp</td>\n",
+       "      <td>x90p</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.378293</td>\n",
+       "      <td>2021-07-20 22:23:41.990000+0200</td>\n",
+       "      <td>True</td>\n",
+       "      <td>f142b771-79bd-46ea-be11-32e9d0b7e2fa</td>\n",
+       "      <td>default</td>\n",
+       "      <td>(0,)</td>\n",
+       "      <td>amp</td>\n",
+       "      <td>x90p</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.787714</td>\n",
+       "      <td>2021-07-20 21:27:25.663000+0200</td>\n",
+       "      <td>True</td>\n",
+       "      <td>dedf23f2-98f6-4714-aaba-37806fa94f51</td>\n",
+       "      <td>default</td>\n",
+       "      <td>(0,)</td>\n",
+       "      <td>amp</td>\n",
+       "      <td>xp</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.801978</td>\n",
+       "      <td>2021-07-20 22:21:40.070000+0200</td>\n",
+       "      <td>True</td>\n",
+       "      <td>d6a5c8a7-173a-4cd1-9453-5fafe1aab331</td>\n",
+       "      <td>default</td>\n",
+       "      <td>(0,)</td>\n",
+       "      <td>amp</td>\n",
+       "      <td>xp</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      value                        date_time  valid  \\\n",
+       "0  0.500000  2021-07-20 19:25:02.649331+0000   True   \n",
+       "1  0.500000  2021-07-20 19:25:02.649316+0000   True   \n",
+       "2  0.393857  2021-07-20 21:27:25.663000+0200   True   \n",
+       "3  0.378293  2021-07-20 22:23:41.990000+0200   True   \n",
+       "4  0.787714  2021-07-20 21:27:25.663000+0200   True   \n",
+       "5  0.801978  2021-07-20 22:21:40.070000+0200   True   \n",
+       "\n",
+       "                                 exp_id    group qubits parameter schedule  \n",
+       "0                                        default     ()       amp     x90p  \n",
+       "1                                        default     ()       amp       xp  \n",
+       "2  dedf23f2-98f6-4714-aaba-37806fa94f51  default   (0,)       amp     x90p  \n",
+       "3  f142b771-79bd-46ea-be11-32e9d0b7e2fa  default   (0,)       amp     x90p  \n",
+       "4  dedf23f2-98f6-4714-aaba-37806fa94f51  default   (0,)       amp       xp  \n",
+       "5  d6a5c8a7-173a-4cd1-9453-5fafe1aab331  default   (0,)       amp       xp  "
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(cals.parameters_table(qubit_list=[qubit, ()], parameters=\"amp\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "asian-fault",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ScheduleBlock(Play(Drag(duration=320, amp=(0.378293207284159+0j), sigma=80, beta=0.5), DriveChannel(0)), name=\"x90p\", transform=AlignLeft())"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cals.get_schedule(\"x90p\", 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "turkish-moment",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ScheduleBlock(Play(Drag(duration=320, amp=(0.801978295624676+0j), sigma=80, beta=-0.815154476359888), DriveChannel(0)), name=\"xp\", transform=AlignLeft())"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cals.get_schedule(\"xp\", 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "mature-induction",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ScheduleBlock(Play(Drag(duration=320, amp=(-0.801978295624676+0j), sigma=80, beta=-0.815154476359888), DriveChannel(0)), name=\"xm\", transform=AlignLeft())"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cals.get_schedule(\"xm\", 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "documented-aside",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/qiskit_experiments/calibration_management/calibrations.py b/qiskit_experiments/calibration_management/calibrations.py
index cb9cb786be..3adf7770c8 100644
--- a/qiskit_experiments/calibration_management/calibrations.py
+++ b/qiskit_experiments/calibration_management/calibrations.py
@@ -14,7 +14,7 @@
 
 import os
 from collections import defaultdict
-from datetime import datetime
+from datetime import datetime, timezone
 from typing import Any, Dict, Set, Tuple, Union, List, Optional
 import csv
 import dataclasses
@@ -380,7 +380,7 @@ def add_parameter_value(
         qubits = self._to_tuple(qubits)
 
         if isinstance(value, (int, float, complex)):
-            value = ParameterValue(value, datetime.now())
+            value = ParameterValue(value, datetime.now(timezone.utc))
 
         param_name = param.name if isinstance(param, Parameter) else param
         sched_name = schedule.name if isinstance(schedule, ScheduleBlock) else schedule
diff --git a/qiskit_experiments/calibration_management/update_library.py b/qiskit_experiments/calibration_management/update_library.py
index 44f6052178..393106f3fd 100644
--- a/qiskit_experiments/calibration_management/update_library.py
+++ b/qiskit_experiments/calibration_management/update_library.py
@@ -13,7 +13,7 @@
 """A library of experiment calibrations."""
 
 from abc import ABC
-from datetime import datetime
+from datetime import datetime, timezone
 from typing import List, Optional, Tuple, Union
 import numpy as np
 
@@ -58,7 +58,7 @@ def _time_stamp(exp_data: ExperimentData) -> datetime:
         if all_times:
             return max(all_times)
 
-        return datetime.now()
+        return datetime.now(timezone.utc)
 
     @classmethod
     def _add_parameter_value(
diff --git a/qiskit_experiments/test/utils.py b/qiskit_experiments/test/utils.py
index 7d94822ed2..297e737d86 100644
--- a/qiskit_experiments/test/utils.py
+++ b/qiskit_experiments/test/utils.py
@@ -14,7 +14,7 @@
 
 import uuid
 from typing import Optional, Union, Dict
-from datetime import datetime
+from datetime import datetime, timezone
 import time
 
 from qiskit.providers.job import JobV1 as Job
@@ -45,7 +45,7 @@ def submit(self):
     @staticmethod
     def time_per_step() -> Dict[str, datetime]:
         """Return the completion time."""
-        return {"COMPLETED": datetime.now()}
+        return {"COMPLETED": datetime.now(timezone.utc)}
 
     def status(self) -> JobStatus:
         """Return the status of the job, among the values of ``JobStatus``."""