ex4.ipynb

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    "# Exercise 4 - Transmon qubit\n",
    "\n",
    "## Historical background\n",
    "\n",
    "In quantum computing, a qubit is the basic unit of quantum information. It is essentially a two-level quantum mechanical system, can be implemented in many physical systems, including natural systems such as electron spins (spin qubit), atomic levels in ions (trapped ion qubits) or artificial systems such as states in a superconducting circuit (superconducting qubit).\n",
    "\n",
    "The original superconducting qubit was the Cooper Pair Box debuted in 1999[1], in which a well-defined number of Cooper pairs (bound electrons in a superconductor) occupy an island of superconductor weakly coupled to a lead by a Josephson junction. In Cooper pair box qubit, the energy difference between the $|0\\rangle$ state (no Cooper Pairs) and $|1\\rangle$ state (a single Cooper Pair)is strongly dependent on any stray or applied electrostatic charges in or around the system. This sensitivity to charge noise leads to qubit dephasing. The qubit dephasing time ($T_2$) for Cooper pair box qubit is typically limited to ~ 1$\\mu s$.\n",
    "\n",
    "The key discovery behind the transmon qubit, first proposed in 2007[2], is that this energy dependence on voltage (known as dispersion) is periodic. By introducing a shunting capacitor, the ratio between the Josephson energy and charging energy $E_J/E_c$ become very large ~ 50, leading to a flat charge dispersion. While Cooper pair box is very sensitive to the charge noise (i.e. uncertainty in x axis position in Fig. 1a), it is largely suppressed in transmon qubit (Fig. 1d). The qubit dephasing time ($T_2$) for transmon qubit is improved significantly. In IBM Quantum systems, $T_2$ is typically ~ 100 $\\mu s$. See [`ibmq_santiago`](https://quantum-computing.ibm.com/services?skip=0&systems=all&system=ibmq_santiago) for example.\n",
    "\n",
    "<figure>\n",
    "    <img src=\"resources/transmon-charge-dispersion.png\" alt=\"Transmon charge dispersion\" style=\"width:50%\">\n",
    "    <center><figcaption><b>Fig. 1</b> Charge dispersion of qubit for different ratios $E_J/E_c$. (from Ref [2])</figcaption></center>\n",
    "</figure>\n",
    "\n",
    "In this exercise, you will investigate the characteristic energies of the transmon qubit by performing spectroscopy with a real IBM Quantum system. the ratio between the Josephson energy and charging energy $E_J/E_c$ is the key to solving the charge noise problem of its predecessor, the Cooper Pair Box qubit.\n",
    "\n",
    "### References\n",
    "\n",
    "1. Nakamura, Yasunobu, Yu A. Pashkin, and Jaw Shen Tsai. \"Coherent control of macroscopic quantum states in a single-Cooper-pair box.\" Nature 398.6730 (1999): 786-788.\n",
    "2. Koch, Jens, et al. \"Charge-insensitive qubit design derived from the Cooper pair box.\" Physical Review A 76.4 (2007): 042319.\n",
    "3. Krantz, Philip, et al. \"A quantum engineer's guide to superconducting qubits.\" Applied Physics Reviews 6.2 (2019): 021318."
   ]
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   "source": [
    "## Energy levels and characteristic energies of the transmon qubit\n",
    "\n",
    "The Hamiltonian of quantum harmonic oscillator (QHO) can be obtained by quantizing Hamiltonian of a linear LC circuit. Its energy levels are equally spaced (Fig. 2c). By introducing a non-linear circuit element, the Josephson junction, the energy levels of the transmon qubit is modified (Fig. 2d). The energy levels are no longer equally spaced. Higher energy level spacing is smaller than those of lower energies. The anharmonicity $\\delta$ is defined as the energy difference between $|1\\rangle \\rightarrow |2\\rangle$ and $|0\\rangle \\rightarrow |1\\rangle$ transition: $\\delta = \\hbar \\omega_{12} - \\hbar \\omega_{01}$ or simply $\\omega_{12} - \\omega_{01}$ (we'll set $\\hbar \\equiv 1$ for the remainder of this discussion). By tunning the microwave frequency to $\\omega_{01}$, we can effectively address the multi-level transmon as a two-level system, i.e. a qubit.\n",
    "\n",
    "<figure>\n",
    "    <img src=\"resources/transmon-energy-levels.png\" alt=\"Transmon energy levels\" style=\"width:50%\">\n",
    "    <center><figcaption><b>Fig. 2</b> Energy potential for a quantum harmonic oscillator and a trasmon qubit. (from Ref [3])</figcaption></center>\n",
    "</figure>\n",
    "\n",
    "In superconductors, electrons form Cooper pairs with the effective charge of $2e$, where $e$ is the charge of a single electron. The charging energy $E_c$ is the energy cost of adding a single Cooper pair to the qubit and is related to the capacitance of the superconducting circuit. The Josephson energy $E_J$ is the energy of the Cooper pairs tunneling through the Josephson junction. In transmon qubits, the inductive energy is wholly provided by the Josephson junction, but this is not true of other styles of superconducting qubit (for information on the design and analysis of superconducting qubits, [Qiskit Metal](https://qiskit.org/metal/) has recently been released!).\n",
    "\n",
    "By deriving the Duffing oscillator Hamiltonian from the Cooper Pair Box Hamiltonian, these energies can be related to the relevant transmon frequencies as\n",
    "\n",
    "$$\n",
    "f_{01} = \\sqrt{8 E_c E_J} - E_c \\qquad E_c = -\\delta = f_{01} - f_{12},\n",
    "$$\n",
    "\n",
    "We will use $f$ to replace $\\omega$ as the symbol for frequency for the rest of the notebook, because it is more commonly used for microwave. The anharmonicity $\\delta$ is wholly provided by the charging energy $E_c$. Physically, this is incorporated as a large *shunting* (parallel) capacitor to the junction. This addition allows well-separated transmon levels while suppressing susceptibility to the charge noise. "
   ]
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   "cell_type": "markdown",
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   "source": [
    "## Qiskit Pulse overview\n",
    "\n",
    "So far, you have been playing with qubits in the abstract quantum circuit level. Physically the quantum gates in circuits are implemented as microwave pulses. Qiskit Pulse provides you pulse level access to manipulate the microwave pulses sending to IBM Quantum system.\n",
    "\n",
    "As a brief overview, Qiskit Pulse schedules (experiments) consist of Instructions (e.g., Play) acting on Channels (e.g., the drive channel). Here is a summary table of available Instructions and Channels:\n",
    "\n",
    "![image](resources/pulse-instr.png)\n",
    "\n",
    "For more details, this table summarizes the interaction of the channels with the actual quantum hardware:\n",
    "\n",
    "![image](resources/pulse-user-device-layers.png)\n",
    "\n",
    "You can learn more about Qiskit Pulse on [Qiskit documentation](https://qiskit.org/documentation/apidoc/pulse.html). However, we find it is more instructive to begin with guided programming in Pulse. Below you will learn how to create pulses, schedules, and run experiments on a real quantum system. "
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's get started!\n",
    "\n",
    "<div class=\"alert alert-block alert-success\">\n",
    "\n",
    "**Goal**\n",
    "    \n",
    "Find $|1\\rangle \\rightarrow |2\\rangle$ transition frequency $f_{12}$.\n",
    "    \n",
    "**Plan**\n",
    "    \n",
    "1. **(Tutorial) Find $|0\\rangle \\rightarrow |1\\rangle$ transition frequency $f_{01}$** using spectroscopy (sweeping frequency).\n",
    "1. **(Tutorial) Calibrate X-180 pulse amplitude** using Rabi oscillation (sweeping amplitude).\n",
    "1. **(Problem) Find $|1\\rangle \\rightarrow |2\\rangle$ transition frequency $f_{12}$** using the calibrated X-180 pulse and spectroscopy (sweeping frequency).\n",
    "    \n",
    "</div>\n",
    "\n",
    "<div class=\"alert alert-block alert-danger\">\n",
    "\n",
    "**You will only need to <a href=\"#problem\">modify one cell</a> within this notebook.** You may also need to <a href=\"#fit-f12\">modify the fitting parameters</a> to get a good fit. But other than that, in rest of the cells, nothing needs to be modified. However, you will need to execute the cells by pressing `shift+Enter` in each code block. \n",
    "\n",
    "</div>\n",
    "\n",
    "In order to keep things tidy and focus on the important aspects of Qiskit Pulse, the following cells make use of methods from the `helper` module. For the gory details, please refer to the [Investigating Quantum Hardware Using Microwave Pulses\n",
    "](https://qiskit.org/textbook/ch-quantum-hardware/index-pulses.html) chapter of the Qiskit Textbook and Lectures 16-21, Labs 6-7 of the [Qiskit Global Summer School](https://qiskit.org/learn/intro-qc-qh/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import helper module from local folder\n",
    "import sys\n",
    "import os\n",
    "sys.path.append(os.getcwd()) \n",
    "from resources import helper\n",
    "\n",
    "# Numerical and plotting tools\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Import SI unit conversion factors\n",
    "from resources.helper import GHz, MHz, kHz, us, ns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\">\n",
    " \n",
    "**Special provider**\n",
    "    \n",
    "We have reserved a brand new `ibmq_jakarta` quantum system for the challenge. Participants will have exclusive access to the system during the challenge period (May 20 - 26, 2021) through a special [provider](https://quantum-computing.ibm.com/composer/docs/iqx/manage/provider/#providers). You need to **complete at least one of the five exercises** in the challenge to get assigned to a special provider for the challenge. When you do, you will receive an email with header `You have been added to a new project in IBM Quantum` in the email address associated with your IBM Quantum account. If everything work properly, you should be able to see the information of the special provider in [your account details page](https://quantum-computing.ibm.com/account).\n",
    "    \n",
    "Your IBM Quantum account needs to be associated with an IBMid. Otherwise, you will encounter this error `Login with some authorized required.`. Please [register a new IBMid](https://auth.quantum-computing.ibm.com/auth/idaas) using the SAME email address associated with your IBM Quantum account and login to [IBM Quantum](https://quantum-computing.ibm.com) again.\n",
    "    \n",
    "**Note: It may take up to 12 hours to get assigned to the provider upon completion of other exercises.** If you face any issues, please reach out to us in [#iqc2021](https://qiskit.slack.com/archives/C021UTFN9GE) channel. Join the Qiskit Slack workspace [here](https://ibm.co/joinqiskitslack) if you haven't already.\n",
    "    \n",
    "</div>\n",
    "\n",
    "Run the cell below to check if you have been assigned to a special provider. If you are added to a special provider, you should be able to see in the output a provider looks like this `<AccountProvider for IBMQ(hub='iqc2021-n', group='challenge-m', project='ex4')>` where `n` is a number goes from `1` to `10` and `m` is a number goes from `1` to `1000`. You will need this information to get access to `ibmq_jakarta` quantum system reserved for the challenge. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<AccountProvider for IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <AccountProvider for IBMQ(hub='iqc2021-2', group='challenge-36', project='ex4')>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing standard Qiskit libraries\n",
    "from qiskit import IBMQ\n",
    "from qiskit.tools.jupyter import *\n",
    "\n",
    "# Loading your IBM Quantum account\n",
    "IBMQ.load_account()\n",
    "IBMQ.providers() # see a list of providers you have access to"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "VBox(children=(HTML(value=\"<h1 style='color:#ffffff;background-color:#000000;padding-top: 1%;padding-bottom: 1…"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<IBMQBackend('ibmq_jakarta') from IBMQ(hub='iqc2021-2', group='challenge-36', project='ex4')>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the special provider assigned to you using information from the output above\n",
    "hub_name = 'iqc2021-2' # e.g. 'iqc2021-1'\n",
    "group_name = 'challenge-36' # e.g. 'challenge-1'\n",
    "project_name = 'ex4' # Your project name should be 'ex4'\n",
    "provider = IBMQ.get_provider(hub=hub_name, group=group_name, project=project_name)\n",
    "\n",
    "# Get `ibmq_jakarta` backend from the provider\n",
    "backend_name = 'ibmq_jakarta'\n",
    "backend = provider.get_backend(backend_name)\n",
    "backend # See details of the `ibmq_jakarta` quantum system"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Instantiate channels and retrieve measurement schedule\n",
    "\n",
    "We will use the same measurement schedule throughout, whereas the drive schedules will vary. For any backend, we can ask for its default measurement pulse instead of calibrating it separately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import pulse\n",
    "from qiskit.pulse import Play, Schedule, DriveChannel\n",
    "\n",
    "# Please use qubit 0 throughout the notebook\n",
    "qubit = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, save the backend configuration and the sampling time $dt$. We will exclude the `AcquireChannel`s when plotting for clarity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sampling time: 0.2222222222222222 ns\n"
     ]
    }
   ],
   "source": [
    "backend_config = backend.configuration()\n",
    "exc_chans = helper.get_exc_chans(globals())\n",
    "dt = backend_config.dt\n",
    "print(f\"Sampling time: {dt*1e9} ns\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `instruction_schedule_map` provides the parameters of the default calibrated pulses used by the backend. In particular, we see that this \"native gate set\" consists of a subset of operations you can perform on a [`QuantumCircuit`](https://qiskit.org/documentation/apidoc/circuit.html#gates-and-instructions), and is the gate set that the `QuantumCircuit` is transpiled to by Qiskit. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['cx', 'id', 'measure', 'rz', 'sx', 'u1', 'u2', 'u3', 'x']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "backend_defaults = backend.defaults()\n",
    "center_frequency = backend_defaults.qubit_freq_est\n",
    "inst_sched_map = backend_defaults.instruction_schedule_map \n",
    "inst_sched_map.instructions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The instructions consist of the native gate set of the backend: single-qubit gates square-root of $X$, `sx`, and parameterized rotation about the $Z$-axis, `rz(θ)`, and the two-qubit controlled-NOT, `cx`. There are several other gates listed here, in particular the soon-to-be deprecated [\"U-gates\"](https://qiskit.org/textbook/ch-states/single-qubit-gates.html#generalU3) based on Euler angles, summarized below for completeness\n",
    "\n",
    "Basis Gate | Operation\n",
    " --- | ---\n",
    " `u1(λ)` | `rz(λ)`\n",
    " `u2(φ,λ)` | `rz(φ+π/2) sx rz(λ-π/2)`\n",
    " `u3(θ,φ,λ)` | `rz(φ+π) sx rz(θ+π) sx rz(λ)`\n",
    " `id` | identity\n",
    " `x` | $X$ for echoes in dynamical decoupling\n",
    " `measure` | measurement\n",
    "\n",
    "One thing to note is that each `sx` consists of one physical pulse and the `rz`s are implemented in software by [frame changes](https://arxiv.org/abs/1612.00858). From the `instruction_schedule_map` we can retrieve the default measurement pulse instead of building it from scratch. This also serves as a useful starting point from retrieving calibrated pulses and manipulating them further."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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V8Bvg48BtwCeANXP66YV8w0m9J++IiCclLcj7ru2W2hoAD18/oeoqmJmZmdkyrEcFJRExV9KNpCFbt+WfN0bEK7nzpGYIMLdOEXPyPmsjBxZmZmZmVpUeFZRklwFnSzoG2Ac4suL6LBMcVJiZmZnZsqonBiXjgPOBHwOrUH8o1hxgtTrpQ/K+Hs0BhJmZmZnZYj0uKImI+ZKuA44GroiI+XWyTSPNHXmHpPcCK+d9TJ06lVUHDezq6jZlx1U2rroKZmZmZmY9Vo8LSrLfACsC5zTYfwNwnKRBEfFaTtsfeB2YDLDlllsy+dbburyibbHw1kerroKZmZmZWdPW3G2LbjlPjwxKImISMKmFLOeQ5ppcJek0YENgNPCL0jLBPcqKn9zond8doJiZmZmZJT0yKGlNRMyR9Cng16Q5J3OBs0iByTLBAYqZmZmZWdIjgpKIGE0rAUVEDC29fgjYsetq1X1qAYqDEzMzMzPri3raE937tBU/udESPShmZmZmZn1BtwYlkvaU9JSkeZK27M5zL0scmJiZmZlZX9LdPSVnAEdExMCImNrN516muNfEzMzMzPqK7g5K1gf+WW+HpB4xv6WncXBiZmZmZr1dq0GJpBmSjpV0v6RXJF0uaUBh/2GSHpU0W9I4SevUKWNFSfOAfsB9kh4rlH28pPuB+ZL6S/oPSXdKmivpPkkjCuVsIGmypNck3Szp15J+3wmfQ4/n4MTMzMzMeqtme0r2A3YGNgA2B0YCSNoR+GnevzYwE7isfHBELIyI2uPVPxwR7yvs/iKwGzAYeBcwHjgVWB04FviTpDVz3j8A9wBDgVOAg5usf6/h4MTMzMzMeptmh0z9T0Q8CyDpWmCLnH4gMCYi7s37TgDmSBoWETPaUPZT+fiDgOsj4vq872ZJU4BdJf0F2AbYKSIWArfmuvRJXkbYzMzMzHqLZntKni/8vgCo9XqsQ+odASAi5gEvA+u2oQ5PFX5fH9g3D92aK2ku8HFSL8w6wJyImF/IP5M+zr0mZmZmZras6+jk8mdJgQQAklYB1gCeaUMZUfj9KeB3EXFYOZOk9YEhklYpBCbrlY7vk9xrYmZmZmbLso6uvnUpcIikLSStCPwEuKsNQ7fKfg98TtJnJfWTNEDSCEnviYiZwBTgJEkrSPo48LkO1r9Xca+JmZmZmS2LOhSURMQtwA+BPwHPAe8DDuhAeU8BewDfA2aRek6OK9TzS8C2wGzgR8DFxePzQxk/0d7z9waeCG9mZmZmyxpFLLujnySNBjaKiIPK+z7ykY/E5Ftv6/5K9TAe0mVmZmZm7bXmblvcExFbd/V5uvvhidbN3GtiZmZmZj2dg5I+wIGJmZmZmfVkHV19q1IRMbrqOiwrvEKXmZmZmfVU7inpY9xrYmZmZmY9jYOSPsgrdJmZmZlZT+KgpA9zYGJmZmZmPYGDkj7OvSZmZmZmVjUHJQa418TMzMzMquOgxN7hwMTMzMzMquCgxJbgwMTMzMzMupuDEluK55mYmZmZWXdyUGINOTgxMzMzs+7goMRa5cDEzMzMzLqSgxJrigMTMzMzM+sqDkqsaR7OZWZmZmZdwUGJtZmDEzMzMzPrTA5KrN0cmJiZmZlZZ3BQYh3iXhMzMzMz6ygHJdYpHJyYmZmZWXs5KLFO5eDEzMzMzNrKQYl1CQcnZmZmZtYsByXWpRycmJmZmVlr+lddAesbioHJwlsfrbAmZmZmZtbTuKfEup17T8zMzMysyD0lVplyYOIeFDMzM7O+yUGJ9RjuPTEzMzPrmzx8y8zMzMzMKuWgxMzMzMzMKuWgxMzMzMzMKuWgxMzMzMzMKuWgxMzMzMzMKuWgxMzMzMzMKuWgxMzMzMzMKuWgxMzMzMzMKuWgxMzMzMzMKuWgxMzMzMzMKuWgxMzMzMzMKuWgxMzMzMzMKuWgxMzMzMzMKuWgxMzMzMzMKuWgxMzMzMzMKuWgxMzMzMzMKuWgxMzMzMzMKuWgxMzMzMzMKuWgxMzMzMzMKuWgxMzMzMzMKuWgxMzMzMzMKqWIqLoOXULSLGBmxdUYCrxUcR2s53M7sWa5rVgz3E6sWW4r1owPRMSgrj5J/64+QVUiYs2q6yBpSkRsXXU9rGdzO7Fmua1YM9xOrFluK9YMSVO64zwevmVmZmZmZpVyUGJmZmZmZpVyUNK1zqu6ArZMcDuxZrmtWDPcTqxZbivWjG5pJ712oruZmZmZmS0b3FNiZmZmZmaVclDSySRtImmCpAWSnpV0sqR+VdfLuoekfSWNk/SMpHmS7pH0xVKe/SVdJek5SSFpZIOy1pV0taTXJL0k6deSVu6WN2LdKn/X83J7GJjT1pZ0uqT78r6nJF0kaZ0Gx7ut9FKS+kv6rqRHJC2U9LSkswr7V5D0R0mPS3pd0ixJN0jaqk5Z/hvVS0k6QNK9+f+LZyRdXO//i0L+s/L/OWfU2ed20ktI2kjSuZLul/S2pEl18kjS9/Lfmdcl3Sppi1Kekbm9lLdvtLWsRnrtksBVkDQEuAV4CNgDeB9wJin4+0GFVbPucwzwBHA0ae33XYE/SBoaEb/KefYBhgHXAV+rV4ik5YGbgDeBA4DBwC/yz4O6rPZWldOBecAqhbStgD2B84G7gHcBo4E7JW0WEfPAbaWPGAvsCJwETAPeC2xS2N8PCOCnwGPAqqT/gyZK2jIiHgf/jerNJH0euBT4X+A4YG3gVGC8pK0iYlEp/ybAocCrdcpyO+ldNiVdi/wNWL5Bnu8CPyS1nWmka5lb8t+a50t5dwReL7x+vANlLSkivHXSBpwAzAFWLaSNAhYU07z13g0YWiftD8AThdfL5Z8DSRcSI+sc80XgbWCDQtp+wCLg/VW/T2+d2mY+CcwGjs3tYWBOHwz0L+XdOOc52G2lb2zAzsBbwCZtPG4gsBA4ppDmv1G9dAMuA+4ppX0+/3/xwTr5JwCnADOAM0r73E560Va75si/XwlMKu0fALwCnFhIWwWYBZxaSBtZ/BvV4FxNldVo8/CtzrULcFNEFO88XAasBGxfTZWsO0VEvSfjTgXWKeRZVCdP2S7A3yPiiULan0l3w3fuSB2t58jDIX4FnEzpqcoRMTci/l1Ke5h0YVAckuG20rt9FZgYEQ+18bj5wBvACoU0/43qvZYnXQwWzc0/VUyUtA8wHPhZg7LcTnqRJq45PkbqXf1j4Zj5wLWkttAWHSrLQUnnGk7qqnpHRDxJuogYXkmNrCfYDni4jcfUa0tvkoZmuC31Ht8AViQNuWiVpM2BlVmyPbmt9G7bAg/neUKv5jH+VzWYW6Q8/+TdwM9JPWiXFrL4b1TvNQb4hKSvSFpV0sak4VtLBLSSViINxfpuvlisx+2kbxlO+r/ikVL6v6j/fT8m6d+Spkv6egfLWoKDks41hMV3Jorm5H3Wx0j6FPAF0h+BtnBb6uUkrUEaPnFMRLzVRP7lgF+S/rMfV9jlttK7vZs0bGIL0pyhQ0jzja6WpFLe40lDvZ4DDgZ2jYiZhf1uK71URIwntZPzSD0m00lzjfYuZT2B1D5+30Jxbid9yxBgXkS8XUqfA6wsqdbb+hxprsiXgc+R5qicI+nodpRVlye6m3URScNI80muiYix1dbGeqAfA3+LiOubzP9TUq/b9s0EMdZrKG97RMTLAJKeAyaTJpxOKOQdS5qgvDZwOHCdpE+2Y+iXLWMk7QCcQ7pxcQOLF8a4WtJOEfG2pA1Ic9d2iDzY36xZEXETaVGVmhskDQB+IOmXTQ5Nb5F7SjrXHGC1OulD8j7rIyStTvrDMBM4sB1FuC31YpI2Jc0VOFnSYEmDScOyAFbLQyyK+Q8nrWRycETcVSrObaV3mwM8UAtIsttJc4aKK3AREc9HxJSIuJZ0J/Nl0ko4xbLcVnqnM4FxEXF8REyKiMtJvfQjSCtoQZpDcgMwvfD/znLAivl1refN7aRvmQMMrLPk8xBgQR4O3MiVwOqkFUU7WpaDkk42jdKYOUnvJV1sTKt7hPU6Ss+HuI40wXT3iFjQjmLqtaUVgA1xW+oN3k+amPpX0n/ic1g8r+Rp0uR3ACTtnV+PyhcaZW4rvdu/KE1UzkRaYa2uvEjCA6R2UOO/Ub3XcOAfxYSImE5auvV9OekDwF4s/j9nDml56SPy7+vmfG4nfcs00lC/jUrpS80tqiNKPztSloOSTnYD8FlJgwpp+5P+U5hcTZWsO0nqD1xBuujcOSJebGdRNwDbSFq/kPZ50qToGztWS+sBbgd2KG2n5X27kp5bgqQRwCXAryJiqQecZW4rvdt1wIckDS2kfZIU1N7X6KA8rOIjpOcm1fhvVO81k/R9v0PSB0krZs3ISV9j6f93XiCtlLQDadlWcDvpa+4kPa9m31pCvrn6OVJbaMk+pJUja3PXOlIW8rDCzpMfOPQQ8CDpAmND0kPMzo4IP3CoD5B0HnAYcBRwd2n31IhYmB9atQlpPe/fke6QTwJmRcTkXM7ypKWEF5Imlq0GnAXcEhF+IF4vJGkkcCEwKCLm5QuKv5IuKL7J4jtRkNrKY/k4t5VeTNKqpL8pzwA/AQaR/r5Mi4hP5zxfJC23eSPwLIvnlGwNfCwipuZ8/hvVS0k6ivTv/iwWzyk5kdRjv1mjlbYkzQCujIhjC2luJ71IDgp2zS+/Q1qy90f59fURsUDSCSz9wMNtgU0j4oVczp9I1zX3k3pD9ic9oPfIWPxwaJopq6EqHuTSmzfSxeZE0h2F50ir6/Srul7euu37n0G6eKy3Dct5RjfYP6lU1ntIz5uYRxob/r/AylW/R29d1nZGsuTDE0e20JbGuq30nY00FOJ60rNH5pAmtA8p7N8SGA88TwpOZwCX54uAcln+G9ULN9Jwvm/mC8b5pCD2cmDDVo6bQenhiW4nvWsjzfdo7bpEwPdJw4dfB24DtiyV8xPSqm4Lcp57gC83aIstltVoc0+JmZmZmZlVynNKzMzMzMysUg5KzMzMzMysUg5KzMzMzMysUg5KzMzMzMysUg5KzMzMzMysUg5KzMzMzMysUg5KzMzaQNJISfdIek3SHElTJf2ik8/xUUmjO7PMnkzSaEkvdUI5G+eyBpfSR0oKSQM7eo6OkjRO0o9aybN7ru+w/Hqt/L6GlfJtLWm2pNW6rsZmZt3DQYmZWZPyk2rPB24C9gK+AlwDfL6TT/VRFj9x15q3MelzG1xKHw9sR3roV2UkbQvsCPyqtbwla5He17BiYkRMAaYCR3dG/czMqtS/6gqYmS1DjgDOjYjvFdKulXRSVRWy1kXELGBW1fUAjgSuiYjZnVjmhcAZkk6NiH93YrlmZt3KPSVmZs0bDDxfToyIqP0u6W5JY8t5JI2VNDX/vrykMyQ9KWmhpGclXS1pBUkjyXfS8xCekDSpUM5mksbn4WOvSbpC0rsL+0fkYz4l6RpJ8yU9IukzkvpJOl3SS5KekXRMM29a0mGSHpD0hqQXJF0paTVJu0paJGmDUv4NcvoehbQ982fzuqSXJV0vaf0Wzrm6pPPy+d6QdGfuaWiUfwRwbX75RP4MZuR9SwzfkjQsvz5A0oWSXpX0tKSD8v5R+TuZJek0ScuVztXid9CgfoOAPYErS+nKQ7NezGVdDKxa2D8MeCC//EutTRSKGAesDny2pfObmfV0DkrMzJp3L/Dfkg6WtEaDPBcA+xTnL+Tf9wHG5KQTgAOBHwKfBr4NvAL0Iw01OjPn2y5vh+dyNgLuAAYABwEjgU1JvTUq1eNc4HbShfBM0sXwr4FBwJfy6zNbutDP5/xBLmsy8AXgm7muA0nD2J4FDi4dNhJ4Mb8XJH0ZuAp4DNgPOAR4GFizwTlXBG4BdgKOy+edBdzSwsX/vcCx+fe9SJ/bni29N+A04Dlgb+A24CJJZ5KGz30VOBsYletcq1tbvoOijwErAXeW0o8ETgTOI7WR14GfF/Y/R2orAN9icZsAICJeBf5J+qzMzJZdEeHNmzdv3prYgM2Bx4EAFpEuBk8GVi3kWRWYDxxSSPsqsBBYI7++DjizhfMcQe6AKaX/DpgOrFBIez/wNrBbfj0i1+9HhTyb5LSJhbTlSL0+p7VQj8GkeRi/aCHPqcATgPJrATOAMwrneQa4qoUyRgMvFV4fCrwJvL+Q1p8U1JzeQjm75/c5rJQ+MqcPzK+H5dcXlr63t4BHgH6F9LuBy9vyHTSo2/eAWaW0fqSg7jel9JuL7wPYLL8e0aDsscAdVf/78ObNm7eObO4pMTNrUkTcD3yQNLH9/0gX4D8EptR6RiLdub6SdCFcMxIYFxEv59f/AEbmYUKbt3KHvWgn4GpgkaT+kvqTAoIZwNalvBMKvz+af04svJdFpABr3RbOtx3p7v6FLeQZA6xPCoYAdsiva8d8AFinlTLKdgLuIQ3Dqr1PSL015ffZEe98Rvl7mwVMjoi3C3keZcnPqC3fQdG7gfIKY+8F1iYtllB0VRveA7ncFoePmZn1dA5KzMzaICIWRsS1EXFERGwCfI10p/zQQrYLgE9I2lDS+4BPsHjoFqTehf8lDcu6D3hK0lFNnH4ocDzpjn5x25B0gVs0t1DnN8tp2ZukYUiN1IaoPdcoQ0Q8DkwiDcki/7w7Iv7ZbBl1DAX+g6Xf5yEs/T47Ym7p9ZsN0oqfUVu+g6IBpN6yolog8WIpvfy6NQtp+Xs0M+vxvPqWmVkHRMQFkn4ODC+k3SrpEVIPiUhDdP5fYf8bpHkEJ0p6P/AN4GxJ0yPixhZON5t0l/78Ovs6/JyPOmo9O2u3Uv75wG+VlkzeC/hOgzKaNRuYQpq/Ula+sO9u7f0OZrP0UsW1RRPWKqWXX7dmcC7fzGyZ5aDEzKxJktaKiBdLaWsCqwEvlLKPIU9QBy4uDQl6R0Q8IulY0iTmTYAbSXfnkTQgBzA1E0iTqu+JiFiqsM73V9LE64NZPIm8nqtIPT+XkXrgLyvsm06aU3Iwi1fHas0E4DPAk+XPuxW1HqGu7DVo73cwHVhH0ooRUQusniIFJnuQvveavUrHtva+hpEWDjAzW2Y5KDEza94Dkq4h9Xq8SJo7cSxpMvhFpbwXkYZp9ac0n0LS1aQ5E1NJF/375Hy35izT8s+jJE0EXo2I6aQJ4XcD4yWNId2ZX5e0gtfYiJjUWW8UICLmSjoF+LGkFYDrgRWB3YCTIuKZnO8NSZeQAqtLI2JuoYxFkkYBl+Q8l5Imbe+Y806pc+qLSb1HkySdQZr7sgZpVaznI+KsBlWenn9+XdJlwIKIeKBB3vYaTfu+gzuA5YEPkXqBiIi3cy/bGUpPtL+NtBLYB0vHPkkODiW9ArxV+ty2Jq0kZma2zPKcEjOz5p1Muiv9P6TA5BTSClwfjYgnihkj4nngLtKqSOW72HeSlrn9A2mS81bA3oULzduA04Gjchnn5jIfJs21WEBaQvYG4CTSkKZH6QIR8VPSMKqdcl3PJQ0Xeq2U9c/555hSOhHxB9LF9nDSIgAX59/rPtAw9w7tQFqF6iTSZ/1L0tydu1uo60xSkLgXKQhotmemae39DvJxDwK7lHadDfyEFIT9ibTU8qjSsW8Ah5HayWTg77V9krYkLa3c1snxZmY9irpnBICZWd8iaXXSsKUjIuKCquvT1fId//2ADfPKXlYi6Wjg0IjYrBPL/CmwTUT4OSVmtkxzT4mZWSeSNCg/kPDXpN6ESyuuUpeS9AFJe5J6U37tgKRF5wFrSuqUAELSKqQelFM7ozwzsyp5TomZWefaCvgL6SnqX4mIBRXXp6udC2wLjCMNa7MGImK+pIOBVTqpyPWAkzt7LpGZWRU8fMvMzMzMzCrl4VtmZmZmZlYpByVmZmZmZlYpByVmZmZmZlYpByVmZmZmZlYpByVmZmZmZlYpByVmZmZmZlap/w9eX/21XX8UWAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 936x118.8 with 1 Axes>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Retrieve calibrated measurement pulse from backend\n",
    "meas = inst_sched_map.get('measure', qubits=[qubit])\n",
    "meas.exclude(channels=exc_chans).draw(time_range=[0,1000])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "\n",
    "## Step 1 (Tutorial): Find $|0\\rangle \\rightarrow |1\\rangle$ transition\n",
    "    \n",
    "You don't need to modify any code in this section. Just go through all the cells and make sure you understand the steps. Pay close attention to how `spec01_scheds` is built. You will need to build similar pulse schedules in <a href=\"#problem\">the final problem</a>. You can learn more about how to build pulse schedule in [this tutorial](https://qiskit.org/documentation/tutorials/circuits_advanced/06_building_pulse_schedules.html).\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x237.6 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.pulse import DriveChannel, Gaussian\n",
    "\n",
    "# The same spec pulse for both 01 and 12 spec\n",
    "drive_amp = 0.25\n",
    "drive_duration = inst_sched_map.get('x', qubits=[qubit]).duration\n",
    "\n",
    "# Calibrated backend pulse use advanced DRAG pulse to reduce leakage to the |2> state.\n",
    "# Here we will use simple Gaussian pulse\n",
    "\n",
    "drive_sigma = drive_duration // 4 # DRAG pulses typically 4*sigma long. \n",
    "spec_pulse = Gaussian(duration=drive_duration, amp=drive_amp, \n",
    "                        sigma=drive_sigma, name=f\"Spec drive amplitude = {drive_amp}\")\n",
    "\n",
    "# Construct an np array of the frequencies for our experiment\n",
    "spec_freqs_GHz = helper.get_spec01_freqs(center_frequency, qubit)\n",
    "\n",
    "# Create the base schedule\n",
    "# Start with drive pulse acting on the drive channel\n",
    "spec01_scheds = []\n",
    "for freq in spec_freqs_GHz:\n",
    "    with pulse.build(name=\"Spec Pulse at %.3f GHz\" % freq) as spec01_sched:\n",
    "        with pulse.align_sequential():\n",
    "            # Pay close attention to this part to solve the problem at the end\n",
    "            pulse.set_frequency(freq*GHz, DriveChannel(qubit))\n",
    "            pulse.play(spec_pulse, DriveChannel(qubit))\n",
    "            pulse.call(meas)\n",
    "            \n",
    "    spec01_scheds.append(spec01_sched)\n",
    "\n",
    "# Draw spec01 schedule\n",
    "spec01_scheds[-1].exclude(channels=exc_chans).draw(time_range=[0,1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/qiskit/compiler/assembler.py:309: RuntimeWarning: Dynamic rep rates are supported on this backend. 'rep_delay' will be used instead of 'rep_time'.\n",
      "  warnings.warn(\"Dynamic rep rates are supported on this backend. 'rep_delay' will be \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60a80c275f34b368384d4786\n",
      "Job Status: job has successfully run\n"
     ]
    }
   ],
   "source": [
    "from qiskit.tools.monitor import job_monitor\n",
    "\n",
    "# Run the job on a real backend\n",
    "spec01_job = backend.run(spec01_scheds, job_name=\"Spec 01\", **helper.job_params) \n",
    "\n",
    "print(spec01_job.job_id())\n",
    "job_monitor(spec01_job)\n",
    "\n",
    "# If the queuing time is too long, you can save the job id \n",
    "# And retrieve the job after it's done\n",
    "# Replace 'JOB_ID' with the the your job id and uncomment to line below\n",
    "#spec01_job = backend.retrieve_job('JOB_ID')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit the Spectroscopy Data\n",
    "\n",
    "We will fit the spectroscopy signal to a *Lorentzian* function of the form\n",
    "\n",
    "$$ \\frac{AB}{\\pi[(f-f_{01})^2 + B^2]} + C $$\n",
    "\n",
    "to find the qubit frequency $f_{01}$ with these fitting parameters:\n",
    "\n",
    "\n",
    "Parameter | Corresponds to\n",
    " --- | ---\n",
    " $A$ | amplitude\n",
    " $f_{01}$ | 01 frequency guess (GHz)\n",
    " $B$ | scale\n",
    " $C$ | offset\n",
    "\n",
    " \n",
    " We will use the `SpecFitter` from the `helper` module that is based on the fitters from `qiskit.ignis.characterization.fitters` library.\n",
    " \n",
    "<div class=\"alert alert-block alert-danger\">\n",
    "\n",
    "**Note:** You may need to modify the fitting parameters below to get a good fit.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spec01 frequency is 5.237505 GHz\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from resources.helper import SpecFitter\n",
    "\n",
    "amp_guess = 5e6\n",
    "f01_guess = 5\n",
    "B = 1\n",
    "C = 0\n",
    "fit_guess = [amp_guess, f01_guess, B, C]\n",
    "\n",
    "fit = SpecFitter(spec01_job.result(), spec_freqs_GHz, qubits=[qubit], fit_p0=fit_guess)\n",
    "fit.plot(0, series='z')\n",
    "f01 = fit.spec_freq(0, series='z')\n",
    "print(\"Spec01 frequency is %.6f GHz\" % f01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare your result with the calibrated qubit frequency from the backend! If things are working properly, your result should be very close to the calibrated value ($\\pm$1 MHz)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Qubit frequency error is 1.109520 MHz\n"
     ]
    }
   ],
   "source": [
    "# Retrieve qubit frequency from backend properties\n",
    "f01_calibrated = backend.properties().frequency(qubit) / GHz \n",
    "f01_error = abs(f01-f01_calibrated) * 1000 # error in MHz\n",
    "print(\"Qubit frequency error is %.6f MHz\" % f01_error) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "🎉 Congratulations! You have successfully ran your first pulse experiment on a real quantum system and analyzed the data. This was not possible without access to an advanced research lab before [less than two years ago](https://www.ibm.com/blogs/research/2019/12/qiskit-openpulse/), when we released Qiskit Pulse and provided pulse access in our open quantum system `ibmq_armonk`. Now you can do experimental quantum physics on your laptop in the comfort of your home. This is incredible!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "\n",
    "## Step 2 (Tutorial): Calibrate X-180 pulse amplitude using Rabi oscillation\n",
    "\n",
    "You don't need to modify any code in this section. Just go through all the cells and make sure you understand the steps. \n",
    "</div>\n",
    "\n",
    "### Build Rabi schedule from measured frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x237.6 with 1 Axes>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_rabi_amp = 0.75\n",
    "rabi_amps = helper.get_rabi_amps(max_rabi_amp)\n",
    "\n",
    "rabi_scheds = []\n",
    "for ridx, amp in enumerate(rabi_amps):\n",
    "    with pulse.build(name=\"rabisched_%d_0\" % ridx) as sched: # '0' corresponds to Rabi\n",
    "        with pulse.align_sequential():\n",
    "            pulse.set_frequency(f01*GHz, DriveChannel(qubit))\n",
    "            rabi_pulse = Gaussian(duration=drive_duration, amp=amp, \\\n",
    "                              sigma=drive_sigma, name=f\"Rabi drive amplitude = {amp}\")\n",
    "            pulse.play(rabi_pulse, DriveChannel(qubit))\n",
    "            pulse.call(meas)\n",
    "    rabi_scheds.append(sched)\n",
    "\n",
    "# Draw rabi schedule\n",
    "rabi_scheds[-1].exclude(channels=exc_chans).draw(time_range=[0,1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/qiskit/compiler/assembler.py:309: RuntimeWarning: Dynamic rep rates are supported on this backend. 'rep_delay' will be used instead of 'rep_time'.\n",
      "  warnings.warn(\"Dynamic rep rates are supported on this backend. 'rep_delay' will be \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60a80c753d517f191dd23597\n",
      "Job Status: job has successfully run\n"
     ]
    }
   ],
   "source": [
    "# Run the job on a real device\n",
    "rabi_job = backend.run(rabi_scheds, job_name=\"Rabi\", **helper.job_params)\n",
    "\n",
    "print(rabi_job.job_id())\n",
    "job_monitor(rabi_job)\n",
    "\n",
    "# If the queuing time is too long, you can save the job id \n",
    "# And retrieve the job after it's done\n",
    "# Replace 'JOB_ID' with the the your job id and uncomment to line below\n",
    "#rabi_job = backend.retrieve_job('JOB_ID')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit the Rabi Data\n",
    "\n",
    "We will fit the Rabi signal to a sinusoidal function of the form\n",
    "\n",
    "$$ a \\cos(2\\pi f x + \\phi) + c $$\n",
    "\n",
    "to find the Rabi period $T = 2\\pi/f$ with these fitting parameters:\n",
    "\n",
    "\n",
    "Parameter | Corresponds to\n",
    " --- | ---\n",
    " $a$ | amplitude\n",
    " $f$ | Rabi drive frequency\n",
    " $\\phi$ | phase offset\n",
    " $c$ | offset\n",
    "\n",
    "\n",
    "We will use the `RabiFitter` from the `qiskit.ignis.characterization.calibration.fitters` library.\n",
    "\n",
    "<div class=\"alert alert-block alert-danger\">\n",
    "\n",
    "**Note:** You may need to modify the fitting parameters below to get a good fit.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pi amplitude is 0.205\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from qiskit.ignis.characterization.calibrations.fitters import RabiFitter\n",
    "\n",
    "amp_guess = 5e7\n",
    "fRabi_guess = 2\n",
    "phi_guess = 0.5\n",
    "c_guess = 0\n",
    "fit_guess = [amp_guess, fRabi_guess, phi_guess, c_guess]\n",
    "\n",
    "fit = RabiFitter(rabi_job.result(), rabi_amps, qubits=[qubit], fit_p0=fit_guess)\n",
    "fit.plot(qind=0, series='0')\n",
    "x180_amp = fit.pi_amplitude()\n",
    "print(\"Pi amplitude is %.3f\" % x180_amp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id='problem'></div>\n",
    "<div class=\"alert alert-block alert-success\">\n",
    "\n",
    "## Step 3 (Problem): Find $|1\\rangle \\rightarrow |2\\rangle$ transition frequency\n",
    "    \n",
    "In order to observe the transition between the $|1\\rangle$ and $|2\\rangle$ states of the transmon, you need to: \n",
    "\n",
    "1. Apply an $X_\\pi$ pulse to transition the qubit from $|0\\rangle$ to $|1\\rangle$.\n",
    "1. Apply a second pulse with varying frequency to find the $|1\\rangle \\rightarrow |2\\rangle$ transition.\n",
    "\n",
    "</div>\n",
    "<div class=\"alert alert-block alert-danger\">\n",
    "The cell below is the only one you need to modify in the entire notebook. \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define pi pulse\n",
    "x_pulse = Gaussian(duration=drive_duration,\n",
    "                     amp=x180_amp, \n",
    "                     sigma=drive_sigma,\n",
    "                     name='x_pulse')\n",
    "\n",
    "def build_spec12_pulse_schedule(freq, anharm_guess_GHz):    \n",
    "    with pulse.build(name=\"Spec Pulse at %.3f GHz\" % (freq+anharm_guess_GHz)) as spec12_schedule:\n",
    "        with pulse.align_sequential():            \n",
    "            # WRITE YOUR CODE BETWEEN THESE LINES - START\n",
    "            pulse.set_frequency(freq*GHz+anharm_guess_GHz, DriveChannel(qubit))\n",
    "            pulse.play(x_pulse, DriveChannel(qubit))\n",
    "            pulse.call(meas)\n",
    "            # WRITE YOUR CODE BETWEEN THESE LINES - END\n",
    "    return spec12_schedule"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The anharmonicity of our transmon qubits is typically around $-300$ MHz, so we will sweep around that value. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x237.6 with 1 Axes>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anharmonicity_guess_GHz = -0.35 # your anharmonicity guess\n",
    "freqs_GHz = helper.get_spec12_freqs(f01, qubit)\n",
    "\n",
    "# Now vary the sideband frequency for each spec pulse\n",
    "spec12_scheds = []\n",
    "for freq in freqs_GHz:\n",
    "    spec12_scheds.append(build_spec12_pulse_schedule(freq, anharmonicity_guess_GHz))\n",
    "\n",
    "# Draw spec12 schedule   \n",
    "spec12_scheds[-1].exclude(channels=exc_chans).draw(time_range=[0,1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/qiskit/compiler/assembler.py:309: RuntimeWarning: Dynamic rep rates are supported on this backend. 'rep_delay' will be used instead of 'rep_time'.\n",
      "  warnings.warn(\"Dynamic rep rates are supported on this backend. 'rep_delay' will be \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60a81c9c5e38ea25ba4cd9c6\n",
      "Job Status: job has successfully run\n"
     ]
    }
   ],
   "source": [
    "# Run the job on a real device\n",
    "spec12_job = backend.run(spec12_scheds, job_name=\"Spec 12\", **helper.job_params)\n",
    "\n",
    "print(spec12_job.job_id())\n",
    "job_monitor(spec12_job)\n",
    "\n",
    "# If the queuing time is too long, you can save the job id \n",
    "# And retrieve the job after it's done\n",
    "# Replace 'JOB_ID' with the the your job id and uncomment to line below\n",
    "#spec12_job = backend.retrieve_job('JOB_ID')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit the Spectroscopy Data\n",
    "\n",
    "<div id='fit-f12'></div>\n",
    "\n",
    "We will again fit the spectroscopy signal to a Lorentzian function of the form\n",
    "\n",
    "$$ \\frac{AB}{\\pi[(f-f_{12})^2 + B^2]} + C $$\n",
    "\n",
    "to find the frequency of the $|1\\rangle \\to |2\\rangle$ transition $f_{12}$ with these fitting parameters:\n",
    "\n",
    "Parameter | Corresponds to\n",
    " --- | ---\n",
    " $A$ | amplitude\n",
    " $f_{12}$ | 12 frequency guess (GHz)\n",
    " $B$ | scale\n",
    " $C$ | offset\n",
    " \n",
    " \n",
    "<div class=\"alert alert-block alert-danger\">\n",
    "\n",
    "**Note:** You may need to modify the fitting parameters below to get a good fit.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spec12 frequency is 4.886692 GHz\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "amp_guess = 2e7\n",
    "f12_guess = f01 - 0.3\n",
    "B = .1\n",
    "C = 0\n",
    "fit_guess = [amp_guess, f12_guess, B, C]\n",
    "\n",
    "fit = SpecFitter(spec12_job.result(), freqs_GHz+anharmonicity_guess_GHz, qubits=[qubit], fit_p0=fit_guess)\n",
    "fit.plot(0, series='z')\n",
    "f12 = fit.spec_freq(0, series='z')\n",
    "print(\"Spec12 frequency is %.6f GHz\" % f12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grading your answer for ex4. Please wait...\n",
      "\n",
      "Congratulations 🎉! Your answer is correct.\n",
      "Feel free to submit your answer.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check your answer using following code\n",
    "from qc_grader import grade_ex4\n",
    "grade_ex4(f12,qubit,backend_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Submitting your answer for ex4. Please wait...\n",
      "Success 🎉! Your answer has been submitted.\n"
     ]
    }
   ],
   "source": [
    "# Submit your answer. You can re-submit at any time.\n",
    "from qc_grader import submit_ex4\n",
    "submit_ex4(f12,qubit,backend_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating $E_J/E_c$\n",
    "\n",
    "Modifying the equations in the introduction section, we can calculate $E_c$ and $E_J$ using $f_{01}$ and $f_{12}$ obtained from the pulse experiments:\n",
    "\n",
    "$$\n",
    "E_c = -\\delta = f_{01} - f_{12} \\qquad E_J = \\frac{(2f_{01}-f_{12})^2}{8(f_{01}-f_{12})}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Ec = f01 - f12\n",
    "Ej = (2*f01-f12)**2/(8*(f01-f12))\n",
    "print(f\"Ej/Ec: {Ej/Ec:.2f}\") # This value is typically ~ 30 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Additional information\n",
    "\n",
    "**Created by:** Nick Brønn, Junye Huang\n",
    "\n",
    "**Version:** 1.0.0"
   ]
  }
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