{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f1c7ce88",
   "metadata": {},
   "source": [
    "# IBM Optimizations using Qiskit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "varied-shame",
   "metadata": {},
   "source": [
    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Infleqtion/client-superstaq/blob/main/docs/source/optimizations/ibm/ibmq_compile_qss.ipynb) [![Launch Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/Infleqtion/client-superstaq/HEAD?labpath=docs/source/optimizations/ibm/ibmq_compile_qss.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8c25005",
   "metadata": {},
   "source": [
    "Below is a brief tutorial on Superstaq optimizations for IBM Quantum superconducting qubit devices. For more information on IBM Quantum, visit their website [here](https://www.ibm.com/quantum)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9a7d9b5",
   "metadata": {},
   "source": [
    "## Imports and API Token\n",
    "\n",
    "This example tutorial notebook uses `qiskit-superstaq`, our Superstaq client for Qiskit; you can try it out by running `pip install qiskit-superstaq[examples]`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4316dd2c-bad2-42f6-ab39-383a1ba6a6d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    import qiskit_superstaq as qss\n",
    "except ImportError:\n",
    "    print(\"Installing qiskit-superstaq...\")\n",
    "    %pip install --quiet 'qiskit-superstaq[examples]'\n",
    "    print(\"Installed qiskit-superstaq.\")\n",
    "    print(\"You may need to restart the kernel to import newly installed packages.\")\n",
    "    import qiskit_superstaq as qss\n",
    "\n",
    "import random\n",
    "\n",
    "import numpy as np\n",
    "import qiskit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b4d8cf9",
   "metadata": {},
   "source": [
    "To interface Superstaq via Qiskit, we must first instantiate a provider in `qiskit-superstaq` with `SuperstaqProvider()`. We then supply a Superstaq API token (or key) by either providing the API token as an argument of `qss.SuperstaqProvider()` or by setting it as an environment variable (see more details [here](https://superstaq.readthedocs.io/en/latest/get_started/basics/basics_qss.html#Set-up-access-to-Superstaq%E2%80%99s-API))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8d8d329d-5c52-44bd-9e35-3052268a4530",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Provide your Superstaq API key using the \"api_key\" argument\n",
    "provider = qss.SuperstaqProvider()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4828ee8",
   "metadata": {},
   "source": [
    "## Single Circuit Compilation\n",
    "\n",
    "Let us start by creating an example Qiskit circuit that we will then compile and optimize for the 156-qubit IBM Quantum `Kingston` processor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1e68dcda-f84a-4b93-aea0-f64c1a5108f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 663.998x284.278 with 1 Axes>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a two-qubit qiskit circuit\n",
    "rng = np.random.default_rng(random.getrandbits(128))\n",
    "theta = rng.uniform(0, 4 * np.pi)\n",
    "circuit = qiskit.QuantumCircuit(2)\n",
    "circuit.cx(0, 1)\n",
    "circuit.rz(theta, 1)\n",
    "circuit.cx(0, 1)\n",
    "circuit.measure_all()\n",
    "\n",
    "# Draw circuit for visualization\n",
    "circuit.draw(output=\"mpl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2116e292",
   "metadata": {},
   "source": [
    "We will now compile the above circuit to IBM's `Kingston` processor and visualize the differences by drawing the compiled circuit. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "135bc08f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1661.94x284.278 with 1 Axes>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compile with ibmq compile\n",
    "compiler_output = provider.ibmq_compile(circuit, target=\"ibmq_kingston_qpu\")\n",
    "\n",
    "# Call circuit from the compiler output to get the corresponding output circuit\n",
    "output_circuit = compiler_output.circuit\n",
    "\n",
    "# Visualize the compiled circuit\n",
    "output_circuit.draw(\"mpl\", idle_wires=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa165427",
   "metadata": {},
   "source": [
    "The resulting output is now a circuit compiled to `Kingston`'s native operations. With Superstaq compilation, you can also observe the gates and gate times for the compiled circuit using Qiskit’s timeline drawer (see [Qiskit Timeline Visualizations Documentation](https://docs.quantum.ibm.com/api/qiskit/qiskit.visualization.timeline_drawer)). \n",
    "\n",
    "Additionally, to ensure that the drawer has all the information on the instruction durations, we must also provide the `qiskit.transpiler.Target` of the corresponding backend the timeline is being generated for. It can be retrieved like so: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "12e7cd22",
   "metadata": {},
   "outputs": [],
   "source": [
    "backend_target = provider.get_backend(\"ibmq_kingston_qpu\").target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3ca9f750",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x300 with 1 Axes>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The pulse gate circuit is obtained via the `pulse_gate_circuit` attribute\n",
    "pulse_circuit = compiler_output.pulse_gate_circuit\n",
    "\n",
    "# Estimate circuit duration for the timeline drawer\n",
    "pulse_circuit.duration = pulse_circuit.estimate_duration(backend_target, unit=\"dt\")\n",
    "\n",
    "custom_style = {\"formatter.general.fig_width\": 30, \"formatter.general.fig_unit_height\": 1}\n",
    "style = qiskit.visualization.timeline.IQXStandard(**custom_style)\n",
    "\n",
    "qiskit.visualization.timeline_drawer(\n",
    "    pulse_circuit, idle_wires=False, style=style, target=backend_target\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30c3a395",
   "metadata": {},
   "source": [
    "## Multiple Circuits Compilation\n",
    "\n",
    "All the functionalities we have seen so far can also be used on a multiple-circuit input as well. To illustrate this, let us create a different example two-qubit circuit: a Bell-state circuit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1520302f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4aT0RWZSvry/eeecdqcsgMhm2SInIoqqqqnD37l1UVVVJXQqRSTBIiciiMjMzMWvWLGRmZkpdCpFJ8NIuERE1IwgCdDqd1GUYRKFQQCaTWfy8DFIiImpGp9MhISFB6jIMMmvWLCiVlo81XtolIiISgUFKREQkAi/tEpFF9e3bF+fPn5e6DCKTYYuUiIhIBAYpkYUIggBBEBq/flTduXMHixYtwp07d6QuhcgkeGmXyEyqa3TY//2/kHTpPi6kaZFyLR/lFbUAgJx/VyBwyl4M7eeOYaHumDHRH30Cu0pbsIVUVFTg6tWrqKiokLoUIpNgkBKZWE5eObbvTcefEzKQV1DZ6nZZOWXIyilDwrEs/L8/XcATI33wu7kh+MVEf0nuhSMi4zBIiUxEEATs3Hcdr206h5KyGoP3P34uB8fP5eCJkT74v9Xh8PfhpO5E1oB9pEQmUFBchajffofn3j5tVIg+7Pi5HPSfuQ979t8wUXVEZE5skRKJlJdfgYjFh3HlRqHJjln2oAYL3jgFbVEVXn2mv8mO2xF4e3tj9erV8Pb2lroUIpNgkBKJUFxajckvHjFpiD7stY3n4GivxOLZfc1yfCl06dIFkZGRUpdBVqiyshLV1dVwcXGRupQmGKREIsSu/xGXMwrMeo4la3/AqAGeGBCsNut5LKWwsBDHjh1DREQEXF1dpS6HzKyyshI3b95EZmYmbt++jZKSEtTW1sLGxgZubm4IDAxEUFAQgoKC2pwnt7KyEuvXr0d5eTlWrVrVocKUQUpkpIOn/oVPEw3rx0yOnw4vdwdotA8wfF6iXvvU1Nbh16tO4dxn02FjY/3DGu7fv4+NGzciLCyMQdqJ3blzB0ePHkVSUlKbz549efIkgPorFRMnTsQTTzwBd3f3Jts0hGh6ejoAYMuWLXjzzTc7zOh26/+ttAJarRYrVqxAr169oFKp0L17d8TGxqK8vBzPPvssZDIZ3n//fanLJANUVevwwjtnDN7Py90Bft0c4eXuYNB+P13Lx7b4VIPPR2RpRUVF2Lx5M+Li4nDs2DG9H+BeXFyMffv2YenSpfj0008b9/t5iDo4OOBXv/pVhwlRgC1Ss0tJSUFkZCQ0Gg0cHR3Rr18/5OTkYOvWrbh16xYKCuovCw4aNEjaQskgCUezcC/vgUXPue1vaYidHwqFgp9/qWM6e/Ysdu7cibKyssZlKpUKo0ePRp8+fRAUFARPT08olUpUV1fj3r17yMzMxNWrV3Hx4kXodDoIgoDDhw/jp59+wnPPPYeEhIQmIfrGG2+gZ8+eUr3FFjFIzUir1SI6OhoajQbLli3DW2+9BWfn+nsDN2zYgLi4OCiVSshkMgwYMEDiaskQH+xNs/g5s3LKcPh0NqaN72HxcxO1Z9++fdi7d2/jaxcXF8ycORPjxo2Dg0PzKzBKpRK9e/dG7969MXnyZBQUFODYsWPYv38/ampqoNFosGbNmsbtO2qIAry0a1Yvv/wysrOzsWTJEmzatKkxRAFgxYoVGDhwIGpraxEQENChOs6pbbezS3A2JU+Sc/9l/01JzmtKDg4OGDlyZIt/XMk6ffPNN01CdNSoUdi0aROmTJmi9/dZrVbjqaeewrp165qFpZ2dXYcNUYBBajbp6enYu3cv3N3d8e6777a4zdChQwEAAwcObFzWELwjRoyAnZ1dh+oHoHrnr/xbsnMnX5Xu3KbSo0cPbNu2DT16sGXdGVy8eBHx8fGNr+fPn49XXnnF6MaBm5sbbG1tmyyrra2Fvb29qDrNiUFqJvHx8airq8P8+fPh5OTU4jYNPxgPB+nNmzeRkJAALy8vDB8+3CK1kmEupGolO3dWThnyi1qfv9ca6HQ6lJWVQafTSV0KiVRWVoY///nPja/nzZuH6Ohoo4/384FFCoUCQP3PzI4dO1BXVyeuYDNhkJrJiRMnAAATJkxodZvs7GwATYN03LhxyM3NRWJiIiIiIsxbJBkl9VaRpOdPk/j8Yt24cQMTJ07EjRucAtHa7dmzB0VFRQCAwYMHY/r06UYfq6XRuatWrUK3bt0AANevX8eRI0dE12wOHGxkJg3PWvT3929xfW1tLc6cqb994uEglctN/9lm2LBh0Gg0Jj/uo+rfzr8BbAJaXNdwn2hrvNztG/+9e3Rum+dp7V7TGbPnQlXTcfpKZ8+ebdD2eXn1/cuHDx/GxYsX9dpnxowZBtfV0cz4zStwdHJBriYXfn5+zV53NLa2tq12SwH1gylPnToFoD70nn/+eaO7oloK0YY+0RdffBHvvPMOBEHA/v37MXny5FYnbggODkZ1dbVRNXh5eeHChQtG7csgNZPy8nIAaPWZi3v37oVWq4WzszMCAwPNWotGo8G9e/fMeo5HSmAVYNPyqob7RNujVMj12q4l+VotUNZxvp8NP+v6avidqKio0HvfzvDzW/efS9l1Oh3u3bvX7HVHY2dn1+b648ePNz6gPjIyEmq1cTNvtRWiABASEoKhQ4fiwoULKCwsxKVLlzBixIgWj5WTk6P3faumxCA1Ey8vr8Zv+ujRo5usy83NxfLlywEAAwYMMPuAIi8vL7Me/1GjtZWhtV9Vjbbte0u93O2hVMhRq6uDRtv2g61bO5a7mwvsuvjqU6pFODoa9oGgITzt7e313tfXt+O8X2PJ/9PfJ1co4Ovr2+x1R/PzAT8Pq6ura+y+ksvleOKJJ4w6R3sh2mDSpEmNrcXjx4+3GqQ+Pj6iWqTGYpCaSUREBNLT07F+/XpMmjQJwcHBAIDk5GQ888wz0GrrB6xYYiIGYy9XUMuW//E8Nn16pcV17U37d/foXPh1c4RGW4Hukz436vwZl/8BdZe2WwuWlJycbND2165dQ3x8PCIjI9G3r36T8b/33ntGVNaxrP3gM5SUlcPbyxvZ2dnNXnc0tbW1SEhIaHGdRqNBcXExgPquKWNao/qGKACEhYVBrVajoKAAGRkZqKura7Eb7Pr1623O12suHGxkJitWrICbmxvu3r2L0NBQhIWFoXfv3hgxYgSCgoIwceJEAE37R8k6DO3nJtm5A32dO1SIGqNXr1749ttv0atXL6lLISNlZmY2fm3M99GQEAXqW70N6yorK5Gbm2tE1ebDIDUTPz8/JCUlYerUqVCpVMjKyoJarcZHH32EgwcP4vr16wAYpNZoRJiHhOd2b3+jDk6pVMLV1VWSlgOZRlZWVuPXQUFBBu1raIi2dJ6Hg7wj4E+yGYWEhODAgQPNlpeVlSErKwtyuRz9+3euhzY/CoL8XBA+uBtO/3Tf4udeEN3b4uc0tezsbGzZsgWvvvpqhxytSu17eC7dnz+ppS3GhihQP1FDA0MHuJkbg1QCqampEAQBwcHBLU6f9dVXXwEA0tLSmrwOCAjAsGHDLFcoteq3T4dYPEgDfZ3x5JiONyjFUGVlZUhKSsLzzz8vdSlkpOjoaIwdOxY1NTUGBWlGRgauXbsGwPC5c/v374+4uDjY2trC29vbqLrNhUEqgStX6geqtHZZd86cOS2+XrhwIXbv3m3W2kg/syYFoPt7jrirsdwn45d/2Y9PfqEOwcfHBz4+PgbvN3DgQCxduhS7du3C66+/btDcuWq12uhbbMyNQSqB9oK04d4s6rhsbRT46A9jEfW77yxyvmGh7lgyr59FzkVkTmPGjMGgQYM61UML+PFWAu0FKVmHyMe6Y9GMYIP20WgfIPt+ebv3mz7M1kaO3f8zDkolf12pc+hMIQqwRSqJhhuZyfptWT4SKdfycSk9X6/t27vPtCUfrhqL0F6uBu/XUXl4eCA2NhYeHtKNfiYyJX7EJRLBxckWRz58EoP6mqfvZuvrowxu9XZ0bm5umD9/fpNRmETWjEFKJJKH2h7/+CQK08Z1N9kxXZxs8Ld1j2PpL0NNdsyOoqSkBMeOHUNJSYnUpRCZBIOUyAS6utghcdskfLpmHLo6tz5HqT6mjPXD1a9nYl6U/iMarUlOTg5WrlyJnJwcqUshMgn2kRKZiEwmw4LpvfHkWD/s+CIdHydkICdP/0FFU8b64XdzQzB1XHezP8iAiEyHQUpkYt3c7PHWS0Ow8rlBOHT6Lk5fuo+LaVr8dC0fRaX1T6ZQKGTo6eeCof3cMLSfO34x0R89u7tIXDkRGYNBSmQmNjZyxEzwR8yE/z7cva5OgE4nwMaGvSpEnQWDlMiC5HIZ5PJH+7KtnZ0d+vTp0+6Do4msBYOUiCwqMDAQe/bskboMIpPh9SUiIiIRGKREZFEZGRkYO3YsMjIypC6FyCQYpERkUYIgoKamhg9noE6DfaRERNSMQqHArFmzTHa8jR/tRWl5OZwdHbH8haebvTYFhUJhkuMYikFKRETNyGQyKJWmiwgBQJ1Q/69SqWz22prx0i4REZEI1v0xgIisTkBAAOLj4+Hr6yt1KUQmwSAlIotSqVTo2bNzTshPjyZe2iUii8rNzcWaNWuQm5srdSlEJsEgJSKLKi4uRmJiIoqLi6UuhcgkGKREREQiMEiJiIhEYJASERGJwFG7RCTK8OHDDdrez88Pb731FiIiIuDt7W2mqogsh0FKRBbl7e2Nt99+W+oyiEyGl3aJiIhEYJASERGJwCAlIiISgUFKREQkAoOUiIhIBAYpERGRCAxSIiIiERikREREIjBIiYiIRGCQEhERicAgJSIiEoFBSkREJAKDlAAAGzduxOjRo+Hq6oquXbsiPDwcR44ckbosImrDoUOHMGjQINjZ2SEgIACbN2+WuiSLOnXqFGJiYuDv7w+ZTIY1a9ZIUgeDlAAAJ06cwKJFi/CPf/wD58+fx5gxYzBt2jScOXNG6tKIqAUXLlxATEwMIiMjkZKSgrfffhsrV67Ejh07pC7NYsrKytCvXz9s2LABXl5ektXBx6gRAODw4cNNXm/YsAFHjhzB119/jbFjx0pUFRG1ZvPmzRg+fDjeffddAEBISAhSU1Oxbt06vPjiixJXZxlRUVGIiooCAMTFxUlWB4OUWlRXV4eSkhI4OjpKXQqRVXlQUYlsjbbZ8lqdrvHf65nZzV4/zKebG5wc7Ns8z5kzZ/Dss882WTZlyhRs2rQJ2dnZ8PPzE/M2RMm8m4uaWl2TZYa8fycHFXy6uVumWBNgkFKL1q5di6KiIixevFjqUoisip2tLb47lYxszb9bXP+gohI7vzjU6utu7q5YsnBGu+fJzc1tdjmz4XVubq6kQZpzPx/7j59tcV17718G4Pl508xdokmxj5Sa2b59O9auXYuvvvpK0l9GImukUMjx1LQJUCoVhu8rr9/XRmndbZzRQ0PRy9/XqH3Dhw9AUA8fE1dkXgxSamLTpk1Yvnw5EhMTERERIXU5RFbJ060roh4fafB+EeFD4avnJU1vb29oNJomy+7fv9+4TkpymQxzosZDZWdr0H7d3F0xedwwM1VlPgxSavTmm29i9erVOHToEEOUSKRRQ0LRO0D/Vpm/bzeMHzlQ7+3Hjh2Lb7/9tsmyI0eOwN/fv0NcSeri4oRfTA7Xe3uFXI6noydaZWucQUoAgFdeeQUbN27Enj170KdPH2g0Gmg0GhQXF0tdGpFVkstkmB31OOxVdu1ua2ujxFNTJ0Au1/9P8quvvorz58/jjTfewLVr1/Dpp59i27ZteP3118WUbVKD+vXCgL5Bem076bFh8PF0M+j4ZWVlSElJQUpKCqqrq6HRaJCSkoKbN28aU67RZIIgCBY9I3VIMpmsxeULFy7E7t27LVsMUSdyOe0m4vefaHObmVPGYcTAvgYf++DBg1i5ciWuXbsGLy8vxMbG4rXXXjO2VLN4UFGJ93Z+hZKyB61uE+DnhcXzphn0QQIAvv/+e0yYMKHZ8vHjx+P77783tFSjMUjJIA8qKlFQVAo/bw+pSyGyGvGJx3E5/VaL60J69cCCmU+2+mG2M7iemd1kZO7DbG1tEPubWXDr6mLhqkyHl3bJIEnJV/D+X/bh4IkfpS6FyGrETA6Hi1Pze7Id7VWYOWVcpw5RAAgO9MPoIaEtroueONqqQxRgkJIByisqcfbiVQD1l2KISD8OKjvMiRrfbPnMKePg7OggQUWWF/n4SHiouzRZFtLLH8MG9JGoItPpNEEqk8kaP9Xt378fjz32GFxcXODu7o7Zs2fj1q3/XlY5cOAAxo8fj65du8LFxQUxMTG4ceNGq8euqqrCn/70J4wZMwZdu3aFSqVCnz59sHz5cmi1zWcwAYBz584hLi4Ow4cPh5eXF+zs7ODr64unnnoKycnJrZ7r0KFDiIqKgqenJ2xsbODm5oaQkBAsWrRI8nlvTydfQVV1Dbw93dCvt7+ktRBZm96Bfhgz9L+tsqFhwQgNDpCuIAuztVHiqWkTIP/P32lHBxVmdZbWuNBJABAACO+//74AQPDx8REGDx4s2NvbCwAEX19fIS8vT9iyZYsAQPD29m6y3svLS8jLy2t2XI1GIwwePFgAIMjlcsHf318ICwsT7OzsBABCjx49hFu3bjXbr2fPngIAQa1WC6GhocLgwYMFNzc3AYCgVCqFr776qtk+H3zwQeP7cHNzE4YMGSKEhIQITk5OAgDhhRdeMMv/O32UPagQ3ty8U4hb95FwNSNTsjqIrFlVdY2w6eO9wroP/yZUVFZJXY4kjiZdqP87cj1T6lJMptMMNmr4VOPg4IAPP/wQCxYsAAAUFBRgypQpSE5OxrRp03D8+HHs2LGjcX1+fj6efPJJXLx4EXFxcVi3bl3jMQVBwIQJE3Dy5ElMnToV77//PgICAgDUD7uOjY3Fzp07MXr0aJw923Q6rL/85S8YM2YMevXq1bisrq4O33zzDRYsWAClUom7d+/CyckJAFBbWwtPT08UFhZi+/btWLx4MRQKRWMdSUlJyM/Px4wZ7U8d9nPbPv0apWUVBu/3sKrqalRV10Aul7c7BygRtU6n00EAoFQYPvNRZyAIAmpqamFrayN1KU04O9lj6cKZRu3b6YJ06dKl2Lp1a5N1R44cQWRkZKvrDx8+jKioKAwYMACXL19uXH7o0CFMnToV/fv3R3JyMlQqVZP9dDodRowYgUuXLuH06dN6PyXlD3/4A9asWYP4+HjMnTsXAKDRaODt7Q1XV1cUFBQY9ubbsfaDz1BSVm7SYxIRdSYuTo5Y+bv5Ru1rfVNItOO5555rtmzIkCF6rb99+3aT5QkJCQDq76X8eYgCgEKhwPTp03Hp0iV8//33zYL0xo0b+Pzzz3H58mXk5+ejpqYGAJCXlwcASElJaQxSDw8PqFQqFBUV4ejRo5g0aZLe77k9zk7iWpBsjRJRZyfm72SnC9KePXs2W+bh4dHmek9PTwD1l2sf9s9//hMAsGvXLvz9739v8XwNc1veu3evyfI//vGPeP3111FbW9tqrfn5+Y1fKxQKxMbGYv369Zg8eTKGDBmCiIgIhIeHY/z48XBxMX54uLGXK4D6kbobdsQDAObHRDxSgyOIiPTR6S7ttvZ2jFnfu3dvvaeaengGoDNnziA8PBwKhQKrV69GTEwMAgIC4OjoCJlMhp07d+LZZ59tNmtQXV0dtm/fjg8++ADXrl1rXG5nZ4df/vKX2LRpE9RqtV71PExMHylbo0T0KBDTR9rpWqSm1DAQKDExEdHR0Xrvt2fPHgDAsmXL8MYbbzRb/3BL9GFyuRxLlizBkiVLkJ2djaSkJBw9ehRffPEFdu3ahbt37+Lo0aMGv4/SsgrRfaR1dXXsZyUiagGDtA2hoaFISUnB1atXDQrSzMxMAEB4eMtPPvjxx/ZnBfLz88O8efMwb948LFu2DGFhYTh27BgyMzMRGBiody2A8df+2RolokcF+0jNZPbs2fjss8/w8ccfY+nSpY0t1PbY29d/Q37+rECgfgDSgQMHDKojNDQUXbp0QVFREXJycgwOUmMuV7BvlIhIP51mZiNziImJwfjx45GVlYXJkyfj6tWrTdbX1dXhhx9+wIsvvthkxO9jjz0GAHj33XebzKiUmpqK6OjoFp9wkJaWhueffx4//vhjk35anU6H9957D0VFRVCpVAgNbXm+SlPjLEZERPrhYKN21mu1WsTExDROuODv7w8vLy9UVFTg1q1bKC+v7zdMT09H3771j0EqLS3FkCFDcPPmTdjY2KBPnz6oq6tDeno6vL298dvf/harVq1qMtgoJSUFgwcPBgA4OzujZ8+eUCgUyMrKauxT3b59O1566SWj/v8YoqE1WlVdg2dmTGZrlIioDWyRtsPd3R0nT57E7t27MWnSJJSXl+PChQvIzMxEr169EBsbi5MnTyI4OLhxH2dnZ5w+fRqLFi2Cq6srMjIyUFZWhhdeeAGXLl2Cr69vs/MEBwfjk08+wdNPPw1vb2/cvn0bly9fhkqlwpw5c5CUlGSREAWAyqpq9PDxZGuUiEgPnaZFSqZXWVUNlZ2t1GUQEXVoDFIiIiIReGmXiIhIBAYpERGRCAxSIiIiERikREREIjBIiYiIRGCQEhERicAgJSIiEoFBSkREJAKDlIiISAQGKRERkQgMUiIiIhEYpERERCIwSImIiERgkBIREYnAICUiIhKBQUpERCQCg5SIiEgEBikREZEIDFIiIiIRGKREREQiMEiJiIhEYJASERGJwCAlIiISgUFKREQkAoOUiIhIBAYpERGRCAxSIiIiERikREREIjBIiYiIRGCQEhERicAgJSIiEoFBSkREJAKDlIiISAQGKRERkQgMUiIiIhEYpERERCIwSImIiERgkBIREYnAICUiIhLh/wMolq4sRgvBHQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 580.387x284.278 with 1 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create second circuit\n",
    "bell_circuit = qiskit.QuantumCircuit(2)\n",
    "bell_circuit.h(0)\n",
    "bell_circuit.cx(0, 1)\n",
    "bell_circuit.measure_all()\n",
    "\n",
    "# Draw second circuit for visualization\n",
    "bell_circuit.draw(\"mpl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c055c11",
   "metadata": {},
   "source": [
    "By passing multiple circuits as a list to the `ibmq_compile` endpoint, we can compile all of them individually with a single call to the endpoint. This will return all the corresponding compiled circuits and pulse gate circuits back as a list, like so:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9fa01c3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create list of circuits\n",
    "circuit_list = [circuit, bell_circuit]\n",
    "\n",
    "# Compile list of circuits\n",
    "compiler_output_list = provider.ibmq_compile(circuit_list, \"ibmq_kingston_qpu\")\n",
    "\n",
    "# The list of compiled output circuits is stored in the `circuits` attribute instead of `circuit`. Likewise for\n",
    "# pulse sequences.\n",
    "output_circuits = compiler_output_list.circuits\n",
    "pulse_gate_circuits = compiler_output_list.pulse_gate_circuits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "736f6d27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiled circuit 1 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1661.94x284.278 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize the first compiled circuit\n",
    "print(\"Compiled circuit 1 \\n\")\n",
    "output_circuits[0].draw(\"mpl\", idle_wires=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ad90e8aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Estimate circuit durations for the timeline drawer\n",
    "for pg_qc in pulse_gate_circuits:\n",
    "    pg_qc.duration = pg_qc.estimate_duration(backend_target, unit=\"dt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4f3c6525",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x300 with 1 Axes>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize the pulse gate circuit for the first compiled circuit\n",
    "qiskit.visualization.timeline_drawer(\n",
    "    pulse_gate_circuits[0], idle_wires=False, style=style, target=backend_target\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "714c2859",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiled circuit 2 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 909.437x284.278 with 1 Axes>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize the second compiled circuit\n",
    "print(\"Compiled circuit 2 \\n\")\n",
    "output_circuits[1].draw(\"mpl\", idle_wires=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "91121471",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x300 with 1 Axes>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize the pulse gate circuit for the first compiled circuit\n",
    "qiskit.visualization.timeline_drawer(\n",
    "    pulse_gate_circuits[1], idle_wires=False, style=style, target=backend_target\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "490036bb",
   "metadata": {},
   "source": [
    "## Using the Superstaq Simulator\n",
    "\n",
    "Lastly, we will show (a) how to execute a circuit on a backend and (b) how to simulate circuit execution. Simulation is available to free trial users, and can be done by passing the `\"dry-run\"` method parameter when calling `run()` on the Superstaq provider."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "331aec28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'00': 504, '11': 496}\n"
     ]
    }
   ],
   "source": [
    "# Get IBM Quantum backend from provider\n",
    "backend = provider.get_backend(\"ibmq_kingston_qpu\")\n",
    "job = backend.run(\n",
    "    bell_circuit,\n",
    "    shots=1000,\n",
    "    method=\"dry-run\",  # Specify \"dry-run\" as the method to run Superstaq simulation\n",
    ")\n",
    "\n",
    "# Get the counts from the measurement\n",
    "print(job.result().get_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "324f3dd0",
   "metadata": {},
   "source": [
    "### Addendum: Compiling via Superstaq backend\n",
    "You can also compile circuits via the `compile` method of the backend directly. This is identical to calling `ibmq_compile` on the provider, but more closely matches Qiskit syntax."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b45ebadf",
   "metadata": {},
   "outputs": [],
   "source": [
    "compiler_output = backend.compile(bell_circuit)\n",
    "output_circuit = compiler_output.circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9eb7512b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xAsePH681b6qx7RrDkK8jiiuAwb8I3qzR2oY/jbbhT4u6z4SERFiL1IPLi0qwufP0RrXNS0gDdIBjgBdyE9Jg7+uGgo1ZDW438KOXIbdQIjchFb1enYjkXSdQkNLwdvokJCbAwsaq4YaN1NL6gJivvxiejQYu5Aj7Y3tWaHu8eDPZOAUJJGQMAM1jHJj7GACkGQdCV4eoqKjAM888A3t7e0FTEz/66COBlTVNRUWF3nOPkpOTof3fPP0+ffoYNMWyseEZqAx2HTp0wM2bN5GamorS0tI6j5AnJCQYPN2TmsZsTyJ844030LZtW6SmpiIwMBA9e/aEv78/wsLC0KlTJwwbVrleaVOWpgGALl26oG/fvnjqqadw+PBhFBQUYOXKlQa3o+Yh9eBpdBgZ+r+rrjV89Kfbc6PhPrAHYtf8F8ee/xByhQID177U4HbUckztLKy9Sg6Mr/sUjBaD44AaQ6lUwsnJqUUHvRs3blT/3KlTJ8HbCwnPDz+PTqerccEWah7MNkB7eXkhOjoaY8aMgZWVFZKTk+Hs7Ix//etf2LNnDxISEgA0PUA/yNHREX5+frh2rfYyT4a0I+mkHjiNDiNC4DH0EWT8dqHetva+bghaOBW3zyXi0vqfkJuQhtgPf4Bb/0B0e260iSomY4vwAKY28nNVLgPeDwbcjDtjwOg4Dqgx0tLSMH/+/Ba9NGtGxv0TXfUtPqCPIeEZAHx8fKp/NvV8cGqY2QZoAOjWrRt2796NgoICFBQU4OTJk5g1axbu3buH5ORkyOVy9OjRQ7Tnu3XrFuLj49G5c/2HqhrbjqSTdfIKHDq5o2NkWK2rrtUgk2HQR3Mgl8vx+wNLdV36dCeyY68haOFU2Hc05mlLZErzAoHZXQGLet5ZHS2BNWHAUHfT1WUsHAfUGIWFhYiOjkZhYWHDjZspa2truLi4oE2bNjWuWtyQhIQEg8IzUHkyoaOjI1xdXWFhYWFw7WQcLff7FCOKi4uDTqdDQEAAbGxqL3C/bds2AMDly5dr3Pbx8UFISAgAYPr06fDz80Pv3r3h6OiIxMRErF27FkqlEvPmzaveV2PbUfOi02iRfux85c/1rF8bOPtxtA/ritP//BZ5ifePIOi0WvwetR6PH1iFgWtfwr4Ji41eMxmfTAb8PQCY2BHYdRPYnw4k5N9//J9BwGPugKVCuhrFxHFA5mLatGmYNm2a4O1cXFzg5OQEtVotKDwDwIABAzBgwADBz0mmwQBdh4sXLwLQP31j0qRJdd6eOXMmNm7cCADo168fNm3ahI8//hglJSXo0KEDwsPDsXDhwhpf/zS2HTU/N/fG1LuCQBt/TwS98RRunY5H3Bc/13q86ivs4IXT0O250bjylRHPXiKTclIBM/0r/wvbVbm4hRxApJfUlYmP44BIP2dnZyxatAg7duzAjBkzGh2eqfljgK5DQwFa14hll+bMmYM5cxpe57Ox7aj5Sdn9R72P5yWm41vfqfW2ubjuR1xc96OYZRGZFMcBUf2cnZ3x97//XeoySGQM0HVoKECTMDnHtyH/wmF0eG4tbqx+CiWplyG3tIayTTt4v/g5rNz9pC6RiMiozPl90NXVFVFRUQZf24CoOWKArsORI0ekLqFVyf3zRziHzwAAuI6YBYfgSMhkMtzasx4p6/+OLu8fk7ZAEl1FYS4uz+0BbVkxLF06QFdeilL1DTgPfRo+r2yQujwikzPn98G2bdsaNH+YqDljgKYmqy8sdXzxcxRePQ6fqI2QKS3QJuT+clW2Af2Q9dNqCSsnY1HaOcL50alQWNvDfco7yDu7H+ptyxieqdXi+6B++fn5iImJQVhYGBwcDL3oPVHzYtbL2JE4qsJS+8dfRfePYuH13Eew7dIPPq9sQMHFo7DtOgAyZe0leG7t/hiOYXVfnpRavqKkWFh36lP58/UzsPnfz0StEd8H9cvIyMDChQtrrKVM1NIxQJMo9IWl3JM/wanf+FrtM/+7DKWZ1+A5Y7lJ6yTTKU6Kre4HDNBkDvg+SGQ+GKBJFHWFJZ1Oh7xz++EQFFmjrfrH1cj9Ywf8Fu2FXFV7nW1q+crupAOQwbKtJwCgOPkCrDv2lLYoIiPj+yCR+WCApibTF5aKEmJg7dUNCmu76rZZO9cgJ3oL/N87CKWdozQFk9EV3ThX44izwtYRt375TMKKiIyL74NE5oUnEVKT6QtLSgcXOPb9S/X9ZdlpSPt6PizdOiHhH+EAAJlShW6rT5q6ZDIyx9CxcAwdW32724enJKyGyPj4PqifSqVCly5doFKppC6FSDQM0NRk+sJS3JxAtF96tPp+SxcvBO9s+CI0REQtDd8H9fP19cW3334rdRlEomKAJqMJXB8ndQlERJLi+yBR68Q50ERERGQ08fHxGDhwIOLj46UuhUg0DNBERERkNDqdDuXl5dDpzGvqCrVuDNBERERERAJwDjQBAKwUQPTohts1J1YK8faltFZh2vXvxNuhCSitxT2jvaX1ATFff+IYAFreGAA4DsSkUCgwceJE0fa36l9bUXDvHuxtbfH6C1Nq3RaDQsEOIBUGaAIAyGSAtRn3BplMBgsbK6nLkJS59wFzxzHAMWDuZDIZlErxOoAOgFZX+X+lUlnrNrVsfAWJiIjIaHx8fLBlyxZ4enpKXQqRaBigiYiIyGisrKzQuXNnqcsgEhVPIiQiIiKjyczMxNKlS5GZmSl1KUSiYYAmIiIio8nLy8OuXbuQl5cndSlEomGAJiIiIiISgAGaiIiIiEgABmgiIiIiIgEYoImIiMhonJ2dMXPmTDg7O0tdCpFoGKCJiIjIaORyOSwsLCCXM3JQ68HeTEREREaTnZ2NDRs2IDs7W+pSiETDAE1EREREJAADNBERERGRAAzQREREREQCMEATERGR0djb22PUqFGwt7eXuhQi0SilLoCIiIhaL09PT7z33ntSl0EkKh6BJiIiIqMpLS1FamoqSktLpS6FSDQM0ERERGQ0SUlJmDhxIpKSkqQuhUg0nMJBAACdDijRSF2FMFYKQCYTZ186nQ4VxS3r6IjSWgWZWP8AaHl9QMzX31y0xH5uKLHHB5E50Ol00Gha0AcBAIVCIclYZ4AmAJXBafAvUlchTPRowFqkHlxRXIrNnaeLszMTmXb9O1jYWIm2v5bWB8R8/c1FS+znhhJ7fBCZA41Gg+3bt0tdhiATJ06EUmn6DwNO4SAiIiIiEoABmoiIiIhIAH4BSkREREbTtWtXxMTESF0Gkah4BJqIiIiISAAGaCIiIjKalJQUPPvss0hJSZG6FCLRMEATERGR0RQXF+PSpUsoLi6WuhQi0TBAExEREREJwABNRERG5dY/EM9kboPf5KFSl0JEJAquwkEGK7h4DAn/CK9xn9zKFiqPALQd+jTajX0FMgW7WGvGPtA6uPUPxKgd71bf1mo0KC8oRpH6Lu5cuIGkn35H+tFY6QokImpm+MlGTeb06F/RJng0oNOhPEeNO8c2Ie3r11CSdgUdX/5S6vLIBNgHWocbO6KRduQsIJPBwtYabfw84D0qDH6ThyLj1/M4NutDlOUXSV0mtTDu7u5499134e7uLnUpRKJhgKYms+kUhLZD718e2HX0S4h7qSuyD26Ax/T3YdHGVcLqTO+ZzG2i7Wuj+5Oi7cuY2AdahzsXk3Bje3SN+04t/gbB70xHj9nj8Ojn83Bo2vsSVUctVZs2bRAZGSl1GdRC3b59G66uze8zhAGaRKewsoVtl37IPbENperrZhWebD1dELN4Iy5/uVvqUiRlzn2gtdFptTj97ia49vGH17A+aBfWFbdirgIALOxt0GvuBHQc0xe2Hi4oLyxCxm8XcfaD71F481b9O5bJ0GvueHgM7Y02ndxh6WiH4tu5SDt0FudWbEFpTiEAwKqtAyad/ReSd/+J6Jc/rrWbvsv+jq4zR2B735dRmHZb9N+fmi4nJweHDh1CREQEnJycpC6HjEyr1SI1NRXXr19HUlIS1Go1ysrKIJfLYWdnh44dO8LX1xf+/v5wcHCod1/79u3Dd999h3nz5iE4ONhEv0HjMECTUZSqrwMAlHbOEldiWh2GhyD1wGmpy2gWzLUPtFaJ3x9G+77d4BURhFsxV2Fhb4PRP78PO08XJP7nCHLjU2HdzgldnxmJsb8sx8+j3sS9tGy9+1NYKtHjxSeQvOdPpO47hYriUrR9pDP8/zoM7cO64ueRb0JbXoGSO/lIPXAaHSPDcNLBpsYUEoXKAp3GD0JG9EWG52YsKysLq1atQs+ePRmgW7HCwkL8+uuvOHjwINRqtd52p06dAgDI5XKEhoZixIgR6N69O2QyWY12+/btw8aNGwEAa9euxcqVK+Hh4WG0+oUy+wCdnZ2NlStXYseOHUhLS4OrqysmTJiAZcuWYe7cufj666+xbt06zJkzR+pSmy1taREq8rOh0+lQkaPG7X1foPjGOdj4h8HKM0Dq8kzKoZMbrm7U/8bRWrEP1KbVAZdzAd3/buvqa9wC3L1SeREMh06VH2B93pgCe+922DN2IXIu379AxrUfjuKJI2vQ5/9Nwe+vfqp3f5rScmzt/Tw0JWU17r99Oh4D17wE71GhSP75DwBAwneH4DO2P3zHD0b8N/ur23Yc0w8qRzskbj4k2u9JRMJotVrs27cPW7duRWlpqaDtTp48iZMnT6Jz58548cUX4eXlBaBmeAaAcePGNbs59GYdoGNjYxEZGQm1Wg1bW1t0794dGRkZ+OSTT3D9+nXcvXsXANC7d29pC23mMrcsRuaWxTXuc+w/Ad4v6P/wbI2UNlYoLyyRugxJsA/cV1gO/JQCbEsG0h44304H4PnfgSd9geEegFymbw/NU3lB5UUwLO2sAQCdJgxG1skrKFLfhcrZvrpdRVEpbp9NhMeQRxrcZ1V4lsnlsLCzgkypQObvlwAALkH+1QE649fzKEjJgv/UYTUCtP9fh6Hkbj5u7jslzi9JRIKo1Wp8/vnniI+Pr3F/YGAg+vTpA19fX3h7e8Pa2hparRZ3797FjRs3cO3aNRw/fhx5eXkAgOvXr+Ott97CpEmTYGlpiW+++aZ6XxMmTMCkSZNqHaGWmtkG6OzsbDz++ONQq9WYP38+Fi9eDHv7yg+BlStX4s0334RSqYRMJkOvXr0krrZ5cxk5C04DJkGnKUdxykWod6xAWXYaZBZW1W0K4qJx7b3aJ5HoKsqg02oQ/KPGlCUbhceQXkj/9bzUZUiCfaCSugiYexK4UVD34+fuVv53NBN4rw9gqTBtfU1hYV8ZnMsKi2HV1gFWzg7wHNobf437vzrbazUNv54+j/dH4OxxcO7hA4WlRY3HVG3satxO+P4wghdMhXOgD+7GJcPOux3cBgTi8oZfoC2vMPC3IiJDJSUlYfny5cjPz6++b9iwYRgzZgw8PT3r3MbNzQ1ubm4YMGAApk6dipiYGGzbtg0ZGRmoqKjAli1barRvruEZMOMAPXfuXKSlpWHOnDlYvXp1jcfeeOMNfP/99zh//jx8fX0bnORu7lTu/nDoHQEAaBMcCbtugxC/YBBufj4bnV7/DwDAPnAw+mwtrLFd2Z0MXJ0fAtcxrWN6TLvQrjiz9Lsa9wUtmIpecyfg93mf4dp/jtTaZtT2d+EaHICfR76B3PhUU5UqOvYBIK8MmPMnkFzYcNtDGYBCBiwNAprh50KdnLt1BADkX8+oLjrj1/O4+OlPBu3Pe3RfDP1yPm6fTUTMO/+Hexl3oCktg0whx4gt70D20CH6a1uOoM//mwz/vw7DyX98Df+/DoNMLuf0jRbAxsYGffv2hY2NjdSlkEhSU1OxdOlS3Lt3DwDQrl07zJ49G927d2/0PpRKJQYMGICQkBD88MMP2L275sn348ePb7bhGTDTKxFeuXIFW7duhYuLC5YvX15nm6qzPR955P7XkFWBOywsDCqVSu+LGh0djYiICLi7u0OlUsHLywtTpkzBlStX6q0rMjISMpkMS5YsMewXaybsug2A89CnkfP7VhReOVFnG215KW58MAF23QfBfdJCE1doBDIZIKtcseBBsat/QM6VFIQtmQkb95on03WfNRZuAwIRu3priw7PdTHHPvDd9caF5yr704FT+s+xa3b8pz4GAEg7dAYld/JRmlsIC3sbZEZf1PtffTo/+Sgqikux78nFiN90AGmHziAz+iLu6TkZsPh2LlIPnkGnCYOhtFbBb3I4bp9JQG5Cmui/K4nL29sb69atg7e3t9SlkAiKioqwYsWK6vDcpUsXLF++XFB4fpClpSVcXFxq3W9hYdFswzNgpgF6y5Yt0Gq1mDZtGuzs7OpsY21d+XXlgwH62rVr2L59O9zc3BAaGqp3/zk5OejZsyc++eQTHDhwACtWrEBcXBz69++PtLS63+x/+OEHxMbGGv5LNTPuU94B5ApkfL+ozsdvfjYb2vIS+ERtNG1hRuLaxw/Z567Vul9bXoHoqPVQ2qgwcM1L1fc7dPZA0Ft/xe0zCbj02S5Tlmoy5tQHyjTAzpSG2z3sv8milyI6mVyOkEUz0L5vN6QeOoNbp+IBnQ43dkTDNcgfHcf0q3M7q7b1f3On01T+sSmT1fwY6vWq/rXPEzYfgsrJHv1XzoKtR1skfH9Y4G9DUtBoNCgsLISmEdN6qPnbvHkzsrMr//rv3Lkz3nrrLdja2hq8v4dPGKyyfft2pKY234NLZjmF48iRyq/Sw8PD9bapCroPBuhHH30UmZmZAIAlS5bg+PHjdW47btw4jBs3rsZ9oaGh6NKlC7Zv346oqKgaj+Xn5+PVV1/F6tWrMX36dLQGVu5+cB78FO7+uhkFcdGwDxxc/ditnz9B3und6Lr6FOSqlveVXvt+3XDrVHx1AAAAz/A+iNOz9vPdi0m4sO5H9H5tEgKmRyDx+yMY/MkrAIDoqPW1jlq3Fq25DzzsxC3gblnD7R72mxrILwMcLMWvyRBte/qi08TK1+nBKxHadWiH9GOx+O2l++swn/1gC9qFdsXQL19D8q4/cPtsAjRlFbDzcoXXY31w58KNelfhSNn9J3zG9sfIbYtx/b+/Qm6hhPeoUCitVXq3ST8ai8LUW+j85BCUFxYj6ae634OpeUlMTMSMGTOwadMmdO3aVepyqAkuXbqEw4cr/3BVqVSIioqqPuBoiIfD84QJE1BeXo6ff/4ZGo0Gn3/+OZYuXQq5vPkd7zXLAJ2SUnmoqGPHjnU+XlFRUR2OHwzQTXkB27ZtC6Byzs/D3n77bQQEBGDatGmiBOiQkJB612Csi8zSGu0/Smzycz/IbdLbuBu9BRnfL0KX948CAAouHEXapjfhv2gvVO19mrT/gAB/6MqKRagUsNDJsRhhDbbzGTcAAz98EYdnfgD1ibj72zvYoLxA/yWOz6/dBu8RIQhZNAPOgT5wDfJHzJKNlfNJDRTgH4BymXjhu6X1ATFf/6ayGfIMHKYsFbydRgf0HBAOjVrcf3d9GurnnSYMRqcJg6HVaFBxrwT3Mu9C/cdlJL35JdKPxtZoW15QhF+eeBuBs8fB9/H+6DAyBDqNFvcy7+BWzFUkbK7/6HDSzuNQ2lkjcNYYhC6agdK8e0g9eBpn3t+MqVc21r2RToeELUcQ9MZTSNp1AhVF+le9EXt80H1PPinsCqm3blVeVGfv3r04c+ZMo7cbP368oOdpbsb/7VXY2jkgU50JLy+vWrebI0tLS71TWwFg586d1T9PmzYN7dq1M/i56grPkyZNQnl5Oc6ePYv09HTcuHEDFy9erJHFHhYQEICyMgOOYKDypMbTpw27doNZBuiqeTvFxXV/+G7duhXZ2dmwt7eHr6+vwc+j0Wig1WqRkpKCBQsWwM3NDZMnT67R5vTp0/j3v/8t6E2lIWq1Gunp6YK2kats0F7g89j3HIrgnfpXt7Xu0K3GygqlWcm4sWoyvJ5ZBfueQwU+W20ZGRnQluoPrUJYyhRozD9A8q4TcPB1Q4eRodUB2tbTRe+8zSq6Cg2io9Zj7N4P0PWZUcg6eQWXv9zTpJozMjNQphPvK9GW1gfEfP2byjU3B4aeanwrS40SgePVUPr6ufqPOIMuG68pLsOFtdtwYW39l6/Xt//EzYfqPAmwvlq0peWV2zYwfUPs8UH3VX2GNlbVZ21xcbGgbYV+jjU3VSvRaDUapKen17rdHKlU+r8BysjIwMWLlec2tG/fHhEREQY/j77wLJPJYGlpiSlTpmDNmjUAgAMHDtQboDMyMgStPy0WswzQbm5uyMnJwdmzZ9G/f/8aj2VmZuL1118HAPTq1atJE9iHDBlSfSTbz88PR44cqXE9d41GgxdeeAFz5sxBYGCgwc/zMDc3N8HbyCwN/wqmMbSlRbi+/C9oEzYO7URaccHDw0PUI9Bo5MGqlL0xeGzjmzi1eCOAxl99sDy/CNqyCigsLZB2+Cyga9qlNTzcPUQ/Am1MYvcBMV//plJpDQvyOk0FXKxk0OlZ8klsQvp5cyRTyBHw9HDcvZyC22cS6m0r9vig+4TOd60KzdbW1oK21bcUWkshVyiq/+/p6VnrdnNkaal/PtnRo0erfx4+fLjB38rXF56rBAcHw9nZGXfv3sXZs2dx9+5dODvXfVVbDw+PJh2BNpRZBuiIiAhcuXIFK1aswPDhwxEQUHmltFOnTuHpp5+unhzf1AuofPXVV8jNzUVSUhJWrVqFESNG4Pjx49VnIq9fvx5ZWVmir7phyNcRxRXA4F9ELaOGnBPbUZx0HiXpCcj5fWutxwPXX4alq7AztBMSEmEtUg8uLyrB5s6Nmz6Tl5AG6ADHAC/kJqTB3tcNBRuzGtxu4EcvQ26hRG5CKnq9OhHJu06gIKXh7fRJSEyAhY1Vww0bqaX1ATFf/6Yq1wJjDwJ3BB4EifBSYkXiZeMUVQch/bw5sevQDq4hAfAeGQoHHzf8Onttg9uIPT7ovqpLMTfW1atXsWXLFkRGRgqaA/3RRx8JrKx5WfbpZuQX3oO7mzvS0tJq3W6OKioqsH379jofu3r1avXPjz76qEH7b0x4BgCFQoHBgwdj586d0Ol0SExMRN++fevcZ0JCQp3TY42tmXz8mFbVOs+pqakIDAxE165dUVJSgmvXriEyMhI+Pj7Yv39/vV8ZNEaXLl0AAH379sWoUaPg4+ODlStXYv369cjOzsY777yD1atXo6KiArm5udXblZSUIDc3Fw4ODs1y4rwh2oY/jbbhT0tdhmhSD55Gh5GhKEzLRnlhw0dBuz03Gu4De+DM8u+Rui8Gjx9YhYFrX8K+CYsb3La1aG194EEWcuAvHYGv6j8oWsskw2eImRW3/t0x6OM5KLmTh9gPf0DSTp482JL4+flh//791Rcro5ZHo9EgOTkZQOVRW0Ouj9HY8FzF39+/+ucbN27oDdBSaR3pTCAvLy9ER0djzJgxsLKyQnJyMpydnfGvf/0Le/bsQUJC5adgUwP0gxwdHeHn54dr1yqXOktLS0NBQQFeeOEFODk5Vf8HACtWrICTkxNu3rwp2vOTuFIPnEaHESHwGPoIMn67UG9be183BC2citvnEnFp/U/ITUhD7Ic/wK1/ILo9N9pEFZOxTe8MdBKQD0Z7AcFtjVdPa3Lth2PY6P4k/tPjOcSu/kHqckggpVIJJycnSY4SkjgyMjJQXl55/oEh54YJDc8PP09VeG9OzLY3d+vWrdZVbwCgsLAQycnJkMvl6NGjh2jPd+vWLcTHx1f/BeXn51djPlGV8PBwzJw5E88880yT5uaQcWWdvAKHTu7oGBmG36P0L9UFmQyDPpoDuVyO3x9Ysu7SpzvRcXRfBC2cirRDZ5o0lYOaB3sLYH0/YO6fwDU9l/KuMsoTeKd3y7kKIVFTpKWlYe3atZg3b16zXX2C6ldYeP8qUVWrijWWIeEZAJydnSGTyaDT6Wo8f3NhtgFan7i4OOh0OgQEBNR52dFt2yrPNr98+XKN2z4+PggJCQEATJ8+HX5+fujduzccHR2RmJiItWvXQqlUYt68eQAAOzs7DB06tM4afHx89D5GzYNOo0X6sfOVP9ezjnPg7MfRPqwrTv/zW+Ql3j/rWqfV4veo9WY5laM1a2cNfD0Y2HWz8iIpKQ+954e5ABN9gHB3QM7wTGaisLAQ0dHReP7556UuhQzUsWNHLFq0COXl5XVeNVCf0tJS7Nu3r/p2Y8MzAMhkMrzxxhtQKpV6L3onJQboh1Qt0aJv+sakSZPqvD1z5szqv7D69euHTZs24eOPP0ZJSQk6dOiA8PBwLFy4UO/a09Ty3NwbU+9KGm38PRH0xlO4dToecV/8XOvxqqkcwQunodtzo3HlKyOewUcmY6MEnuoETPEF4vOA7FJAKQO8bCv/IyJqaWxsbAy6VLdKpcKiRYvw3nvvYcCAAY0Oz1X69Okj+DlNhQH6IQ0FaF0jlh6bM2cO5swxbJmuxuyfmoeU3X/U+3heYjq+9Z1ab5uL637ExXU/ilkWNRMyGdDVUeoqiIik5ezsjGXLlsHa2rpJSwM3NwzQD2koQJNwOce3If/CYXR4bi1urH4KJamXIbe0hrJNO3i/+Dms3P2kLpGMiK9/yzL8P+/A2tUR0GpRfq8EJ//xNfIS0zDki3lo4+8FTUkZSrLz8Mdb/0ZBct1XPPWKCEbo4hmQyeXIuXoTv0etR3lhMdz6ByJi80LkX8/Agaf+iZI7+QbVOPjTKHgM6oGknccRs2ij4b8sEZlEXVNiWzoG6IccOXJE6hJandw/f4Rz+AwAgOuIWXAIjoRMJsOtPeuRsv7v6PL+MWkLJKPi69+y/DrrQ5TlV14YxjsyDIM+ehl7xixA/LcHkX7kHACg699GYeCHL2LfxNpz95U2Vhi45kXsm7AIedcy0Pf95/DIvCdx+p/fAgDyr2dg1/DXm1Rj9Msfo/f8ybBs0/o+lFsjV1dXREVF1biQGFFLxwBNTVZRmIvLc3tAW1YMS5cO0JWXolR9A85Dn0bHFz9H4dXj8InaCJnSAm1C7i/bZhvQD1k/rZawchIDX//WpSo8A4ClvQ2g00FTWl4dngHg9tlE9HhxXJ3bew7rg7uXkpB3LQMAcPWb/Rix5Z3qAP2wUdvfRfb563AN8od1eydk/nYBf7z5JQDAf+pjCHxhLLRlFZAp5Dg+/wtkn0sU61clE2nbti2mTZsmdRlEomKApiZT2jnC+dGpUFjbw33KO8g7ux/qbcvg88oG5J87ANuuAyBTWtTa7tbuj+EY9oQEFZOY+Pq3PoM+eQXuAwIBAAenL6v1ePe/j8bN/XVfjc7O0wWFaberbxem3oJ1e0fIFPovO2Dv0x77Ji6G3EKJv/y6Fq7BAbh9JgGhi2fgx8FRKL6VC5lSAYVl7X5EzV9+fj5iYmIQFhZm0AU4iJojs7yQComvKCkW1p0qz5Ytun4GNv/7OffkT3DqN75W+8z/LkNp5jV4zlhu0jrJOPj6ty6/z12H/4bMxtkVWxDyj5qX/u45dwLsfdxwZtlm0Z4veecJ6DRaaErKcDcuGfY+7QEAmb9fwuB1c9Ht76Nh790OFUUloj0nmU5GRgYWLlyIjIwMqUshEg0DNImiOCm2OjRVBSidToe8c/vhEBRZo636x9XI/WMH/BbthVzFOYytAV//1un6f3+F24BAqJwq12ANnD0OHUf3xaFp70NTXFbnNoXp2bDzuj/X1a5DOxRn5UKn0b9euqb0/r50Gi3kCgUA4Ohzq3Bm2WbIlUpEfLcQvk8MFOPXIiJqMgZoarKyO+kAZLBs6wkAKE6+AOuOPVGUEANrr25QWN9fAD1r5xrkRG+B/3sHobRzlKZgEhVf/9bD0sEG1u2dqm97jwpFaU4hSnMK0f2FsfAdPxAHprxXY570w9KPnoNzz05o4+cBAOg6cySSdh4XXItMIYe9jxvunL+OuC92IWX3n3DpwxVbiKh54BxoarKiG+eqjz4CgMLWEbd++QxKBxc49v1L9f1l2WlI+3o+LN06IeEf4QAAmVKFbqtPmrpkEhFf/9bDwsEGQ7+cD6WVJXRaHUru5OPwjOWwcXdG2JJnkJ+sxqhtSwAAmrIK7BmzAADQ+/UpKM7KQfymA6i4V4IT8z/HsP97EzKFHLnxqYieu15wLTKFHAPXvgSVox10FRqU3MnH7/M+FfPXJSIyGAM0NZlj6Fg4ho6tvt3tw8qTi+LmBKL90qPV91u6eCF4Jy8U09rw9W897qVlY8/oBXU+ttH9Sb3bxa7aWuN26oHTSD1wulHP+fBSeMee//D+Y+MXNWof1LypVCp06dIFKpVK6lKIRMMpHGQ0gevjYOHYTuoySCJ8/elhmvIKqJzsMe7gKli1NXw1hsGfRqHTxMEoKygWsToyFl9fX3z77bfw9fWVuhQi0fAINBERmcTt0/H4b8jsJu8n+uWPRaiGiMhwPAJNRERERhMfH4+BAwciPj5e6lKIRMMATUREREaj0+lQXl4OnY7nQFDrwSkcBACwUgDRoxtu15xYKcTbl9JahWnXvxNvhyagtBb3hJyW1gfEfP3NRUvs54YSe3wQmQOFQoGJEyeKtr9V/9qKgnv3YG9ri9dfmFLrthgUCmk+DBigCQAgkwHWZtwbZDIZLGyspC5DUubeB8wB+zkR1Ucmk0GpFO+DQAdAq6v8v1KprHW7JeMUDiIiIiIiAVp2/CciIqJmzcfHB1u2bIGnp6fUpRCJhgGaiIiIjMbKygqdO3eWugwiUXEKBxERERlNZmYmli5diszMTKlLIRINAzQREREZTV5eHnbt2oW8vDypSyESDQM0EREREZEADNBERERERAIwQBMRERERCcBVOIiIiKjRQkNDBbX38vLC4sWLERERAXd3dyNVRWRaDNBERERkNO7u7liyZInUZRCJilM4iIiIiIgEYIAmIiIiIhKAAZqIiIiISAAGaCIiIiIiARigiYiIiIgEYIAmIiIiIhKAAZqIiIiISAAGaCIiIiIiARigiYiIiIgEYIAmIiIiIhKAAZqIiIiISAAGaCIiIiIiARigzdCqVavQv39/ODk5wdHREYMGDcK+ffukLouIiIjq8dtvv+GJJ55Ax44dIZPJsHTpUqlLMrlffvkFvXv3hkqlgo+PD9asWSNJHQzQZujIkSN49tlncfToUcTExGDAgAEYO3Ysjh8/LnVpREREpEdhYSG6d++OlStXws3NTepyTO706dN44oknEBkZidjYWCxZsgQLFy7EF198YfJalCZ/RpLc3r17a9xeuXIl9u3bhx07dmDgwIESVUVERET1GT16NEaPHg0AePPNNyWuxvTWrFmD0NBQLF++HADQrVs3xMXF4YMPPsDs2bNNWgsDNEGr1SI/Px+2trZSl0JERNTi3EjNREWFpsZ9FRpN9f8TktJq3X6Qna01PNq1NU2xRnAnJx93cvNr3S/k36BzRw8o5PVPjDh+/Diee+65GveNGjUKq1evRlpaGry8vJryawjCAE1YtmwZcnNzMWvWLKlLISIianHS1bex58ifdT5WVFyCr3/4Re9tGYBZUx83dolGJZfLsPmngygtK6/z8Yb+DUJ6dUGAb8PhNzMzs9bUlarbmZmZJg3QnANt5j777DMsW7YM27ZtM2nHIyIiai0GhvREJ28Pg7Z9tO8j8O3gLnJFpuXUxh7jhhs2BdS5jT0eH9Zf5IqMjwHajK1evRqvv/46du3ahYiICKnLISIiapHkMhkmjxkKlaWFoO3cXJ0xfFCIkaoyraBAf/QI8BW0jQzA5LHhUKksG9Xe3d0darW6xn1ZWVnVj5kSA7SZWrRoEd5991388ssvDM9ERERN5Ohgh7+MGNTo9gqFHE89PgxKpcKIVZmOTCbD+JGDYWdr3ehthvTrDR+vxq8mMnDgQOzfv7/Gffv27UPHjh1N/i06A7QZevXVV7Fq1Sp8++236NKlC9RqNdRqNfLy8qQujYiIqMXq3d0PPbs07ijsyMGhcHN1FrT/wsJCxMbGIjY2FmVlZVCr1YiNjcW1a9cMKVd0tjZWeDJySKPaurdri4hBwYL2P2/ePMTExODtt9/G1atX8c0332DdunV46623DCm3SWQ6nU5n8mclSclksjrvnzlzJjZu3GjaYoiIiFqRe8Ul+OjrbSgoLNLbxreDO55/agzkDaw68bBjx44hPDy81v1DhgzBsWPHhJZqND/uj8bJ2Ct6H1co5Hhl5gTBf0AAwJ49e7Bw4UJcvXoVbm5uiIqKwmuvvdaUcg3CAE31upOTDx10cHFqI3UpRERELUL89Zv4v211X+FXZWmBqGefhHMbexNXZTqlZeX4ZON23MmpvbQdAIwO74dHw3qZuCpxcQoH1WvvsZNY8+8f8Oe5y1KXQkRE1CJ06eyNfn261/nY4xEDWnV4Bir/SJgyJrzOb7w7ebtjUGhPCaoSFwM06ZV56w4uJSRBp9MJmuRPRERk7kYP7Vvr29vAAB8E9wiQqCLT8vZsj/B+vWvcp7K0wKTRQyHXM5W0JWGAlphGo8G3336LESNGwNXVFSqVCt7e3hg1ahQ2bNgAjUbT8E6M5PDxswCAnl07GTRPiYiIyFxZWlpg8tjw6rBoZ2uN8SMH6z0PqTV6bGAwPNu7VN8eN3wgnFrJ0XcGaAnl5+dj+PDhmDFjBg4ePAhLS0s88sgj0Gq1OHDgAJ5//nkUFBRIUlvV0WcZgGEDgiSpgYiIqCXz9miHof37AAAmjnoUdjaNX+KtNVAo5Jg8NhxKpQKBAT4ICvSXuiTR8CRCCU2aNKn6CoCbNm2qcWZtVlYWvvrqK0RFRcHW1lbQftd9swMFhcVNqq2opAQVFRpYKBWwtrJq0r6IiIjMlU6nQ3l5BSwFXmSlNSkrL4dSqWx2Uzfs7azxyswJBm3LAC2RM2fOICQkBEqlEufOnUOPHj1E2/eyTzcjv/CeaPsjIiIiam0c7Gyx8OVpBm2rFLkWaqSffvoJADBmzBhRwzNQ+RdVU/DoMxEREbV2TclLDNASuXy5clm4/v37i75vQ7+OAAD17bv46OttkAF4ecZ4njxIRERE9BAGaInk51cuLt6mjfgXKGnKHOiikhIAgFKpwNc/7BWzLCIiIqJmoylzoBmgJeLg4AAAyMvLE33fBYXFTZ4DXV6hQTnnURMRERHVwgAtkcDAQOzYsQN//PGH6Ps2dE4P5z4TERGRuWjKHGiuwiGRc+fOISgoCBYWFoiNjUX37nVf8tNUHpz7HPXsk5z7TERERKQHL6QikT59+mDy5MkoLy9HZGQkfv311xqPZ2VlYfny5bh3zzTTKA4fPwOAVx0kIiIiagiPQEsoPz8fTzzxBI4dOwYA8PT0hIeHBzIzM5Geng6dToecnBw4OjoatQ4efSYiIiJqPB6BlpCDgwMOHTqEr776CkOHDkVRURHOnz8PuVyOkSNH4quvvoK9vfGvGV9WXgGP9m159JmIiIioEXgEmgBUXmq0rKwcKpWl1KUQERERNWsM0EREREREAnAKBxERERGRAAzQREREREQCMEATEREREQnAAE1EREREJAADNBERERGRAAzQREREREQCMEATEREREQnAAE1EREREJAADNBERERGRAAzQREREREQCMEATEREREQnAAE1EREREJAADNBERERGRAAzQREREREQCMEATEREREQnAAE1EREREJAADNBERERGRAAzQREREREQCMEATEREREQnAAE1EREREJAADNBERERGRAAzQREREREQCMEATEREREQnAAE1EREREJAADNBERERGRAAzQREREREQCMEATEREREQnAAE1EREREJAADNBERERGRAAzQREREREQCMEATEREREQnAAE1EREREJAADNBERERGRAAzQREREREQCMEATEREREQnAAE1EREREJAADNBERERGRAAzQREREREQCMEATEREREQnw/wFATRS5EW3CCAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 909.437x284.278 with 1 Axes>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize the compiled circuit\n",
    "output_circuit.draw(\"mpl\", idle_wires=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d94c141c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x300 with 1 Axes>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pulse_circuit = compiler_output.pulse_gate_circuit\n",
    "\n",
    "# Estimate circuit duration for the timeline drawer\n",
    "pulse_circuit.duration = pulse_circuit.estimate_duration(backend_target, unit=\"dt\")\n",
    "\n",
    "# Visualize the pulse gate circuit\n",
    "qiskit.visualization.timeline_drawer(\n",
    "    pulse_circuit, idle_wires=False, style=style, target=backend.target\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}