qiskit/fundamentals/6_qiskit_jupyter_tools.ipynb

{
 "cells": [
  {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![qiskit_header.png](attachment:qiskit_header.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "    <b>Important:</b> This notebook uses <code>ipywidgets</code> that take advantage of the javascript interface in a web browser.  The downside is the functionality does not render well on saved notebooks.  Run this notebook locally to see the widgets in action.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Qiskit Jupyter Tools\n",
    "\n",
    "Qiskit was designed to be used inside of the Jupyter notebook interface.  As such it includes many useful routines that take advantage of this platform, and make performing tasks like exploring devices and tracking job progress effortless.\n",
    "\n",
    "Loading all the qiskit Jupyter goodness is done via:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-09T15:23:33.875002Z",
     "start_time": "2019-08-09T15:23:29.481034Z"
    }
   },
   "outputs": [],
   "source": [
    "from qiskit import *\n",
    "import qiskit.tools.jupyter  # This is the where the magic happens (literally)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of contents\n",
    "\n",
    "1) [Automatic Job Tracking](#tracking)\n",
    "\n",
    "\n",
    "2) [Backend Details](#details)\n",
    "\n",
    "\n",
    "3) [Overview of Backends](#overview)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-09T14:27:22.788139Z",
     "start_time": "2019-08-09T14:27:20.241689Z"
    }
   },
   "outputs": [],
   "source": [
    "IBMQ.load_account();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-09T14:27:41.857406Z",
     "start_time": "2019-08-09T14:27:41.854349Z"
    }
   },
   "outputs": [],
   "source": [
    "provider = IBMQ.get_provider(group='open')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Automatic Job Tracking <a name=\"tracking\"></a>\n",
    "\n",
    "Perhaps the most useful Jupyter tool is the `job_watcher`.  Once loaded, this widget automatically tracks the jobs submitted by the user, and displays this information in a window floating in the upper left corner of the notebook.  \n",
    "\n",
    "To start the monitor you run the Jupyter magic:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-09T14:26:08.275802Z",
     "start_time": "2019-08-09T14:26:08.251911Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Accordion(children=(VBox(layout=Layout(max_width='710px', min_width='710px')),), layout=Layout(max_height='500…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div.job_widget')\n",
       "        .detach()\n",
       "        .appendTo($('#header'))\n",
       "        .css({\n",
       "            'z-index': 999,\n",
       "             'position': 'fixed',\n",
       "            'box-shadow': '5px 5px 5px -3px black',\n",
       "            'opacity': 0.95,\n",
       "            'float': 'left,'\n",
       "        })\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%qiskit_job_watcher"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should now see a small window titled \"IBMQ Jobs\" in the upper left corner of the notebook.\n",
    "\n",
    "Now, let's submit a job to a device:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-09T14:28:39.111265Z",
     "start_time": "2019-08-09T14:28:37.321568Z"
    }
   },
   "outputs": [],
   "source": [
    "backend = provider.get_backend('ibmq_essex')\n",
    "\n",
    "qc = QuantumCircuit(2, 2)\n",
    "qc.h(0)\n",
    "qc.cx(0, 1)\n",
    "qc.measure([0,1], [0,1])\n",
    "\n",
    "job = execute(qc, backend)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Opening the job watcher you will see that the job has been added to the list of jobs, and its status and queue position (if any) are being automatically tracked and updated.  If you want to kill the job watcher you can do so by calling:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-09T14:31:46.991455Z",
     "start_time": "2019-08-09T14:31:46.988055Z"
    }
   },
   "outputs": [],
   "source": [
    "%qiskit_disable_job_watcher"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although the watcher itself is killed, the underlying framework is still tracking jobs for you and will show this information if loaded once again."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Viewing Backend Details <a name=\"details\"></a>\n",
    "\n",
    "The IBM Q devices contain a large amount of configuration data and properties.  This information can be retrieved by calling:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-09T14:33:10.432853Z",
     "start_time": "2019-08-09T14:33:10.429483Z"
    }
   },
   "outputs": [],
   "source": [
    "config = backend.configuration()\n",
    "params = backend.properties()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, parsing through this information quickly becomes tedious.  Instead, all the information for a single backend can be displayed graphically by just calling the backend instance itself:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-09T14:34:40.067963Z",
     "start_time": "2019-08-09T14:34:39.397926Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "793891a44b2f4bc995e1e72091107e52",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value=\"<h1 style='color:#ffffff;background-color:#000000;padding-top: 1%;padding-bottom: 1…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<IBMQBackend('ibmq_essex') from IBMQ(hub='ibm-q', group='open', project='main')>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "backend"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This widget displays all the information about a backend in a single tabbed-window."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting an Overview of Backends <a name=\"overview\"></a>\n",
    "\n",
    "Instead of a single backend, you may be interested in seeing all the backends at once to compare, for example, average CNOT error rates.  This is done using the `backend_overview` widget:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-09T14:37:26.795312Z",
     "start_time": "2019-08-09T14:37:13.949438Z"
    }
   },
   "outputs": [
    {
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\n",
          "text/plain": "<Figure size 360x360 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "d440771d473e4c8a9d595f12295b1302": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "width": "190px"
      }
     },
     "d51f30a7d2f84933a2ec467c4fadcdd7": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "align_items": "center",
       "display": "flex-inline",
       "grid_area": "right",
       "max_height": "325px",
       "padding": "10px 10px 10px 10px",
       "width": "auto"
      }
     },
     "d54207c3781c45819f4da6032f576599": {
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      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "width": "190px"
      }
     },
     "d6fd1906459d42f6aa08c2687a1adfd4": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_24d62679b4bf4e12853fce863379f446",
       "style": "IPY_MODEL_584d249f0a6645e184e213f963cd7ae4",
       "value": "8"
      }
     },
     "d8d30aac555e45ad8637a883d6591087": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "dbbee07d07a14f419233b95a085daf98": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "margin": "0px 0px 0px 35px",
       "width": "600px"
      }
     },
     "e055d89ead8947c59071023f55496535": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "grid_area": "left"
      }
     },
     "e0b06be4697646288e4dc53f527d9a15": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "margin": "0px 0px 0px 0px"
      }
     },
     "e85a88503db641f99ec28390534be42c": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "eff5911361da43c4859ca2807456fc5d": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_8fcabc865c5f418e8347c2523270d888",
       "style": "IPY_MODEL_2e84ad3c674341c4a529a1a8cf86cea6",
       "value": "<h5>Status</h5>"
      }
     },
     "f157551f44d149b59a0a350d0ea7cdbf": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_f266b9839a854e06be5a852347ee1264",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 792x648 with 5 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "f266b9839a854e06be5a852347ee1264": {
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       "display": "flex-inline"
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       "style": "IPY_MODEL_b63d67e1f6fb445c8a197aa8d6768ccc",
       "value": "5df003fb11bc0b00129883a9"
      }
     },
     "fb81d28592764b79842a0fbdc3589b5e": {
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      "model_name": "DescriptionStyleModel",
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     "fc17a6e739834f78a44e5c70b9346d90": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "ButtonStyleModel",
      "state": {}
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    },
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}