{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Running circuits on quantum hardware: compiling circuits\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ibmqfactory.load_account:WARNING:2022-06-09 13:30:21,606: Credentials are already in use. The existing account in the session will be replaced.\n"
     ]
    }
   ],
   "source": [
    "from qiskit import IBMQ\n",
    "provider = IBMQ.load_account()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The backends we have access to"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('ibmq_qasm_simulator', 32),\n",
       " ('ibmq_armonk', 1),\n",
       " ('ibmq_santiago', 5),\n",
       " ('ibmq_bogota', 5),\n",
       " ('ibmq_lima', 5),\n",
       " ('ibmq_belem', 5),\n",
       " ('ibmq_quito', 5),\n",
       " ('simulator_statevector', 32),\n",
       " ('simulator_mps', 100),\n",
       " ('simulator_extended_stabilizer', 63),\n",
       " ('simulator_stabilizer', 5000),\n",
       " ('ibmq_manila', 5)]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[(b.name(), b.configuration().n_qubits) for b in provider.backends()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If you have access to more than one hub:\n",
    "#provider = IBMQ.get_provider(\"ibm-q-internal\")                  \n",
    "#[(b.name(), b.configuration().n_qubits) for b in provider.backends()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2d72d0d8426a40a9b2bffc4f80814b2f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value=\"<h2 style ='color:#ffffff; background-color:#000000;padding-top: 1%; padding-bottom…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from qiskit.tools.jupyter import *\n",
    "%qiskit_backend_overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "05ad37b6c23f4410b831e3ab78d6dab4",
       "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_quito') from IBMQ(hub='ibm-q', group='open', project='main')>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.providers.ibmq import least_busy\n",
    "\n",
    "backend = least_busy(provider.backends(\n",
    "                simulator=False,\n",
    "                filters=lambda b: b.configuration().n_qubits >= 2))\n",
    "backend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "106ec722481d4c358a0c80ef7e433e0c",
       "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_quito') from IBMQ(hub='ibm-q', group='open', project='main')>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "backend"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's go back to Bell state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 417.879x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit import QuantumCircuit\n",
    "circuit = QuantumCircuit(2)\n",
    "circuit.h(0)\n",
    "circuit.cx(0, 1)\n",
    "circuit.measure_all()\n",
    "circuit.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember how to run it in a simulator?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit import Aer\n",
    "from qiskit.visualization import plot_histogram\n",
    "sim = Aer.get_backend('aer_simulator')\n",
    "result = sim.run(circuit).result()\n",
    "counts = result.get_counts()\n",
    "plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "job = backend.run(circuit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Traceback \u001b[1;36m(most recent call last)\u001b[0m:\n",
      "  Input \u001b[0;32mIn [81]\u001b[0m in \u001b[0;35m<cell line: 1>\u001b[0m\n",
      "    job.result()\n",
      "\u001b[1;36m  File \u001b[1;32m/opt/conda/lib/python3.8/site-packages/qiskit/providers/ibmq/job/ibmqjob.py:290\u001b[1;36m in \u001b[1;35mresult\u001b[1;36m\u001b[0m\n",
      "\u001b[1;33m    raise IBMQJobFailureError(\u001b[0m\n",
      "\u001b[1;31mIBMQJobFailureError\u001b[0m\u001b[1;31m:\u001b[0m \"Unable to retrieve result for job 62a1f6cd129da2b880c1e819. Job has failed: The Qobj uses gates (['h']) that are not among the basis gates (['id', 'rz', 'sx', 'x', 'cx', 'reset']). Error code: 1106.\"\n",
      "\n",
      "Use %tb to get the full traceback.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       ".button {\n",
       "  border: none;\n",
       "  color: white;\n",
       "  padding: 4px 8px;\n",
       "  text-align: center;\n",
       "  text-decoration: none;\n",
       "  display: inline-block;\n",
       "  font-size: 12px;\n",
       "  margin: 4px 2px;\n",
       "  transition-duration: 0.2s;\n",
       "  cursor: pointer;\n",
       "}\n",
       ".iqx-button {\n",
       "  background-color: #0f62fe; \n",
       "  color: white; \n",
       "}\n",
       ".iqx-button:hover {\n",
       "  background-color: #0043ce;\n",
       "  color: white;\n",
       "}\n",
       "</style>\n",
       "<a href=\"https://quantum-computing.ibm.com/docs/manage/errors#error1106\" target='_blank'><button class='button iqx-button'>View solution for error #1106</button></a>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "job.result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 417.879x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.draw('mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600.731x385.28 with 1 Axes>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit import transpile\n",
    "\n",
    "transpiled_circuit = transpile(circuit, backend)\n",
    "transpiled_circuit.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "job = backend.run(transpiled_circuit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<JobStatus.DONE: 'job has successfully run'>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job.status()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = job.result()\n",
    "counts = result.get_counts()\n",
    "plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 545.638x204.68 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.visualization import plot_circuit_layout, plot_gate_map\n",
    "\n",
    "display(transpiled_circuit.draw(idle_wires=False))\n",
    "display(plot_gate_map(backend))\n",
    "plot_circuit_layout(transpiled_circuit, backend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 658.679x264.88 with 1 Axes>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# a slightly more interesting example:\n",
    "circuit = QuantumCircuit(3)\n",
    "circuit.h([0,1,2])\n",
    "circuit.ccx(0, 1, 2)\n",
    "circuit.h([0,1,2])\n",
    "circuit.ccx(2, 0, 1)\n",
    "circuit.h([0,1,2])\n",
    "circuit.measure_all()\n",
    "circuit.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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37BqAFY6fNddPriRkZLV0m0kKy2s0rjf58vFDMjNCfTivYNdPdpnE+J/WmPMZ5G/rYXP95UpCRoY0uzRttZkpDfB1vn7cg/ccOS19MNe1hIysFm+WJq30jT7G19v17uPShPkFu36yy8yKO2Od28MqEF+v60ASCHU9depUq0PwmCJFmHo9EDHmMoDMnz9f//nPf7Rs2TIdOHBA5cuXV9euXfXqq6+qVq1alsaWknpJvyz7SJJUNaq+pbE4KyXpvGa8P0i7180o0Psf/a81Z4z+WNe+bvl285Of8UPM/x/7JvfXL1wyDyeevoGpz/MzN8FMh5sfR3V98rwZffSXXiyvkZ+f4kxiRX4c1fWhU2aphfu6MjouL3a7eShwKJ+kLEf1LEk7jpoZSga2dW98sN7aPdICB0l/jtpIcqqZieXZG6WijHbP029bpNX5TOMqOa7rs0nSF79LT/Zhuaz8zPhT2nwo/30c1fXxs9LXS6WRPaxfXsOXz6+nrDJLueXHUV3vSzQJyPd0cm9sQEGkpZvjbH4PFZw5d4rbLVUtI3Vv5NbwXObLx4+kFHONml9SljN1vXizVL2M1Mra204+7fRF6cvfzRKceXGmrn9ea9p1/YrujQ9wJ18+7sF7UtLMNWpeSxhKzh33Vuwwx73OFjclX27X55NNf57fQB5n6nruBqlaWalZNffG5ypfruu88BzJOmPGjNFrr71mdRj5KlGihPr06aPWrVurUaNGCgsL0/nz55WQkKBVq1Zpzpw5Sk7OfrCMjIzUvHnzNHHiRE2YMMGiyOEJPBoMIB9//LFOnTqlZ555RvXr19e+ffv0+uuvq1WrVlqzZo1q1qzp9Zi+nf83TV78ti4mn1VwcBE9MfBz1a7cTJI084+Jmhf3dea+hxJ3qmmtLvrrXfmcHXjR9lVTVa/dQN369K9Wh+IUf65rX3byvHkQ6g7HzpopuW9t6Z7yAs3Bk2aklTvsPi4t3mL9TVhftfWwtMxBopGz1u83ax23qume8gJN3O6Cz0aS09JtUvPq3IQNJGeTpKmr3FPWqQvmRv3gdu4pL9AcO2vWMXaHQ6fMDau+zdxTXqDZc1xa6KaRvNuOSMu3SZ0suj/k6+fXG/Y7TjRyVtxu08dYfRMWmLvBXBe4w6/rpOgqUnkLlp3y9eOHZM4bTrpphsEpq805aomi7ikv0Ez+wyw15Q7frzCJuGEMnoSP8YfjHrxn1p8Fn/Eqp5/XSo0qS2WLu6c8V/hDu/4pztxbcIdJf0h1ykvFwtxTniv8oa7zwnMk60yePNlnkzIqVaqkl156Sffcc4+KF7/6AHbbbbdJko4dO6bPP/9cY8eO1dmzZzMTMlq2bKlixYrpiy++UFKSm77ksBxjiAPIhAkTNHfuXD3wwAOKjY3V3XffrdmzZ+vUqVP66KOPLInprh4v6KfXT2nKK8fVtuENWrd9YeZrfdsO179GLtK/Ri7SC0O+V3hoMQ3r8zdL4szN9tXTVLftAElSWuolffN8jH775sls+6yd9a6+eLSGks+fsiDC7Py5rn3Zwk1mBLK7/L5FOmdxH7rjqFljMMPY/5m4UtKsi0kyCRnunGJ43gbr/06+ataf7i/Pyukk7XYz28B7c65se2+OFL/H2rjS7dLs9e4t093lwVpLthZsCuu8rNhukgmttPu49J8lV/78xs/Sok1SssVLIyzY6N4+YeEmlnvIy5yEgi3Fk195afmMLPYkXz6/ttulmR7oY3xhemhcu5JS3JfUJZmllOZbtNyDLx8/JJPMucJNSdqSSTj4bYv7yiuIC8lm5sVXf7yy7bsV7kvyKaj9iWY5QndJPC+tclNCHnzfiXPSj3FX/vziVLP0oS8u2errxz14z/lkM1DKXS6lmmtKK/h6uz5+1r19wrkkadk295XnCl+v6/zwHAk5DRkyRBs2bNDIkSNVvHhx/fbbb3rllVfUv39/9e7dW4MHD9bYsWO1du1alStXTn/961+VkJCgfv36ZSZkbN26Vddddx0JGQGGpIw8pKen6+2331a9evUUHh6u5s2ba/HixWrQoIEefPDBQpV98uRJ9enTR3/+mftTMbvdrpdfflkHDrg2tLZcuXJXbatZs6aioqK0f78brwALoEREpJ4Y+LlWbv5VyxKmZ3stPT1dY78bouF9x6pimZrWBJjDucQDCouIVGi4yWALDglVn4e/0foFH2vfhgWSpOP71mvZ5Od1/UP/UVix0hZGm52/1bUvu5Qq/bHTvWWmpru/TFcs3mzWZ16f5ZBw9Iw0dbX0wTzrHpqdueh42RJXnU+W1u11b5mB4NApx8uWuOrYWTOa2QrpdnOz9d9LpF1Z/l67jktfLZG+X2ndeuLbDpu6cacdR82/IfxfWrqZAcCd7LLuBopkppZ9d7ZJkspw4qxZa/7dOea4bIWLl9w3m0CGS6nSKgv7c1914py00U2zA2U4fdG9D7MKwhfPr/eeMA/63OnASZNYBVglbpd7E+Ilac1u87DeKr54/JDM8pzuPkdescMkwljh1AXpX7PM7ChZZ//4Y4f09kz3X2u6YqkHzs2WbCWJ7lqw+7j01q/mPk6Gc0km2eytX61POMqLrx734D1/7HR/f7Bql7WJ/r7arj3RxyzbJqVblBQv+W5d54XnSMjpxRdf1H//+19FRkbq119/VXR0tGJjY/Xqq6/qxx9/1Jw5czRp0iQ9//zzatmypTp27KhVq1apevXqmjZtWmZCRvfu3XXw4EGr/zpwM5Iy8jB8+HC9/vrrGjFihGbOnKlBgwbpzjvv1M6dO9WqVatClZ2cnKzZs2crMTH3u2gnT57UlClTFBsbq337CnflmJCQoGPHjik6OrpQ5bhDyYgyur3LE/pi1vNKz9Kzfz33VdWq2FSdmvSzLLZLSed06eKVJ2dbln2rhp3uzrZP2arR6jjwTc359F6dP3VYsyYMUfNeo1W1Uay3w3XIl+van+w+7plRsJss6kt3Hr0ywiLrDZyMX/ccl6bFXfU2r9h62DMPzq2qa1/mqTqxqq6XbL2S6JS1CWW08ZU7pKVbvR6WJM/VyeZDnikX3nXwpHTGA8nuVrWPAyelH1aY72G27+Ll/x88Jf2w0vtxSab/88TMSXwXr7b1sHtnycjgC3Xta+fXmzxUJ75Q17h2eaJdp6S5PyHZVb52/JA8c556Nsn091b49xIp8dzV2+0y1wX/XmLdbGKeqOvDp03SIgLXpVTp04Xm/7m5eEn6dJF1s4k54ovHPXjPZg8c95JSrE8e9sV27Ym6PnlBOuKmpWcKyhfrOgPPkXzL4sWLrQ4hmwceeECvv/660tLS9NBDD+mmm27Sxo35T923fPly3XDDDTp8+LBsNpvsdrvGjBlDQkaAIikjF999952++uor/fzzz3rqqafUvXt3vfDCC+rQoYNSU1PVsmVLt3zOpUuXlJSUdNVPRESEZs2apdDQUMXGxmr37t0FKj8lJUUjRoxQVFSURowY4ZaYC+u2Lo8q8cwhzY37jyRpzbb5its6Rw/c+JZlMe1a+6smv9ZZ6+d/nLlt/6ZFqta4+1X7xvT+i8pUbqRvnm+moKAQdRjwujdDdYkv1rW/cffowwz7Eq0Z1fLbFsnmYJ9VO61ZXmWfB+sa2XmqTjz1fclPuj37qKHc2GT2seI7F0h1DffzVPs4eMqaUapLnEh+Wr8v9wcmnkYf4z37TnioXB+pa186v/bYeaqH/g0BZ3jy+stqvnT8SEv33Ah7K44h+xLNjHl5ne7bZf7OVswmdjbJc8tMcLwObGt2Sxcu5d+uT12wfjax/PjScQ/eY7cH9vWXL7XrS6nSYQ8lT1DXueM5ku/ZsGGD1SFkql27tsaNGyfJDPr/5JNPnHpfZGSkZs+erYoVK+rEiROy2Wz65z//qZIlS3oyXFjEZrcz4V1OTZs2VdWqVTVz5sxs25999lmNGzdOZ8+eVVhYmHbv3q2hQ4fq0KFDCgsL04QJE9SlSxeH5R8+fFiVKlVyOp6uXbu6nPFlt9t177336rvvvtMvv/yi66+/3qn32WyOHtkabz+0UM3rdHMpppwSzxzWU59015vDZ7o0BdK6HYv01MdXd3TOuP2FharaqNtV27etnKw/pr+hIW+u07E98dq2crI6Dsp9raxVP4/VsknPq8uQd9Sy7+NOfe7+TYs09W8Fi9lf6zoQdPu/99X8+tHZto0fUrCyHvsm+58/erC0Ll04XcDICmbk52cUGl7C4X6/vjdQ2/+Y4oWIrrjxL1NUt+3t2ba5o65Tki9owvBihYgs8Awcs1SV63fMtq0gdZ2zTZ86skP/frJuISJzXakKdXTvv5xbDPvLx2vrzDHvLr587zs7Vap8rWzb3FHXB7cs0eTXHZ9vwLd1GPiG2t76QrZt7upjvni0hs6e8O76TcPf36/ikVUc7jfv8/u1YdFEL0R0Rc8HJio69r5s29xV1+8PDVV6moXz6PqYW5+eoZrN+2bb5o66vnj2hD4dGVWIyK5wx7m1VLDza3eeW9/1t7UqVyMm2zZ39DHH963XN39tVvDAgAKy2YL0l6+zZxW661i98fd/a+4n9xassBwC4fq8eJkqGv5e9ie57qrr1f/7u5b+8NcCRlYwrW9+Tp0Gj813H7vdrmN74vXdi+4ZZOWsCrVb647XVmXb5q66Xvz1o4qf/V4BI4Ovu+GRSarT5jYFBYXkuU96eqo2Lv5S8ycWbpltRwLhuAfvKRJWTA9PzJ6J767j3p/zPtLCrx4uYGTZWdWu3dmmS1esp6FvZx8d4a66Xj5ljP74yT1JBP5Y13k9Q5J4jpQbTx2rH3/ccZ2NGzfO4X4ZiRKe9uOPP6pfv3769ttvNWSIc1/GyMhIzZs3L3PJkh49emjy5Mlq37693n77bT399NMejjp/z/7dJJb847kR2X73dVbE7WyqRd5ndteo/fv3KyEhIdcv8t69exUdHa2wsDBJ0ogRIzR48GA9/PDDWrZsmQYOHKhdu3YpNDTUqc9688031bRp01xfS09P13PPPacdO3YU6Is3evRo/fe//9W3337rdEKGt/133us6n3Ra//zh3sxt1co10GMDnMsgc6daLW7WvIkP6NjeP7Vpyddqel3uX9Dj+9brj+lvqNVNz2rlj6+qbpvbVTKqupejdZ0v1bU/sQUFe65sm/cnKgoKLuLUfsFO7udOnqprT/4b+qsgD9WJp8rNT3Cwc/2tZNZ09DZP1QntOjB48jtjC/J+H+Psd8yK76JH+/OgIMmCmUl8lef6c9+b4NHq82vOnRBoPPk9t+I8NT+WHz9snuwXLbgmcOLcwmazKbhImBeiyfm5gVXX8J6gkFDJ7mDgnP3yfn7A6uMevMez116+ddyzul17sj44d8obz5GQmxo1auiWW25RcnKyU8kk0tUJGd27d9fBgwf1yCOPaNWqVbrvvvs0ZswYXbzImnWBhJkyclixYoU6dOigX3/9VTfccEPm9osXL6pOnTrq27evJk6cqOPHj6tGjRpKTEzMTNJo06aN3njjDfXu3Tvfz8iYKWPhwoXq1q3bVa+npaVp2LBhmjRpkn788Uf17dv36kLy8eSTT2rcuHH6/PPPdd999zl+QwGs/l46ZdEUeaWrSq3vKNh7358r7Tia+2tzPxuusIjSOnN0p256/MerXk9NSdb3Y9qodoub1XHQ3zTnk2E6e3y3+v91vsMbSHXKS4/0KljM/lrXgeDXeGmukzNgZWQi58wszk2QTXprsBTi5fPbf84w09Q6Oug/1VeqWsYrIWX6drn0x07n9nWlrkuGS6/d7ni/a8nHC5xfL96Vuq4aKT11g+P93Ck5RXpxqlkrPD9FgqU3BkhhXk5FfWuGc1NDu1LPktS4svQgg4X83oKN0s9rndvX1Tby5kApwsv3Z9+fK+085nipoNE9pboVvBNThh/jHC91lMGVug4Nlv4xWHJyorlrwr+XSGv3OLevK3Vdtrj00q0FjyurQDm3zu+6JidX6rpWOelR38zpR4Cz26XnJknJqY73dbVf7NJAur11wWPLKhCOIUkppq6d4Wpd39hc6tWkYHEVVPxe6avf89/HJimmhjS0s1dCynTsjPS3/zm3r6t1fWd7qV2dgsUF3/dLvDTPiftRN8dIPaI9G0sgHPfgPWnp0jM/mP874upxr0dj6eYWBY8tK6vatTvb9Nkk6aWpzu3ral3f1kqKbViwuHLyx7p2dK3Fc6TsPHWs3rzZ8Y2cRo0aadOmTfnu07ChmxpzFt26dcu2usFzzz2nsWPH6ptvvtHdd9/t8P15JWRkWLlypdq2bauBAwdqypQrM5rHxsZq0aJFbv275Oe5f3wqSfr7sw9m+93X+XLcvjfkyGJRUWZa3K1bs0/99NZbb+nQoUNq1aqVJDNrRoUKFTITMiSpVq1a2rPHybuQ+Thz5oy2bNmi6dOnu5yQ8cILL+idd97Re++957GEjEDVsNPdWjfnfVVvmvtdyGU//FXBIaFq1/8VSVK3/3tPZ47v1pqZ73gvSHhVFQ8lJlQq7f2EDEnqXD//hAybTape1vsJGZLnPtNT/4b+LJDqOqyI1Ka2ucmaF5ukdrW9n5AhmUQVT6jioXLhXZ76dyxb3PsJGdLlPiafTsYmqVwJc4PB2zxV11UiScjIyVN17anjqT+jrhFobDbatbeEF5GiHK9qWSBWXEs2qSIVD8v/msAuqVM9b0V0RdkSnrsOqca1bkDr4MTKoME2EnPge4KDpMqlPVO2FX2MLysRLpUq6pmyqev88RzJd7z66qtWhyBJatu2rSRp5syZDvd1lJCRtZw2bdq4P1hYiqSMHGrXrq1mzZrpzTff1H/+8x/Nnz9fI0eO1BdffCFJmUkZhVGxYkXZ7fZcZ8mQzJdyxYoVDmfcyOmf//yn3nzzTQ0ePFitW7fWihUrMn82btxY6LgDXdVG3VQiqobqd7g6rW9vwnwlLPxUfUZ+o+AQs7RDaNESuv6hr7Vi2ss6vm+9t8OFF9SMyv/GTkHVcs+S6C5rUyvvh2E2SSFB0gCL+vla5TxUrkV17csCra77NJVKReT+XbVJKh0hXZ/7SmEe57G69lC58K5qZcwNK3eradF3sVk1KbpK7q/ZJAUFSYPbWZPE4KnvTE2+i1epTV17DXWNQMS5k/d4ok6CLif5e1tIsDnHkPK+fm9b25rE0CCbZ46r4UWkCqXcXy58R9niUp9m+e9zS0upeLh34gFc4bHrL+7xXcUTdR0STEKrIzxH8h2DBg2yOgRJUtOm5ubz2rX5T4nrTEJG1nIyykXgICkjh6CgIE2ePFnR0dEaOXKkhg0bpqioKI0aNUrBwcFq1sycEVevXl1HjhxRcnJy5nt37dqlGjVquCUOWwHuWP/666+SpB9++EEdOnTI9vPwww+7Ja5AZrPZNPDF3xRe7OqzjupNeujhiecUWblBtu1VGnTWqInnFVWNg2MgKh0hNars/nLbOzHiwRNCgqUR3c0IoZAcR//qUWZqNCtuoknmZN/dJ/xBNqkto0au0rCS+zPpw0LMdMBWKFlUeqy3FF01+01Ym8y2x3qbfazQoob7R8aVjpAaVHJvmbBGRJjUvJr7y3VmVJ0nBAdJw7qYKU6L5JgNqkqkNKqH95ctyVCuhGc+26r+3JfVKieVL+neMoODTGIpsmtSVSoW5ng/V0SEmgQrwCrtPXDuXtsDx6VA0MEDdd2smvuPS85qWk16oNvV/9bhRaTeTaU7LEoMlTxT1+3qeCa5F76ldxMzcKZEjsSL0hHSXR3ct7QA4G6e6M8bVpIii7m/XH/niWvSljXMzLTIG8+RfEejRo2sDkGSNGXKFH355Ze5Jlhk9d133zlMyJDM0i1ffPGF5s6d64lwYSELJvP2ffXr19fChQuzbbvnnnvUuHFjFS1qnuxERUWpU6dOmjhxoh5++GEtW7ZMBw4cUPfu1i3y7s21hAJVsUiedCG72IbSxvz7UpfUKW/tFHChIdLAtmat3x1HpdR0qUJJqbLFGdA2m6nrb5a7r8zm1c3NCmQXHCR1bSD9L959Zbava254WqV0hHR/rHTyvLTnhEnIqF7W+gv28CKmbhY7XgLRaV0bcAM2kHRtKK0p/Mp3mapGWjMKNENIsFl7tm8zafsR6VKqeTjiC1OfdmtoYnKXRpVN/4nsMvrzyX+4r8zWtRgFmpuQYLNs0Gw3DrTqVO/qpCrAm8qVNLMubTjgvjJ5aJm7WuXMrF37Et1XptV13biK6Z/3nJASz5lz8boVzDWwlZpWM9clJ8+7p7xgm9TZgqVY4H02m+nrO9Q157Hnk6USRaU65cwsdICvqhwp1a8obT3svjKt7mN8Vf2KUsVS0uHT7inPJnPfCY7xHAlZ/fWvf3Vqv6efflrFixfXoEGD8k3g2LJli4YPH+6u8OBDOIVz0urVq69auuTjjz/W999/r/r16+vBBx/Ud999p9BQCxbx9jPHTx/QhOmPadPelXr0g4567MPO+ujnx60OKyBR14XXoJLUuqZ7ygoJlga1c09ZhRURZm4OtahhfUJGhta1zMWEO0SEmgeDyF1sQ/f9u0cWMw9gfUFkMSmmuknIsTohI0PfZlKkm5KDqkZyIyLQ1IwyN1rdIcgmDW5v3SjQrMKLmFH8LWv6RkKGZB7yuWsGgNAQ65b78gcd6rhvGt0S4dItLdxTlidknGtnmPrbOD32YWevfX7PaPfNAFCuhNSriXvKAgrj9tbum2msSVXfnf3F6uOHzWaW/Ahy03lDx3q+sUyMzWbOr1rWNEkaVidkSCah+g433ge4vqlJYMK1IzjI3JtqWVOqV8G/EjKsPtbBOgPbuC/Zt2UNz8xk7A5Wt/Egm3RHe/ctvx3b0Heu4SWebXgTde0d69evV+fOnR3OqIHA5QOXJ77v3Llz2rp161VLgNSuXVu//fabRVH5r7itc9Wqfi9VKF1D/xyxQKFFwjX22yHadWi9alVi+iZ3oq7do39rM9rm2Nm893nsGyfKacWo2vzYbNKd7aVxs6UzF/Pez1Fd22TKsWrJCn8QEizd01F6d46UlJL3fo7qOiTIlGPlLBm+LryIdHcn6aP5Zmaa3Dhz/AgvIg3pyCwZgejmGGnXMenAybz3caaN3BRjRrwidzabmSnqwEnpxLm893Omrge2Met8I3dBQdKQDtL42dK55Lz3c1TXQTYzNbdVU+E7I+NcW5IupSZrx8F4r35+kcv9+fvzzMw0eXFU10WCpXs6+cbDS6BMcWlQW+nrZXnv48yxOrKYKccXkhVzY/XxQzIPXW5pIf20Ju99nKnrSqV9O4HOFzSoJF3XSFqwKe99nKnrOuVNQh7gL3zhWAdrlCtpEi2/X5n3Ps4c96JKSLf7cEK8L7TxmlFS3+bSjHV57+NMXVcrI93Q3H1xuQPPNrzHX+u6W7duVocAuITbLk4oXry40tLSrA7DL63bsUiv/Ps21a7UXIcTd6lO5RiViCij0f3eV9GwK3ezg4OKKCiIuXILg7r2nIgwaVRPacJ86eiZgpXRr6UZPYT8RRaTHu5hHmCfzicxIy8ZD3Ca+uiIOF9SqbT00HXSJwuki/kkZuSlSLB0X1eptoVLJfiLOuVNXX3xu5RagNOJoqHSiO7m3wyBJ6yI+S5+vCD/xIz89GkqXdfYvXEFohLhpo+ZMD//xIy82GRmyGhT2+2hBZyoEpfreoF0Lsn19wfbpP/r7Dsj4nI7z35t2HT9uXOxRvd7X5I064+J6tV6qP49e4xXY6tWVhrRTfp0kZScT2JGXsJCzBJg1cu6OzKg4FrVki6lSZNWSvYCvL/M5WsKX0jS9uXjhyR1a2TqOr8HOfmpXFoaeR1J2s64uYWUkib9vrVg769TXnqgG0na8F353ZeUrD3WwRrt65o+Ztrqgr2/3OVrCl9I0vb1/rxXtJSSKs3dULD3Vytj7ktYmaTNsw3vCaS6/uijj6wOAXAJp/LwqKa1uqpBtbb618hFalY7Vn/pP0FJl85nO7jvPPinTp8/phoVeJpQGNS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      "text/plain": [
       "<Figure size 2827.99x385.28 with 1 Axes>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transpiled = transpile(circuit, backend)\n",
    "transpiled.draw(fold=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initial layout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transpiled = transpile(circuit, backend, initial_layout=[0, 2, 3])\n",
    "display(plot_circuit_layout(transpiled, backend))\n",
    "plot_gate_map(backend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3189.19x385.28 with 1 Axes>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transpiled.draw(fold=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimization level\n",
    "\n",
    "Higher levels generate more optimized circuits, at the expense of longer transpilation time.\n",
    "\n",
    " * 0: no explicit optimization other than mapping to backend\n",
    " * 1: light optimization by simple adjacent gate collapsing.(default)\n",
    " * 2: medium optimization by noise adaptive qubit mapping and gate cancellation using commutativity rules.\n",
    " * 3: heavy optimization by noise adaptive qubit mapping and gate cancellation using commutativity rules and unitary synthesis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 598.479x264.88 with 1 Axes>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit = QuantumCircuit(3)\n",
    "circuit.h(0)\n",
    "circuit.cx(0, 1)\n",
    "circuit.cx(1,2)\n",
    "circuit.cx(0,2)\n",
    "circuit.measure_all()\n",
    "circuit.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "level0 = transpile(circuit, backend, optimization_level=0)\n",
    "level1 = transpile(circuit, backend, optimization_level=1)\n",
    "level2 = transpile(circuit, backend, optimization_level=2)\n",
    "level3 = transpile(circuit, backend, optimization_level=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6 10\n",
      "6 10\n",
      "6 10\n",
      "6 10\n"
     ]
    }
   ],
   "source": [
    "for level in [level0, level1, level2, level3]:\n",
    "    print(level.count_ops()['cx'], level.depth())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transpiling is a stochastic process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transpiled = transpile(circuit, backend, optimization_level=2, seed_transpiler=0)\n",
    "transpiled.depth()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transpiled = transpile(circuit, backend, optimization_level=2, seed_transpiler=1)\n",
    "transpiled.depth()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Playing with other transpiler options (without a backend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 598.479x264.88 with 1 Axes>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transpiled = transpile(circuit)\n",
    "transpiled.draw(fold=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set a basis gates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['id', 'rz', 'sx', 'x', 'cx', 'reset']"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "backend.configuration().basis_gates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 598.479x264.88 with 1 Axes>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transpiled = transpile(circuit, basis_gates=['x', 'cx', 'h', 'p'])\n",
    "transpiled.draw(fold=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set a coupling map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0, 1], [1, 0], [1, 2], [1, 3], [2, 1], [3, 1], [3, 4], [4, 3]]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "backend.configuration().coupling_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EEbtoTIWIiJ3+e7l4geJGmw/Dqh+dVlKZ8N0huHrNtm2zc6xdz7KyS7YmkZKydOlSo0sQN+Tn50enTp3o06cPPXv2pGHDhoVu37p1awYOHOiS2hQqbHD69GmeeeYZunXrRsWKFTGZTOzZs8foskTEANk58NnmogNF3F+sX4X5ai8cU7dpm3x7EOK3wbyviw4W2TnwyXfWQfL/3uKa+kScbcqUKUaXIG6iSpUqPP3002zfvp0rV67w/fffs3btWtatW8exY8c4f/48H3/8MV26dMmzX+vWrVm3bh3x8fF07969xOtUqLDB4cOHWbx4MdWqVSM8PNzocsRg/70Ma3+ClT9Y3+hoVp+yZcsROHnBOW1ZsL5Rtlic015pFhoEtW6Ds5cKDxbXA8Wuk1DBG+5q7tIyS61Lv8O6fdbr3rp9kPKb0RWVfvHx8UaXIG7gkUce4fjx48ybN48OHTpgMpn46aefSEpKwmw28+uvvxIQEEB0dDSbN2/myy+/pF69ermBombNmiQlJfH999+XeK0KFTbo0aMH586dIzExkQcffNDocm6RMCOC75fPsHm5FE9aBvxjA7z2BSTuhnX7Yel2mPK5NWS46xtDT319uGPdFgtsPOjcNn9JMf5uhTue65tV9YPY3oUHi5sDxVN3Q8MahpSbL084zzfLzoGE72H6cli509qlbOVOeHk5LN7qvt3LPPFci9zI29ubTz75hE8//RR/f3++++47oqKiqFKlCqGhofTt25eIiAhq165N06ZNee2110hJSeGee+5h//79fPvtt9SsWZPExEQGDx5MenrJfwLq8lCRk5PD7Nmzadq0KRUqVKBt27aYzWaaN2/OyJEjHWo7JSWFyMhIdu/ene96i8XC1KlTOXPmjF3tenkpe5V12TnWPvR783npZOdYQ8ZXe11elrjYmRQ4e9n57W475vw2S6OigoU7BwpPFf89bPz5jw9Nrn92YsE6Lkjdy0Scz8vLi0WLFjF06FBSU1N5/PHHueuuu4iPjyctLe2W7Q8fPszEiRNp1aoVZrOZ2267jWrVqrFlyxaXBQowIFSMGDGCV155hZiYGFatWkVUVBQPP/wwR48epUOHDg61nZ6ezpo1a7h48WK+61NSUkhISCA8PJxTp045dCwpW/aeKfrT5DU/we8ZrqlHjHHCSd2eXNVuaZRfsLhOgcK5/nvZ2t2vMNuPW++2ifOZzWajSxCDjB07lvvvv59Lly4RERHBwoULbdqvRo0aeWYNa9SoEZUrVy6pMm/h0lCxaNEiPvroI1auXMkLL7xAz549mTRpEl27diUrK4v27ds75TgZGRlcu3btlq+KFSuyevVqfHx8CA8P5/jx4045npR+Ww4X/UyBrBzYedwl5YhBzl4qmXb/ewlyckqm7dLo5mBxnQKFc20tIlAAmCg6eEjx7N2r299lUZMmTZgxw9pF7y9/+Qs//PCDTfvdOIYiMTGRb775htq1axMXF1eC1ebl0udUzJw5k8jIyFsGOzdp0gRvb29CQ0MB64wH//73vzl8+DBLlixhyJAhdh2nX79+Nm03bNgwl34SYLLxSVf3T1pPYMsIu9r+fsXf2JE4O8+yzGtXCWrd2+Y2zOYNPNO3p13HLSv+8uqP1GgQWug2FouFF//vVTYtmVSitRj1+oDiv0aKUzO43+u6z8iFtOrxWJ5lRc3wVND6MZ/+8X2OBXz9KpKVcettbXuVlnNti8r+gQx9fS++frcBsGhGP15/cK3Lju+Jv4v2iIxdRNPOQ/AqV/BbhezsLD7810ru73R/idZSGs/12LFjC10/d+5cm7aR4hv/2nuA9f3Zjd8baezYsVSoUIFPPvmExMREm/a5OVAMHjyY+vXrs2/fPh555BEmT57MiRMn8uxjNptt/lktNg4adVmoOH36NHv27Mn3F+TkyZOEhITg6+sLQGRkJI899hiPP/54sY41c+ZM2rRpk++6nJwcJkyYwJEjRxg3blyx2ndHnQdOovOgyXmWJcyIMKaYUij9t0tYcrIxeZUrcBuTyUT67yXQ4d4JPPX14W51Z2c5v3+bxWLBZDKRk53p9Lbt4W7nuihe5crT/S9/zw0UAN0ffoPPj/9AWup5AysrnCed54y0y0XfojX9bzs35EnnWgSgUqVKREdHA/Daa6/ZtE9+gSI9PZ2jR4+yZMkSoqOjGTlyJJMmlewHnuDiUAFQp06dPMvT0tIwm830798/d1m3bt0cOlbXrl2JiIi4ZXl2djbDhw/n6NGjLF++PM8xXcHWpPd2Ehz5tYSLyUd4eAQJM9x0CiODfXvQOtNTUVZ/9jo1qrxeorUY9fqA4r9GPLHm/KzbZ5355kY33nG40fU7FAWtv85kMhFQGbKznBMqSsu5LszNszxF32l9JgVBoUxdlExsL6hcocTLKPXn+tB/Yf7XhW/j5VWeN6cOp+V7w0u0ltJ4rg8cOFDo+rlz5xY5gc2cOXOcWVKZM+H19wHr+7Mbv3eViIiIPD1mOnXqRJUqVdixYwf79u0rcv+CAsV1H3/8MdHR0fTq1euWUBEeHs6GDRuc9rOAC8dU1Khh7eR66NChPMtnzZrF2bNnHR6kbYsrV65w8OBBVqxY4fJAIZ6tUzBUqVD4h3ZhQVCjiutqEtdr4O9Z7ZZG+U0bG1K/6OlmxX5Na1tfmwVd9kxAvWrQvK4LiypDpk+fbnQJ4mLX3wtv3bq1yG2LChQA27ZtA6Bt27aUL1/y9xFcdqciODiY0NBQZs6cib+/P/Xr1ychISG3v5gzQkWdOnUKTZjVq1dny5Ytxeovl5CQAMD27daPq5OSkjhw4ACVKlVSQCkDKnjD072sn9ql3vBmxWSyTrXYtDY83KXg/aV0aFTT+gm4s9+whjZwbnulVWHPobg+eHveV38EC1fdsSitTCZ4MgIWrLOeUxN/TCkL1hA3sid4GdsFvdSKiooyugRxsfr16wNw5Ejhsx/YEigALl++THJyMjVr1qR69eokJ5fsQ5FcFiq8vLyIj48nJiaGUaNGERAQwLBhw4iNjeWll17KHaRd0oo7AOeBBx7I8+/nnnsOgNtvv93wWaSGTN5g13IpnrrVYNJ9sOOY9SnIAG0CoUtjaFHPff+weurrwx3rLl8OujaGJCdOylKlgvGhwh3P9c1sebCduwcLTzjPN7vND56PhN2nrM9T2f+LdXl0N2gbZP2dcEeeeK5v1rJlS/bv3290GeJC48ePZ/r06WRkFD5+LyAggEqVKtn0YLvmzZuTkZHBb7/95uxyb+HS2Z+aNWvG+vXr8yyLjo6mVatW+Pn5ubIUu7myj524rwrecGezP0LF4z2MrUdcL6KldQrNVCfdrRgQ5r5vzNzJ+v22PYfi5mCxeCuMCM9/W7FN+XLQvqH16/oYoQ6NjKxIpHTKzMzk8uWiJz4wm83cdddd7Nu3r8gH26WkuO5BMoY/Knr79u23dH36v//7PwIDA9m8eTMxMTEEBgYWeStIRMQVKvnCg3c4p62W9eCOYOe0VdqFt7COW7LlORRV/WB0b+tYi8EdXVOfiIgr7dy502VPyraVoaHi6tWrHDp06JaH3r3yyiucPn2a9PR0Lly4wOnTp2ncuLFBVYqI5NU6EAYW8azOMZ8WPvNTA3949M6iZ+wUK+9y8Fh32x9sd5ufdTxA9UolWpZIiclvFksRd+bS7k83q1y5MtnZ2UaWICJSLD1bQkUf61TDGVn27ds6EP7SFfx8SqY2EfF8CxYsMLoEEbsY3v1JRMRT3dEYxg+whgRbbjj4V4Kh3WBEDwUKESncqFGjjC5BxC6G3qkQEfF0AZXhiXA4n2qdHefEefglBdIyoZwX1KwCgf7W4NGyLnjpoxwRsYGzH0wmUtIUKkREnKBGFejvmpmxRURE3I4+MxMREREREYcoVIiIiIi4GT34TjyNuj+5ofrVy9ZxxT5G/n8q7rE9sWZPpXPtOjrXrlMWz/WSJUuIiooy5uDiEmFhYXbvc/TkWQCCg+rm+d4Vxy6KQoUb0sOapDCe+PrwxJo9lc616+hcu05ZPNdTp05VqCjl4uLi7N5nwuvvA/Da+JF5vncH6v4kIiIiIiIOUagQERERERGHKFSIiIiIuJn58+cbXYKIXRQqRERERNxMSEiI0SWI2EWhQkRERMTNhIeHG12CiF0UKkRERERExCEKFSIiIiJuplOnTkaXIGIXhQoRERERN7Nt2zajSxCxi0KFiIiIiIg4RKFCREREREQcolAhIiIi4mYSEhKMLkHELgoVIiIiIiLiEIUKERERETczZMgQo0sQsUt5owuQW32+Hc6kuP649avD4I7F29eomsGxuj2RJ55rT6xZRNyHriGuM2bMGHbt2mXIscPCwoiLizPk2OI4hQo3dCYFjvxqdBX28cSaPZUnnmtPrFlE3IeuIa6za9cuzGaz0WWIB1L3JxERERE3Exsba3QJInZRqBARERFxM6NHjza6BBG7KFSI2Cn12h/fn7wAGVnG1SIi4gppGXD8/B///j3DuFrKih49ehhdgohdNKZCxAbnLsN3P8PuU3Dp9z+Wz1kNJhPUrQqdgqFzMFTyNa5OERFnufQ7bD4MPxyH5NS8616KhxpVoP3t0LUJVK9kSImlWnJystEliNhFoUKkEGkZsPwH2Hqk4G0sFvjlEqz4AVbthnvD4M5m4GVyVZUiIs6TlQ1JeyFpD+RYCt7ufCqs3QNf7YW7W0FkGyhfznV1ioh7UagQKcDZS/De+rx3JoqSkQVLt8PeMzC8O/h6l1h5IiJOdyUN3t8Apy/avk+OxRos9p2BkT2hWsUSK69MadWqldEliNhFYypE8vHfy/B2kn2B4kYHzlr/MGu8hYh4iqvX4J2v7AsUN/rlknX/1DSnllVmLV261OgSSlTVqlWNLkGcTKHCBl9//TXDhg2jadOmVKxYkYYNG/Loo49y7Ngxo0uTEpCVDR99W/hAxLi/WL8Kc+RX+PJH59YmIlISLBZYtAV+vVL4dkVd+86nwqebre2JY6ZMmWJ0CUXy9vZm0KBBzJ49m6+//poff/yRnTt3smLFCqZMmUKXLl3y3W/WrFl8//331K1b18UVS0lSqLDBu+++yy+//MKLL77IqlWrmDFjBlu3bqVDhw4cP37c6PLEydbusd6pcIZvDsAxjbUTETf3w3Frt01nOHAWvj/qnLbKsvj4eKNLKJC3tzcTJ07kxIkTLFu2jOeff567776b0NBQwsLCuO+++5g+fTqbN2/mhx9+YNCgQbn7zpo1i3HjxtGoUSPatm1r3A8hTqdQYYP58+eTlJTEk08+SXh4OEOHDmXNmjVcunSJBQsWGF0eCTMi+H75DJuXuwN3rTk9E8wHnNeeBWtfYyO567kuiqfWLeJpLBbrwGxn+mpv4YO8S5quHyUnJCSEbdu2MXPmTOrWrcuePXuYOnUqAwYMoG3btrRv356HHnqIN998k+TkZNq1a8eyZctYtGgRb775JuPGjSMzM5MHHniA1atXG/3jiBO5PFTk5OQwe/ZsmjZtSoUKFWjbti1ms5nmzZszcuRIh9pOSUkhMjKS3bt357veYrEwdepUzpyx7+OYmjVr3rKsYcOG1KhRg9OnTxerVnFPO45DupPHQew7AxevOrdNERFnOfqr8+7OXpecCj//17ltivE6derExo0badu2LUeOHKFv3760adOGl19+mcTERHbv3s3OnTtZvHgxY8aMITAwkL/+9a9cvXqVhx56iGeeeSY3UKxYscLoH0eczOWhYsSIEbzyyivExMSwatUqoqKiePjhhzl69CgdOnRwqO309HTWrFnDxYv5jzJLSUkhISGB8PBwTp065dCx9uzZQ3JyMiEhIQ61I+7lUAn8EbQAh391frsiIs5wsITe/JfE9bQsMZvNRpeQR2BgIKtXr6ZatWosXbqU0NBQkpKSCt0nIyODd955h0WLFuUuO3ToEP/5z39KulwxgEtDxaJFi/joo49YuXIlL7zwAj179mTSpEl07dqVrKws2rdv75TjZGRkcO3atVu+KlasyOrVq/Hx8SE8PLzY4yEyMzOJiYmhRo0axMTEOKVmcQ+nijnriVHtiog4qrizPRVF1z3H7N1rcN/Zm/zjH//A39+fVatW8dBDD/H777ZNjzhr1iyefPJJMjMzuXjxIiEhITz77LMlXK0YwaXPqZg5cyaRkZGEh4fnWd6kSRO8vb0JDQ0F4Pjx4wwbNoyzZ8/i6+vL/Pnz6d69u83H6devn03bDRs2zO5PAiwWC0888QTbtm3jiy++ICAgwOZ9TSbbnoZ2/6T1BLaMsKuu71f8jR2Js/Msy7x2laDWvW1uw2zewDN9e9p13OuMqhkcq/tmsR+mUd6nQu6/i5rhqaD1Yz7N+++FnyxlSKchDlZn5Ynnujg1g/Gva5Gy4JG/7aTm7WF5lhXn2nfzdW/z9r3E9m7tWHH/44nXvaKMHTu20PVz5861aRtXGDBgAJGRkVy4cIHhw4eTlWVbP+Hrg7Kvd3nKzMzkyy+/ZMaMGSxcuJBLly7l2d5sNtv8XskZxr/2HmB9f3bj9+7MiJotNk7n5rJQcfr0afbs2ZPvL8jJkycJCQnB19cXgJiYGB588EGefvppNm3axAMPPMCxY8fw8fGx6VgzZ86kTZs2+a7LyclhwoQJHDlyhHHjxtn9c4wePZp//etffPbZZ/Tt29fu/UtK54GT6Dxocp5lCTMijCnGRm5Zc0n9Yhp8kXLLc20DT61bxLOUzPXJZDJ2LhhdP5wnNjYWgFdffZVz587ZtM/NgeL6GIq1a9fSt29fHnvsMeLi4kqqZDGAS0MFQJ06dfIsT0tLw2w2079/fwDOnz/Pxo0bWblyJQDdunWjXr16rF+/3uY7EF27diUiIuKW5dnZ2QwfPpyjR4+yfPny3GPa6vnnn2fBggX885//5MEHH7RrX7A96b2dZH3GgauFh0eQMKN403UYVTM4VvfNpn4Ol294cNPNn7xdd/1TuoLW3yz64cF8EeecGj3xXHtizSJlxfyvbx3/4IxrX8ewlnzspAdWlMZryIEDhU81OHfu3CInsJkzZ44zSwIgIiIiTy+O6tWr079/f9LT01m4cKFNbRQUKAAWLFhA3759efjhh28JFeHh4WzYsMEZP4ZNJrz+PmB9f3bj9+7MnWt22ccINWrUAKwDdG40a9Yszp49mztI++TJk9SuXTv3rgVAo0aNOHHihMM1XLlyhYMHD7JixQq7A8WkSZOYM2cOb731Fo8//rjDtYh7CvT3rHZFRBwVWL1k2m1ge+9gycf06dONLgEgd7zrjh07CpwI50aFBQqwPlAYoG3btnh7ezu/YDGMy+5UBAcHExoaysyZM/H396d+/fokJCSQmJgI4PDMT2C9C1JYWqtevTpbtmyxu+/ZG2+8wcyZM3nwwQfp2LEjW7ZsyV1322230apVq2LXLO6lcS3nPQDq5nZFRNxR41qwbr/z2w2+dTZ2sUNUVJTRJQDkznL5448/FrltUYECIDU1lcOHD9OkSRMaN25c5B0b8RwuCxVeXl7Ex8cTExPDqFGjCAgIYNiwYcTGxvLSSy/lDtIOCgri3LlzpKen596tOHbsGLfffrtT6ijOYJYvv/wSgMWLF7N48eI861x9qy4/Qybnf/yClrsDd625UzB8+SNk5zivzeCaUKeq89qzl7ue66J4at0inqZFPahWES7ZNpmPTW6rAK0DndeevUrD9aNly5bs318Cac9O27dv5+WXX2bz5s2FbnfvvfcWGSiue/PNNwkICCAlJcXZ5YqBXDr7U7NmzVi/fn2eZdHR0bRq1Qo/Pz/A2k3qzjvv5IMPPsgdqH3mzBl69jRu9hajQ4O4TpUK0DkYNh92Xps9WzqvLRERZyvnBeEtYMUPzmuzRwtru+L5Nm3axKZNm4rc7j//+Q+zZ89m48aNRT7Y7p133nFWeeJGXBoq8rN9+3a6dOmSZ9m7776bOyuAj48PixYtsnnmJxFH3RtmfQr2jQO2iyssCNo0cLwdEZGS1KM57DwBJy843lagvz5MKauKM6umlB6GhoqrV69y6NAhnn766TzLg4OD+eabbwyqSsq6ir4w9E54bx1kFdANypaZT2pWgSGdnFubiEhJKOcF0d0gbi38ll7wdkVd+yr6WNvRXQrH5TeLpYg7MzRUVK5cmezsbCNLEMlX09owIhw+/AYyi/ESrVMVnrobKlcoelsREXdQ8zaI7QXvrocrxbhTW9nXet2rbeAYstJkwYIFRpcgYhd9liBSgJb14MUB9s9g0qM5jI20DnwUEfEk9arDi/dYu27aI7QBjB+g6bOdadSoUUaXIGIXw8dUiLizmlVgdB/Yexq++xkOnoX8Ji32KQ8dGsJdzaB+Cc35LiLiCpUrwGPdrQ+b23gIdp/Kf0Y8L5M1TNzZDJrUgmJMriiF0CQx4mkUKkSK4GWyDrZu0wCuZcKZFPj1ivWPrG95a4ioXVV9iEWkdGlcy/qVmQ1nL1m/MrPBu5y1i2e96tbvRURAoULELhW8//hDKyJSFniXg6AA65eISEH02aqIiIiIm3GHB9+J2EOhQkRERMTNLFmyxOgSROyi7k9uyKiBvo4c18jByWVtYLQnnmtPrFlE3EdZvIZMnTqVqKgolx83LCzM7n2OnjwLQHBQ3Tzfu+LY4j4UKtzQ4I5GV2A/T6zZU3niufbEmkXEfega4jpxcXF27zPh9fcBeG38yDzfS9mi7k8iIiIiIuIQhQoRERERNzN//nyjSxCxi0KFiIiIiJsJCQkxugQRuyhUiIiIiLiZ8PBwo0sQsYtChYiIiIiIOEShQkREREREHKIpZUVERERcrEWLFoWunzp1apHbiLgT3akQERERcTPTpk0zugQRuyhUiIiIiIiIQxQqRERERETEIQoVIiIiIiLiEIUKERERERFxiEKFiIiIiIg4RKFCREREREQcolDhJvr27UtYWBht2rRhyJAhXLlyxeiSRERERErchg0bCAkJoUmTJjzxxBNkZ2cbXVKRnn32WQIDAylf3nMe+Xbq1Cl69epFy5YtCQkJYeLEiU5tX6HCTcTHx7Nr1y5++uknAgMDmTNnjtEliYiIiJSonJwcnnjiCeLj4zl8+DBXrlzhX//6l9FlFemBBx5g+/btRpdhl/Lly/P666+zf/9+du7cycaNG1mxYoXT2leocBNVq1YFrL9c165dw2QyGVyRiIiISMnatm0b9erVo1WrVgCMGDGCpUuXGlxV0e666y7q1KljdBl2qVu3Lh07dgTAx8eHdu3acfLkSae1r1DhRv785z9Tq1YtDh48yPPPP290OSIiIiIl6vTp0zRo0CD330FBQZw6dcrAisqGixcvsnz5cvr06eO0Nj2nI1gZsGzZMjIyMhgxYgQJCQk89thjRpckIiIikofFYiFhlZlfzl3Is/zNhUvz/b5Lu1bcEdaywLZc5ciJX/hi3eZbludXdyW/Cjx6fz98vI19q5ydk8Ony5NIuXw1z/KCznWvbu1p3bxRoW1mZGQwZMgQnn32WVq0aOG0WnWnws34+Pjw0EMPsWzZMqNLEREREbmFyWSiR6dQki9c4uyvfwSLm78/++sFsrNzaN+6aYFtNWjQIM+diZMnTxIYGFgidQcH1aV61cq5tRVWd9f2IYYHCoByXl7c1SmU/9pQs6+PN62a3l5oe9nZ2TzyyCOEhYU5vVeMQoUbSE1N5ezZs4B1TMXKlSsJCQkxuCoRERGR/NWu6U+/Hp0K3aaclxcP3tsT70JmSOrYsSOnT59m3759AHzwwQcMHjzYqbVeZzKZGNyvB5Ur+hW6XYfWzQhp1rBEaiiO4AZ16d45tNBtfH28ifpTT7y8Cn9rP3LkSKpUqcLf//53Z5YIKFS4hdTUVO677z5CQ0MJDQ0lKyuLyZMnG12WiIiISIHu7NSG4KB6Ba7vfVcH6teuUWgb5cqV45///CdDhgyhcePGVK5cmejoaGeXmqtyJT8G9+9R4Ppqt1Xm3t7dimwnJiaGwMBAsrOzCQwMJDY21pll3qJv907Uqelf4Pp7e3XDv2qVQtv47rvv+PDDD9m+fTvt2rUjLCyMt956y2k1miyu7MwmxWKxWDQblIiIiLidS1euMveDeNIzMvMsv71+bUY+ci/livjk3ChLV33Dtt0H8iwzAU8+ci/BDeoaU1QRfvn1AvP+3zKyc3LyLG/VtCHRf+5j+HtF9/w/LblSr/5O3IcJHD111uhSRERERPKodltlBva5M88yH+/yRA3o6baBAuBPd3e55ZP97p1D3TZQANSrFUDfHh3zLKtc0Y/Bkd0NDxTgAaFi2rRpmEwm9uzZw4ABA6hcuTJ169bljTfeAGDVqlW0b9+eihUr0q5dOzZu3Jhn/02bNtGvXz+qVq2Kn58f3bt3v2Wb7du3ExUVRVBQEH5+fjRp0oS//vWvXL58Oc92hw8fZsiQIdSpUwdfX1/q16/Pfffdx4ULeWc/cCbz1h9JvnCJ2ypXLLFjiIiIiBRXu5CmtG72x4xDf7q7KwHVbzOwoqL5+voQ9aeeuW/G69T0p2/3wseIuIPunUJpGPjH8zEG9y96jIiruH2ouO6BBx7g7rvvzp1T98UXX2TChAmMGzeOF198kfj4eCwWCwMHDiQ1NRWAtWvXEhERgclkYuHChSQkJFClShV69erFtm3bcts+fvw4bdq04Z133mH16tVMnDiRVatWcc899+SpYcCAAZw4cYK3336bpKQk5s6dS+3atUlLSyuRnzn16u9s2bWPsJCm1KhetUSOISIiIuIIk8nEn/t1p3IlP1o0DqJTW+dNU1qSGgbWIfyOtpTz8iLqTz0pX76c0SUVycvLi6gBEfj4eNMptAWtmhQ+25Mruf2YimnTpjF9+nQWLFjAU089BUB6ejq1a9fm999/59ChQzRs2BCAdevW0atXLxISErj//vtp1qwZNWrUYOPGjbmj4bOysmjdujXBwcEkJibme8ysrCw2b95Mjx492LlzJ2FhYZw/f56aNWuyfPlyBg4cWKyfZcLr7xdrPxERERERI7w2fqRN23nMnYob7xr4+voSHBxMy5YtcwMFkPsAj1OnTnH48GF+/vlnhg4dSk5ODllZWWRlZQHQu3dvzGZz7n5Xr15l8uTJNG3alAoVKuDt7U2PHtaZAQ4ePAhAQEAAwcHBTJgwgffff58DB/IO7hERERERKauMf6qHjfz9806j5ePjQ4UKFW5ZBnDt2jXOnTsHQGxsbIHTfKWlpeHn58fjjz/OqlWrmDZtGu3bt6dKlSqcOnWKwYMH53ZtMplMfPXVV7z88stMnjyZ5OTk3CnExo8fb9MAGVuTHsAXX29m0449PPdklLo+iYiIiIhb85hQYa+AgADA2n1qwIAB+W7j6+vLtWvXWLZsGVOmTMnzZMGbB2kDNGrUiIULF2KxWNi7dy8ffvghEydOpEaNGjzxxBNF1lSc7k+z319s9z4iIiIiIs5g64fipTZUNG/enODgYH766SemTp1a4Hbp6elkZWXh7e2dZ/mHH35Y4D4mk4nWrVszZ84c3n33XX766Sen1S0iIiIi4mlKbagwmUy8++67DBgwgIEDBzJ06FBq1apFcnIyP/zwA5mZmbzxxhtUrVqVbt26MXv2bGrXrk29evVYsmQJW7duzdPe7t27eeaZZ4iKiqJp06YAxMfHk5aWRr9+/WyqyZakl3r1d15/bxGhLRoTNSDC7p9bRERERMTVSm2oAOjTpw+bNm3ib3/7G6NGjSI1NZVatWrRvn17nnzyydztPvvsM0aPHs2YMWMoV64cf/rTn1i8eDEdO/7xgJE6derQsGFD3nzzTU6fPo23tzctW7ZkyZIlt0w96wjz1h/Jyc7h7m7tnNamiIiIiEhJcvspZcsSi8VCfOIGTJh4QHcpRERERMRDKFS4oZycnNznaoiIiIiIuDuFChERERERcYg+DhcREREREYcoVIiIiIiIiEMUKkRERERExCEKFSIiIiIi4hCFChERERERcYhChYiIiIiIOEShQkREREREHKJQISIiIiIiDlGoEBERERERhyhUiIiIiIiIQxQqRERERETEIQoVIiIiIiLiEIUKERERERFxiEKFiIiIiIg4RKFCREREREQcolAhIiIiIiIOUagQERERERGHKFSIiIiIiIhDFCpERERERMQhChUiIiIiIuIQhQoREREREXGIQoWIiIiIiDhEoUJERERERByiUCEiIiIiIg5RqBAREREREYcoVIiIiIiIiEMUKkRERERExCH/HytT+Z2g+aSTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1027.24x264.88 with 1 Axes>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.transpiler import CouplingMap\n",
    "\n",
    "transpiled = transpile(circuit, coupling_map=CouplingMap([(0,1),(1,2)]))\n",
    "transpiled.draw(fold=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set an initial layout in a coupling map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 846.929x264.88 with 1 Axes>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transpiled = transpile(circuit,\n",
    "                       coupling_map=CouplingMap([(0,1),(1,2)]),\n",
    "                       initial_layout=[1, 0, 2])\n",
    "transpiled.draw(fold=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set an initial_layout in the coupling map with basis gates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1147.93x264.88 with 1 Axes>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transpiled = transpile(circuit,\n",
    "                       coupling_map=CouplingMap([(0,1),(1,2)]),\n",
    "                       initial_layout=[1, 0, 2],\n",
    "                       basis_gates=['x', 'cx', 'h', 'p']\n",
    "                      )\n",
    "transpiled.draw(fold=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transpiled.count_ops()['cx']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plus optimization level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1087.73x264.88 with 1 Axes>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transpiled = transpile(circuit,\n",
    "                       coupling_map=CouplingMap([(0,1),(1,2)]),\n",
    "                       initial_layout=[1, 0, 2],\n",
    "                       basis_gates=['x', 'cx', 'h', 'p'],\n",
    "                       optimization_level=3\n",
    "                      )\n",
    "transpiled.draw(fold=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transpiled.count_ops()['cx']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Last parameter, approximation degree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1087.73x264.88 with 1 Axes>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transpiled = transpile(circuit,\n",
    "                       coupling_map=CouplingMap([(0,1),(1,2)]),\n",
    "                       initial_layout=[1, 0, 2],\n",
    "                       basis_gates=['x', 'cx', 'h', 'p'],\n",
    "                       approximation_degree=0.99,\n",
    "                       optimization_level=3\n",
    "                      )\n",
    "transpiled.draw(fold=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transpiled.depth()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1087.73x264.88 with 1 Axes>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transpiled = transpile(circuit,\n",
    "                       coupling_map=CouplingMap([(0,1),(1,2)]),\n",
    "                       initial_layout=[1, 0, 2],\n",
    "                       basis_gates=['x', 'cx', 'h', 'p'],\n",
    "                       approximation_degree=0.01,\n",
    "                       optimization_level=3\n",
    "                      )\n",
    "transpiled.draw(fold=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transpiled.depth()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Qiskit is hardware agnostic!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install qiskit-ionq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_ionq import IonQProvider\n",
    "provider = IonQProvider(<your token>)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "[(b.name(), b.configuration().n_qubits) for b in provider.backends()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "circuit = QuantumCircuit(2)\n",
    "circuit.h(0)\n",
    "circuit.cx(0, 1)\n",
    "circuit.measure_all()\n",
    "circuit.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "backend = provider.get_backend(\"ionq_qpu\")\n",
    "job = backend.run(circuit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_histogram()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "job.get_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_histogram(job.get_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<frozen importlib._bootstrap>:219: RuntimeWarning: scipy._lib.messagestream.MessageStream size changed, may indicate binary incompatibility. Expected 56 from C header, got 64 from PyObject\n"
     ]
    },
    {
     "data": {
      "text/html": [
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    "import qiskit.tools.jupyter\n",
    "%qiskit_version_table\n",
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      "state": {
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          "text/plain": "<Figure size 360x360 with 1 Axes>"
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          "text/plain": "<Figure size 360x360 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        }
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       "value": "<table><style>\ntable {\n    border-collapse: collapse;\n    width: auto;\n}\n\nth, td {\n    text-align: left;\n    padding: 8px;\n}\n\ntr:nth-child(even) {background-color: #f6f6f6;}\n</style><tr><th></th><th></th></tr><tr><td>local</td><td>False</td></tr><tr><td>measure_esp_enabled</td><td>False</td></tr><tr><td>simulator</td><td>False</td></tr><tr><td>clops</td><td>2506</td></tr><tr><td>memory</td><td>True</td></tr><tr><td>multi_meas_enabled</td><td>True</td></tr><tr><td>rep_times</td><td>[1000.0]</td></tr><tr><td>description</td><td>5 qubit device Quito</td></tr><tr><td>acquisition_latency</td><td>[]</td></tr><tr><td>allow_q_object</td><td>True</td></tr><tr><td>credits_required</td><td>True</td></tr><tr><td>meas_lo_range</td><td>[[6.807868157000001, 7.807868157000001], [6.7292695270000005, 7.7292695270000005], [6.991060108, 7.991060108], [6.8547510460000005, 7.8547510460000005], [6.904365522000001, 7.904365522000001]]</td></tr><tr><td>url</td><td>None</td></tr><tr><td>dynamic_reprate_enabled</td><td>True</td></tr><tr><td>default_rep_delay</td><td>250.0</td></tr><tr><td>n_registers</td><td>1</td></tr><tr><td>channels</td><td>{'acquire0': {'operates': {'qubits': [0]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire1': {'operates': {'qubits': [1]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire2': {'operates': {'qubits': [2]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire3': {'operates': {'qubits': [3]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire4': {'operates': {'qubits': [4]}, 'purpose': 'acquire', 'type': 'acquire'}, 'd0': {'operates': {'qubits': [0]}, 'purpose': 'drive', 'type': 'drive'}, 'd1': {'operates': {'qubits': [1]}, 'purpose': 'drive', 'type': 'drive'}, 'd2': {'operates': {'qubits': [2]}, 'purpose': 'drive', 'type': 'drive'}, 'd3': {'operates': {'qubits': [3]}, 'purpose': 'drive', 'type': 'drive'}, 'd4': {'operates': {'qubits': [4]}, 'purpose': 'drive', 'type': 'drive'}, 'm0': {'operates': {'qubits': [0]}, 'purpose': 'measure', 'type': 'measure'}, 'm1': {'operates': {'qubits': [1]}, 'purpose': 'measure', 'type': 'measure'}, 'm2': {'operates': {'qubits': [2]}, 'purpose': 'measure', 'type': 'measure'}, 'm3': {'operates': {'qubits': [3]}, 'purpose': 'measure', 'type': 'measure'}, 'm4': {'operates': {'qubits': [4]}, 'purpose': 'measure', 'type': 'measure'}, 'u0': {'operates': {'qubits': [0, 1]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u1': {'operates': {'qubits': [1, 0]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u2': {'operates': {'qubits': [1, 2]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u3': {'operates': {'qubits': [1, 3]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u4': {'operates': {'qubits': [2, 1]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u5': {'operates': {'qubits': [3, 1]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u6': {'operates': {'qubits': [3, 4]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u7': {'operates': {'qubits': [4, 3]}, 'purpose': 'cross-resonance', 'type': 'control'}}</td></tr><tr><td>dt</td><td>0.2222222222222222</td></tr><tr><td>u_channel_lo</td><td>[[{'q': 1, 'scale': (1+0j)}], [{'q': 0, 'scale': (1+0j)}], [{'q': 2, 'scale': (1+0j)}], [{'q': 3, 'scale': (1+0j)}], [{'q': 1, 'scale': (1+0j)}], [{'q': 1, 'scale': (1+0j)}], [{'q': 4, 'scale': (1+0j)}], [{'q': 3, 'scale': (1+0j)}]]</td></tr><tr><td>online_date</td><td>2021-01-08 05:00:00+00:00</td></tr><tr><td>hamiltonian</td><td>$$\\begin{align} \\mathcal{H}/\\hbar = & \\sum_{i=0}^{4}\\left(\\frac{\\omega_{q,i}}{2}(\\mathbb{I}-\\sigma_i^{z})+\\frac{\\Delta_{i}}{2}(O_i^2-O_i)+\\Omega_{d,i}D_i(t)\\sigma_i^{X}\\right) \\\\ & + J_{0,1}(\\sigma_{0}^{+}\\sigma_{1}^{-}+\\sigma_{0}^{-}\\sigma_{1}^{+}) + J_{1,2}(\\sigma_{1}^{+}\\sigma_{2}^{-}+\\sigma_{1}^{-}\\sigma_{2}^{+}) + J_{1,3}(\\sigma_{1}^{+}\\sigma_{3}^{-}+\\sigma_{1}^{-}\\sigma_{3}^{+}) + J_{3,4}(\\sigma_{3}^{+}\\sigma_{4}^{-}+\\sigma_{3}^{-}\\sigma_{4}^{+}) \\\\ & + \\Omega_{d,0}(U_{0}^{(0,1)}(t))\\sigma_{0}^{X} + \\Omega_{d,1}(U_{1}^{(1,0)}(t)+U_{3}^{(1,3)}(t)+U_{2}^{(1,2)}(t))\\sigma_{1}^{X} \\\\ & + \\Omega_{d,2}(U_{4}^{(2,1)}(t))\\sigma_{2}^{X} + \\Omega_{d,3}(U_{6}^{(3,4)}(t)+U_{5}^{(3,1)}(t))\\sigma_{3}^{X} \\\\ & + \\Omega_{d,4}(U_{7}^{(4,3)}(t))\\sigma_{4}^{X} \\\\ \\end{align}$$</td></tr><tr><td>conditional</td><td>False</td></tr><tr><td>coupling_map</td><td>[[0, 1], [1, 0], [1, 2], [1, 3], [2, 1], [3, 1], [3, 4], [4, 3]]</td></tr><tr><td>discriminators</td><td>['hw_centroid', 'quadratic_discriminator', 'linear_discriminator']</td></tr><tr><td>rep_delay_range</td><td>[0.0, 500.0]</td></tr><tr><td>qubit_channel_mapping</td><td>[['u1', 'd0', 'u0', 'm0'], ['u0', 'm1', 'u4', 'u3', 'u5', 'u2', 'd1', 'u1'], ['m2', 'u4', 'u2', 'd2'], ['m3', 'u7', 'u3', 'u5', 'u6', 'd3'], ['u7', 'u6', 'd4', 'm4']]</td></tr><tr><td>qubit_lo_range</td><td>[[4.800541040012398, 5.800541040012398], [4.580580828765353, 5.580580828765353], [4.822189923881474, 5.822189923881474], [4.6636068164653475, 5.6636068164653475], [4.552327895072668, 5.552327895072668]]</td></tr><tr><td>timing_constraints</td><td>{'acquire_alignment': 16, 'granularity': 16, 'min_length': 64, 'pulse_alignment': 1}</td></tr><tr><td>supported_features</td><td>['qobj']</td></tr><tr><td>sample_name</td><td>family: Falcon, revision: 4, segment: T</td></tr><tr><td>open_pulse</td><td>False</td></tr><tr><td>n_uchannels</td><td>8</td></tr><tr><td>processor_type</td><td>{'family': 'Falcon', 'revision': 4, 'segment': 'T'}</td></tr><tr><td>uchannels_enabled</td><td>True</td></tr><tr><td>conditional_latency</td><td>[]</td></tr><tr><td>supported_instructions</td><td>['reset', 'x', 'setf', 'play', 'shiftf', 'rz', 'measure', 'u3', 'cx', 'acquire', 'sx', 'u2', 'u1', 'delay', 'id']</td></tr><tr><td>input_allowed</td><td>['job', 'runtime']</td></tr><tr><td>allow_object_storage</td><td>True</td></tr><tr><td>live_data</td><td>False</td></tr><tr><td>meas_levels</td><td>[1, 2]</td></tr><tr><td>pulse_num_qubits</td><td>3</td></tr><tr><td>meas_kernels</td><td>['hw_boxcar']</td></tr><tr><td>dtm</td><td>0.2222222222222222</td></tr><tr><td>backend_name</td><td>ibmq_quito</td></tr><tr><td>pulse_num_channels</td><td>9</td></tr><tr><td>meas_map</td><td>[[0, 1, 2, 3, 4]]</td></tr><tr><td>parametric_pulses</td><td>['gaussian', 'gaussian_square', 'drag', 'constant']</td></tr></table>"
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\n",
          "text/plain": "<Figure size 360x360 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "32e94fb350934b24b7b3c10ac6cf3f4f": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLMathModel",
      "state": {
       "layout": "IPY_MODEL_3e9546f1bb674cdeacb6ef390991be2b",
       "style": "IPY_MODEL_f78da227947145748698cdbe42cca795",
       "value": "<table><style>\ntable {\n    border-collapse: collapse;\n    width: auto;\n}\n\nth, td {\n    text-align: left;\n    padding: 8px;\n}\n\ntr:nth-child(even) {background-color: #f6f6f6;}\n</style><tr><th></th><th></th></tr><tr><td>local</td><td>False</td></tr><tr><td>measure_esp_enabled</td><td>False</td></tr><tr><td>simulator</td><td>False</td></tr><tr><td>clops</td><td>2454</td></tr><tr><td>memory</td><td>True</td></tr><tr><td>multi_meas_enabled</td><td>True</td></tr><tr><td>rep_times</td><td>[1000.0]</td></tr><tr><td>description</td><td>5 qubit device Belem</td></tr><tr><td>acquisition_latency</td><td>[]</td></tr><tr><td>allow_q_object</td><td>True</td></tr><tr><td>credits_required</td><td>True</td></tr><tr><td>meas_lo_range</td><td>[[6.801489952000001, 7.801489952000001], [6.8934333720000005, 7.8934333720000005], [6.860065181, 7.860065181], [6.8033823270000005, 7.8033823270000005], [6.926310916, 7.926310916]]</td></tr><tr><td>url</td><td>None</td></tr><tr><td>dynamic_reprate_enabled</td><td>True</td></tr><tr><td>default_rep_delay</td><td>250.0</td></tr><tr><td>n_registers</td><td>1</td></tr><tr><td>channels</td><td>{'acquire0': {'operates': {'qubits': [0]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire1': {'operates': {'qubits': [1]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire2': {'operates': {'qubits': [2]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire3': {'operates': {'qubits': [3]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire4': {'operates': {'qubits': [4]}, 'purpose': 'acquire', 'type': 'acquire'}, 'd0': {'operates': {'qubits': [0]}, 'purpose': 'drive', 'type': 'drive'}, 'd1': {'operates': {'qubits': [1]}, 'purpose': 'drive', 'type': 'drive'}, 'd2': {'operates': {'qubits': [2]}, 'purpose': 'drive', 'type': 'drive'}, 'd3': {'operates': {'qubits': [3]}, 'purpose': 'drive', 'type': 'drive'}, 'd4': {'operates': {'qubits': [4]}, 'purpose': 'drive', 'type': 'drive'}, 'm0': {'operates': {'qubits': [0]}, 'purpose': 'measure', 'type': 'measure'}, 'm1': {'operates': {'qubits': [1]}, 'purpose': 'measure', 'type': 'measure'}, 'm2': {'operates': {'qubits': [2]}, 'purpose': 'measure', 'type': 'measure'}, 'm3': {'operates': {'qubits': [3]}, 'purpose': 'measure', 'type': 'measure'}, 'm4': {'operates': {'qubits': [4]}, 'purpose': 'measure', 'type': 'measure'}, 'u0': {'operates': {'qubits': [0, 1]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u1': {'operates': {'qubits': [1, 0]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u2': {'operates': {'qubits': [1, 2]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u3': {'operates': {'qubits': [1, 3]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u4': {'operates': {'qubits': [2, 1]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u5': {'operates': {'qubits': [3, 1]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u6': {'operates': {'qubits': [3, 4]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u7': {'operates': {'qubits': [4, 3]}, 'purpose': 'cross-resonance', 'type': 'control'}}</td></tr><tr><td>dt</td><td>0.2222222222222222</td></tr><tr><td>u_channel_lo</td><td>[[{'q': 1, 'scale': (1+0j)}], [{'q': 0, 'scale': (1+0j)}], [{'q': 2, 'scale': (1+0j)}], [{'q': 3, 'scale': (1+0j)}], [{'q': 1, 'scale': (1+0j)}], [{'q': 1, 'scale': (1+0j)}], [{'q': 4, 'scale': (1+0j)}], [{'q': 3, 'scale': (1+0j)}]]</td></tr><tr><td>online_date</td><td>2021-01-08 05:00:00+00:00</td></tr><tr><td>hamiltonian</td><td>$$\\begin{align} \\mathcal{H}/\\hbar = & \\sum_{i=0}^{4}\\left(\\frac{\\omega_{q,i}}{2}(\\mathbb{I}-\\sigma_i^{z})+\\frac{\\Delta_{i}}{2}(O_i^2-O_i)+\\Omega_{d,i}D_i(t)\\sigma_i^{X}\\right) \\\\ & + J_{0,1}(\\sigma_{0}^{+}\\sigma_{1}^{-}+\\sigma_{0}^{-}\\sigma_{1}^{+}) + J_{1,2}(\\sigma_{1}^{+}\\sigma_{2}^{-}+\\sigma_{1}^{-}\\sigma_{2}^{+}) + J_{1,3}(\\sigma_{1}^{+}\\sigma_{3}^{-}+\\sigma_{1}^{-}\\sigma_{3}^{+}) + J_{3,4}(\\sigma_{3}^{+}\\sigma_{4}^{-}+\\sigma_{3}^{-}\\sigma_{4}^{+}) \\\\ & + \\Omega_{d,0}(U_{0}^{(0,1)}(t))\\sigma_{0}^{X} + \\Omega_{d,1}(U_{1}^{(1,0)}(t)+U_{3}^{(1,3)}(t)+U_{2}^{(1,2)}(t))\\sigma_{1}^{X} \\\\ & + \\Omega_{d,2}(U_{4}^{(2,1)}(t))\\sigma_{2}^{X} + \\Omega_{d,3}(U_{6}^{(3,4)}(t)+U_{5}^{(3,1)}(t))\\sigma_{3}^{X} \\\\ & + \\Omega_{d,4}(U_{7}^{(4,3)}(t))\\sigma_{4}^{X} \\\\ \\end{align}$$</td></tr><tr><td>conditional</td><td>False</td></tr><tr><td>coupling_map</td><td>[[0, 1], [1, 0], [1, 2], [1, 3], [2, 1], [3, 1], [3, 4], [4, 3]]</td></tr><tr><td>discriminators</td><td>['hw_centroid', 'quadratic_discriminator', 'linear_discriminator']</td></tr><tr><td>rep_delay_range</td><td>[0.0, 500.0]</td></tr><tr><td>qubit_channel_mapping</td><td>[['u0', 'd0', 'm0', 'u1'], ['u0', 'm1', 'u5', 'u3', 'u1', 'u2', 'u4', 'd1'], ['u4', 'd2', 'm2', 'u2'], ['u7', 'u5', 'd3', 'u3', 'u6', 'm3'], ['d4', 'u7', 'm4', 'u6']]</td></tr><tr><td>qubit_lo_range</td><td>[[4.590212061000103, 5.590212061000103], [4.745507603451665, 5.745507603451665], [4.861500828470743, 5.861500828470744], [4.670645325553972, 5.670645325553972], [4.758644659191847, 5.758644659191847]]</td></tr><tr><td>timing_constraints</td><td>{'acquire_alignment': 16, 'granularity': 16, 'min_length': 64, 'pulse_alignment': 1}</td></tr><tr><td>supported_features</td><td>['qobj']</td></tr><tr><td>sample_name</td><td>family: Falcon, revision: 4, segment: T</td></tr><tr><td>open_pulse</td><td>False</td></tr><tr><td>n_uchannels</td><td>8</td></tr><tr><td>processor_type</td><td>{'family': 'Falcon', 'revision': 4, 'segment': 'T'}</td></tr><tr><td>uchannels_enabled</td><td>True</td></tr><tr><td>conditional_latency</td><td>[]</td></tr><tr><td>supported_instructions</td><td>['shiftf', 'play', 'reset', 'sx', 'setf', 'acquire', 'u3', 'cx', 'u1', 'measure', 'x', 'rz', 'u2', 'delay', 'id']</td></tr><tr><td>input_allowed</td><td>['job', 'runtime']</td></tr><tr><td>allow_object_storage</td><td>True</td></tr><tr><td>live_data</td><td>False</td></tr><tr><td>meas_levels</td><td>[1, 2]</td></tr><tr><td>pulse_num_qubits</td><td>3</td></tr><tr><td>meas_kernels</td><td>['hw_boxcar']</td></tr><tr><td>dtm</td><td>0.2222222222222222</td></tr><tr><td>backend_name</td><td>ibmq_belem</td></tr><tr><td>pulse_num_channels</td><td>9</td></tr><tr><td>meas_map</td><td>[[0, 1, 2, 3, 4]]</td></tr><tr><td>parametric_pulses</td><td>['gaussian', 'gaussian_square', 'drag', 'constant']</td></tr></table>"
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\n",
          "text/plain": "<Figure size 360x360 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "4330e9bd9af34e72a3abb73c6e2894f4": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "43c149b1828c42f3a51979ae106fffb2": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_b602927bdbd64149b9858cbc84dcd67e",
       "style": "IPY_MODEL_5940b0bbe0184b95a1ea7d595c49b052",
       "value": "<h4>ibmq_santiago</h4>"
      }
     },
     "4460d1d5955047a4becf5f54cecbfcd9": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_9a344cbfec744af2b2a7ba56ecac02b9",
       "outputs": [
        {
         "data": {
          "image/png": 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       "value": "<h1 style='color:#ffffff;background-color:#000000;padding-top: 1%;padding-bottom: 1%;padding-left: 1%; margin-top: 0px'>ibmq_quito</h1>"
      }
     },
     "48b7f99d55d34169896dcc15053d4815": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "LabelModel",
      "state": {
       "layout": "IPY_MODEL_fe2279c61ece4800ad19ad2d59d5f122",
       "style": "IPY_MODEL_78e9c3c2da134b2da9982035cb38a11c",
       "value": "241"
      }
     },
     "49179e6fa6d745b397e8c58d86c3adf5": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_1f59bb7ff9ac48629c6d458b3016d640",
       "style": "IPY_MODEL_14e3da6908604510b787a4b85232b170",
       "value": "ibmq_belem"
      }
     },
     "4a3b4365219d459b981bebfb9bde305c": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "grid_gap": "0px 25px",
       "grid_template_columns": "250px 250px 250px 250px 250px 250px 250px ",
       "grid_template_rows": "auto"
      }
     },
     "4a521c60a0124d25b72ca505e8637a79": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_c2fd0dd3093e415598340aaf66284752",
       "style": "IPY_MODEL_1d1374173b124b6b81548037bff37049",
       "value": "<h5 style='color:#dc267f'>False</h5>"
      }
     },
     "4a59d71386164b66998b372e501cff44": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "4a5b83f180084158bc7cfc24fffdf02a": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "align_items": "center",
       "display": "inline-flex",
       "flex_flow": "column"
      }
     },
     "4adbb04896884d63aef759abd876bcda": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_5d14d33cc1634329909c2b822791d037",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 792x648 with 5 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "4aed05607d224f95a0701e995eba56d3": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "4af5a03525e3443aa3c2b2dbe1abdd42": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "4b2f6d504d4047c09d817fa779e17875": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "4bbd9989d3b24adcb9ad4df645f65aea": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "4bdb709305d549b19a85a08cebb95807": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_d1a44d1c4a134fd08ad0c0136abe1477",
       "style": "IPY_MODEL_b8f5429222a549b3935e99fd63c32b54",
       "value": "<h5><b>5</b></h5>"
      }
     },
     "4be8969063eb473cad21c5091efbeffa": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "GridBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_861f9268deb1449197bc98af04a4be91",
        "IPY_MODEL_c27a96eccea94c8495077fe78580688c",
        "IPY_MODEL_60934d7358814797b2fd3fbbdef0ed2d"
       ],
       "layout": "IPY_MODEL_697dd433bea049e9a623a7c6d8f84951"
      }
     },
     "4c173c2fb86148ae9c87026708e00210": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_466f90dd9d5b4e8f95b73238fecae701",
       "style": "IPY_MODEL_c275b6af217b4ac295912f9aa54124ad",
       "value": "<h5 style='color:#dc267f'>False</h5>"
      }
     },
     "4c1acdc37f584aa69efa98fbac710849": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_0b3623f5cf2e4136a11bbf42898cb91f",
       "outputs": [
        {
         "name": "stderr",
         "output_type": "stream",
         "text": "<frozen importlib._bootstrap>:219: RuntimeWarning: scipy._lib.messagestream.MessageStream size changed, may indicate binary incompatibility. Expected 56 from C header, got 64 from PyObject\n"
        },
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 792x648 with 5 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "4c92e814927346a4b5485b85ac69637a": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_085f7f4b0e114c8c9e744bc20530f751",
       "style": "IPY_MODEL_81d7988ea2c14559b8fb5b38ef7bc1be",
       "value": "-"
      }
     },
     "4cb8754430244e1ebf3b8670aecf7f46": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_552e5a08ef524baa8e82243bc4e1a956",
       "outputs": [
        {
         "data": {
          "image/png": 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          "text/plain": "<Figure size 360x360 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "5fad9047d5be4233980f63b499ecb25d": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_ce8a222abf274cd59d8545226f5182b8",
       "style": "IPY_MODEL_e4a7b04587d34654812d8fa6bee939be",
       "value": "ibmq_quito"
      }
     },
     "600bc54a6dbb4d06adae9387eb6874c7": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_693fe5810b8340eeb87792cb68f0a0f3",
       "style": "IPY_MODEL_3b56fce3a54d45369cc60c207c45e255",
       "value": "<h5>0.0294</h5>"
      }
     },
     "606ed51d3a01442ab275827b38db8e63": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_3ab89c0ce3aa48c39c74c02d2abba3d0",
       "style": "IPY_MODEL_fb14e1dd242341a89e67a41154f910d6",
       "value": "<h5>0.0215</h5>"
      }
     },
     "60934d7358814797b2fd3fbbdef0ed2d": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLMathModel",
      "state": {
       "layout": "IPY_MODEL_b5cad1a869dc4b4a8ab3963d5a331389",
       "style": "IPY_MODEL_73d7c0c487de444d84974a60af96581b",
       "value": "<table><style>\ntable {\n    border-collapse: collapse;\n    width: auto;\n}\n\nth, td {\n    text-align: left;\n    padding: 8px;\n}\n\ntr:nth-child(even) {background-color: #f6f6f6;}\n</style><tr><th></th><th></th></tr><tr><td>local</td><td>False</td></tr><tr><td>measure_esp_enabled</td><td>False</td></tr><tr><td>simulator</td><td>False</td></tr><tr><td>clops</td><td>2506</td></tr><tr><td>memory</td><td>True</td></tr><tr><td>multi_meas_enabled</td><td>True</td></tr><tr><td>rep_times</td><td>[1000.0]</td></tr><tr><td>description</td><td>5 qubit device Quito</td></tr><tr><td>acquisition_latency</td><td>[]</td></tr><tr><td>allow_q_object</td><td>True</td></tr><tr><td>credits_required</td><td>True</td></tr><tr><td>meas_lo_range</td><td>[[6.807868157000001, 7.807868157000001], [6.7292695270000005, 7.7292695270000005], [6.991060108, 7.991060108], [6.8547510460000005, 7.8547510460000005], [6.904365522000001, 7.904365522000001]]</td></tr><tr><td>url</td><td>None</td></tr><tr><td>dynamic_reprate_enabled</td><td>True</td></tr><tr><td>default_rep_delay</td><td>250.0</td></tr><tr><td>n_registers</td><td>1</td></tr><tr><td>channels</td><td>{'acquire0': {'operates': {'qubits': [0]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire1': {'operates': {'qubits': [1]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire2': {'operates': {'qubits': [2]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire3': {'operates': {'qubits': [3]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire4': {'operates': {'qubits': [4]}, 'purpose': 'acquire', 'type': 'acquire'}, 'd0': {'operates': {'qubits': [0]}, 'purpose': 'drive', 'type': 'drive'}, 'd1': {'operates': {'qubits': [1]}, 'purpose': 'drive', 'type': 'drive'}, 'd2': {'operates': {'qubits': [2]}, 'purpose': 'drive', 'type': 'drive'}, 'd3': {'operates': {'qubits': [3]}, 'purpose': 'drive', 'type': 'drive'}, 'd4': {'operates': {'qubits': [4]}, 'purpose': 'drive', 'type': 'drive'}, 'm0': {'operates': {'qubits': [0]}, 'purpose': 'measure', 'type': 'measure'}, 'm1': {'operates': {'qubits': [1]}, 'purpose': 'measure', 'type': 'measure'}, 'm2': {'operates': {'qubits': [2]}, 'purpose': 'measure', 'type': 'measure'}, 'm3': {'operates': {'qubits': [3]}, 'purpose': 'measure', 'type': 'measure'}, 'm4': {'operates': {'qubits': [4]}, 'purpose': 'measure', 'type': 'measure'}, 'u0': {'operates': {'qubits': [0, 1]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u1': {'operates': {'qubits': [1, 0]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u2': {'operates': {'qubits': [1, 2]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u3': {'operates': {'qubits': [1, 3]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u4': {'operates': {'qubits': [2, 1]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u5': {'operates': {'qubits': [3, 1]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u6': {'operates': {'qubits': [3, 4]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u7': {'operates': {'qubits': [4, 3]}, 'purpose': 'cross-resonance', 'type': 'control'}}</td></tr><tr><td>dt</td><td>0.2222222222222222</td></tr><tr><td>u_channel_lo</td><td>[[{'q': 1, 'scale': (1+0j)}], [{'q': 0, 'scale': (1+0j)}], [{'q': 2, 'scale': (1+0j)}], [{'q': 3, 'scale': (1+0j)}], [{'q': 1, 'scale': (1+0j)}], [{'q': 1, 'scale': (1+0j)}], [{'q': 4, 'scale': (1+0j)}], [{'q': 3, 'scale': (1+0j)}]]</td></tr><tr><td>online_date</td><td>2021-01-08 05:00:00+00:00</td></tr><tr><td>hamiltonian</td><td>$$\\begin{align} \\mathcal{H}/\\hbar = & \\sum_{i=0}^{4}\\left(\\frac{\\omega_{q,i}}{2}(\\mathbb{I}-\\sigma_i^{z})+\\frac{\\Delta_{i}}{2}(O_i^2-O_i)+\\Omega_{d,i}D_i(t)\\sigma_i^{X}\\right) \\\\ & + J_{0,1}(\\sigma_{0}^{+}\\sigma_{1}^{-}+\\sigma_{0}^{-}\\sigma_{1}^{+}) + J_{1,2}(\\sigma_{1}^{+}\\sigma_{2}^{-}+\\sigma_{1}^{-}\\sigma_{2}^{+}) + J_{1,3}(\\sigma_{1}^{+}\\sigma_{3}^{-}+\\sigma_{1}^{-}\\sigma_{3}^{+}) + J_{3,4}(\\sigma_{3}^{+}\\sigma_{4}^{-}+\\sigma_{3}^{-}\\sigma_{4}^{+}) \\\\ & + \\Omega_{d,0}(U_{0}^{(0,1)}(t))\\sigma_{0}^{X} + \\Omega_{d,1}(U_{1}^{(1,0)}(t)+U_{3}^{(1,3)}(t)+U_{2}^{(1,2)}(t))\\sigma_{1}^{X} \\\\ & + \\Omega_{d,2}(U_{4}^{(2,1)}(t))\\sigma_{2}^{X} + \\Omega_{d,3}(U_{6}^{(3,4)}(t)+U_{5}^{(3,1)}(t))\\sigma_{3}^{X} \\\\ & + \\Omega_{d,4}(U_{7}^{(4,3)}(t))\\sigma_{4}^{X} \\\\ \\end{align}$$</td></tr><tr><td>conditional</td><td>False</td></tr><tr><td>coupling_map</td><td>[[0, 1], [1, 0], [1, 2], [1, 3], [2, 1], [3, 1], [3, 4], [4, 3]]</td></tr><tr><td>discriminators</td><td>['hw_centroid', 'quadratic_discriminator', 'linear_discriminator']</td></tr><tr><td>rep_delay_range</td><td>[0.0, 500.0]</td></tr><tr><td>qubit_channel_mapping</td><td>[['u1', 'd0', 'u0', 'm0'], ['u0', 'm1', 'u4', 'u3', 'u5', 'u2', 'd1', 'u1'], ['m2', 'u4', 'u2', 'd2'], ['m3', 'u7', 'u3', 'u5', 'u6', 'd3'], ['u7', 'u6', 'd4', 'm4']]</td></tr><tr><td>qubit_lo_range</td><td>[[4.800541040012398, 5.800541040012398], [4.580580828765353, 5.580580828765353], [4.822189923881474, 5.822189923881474], [4.6636068164653475, 5.6636068164653475], [4.552327895072668, 5.552327895072668]]</td></tr><tr><td>timing_constraints</td><td>{'acquire_alignment': 16, 'granularity': 16, 'min_length': 64, 'pulse_alignment': 1}</td></tr><tr><td>supported_features</td><td>['qobj']</td></tr><tr><td>sample_name</td><td>family: Falcon, revision: 4, segment: T</td></tr><tr><td>open_pulse</td><td>False</td></tr><tr><td>n_uchannels</td><td>8</td></tr><tr><td>processor_type</td><td>{'family': 'Falcon', 'revision': 4, 'segment': 'T'}</td></tr><tr><td>uchannels_enabled</td><td>True</td></tr><tr><td>conditional_latency</td><td>[]</td></tr><tr><td>supported_instructions</td><td>['reset', 'x', 'setf', 'play', 'shiftf', 'rz', 'measure', 'u3', 'cx', 'acquire', 'sx', 'u2', 'u1', 'delay', 'id']</td></tr><tr><td>input_allowed</td><td>['job', 'runtime']</td></tr><tr><td>allow_object_storage</td><td>True</td></tr><tr><td>live_data</td><td>False</td></tr><tr><td>meas_levels</td><td>[1, 2]</td></tr><tr><td>pulse_num_qubits</td><td>3</td></tr><tr><td>meas_kernels</td><td>['hw_boxcar']</td></tr><tr><td>dtm</td><td>0.2222222222222222</td></tr><tr><td>backend_name</td><td>ibmq_quito</td></tr><tr><td>pulse_num_channels</td><td>9</td></tr><tr><td>meas_map</td><td>[[0, 1, 2, 3, 4]]</td></tr><tr><td>parametric_pulses</td><td>['gaussian', 'gaussian_square', 'drag', 'constant']</td></tr></table>"
      }
     },
     "60ef035220754eac86341905cb081712": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_8281c28d3ba04ed3a80ae668f114d946",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 360x360 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "611792fedaef4e76aea840e63f13b77c": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "justify_content": "center"
      }
     },
     "6135fb17e71b4f268a80639d2ce2e40f": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "6137abd4f2944606abd29b70e7b0bd08": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "margin": "8px 0px 0px 0px"
      }
     },
     "614a1b24777e49bf81ff7a724de6d2c8": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "min_width": "auto"
      }
     },
     "615f0e3ac7224330b5d5dc69105c84bb": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_86992922ff734fc1a3876e10f2f84daf",
       "style": "IPY_MODEL_ddf7edf1e6414c6d8683484c65b1d8d0",
       "value": "<table><style>\ntable {\n    border-collapse: collapse;\n    width: auto;\n}\n\nth, td {\n    text-align: left;\n    padding: 8px;\n}\n\ntr:nth-child(even) {background-color: #f6f6f6;}\n</style><tr><th></th><th>Frequency</th><th>T1</th><th>T2</th><th>RZ</th><th>SX</th><th>X</th><th>Readout error</th></tr><tr><td><font style='font-weight:bold'>Q0</font></td><td>5.30054 GHz</td><td>66.32672 us</td><td>108.95054 us</td><td>0</td><td>0.00041</td><td>0.00041</td><td>0.037 </td><tr><td><font style='font-weight:bold'>Q1</font></td><td>5.08058 GHz</td><td>93.66322 us</td><td>114.37514 us</td><td>0</td><td>0.00059</td><td>0.00059</td><td>0.0385 </td><tr><td><font style='font-weight:bold'>Q2</font></td><td>5.32219 GHz</td><td>50.77516 us</td><td>67.74683 us</td><td>0</td><td>0.00028</td><td>0.00028</td><td>0.0657 </td><tr><td><font style='font-weight:bold'>Q3</font></td><td>5.16361 GHz</td><td>126.81019 us</td><td>23.87534 us</td><td>0</td><td>0.00035</td><td>0.00035</td><td>0.0343 </td><tr><td><font style='font-weight:bold'>Q4</font></td><td>5.05233 GHz</td><td>83.35941 us</td><td>128.24079 us</td><td>0</td><td>0.00034</td><td>0.00034</td><td>0.0392 </td></table>"
      }
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          "text/plain": "<Figure size 360x360 with 1 Axes>"
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      "state": {
       "layout": "IPY_MODEL_79504bc79d224a0abe7e4c38dd5decb0",
       "outputs": [
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          "text/plain": "<Figure size 360x360 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
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     "879bf8a6a22a4e7facb5a8080d8f58f9": {
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     "879cac8104da4411952e0585ffa67ce0": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "LabelModel",
      "state": {
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     "87cf842d183b48b4a2ba4aa5346ba3cb": {
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      "model_name": "LayoutModel",
      "state": {
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       "justify_content": "center",
       "margin": "0px 0px 0px 0px",
       "max_height": "250px",
       "max_width": "250px",
       "min_height": "250px",
       "min_width": "250px"
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     "87d85ea714034f15b1055f4d0f0cac6d": {
      "model_module": "@jupyter-widgets/base",
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      "model_name": "LayoutModel",
      "state": {
       "grid_template_areas": "\n                                       \". . . . right \"\n                                        ",
       "grid_template_columns": "20% 20% 20% 20% 20%",
       "width": "100%"
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     },
     "889e9a5f95864b23bbb54599b5f6e821": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLModel",
      "state": {
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       "style": "IPY_MODEL_f44a776803e749c6a554472676ad262b",
       "value": "<h5>NA</h5>"
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     },
     "88a9ecdc84164c72855607f3abbf55eb": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "LabelModel",
      "state": {
       "layout": "IPY_MODEL_614a1b24777e49bf81ff7a724de6d2c8",
       "style": "IPY_MODEL_796abb42af8241ecb5df1ead17c25b4a",
       "value": "50"
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     },
     "88cfdbf14dbc4d70a55e8fc06c71ddd1": {
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      "model_name": "DescriptionStyleModel",
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     },
     "88e083a7da464f08b9450e4089b35aec": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_e3afb618db2e456780bbcbbae8806dc8",
       "style": "IPY_MODEL_531ec14ac65e4fdc801007e7b38c264f",
       "value": "<table><style>\ntable {\n    border-collapse: collapse;\n    width: auto;\n}\n\nth, td {\n    text-align: left;\n    padding: 8px;\n}\n\ntr:nth-child(even) {background-color: #f6f6f6;}\n</style><tr><th></th><th>Frequency</th><th>T1</th><th>T2</th><th>RZ</th><th>SX</th><th>X</th><th>Readout error</th></tr><tr><td><font style='font-weight:bold'>Q0</font></td><td>5.09021 GHz</td><td>116.6279 us</td><td>113.41316 us</td><td>0</td><td>0.0002</td><td>0.0002</td><td>0.0165 </td><tr><td><font style='font-weight:bold'>Q1</font></td><td>5.24551 GHz</td><td>71.52067 us</td><td>81.59946 us</td><td>0</td><td>0.00325</td><td>0.00325</td><td>0.039 </td><tr><td><font style='font-weight:bold'>Q2</font></td><td>5.3615 GHz</td><td>79.0816 us</td><td>72.69211 us</td><td>0</td><td>0.00028</td><td>0.00028</td><td>0.021 </td><tr><td><font style='font-weight:bold'>Q3</font></td><td>5.17065 GHz</td><td>125.36185 us</td><td>157.39478 us</td><td>0</td><td>0.17466</td><td>0.17466</td><td>0.1295 </td><tr><td><font style='font-weight:bold'>Q4</font></td><td>5.25864 GHz</td><td>66.81345 us</td><td>95.27688 us</td><td>0</td><td>0.00024</td><td>0.00024</td><td>0.0262 </td></table>"
      }
     },
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     },
     "894bd405639e45d8a6321fcbc3e4ddfc": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLModel",
      "state": {
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\n",
          "text/plain": "<Figure size 792x648 with 5 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "8eb95166aac94e05b2d36d21e9937ba9": {
      "model_module": "@jupyter-widgets/controls",
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      "model_name": "HTMLModel",
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       "style": "IPY_MODEL_b92edfe6cc6b4b14985c8e8253e941f4",
       "value": "<h5 style='color:#34bc6e'>True</h5>"
      }
     },
     "8ef94de4e1fc4f258186250a446960ec": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLMathModel",
      "state": {
       "layout": "IPY_MODEL_ccd1f87cf282426b8bc3ae0a7cc213e6",
       "style": "IPY_MODEL_c2bb0269dd5e429b83fce3d3afeef97a",
       "value": "<table><style>\ntable {\n    border-collapse: collapse;\n    width: auto;\n}\n\nth, td {\n    text-align: left;\n    padding: 8px;\n}\n\ntr:nth-child(even) {background-color: #f6f6f6;}\n</style><tr><th>Property</th><th>Value</th></tr><tr><td><font style='font-weight:bold'>n_qubits</font></td><td>5</td></tr><tr><td><font style='font-weight:bold'>quantum_volume</font></td><td>16</td></tr><tr><td><font style='font-weight:bold'>operational</font></td><td>True</td></tr><tr><td><font style='font-weight:bold'>status_msg</font></td><td>active</td></tr><tr><td><font style='font-weight:bold'>pending_jobs</font></td><td>2</td></tr><tr><td><font style='font-weight:bold'>backend_version</font></td><td>1.0.42</td></tr><tr><td><font style='font-weight:bold'>basis_gates</font></td><td>['id', 'rz', 'sx', 'x', 'cx', 'reset']</td></tr><tr><td><font style='font-weight:bold'>max_shots</font></td><td>20000</td></tr><tr><td><font style='font-weight:bold'>max_experiments</font></td><td>100</td></tr></table>"
      }
     },
     "8f1d9f3cc258485eb917a7814876b654": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "8f6f1d9abab64f4cb686b47f566ecc0c": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "8f6f8e14e2f4441da90a4379c2f1886d": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "justify_content": "center"
      }
     },
     "8f76c81db92b4ea48989a0edce951b89": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "LabelModel",
      "state": {
       "layout": "IPY_MODEL_80ea652c98734c099061645156acac06",
       "style": "IPY_MODEL_dae02cf6b5e440f0874e4d4d810612d5",
       "value": "Num. Qubits"
      }
     },
     "8fc34b3d674f42428f07df90c3d41d2b": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "8ff89fb8282a447d96549f352caa16b5": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "margin": "0px 5px 0px 0px",
       "width": "32px"
      }
     },
     "905f0221ed044743b14c5f8d31f6754d": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_8d7430fa55f1438d8f8b8b1e388971d4",
       "style": "IPY_MODEL_0175590e45f94a0f9f8502fd5df195d2",
       "value": "<h4>ibmq_belem</h4>"
      }
     },
     "90879ea8dbb949e79ef7bfa8d3cc3b35": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "908bcc56cd3a42b4abbea6236f3e09e1": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "9096bf548b1a4cec86933972384b685c": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "90bfb42798a7484e8b821b0b6f0073e7": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_7f7260e6b0a24b2b83f712a0fd6d473a",
       "outputs": [
        {
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\n",
          "text/plain": "<Figure size 360x360 with 1 Axes>"
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          "text/plain": "<Figure size 360x360 with 1 Axes>"
         },
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         "output_type": "display_data"
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     "c4abc7349d3643349b25952899df8d36": {
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     "c4ae4650b4b2466593837742e29f01bb": {
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      "state": {
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       "value": "<table><style>\ntable {\n    border-collapse: collapse;\n    width: auto;\n}\n\nth, td {\n    text-align: left;\n    padding: 8px;\n}\n\ntr:nth-child(even) {background-color: #f6f6f6;}\n</style><tr><th></th><th></th></tr><tr><td>local</td><td>False</td></tr><tr><td>measure_esp_enabled</td><td>False</td></tr><tr><td>simulator</td><td>False</td></tr><tr><td>clops</td><td>2506</td></tr><tr><td>memory</td><td>True</td></tr><tr><td>multi_meas_enabled</td><td>True</td></tr><tr><td>rep_times</td><td>[1000.0]</td></tr><tr><td>description</td><td>5 qubit device Quito</td></tr><tr><td>acquisition_latency</td><td>[]</td></tr><tr><td>allow_q_object</td><td>True</td></tr><tr><td>credits_required</td><td>True</td></tr><tr><td>meas_lo_range</td><td>[[6.807868157000001, 7.807868157000001], [6.7292695270000005, 7.7292695270000005], [6.991060108, 7.991060108], [6.8547510460000005, 7.8547510460000005], [6.904365522000001, 7.904365522000001]]</td></tr><tr><td>url</td><td>None</td></tr><tr><td>dynamic_reprate_enabled</td><td>True</td></tr><tr><td>default_rep_delay</td><td>250.0</td></tr><tr><td>n_registers</td><td>1</td></tr><tr><td>channels</td><td>{'acquire0': {'operates': {'qubits': [0]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire1': {'operates': {'qubits': [1]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire2': {'operates': {'qubits': [2]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire3': {'operates': {'qubits': [3]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire4': {'operates': {'qubits': [4]}, 'purpose': 'acquire', 'type': 'acquire'}, 'd0': {'operates': {'qubits': [0]}, 'purpose': 'drive', 'type': 'drive'}, 'd1': {'operates': {'qubits': [1]}, 'purpose': 'drive', 'type': 'drive'}, 'd2': {'operates': {'qubits': [2]}, 'purpose': 'drive', 'type': 'drive'}, 'd3': {'operates': {'qubits': [3]}, 'purpose': 'drive', 'type': 'drive'}, 'd4': {'operates': {'qubits': [4]}, 'purpose': 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J_{3,4}(\\sigma_{3}^{+}\\sigma_{4}^{-}+\\sigma_{3}^{-}\\sigma_{4}^{+}) \\\\ & + \\Omega_{d,0}(U_{0}^{(0,1)}(t))\\sigma_{0}^{X} + \\Omega_{d,1}(U_{1}^{(1,0)}(t)+U_{3}^{(1,3)}(t)+U_{2}^{(1,2)}(t))\\sigma_{1}^{X} \\\\ & + \\Omega_{d,2}(U_{4}^{(2,1)}(t))\\sigma_{2}^{X} + \\Omega_{d,3}(U_{6}^{(3,4)}(t)+U_{5}^{(3,1)}(t))\\sigma_{3}^{X} \\\\ & + \\Omega_{d,4}(U_{7}^{(4,3)}(t))\\sigma_{4}^{X} \\\\ \\end{align}$$</td></tr><tr><td>conditional</td><td>False</td></tr><tr><td>coupling_map</td><td>[[0, 1], [1, 0], [1, 2], [1, 3], [2, 1], [3, 1], [3, 4], [4, 3]]</td></tr><tr><td>discriminators</td><td>['hw_centroid', 'quadratic_discriminator', 'linear_discriminator']</td></tr><tr><td>rep_delay_range</td><td>[0.0, 500.0]</td></tr><tr><td>qubit_channel_mapping</td><td>[['u1', 'd0', 'u0', 'm0'], ['u0', 'm1', 'u4', 'u3', 'u5', 'u2', 'd1', 'u1'], ['m2', 'u4', 'u2', 'd2'], ['m3', 'u7', 'u3', 'u5', 'u6', 'd3'], ['u7', 'u6', 'd4', 'm4']]</td></tr><tr><td>qubit_lo_range</td><td>[[4.800541040012398, 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     "e4330e8646384ac390d9423008233944": {
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        {
         "data": {
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          "text/plain": "<Figure size 360x360 with 1 Axes>"
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       "value": "<h5>0.0496</h5>"
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       "style": "IPY_MODEL_0f01677465d6478a9daa713949c55872",
       "value": "<h5><b>5</b></h5>"
      }
     },
     "effc87c8d1d246fca6f9310d1ff64ab1": {
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     },
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      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
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        "IPY_MODEL_3ae343a21678452986a9d0641b4e3cc2",
        "IPY_MODEL_3c820910cec2484b971188cb92159ac0",
        "IPY_MODEL_54956e3476dc47c28d69325033b8f705",
        "IPY_MODEL_33be807f54e34149a88e91d814f37a3d",
        "IPY_MODEL_7e7591510a124b6c990b084f5e5e1691",
        "IPY_MODEL_168f8bc2978f487c91c150ae5ed0b5f9",
        "IPY_MODEL_b148b93dd12c4dc69df204ca93b7949f",
        "IPY_MODEL_0fb5c183c7914026b4f574c32e942d5c",
        "IPY_MODEL_889e9a5f95864b23bbb54599b5f6e821",
        "IPY_MODEL_53f179f242e84cdbbcdff428acb63e1b"
       ],
       "layout": "IPY_MODEL_0f590fffb2324710a7b19030736d9843"
      }
     },
     "f05d91ced3984a9189b93924dbb4dc20": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "LabelModel",
      "state": {
       "layout": "IPY_MODEL_98ca76c324b64b3cae38a06514f80ac1",
       "style": "IPY_MODEL_e57b03a5367046a0bf0ca79f7692f632",
       "value": "316"
      }
     },
     "f0795e25d2fd49c3b98ef0157d69ba68": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "f0804c9b6955446a997989a62b3dd3db": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_78e0a8e814ad483cb9ef807a885e13e5",
        "IPY_MODEL_77433a4344cc49a9a24477567c79842f",
        "IPY_MODEL_0d4cd2e2578b48a683d54d1d0ea3722e",
        "IPY_MODEL_57aeff20a28841e591ce92c9f6c1418d",
        "IPY_MODEL_b67f454b593c40c395a302db4bfb76eb",
        "IPY_MODEL_f32b26dc2fad4881ae1bd07445713896",
        "IPY_MODEL_4f3f97a379c44bc18b49bc80a8032a26",
        "IPY_MODEL_0dd88a1526ae4c67875660d781469d62"
       ],
       "layout": "IPY_MODEL_a73e70dfc21e4d2398512971bc136420"
      }
     },
     "f0ce2190ae80461bb0e586e996304368": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "grid_gap": "0px 0px",
       "grid_template_areas": "\n                                                   \"top top top\"\n                                                   \"left middle right\"\n                                                   ",
       "grid_template_columns": "33% 33% 33%",
       "grid_template_rows": "auto auto"
      }
     },
     "f12274a9db9c4429a7069195b5a7b78b": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "grid_area": "bottom",
       "width": "auto"
      }
     },
     "f1f0763edc0742689e98ca574d1a9bd0": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "f21c818cef9049c7b93a12c30f51e8cc": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_53d3aaf13c1b4991824ad3e6e6aad3ec",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 360x360 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "f2547c47ad89498684f6561f36a4b729": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "min_width": "auto"
      }
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