Grover_4qubits.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1326b779-9d32-4a13-9754-335975b675fd",
   "metadata": {},
   "source": [
    "## Grover's algorithm on a 4-qubit space using the V-Oracle model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a8686e1",
   "metadata": {},
   "source": [
    "Search for the state |1111>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bf3b38a-dbbb-43ba-9937-59e9819baa84",
   "metadata": {},
   "source": [
    "Import the required packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "291303e1-734a-4a8d-9e44-65cbaf1e250b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import QuantumRegister, ClassicalRegister, QuantumCircuit\n",
    "from qiskit import Aer, assemble\n",
    "from qiskit.visualization import plot_histogram"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "047d6ef6-6001-4d8f-b4b2-2122739466b2",
   "metadata": {},
   "source": [
    "Define the registers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aacaf33c-beb3-41f9-8d9e-3f9aabcaf76e",
   "metadata": {},
   "outputs": [],
   "source": [
    "qreg_q = QuantumRegister(6, 'q')\n",
    "creg_c = ClassicalRegister(4, 'c')\n",
    "circuit = QuantumCircuit(qreg_q, creg_c)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d63d5d1-9890-4daf-8edf-aeeddb5c2065",
   "metadata": {},
   "source": [
    "Initialise the states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "73051513-8a76-4b78-a689-4c704e1a104a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x7f94e29d1c00>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.h(qreg_q[0])\n",
    "circuit.h(qreg_q[1])\n",
    "circuit.h(qreg_q[3])\n",
    "circuit.h(qreg_q[5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a03f1922-c1e3-49ea-944b-5da97931be59",
   "metadata": {},
   "source": [
    "Quantum barrier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fc8d51ff-a501-4b49-afe7-6e455b220c62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x7f94e29da680>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.barrier(qreg_q[0], qreg_q[1], qreg_q[2], qreg_q[3], qreg_q[4], qreg_q[5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79ac8a3a-6967-4e1c-923f-01d7a5174287",
   "metadata": {},
   "source": [
    "Iteration 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b93b7090-e00f-4874-9a80-66886ad3606e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x7f94e29856c0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Oracle iteration 1\n",
    "circuit.ccx(qreg_q[0], qreg_q[1], qreg_q[2])\n",
    "circuit.ccx(qreg_q[2], qreg_q[3], qreg_q[4])\n",
    "circuit.cz(qreg_q[4], qreg_q[5])\n",
    "circuit.ccx(qreg_q[2], qreg_q[3], qreg_q[4])\n",
    "circuit.ccx(qreg_q[0], qreg_q[1], qreg_q[2])\n",
    "# barrier\n",
    "circuit.barrier(qreg_q[0], qreg_q[1], qreg_q[2], qreg_q[3], qreg_q[4], qreg_q[5])\n",
    "# diffuser iteration 1\n",
    "circuit.h(qreg_q[0])\n",
    "circuit.h(qreg_q[1])\n",
    "circuit.h(qreg_q[3])\n",
    "circuit.h(qreg_q[5])\n",
    "circuit.x(qreg_q[0])\n",
    "circuit.x(qreg_q[1])\n",
    "circuit.x(qreg_q[3])\n",
    "circuit.x(qreg_q[5])\n",
    "circuit.ccx(qreg_q[0], qreg_q[1], qreg_q[2])\n",
    "circuit.ccx(qreg_q[2], qreg_q[3], qreg_q[4])\n",
    "circuit.cz(qreg_q[4], qreg_q[5])\n",
    "circuit.ccx(qreg_q[2], qreg_q[3], qreg_q[4])\n",
    "circuit.ccx(qreg_q[0], qreg_q[1], qreg_q[2])\n",
    "circuit.x(qreg_q[0])\n",
    "circuit.x(qreg_q[1])\n",
    "circuit.x(qreg_q[3])\n",
    "circuit.x(qreg_q[5])\n",
    "circuit.h(qreg_q[0])\n",
    "circuit.h(qreg_q[1])\n",
    "circuit.h(qreg_q[3])\n",
    "circuit.h(qreg_q[5])\n",
    "# barrier\n",
    "circuit.barrier(qreg_q[0], qreg_q[1], qreg_q[2], qreg_q[3], qreg_q[4], qreg_q[5])\n",
    "circuit.barrier(qreg_q[0], qreg_q[1], qreg_q[2], qreg_q[3], qreg_q[4], qreg_q[5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e296df55-0847-4f7b-a9ca-ad053d77e2d9",
   "metadata": {},
   "source": [
    "Iteration 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e6bb1308-0d33-4c8d-a4c8-61e5ed0e4e27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x7f94e297bdc0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Oracle iteration 2\n",
    "circuit.ccx(qreg_q[0], qreg_q[1], qreg_q[2])\n",
    "circuit.ccx(qreg_q[2], qreg_q[3], qreg_q[4])\n",
    "circuit.cz(qreg_q[4], qreg_q[5])\n",
    "circuit.ccx(qreg_q[2], qreg_q[3], qreg_q[4])\n",
    "circuit.ccx(qreg_q[0], qreg_q[1], qreg_q[2])\n",
    "# barrier\n",
    "circuit.barrier(qreg_q[0], qreg_q[1], qreg_q[2], qreg_q[3], qreg_q[4], qreg_q[5])\n",
    "# diffuser iteration 2\n",
    "circuit.h(qreg_q[0])\n",
    "circuit.h(qreg_q[1])\n",
    "circuit.h(qreg_q[3])\n",
    "circuit.h(qreg_q[5])\n",
    "circuit.x(qreg_q[0])\n",
    "circuit.x(qreg_q[1])\n",
    "circuit.x(qreg_q[3])\n",
    "circuit.x(qreg_q[5])\n",
    "circuit.ccx(qreg_q[0], qreg_q[1], qreg_q[2])\n",
    "circuit.ccx(qreg_q[2], qreg_q[3], qreg_q[4])\n",
    "circuit.cz(qreg_q[4], qreg_q[5])\n",
    "circuit.ccx(qreg_q[2], qreg_q[3], qreg_q[4])\n",
    "circuit.ccx(qreg_q[0], qreg_q[1], qreg_q[2])\n",
    "circuit.x(qreg_q[0])\n",
    "circuit.x(qreg_q[1])\n",
    "circuit.x(qreg_q[3])\n",
    "circuit.x(qreg_q[5])\n",
    "circuit.h(qreg_q[0])\n",
    "circuit.h(qreg_q[1])\n",
    "circuit.h(qreg_q[3])\n",
    "circuit.h(qreg_q[5])\n",
    "# barrier\n",
    "circuit.barrier(qreg_q[0], qreg_q[1], qreg_q[2], qreg_q[3], qreg_q[4], qreg_q[5])\n",
    "circuit.barrier(qreg_q[0], qreg_q[1], qreg_q[2], qreg_q[3], qreg_q[4], qreg_q[5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fab5ab87-a345-49f7-b1ba-30c551feaa82",
   "metadata": {},
   "source": [
    "Iteration 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "df8ba417-e447-49d6-83b0-a739196e8dc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x7f94e2988380>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Oracle iteration 3\n",
    "circuit.ccx(qreg_q[0], qreg_q[1], qreg_q[2])\n",
    "circuit.ccx(qreg_q[2], qreg_q[3], qreg_q[4])\n",
    "circuit.cz(qreg_q[4], qreg_q[5])\n",
    "circuit.ccx(qreg_q[2], qreg_q[3], qreg_q[4])\n",
    "circuit.ccx(qreg_q[0], qreg_q[1], qreg_q[2])\n",
    "# barrier\n",
    "circuit.barrier(qreg_q[0], qreg_q[1], qreg_q[2], qreg_q[3], qreg_q[4], qreg_q[5])\n",
    "# diffuser iteration 3\n",
    "circuit.h(qreg_q[0])\n",
    "circuit.h(qreg_q[1])\n",
    "circuit.h(qreg_q[3])\n",
    "circuit.h(qreg_q[5])\n",
    "circuit.x(qreg_q[0])\n",
    "circuit.x(qreg_q[1])\n",
    "circuit.x(qreg_q[3])\n",
    "circuit.x(qreg_q[5])\n",
    "circuit.ccx(qreg_q[0], qreg_q[1], qreg_q[2])\n",
    "circuit.ccx(qreg_q[2], qreg_q[3], qreg_q[4])\n",
    "circuit.cz(qreg_q[4], qreg_q[5])\n",
    "circuit.ccx(qreg_q[2], qreg_q[3], qreg_q[4])\n",
    "circuit.ccx(qreg_q[0], qreg_q[1], qreg_q[2])\n",
    "circuit.x(qreg_q[0])\n",
    "circuit.x(qreg_q[1])\n",
    "circuit.x(qreg_q[3])\n",
    "circuit.x(qreg_q[5])\n",
    "circuit.h(qreg_q[0])\n",
    "circuit.h(qreg_q[1])\n",
    "circuit.h(qreg_q[3])\n",
    "circuit.h(qreg_q[5])\n",
    "# barrier\n",
    "circuit.barrier(qreg_q[0], qreg_q[1], qreg_q[2], qreg_q[3], qreg_q[4], qreg_q[5])\n",
    "circuit.barrier(qreg_q[0], qreg_q[1], qreg_q[2], qreg_q[3], qreg_q[4], qreg_q[5])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b0fe431-fbbe-4d22-9a4d-8dfe77f182fd",
   "metadata": {},
   "source": [
    " Measurements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8f13f5b6-0816-440e-ad5b-cb35e6719ff3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x7f94e2980380>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.measure(qreg_q[0], creg_c[0])\n",
    "circuit.measure(qreg_q[1], creg_c[1])\n",
    "circuit.measure(qreg_q[3], creg_c[2])\n",
    "circuit.measure(qreg_q[5], creg_c[3])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4df781c0-95cd-4249-bd29-f22de5a8307f",
   "metadata": {},
   "source": [
    "Simulation of the circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dc5abcb9-e80f-45b2-bdcf-e7498ad8cf1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aer_sim = Aer.get_backend('aer_simulator')\n",
    "qobj = assemble(circuit)\n",
    "result = aer_sim.run(qobj).result()\n",
    "counts = result.get_counts()\n",
    "plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb4a160c-80d3-410d-94d0-c1c714f9776f",
   "metadata": {},
   "source": [
    "Visualization of the circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0270ea6f-43a8-4d1e-bf0e-ee38696bca52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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spa/ODFtWe7JK/YZfHWfGMn39mklxPeeMrCcek39kcVzP8fl88hWPUGjp+/J/dop8OTlxv29ZWZl8Yy+L+3nxKBrwEf142r+irhesk7p3vUAHj+1OaR4gGW64fLqm3/jTyCuFpO0b9utSX/eUZkmkfkjma4ipet2SWh1JU2PguWbNmhV1vVmzZiUrUkySkTvdmRsa0KNIv7p3VdT1QkFpUP9Cbdu3Lg2pgJa5esxt+s/PPhd1vSP7T8rna5OGRE2zue65OMa4Op7HqvT6Wbrh8q8py5/V/Eoh6R+v/ksjPnlFSrMAyfLk3Ys04sIrI64TCAb0+EM/11N/+I+UZsnEGmJtA0ySevbsGba8urpa5eXluu6660zEitm4G2Zo3I3fC1s276ESM2HiENq8WcEXfif/LZ9R8PkX5L/ycvm6p/aLdiJOnGpi4q9mVNt6bihwjuoYtutQKBTX9p9urtSQhlyt10ideP7GbP57BBqKZYyRpJOn2G9KJlfHGFfG8+pTVfJHOZkhGAzoxCluCQl3VJ+sVCAYiNjY9ft8Mdd1E2yuIVY2wPLz8yVJFRUVYc2uxx9/XLt379bo0aNb/B5nLq0sKyuLuN5P/y5t3Nfit0vIxIklmvdQYrM2Xr3kXS06fCjm9UM1tap77An5b7pRWXfcrtDhwwo8/iNlPf6ofP7Yr5QtKSnRP1I802QoJL31P1FO5fdJXfpJx5kJH46oOeHdmSvSqfw+n0/jPz5QoUdT+zcWb/2Q7Kghpup1S2p1JOvWRT+zaNasWVHvivzkk08mK1JMkpE73ZnP9e5zUuV+NX8ZpE/q0EPaf2RHOmMBCaur8e4eFu1GKsM+0lUhgzOG21z3XBxjXB3PY1W5z6vXkfj9Wbr96x/X//c/zIQPN+xcKa39a+R1fD6/fvzsDD3TY0ZKs9heQ0pKSuLKJlk6B9jAgQNVXFysRx55RM8995xef/11lZaW6plnnpEk7gCZAsFnfiNfdrb8t31ekpR191cV2rdfwfkvG07WmM8nDbg0ykohqf/YtMQBkqJVW+mCSHND+iR/ttR7ZNoixcWlGpJJHnjgAdMREmJ77gHjFHUOsAFMPQqHZLeS+lwceR2fX+rb8mPMKWd7/XCdS+N5++5S534RVvBJrdpJPYemLRLQYj2HSrl5ijgPWOe+3oE4G9leQ6xsgPn9fs2dO1dFRUUqLS3VHXfcofz8fE2bNk3Z2dkqLo7/OlQ0L7hsuYKv/kVZ3/2OfNneSYG+tm2V9Z1vKfjc8wpt3mw4YWMXjGjQBGuiOAy9Wuo6IJ2JgJYbMknKH3j6l3O266xsadRNUpuOaY8VlYs1JFNMmTLFdISE2J67x1BpUITpNwZNkLoPTl8eIBkuusLbtiU1GmP8WVLxDVK7rmmPFTfb64fLXBzPR3xKan+mEXDOdt2qrTR6spTVKu2xgIRl5UgXT/a23zCnt+/23b3t3kYu1BArL4GUpMGDB2vhwvD7Nd92220qLCxUmzb1k3PW1tYqEAgoGAyqtrZWJ0+eVG5ubtTJ7VNl8vfK4lpuA//Fo+Rf8FLj5cOL5P+jHZ3ac/l83peTboOkHSuk3afnK+47Wuoz0o0dOOBc/mxp5Kelg5u97frARm/5wMul3sVSbjuz+ZrjYg2R3KzX5yosLNTatRbfBqgZLuQecKnU9UJpx/L6O+P1Hin1GSW172YyGZAYv18a/gmp9whvjNlX4S2/8DJv227d3my+WLlQPyQ3xxgXx/NWbaSxn5P2rffu9nhoq7d88FXSBUVSdq7ZfEAi8vKlj0yVdq+WPnzDW9aln3TBcO8AXKT7PpjkQg2x8gyw5ixZsqTR5Y9f+cpX1KZNG7355pv63Oc+pzZt2mjr1q2GEiLdOvaSiq6t/33IVTS/4DafzzsLbNSn65cNHG9v8wvIZO27S4XX1P9e+FGaX3Cbzyd16S8VX1+/7KIr3Gl+AU3xZ3mXjY3+TP2yfqNpfsFt2bnhl6WP/ozUs9De5pcrnGmAVVVVqaKiotEE+M8++6xCoVDYz4ABA8yEBAAAAAAAgHWsvQTyXHl5eQoEAqZjAABgjUTufmMDV3MDMI/6AQBIlDNngAEAgHCzZ882HSEhruYGYB71AwCQKBpgAAA4qrS01HSEhLiaG4B51A8AQKJogAEA4KiysjLTERLiam4A5lE/AACJogEGAAAAAACAjEYDDAAAAAAAABmNBhgAAI5au3at6QgJcTU3APOoHwCARGWbDmC73p3dfO+R7dsnL4gD7wsgeUz+HbfkvU3Va5PjxIsvvqgpU6aYC5AgV3MDMM9U/XBxjHF1PAdgh0ysITTAorjpEtMJEvOjIcNMRwDgKFfrh6v1uiXuv/9+JxtJruYGYJ6p+uHiGOPqeA7ADplYQ7gEEgAAAAAAABmNBhgAAAAAAAAyGg0wAAAc9dRTT5mOkBBXcwMwj/oBAEgUDTAAABxVVFRkOkJCXM0NwDzqBwAgUTTAAABw1MSJE01HSIiruQGYR/0AACSKBhgAAAAAAAAyGg0wAAAcNXbsWNMREuJqbgDmUT8AAImiAQYAgKMWL15sOkJCXM0NwDzqBwAgUTTAAAAAAAAAkNFogAEAAAAAACCj0QADAMBR8+bNMx0hIa7mBmAe9QMAkCgaYAAAAAAAAMhoNMAAAHDU5MmTTUdIiKu5AZhH/QAAJIoGGAAAAAAAADIaDTAAAAAAAABkNBpgAAA4atq0aaYjJMTV3ADMo34AABJFAwwAAEdNnz7ddISEuJobgHnUDwBAomiAwWmhkFR9tP73U1XmsgDJEgpKxw/V/1570lwW2G3ChAmmIyTEldyhkHTyWP3vJ495ywCXhULSicP1v9ecMJclEa7UD6RXMCBVHaj/va7GXBYgWRpux1X7ve0cLZNtOgAQr1BQOrhF2vmBdGRHeHPgzV9IrdpJXfpJfUZKHXtLPp+xqEDMAnXS3g+lXSulY3ukYF39Y+U/k9p0lPIv8rbrdl3N5YRd9u/fbzpCQmzOHQpJh7ZJO1dIh7dLtdX1j/3zaSmnjdS5r9RnlPe/jDFwQTAg7VvvjTFHd0mB2vrHFj0ltW4vdR3obdftuxmLGROb6wfSq+6UtGu1tGeNVLlfCjVoDpT9RGrXReo+WOpdLLXuYC4nEI+Tld4+yL6K8APi78yRfH6pfXep5zCp1zApp7W5nK6iAQanHNsjrflL+BGec9Ucl/as9X469ZGGfUxq2zl9GYF47Vsvrft75KPw1Uel7e97Pz2HSUMmeV/EASRP1X5vjDm2t/l1aqu9ndJ9FVKHntKwa6W8/PRlBOJ1cLO05m/Sqcrm1znzhWvnCql7gTT0au+AImCjUMjbVtcvkgIRzvQ6fkja/I60+V2p/xhp4OVSVk76cgLxCNRJm/4lbV0iqZmzzUNB7/vwsT3SxjelQRO8AxccjIsdDTA4Y+sSaX25mi0ITTmyw+uWF10r9RiasmhAQoJBr/G1a2V8z9uzRjq0VRr1ae8LOM5fw4YNMx0hITbm3vmBtO4f3s5lrI7tkd59Thr6Uan3iNRlAxIRCkkVZdL2pfE9b9966fAOqfh67yxH29hYP5A+dTXSyj96jd2YhbzvEQc2SaNu9s6qB2xSfVRa/pJ0/GDszwnUSh++Lh3YKI24Xspulbp8mYQ5wOCELe9J68sUV/PrjGCdtPJP0p51yU4FJC4Ukla/Gn/z64ya49LSFyOfqYLMN3/+fNMREmJb7h3LpbV/i6/5dUYoKK39q9dAA2wRCnkN3XibX2fUVkvL5nuNMNvYVj+QPoFar0kQV/OrgeOHpCW/C5/bETDt5DFp6e/ia341dHCLV68jnQ2JejTAYL1D26QNiyKvc/W93k8ka14Lv44aMGnbUmlvlKZstO06UCN98Acmej2f3XfffaYjJMSm3Mf2SOtej7xOLGPM2r/TkIY9dq3yLhGLJNp2HayTVi6QaqqbX8cEm+oH0mt9uXd1RyTRtutTld6B8UQOeADJFgpKK//sXYbenFj2QY7uPH2lFKKiAQarBWq9I+vJEAx4c7vYcgevYNBrXNiSJ5MFA3Z91ieOeNftJ8PJY9EbxMhcc+fONR0hIbbkDgak1a8pobOLGwl5B1qClnypCjHGpI1t4/mpKqliYXJeq+ZE8l4rWWypH0ivw9u9s3WT4eguadv7yXmtZAjUed95bKkhmSoU8j7nhjcBMW37Mq95lQw7VngnjiAyq+cACwaDevLJJ/XLX/5S27dv15AhQ/STn/xEd955pyZOnKinn37adESk2J613jXRyXJ0l3R4m9Slf/JeM17H9njzEOyr8L6gZLWSLhgh9b/EuwsTkufw9vo5HxTyJo3vPVLqN1pq1dZcrm2Lk3sb450fSBeOl3KZsBiIy/4NiV9y0JSqA9KBDd5dx0yp2u/VvT1rvTHGny1dMFzqd4nUtpO5XJno6K7T4/l6SSEpO9ebC67fJVJunrlc299P7qUwe9ZIAz/C9gOzNr+b3Nfb+p7U92LJn5Xc141VKCTtXuP9vVaePnu4bRcvU+9ic7kyUTDg7StvXyadOH01UPvuUt/RUq8icxPIBwPeND/JtOVdqUu/5L5mprH6DLCpU6fqwQcf1F133aXXXntNU6ZM0a233qpNmzZpzJgxpuMhDXZEOX0/oddcnvzXjNW+CmnxC9LeD+tPvQ7UeHN0vPtc5LtbIj47VkhLf1/f/JK8OU22vCO993zkU41Tqa7G2+FJplAw8bnEgPNZKsYDk2PMwS3Su897NebMGBOs8zK9+5x3AAbJsWettPi39c0vSao75TXE3v1f6cRhM7mCAWlnCsaDaJdTAql04rB0aEtyX7PmxOm/XwNCIe+qlDWvSZX76pefOORNar7i5eQeKD2fBQPSile8z7VhXa48fdfn1a+ZO/Nu/wZvTt9kOrSVKX+isbYB9sILL2jOnDlasGCB7r33Xk2aNEkzZszQ+PHjVVdXpzFjxujUqVP64he/qN69e6tTp0666qqrtHbtWtPRkSQ11fVHRJLp0DYzhe5UVYM5B5p4/9qT3nxOnP7cclX7vbsrSmrysz5Z6U1Ab8LRXak59frQ1uS/JuxXXu7mhA825A7URp9LJhGHd3hNp3Q7O4YE1GTdC9R6XwL4UtVy1UdOjyEhNflZ15yQPlhgZjyv3Ocd7Ek2m8YYG+oH0itV25+p7XrnB9Lu1ad/aaJOHNwibX4nnYky1+Z3Gtw0oeFnffrfe9aYa/Bn2nbtCmsbYI8++qiuvfZaTZw4MWz5oEGDlJOToxEjRqiurk6DBg3Se++9p4MHD+rqq6/WLbfcYigxki0VzS/JO0JbfSQ1rx3Jzg+iTLgZOn2Ei2u3W2z78igrhLzLI02ccZeq7frYXpqn56PVq1dHX8lCNuSuOpCav5lQ0Ext2b06SnM95B2I2b8xbZEy1o4Pomw7Ie9AzNFdaYt0VqrGmKoD3jxFNrChfiC9UnWDERNnxYZC3mWP0exYzgGLlgoGYjsre9v7ZvahU7Zdc0OeiHyhkH1fmXbs2KG+ffvq17/+taZOnRr22K233qp169Zp2bJljZ5XWVmpDh06qLq6Wq1bt474Hj5TF/siZteOnapvTfl12LJod8Bozj9mhv9+7y8macXGssReLEFPlpZr+IVXyOdrvu8cCgX1u4X/pWde+//SmCzzPPfdjerVdWDU9X7+ytf1yr9+moZE9b726Z/p+o9MC1uWrO36hu931Anu7Z0x7rnnnqjrzJo1K+p6s2bNSlakmCQjdzoyXzniZt337/PCliXrb/H+Z2/UW6v/kGCyxPzwjgUaN/Q6ZUWYOCYQDOjP7/xSP315WrPrILqn/mOJBvUeHXVf8tm/fl//94+H0pTKM/Xah3Xrv4XvQyRru/7cw/20/8j2BJPFxtW6h9R65MuvaeyQa8OWJWO7Pnr8gCb/oFsLksWvU153zb0/tg5F6Y9Ha8POxt95EZuLLhipX9yzPKZ1b/lhLx2qTG9H9MX796pzXvezvyerVi+t+Ju++6uPtSCZe+JpaVl5BtiOHd41CT179gxbXl1drfLy8mbn/3rrrbc0YMCAqM0vuCFSo6il/AZmlszKyon6/ykUkrL8Vt+bwglZWbF9hrGul0yp3K6zfMyYCsQq48YYf7aiH9oLMcYkgTeeR/60QyEznzVjDDKRP0Xbtd/ANh1PXaBet4ztn3WqtutUjgOZwMozwDZs2KCCggLNmjVL3/jGN84uf+CBB/SDH/xAP//5z3X33XeHPefw4cMaO3asfvjDH+pzn/tc1PcoKSmRJJWVlSUxOZJp33pvPpNYnOmYn9sBb86l/+7d/SOd1v09tkn9i66Teg1LfZ5MtuKV05f5RKluo6ek/04pG/8Z+7wO8WzXvixp0te5a1AmWbduXdR1CgsLo859OXTo0GRFikkycqcj86Ft0vsvxrZuvGPMmFukzn0Ty5WoDYtiu5vUkH/z7jKGxK1+7fTNTKKMMcU3SN0L0hLprK2LpfUxTpEV73Zd8jXvTpep5GrdQ2qt/JO0N/qmISm+7bptZ+kjX0o8VyKCQenN2dHn6vNlSRNKpRzO60hY3Slp0VPRLyXNaSNd+dX070O/9Uz9XSkjibdW9xgijfhU4rlckkhPx8r24MCBA1VcXKxHHnlEzz33nF5//XWVlpbqmWeekaRGZ4BVV1fr+uuv1y233BJT8wtuSFWDyp8lteuamteOpM+o6Otk50rdB6c8SsbrM0qRv5j4pDad0v8FVZLa90jR6+bT/DofPfDAA6YjJMSG3Kk8CJLuAyyS1Ls4+jr+bA6wJEMsY0yrtlL+RWkK1ECqxpi2nVPf/IqVDfUD6dUhVftOBmq13y/1GRl9vZ6FNL9aKjtX6hnDmNe72Mw+dKq2v1SNA5nCygaY3+/X3LlzVVRUpNLSUt1xxx3Kz8/XtGnTlJ2dreLi+r28uro6TZkyRQUFBXr44YcNpkayte4g5bZP/ut26GWmyOV1kwZc2syDPu9n2LWSgavyMk6X/lKv4c086JN8fqnoWsnEVIAdL/AyJP11+yT/NWG/KVOmmI6QEBty57ROzcGQvG5mGgVtOkmDJjTz4OmaU/hRe5oYLuvQU+rb9Gwc3hjjk4Z93Puim24deqRmH6dj7+S/ZqJsqB9Ir04p2v5S9brR9B/njRVN8kmt20uDrkxrpIx10RXed8rm9r3z8qUB49Ia6axM265dYWUDTJIGDx6shQsX6vjx49q2bZsefPBBrVy5UoWFhWrTps3Z9b785S8rGAzq6aefNpgWqeDzxXZEO16peM1YXXSFNPTqxo299t2liyen/1KJTOXzScM+5u085LQJf6xTb+mSz0qdDDWMcttJ3VJwVkDvEcl/TdivsLDQdISE2JK7dwxH4eN+TYNjzIBx3oGU1h3Dl7frKo38tNSryEyuTOPzSYNLpMGTpFbtwh/r0FMa/Rkp/0Ij0ZSdK/VIwdV/Jrfrc9lSP5A+HXp5jYpk8mfHdnZQKmS38vZFzz3zyOf3Ll8b+3lvfxEtl9vO+zx7DvU+3zP8WdIFI6QxnzV3YKhnobcdJlO7rqcPtqNZ1jbAmrJkyZKwyx+3bt2qOXPm6I033lCnTp2Ul5envLw8bdu2zWBKJFPvYikrJ3mvl9ve7CWGPp936cQVX6lfdtnt0qW3SV37G4uVkXw+74y7K79av2z8VG+Ho2Mvc7kkqd8lyX29rgOSv2MInA96DUvuJSY5bcxfYnjBcOnyL9f/fum/e+NMKhrv5zOfT+o3Rrrirvpll31RGvd5M5fXN9R3tJJ6pnHHXubHTZzffL7k7ztdMNzsJYbZuVLhNdKVpfXLrrhLGvFJKTfPXK5MlNtOGv6J8Hp9Zal3sNzkNpDTOvkHsPtdYuYKF5c40wCrqqpSRUWFRo8efXZZ//79FQqFVF1draqqqrM//fqleVZrpExuO6mgJHmvN+waOy4xbHgEotlToJEUDY+stetiLkdDnfsk72h6Vo409KPJeS3gfJPTWhpydfJeb+jVdlxi2HDnt313doZTqeFljrYciOjQQ+qfpGaBzy8VfoxtCOb1KvKmuEiGXIsuMWzYgOGsr9Rq+PnaMr/aRVd4l7wmQ+d+XmMXkVnQCohNXl6eAoEot3BARupdLB3aKu2raH6dWO6K0X+s1NXQJQnAuQpKpKO7par9za8Ty3ZdeI3UpmP09ZCZztz9xjU25e4xxBtjdq1sfp1Y/hZ7F3uvBdhg4OXSkZ3S0V3NrxPLdj3kKnsae2fYVD+QPmemt1j8gnSqqvn1om3Xvixp+HV2HKwAsnO9s9Pen9v83SpjqdW5eebmN3aNM2eA4fzl83mFoSWXLvYdHWFyYMCA7FbS6MktuAOMzzsq35OpUM5rs2fPNh0hITbl9vm8syhbMj/WBcO9s78AW2RlS6NuatlcMIMnxXYH63SzqX4gvVp3kEZPSfyMGX+2NOrT5i9TBhrq1MebpzPRaX9y25/+u+iQ3FyZigYYnODPkkZ8Shryb/EVh5w2p593FR1x2KdVO+mSW+Of16JdF2ns55j4HlJpaWn0lSxkW26/35s8ftjH4jsrIDvXu9tf4cfCL20HbJDTWhpzi3ThZYprTrA2Hb3n9WvuTpeG2VY/kF7tunhzG8Y7gX3H3t58iF0HpCQW0CJdB3jbdbx3cOxZ6D3PlmleXODMJZCAzyf1vVjqNkjasULa9YFUc6LpdVt3kPqMlC4ollq1aXodwAZZOd7dxHoVSTuWSbvXSMG6ptdt38M7Gt+z0I657GBeWVmZ6QgJsTG3z+fdEarrQGnnCmnnB81fZpOb591Bsncxc7bAbv4sb46ZnoXS9uXS7tVSoKbpddvlS31HeTdyyGqVzpTxsbF+IL1y2niXMfYu9vad9q2XQsGm1+3c19t36l7AgQrYrW1n766U+9ZLO5ZLh5u5r5/P723PfUZxNmMi+AoF57RuLw26QrroI9LxQ1LlPq8R5vN5X0Ta9/SOXnLGF1zSvps3n9eQq6SqA952XXfKG+TadPImNeauQEDq5baTBn5EunC8dOKwVLlXOnW8/rH2PbydVMYYuKRdV2nov3kHXMLGGJ/UumP9GMN2DZd07uP9BGqkyv3evKqBWm+er3ZdvHrNgXC4xOeTegz2fmqqvX2QE4e8+cGycrybp7XvZvdBCtvRAIOzfH5vYlbbJmcFWsKfLXXo6f0AMMfn875AcVkBMok/y2t2dehhOgmQPFmtvEvH4r18DLBZqzbepZFctptcnAgKAICj1q5dazpCQlzNDcA86gcAIFE0wAAAcNSLL75oOkJCXM0NwDzqBwAgUTTAAABw1P333286QkL+//buPT6K+tD7+Hc3CUkgIJcgIBcxEiAJBCSCUJWEapXi9SiNUsupoMUnBM8jSit9UBBvKEWCtcJRjx6xltOSIJpjsS0qiRdEAUUFglEuQgAlIgrhnmSfP1YCcUM2GXbymxk+79eLl8nszO436+5vdr87v1m35gZgHuMHAMAqCjAAAAAAAAB4GgUYAAAAAAAAPI0CDAAAl5o7d67pCJa4NTcA8xg/AABWUYABAOBSaWlppiNY4tbcAMxj/AAAWEUBBgCAS2VmZpqOYIlbcwMwj/EDAGAVBRgAAAAAAAA8jQIMAAAAAAAAnkYBBgCASw0cONB0BEvcmhuAeYwfAACrKMAAAHCplStXmo5giVtzAzCP8QMAYBUFGAAAAAAAADyNAgwAAAAAAACeRgEGAIBLFRQUmI5giVtzAzCP8QMAYBUFGAAAAAAAADyNAgwAAJcaOXKk6QiWuDU3APMYPwAAVkWbDuB0L62Stu8xc9ud20jXnW9t27s+W6+P9+2LbKAG6NeypR7rldrktws4lannomT9+ejWcQ8AALuY2je68f2AxHsC4Mfc+P7ci+8JKMDC2L5H2rjLdIrG+3jfPr2151vTMYDTnhufi24d9wAAsIsb941ufA0CeJUbn49uHPfCYQokAAAulZubazqCJW7NDcA8xg8AgFUUYAAAuNSECRNMR7DErbkBmMf4AQCwigIMAACXGjp0qOkIlrg1NwDzGD8AAFZRgAEA4FLl5eWmI1ji1twAzGP8AABYRQEGAAAAAAAAT6MAAwDApVJT3fkV827NDcA8xg8AgFUUYAAAuNSiRYtMR7DErbkBmMf4AQCwigIMAACXmjp1qukIlrg1NwDzGD8AAFZRgAEG7P3q+M+b3pMO7TWXxcsCAWnPtuO/b/lAOrzfXB4g0vLz801HsMStud1i3wnnCN/4rnTgO2NRPC0QkL7fcfz3zSukQ/vM5TldMH7AK6qrpK83HP99x6dS1VFzebys6mjw/j3mqw3B+x+nH0cXYNXV1Zo1a5aSk5MVFxenfv36qbi4WL169dK4ceNMx6tTwYNZ+uDlBxu83AkCR4/q6P+ZoKqnnqm1vGrxyzr6q18rsJ/GIFKOHpJWL5Q+ePH4sk3vSu88LX32phSoNpfNaw7tC97Pq/92fNkXb0nvPCVtWh584+JEbn0+unHsA7ym8oi0ZrH0/vzjyza/Jy3/L2n9P3ixH0lHDkir/yqtXHB82cZ3gvvzz4udu49B03HjftGtr0Hc6Lvtwdekn756fNn6f0pvz5PKvzCXy4u+2Ri8X9f/8/iyta9Kbz8l7Skzl6s+bn0uumHcc3QBNnbsWD3wwAO67bbb9Nprryk7O1ujRo3Spk2blJGRYTqeZ/hiYhQ9+beqfnWJqj9aI0kKbN6i6ufmK+p3k+Rr0cJsQI8IVEsfLZL2bK378m0fBl8049RVHZE+XCjt2xV6WaA6WIBtXdX0uRqC5yMAKwIB6ZNXgi/067JjrbRhadNm8qrqKumjguAb2BAB6cuVwSPvALfhNUjTqPhG+jBfOnIw9LLKI9LHr9SewQDr9pQF78/KI6GXHT0YHMsrykMvM43non0cW4AtWLBA8+fPV2FhoSZNmqRhw4ZpypQpGjJkiCorK2sKsJtuukkdOnTQGWecoUGDBum9994znNydfN3Pln/sr1X1h9kKfPutKh+ZKf81V8mf3td0NM/4ZpO0d2f962z9UDpc0TR5vGxniXRgj6R6PoHftDxYlDkRz0c0VHGxO1tzt+Z2su/KpG+/rH+dHWt/GBtxSnZ9XvcHLCf68oO639zi1DF+2IvXIPbb8sEPR+TW8zp14/Imi+Npm96t54jcQPD/w5YPmjRSg/FctIdjC7AZM2Zo+PDhyszMrLW8R48eiomJUd++wf/xU6ZM0bZt2/T9999r2rRpuv76603E9QT/tdfId3Y3Vd6WK/n98v96tOlInrJjnSRfmJUC0lclTZHG206c438yVUelXQ4+xJznIxpi3bp1piNY4tbcTrZjrcLvYyTt5K4/ZQ25rwPVtc/tg8hh/LAfr0HsU3Xkh7GhvmnSAem7bdLB75sqlTcd2vvDkXRh7uuvP6v7CDEn4LkYedGmA9SlrKxMa9eu1cSJE0Mu27p1q9LS0hQbGytJSk1NlSQFAgHFxMToq6++0qFDhxQXF1fvbRz79Mjnq/8VzPVTlqlLSlaj8n/wykNavWRWrWVHD1WoW59LG3U9xcVF+o/LhjVqm2Oi/vCI/P3SG7WNz+eTL72vAqs/lP/GbPliYhp9u0VFRfINHNzo7U4HT9y+Qr27XVDvOtXVVZrxwON66n/vaqJU3vQ/92xT4hldwq73f8f/VvnFs8KudyqsPBcls89HK+OeFJmx71TGPa+pax/4Y3l5eWHXy8vLi1SkBolE7qbO7AUzbv2HBiT/TH7/yT/brKqq1Nw/ztfsi29twmTe88xda9W9Y1q96wQCAU39/UN6/p/3NlEqb3DyuGfqPUFTvx+QeE9gp/ZndNGCexo2vzGj3xCVfLnC5kTe1avrQP3pP8If3hWolrp07K6v94Q5jPoUufH9uVu7kPo4tgCTpI4dO9ZafvDgQRUXF2vEiBG1lt90001atGiRDh8+rNzc3LDll90GXTNFg669p9ayggezzIRphMDmzape8Ff5b/iFql9cIP/FF8p35pmmY3nG3v27VV1dJb8/6qTr+P1R2nfg2yZM5U179+9W21Znye+r/yBXJ9/Xbnw+unXsA7xg34FvFVC16ju43+f3O3rcc4vv938Tdn/u8/m07yD39enOrftFN74GcYv9h74PO34cw3h9ahp6/1UHqlVx8Dt7w1jkxuei08c9RxZgiYmJkqTS0tJaZdfMmTO1c+dODRgwoNb6f/nLX/T8889ryZIl2revYd8/fWxqZVFRUb3rPbFU2hjmPA92yczMUsGD1r5G6NJV7+utPQ0fNANHjqrykT/If921ihrzawX27FHVzMcUNXOGfPV8mvxjWVlZep2vPqrTznXSutfCr/dM/oP6SxtnfEuGW235IPiNj/XxRUmFxc8qJv5ZW7M09rkomX8+unXc85oNG8LPn8rLywv7rcizZ8+OVKQGiUTups7sBeVfSB+/XP86fp9fs579rZ7u8NsmyeRVZR834AsFfNKLr85WQUsey43h5HHP1L6xKd8PSOZfg5wO1rwkfbNZJ5+a55MSEqWtX3+mMJOVEMb7L0j7ylXvfd0+yd8kBZgb3587/T1BVlZWo6/XkecAS0pKUnp6uh5++GG98MILeuONN5STk6PnnntOkur8BsiYmBhdc801+sMf/qDS0tKmjux61c/9t3zR0fKPvkmSFDX+/yiwq1zVixYbTuYdZ/aS4lur3vOGdOgtNW/TVIm866y+Uky86r2vu/b/YR0H4vmIhpo+fbrpCJa4NbeTJSYF3zDVN+616y616tBUibyrY4oU21L13tdnpUlxLZss0mmF8cNevAaxX/f6z4giBaSkIaL8ioBzhqj+c4BJOmdQk0RpNJ6L9nBkAeb3+5Wfn6+0tDTl5ORozJgxSkxMVG5urqKjo5WefvK5s0eOHNGWLVuaLqwHVH+0RtVL/qGoyb+TLzp4UKCveXNF/e4uVb/wogKbNxtO6A1R0dKAX0jxZ/zogh92bolJUurlTR7Lk5rFSxnZUrPmP7rgh/u6U5rUIzNkM0fg+YjGyM7ONh3BErfmdjKfXzpvpJTQ7scXBP/TuqvU96omj+VJ0c2C+5iQguuH+7p9stS7cac6QSMwftiH1yBNo3Vnqe+VwXE7hE/q9VPpzJ5NHsuTzjw2HtdRJvr8Up8rpNbhTxvc5Hgu2seRUyAlqWfPnlq2bFmtZaNHj1ZKSori44OHbezevVtvvvmmrrjiCkVHR+uZZ57Rjh07QqZINqWR9xQ1arkT+M/rL3/hS6HL+6TJ/780zJEUf4Y0ZIxU/rn06avBZZ1SpbP6BAdfPumJnIT20oW3Br/ZZf0/gss695U6p0utOta/rUlufT66cezzgpSUFJWUuO+rY92a2+liE6RB/y59s0n65OXgso69pU59pLbd2MdEUvM20k/GSrs+l9b+PbjsrDTprHTpjE7c13Zyy/jhxv2iW1+DuFGHXlKbLtL2tdLGt4PLug+WuqRLca3MZvOaLv2DBxqUfSJt+eE7Bc69KDhjJLaF0Wgn5dbnohvGPUceAXYyq1atCpn++MQTT6hTp07q0KGD/vrXv+rvf/97zTnEACfyRwWnOh6T9nOpTVdeLNshKiZYLh6Tcpmzyy8AOFV+v3Rmj+O/97lCanc2+xg7+KOD0yGPSR0utT6L+xpAwzRrIZ1zwnTIHhdRftklrlXw/j3mnMHOLb9gL8ceAfZjFRUVKi0t1fjx42uWtWvXTm+9FeZM1wAAAAAAADituaYAS0hIUFVVlekYAAA4hpVvv3ECt+YGYB7jBwDAKldNgQQAAMfNmzfPdARL3JobgHmMHwAAqyjAAABwqZycHNMRLHFrbgDmMX4AAKyiAAMAwKWKiopMR7DErbkBmMf4AQCwigIMAAAAAAAAnkYBBgAAAAAAAE+jAAMAwKVKSkpMR7DErbkBmMf4AQCwKtp0AKfr3Madt92vZcvIBXHB7QJOZfI5YfW23TrunY4WLlyo7Oxs0zEaza25AZhnavwwtX9y4/sB07cNOJEb35978T0BBVgY151vOoE1j/VKNR0BgNz5XHTruHc6mjZtmiuLJLfmBmCeqfHDjftGN74GAbzKjc9HN4574TAFEgAAAAAAAJ5GAQYAAAAAAABPowADAMCl5s6dazqCJW7NDcA8xg8AgFUUYAAAuFRaWprpCJa4NTcA8xg/AABWUYABAOBSmZmZpiNY4tbcAMxj/AAAWEUBBgAAAAAAAE+jAAMAwKUGDhxoOoIlbs0NwDzGDwCAVRRgAAC41MqVK01HsMStuQGYx/gBALCKAgwAAAAAAACeRgEGAAAAAAAAT6MAAwDApQoKCkxHsMStuQGYx/gBALCKAgwAAAAAAACeRgEGAIBLjRw50nQES9yaG4B5jB8AAKsowAAAAAAAAOBpFGAAAAAAAADwNAowAABcKjc313QES9yaG4B5jB8AAKsowAAAcKkJEyaYjmCJW3MDMI/xAwBgFQUY0MQCAenAnuO/794iHd5vLI6nBaql/buP//7tVunIQXN5gEgbOnSo6QiWuDW3GwQC0sHvjv++e7N0aF9wOSIrEJD2f3v8991fSkfYn9uO8QNeUV0pfb/z+O/f7ZCqjprL42VVR4P37zHf75SqKs3lgTnRpgMAp4u9X0lla6Rdn0uVh48v/6gg+N/YllKnVKlLPymulZGInhAISN9tD97X32ys/ULiw4XB/8a3ls7qI53VV4ptYSIlEBnl5eWmI1ji1txOVlEubVsj7SqVjp5Q9H+0KPjfZi2kTilS5/5S89YGAnpEIPDD/vwjadcXUtWR45d9lB/8b1wrqVOa1DldimtpJqeXMX7AzaqrpPLPpbKPg69XA9XHL1u1QJJPatVR6pIudegtRcUYi+p6VUelrz+Ttn8sff+VpBM+CFr5F8nnl844S+rSXzozWfJHmUqKpkQBBtjsyEHpszekrzfUv97hfdKW96UvV0rnDJa6X8BA3FiH9kkl/woe8VCfg99JG9+RNq+QelwsdR0g+XxNEhEAIqrysFRaJO34tP71juyXvlwlfblaOvt8KelCKYpXgY1yZL9U8nrwzWt9Du2VNr8X3KcnXSidPVDyM+cCOO19v0Na9w/pwLf1rBSQ9u6U1u+UNi6XUi+T2p3TZBE9Y/cWaf0/g++vTiZQLX1XFvzXvK2UNjxYiMHbeOkD2GjfruCn742ZEhGoljYtl77ZJPW/TmrW3L58XvLtVumTV2ofXRdOdaVUuixYmPW9WopuZl8+wA6pqammI1ji1txOs393cB9zaG8jNgoEP2jZvVnqfz1HKDXU9zulNS/VProunEC1tPFtafcmqd+/STFx9uU7nTB+wI22rg5+WKFGTEc/vC84xp89KPiBLR/WhhcIBD/k3vJ+47Y78K208n+k5Mzgh0TwLj6PAmxSUS6t/pv184Hs/Ur6MF86eiiyubzouzJpzaLGlV8n2r0l+MaGcwHAbRYtWmQ6giVuze0kB74L7mMaVX6doOKb4LRwzlkV3t6vf9gfWzyH5Hfbg6c7qDwSfl2Ex/gBt9m6OviBa2PKrxN9+YH0xVsRjeRZX7zd+PKrRkD6vCh4tDS8iwIMsEFVpfTpq/UXMpdOCv6rT0W59Nmbkc3mNUcPBe/r6qqTr9OQ+/q7MmnTu5HNBtht6tSppiNY4tbcTlFdLa19VTpy4OTrNGTcO7BHWv8vTpBfn6oj0qf/W/tcXz/WkPt671fS58WRzXa6YvyAm3y/84cjv+rRkDHky5VS+caIxfKkbzYGy8L6NOS+/ry49pcTwFsowAAbbFlR+9sHT8VX64PTIZ3gSKVUvk/6tsI5b5g+L5YOV0Tmur5cFXyTArhFfn6+6QiWuDW3U2xbHbmx6puNwZMEO0FlVXAfs7tCqnbIPmbju7W/VfNUbP84OF0fp4bxA25RXS2t/4csH/n1YyX/sj7bIdL2H5Z27Q3+1wkqj0glSyN0ZQFp3Wv1f7gO93L0OcCqq6s1e/ZsPfXUU9q2bZt69eqlP/7xjxo3bpwyMzP19NNPm44IhKg8Im39MLLXueUDKTEpstfZGBWHpH+tld7fKB3+YZpg+5ZSVm9pSLLkN3ROgsMV0o61EbzCH86N0/eqCF4nAERQdVVwnIqkLe9LHXqZO7/MwSPS0nXSe59LB3/45t62LaShvYL/TJ1A/uih4De1RdKXH0htu0X2OgE40zcbI/eBuBScsr5zXfDLm0zZ8o30r0+l9TuOL0s9S/pZH+mc9uZy7VwXuQ/EpeA5wco3Sh16Ru464QyOPgJs7NixeuCBB3TbbbfptddeU3Z2tkaNGqVNmzYpIyPDdDygTl9vqH+qhBXflQXP12LCvoNS3j+ltz47Xn5J0jf7pPyV0t/eN3c02PZPFbFP1Y7Z9TnnxAHgXOVf1D/10YqK8uC3jplw4LD0+L+kN9cfL78k6dv90ssfSvPfDR5FYcLO9cEvS4mk3Vsid0QZAGeLdIFu13U21Noy6Y//kkp+tL8o2Sk9sVT6dJuZXFLwCFs3XCfMc2wBtmDBAs2fP1+FhYWaNGmShg0bpilTpmjIkCGqrKwMKcD+9re/yefzqaCgwFBiIGiPTYO/XdcbTsGq4JTHHzvWO72/UfroyyaNVGOPDVNJAtXSdzvCrwc4QXGxO08q5NbcTmDXvuBbQ/uYwo+kr74/+eUfb5VWGDrvjR37GEnaU2bP9Z4uGD/gBtXVwQ+wI23/7sh/CNIQh45KL7wb/ND7xx98H1v253eDR/Q2tSMH7TlQ4LsypkF6kWMLsBkzZmj48OHKzMystbxHjx6KiYlR3759a5YdOHBADz30kNLS0po6JhBi79f2XO8+m663Pt8fkD7ZVv9BVj5Jb5c2VaLjAgFp3y57rtuu/4dApK1bt850BEvcmtsJbNvHGDj/4YHD0qrN9a/jU/AIZBNHGtu132Ufc2oYP+AGB3ZH/gjSY0yMIas2B88FfLKhOCDpSFX4Md0Odo3V1VWRncIKZ/AFAk45lfVxZWVl6tq1q5599lmNHTu21mWjRo3Shg0b9NFHH9Usu/fee9WmTRsVFhZqwoQJGjlyZNjb8Jk60QU8b/H9e5QQ37rm93DfNHIyr8+q/fv7JX/XPc9daT2YBUkZ1+iqiS+HXa+6ulJP/HuM/YFO0Cw6Tn+fUfs76SN1X7/2/n9pdsFvLCYDImPixIlh18nLywu7Xl5eXqQiNUgkcjd1Zjd58f9tUYc2Z9daZmXs+/G4t3bzO5o49+JTSNZ4XVKydP2UZQ1ad+4tCTp6uGnnpy+ZcVgx0c1qfo/UPuatj/P1wIvZp5DMu9w67gE/NiD5Uj06rvZZ2SM1hvzhbzfrX6vmW0xmzfDxf1HyBdnyR538FOLVVZX6/IN8/ePJXzZhMunSjNG6+8YXai+L0H39+2cu16rSf1lMhqbSmErLkUeAlZUFjxft2LFjreUHDx5UcXFxremPW7ZsUWFhoW6//fYmzQicjE/2lKs+X9M/XRtaFPvka/KzJ9tZYpu4rwGgIby0j2nUfsPAB5d27Wd8ps7qD6DJ2Po61ab9QP032tDbNDBW23mbHDTjOY48AuyLL75QcnKy8vLydMcdd9Qsnz59uu677z49+eSTGj9+vCTpuuuu080336yrr75aWVlZDT4CLCsrS5JUVFRkw1+A09nyZ6UDe8Kvd+yTiR9/0nAyndKktJ9bz2XFdwek6YvDT4Hs1k6aOLypUgUFAlLREw37woHG3tfdL5B6NO2BEECIDRs2hF0nJSVFJSUl9a7Tu3fvSEVqkEjkburMbvLBi9LeBkxXbOy41z5Z6neN9VxW7D8sTX1JqqrnJPc+Se1bSb+/sunfh7z9lHR4X/j1Gntfd+kv9b7UcixPc+u4B/zYvl3S+y+EX09q/BjS/7qm/3b44g3S4tXh17s2I/gt8U3pm83SmkUNW7ex9/Wg0VKrDtZywX5WOp2TH8NoUFJSktLT0/Xwww+rbdu26ty5swoKCrRkyRJJqjkC7M0339TevXt19dVXm4wL1NKyQ8MKsMYyMfi2bi716RL81pf65vxf3KspUwX5fFLLM+05wWhLdnRwienTp5uOYIlbcztByw4NK8Aay8Q+pkWsNKC7tGpT/fuYob3MfAjf8syGFWCNvl72MaeE8QNu0KKd5I+y5yTqJsaQgUnSq2ukyqqTj9fRfmnQOU2ZKsiu/ZcvSkpItOe6YY4jj8H2+/3Kz89XWlqacnJyNGbMGCUmJio3N1fR0dFKT0+XJL311ltasWKFEhMTlZiYqHfffVdjx47VjBkzDP8FOJ216equ6w3n+oHSGc1PfkDzgLODb2BMsOU+8UmtO9twvYANsrPdeR4ht+Z2Atv2MV3sud5wrjlPSmx58svTOktDejRdnhN57b72CsYPuIE/yp7Xk83bSrEtIn+9YW+3mXTTTyT5Qt8T+H74d9NPpOaxTZ+tWfNg4Rhprc8K/n+EtziyAJOknj17atmyZdq/f7+2bt2qBx54QJ9++qlSUlIUHx8vSbrzzju1YcMGrVmzRmvWrNH555+vWbNmKTc313B6nM469paiInw++DPOkhLaR/Y6G6p18+D0xsE9pOio2suvGSD96ieS39D0+M59FfFTDZzZw8wLC8CKlJQU0xEscWtuJzizhxQTH9nrbNFOOsNQ8Z8QJ91xuXRxT6nZCfMSWsZJV/STxg6Vogy9Wu2UFvk3P23Plpq3iex1nm4YP+AWnftF/jq72HCdDdW/mzT+EqnHj464OrdDcPl5Z9e9XVOw47624zphnmMLsLqsWrWq1gnwW7VqpS5dutT8i42NVdu2bdWqVSuDKXG6i44Nnt8jkroPiuz1NdYZ8dINF0gPXn982dRrpGEpkslz+ca1kjpG+HXw2QMje30AEEn+aKnb+ZG9zu6DzJ7nt0Vs8GjjB07Yx9z3b9LP+pgrvySpWbx0Vnpkr5N9DHD6aN8jsoV3THywmDcpuYOUe2lwjD5mwqVScseTb9MUzkoLHgkWKfGtpTOTI3d9cA7XFGAVFRUqLS3VgAEDTrpOUVFRg06AD9gt6SeR2+F16BXcgTpB3AlHtjnlS6x6DpNiIrTD65oRPNoOAJzs7POlhDMjc13tuksdUyNzXacq9oQjwEwWXyfqcXHww5ZI6NQneH8DOD34o6TUCH5JVO+fSTFxkbu+U9E6gmVTJETHBu+fSEkbzvRHr3LkSfDrkpCQoKoqG84iCNggKkbqc6W0+m8n/5bChnz7SIu2Ui++KapezeKlvldKHxVIgZN8k1hD7uszzpJ6XBTZbIDdjn37jdu4NbdT+KOkvldIq/5HOnqo7nUaMu7FnxF8c8a3vJ9cdLPgPmb1Qqm6su51GnJfJ7SXeg2LbLbTFeMH3KR152CR/sXbJ1+nIWNIl/OkDj0jl8uLzkyWug6Qtn148nUacl+fe5HUmnM1epZDPl8DvKdVB2nAL6x/UpPQXhpwQ7DgQf3adpP6/Zv1c6+17iKdd33kz90G2G3evHmmI1ji1txO0qKdlHGD1MziOQubt5EGZEuxCZHN5UVnnCWdNzJ4hIEVrToGXw9Y3R61MX7Abc4eJJ17sfXtu54n9fpp5PJ4Wc9hwRkdVp17kdT9gsjlgfNQgAE2OqOTNHhMI6cw+oI7yoE3cTL2xkg8R7rg14371i6fP/ip3IBs3pjAnXJyckxHsMStuZ0mob00+ObGnwux63nSBaODR4ChYdp0Cd7X7c5pxEY+6ZzB0vmjIntumtMd4wfcxueTzrkg+HqzMeNus+ZS+jVSr0s4UrehfL7g0bb9rm3cB0TxZwT//5wzmPva61wzBRJwq9gWwZ3X99ulbWuk8i/qnkYREy91Sg1+40iLtk0e0xOatw7uvL79Utr2kbR7ixSoY+Z0bIJ0Vh+pc3rkzu0CmFBUVGQ6giVuze1EzeKlPlcEp32UrZF2lUpVR0PXi44Lfktxl/5SQmJTp/SGuJZS/+ukPduC9/U3m+renzdrHjxRded+wf0SIovxA27VtluwSP/6M6nsY2nvzrr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      "text/plain": [
       "<Figure size 1591.6x1408.68 with 1 Axes>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.draw(output=\"mpl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da374a0d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "eca16aa7a5f8b35a277530dea12079435ebf8cbf4ee51249fed3c4b16880e759"
  },
  "kernelspec": {
   "display_name": "Python 3.9.7 ('base')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}