Tket Example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import qiskit\n",
    "import pytket\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First create the circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.aqua.algorithms import Grover\n",
    "from qiskit.aqua.components.oracles import TruthTableOracle\n",
    "from qiskit import IBMQ\n",
    "\n",
    "oracle = TruthTableOracle('0100')\n",
    "grover = Grover(oracle)\n",
    "circuit = grover.construct_circuit(measurement= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1043.05x264.88 with 1 Axes>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.draw('mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert the circuit to tket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tim/anaconda3/envs/planqk/lib/python3.7/site-packages/qiskit/providers/ibmq/ibmqfactory.py:192: UserWarning: Timestamps in IBMQ backend properties, jobs, and job results are all now in local time instead of UTC.\n",
      "  warnings.warn('Timestamps in IBMQ backend properties, jobs, and job results '\n",
      "ibmqfactory.load_account:WARNING:2020-11-30 11:19:17,052: Credentials are already in use. The existing account in the session will be replaced.\n"
     ]
    }
   ],
   "source": [
    "provider = IBMQ.load_account()\n",
    "qiskit_backend= provider.get_backend(\"ibmq_16_melbourne\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import transpile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "bcircuit = transpile(circuit, backend= qiskit_backend, optimization_level= 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1701.68x987.28 with 1 Axes>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bcircuit.draw('mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bcircuit.depth()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pytket.qiskit import qiskit_to_tk\n",
    "circuit = qiskit_to_tk(circuit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[U3(1*PI, 0*PI, 1*PI) o[0]; U2(0*PI, 1*PI) v[0]; U2(0*PI, 1*PI) v[1]; U2(0*PI, 1*PI) o[0]; X v[1]; CCX v[0], v[1], o[0]; X v[1]; Barrier v[0], v[1]; U2(0*PI, 1*PI) v[0]; U2(0*PI, 1*PI) v[1]; U3(1*PI, 0*PI, 1*PI) v[0]; U3(1*PI, 0*PI, 1*PI) v[1]; U2(0*PI, 1*PI) v[1]; CX v[0], v[1]; U3(1*PI, 0*PI, 1*PI) v[0]; U2(0*PI, 1*PI) v[1]; U2(0*PI, 1*PI) v[0]; U3(1*PI, 0*PI, 1*PI) v[1]; U2(0*PI, 1*PI) v[1]; Barrier v[0], v[1]; Measure v[0] --> m[0]; Measure v[1] --> m[1]; ]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 2.43.0 (0)\n",
       " -->\n",
       "<!-- Title: Circuit Pages: 1 -->\n",
       "<svg width=\"1308pt\" height=\"168pt\"\n",
       " viewBox=\"0.00 0.00 1307.60 168.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 164)\">\n",
       "<title>Circuit</title>\n",
       "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-164 1303.6,-164 1303.6,4 -4,4\"/>\n",
       "<g id=\"clust1\" class=\"cluster\">\n",
       "<title>cluster_q_inputs</title>\n",
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      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pytket.utils import Graph\n",
    "Graph(circuit).get_DAG()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.depth()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pytket.backends.ibm import IBMQBackend\n",
    "backend = IBMQBackend(\"ibmq_16_melbourne\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "backend.compile_circuit(circuit, optimisation_level= 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
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\n",
      "text/plain": [
       "<Figure size 1684.43x565.88 with 1 Axes>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pytket.qiskit import qiskit_to_tk, tk_to_qiskit\n",
    "tk_to_qiskit(circuit).draw('mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tk_to_qiskit(circuit).depth()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[node[0],\n",
       " node[1],\n",
       " node[2],\n",
       " node[3],\n",
       " node[4],\n",
       " node[5],\n",
       " node[6],\n",
       " node[7],\n",
       " node[8],\n",
       " node[9],\n",
       " node[10],\n",
       " node[11],\n",
       " node[12],\n",
       " node[13],\n",
       " node[14]]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "backend.device.nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OPENQASM 2.0;\n",
      "include \"qelib1.inc\";\n",
      "\n",
      "qreg node[3];\n",
      "creg m[2];\n",
      "u3(0.5*pi,0.25*pi,0.0*pi) node[0];\n",
      "u3(3.0*pi,0.0*pi,1.0*pi) node[1];\n",
      "u3(3.5*pi,1.25*pi,0.0*pi) node[2];\n",
      "cx node[0],node[1];\n",
      "u1(1.75*pi) node[1];\n",
      "cx node[2],node[1];\n",
      "u1(0.25*pi) node[1];\n",
      "cx node[0],node[1];\n",
      "u1(1.75*pi) node[1];\n",
      "cx node[2],node[1];\n",
      "u3(0.5*pi,0.0*pi,1.25*pi) node[1];\n",
      "cx node[0],node[1];\n",
      "cx node[1],node[0];\n",
      "cx node[0],node[1];\n",
      "cx node[2],node[1];\n",
      "u3(1.0*pi,0.0*pi,0.75*pi) node[1];\n",
      "cx node[2],node[1];\n",
      "barrier node[2],node[1];\n",
      "u1(1.0*pi) node[1];\n",
      "u3(0.5*pi,0.0*pi,0.0*pi) node[2];\n",
      "cx node[2],node[1];\n",
      "u1(1.0*pi) node[1];\n",
      "u3(3.5*pi,0.0*pi,0.0*pi) node[2];\n",
      "barrier node[2],node[1];\n",
      "measure node[2] -> m[0];\n",
      "measure node[1] -> m[1];\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from pytket.qasm import circuit_to_qasm_str, circuit_from_qasm_str\n",
    "qasm = circuit_to_qasm_str(circuit)\n",
    "print(qasm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "qasm = qasm.replace('FCNode', 'fcnode')\n",
    "circuit = circuit_from_qasm_str(qasm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'node'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(map( lambda q : q.reg_name  ,circuit.qubits)).pop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 2.43.0 (0)\n",
       " -->\n",
       "<!-- Title: Circuit Pages: 1 -->\n",
       "<svg width=\"1772pt\" height=\"151pt\"\n",
       " viewBox=\"0.00 0.00 1771.80 150.92\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 146.92)\">\n",
       "<title>Circuit</title>\n",
       "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-146.92 1767.8,-146.92 1767.8,4 -4,4\"/>\n",
       "<g id=\"clust1\" class=\"cluster\">\n",
       "<title>cluster_q_inputs</title>\n",
       "<path fill=\"lightgrey\" stroke=\"lightgrey\" d=\"M31.53,-46C31.53,-46 36.07,-46 36.07,-46 38.33,-46 40.6,-48.27 40.6,-50.53 40.6,-50.53 40.6,-85.47 40.6,-85.47 40.6,-87.73 38.33,-90 36.07,-90 36.07,-90 31.53,-90 31.53,-90 29.27,-90 27,-87.73 27,-85.47 27,-85.47 27,-50.53 27,-50.53 27,-48.27 29.27,-46 31.53,-46\"/>\n",
       "</g>\n",
       "<g id=\"clust2\" class=\"cluster\">\n",
       "<title>cluster_c_inputs</title>\n",
       "<path fill=\"lightgrey\" stroke=\"lightgrey\" d=\"M1608.53,-13C1608.53,-13 1613.07,-13 1613.07,-13 1615.33,-13 1617.6,-15.27 1617.6,-17.53 1617.6,-17.53 1617.6,-37.47 1617.6,-37.47 1617.6,-39.73 1615.33,-42 1613.07,-42 1613.07,-42 1608.53,-42 1608.53,-42 1606.27,-42 1604,-39.73 1604,-37.47 1604,-37.47 1604,-17.53 1604,-17.53 1604,-15.27 1606.27,-13 1608.53,-13\"/>\n",
       "</g>\n",
       "<g id=\"clust3\" class=\"cluster\">\n",
       "<title>cluster_q_outputs</title>\n",
       "<path fill=\"lightgrey\" stroke=\"lightgrey\" d=\"M1727.73,-56C1727.73,-56 1732.27,-56 1732.27,-56 1734.53,-56 1736.8,-58.27 1736.8,-60.53 1736.8,-60.53 1736.8,-95.47 1736.8,-95.47 1736.8,-97.73 1734.53,-100 1732.27,-100 1732.27,-100 1727.73,-100 1727.73,-100 1725.47,-100 1723.2,-97.73 1723.2,-95.47 1723.2,-95.47 1723.2,-60.53 1723.2,-60.53 1723.2,-58.27 1725.47,-56 1727.73,-56\"/>\n",
       "</g>\n",
       "<g id=\"clust4\" class=\"cluster\">\n",
       "<title>cluster_c_outputs</title>\n",
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       "</g>\n",
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       "<title>cluster_10</title>\n",
       "<path fill=\"lightblue\" stroke=\"lightblue\" d=\"M63.6,-61C63.6,-61 181.2,-61 181.2,-61 186.7,-61 192.2,-66.5 192.2,-72 192.2,-72 192.2,-83 192.2,-83 192.2,-88.5 186.7,-94 181.2,-94 181.2,-94 63.6,-94 63.6,-94 58.1,-94 52.6,-88.5 52.6,-83 52.6,-83 52.6,-72 52.6,-72 52.6,-66.5 58.1,-61 63.6,-61\"/>\n",
       "<text text-anchor=\"middle\" x=\"122.4\" y=\"-82\" font-family=\"Times,serif\" font-size=\"10.00\">U3(0.5*PI, 0.25*PI, 0*PI)</text>\n",
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       "<g id=\"clust6\" class=\"cluster\">\n",
       "<title>cluster_11</title>\n",
       "<path fill=\"lightblue\" stroke=\"lightblue\" d=\"M75.6,-94C75.6,-94 169.2,-94 169.2,-94 174.7,-94 180.2,-99.5 180.2,-105 180.2,-105 180.2,-116 180.2,-116 180.2,-121.5 174.7,-127 169.2,-127 169.2,-127 75.6,-127 75.6,-127 70.1,-127 64.6,-121.5 64.6,-116 64.6,-116 64.6,-105 64.6,-105 64.6,-99.5 70.1,-94 75.6,-94\"/>\n",
       "<text text-anchor=\"middle\" x=\"122.4\" y=\"-115\" font-family=\"Times,serif\" font-size=\"10.00\">U3(3*PI, 0*PI, 1*PI)</text>\n",
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       "<title>cluster_12</title>\n",
       "<path fill=\"lightblue\" stroke=\"lightblue\" d=\"M257.8,-42C257.8,-42 375.4,-42 375.4,-42 380.9,-42 386.4,-47.5 386.4,-53 386.4,-53 386.4,-64 386.4,-64 386.4,-69.5 380.9,-75 375.4,-75 375.4,-75 257.8,-75 257.8,-75 252.3,-75 246.8,-69.5 246.8,-64 246.8,-64 246.8,-53 246.8,-53 246.8,-47.5 252.3,-42 257.8,-42\"/>\n",
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       "<g id=\"clust8\" class=\"cluster\">\n",
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       "<path fill=\"lightblue\" stroke=\"lightblue\" d=\"M452,-75C452,-75 504.6,-75 504.6,-75 510.1,-75 515.6,-80.5 515.6,-86 515.6,-86 515.6,-97 515.6,-97 515.6,-102.5 510.1,-108 504.6,-108 504.6,-108 452,-108 452,-108 446.5,-108 441,-102.5 441,-97 441,-97 441,-86 441,-86 441,-80.5 446.5,-75 452,-75\"/>\n",
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       "<text text-anchor=\"middle\" x=\"606.5\" y=\"-96\" font-family=\"Times,serif\" font-size=\"10.00\">U1(1.75*PI)</text>\n",
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       "<path fill=\"lightblue\" stroke=\"lightblue\" d=\"M665,-58C665,-58 675.2,-58 675.2,-58 680.3,-58 685.4,-63.1 685.4,-68.2 685.4,-68.2 685.4,-95.8 685.4,-95.8 685.4,-100.9 680.3,-106 675.2,-106 675.2,-106 665,-106 665,-106 659.9,-106 654.8,-100.9 654.8,-95.8 654.8,-95.8 654.8,-68.2 654.8,-68.2 654.8,-63.1 659.9,-58 665,-58\"/>\n",
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       "<text text-anchor=\"middle\" x=\"767.2\" y=\"-96\" font-family=\"Times,serif\" font-size=\"10.00\">U3(0.5*PI, 0*PI, 1.25*PI)</text>\n",
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       "<g id=\"clust16\" class=\"cluster\">\n",
       "<title>cluster_21</title>\n",
       "<path fill=\"lightblue\" stroke=\"lightblue\" d=\"M859.2,-77C859.2,-77 869.4,-77 869.4,-77 874.5,-77 879.6,-82.1 879.6,-87.2 879.6,-87.2 879.6,-114.8 879.6,-114.8 879.6,-119.9 874.5,-125 869.4,-125 869.4,-125 859.2,-125 859.2,-125 854.1,-125 849,-119.9 849,-114.8 849,-114.8 849,-87.2 849,-87.2 849,-82.1 854.1,-77 859.2,-77\"/>\n",
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       "<g id=\"clust17\" class=\"cluster\">\n",
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       "<path fill=\"lightblue\" stroke=\"lightblue\" d=\"M901.8,-77C901.8,-77 912,-77 912,-77 917.1,-77 922.2,-82.1 922.2,-87.2 922.2,-87.2 922.2,-114.8 922.2,-114.8 922.2,-119.9 917.1,-125 912,-125 912,-125 901.8,-125 901.8,-125 896.7,-125 891.6,-119.9 891.6,-114.8 891.6,-114.8 891.6,-87.2 891.6,-87.2 891.6,-82.1 896.7,-77 901.8,-77\"/>\n",
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       "<title>cluster_23</title>\n",
       "<path fill=\"lightblue\" stroke=\"lightblue\" d=\"M944.4,-77C944.4,-77 954.6,-77 954.6,-77 959.7,-77 964.8,-82.1 964.8,-87.2 964.8,-87.2 964.8,-114.8 964.8,-114.8 964.8,-119.9 959.7,-125 954.6,-125 954.6,-125 944.4,-125 944.4,-125 939.3,-125 934.2,-119.9 934.2,-114.8 934.2,-114.8 934.2,-87.2 934.2,-87.2 934.2,-82.1 939.3,-77 944.4,-77\"/>\n",
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       "<g id=\"clust19\" class=\"cluster\">\n",
       "<title>cluster_24</title>\n",
       "<path fill=\"lightblue\" stroke=\"lightblue\" d=\"M987,-56C987,-56 997.2,-56 997.2,-56 1002.3,-56 1007.4,-61.1 1007.4,-66.2 1007.4,-66.2 1007.4,-93.8 1007.4,-93.8 1007.4,-98.9 1002.3,-104 997.2,-104 997.2,-104 987,-104 987,-104 981.9,-104 976.8,-98.9 976.8,-93.8 976.8,-93.8 976.8,-66.2 976.8,-66.2 976.8,-61.1 981.9,-56 987,-56\"/>\n",
       "<text text-anchor=\"middle\" x=\"992.1\" y=\"-92\" font-family=\"Times,serif\" font-size=\"10.00\">CX</text>\n",
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       "<g id=\"clust20\" class=\"cluster\">\n",
       "<title>cluster_25</title>\n",
       "<path fill=\"lightblue\" stroke=\"lightblue\" d=\"M1030.4,-34C1030.4,-34 1139,-34 1139,-34 1144.5,-34 1150,-39.5 1150,-45 1150,-45 1150,-56 1150,-56 1150,-61.5 1144.5,-67 1139,-67 1139,-67 1030.4,-67 1030.4,-67 1024.9,-67 1019.4,-61.5 1019.4,-56 1019.4,-56 1019.4,-45 1019.4,-45 1019.4,-39.5 1024.9,-34 1030.4,-34\"/>\n",
       "<text text-anchor=\"middle\" x=\"1084.7\" y=\"-55\" font-family=\"Times,serif\" font-size=\"10.00\">U3(1*PI, 0*PI, 0.75*PI)</text>\n",
       "</g>\n",
       "<g id=\"clust21\" class=\"cluster\">\n",
       "<title>cluster_26</title>\n",
       "<path fill=\"lightblue\" stroke=\"lightblue\" d=\"M1172.2,-36C1172.2,-36 1182.4,-36 1182.4,-36 1187.5,-36 1192.6,-41.1 1192.6,-46.2 1192.6,-46.2 1192.6,-73.8 1192.6,-73.8 1192.6,-78.9 1187.5,-84 1182.4,-84 1182.4,-84 1172.2,-84 1172.2,-84 1167.1,-84 1162,-78.9 1162,-73.8 1162,-73.8 1162,-46.2 1162,-46.2 1162,-41.1 1167.1,-36 1172.2,-36\"/>\n",
       "<text text-anchor=\"middle\" x=\"1177.3\" y=\"-72\" font-family=\"Times,serif\" font-size=\"10.00\">CX</text>\n",
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       "<g id=\"clust22\" class=\"cluster\">\n",
       "<title>cluster_27</title>\n",
       "<path fill=\"lightblue\" stroke=\"lightblue\" d=\"M1216.6,-36C1216.6,-36 1245.2,-36 1245.2,-36 1251.2,-36 1257.2,-42 1257.2,-48 1257.2,-48 1257.2,-72 1257.2,-72 1257.2,-78 1251.2,-84 1245.2,-84 1245.2,-84 1216.6,-84 1216.6,-84 1210.6,-84 1204.6,-78 1204.6,-72 1204.6,-72 1204.6,-48 1204.6,-48 1204.6,-42 1210.6,-36 1216.6,-36\"/>\n",
       "<text text-anchor=\"middle\" x=\"1230.9\" y=\"-72\" font-family=\"Times,serif\" font-size=\"10.00\">Barrier</text>\n",
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       "<g id=\"clust23\" class=\"cluster\">\n",
       "<title>cluster_28</title>\n",
       "<path fill=\"lightblue\" stroke=\"lightblue\" d=\"M1312.2,-70C1312.2,-70 1349.8,-70 1349.8,-70 1355.3,-70 1360.8,-75.5 1360.8,-81 1360.8,-81 1360.8,-92 1360.8,-92 1360.8,-97.5 1355.3,-103 1349.8,-103 1349.8,-103 1312.2,-103 1312.2,-103 1306.7,-103 1301.2,-97.5 1301.2,-92 1301.2,-92 1301.2,-81 1301.2,-81 1301.2,-75.5 1306.7,-70 1312.2,-70\"/>\n",
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       "<g id=\"clust24\" class=\"cluster\">\n",
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       "<g id=\"clust25\" class=\"cluster\">\n",
       "<title>cluster_30</title>\n",
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       "<polygon fill=\"slategray\" stroke=\"slategray\" points=\"1727.99,-23.81 1725.82,-24.54 1727.01,-23.62 1726.03,-23.44 1726.03,-23.44 1726.03,-23.44 1727.01,-23.62 1726.24,-22.33 1727.99,-23.81 1727.99,-23.81\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.dot.Digraph at 0x7ff3fc6ab490>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Graph(circuit).get_DAG()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "job = backend.process_circuit(circuit, n_shots=1000, valid_check= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ResultHandle('5fc4c664176371001a2224b0', 0)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "status = backend.circuit_status(job)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CircuitStatus(status=<StatusEnum.QUEUED: 'Circuit is queued.'>, message='job is queued')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job Status: job is queued (None)"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mCircuitNotRunError\u001b[0m                        Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/envs/planqk/lib/python3.7/site-packages/pytket/backends/ibm/ibm.py\u001b[0m in \u001b[0;36mget_result\u001b[0;34m(self, handle, **kwargs)\u001b[0m\n\u001b[1;32m    283\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 284\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    285\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mCircuitNotRunError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/planqk/lib/python3.7/site-packages/pytket/backends/backend.py\u001b[0m in \u001b[0;36mget_result\u001b[0;34m(self, handle, **kwargs)\u001b[0m\n\u001b[1;32m    275\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"result\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 276\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mCircuitNotRunError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    277\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mCircuitNotRunError\u001b[0m: Circuit corresponding to ResultHandle('5fc4c664176371001a2224b0', 0) has not been run by this backend instance.",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-27-80bd56087bf5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbackend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/planqk/lib/python3.7/site-packages/pytket/backends/ibm/ibm.py\u001b[0m in \u001b[0;36mget_result\u001b[0;34m(self, handle, **kwargs)\u001b[0m\n\u001b[1;32m    295\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    296\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_monitor\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mjob\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 297\u001b[0;31m                     \u001b[0mjob_monitor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    298\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    299\u001b[0m                 \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"timeout\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"wait\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/planqk/lib/python3.7/site-packages/qiskit/tools/monitor/job_monitor.py\u001b[0m in \u001b[0;36mjob_monitor\u001b[0;34m(job, interval, quiet, output)\u001b[0m\n\u001b[1;32m     84\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     85\u001b[0m     _text_checker(job, interval, _interval_set,\n\u001b[0;32m---> 86\u001b[0;31m                   quiet=quiet, output=output)\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/planqk/lib/python3.7/site-packages/qiskit/tools/monitor/job_monitor.py\u001b[0m in \u001b[0;36m_text_checker\u001b[0;34m(job, interval, _interval_set, quiet, output)\u001b[0m\n\u001b[1;32m     40\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\r%s: %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'Job Status'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m     \u001b[0;32mwhile\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'DONE'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'CANCELLED'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'ERROR'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m         \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minterval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     43\u001b[0m         \u001b[0mstatus\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m         \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "result = backend.get_result(job)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "result.get_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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