4qubit_balanced.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fabc5d5a-4e0d-402a-ba1f-b422689cae59",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1502/83197244.py:14: DeprecationWarning: The qiskit.ignis package is deprecated and has been supersceded by the qiskit-experiments project. Refer to the migration guide: https://github.com/Qiskit/qiskit-ignis#migration-guide on how to migrate to the new project.\n",
      "  from qiskit.ignis.mitigation.measurement import complete_meas_cal, CompleteMeasFitter\n",
      "<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"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Importing standard Qiskit libraries\n",
    "from qiskit import QuantumCircuit, assemble, transpile, Aer, IBMQ\n",
    "# from qiskit.tools.jupyter import *\n",
    "from qiskit.visualization import *\n",
    "# from ibm_quantum_widgets import *\n",
    "# from qiskit.providers.aer import QasmSimulator\n",
    "from qiskit.providers.ibmq import least_busy\n",
    "from qiskit import execute\n",
    "\n",
    "from qiskit.tools.monitor import job_monitor\n",
    "\n",
    "from qiskit.ignis.mitigation.measurement import complete_meas_cal, CompleteMeasFitter\n",
    "from qiskit import QuantumRegister\n",
    "from qiskit.visualization import array_to_latex\n",
    "\n",
    "# Loading your IBM Quantum account(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "df35c79b-29b6-4326-ac38-b8dbebfccbc4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "backend used:  ibmq_belem\n"
     ]
    }
   ],
   "source": [
    "# Load our saved IBMQ accounts and get the least busy backend device with greater than or equal to (n+1) qubits\n",
    "IBMQ.load_account()\n",
    "provider = IBMQ.get_provider(hub='ibm-q')\n",
    "# provider = IBMQ.get_provider(hub='ibm')\n",
    "real_backend = least_busy(provider.backends(filters=lambda x: not x.configuration().simulator))\n",
    "# real_backend = provider.get_backend('ibm_lagos')\n",
    "print(\"backend used: \", real_backend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "516d324d-c671-48e1-b696-716201fa2363",
   "metadata": {},
   "outputs": [],
   "source": [
    "def dj_oracle(case, n):\n",
    "    # We need to make a QuantumCircuit object to return\n",
    "    # This circuit has n+1 qubits: the size of the input,\n",
    "    # plus one output qubit\n",
    "    oracle_qc = QuantumCircuit(n+1)\n",
    "    \n",
    "    # First, let's deal with the case in which oracle is balanced\n",
    "    if case == \"balanced\":\n",
    "        # First generate a random number that tells us which CNOTs to\n",
    "        # wrap in X-gates:\n",
    "        b = np.random.randint(1,2**n)\n",
    "        # Next, format 'b' as a binary string of length 'n', padded with zeros:\n",
    "        b_str = format(b, '0'+str(n)+'b')\n",
    "        # Next, we place the first X-gates. Each digit in our binary string \n",
    "        # corresponds to a qubit, if the digit is 0, we do nothing, if it's 1\n",
    "        # we apply an X-gate to that qubit:\n",
    "        for qubit in range(len(b_str)):\n",
    "            if b_str[qubit] == '1':\n",
    "                oracle_qc.x(qubit)\n",
    "        # Do the controlled-NOT gates for each qubit, using the output qubit \n",
    "        # as the target:\n",
    "        for qubit in range(n):\n",
    "            oracle_qc.cx(qubit, n)\n",
    "        # Next, place the final X-gates\n",
    "        for qubit in range(len(b_str)):\n",
    "            if b_str[qubit] == '1':\n",
    "                oracle_qc.x(qubit)\n",
    "\n",
    "    # Case in which oracle is constant\n",
    "    if case == \"constant\":\n",
    "        # First decide what the fixed output of the oracle will be\n",
    "        # (either always 0 or always 1)\n",
    "        output = np.random.randint(2)\n",
    "        if output == 1:\n",
    "            oracle_qc.x(n)\n",
    "    oracle_qc.name = \"oracle \" + case\n",
    "    return oracle_qc\n",
    "    # oracle_gate = oracle_qc.to_gate()\n",
    "    # oracle_gate.name = \"Oracle\" # To show when we display the circuit\n",
    "    # return oracle_gate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0e937de4-f363-4270-b184-8dd1e545e832",
   "metadata": {},
   "outputs": [],
   "source": [
    "def dj_algorithm(oracle, n):\n",
    "    dj_circuit = QuantumCircuit(n+1, n)\n",
    "    # Set up the output qubit:\n",
    "    dj_circuit.x(n)\n",
    "    dj_circuit.h(n)\n",
    "    # And set up the input register:\n",
    "    for qubit in range(n):\n",
    "        dj_circuit.h(qubit)\n",
    "    # Let's append the oracle gate to our circuit:\n",
    "    # dj_circuit.barrier()\n",
    "    dj_circuit.append(oracle, range(n+1))\n",
    "    # Finally, perform the H-gates again and measure:\n",
    "    # dj_circuit.barrier()\n",
    "    for qubit in range(n):\n",
    "        dj_circuit.h(qubit)\n",
    "    \n",
    "    for i in range(n):\n",
    "        dj_circuit.measure(i, i)\n",
    "    \n",
    "    return dj_circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6e507be0-4e26-4ad4-b117-12450e7ce120",
   "metadata": {},
   "outputs": [],
   "source": [
    "aer_sim = Aer.get_backend('aer_simulator')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0f472397-1d45-4a87-ae72-5cac7807a727",
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "daadac01-43b2-40ea-9c04-d0d4cf1d675a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 387.597x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oracle_qc = dj_oracle('balanced', n)\n",
    "oracle_qc.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "beb78ead-4a4a-4c71-ace0-5b1524202709",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 688.597x385.28 with 1 Axes>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_circuit = dj_algorithm(oracle_qc, n)\n",
    "dj_circuit.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3e5881b9-ae4f-4661-a6cb-604dc01c34a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 748.797x385.28 with 1 Axes>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_dj = dj_circuit.decompose('oracle balanced')\n",
    "new_dj.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "215f06a8-22cd-48bb-ac1d-c2e03cd405b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aer_job = execute(dj_circuit, aer_sim, shots=1000)\n",
    "aer_results = aer_job.result()\n",
    "aer_counts = aer_results.get_counts()\n",
    "plot_histogram(aer_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f64dca63-4b90-4098-ad40-cafe2e3a94f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "job = execute(dj_circuit, real_backend, shots=1000)\n",
    "realqc_results = job.result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9eea3da7-7988-43d3-b821-e0836ac1cc30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IiEiNqU4T6/XAA2a2NXqBmW0B7gNGJjkvERGRtKpOgewOHKwZdSrQIznpiIiIZIbqFMhj8AOTV8WA5slJR0REJDNUp0CW488iq9IdWJ2cdERERDJDdQrka8B/OecaRC9wzh0B3BW8RkREpNaozm0edwNDgKXOuUeBymc/dsF34HHAf6cmPRERkfQ4ZIE0s/XOue8Cj+MLoatcBPwT+JmZrUtdiiIiIjWvWgMFmNmnwHnOuabACfgiuczMNqcyORERkXSJ6YHJQUF8P0W5iIiIZIxYn+YhIiJSJ6hAioiIhFCBFBERCaECKSIiEkIFUkREJIQKpIiISAgVSBERkRAqkCIiIiFUIEVEREKoQIqIiIRQgRQRqSWuuOIKWrRoQWFh4b55Q4cOpaioiKKiIgoKCigqKgJg5cqVNGjQYN+ya6+9Nk1ZZ66YxmIVEZHMdfnll3P99ddz6aWX7pv3wgsv7Pv/L37xC4466qh9Px9//PHMnz+/JlPMKiqQIiK1RN++fVm5cmXoMjPjxRdfZOrUqTWbVBZTE6uISB0wc+ZMWrZsSYcOHfbNW7FiBT179qRfv37MnDkzjdllJp1BiojUAc8//zzDhg3b9/Oxxx7LZ599RvPmzZk7dy4/+tGPWLRoEY0bN05jlplFZ5AiIrXcnj17GD9+PEOHDt03Ly8vj+bNmwPQu3dvjj/+eJYuXZquFDOSCqSISC03efJkOnfuTH5+/r55X3zxBRUVFQAsX76cZcuW0b59+3SlmJFUIEVEaolhw4ZRUlLCkiVLyM/P56mnngJg3Lhx+zWvAsyYMYPu3btTVFTEkCFDeOKJJ2jWrFk60s5YzszSnUONKS4utrKysnSnISIiIa5+KLbXjx6Z+Dqdc3PNrDhsmc4gRUREQqhAioiIhFCBFBERCaECKSIiEkIFUkREJIRG0hERqWXS0Ru0NtIZpIiISAgVSBERkRAqkCIiIiFUIEVEREKoQIqIiIRQgRQREQmhAikiIhKiRgukc66vc+5V59xq55w55y6PWj4mmB85vRv1mjzn3Cjn3Abn3I4gXj4iIiJJVNNnkA2BhcDPgZ1VvGYycGzEdF7U8oeAC4BhwPeAxsBE51xOCvIVEZE6qkZH0jGz14HXwZ8tVvGyXWa2NmyBc+4o4EpghJm9Gcz7N+BT4Czgn8nOWURE6qZMvAZZ6pxb75xb6pwb7ZxrEbGsN1AfeKNyhpmtAj4GvlvDeYqISC2WaWOxTgLGAyuAAuB3wFTnXG8z2wW0AiqADVHvWxcsExERSYqMKpBmNi7ix3855+bim08H4gtnzJxz1wDXALRu3Zrp06cD0L59exo1asSCBQsAaN68Od26dWPGjBkA5ObmUlpayrx589i6dSsAxcXFrFu3jlWrVgHQoUMH8vLyWLhwIQAtWrSgY8eOzJo1C4C8vDxKSkooKytj+/btAPTp04fy8nJWr14NQKdOncjJyeGjjz4CoFWrVrRr147Zs2cD0KBBA/r06cOcOXPYudNfti0pKWHFihWsXetbort27UpFRQVLliwBoE2bNuTn5zNnzhwAGjZsSHFxMbNnz2bXrl0AlJaWsnTpUtavXw9AYWEhu3btYtmyZQC0bduWli1bUlZWBkDjxo3p1asXs2bNYs+ePQD07duXRYsWsXHjRgB69OjBtm3bWL58OQAFBQU0a9aMefPmAdC0aVN69OjBW2+9hZnhnKNfv34sWLCAzZs3A9CrVy82bdrEypUrtZ20nbSd4txO0INYVO4X072dIC+mvNesWZPwdjoYZ2YxJZQszrntwPVmNuYQr1sBPGFm9znnzgCmAC3M7IuI1ywC/mpmvz1YrOLiYqv8goqI1FbZ+jSPdOTtnJtrZsVhyzLxGuQ+zrmjgTbAmmDWXGA30D/iNflAF+CdGk9QRERqrRptYnXONQROCH6sBxznnCsCNgXTncDL+IJYANwDrAf+BmBmW5xzTwH3O+fWAxuBPwAf4m8PERERSYqaPoMsBj4IpgbAfwb/vwvf+eZEYAKwFHgWWAKUmNm2iBgj8QXzBeBtYDvwfTOrqJlfQURE6oKavg9yOuAO8pKzqxFjF3BDMImIiKRERl+DFBERSRcVSBERkRAqkCIiIiFUIEVEREKoQIqIiIRQgRQREQmhAikiIhJCBVJERCSECqSIiEgIFUgREZEQKpAiIiIhVCBFRERCqECKiIiEUIEUEREJoQIpIiISQgVSREQkhAqkiIhICBVIERGJyYMPPki3bt0oLCxk2LBhfP311zz66KOccMIJOOfYsGFDulNMChVIERGpttWrV/PII49QVlbGwoULqaioYNy4cZx66qlMnjyZ73znO+lOMWly052AiIhklz179rBz507q16/PV199RevWrenZs2e600o6nUGKiEi1tWnThltuuYXjjjuOY489lqOOOooBAwakO62UUIEUEZFq27x5MxMmTGDFihV8/vnn7Nixg7Fjx6Y7rZRQgRQRkWqbPHky7dq145hjjqF+/fqcf/75vPPOO+lOKyVUIEVEpNqOO+443n33Xb766ivMjClTptClS5d0p5USKpAiIlJtffr0YciQIfTq1YsTTzyRvXv3cs011/DII4+Qn59PeXk53bt356qrrkp3qglzZpbuHGpMcXGxlZWVpTsNEZGUuvqh2F4/emQqsohdOvJ2zs01s+KwZTqDFBERCaECKSIiEkIFUkREJIRG0hERkWrL1uub8dAZpIiISAgVSBERkRAqkCIiIiF0DTIDFRQU0KhRI3JycsjNzaWsrIxNmzYxdOhQVq5cSUFBAS+++CJNmzZNd6oiIrWWziAz1LRp05g/fz6VAxvce++9nHnmmSxbtowzzzyTe++9N80ZiojUbiqQWWLChAlcdtllAFx22WW88sor6U1IRKSWU4HMQM45BgwYQO/evXnyyScBWLduHcceeywArVq1Yt26delMUUSSpKKigp49ezJo0CAALr/8ctq1a0dRURFFRUXMnz8/vQnWYboGmYFmzZpFmzZtWL9+Pf3796dz5877LXfO4ZxLU3aSKSoqKiguLqZNmzZMnDiRK6+8krKyMsyMjh07MmbMGBo2bJjuNOUQHn74Ybp06cLWrVv3zXvggQcYMmRIGrMS0BlkRmrTpg0ALVq0YPDgwbz33nu0bNmSNWvWALBmzRpatGiRzhQlA1TuWCs9+OCDLFiwgA8//JDjjjuORx99NI3ZSXWUl5fz2muv1YonX9RGKpAZZseOHWzbtm3f/9944w0KCwv5wQ9+wLPPPgvAs88+yw9/+MN0pilpFrZjbdy4MQBmxs6dO9XKkAVGjhzJ/fffT716+++K77jjDrp3785NN93Erl270pSdqEBmmHXr1lFaWkqPHj04+eSTGThwIOeccw633347b775Jh06dGDy5Mncfvvt6U5V0qiqHeuIESNo1aoVixcv5oYbbkhTdlIdEydOpEWLFvTu3Xu/+ffccw+LFy/m/fffZ9OmTdx3331pylB0DTLDtG/fngULFhwwv3nz5kyZMiUNGUmmidyxTp8+fb9lzzzzDBUVFdxwww288MILjBgxIj1JyiG9/fbbvPrqq7z++ut8/fXXbN26lUsuuYSxY8cCkJeXx4gRI/j973+f5kzrLp1BimSZyh1rQUEBF110EVOnTuWSSy7ZtzwnJ4eLLrqIl19+OY1ZyqHcc889lJeXs3LlSsaNG8cZZ5zB2LFj9/U1MDNeeeUVCgsL05xp3aUCKQn7+uuvOfnkk+nRowfdunXjt7/9LQCPPvooJ5xwAs45NmzYkOYsa4+wHetzzz3HJ598Avgd66uvvnpA72fJDsOHD+fEE0/kxBNPZMOGDfz6179Od0p1lppYM0i2PkYmLy+PqVOn0rBhQ3bv3k1paSnnnnsup556KoMGDeK0005Ld4q1nplx2WWXsXXrVsyMHj168Pjjj6c7Lamm0047bd/fydSpU9ObjOyjAikJc87tu99u9+7d7N69G+ccPXv2THNmtV/kjvXtt99ObzIitYyaWCUpKioqKCoqokWLFvTv358+ffqkOyURkYToDFKSIicnh/nz5/Pll18yePBgFi5cqM4FIgeRrZdU6hIVSEmqJk2acPrppzNp0iQVyBSJZceqnapI/NTEKgn74osv+PLLLwHYuXMnb775pnpQikjWU4GUhK1Zs4bTTz+d7t27c9JJJ9G/f38GDRrEI488Qn5+PuXl5XTv3l3jTYpIVlETqySse/fufPDBBwfMv/HGG7nxxhvTkJFI8lxxxRX7Ri9auHDhvvmjRo3iscceIycnh4EDB3L//fenMUtJBZ1BiogcxOWXX86kSZP2mzdt2jQmTJjAggULWLRoEbfcckuaspNUUoGsQ6644gpatGixX+eZ3/zmN3Tv3p2ioiIGDBjA559/nsYMRTJP3759adas2X7zHn/8cW6//Xby8vIA9Pi5WkoFMg5hhebWW2+lc+fOdO/encGDB+/rtJJJwo6Eb731Vj788EPmz5/PoEGDuOuuu2KOe/VDsU2ZIlu3Y7YK+7xfeuklunXrRr169SgrK0tjdrFZunQpM2fOpE+fPvTr14/3338/3SlJCqhAxiGs0PTv35+FCxfy4Ycf0rFjR+655540ZVe1sCPhymcIgn/+ZF16hmC2bsdsFfZ5FxYWMn78ePr27ZvUdT388MMUFhbSrVs3HnrooaTGBtizZw+bNm3i3Xff5YEHHuDCCy/EzJK+HkkvFcg4hBWaAQMGkJvr+zydcsoplJeXpyO1uNxxxx20bduWv/zlL3GdQWar2rYdM13Y592lSxc6deqU1PUsXLiQ0aNH895777FgwQImTpy4byD3ZMnPz+f888/HOcfJJ59MvXr1NCB/LaQCmQJPP/005557brrTqLa7776bVatWMXz4cB599NF0p3NQX375JUOGDKFz58506dKF2bNnp2xdydyONZl3Xffxxx/Tp08fjjjiCHJzc+nXrx/jx49P6jp+9KMfMW3aNMA3t37zzTccffTRSV2HpJ8KZJLdfffd5ObmMnz48HSnErPhw4dn/DMEf/7zn3POOeewePFiFixYQJcuXVKynmRvx5rKO5mqeoxZpissLGTmzJls3LiRr776itdff51Vq1bFHW/YsGGUlJSwZMkS8vPzeeqpp7jiiitYvnw5hYWFXHTRRTz77LN16vJEXaH7IJNozJgxTJw4kSlTpmTNH8uyZcvo0KEDABMmTMjoEXC2bNnCjBkzGDNmDACHHXYYhx12WNLXk+ztWFN5J1tVjzE75ZRT0p3aQXXp0oXbbruNAQMGcOSRR1JUVEROTk7c8Z5//vnQ+WPHjo07pmQHnUEmyaRJk7j//vt59dVXOeKII9KdTqiwI+Hbb7+dwsJCunfvzhtvvMHDDz+c7jSrtGLFCo455hhGjBhBz549ueqqq9ixY0dS15GK7VgTeadCVY8xywZXXnklc+fOZcaMGTRt2pSOHTumOyXJQiqQcQgrNNdffz3btm2jf//+FBUVce2116Y7zQM8//zzrFmzht27d1NeXs6VV17Jyy+/vK/X5t///nfatGmT7jSrtGfPHubNm8d1113HBx98wJFHHsm9994bd7ya2o7JzrsmJfMxZmGf99/+9jfy8/OZPXs2AwcO5Oyzz05K3uvXrwfgs88+Y/z48Vx88cVJiSt1i5pY4xDW5HLllVemIZO6JT8/n/z8/H076SFDhiRUaGpqOyY775qUzMeYVdVUOXjw4ERSDHXBBRewceNG6tevz2OPPUaTJk3iiqNHUtVtKpB1RG34Q2/VqhVt27ZlyZIldOrUiSlTptC1a9d0p3VI2Zp3pGx7jNnMmTPTnYLUAiqQMaoNhSabjRo1iuHDh/PNN9/Qvn17nnnmmbhj1eRzFZOZd0354osvqF+/Pk2aNNn3GLPbbrst7nh6jqVkm6wtkM65nwK3AscCi4CRZqbDxlquqKgoq4Ykq5SNea9Zs4bLLruMiooK9u7dy4UXXsigQYPSnVYoFV9JhawskM65ocDDwE+BWcG//3DOdTWzz9KanEgtUdVjzETqiqwskMDNwBgzGx38fINz7hzgOuCX6UtLki1bm7R1RiOS/bKuQDrnDgN6A7+PWvQG8N2az0ik9lBhF/lWNt4HeTSQA6yLmr8OaFXz6YiISG3ksu0RLc651sBqoJ+ZzYiY/x/AcDPrFPX6a4Brgh87AUtSlNrRQKqG81dsxU5n7GzMWbEVu7q+Y2bHhC3IuiZW/IdUAbSMmt8SWBv9YjN7Engy1Uk558rMrFixFbu2xc7GnBVbsZMh65pYzewbYC7QP2pRf+Cdms9IRERqo2w8gwT4A/Ccc+494G3gWqA18ERasxIRkVojKwukmb3gnGsO/Bo/UMBC4Dwz+zSNaaWyGVexFTudsbMxZ8VW7IRlXScdERGRmpB11yBFRERqggqkiIhICBVIERGRECqQSeKccxH/z5rPNSpvd7DXZopszBmy63shIiqQSWNmFowTi5ntrZyfjB14KnesQd6NK/+fzNjOuZxkxquUjTmD/14451oG6zncOZe0XuTOuZxUHixUxs6mAxKRRKkXaxI451oBQ4Be+OHs3gX+amazk7weh99me8OWxVosnHMnAMOA04ECYDbwd2Cama2LN27IeurB/gcO8eadjTkH7ykCLgXOw48ZXAa8CUwBPjCziiTm7cysIpE4IXFzgSPNbEsy4waxc5Kdb0TsRsCRwHrgCOCrsG0aR9ysyzmIncq8jwUaATvx42WvNbOvkxQ718z2JCNWTOtVgUycc+414ATgY+Bz/FNFCoFPgHuB5+L9UjrnfoZ/IPQcM9sZMb8e/gQq7g3onHsL/4WehR+m7wygFNiIf97m/4t3x+2cuxuYB7xhZtsi5ucAe+PNOxtzDmLMBbbhi/k6YGAwVQBjgF+b2Y44834Gf1D2opltjpifG+Sd0M41eJTcCKAHcBi+qE/AH5TsSCR21HpCD0oqz1rj+FwuxufdK5g1G/gHMNnMllTGTnC75gSpJauApTznIEay8/4pcAV+v7cbP9rZu8BU4C0z25XEvElVkT+AmWlKYMLvoL8A8oOfDweOwhfJ0cBy4OY4Y5cCe4EZwFjgRqBH1GvygN8Ax8YY+/Qg76ZR81sDv8UPCP9HICeBvD8AZuIfTdYv6jUNgEeA42pzzsH7TgvyPjxk2ZXAp/idYKME8l4KfAa8BPwwJO+nga5xxl+Cf5zcT4Pv2hx8YV8I/Die73YQ+8Rge50F5EYtyyE4gI8zdl9gJfA/wLnAZcFnvAtYBfwszrjFwER8i1H9qGW5mZhzDeR9WvC3dx/QJch9dPCdXA3cHb19Y4h9KrAAuAo4LCTveoADmiXyO1S5/mQHrGsTcCcwtYpljYE7gO1ArzhiP4gfX/au4Mv9PjAN+BO+ua4A6BPsIBvGGPsW/BFeg+DnHKBexPJLgC3AGXHkfX+Q57XAo8B0YD7wT/wDrQuBk4O8q10UsjHnIPa1QayWwc95kX/sQD/8WeXgOPL+XZDjD4PPZyJ+J7sUeAw4BTgpnryD+H8FRofM74wf3WQ1cGmcfzvP4pvj3gcmB39LvaJeUxJ832Pa+QEvAk+GzD8iWM9G4Ddx5vxN8Pn+H/BnDjyQ+i4wjhgP1FKVcw3k/b/An0Lm1w++++uBpxLIuwJYA+wBJgGDol5zajA/riJ8sCkrh5rLMJOBnznnzjGzSZELzGyrc+4+/A6wH775LhZHA2Vm9h9BB6Az8IOy98Q3wVyI31FNMbPtMcZ+HbgNOB/4iwVNFpXNIGY21jn34yDvqXHkvdzMngiazXrhd3QnAT8ABgPtgEkW0ZRZS3MGX7TuAIYDfzCzXUHelU23bwVNx98D/hZj7CPxO4/XzGyPc+5lfDEvwZ/9jQPaAP+II2/wR+YfV/4Q0Wy72Dl3I34nONI5N8nM1scY+0T8QWA5/gynP/BD59xn+DPWv+MPBL9nwZ4wBofhd6yVeecFeX8F3Bls44ucc8+Z2coY4nbEnxGV4Q+Y+gJjnXObgVeB5/DbudBibwZMVc6pzvsboJlz7nAz+9o5dziwx8x2A08453YDtzrnupnZohhjF+CHFJ0Y5P1j4KUg5kv4Fp2LgNaWimuUya64dW3CN1/9D/564+1Ab3xnhsrlTfA7gPPjiF0InBsyvwW+o8oY/JnBeXHEzgH+H/6o9El855HmEctb4Zt1hsQRuxVwWsj8o/DNpHfFk3dEzpuyKOfK6/y34VsSJuOv1bSOeE37IO+YmyuBhsApVXwvu+KbpuL6jgRxbsRf6+1Yxe/VNvjux9RCAnQA3gKuCH5uhD9AuA1/JvUefme+l6gm42rGHx58t78b/R0K/m0GrABKYohZgN9RXxf8fDi+U96F+Ovf7wfbcS/wg0zIuYbyPhv/GMILo+bnBv8eiW/+7xdj3Nb4M92fVH4OQFP8QevtQd67gry/H8/3+1CTOukkgXOuLb4Z7ixgM37DrcUfDZYAnSzqQc4xxM41f2ZQD9/evq/ThXPu+8BYMzsqzth5wA34M6QG+EK+Cd9M2QfflNmr6gjVXs9+HYqCvJ83s4ZxxDoCuA5/RtcA/4eXUM7RnQciPuuKZOQcEXcwfifYHv/d2II/++sJbDKzvvHGjlhH9O/yA3znncPjjHcMvgmtPf5s9J/4XrfbguXnA8+aWaM4Yp+AbyJfGjW/Jf4M/if4g5YmMcZ1+J3yaOAc/HW8l/EtLV8G23covjkzpryD3I40s+VR8xvhP6OfAkMTzPlcfGtJUnKugbzzgP/G70vK8M2iL5nZRufc0fj9y0Nm1jiOvJsDR5jZqqj5ufhieQNwY6x5V3v9KpDJ45zrjr8O9l38BeSm+GuIfzCzfyVxPQ5/YfqvQGMzOyvBeJ2BQUARPudj8Wc6j5vZisSyPWBd9fAdM5qZ2YUJxDkO+D7+GtvR+AdmJz3niM864ZyDeG3x10y6AfnBNAnf0znWJspDrcsB/4m/9vmTBOKcgN+B9sNfByoHvsLv0DsDE83s3xPMtbJTzp6Iea8AO8xseJwxGwGX43sLt8SfaWzBn4m0BMaZ2Z0J5h19QPIKsMvMhsYZ70h8L9Yf4luK9qQg56o+67jzDmIMAi7GH/Adg7+uvgdfQP9sZr9PJO8q1vkK/kD2gmTHBhXIuAVnX93x18O2AR/irxeuDZZ3wneW+MZi/JAjYg/G/3EswjdjrTJ/K0A98zed5wJHmdnGOPKv7OCyO2r+MWb2RazxQmKbVdGFPFjeyMy+rGa8hvhrJsOAL/G9KufiP+/dieQcFXsTsAzfkeFfZrYm3pyj1pELYFHXSJxzeRZcj4zXoW7lCA5IjrT4rj9Gx+qKPyjpim/ua4C/hjjN/HWypAhybopvZr3UzN5OMF4nfEtOO/wByeH4jlhzzT+APSmcc02A8cBtZvZ+nDHyzN8S0Qr/veyGb8bOI8GcnXONor8HwWfdOJG8Iw8SnL8XsitwHP7zPhzfg3qZJf/e3IbAKOBhM5ufzNj71qECGR/n3CP44rgGv7MowDerTgDuswSeTRkS+zv4+ytfwTdVLK/63YeM3dvM5kbNOwxf0HZX8bZEYifjHsJn8TuLZfjPIx/Yiu8Z+piZvZXk2JuD2E+a2bQEYpea2ayoeft91i7OG6CriL1fsaw8kIoz97b4a6Un4697LQLeMbMFwTZtYLF3DIuOfRK+R+WSIP6HZra5cofrnGsYyzqidtQHHJTE+3mExD3YAckRsR4sOOe6ADfjz7yW4z/vt4EZ8RyQHST2J/hLEvOBmZHNls65BhZxn3Uc60n4Hsc415vwQeZBWQoubNb2CX+EtBUYQNBJBN+kcDv+y70Tf/0k5vtyqhn7GnyzX6xd3zvgm5kWAn8AekYtd/iedCcTdc9RkmLXjzV28HlswzelVnZWOAq4mm/vx7uTOO6bq2bs3xJ1O0k1Y3cOPo9t+Gt3p0Ytr4c/K/g+we0fSYxduR1jjh28vx2+x/XH+OtJ8/C3cyzC33JRkMDfTljscnwLzB+B46N/lxhiH8OBty5Ufs6VHUYcsd/GEBbXEXX/YMR3KJacj8cfIMwA7sH3Yn4Xf+/fy8BZCXzWYbFnB5/5i8CABD7rlsC/4S87RH/elSdeOUBeHHmHxg553eGx5h1zLqkKXJsn4Ff4o7vKn6Nvcv5vYDERPRUzJPZ/4M+UHsRfG10d/DH+O9A2eE2bYOebnwmxgZHArIifo28WvhbfA69jLPnWQOxf4Qcd+CV+4IE9+BaG3xMUAfw1pr2Vn08mxA7e+wT+FotWEfOOw/cw/RTfY/GHscatZuwvEoj9WPA7rw3+3zVqeU6wrh8TQ5GsRtx6EXFjuhcPeDz4PBpFzGuJv3Y6A3+t98o4P49Dxd6RQOxRwWeyCX+7xXlEFcPgM7klen46Y8f8e6YyeG2d8NcGlwEnRMzL5dsjmsqj5BszLPZf8N26W+GvbVyG70b9If4o/u/47uCLMiU2/haLVcCZUZ9H5WABzfHDzv02jpxTGfsx/DWjZsHUF39w83Hwx/8h/l7NeD7rlMUO4s8Ebgn+X58DD9Kex/cM3Xe2kCGx38Ofhf4Wfwa2F9+s+CuCsxH8SEDLMyFu8L7Xgf8M/p9DVOHG39b0Lr4nZybFno0/Kx2B7xy3C38ZaBTBLT/AfwGfZFLsmHNJ9Qpq4xTsOD/Gn8ldSMhRTPCH9JNMiY3f8V8M/DJqfjN8B4br8c0ue4nxqDLFsQ/HD+q9Bn9G1yDkNfOJYxiuVMUOdkbnAT+Nmn8Y/sh3EL47/15gRKbEjoh1Z/B7R5551OfbA4dS/LXDA+6/TFds/HX6f+LPjurhWyvOxh9MfBZ8Hm/jz0puSnfciPg/D+J0jtqWhwX/74q/tHJ6psTG35/4EnBN8HMuvtn/tmDbVgD/wp+h/jxTYsf1t5DqFdTWKdiQLwAf4Uf9+B1+pJuO+DOpL4gYMCBTYkeso37IvPODP/iYjyhTGRvfW/IPwR/7Evy1q/Px12mfx58FxpVzKmNHrOOA65fBTjYZn3XSY+MHu/gc31JxwI3jwQ7r6zi3ZUpi43tiXkb4+LnH4w82p+Obow84EKrpuBFx2uGby1cQDJoQtbwQP1JNPJ91SmLjb+/5PtAnZNkR+H4GL8bzmaQydjyTerEmILiJdRB+iKz2+NEpmuJHCPmTmY3LlNhV9eALeuVVmJk5534PFJvZaRkUO8f80zka4s8uvofvVNMLfzY1GT9W6D9iiZvK2JX3ToZ9JhGvuRM/IsrZmRK7Mn6wvU7Aj097Cn50l5n4ps+u+AGvV1iM94SmMnb0evDNidG31fwFaBPrd7AG4jbCNykOx59Nv4H/7hXiv5fzzezSTIsdsY4DerA658bgr4d/L1NjV2v9KpCxcc7l4x9tBf40/yN8z9L2+GG/vgI2mNmmDI3t8GcXSyy4ZzNY7vA3J6+2GO+FSmXskHUdhu9VuBPfRLrFkvTIpVTGDlnXacAXFvvYlDUW2/kxNc/CH6SdjL+2vAnfhDvWEruVKWWxI9bh8Ac6FfgzvhnAPWb2cibEDeLUCw7SDsePTdsX31rUC3/mNxYYH/n3lAGx9xsZK2R5A/ztbo+b2d8yJXY8VCBj4Jy7Dn//Vg98sVqO74AyDf+A5FUHeXumxN6B72BQjr9A/4oFz5nLpNjR92Ud6g8nU2MnUypjB/Hr4Q9kjsHv+P8Pf7/clmAna/hrhxsyOPYR+B7Ub1nEyETOD75xlpm9lu64h1jnvvuGnXNHWRIfUJ3K2FHrqY9vLUrqA+NTHTt0fSqQ1RM0eX6C7/n1OP6P5iz8s9C64q+r3GhmH8V602waY3fBF7ObgtgxPW08VbGdc03xHZFewx/lvlP5O0cWs+Am6HKLYZSYNMaOvNm8C7DGYrgJPJWxg/c1Ap7C9+zdiy8GDn+wNhl/VrcseG1MN9zXcOxyfLH9Gn854jkzW1zdeKmOG8Suj78++KmF3OSeyE336YydiFTGToil+CJnbZnwg+LOqWJZKf46ynLgaMVOLHYQdxe+e30F/mzjLvyg75WvaYvvgNBesROLHbz3DnwBPin4uTN+XOHH8cP6/R04Jta4aY79Pv5RTjHHTlXcINZIfGvLM/gOKa048BaMxvhByw/o8JYFsQcS+0AjKYudyFQjK6kNE35knI/wz0uDAx96e1yw/GLFTiw2/jrUE/ib3bvjOxkswxeGd/EjCf0S2B5HzoodHn8mcHPI/By+HYpvUl2JneKcZ+MvncwMtt8KfE/qUvzYyuBvO3pXsROPnchUYyvK9gl/f+JC/M3vkfdwRT7R/h3g3xU7/tj4InsTcEfU/KPwzbdP45uc9xLj09UVu8r4ufgzo7cJzoqIGl4PODPY1j1qe+wU53wMvlPPxcHP+fgHAi8Ntt9c/D1/i/GDcCt2ArETnWpsRdk8EYx7CvwIPxzWNvz1id58O8zUJcH8AsVOOHYewVBk0TumYN5pxDEcnmIfNP4p+Gbb+wgZvxXffLsdfztDrY+dwrjH4g92zg5Z1hM/AMHGYFsqdoKxE51qbEW1YQKa4Ju3rsWPrrE9mD4J/pjuVOzEYvNtx7H20TumiGX/QXzDeil2ePx6+LOmq/GDUGzGtwr0x99/+2P8AArv14XYqcw5iN+AiIG2K6eI5XfjH0qt2EmIncikXqyH4JxrgR9Z/hf4QZp34p9JOAt/7ac+/h7ASRb1ZHTFji12RNybgfX40TLW4IeeGm/+WZgOv+P63MwmKnb8satYXxP8sGoX4x+gvQ3fc/N9/L1+c+pS7BTGDe1N6pw7Aj/K0DNmdp9iJyd2vFQgDyEYtaEbvqfdJvz4oifih31bD/w6gT8SxT503J74HoTlwANm9kYSc67TsYP4jYFtkTum4HaXw/GDUxQCO+LcnlkXu6ZzDnnN4cBQ4HmL4cHIip0iNX3Kmk0T/jR/O9A3at538GMwvoFvSuyl2InFPkjctvgmrTfwY6UmM+c6Gzsi3p+AK/EHOI2reE3TynXX9tgZkHOTFG7HOhU7GVNaVpotE/7I/V9U8WQBfMeJMnxTi2InELsacQ9LYc51Lnbw/mH4jg9f4u9X/RN+oPYT+PbpGg2BV4ATa3vsNOQ8GD/geWXsymHUChU7sdjJmmp8hdk0BRtnCv4enQ6EPznhBvyAv4qdQOxszDmbYwfvrby3sj3+4bP/wo9u8wG+U8QZwHXAN3UhdjbmrNipndKy0mya8N295wc7qcvxzVsNg2VHAH/FD5Wl2AnGzsacszU2vpfmr4B7o+Z3Ax7CX9/cgO8U9FRtj52NOSt26qe0rDTbJvyF+RfwvTU34DtMPI1/huAcYmxuUezalXO2xsY/Pq1z8P/DiLqmhu8YsRcoqguxszFnxU7tpF6sMQi63A/E3xz/NX4kjZcszkGLFbvm4yr2IddRD7+jqnDOXY0fueSIuho7G3NW7ORRgYxTrE8dUOzMi6vYh1zHzfgBox9Q7OzMWbETzEEFUkTCBI8gqkjRQU/Wxc7GnBU7wRxUIEVERA5UL90JiIiIZCIVSBERkRAqkCIiIiFUIEVEREKoQIqIiIRQgRQREQnx/wGK89cPoyt3egAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = realqc_results.get_counts()\n",
    "plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "780f0e35-e6ec-4297-bde3-efc7bd51733b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# QEM\n",
    "qr = QuantumRegister(n)\n",
    "qem_meas_calibs, state_labels = complete_meas_cal(qr=qr, circlabel='mcal')\n",
    "qem_cal_results = real_backend.run(qem_meas_calibs, shots=10000).result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5a16e528-c93c-4f84-9516-075fbe903b94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$\n",
       "\n",
       "\\begin{bmatrix}\n",
       "0.9509 & 0.0326 & 0.0485 & 0.0023 & \\cdots & 0 & 0.0004 & 0  \\\\\n",
       " 0.0101 & 0.942 & 0.0002 & 0.0528 & \\cdots & 0.0048 & 0 & 0.0001  \\\\\n",
       " 0.0144 & 0.0004 & 0.9211 & 0.0328 & \\cdots & 0 & 0.0038 & 0.0002  \\\\\n",
       " 0.0003 & 0.0066 & 0.0084 & 0.8906 & \\cdots & 0.0001 & 0 & 0.0041  \\\\\n",
       " \\vdots & \\vdots & \\vdots & \\vdots & \\ddots & \\vdots & \\vdots & \\vdots \\\\\n",
       " 0 & 0.0001 & 0 & 0 & \\cdots & 0.8282 & 0.0002 & 0.0429  \\\\\n",
       " 0 & 0 & 0.0002 & 0 & \\cdots & 0.0005 & 0.8144 & 0.025  \\\\\n",
       " 0 & 0 & 0 & 0.0001 & \\cdots & 0.0052 & 0.0088 & 0.796  \\\\\n",
       " \\end{bmatrix}\n",
       "$$"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qem_meas_fitter = CompleteMeasFitter(qem_cal_results, state_labels, circlabel='mcal')\n",
    "array_to_latex(qem_meas_fitter.cal_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "237a0603-2f94-43f0-ae42-3fb0c87f9fbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# cal_results = aer_sim.run(qem_meas_calibs, shots=10000).result()\n",
    "# meas_fitter = CompleteMeasFitter(cal_results, state_labels, circlabel='mcal')\n",
    "# array_to_latex(meas_fitter.cal_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "08024d60-6909-40c7-8e1e-f176834af0c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "qem_meas_filter = qem_meas_fitter.filter\n",
    "\n",
    "mitigated_results = qem_meas_filter.apply(realqc_results)\n",
    "mitigated_counts = mitigated_results.get_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "eec9bfb2-1d59-44fd-a953-bf6adab0682a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noisy_counts = realqc_results.get_counts()\n",
    "plot_histogram([noisy_counts, mitigated_counts], legend=['noisy', 'mitigated'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e01a4756-1d73-4776-a2f8-9c78a5cf92bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# GEM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9509da26-8378-419c-bcf5-90ce51e67aab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# not calibrating ancilla\n",
    "# depends on the concrete DJ circuit. this time: x on 1 and 2\n",
    "\n",
    "dj_left0 = QuantumCircuit(n,n)\n",
    "dj_left1 = QuantumCircuit(n) \n",
    "dj_left2 = QuantumCircuit(n)\n",
    "dj_left3 = QuantumCircuit(n)\n",
    "dj_left4 = QuantumCircuit(n)\n",
    "dj_left5 = QuantumCircuit(n)\n",
    "dj_left6 = QuantumCircuit(n)\n",
    "dj_left7 = QuantumCircuit(n)\n",
    "dj_left8 = QuantumCircuit(n)\n",
    "dj_left9 = QuantumCircuit(n)\n",
    "dj_left10 = QuantumCircuit(n)\n",
    "dj_left11 = QuantumCircuit(n)\n",
    "dj_left12 = QuantumCircuit(n)\n",
    "dj_left13 = QuantumCircuit(n)\n",
    "dj_left14 = QuantumCircuit(n)\n",
    "dj_left15 = QuantumCircuit(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "28c7e814-2c41-41e8-b986-ae0b6d0d67c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 507.997x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_left0.h(0)\n",
    "dj_left0.h(0)\n",
    "\n",
    "dj_left0.h(1)\n",
    "dj_left0.x(1)\n",
    "dj_left0.x(1)\n",
    "dj_left0.h(1)\n",
    "\n",
    "dj_left0.h(2)\n",
    "dj_left0.x(2)\n",
    "dj_left0.x(2)\n",
    "dj_left0.h(2)\n",
    "\n",
    "dj_left0.h(3)\n",
    "dj_left0.h(3)\n",
    "dj_left0.measure([0,1,2,3], [0,1,2,3])\n",
    "dj_left0.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "439b7191-f5ac-4307-a2a0-004d8883cbde",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1502/4218642948.py:2: DeprecationWarning: The QuantumCircuit.__iadd__() method is being deprecated. Use the compose() (potentially with the inplace=True argument) and tensor() methods which are more flexible w.r.t circuit register compatibility.\n",
      "  dj_left1 += dj_left0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 507.997x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_left1.x(0)\n",
    "dj_left1 += dj_left0\n",
    "dj_left1.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "517b5812-6472-4eb7-8539-f6e3ce6f30ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1502/2252380090.py:2: DeprecationWarning: The QuantumCircuit.__iadd__() method is being deprecated. Use the compose() (potentially with the inplace=True argument) and tensor() methods which are more flexible w.r.t circuit register compatibility.\n",
      "  dj_left2 += dj_left0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 507.997x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_left2.x(1)\n",
    "dj_left2 += dj_left0\n",
    "dj_left2.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "954f13f0-4bc3-447b-b252-39bed583e5f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1502/810308772.py:3: DeprecationWarning: The QuantumCircuit.__iadd__() method is being deprecated. Use the compose() (potentially with the inplace=True argument) and tensor() methods which are more flexible w.r.t circuit register compatibility.\n",
      "  dj_left3 += dj_left0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 507.997x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_left3.x(0)\n",
    "dj_left3.x(1)\n",
    "dj_left3 += dj_left0\n",
    "dj_left3.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e439ee56-27b2-48b5-8af9-221b8ae768f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1502/264572622.py:2: DeprecationWarning: The QuantumCircuit.__iadd__() method is being deprecated. Use the compose() (potentially with the inplace=True argument) and tensor() methods which are more flexible w.r.t circuit register compatibility.\n",
      "  dj_left4 += dj_left0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 568.197x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_left4.x(2)\n",
    "dj_left4 += dj_left0\n",
    "dj_left4.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3384a01e-eea9-4955-ab6a-1cc5a418c9c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1502/2497025790.py:3: DeprecationWarning: The QuantumCircuit.__iadd__() method is being deprecated. Use the compose() (potentially with the inplace=True argument) and tensor() methods which are more flexible w.r.t circuit register compatibility.\n",
      "  dj_left5 += dj_left0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 568.197x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_left5.x(2)\n",
    "dj_left5.x(0)\n",
    "dj_left5 += dj_left0\n",
    "dj_left5.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "bc944ec6-bb6d-46ea-a133-fe14fffa71ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1502/2426855407.py:3: DeprecationWarning: The QuantumCircuit.__iadd__() method is being deprecated. Use the compose() (potentially with the inplace=True argument) and tensor() methods which are more flexible w.r.t circuit register compatibility.\n",
      "  dj_left6 += dj_left0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 568.197x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_left6.x(2)\n",
    "dj_left6.x(1)\n",
    "dj_left6 += dj_left0\n",
    "dj_left6.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e3f63139-7587-4249-908f-1e7a97cb41dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1502/778788731.py:4: DeprecationWarning: The QuantumCircuit.__iadd__() method is being deprecated. Use the compose() (potentially with the inplace=True argument) and tensor() methods which are more flexible w.r.t circuit register compatibility.\n",
      "  dj_left7 += dj_left0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 568.197x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_left7.x(2)\n",
    "dj_left7.x(1)\n",
    "dj_left7.x(0)\n",
    "dj_left7 += dj_left0\n",
    "dj_left7.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5cdb563d-f0aa-4075-8bd5-5f5e36ccd9cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1502/989489376.py:2: DeprecationWarning: The QuantumCircuit.__iadd__() method is being deprecated. Use the compose() (potentially with the inplace=True argument) and tensor() methods which are more flexible w.r.t circuit register compatibility.\n",
      "  dj_left8 += dj_left0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 507.997x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_left8.x(3)\n",
    "dj_left8 += dj_left0\n",
    "dj_left8.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "5b1681d6-8356-45d2-96cc-12e7c8290829",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1502/209537080.py:3: DeprecationWarning: The QuantumCircuit.__iadd__() method is being deprecated. Use the compose() (potentially with the inplace=True argument) and tensor() methods which are more flexible w.r.t circuit register compatibility.\n",
      "  dj_left9 += dj_left0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 507.997x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_left9.x(3)\n",
    "dj_left9.x(0)\n",
    "dj_left9 += dj_left0\n",
    "dj_left9.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "71c754bb-db2c-4b23-8823-a918d0385889",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1502/2470987004.py:3: DeprecationWarning: The QuantumCircuit.__iadd__() method is being deprecated. Use the compose() (potentially with the inplace=True argument) and tensor() methods which are more flexible w.r.t circuit register compatibility.\n",
      "  dj_left10 += dj_left0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 507.997x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_left10.x(3)\n",
    "dj_left10.x(1)\n",
    "dj_left10 += dj_left0\n",
    "dj_left10.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "52100788-3a7f-423d-bb8b-0fcf5ea9a817",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1502/4258748235.py:4: DeprecationWarning: The QuantumCircuit.__iadd__() method is being deprecated. Use the compose() (potentially with the inplace=True argument) and tensor() methods which are more flexible w.r.t circuit register compatibility.\n",
      "  dj_left11 += dj_left0\n"
     ]
    },
    {
     "data": {
      "image/png": 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PQB4aBqlfP4VT3h20uK21mJlcKy8vD3/5y19w4sSJJqdLTk52WBhAzV3i69atg06ngyy3/HGAakujMbm5uYiIiLAbt3TpUmzcuBGXLl3CmTNnMH/+fJjNZvTv31+RjPIQI+S9WQ3HD9ZD3qfed5JTnsl2arwaaHFbazGzlqlxv96xY4fQtYzNmzdj/Pjx+NOf/uTwPoy5c+e6Kp5DmimN8vJyFBYWYs6cOXbjly1bhmXLlimUioioZdy4ccPuk6JqoZnS8Pb2hsViUToGEdHPmiovhBMRkTqxNIiISBhLg4iIhLE0iIhIGEuDiIiEsTSIiEgYS4OIiIRp5j4NpYT6+Ghy3T07ujBIK61Xq9taqdxKbi+laHG/NhqNTs9TVHIRAODfu7vd49ZYtyMsDQdeDAxWOkKzTBqqdALnaXVbazW3Fmlxv87IyHB6niUra77ufMXiRLvHasDTU0REJIylQUREwlgaREQkjKVBRETCWBpERCSMpUFERMJYGkREJIylQUREwlgaREQkjKVBRETCWBpERCSMpUFERMJYGkREJIzfcutAVi5QelWZdffs2Pxv9Vz4eQFOl5W5NpCAUB+fZn/rq1KZgTvLrdQ+cif7h1bNnz8f+fn5rb5eo9HYrG+rvRuxNBwovQp89b3SKZx3uqwMR69eUTqGU7SYGdDuPqJF+fn5MJvNSsf4WePpKSIiEsbSICIiYSwNIiISxtIgIiJhLA0iIhLG0iAiImEsDSK6q/n5+SEqKgoPPPAAhg0bhrZt2zY5/bx58+Dj49NK6bSH92kQ0V0nLCwMc+bMwfjx49G5c2e756xWK86cOYONGzdi8+bNuHbtWt1zGRkZePLJJzFx4kSMGjWqlVNrg6qPNKxWK9LT0zFw4EB4eXkhNDQUZrMZgYGBSExMVDoeEalMly5dkJmZiby8PCQkJKBz5864fPkyjh49ikOHDuH06dOwWCwIDQ1FRkYGvv76a8yaNQvAj4VRUVGB9PR0hV+Jeqm6NBISErBs2TIkJSXhwIEDmDZtGmbOnImioiKEh4crHa9RO5dH4eSe5cLj1cBWVYWq2XNhWfum3XjL7j2o+u2jsJWXK5SsaVrNrcV9RAsiIyNx9uxZTJkyBWVlZVizZg2Cg4Ph6+sLk8mEMWPGwGg0wsfHBxMnTsT777+P9u3b480338QXX3xRVxiTJk3C/v37lX45qqXa0ti+fTs2bdqEvXv3YtGiRYiOjkZqaioiIyNRXV2NsLAwpSPeNSR3d7gteQrWd/bDeiofAGArLoZ1w2bofr8Ikre3sgFvQ6u5yfXuvfdevPfee+jcuTP+8Y9/YPDgwVi4cCE+++yzBtNWVFRgz549GD16NGbMmIFbt25hwIABsFqtmDZtGgvDAdWWRlpaGsaOHQuTyWQ3fsCAAXB3d4fBYMDVq1cxbtw4BAQEIDQ0FGPGjMGXX36pUGJtk/r2gfzYo7CkvwTblSuoXrEa8oSHIBtClI7WJK3mJtfx8fFBZmYmvL29sXXrVowdOxYlJSVC80ZGRsLLyws2mw2yLPPNqABVlsb58+dx9uxZTJ06tcFzJSUl0Ov18PT0hCRJmD9/PgoLC3H69GmMGzcO8fHxCiS+O8ixEyD19kN1Ugqg00F+NE7pSEK0mptcY8WKFejTpw8++ugjxMfHw2KxCM3302sYixcvhtVqxf/93//BYDC0cGJtU21pAEC3bt3sxt+8eRNms7nu3UCHDh1w//331z0/fPhwFBcXC61DkiShwWzOdjr/ybf/iNcTO9gNFwo/cHo5ZnO2cM76Q3a287klSYJkCAF++AHy6FGQ3N2dXkZ2dutmVkNupfaRO9k/mjPUqv+4NYf633DbpUsXzJo1CxaLBfHx8aiurhbadj8tjEmTJmH16tV4/fXX4e7ujkWLFjWyrc13/bYWpcrS8PX1BQAUFhbajV+1ahUuXrx424vgGRkZiI2Nbel4Dg2bkIrkddfshh4B9ykdyyFbcTGsb+2APH0qrFvfgu17bXzftxZza3UfUZvHHnsMHh4e2LdvHz799FOheeoXRu01jNWrV9dd1/jFL37RkrE1TZWl4e/vD4PBgLS0NGzZsgWHDx9GcnIyNmzYAACNlsZzzz2HL7/8EqtWrRJah81mExpMpihXvjSnmExRwjnrD1FRzuW2VVbVXA+YFAtdQjykEZGwrF4Dm9Xq1HKiolovs1pyK7WP3Mn+0ZyhVv3HrTnUv8Y5evRoAMBf//pXoW12u8IAgK+//hpmsxmenp4YMWJEvW1tuuu3tShVloYsy8jMzIRer0dycjLi4+Ph6+uLlJQU6HS6Buccly9fjnfeeQcHDx50eLcnNc66YSMkNzfIcb8BAOjmzIbt2+9g3bVb4WRN02puco3aU9UnTpxwOG1ThVHr5MmTABp/Y0o1VHtHeEBAAI4cOWI3Li4uDsHBwWjTpk3duOeeew779+/HoUOH0KFDh1ZOeXewnsqHdf9BuP35FUhuNbuE1LYtdIsXwfL0M5CHhkHq10/hlA1pNTe5hpeXFzp16oSKigqUlpY2Oa1IYQCo+/Rljx49XJ73bqHa0mhMbm4uIiIi6n7+9NNP8eyzz6J///52pzaU+BvCtaY8k+3UeDWQhxgh781qOH6wHvI+9b5j12puLe4janTr1i20a9cOXl5eTU6n0+nQo0cPoRv3tm7diszMTNy4ccPVce8amimN8vJyFBYWYs6cOXXj9Hq9U+fiiOjucuPGDYe/4C0WCx5++GEYjUbk5uY2Oe2tW7dw69YtV0a862imNLy9vYU/f01E9FPV1dUOC4PEqPJCOBERqRNLg4iIhLE0iIhIGEuDiIiEsTSIiEgYS4OIiISxNIiISJhm7tNQSs+O2lx3qI+P64K00nqVynyn61ZqH1Fy31SK0Wh0ep6ikosAAP/e3e0et/R671YsDQcmDVU6QfO8GBisdASnaTEzoN19RIsyMjKcnmfJynUAgBWLE+0eU/Pw9BQREQljaRARkTCWBhERCWNpEBGRMJYGEREJY2kQEZEwlgYREQljaRARkTCWBhERCWNpEBGRMJYGEREJY2kQEZEwlgYREQnjt9w6sPDzApwuK1Nk3aE+Ps3+5tesXKD0qosDCejZsfnf+qpUZuDOcs+fPx/5+fkuzSPCaDQ261tfie4ES8OB02VlOHr1itIxnFZ6Ffjqe6VTOEeLmQEgPz8fZrNZ6RhErYKnp4iISBhLg4iIhLE0iIhIGEuDiIiEsTSIiEgYS4OIiISxNIg04J577lE6AhEA3qdB1Kr69u2LiRMnYujQoQgMDISHhwfKy8tx5swZnDx5Ert27cK1a9fs5vH390d2djZef/11vPDCC8oEJ/ovlgZRKwgJCcELL7yAmJgYyHLDA/zIyEgkJibilVdewbZt25CamopLly7VFYafnx9iYmKQnp6OqqoqBV4BUQ1Vn56yWq1IT0/HwIED4eXlhdDQUJjNZgQGBiIxMVHpeI2yVVWhavZcWNa+aTfesnsPqn77KGzl5Qola9rO5VE4uWe58Hg10EJmSZKQmpqK3Nxc/PrXv0ZlZSW2bduGxx9/HBERETAYDIiKisKCBQtw6NAhtG3bFo8//jgKCgowe/bsusLIycnBr371KxYGKU7VRxoJCQnIysrC0qVLER4ejg8//BAzZ87EpUuX8Lvf/U7peI2S3N3htuQpVM+bD2nYvZCHGGErLoZ1w2bo/vg8JG9vpSNSK5EkCevXr0d8fDwA4LXXXsMf/vAHXL58ucG0ZrMZGRkZCAwMxKuvvooHHngAr7/+OgDUFUa5St9w0M+Laktj+/bt2LRpE7Kzs2EymQAA0dHR+Pjjj5GVlYWwsDCFE96e1LcP5McehSX9JUivvoTqFashT3gIsiFE6WjUip5//nnEx8fj+vXrmDJlCg4ePOhwns8//xyzZ89GXl4eOnToAAB49dVXWRikGqo9PZWWloaxY8fWFUatAQMGwN3dHQaDAQAQGxsLg8GAIUOGYNiwYfjHP/6hRNwG5NgJkHr7oTopBdDpID8ap3QkakXDhg3D008/DYvFggkTJggVBvDjRe8OHTrg3LlzAICXX34ZHTt2bMG0ROJUWRrnz5/H2bNnMXXq1AbPlZSUQK/Xw9PTEwCwadMmfPLJJzh16hTWrl2LyZMnw2KxOFyHJElCQ3Z2drNegyRJkAwhwA8/QB49CpK7u9PLyM7OFs5ZfzCbnc998u0/4vXEDnbDhcIPnFqG2ay9zHeeu+E33GZkZECn0+HFF1/E4cOHhTL89KJ3Tk4ODAYDcnJy0L17d6SmpjaS2dzszM0ZatV/rPZBi7mVyCxKlaenzp8/DwDo1q2b3fibN2/CbDYjJiamblztITwA/PDDD5AkCTabrVVyNsVWXAzrWzsgT58K69a3IP9yBKQuXZSO1aRhE1IxLPYZu3E7l0cpE0aQGjOHhYUhMjIS165dw3PPPSc0T/3CqL2GsWDBAuTm5iI+Ph5Lly7FzZs3Wzg9UdNUeaTh6+sLACgsLLQbv2rVKly8eBHh4eF241NSUuDv74/Jkydj165dcHNz3IU2m01oiIqKcjq/rbKq5jrGpFjoEuIhjYiEZfUa2KxWp5YTFRUlnLP+YDI5n9sVTCbtZb7z3PanUGfMmAEA2Lx5M27cuOFw3bcrDADIy8vDRx99hE6dOmHMmDH1Mpuanbk5Q636j9U+aDG3EplFqbI0/P39YTAYkJaWhi1btuDw4cNITk7Ghg0bAKBBafz5z39GUVERsrKy8NRTTyl+0dC6YSMkNzfIcb8BAOjmzIbt2+9g3bVb0VzUOoYOrfkTgIcOHXI4bVOFUav2Ol3tcomUpMrSkGUZmZmZ0Ov1SE5ORnx8PHx9fZGSkgKdTld3Ebw+k8kEWZZx7NixVk78I+upfFj3H4Ruye8h/feIR2rbFrrFi2DdshW24mLFslHrCA6u+RO9p0+fbnI6kcIAUPenZPV6vcuzEjlLldc0ACAgIABHjhyxGxcXF4fg4GC0adMGAFBeXo7Lly+jT58+AIBTp07hq6++wqBBg1o9by15iBHy3qyG4wfrIe9T75HGlGeynRqvBmrN/NJLL6F9+/a4dOlSk9Nt27bNYWEANaWxfPlyFBQUtERcIqeotjQak5ubi4iIiLqfr1+/junTp6O8vBxubm7w8vLC1q1b0bt3bwVT0s/dypUrhaZ75JFHsHz5ciQkJDR5SrWwsBBLly51VTyiO6KZ0igvL0dhYSHmzJlTN65r1644fvy4gqmImu+LL77A9OnTlY5B5BTNlIa3t7fQ/RdERNRyVHkhnIiI1ImlQUREwlgaREQkjKVBRETCWBpERCSMpUFERMJYGkREJEwz92koJdTHR5Pr7qnQ3+y5k/UqlflO1200Gp2ep6jkIgDAv3d3u8ctvV6iO8XScODFwGClIzTLJA1+IaoWMwM1f3DJWUtWrgMArFicaPeYSO14eoqIiISxNIiISBhLg4iIhLE0iIhIGEuDiIiEsTSIiEgYS4OIiISxNIiISBhLg4iIhLE0iIhIGEuDiIiEsTSIiEgYS4OIiISxNIiISBhLg4iIhLE0HEhJSYGbG//sCKnLmDFjYDQaERISgilTpuA///mP0pEcys7Ohl6vx4ABAzBr1ixYLBalIzXpm2++wejRozFo0CDo9Xo8/fTTSkcS8uSTT6JXr14t9nuLpdGEnJwclJeXKx2DqIHMzEzk5+fjzJkz6NWrF9asWaN0pCZZrVbMmjULmZmZ+PLLL/Gf//wHW7duVTpWk9zc3LBy5Up89tlnOHXqFD744AO8/fbbSsdyaOrUqcjNzW2x5bM0bqOiogJLlixBenq60lGIGrjnnnsA1PwyvnXrFiRJUjhR0z766CP06NEDwcE1fwkzISEBu3btUjhV07p3746hQ2v+nKSHhweGDBmCkpIShVM5dt9996Fbt24ttnzJZrPZWmzpGpaamor+/fvjscceg5ubG6qrq5WORBpW+u2/sfOAue7ni99fBgB07/ILu8cA4OnhjkcmP4i2Xp5NLnPixInIyclBSEgI9u3bB29vb5dmttls+Ns7R/Ddv686zAwAI8IHY6ghsNFl7dq1C1lZWdi2bRsA4LPPPsPDDz+MU6dOuTQzABQWfYMD5pN1PzeV26ddG8RNGgN3B6dyrly5AqPRiPfeew9BQUEuz1xtsWDr7kP4oey6w8wAMOaXQzFoQJ8ml9lSv7d4pNGITz75BCdOnEB8fLzSUegu0bObL3p29cXF7y/X/RIA0ODxxe8vI3xwgMPCAIDdu3fjwoUL6NWrF3bu3OnyzJIkYeQwA77/91WHmQHAqB9w22W15nvTgf16ob13W6FtPTx8sMPCqKysxJQpU/Dkk0+2SGEAgJtOhxFDBwtlbtfGC4H9e7dIDhEsjUYcO3YMBQUF6NevH/r27QuLxYK+fftq4mIjqde40ZHoeI9Pk9MMGtDntu/WG+Ph4YEZM2Zg9+7ddxqvUT26+uKBXw5tchqdTsb0cdFw0+luO42fnx+++eabup9LSkrQq1cvl+X8KUmSMDnG5LB4/8c4CEEOfvlaLBY8/PDDMBqNWLhwoStjNjCwby8MDx/c5DRenh6Y8isTZAVPR7I0GpGcnIwLFy7g3LlzOHfuHHQ6Hc6dO4f27dsrHY00zMvTA9N+HYXb/Xdv19YLk8eOdHh9oqysDBcvXgRQc01j79690Ov1Lk77o5HDDOjTs+ttn39w5DB069ypyWUMHToU58+fR0FBAQBg/fr1mDRpkktz/lR777aY+OAvb/v8Lzq0x6+iIxwuJzExET4+PnjxxRddGe+2YkzD0LlTh9s+HzvmPnRo79rTkM5iaTiJl4DoTvTz646R/xPa6HOTxo6Ed7s2DpdRVlaG8ePHw2AwwGAwoLq6Gs8884yro9aRZRnTxkXDw8O9wXP9/LrjvntDHC5Dp9PhL3/5C6ZMmYL+/fvD29sbcXFxLRG3TkiQP4boBzYYL0kSpo2Lhmcjr+enjh07hg0bNiA3NxdDhgyB0WjEK6+80lJxAQDu7m6YPi4astzwjYMhyB+hg/o7XEZSUhJ69eoFi8WCXr16ISUlxaUZeSHcSdnH81H67SVMf2hUk4fjRLdTXW3Bn7bsxreXrtSNGxoSiCm/MimYyrGTp/+FrINH63729HDH/MemODzlpqRbFZV4aX1m3QVmAIiOHIIHR96rYCrHDh/7GIc++PFjsz7ebbHgsSlo28ZLwVQ1eKThhIqKShw9cRqVVdUsDGo2NzcdZjw0CjpdzX+/Tvf44KHRkQqncuxeQyAGDfjxGsD4+0eoujCA2lOC0XU/9+zqi9EjwhRMJCYq0gi/7l3qfp4aY1JFYQB3QWmcOXMGkydPhq+vL7y8vDBw4ECkpqa2yLo+/PhT3LhVgftHhLfI8unno1vnThjzy3shAZj66yh4enooHckhSZIwaexItGvrBX1AX4QNbnjqR4369+mB+4aGwE2nwzQHF+zVQifXfLjA3d0NEUOCEeDvp3SkOpo+PZWXl4eRI0fCz88PixcvRp8+fVBcXIwPP/wQ69evb3LeJSvXtVJKIiL1W7E4UWg6TX+p0sKFC9GuXTucOHGi7g5ZoOZuUyIicj3NHmncuHEDPj4+mDt3Ll5++eUWXVdFRSVWvrEdfj26IH5qTIuui4hIzTR7pHH16lVYrdZm3yDUnNNTnxd9w9NaRHRXEj09pdkL4R07doQsyygtLVU6ChHRz4ZmT08BQHR0NAoKCvDFF1+02N3aR/55Cu8e/QgpcbHw69HF8QxERHcxzR5pAEB6ejrKy8sRERGBTZs24ciRI9i8eTNmzZrlkuVXVFQi5+QnCPT3Y2EQEUHD1zQAIDw8HP/85z+xdOlSLFiwALdu3YKfnx9mzJjhkuVf+aEMbdp48r4MIqL/0vTpqdZgtVohy5o+ICMichmWBhERCeNbaCIiEsbSICIiYSwNIiISxtIgIiJhLA0iIhLG0iAiImEsDSIiEsbSICIiYSwNIiISxtIgIiJhLA0iIhLG0iAiImEsDSIiEsbSICIiYSwNIiISxtIgIiJhLA0iIhLG0iAiImEsDSIiEsbSICIiYSwNIiISxtIgIiJhLA0iIhLG0iAiImEsDSIiEsbSICIiYSwNIiIS9v9bEkSmS/jaJAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 507.997x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_left11.x(3)\n",
    "dj_left11.x(1)\n",
    "dj_left11.x(0)\n",
    "dj_left11 += dj_left0\n",
    "dj_left11.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "7fd7f318-e754-45b2-858b-ac30d76bd881",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1502/4044102656.py:3: DeprecationWarning: The QuantumCircuit.__iadd__() method is being deprecated. Use the compose() (potentially with the inplace=True argument) and tensor() methods which are more flexible w.r.t circuit register compatibility.\n",
      "  dj_left12 += dj_left0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 568.197x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_left12.x(3)\n",
    "dj_left12.x(2)\n",
    "dj_left12 += dj_left0\n",
    "dj_left12.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "cd5cb368-711a-482a-a115-d6a376c4a6c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1502/1494147891.py:4: DeprecationWarning: The QuantumCircuit.__iadd__() method is being deprecated. Use the compose() (potentially with the inplace=True argument) and tensor() methods which are more flexible w.r.t circuit register compatibility.\n",
      "  dj_left13 += dj_left0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 568.197x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_left13.x(3)\n",
    "dj_left13.x(2)\n",
    "dj_left13.x(0)\n",
    "dj_left13 += dj_left0\n",
    "dj_left13.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3341e4e2-6f6f-45f1-ad6d-f2e472b2c7f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1502/3820195286.py:4: DeprecationWarning: The QuantumCircuit.__iadd__() method is being deprecated. Use the compose() (potentially with the inplace=True argument) and tensor() methods which are more flexible w.r.t circuit register compatibility.\n",
      "  dj_left14 += dj_left0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 568.197x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_left14.x(3)\n",
    "dj_left14.x(2)\n",
    "dj_left14.x(1)\n",
    "dj_left14 += dj_left0\n",
    "dj_left14.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c27fbded-3a88-407f-91d4-ebc4893ac6bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1502/1035582658.py:5: DeprecationWarning: The QuantumCircuit.__iadd__() method is being deprecated. Use the compose() (potentially with the inplace=True argument) and tensor() methods which are more flexible w.r.t circuit register compatibility.\n",
      "  dj_left15 += dj_left0\n"
     ]
    },
    {
     "data": {
      "image/png": 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8GQEBAWjRooVEqSwz5uTCuHc/1H9fDuF/n1UntGwJ1exZMLw+F2K/vhC6d5c4pTLIcVvLMTPZV3Z2Nj788EMcP368wekSExMtFh1Q/akqq1evhkqlgig65P6LQ3DYsqtPVlYWQkNDzcbNmzcPH330EUpKSnD69GlMnz4der0ePj4+kmQU+wRD3J1ed3xvDcQ9jvvOfdzcDJvGOwI5bms5ZpYzR3xdb9261apzdevXr8eoUaPwt7/9zeJ9dNOmTbNXPMWSTdmVlZUhPz8fU6dONRu/YMECLFiwQKJURERN48aNG2ZXntO9kU3Zubm5wWAwSB2DiIhkiAd4iYhI8Vh2RESkeCw7IiJSPJYdEREpHsuOiIgUj2VHRESKx7IjIiLFk819dlIJcneX5bo7t7FjkGZar1y3tVS5pdxeUpHj6zo4ONjmeQqKLgIAvLt0NHvcHOtWKpadBUv9AqSO0Chj+kmdwHZy3dZyzS1Hcnxdp6Wl2TzPnMXVX9uzaHac2WNqPB7GJCIixWPZERGR4rHsiIhI8Vh2RESkeCw7IiJSPJYdEREpHsuOiIgUj2VHRESKx7IjIiLFY9kREZHiseyIiEjxWHZERKR4LDsiIlI8fuuBBTPP5uFUaakk6w5yd2/0J+qnZwHFV+0cyAqd2zT+k+nluq2lyn0vmeVKjq9rOZo+fTpyc3MlWXdwcHCjvinCEpadBadKS3H46hWpY9is+Crw4y9Sp7CNXLe1XHPLkRxf13KUm5sLvV4vdQy74mFMIiJSPJYdEREpHsuOiIgUj2VHRESKx7IjIiLFY9kREZHiseyIiMguvL29MXjwYDz22GMICQmBi4vLXadVqVSYMWMGnJ2dmyUb77MjIqJGe/TRRzF16lQMHz4cbdq0MXuuqqoKJ0+exIcffojNmzejvLwcQHXRffzxx5g0aRL69euHZ555pslzOvSendFoRGpqKnr27AlXV1cEBQVBr9fDz88PcXFxUscjIrpvde3aFV988QUOHz6MiRMnok2bNrh06RIyMjJw4MABfPvttxAEAf3798fq1atRUFCAsWPHmhXdb7/9hhUrVjRLXocuu9jYWCxYsADx8fHYt28fxo8fj0mTJqGgoAAhISFSx6uXqbISlQnTYFj1D7Pxhp27UPnnKTCVlUmUrGHbF4bjxK6FVo93BHLc1nLMLGdyfF3LweOPP47Tp09jyJAhuHz5MhYsWABvb2907NgRERERGDZsGHr37o1WrVrhmWeewYkTJ9CuXTts374dZ8+erSm64cOH49ixY82S2WHLbsuWLVi3bh12796NWbNmISIiAsnJyQgLC0NVVRX69u0rdcR6CU5OUM95FcZP98KYkwsAMBUWwrh2PVSvzYLg5iZtQAWR47aWY2aiPxo+fDh27doFd3d3bN++Hb169cIbb7yBwsLCOtOWl5dj8+bNCA0NRVJSEqqqquDj44OKigqMGDGi2YoOcOCyS0lJwYgRI6DT6czG9+jRA05OTtBqtbh69SpGjhwJX19fBAUFYdiwYfjhhx8kSvw7oVtXiM9NgSH1XZiuXEHVoncgjn4SojZQ6miKI8dtLcfMRADQoUMHbN68GU5OTli2bBmio6NRUlJicT5RFPHII49ArVbDZDLB2dkZWq22GRL/IUOzrs1K58+fx5kzZxAdHV3nuaKiImg0Gri4uEAQBEyfPh35+fk4deoURo4ciZiYGAkS1yVGjYbQxQtV8UmASgVxymSpIymWHLe1HDMTrVy5Em3btsXevXsxc+ZMq+apfY5u7ty5AIDU1FR4eXk1ZVwzDlt2QPW7iD+6efMm9Hp9zSHM1q1bY+jQoTXPDxgwoN5d6foIgmDVkJGR0ajfQRAECNpA4Pp1iEMGQ3BysnkZGRkZVuesPej1tuc+8clf8X5ca7PhQv4Rm5ah1zc+s1y3dWNyS525McMfs5v9Hs04yPF1LcdtXd83HvTq1QtPPfUUysrKrL5AsHbRDR8+HCkpKUhPT4ebmxumTZtWz7bWN2pbWeKQZefh4QEAyM/PNxu/ZMkSXLx48a4Xp6SlpSEqKqqp41nFVFgI4+atECdEw7hxM0y/OP73kvQfnYzE1dfMhk6+j0gdyyI5bms5ZpYrub6uHU1CQgIA4OOPP0ZxcbHF6esrujvn6BYtWgSg+iLE5rrPziHLztvbG1qtFikpKdiwYQMOHjyIxMRErF27FgDqLbv58+fjhx9+wJIlS6xah8lksmoIDw+3Ob+porL6PMyYKKhiYyAMDIPhnWUwGY02LSc8PNzqnLUHnc723Pag0zU+s1y3ta25HSFzY4aa/LUeN+cgx9e1HLd17WslAGDIkCEAqsvOkoaKDgC+/vprnD17Fn/6058QFBRUa1vrGrWtLHHIshNFEdu2bYNGo0FiYiJiYmLg4eGBpKQkqFSqOic2Fy5ciE8//RT79+9Hy5YtJUr9O+PajyCo1RAnV98oqZqaANOln2HcsVPiZMojx20tx8x0f2vZsiX8/f1RWVmJnJycBqe1VHR3HD9+HED9Oy9NwSHLDgB8fX1x6NAhlJeXo6ioCAsWLMDp06cREBCAFi1a1Ew3f/587NmzBwcOHEDr1q2lC/w/xpxcGPfuh2rOaxDU1R9QI7RsCdXsWTBu2AiTlecUyTI5bms5ZiZq3749VCoViouLcevWrbtOZ23RAai5cr5Tp05Nkrk2WX1cWFZWFkJDQ2t+/vbbb/Hmm2/Cx8fH7FBSbm5u84f7H7FPMMTd6XXH99ZA3OO479zHzc2wabwjkOO2lmNmOZPj69oRnTt3Dm5ubhbPrzk7O6N9+/ZW3TC+dOlSLF++vOYjxJqabMqurKwM+fn5mDp1as04jUZj0zFbIiKynclkQnl5ucViunnzJp588kn07NkTp06danDaGzdu2DOiRbIpOzc3NxgMBqljEBFRA27cuGGx6KTgsOfsiIiI7IVlR0REiseyIyIixWPZERGR4rHsiIhI8Vh2RESkeCw7IiJSPNncZyeVIHd3Wa67cxs7Bmmm9cp1W0uVW8rtJRU5vq7lKDg4uFHzFRRdBAB4d+lo9rg51m0Jy86CpX4BUkdolDH9pE5gO7lua7nmliM5vq7lKC0trVHzzVm8GgCwaHac2WNHwMOYRESkeCw7IiJSPJYdEREpHsuOiIgUj2VHRESKx7IjIiLFY9kREZHiseyIiEjxWHZERKR4LDsiIlI8lh0RESkey46IiBSPZUdERIrHbz2wYObZPJwqLZVk3UHu7o3+RP30LKD4qp0DWaFzm8Z/Mr1UmYF7yz19+nTk5ubaNY81goODG/3p9ET3G5adBadKS3H46hWpY9is+Crw4y9Sp7CNHDMDQG5uLvR6vdQxiKgBPIxJRESKx7IjIiLFY9kREZHiseyIiEjxWHZERKR4LDsiIlI8lh2RDLRq1UrqCESyxvvsiJpRt27d8NRTT6Ffv37w8/ODs7MzysrKcPr0aZw4cQI7duzAtWvXzObx9vZGRkYG3n//fbz99tvSBCeSOZYdUTMIDAzE22+/jcjISIhi3QMqYWFhiIuLw/Lly7Fp0yYkJyejpKSkpui8vLwQGRmJ1NRUVFZWSvAbEMmbQx/GNBqNSE1NRc+ePeHq6oqgoCDo9Xr4+fkhLi5O6nj1MlVWojJhGgyr/mE23rBzFyr/PAWmsjKJkjVs+8JwnNi10OrxjkAOmQVBQHJyMrKysvDEE0+goqICmzZtwgsvvIDQ0FBotVqEh4djxowZOHDgAFq2bIkXXngBeXl5SEhIqCm6zMxMPP744yw6okZy6D272NhYpKenY968eQgJCcFXX32FSZMmoaSkBK+88orU8eolODlBPedVVL04HUL/hyH2CYapsBDGteuh+utbENzcpI5IzUQQBKxZswYxMTEAgJUrV+KNN97A5cuX60yr1+uRlpYGPz8/rFixAo899hjef/99AKgpujIHfaNEJAcOW3ZbtmzBunXrkJGRAZ1OBwCIiIjAyZMnkZ6ejr59+0qc8O6Ebl0hPjcFhtR3Iax4F1WL3oE4+kmI2kCpo1EzeuuttxATE4Py8nKMGzcO+/fvtzjP2bNnkZCQgOzsbLRu3RoAsGLFChYd0T1y2MOYKSkpGDFiRE3R3dGjRw84OTlBq9UCAKKioqDVatGnTx/0798fX3zxhRRx6xCjRkPo4oWq+CRApYI4ZbLUkagZ9e/fH6+//joMBgNGjx5tVdEBv1+M0rp1a5w7dw4A8N5776FNmzZNmJZI+Ryy7M6fP48zZ84gOjq6znNFRUXQaDRwcXEBAKxbtw7ffPMNcnJysGrVKowdOxYGg8HiOgRBsGrIyMho1O8gCAIEbSBw/TrEIYMhODnZvIyMjAyrc9Ye9Hrbc5/45K94P6612XAh/4hNy9Dr5Zf53nPX/caDtLQ0qFQqLF26FAcPHrQqwx8vRsnMzIRWq0VmZiY6duyI5OTkejLrG525McMdtR9z4LaWMre1HPIw5vnz5wEAHTp0MBt/8+ZN6PV6REZG1oy7c6gHAK5fvw5BEGAymZolZ0NMhYUwbt4KcUI0jBs3Q3x0IIR27aSO1aD+o5PRP2qu2bjtC8OlCWMlR8zct29fhIWF4dq1a5g/f75V89Quujvn6GbMmIGsrCzExMRg3rx5uHnzZhOnJ1Imh9yz8/DwAADk5+ebjV+yZAkuXryIkJAQs/FJSUnw9vbG2LFjsWPHDqjVljvcZDJZNYSHh9uc31RRWX2ebkwUVLExEAaGwfDOMpiMRpuWEx4ebnXO2oNOZ3tue9Dp5Jf53nObH2qfOHEiAGD9+vW4ceOGxXXfregAIDs7G19//TXatm2LYcOG1cqsa3Tmxgx31H7MgdtaytzWcsiy8/b2hlarRUpKCjZs2ICDBw8iMTERa9euBYA6Zff3v/8dBQUFSE9Px6uvvir5yXzj2o8gqNUQJz8DAFBNTYDp0s8w7tgpaS5qHv36VX/l+YEDByxO21DR3XHnPPSd5RKR7Ryy7ERRxLZt26DRaJCYmIiYmBh4eHggKSkJKpWq5uKU2nQ6HURRxNGjR5s58e+MObkw7t0P1ZzXIPxvD1No2RKq2bNg3LARpsJCybJR8wgICAAAnDp1qsHprCk6oPqb0AFAo9HYPSvR/cIhz9kBgK+vLw4dOmQ2bvLkyQgICECLFi0AAGVlZbh8+TK6du0KAMjJycGPP/6IXr16NXveO8Q+wRB3p9cd31sDcY/j7tmNm5th03hH4KiZ3333XTz44IMoKSlpcLpNmzZZLDqguuwWLlyIvLy8pohLdF9w2LKrT1ZWFkJDQ2t+Li8vx4QJE1BWVga1Wg1XV1ds3LgRXbp0kTAl3e8WL15s1XTPPvssFi5ciNjY2AYPvefn52PevHn2ikd0X5JN2ZWVlSE/Px9Tp06tGde+fXscO3ZMwlREjff9999jwoQJUscgui/Ipuzc3Nysun+OiIioNoe8QIWIiMieWHZERKR4LDsiIlI8lh0RESkey46IiBSPZUdERIrHsiMiIsWTzX12Uglyd5flujtL9F2f97JeqTLf67qDg4Ntnqeg6CIAwLtLR7PHTb1eovsVy86CpX4BUkdolDEy/IB8OWYGqr+o1VZzFq8GACyaHWf2mIiaBg9jEhGR4rHsiIhI8Vh2RESkeCw7IiJSPJYdEREpHsuOiIgUj2VHRESKx7IjIiLFY9kREZHiseyIiEjxWHZERKR4LDsiIlI8lh0RESkey46IiBSPZUdERIrHsrMgKSkJajW/9o8cy7BhwxAcHIzAwECMGzcOv/32m9SRLMrIyIBGo0GPHj3w/PPPw2AwSB1JkV5++WV4enrK6u/WTz/9hCFDhqBXr17QaDR4/fXX7b4Oll0DMjMzUVZWJnUMojq2bduG3NxcnD59Gp6enli2bJnUkRpkNBrx/PPPY9u2bfjhhx/w22+/YePGjVLHUqTo6GhkZWVJHcMmarUaixcvxnfffYecnBwcOXIEn3zyiV3XwbK7i9u3b2POnDlITU2VOgpRHa1atQJQXSK3bt2CIAgSJ2rY119/jU6dOiEgIAAAEBsbix07dkicSpkeeeQRdOjQQeoYNunYsSP69esHAHB2dkafPn1QVFRk13UIJpPJZNclKkRycjJ8fHzw3HPPQa1Wo6qqSupIJGPFl37F9n36mp8v/nIZANCx3Z/MHgOAi7MTnh07HC1dXRpc5lNPPYXMzEwEBgZiz549cHNzs2tmk8mEf356CD//etViZgAYGNIb/bR+9S5rx44dSE9Px6ZNmwAA3333HZ5++mnk5OTYNbNcVRkM2LjzAK6XlgOwvK2HPdoPvXp0bXCZzfF3K+fb73H4xDc1PzeU+6G2rTBx1BCIFt6YXblyBcHBwfj888/h7+9vt6zcs6vHN998g+PHjyMmJkbqKKQQnTt4oHN7D1z85XLNHwEAdR5f/OUyQnr7Wiw6ANi5cycuXLgAT09PbN++3e6ZBUHAoP5a/PLrVYuZASBY0+Ouy+J76oapVSoM7NfbqtfHAy1c4efTRYqYdWj9faASRYu5fy65gkf7ay0WXUVFBcaNG4eXX37ZrkUHsOzqdfToUeTl5aF79+7o1q0bDAYDunXrJouLAMhxjRwShjat3BucplePrnfdO6qPs7MzJk6ciJ07d95rvHp1au+Bxx7t1+A0KpWICSMjoFap7jqNl5cXfvrpp5qfi4qK4OnpabecStCzmycGhPRucBpXF2eMe1xnsTSai0olYvzICKjVd/+3B4DBA/rCq2O7BqcxGAx4+umnERwcjJkzZ9ozJgCWXb0SExNx4cIFnDt3DufOnYNKpcK5c+fw4IMPSh2NZMzVxRnjnwjH3f5MPdDSFWNHDLJ4/q20tBQXL14EUH3Obvfu3dBoNHZO+7tB/bXo2rn9XZ8fPqg/OjzUtsFl9OvXD+fPn0deXh4AYM2aNRgzZoxdcypBpK4/Hmrb+q7PRw17BK0ftO/h6nvV7k+tERn+f3d93rPjQ4gI62NxOXFxcXB3d8fSpUvtGa8Gy85GPBxD96K7V0cM+r+gep8bM2IQ3B5oYXEZpaWlGDVqFLRaLbRaLaqqqjB37lx7R60hitXv3p2dneo8192rIx55ONDiMlQqFT788EOMGzcOPj4+cHNzw+TJk5sirqw5OakxYWQERLHuGx6tvzeCevlYXEZ8fDw8PT1hMBjg6emJpKSkpohqJqyvBj27da4z3kmtwoQnIqBSNVw1R48exdq1a5GVlYU+ffogODgYy5cvt2tGXqBio4xjuSi+VIIJTw5u8LAN0d1UVRnwtw07cankSs24foF+GPe4TsJUlp049R+k7z9c87OLsxOmPzfO4qFZst3Boydx4Mjvtw+4u7XEjOfGoWULVwlTNex6aTneXbMNt25X1Iwb/dhAhPVtuqMOtuCenQ1u367A4eOnUFFZxaKjRlOrVZj45OCad7ttW7njySFhEqey7GGtH3r1+P3CiFFDB7Lomkh4WLDZOa7oSJ1DFx0AtHJ/AE8Ne6TmZ9/ungjtEyBhInOyL7vTp09j7Nix8PDwgKurK3r27Ink5OQmWddXJ7/FjVu3MXRgSJMsn+4fHR5qi2GPPgwBQPQT4XBxcZY6kkWCIGDMiEF4oKUrNL7d0Ld3T6kjKZZKrL7ox8lJjdA+AfD19pI6klWCAnogqJcPWri6YFykzqHu/5T1Yczs7GwMGjQIXl5emD17Nrp27YrCwkJ89dVXWLNmTYPzzlm8uplSEhFRU1k0O86q6eTz4Wn1mDlzJh544AEcP3685hMlgOpPZyAiIrpDtnt2N27cgLu7O6ZNm4b33nuvSdd1+3YFFn+wBV6d2iEmOrJJ10VERPYn2z27q1evwmg0NvrG1MYcxjxb8BMPfxIRORBrD2PK9gKVNm3aQBRFFBcXSx2FiIgcnGwPYwJAREQE8vLy8P333zfZp5sc+ncOPjv8NZImR8GrU8Mfd0NERI5Jtnt2AJCamoqysjKEhoZi3bp1OHToENavX4/nn3/eLsu/fbsCmSe+gZ+3F4uOiEjGZHvODgBCQkLw73//G/PmzcOMGTNw69YteHl5YeLEiXZZ/pXrpWjRwoX31RERyZysD2M2B6PRCFGU9Q4wEdF9j2VHRESKx10WIiJSPJYdEREpHsuOiIgUj2VHRESKx7IjIiLFY9kREZHiseyIiEjxWHZERKR4LDsiIlI8lh0RESkey46IiBSPZUdERIrHsiMiIsVj2RERkeKx7IiISPFYdkREpHgsOyIiUjyWHRERKR7LjoiIFI9lR0REiseyIyIixWPZERGR4rHsiIhI8Vh2RESkeCw7IiJSPJYdEREpHsuOiIgU7/8Bw0R3AV1PiaQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 568.197x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_left15.x(3)\n",
    "dj_left15.x(2)\n",
    "dj_left15.x(1)\n",
    "dj_left15.x(0)\n",
    "dj_left15 += dj_left0\n",
    "dj_left15.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "1d2ddff8-4fb7-4102-988a-7ca409e60027",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16\n"
     ]
    }
   ],
   "source": [
    "meas_calibs1 = [dj_left0, dj_left1, dj_left2, dj_left3, dj_left4, dj_left5, dj_left6, dj_left7, dj_left8, dj_left9, dj_left10, dj_left11, dj_left12, dj_left13, dj_left14, dj_left15]\n",
    "print(len(meas_calibs1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "85f93412-9b6c-4d5c-b84e-5a2573a5cd7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# t_qc = transpile(meas_calibs1, aer_sim)\n",
    "# qobj = assemble(t_qc, shots=10000)\n",
    "# results = aer_sim.run(qobj, shots=10000).result()\n",
    "# result_counts = results.get_counts()\n",
    "# print(result_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "1d44f90c-f401-4290-a920-36601ef8313d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1502/3062128249.py:3: DeprecationWarning: Passing a Qobj to Backend.run is deprecated and will be removed in a future release. Please pass in circuits or pulse schedules instead.\n",
      "  real_results_left = real_backend.run(qobj, shots=10000).result()\n"
     ]
    }
   ],
   "source": [
    "t_qc = transpile(meas_calibs1, real_backend)\n",
    "qobj = assemble(t_qc, shots=10000)\n",
    "real_results_left = real_backend.run(qobj, shots=10000).result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "a8dcb035-6215-476e-9ec9-16965b03cdeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "real_counts_left = real_results_left.get_counts()\n",
    "plot_histogram(real_counts_left)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "4a945273-3b78-4df4-9617-b2c92ac03d66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9.576e-01 1.180e-02 1.140e-02 3.000e-04 4.800e-03 0.000e+00 0.000e+00\n",
      " 0.000e+00 1.380e-02 2.000e-04 1.000e-04 0.000e+00 0.000e+00 0.000e+00\n",
      " 0.000e+00 0.000e+00]\n",
      "[3.000e-02 9.484e-01 2.000e-04 5.600e-03 1.000e-04 4.600e-03 0.000e+00\n",
      " 0.000e+00 3.000e-04 1.050e-02 0.000e+00 2.000e-04 0.000e+00 1.000e-04\n",
      " 0.000e+00 0.000e+00]\n",
      "[5.010e-02 5.000e-04 9.193e-01 1.210e-02 2.000e-04 0.000e+00 4.600e-03\n",
      " 2.000e-04 6.000e-04 0.000e+00 1.200e-02 2.000e-04 0.000e+00 0.000e+00\n",
      " 2.000e-04 0.000e+00]\n",
      "[2.300e-03 4.780e-02 2.940e-02 9.053e-01 0.000e+00 2.000e-04 1.000e-04\n",
      " 4.200e-03 0.000e+00 5.000e-04 4.000e-04 9.800e-03 0.000e+00 0.000e+00\n",
      " 0.000e+00 0.000e+00]\n",
      "[4.480e-02 5.000e-04 7.000e-04 0.000e+00 9.189e-01 1.000e-02 1.190e-02\n",
      " 1.000e-04 7.000e-04 0.000e+00 0.000e+00 0.000e+00 1.230e-02 0.000e+00\n",
      " 1.000e-04 0.000e+00]\n",
      "[1.700e-03 4.280e-02 0.000e+00 4.000e-04 2.880e-02 9.091e-01 2.000e-04\n",
      " 6.000e-03 0.000e+00 1.000e-04 0.000e+00 0.000e+00 2.000e-04 1.060e-02\n",
      " 0.000e+00 1.000e-04]\n",
      "[1.900e-03 0.000e+00 4.230e-02 4.000e-04 4.540e-02 3.000e-04 8.884e-01\n",
      " 9.900e-03 0.000e+00 0.000e+00 7.000e-04 0.000e+00 5.000e-04 0.000e+00\n",
      " 1.010e-02 1.000e-04]\n",
      "[0.000e+00 1.900e-03 1.100e-03 4.250e-02 1.600e-03 4.710e-02 2.700e-02\n",
      " 8.665e-01 0.000e+00 0.000e+00 0.000e+00 9.000e-04 0.000e+00 5.000e-04\n",
      " 3.000e-04 1.060e-02]\n",
      "[2.292e-01 2.300e-03 2.600e-03 1.000e-04 1.100e-03 1.000e-04 0.000e+00\n",
      " 0.000e+00 7.434e-01 7.000e-03 9.800e-03 2.000e-04 4.000e-03 2.000e-04\n",
      " 0.000e+00 0.000e+00]\n",
      "[8.100e-03 2.276e-01 1.000e-04 1.000e-03 0.000e+00 1.100e-03 0.000e+00\n",
      " 1.000e-04 2.650e-02 7.274e-01 0.000e+00 5.000e-03 0.000e+00 3.100e-03\n",
      " 0.000e+00 0.000e+00]\n",
      "[1.090e-02 4.000e-04 2.192e-01 1.800e-03 0.000e+00 0.000e+00 1.300e-03\n",
      " 0.000e+00 3.870e-02 1.000e-04 7.166e-01 7.700e-03 0.000e+00 0.000e+00\n",
      " 3.200e-03 1.000e-04]\n",
      "[1.000e-04 1.140e-02 6.700e-03 2.123e-01 0.000e+00 1.000e-04 0.000e+00\n",
      " 1.100e-03 1.300e-03 3.840e-02 2.190e-02 7.031e-01 0.000e+00 0.000e+00\n",
      " 2.000e-04 3.400e-03]\n",
      "[1.040e-02 0.000e+00 1.000e-04 0.000e+00 2.238e-01 2.300e-03 2.300e-03\n",
      " 0.000e+00 3.460e-02 1.000e-04 5.000e-04 0.000e+00 7.100e-01 6.400e-03\n",
      " 9.400e-03 1.000e-04]\n",
      "[4.000e-04 1.030e-02 0.000e+00 0.000e+00 7.300e-03 2.172e-01 1.000e-04\n",
      " 8.000e-04 1.200e-03 3.310e-02 0.000e+00 2.000e-04 1.970e-02 7.041e-01\n",
      " 1.000e-04 5.500e-03]\n",
      "[9.000e-04 0.000e+00 1.040e-02 0.000e+00 1.150e-02 2.000e-04 2.150e-01\n",
      " 1.400e-03 2.100e-03 0.000e+00 3.090e-02 2.000e-04 3.480e-02 1.100e-03\n",
      " 6.834e-01 8.100e-03]\n",
      "[0.000e+00 6.000e-04 3.000e-04 9.400e-03 8.000e-04 1.260e-02 6.700e-03\n",
      " 2.084e-01 1.000e-04 9.000e-04 1.200e-03 3.300e-02 1.000e-03 3.940e-02\n",
      " 2.070e-02 6.649e-01]\n",
      "[[9.576e-01 3.000e-02 5.010e-02 2.300e-03 4.480e-02 1.700e-03 1.900e-03\n",
      "  0.000e+00 2.292e-01 8.100e-03 1.090e-02 1.000e-04 1.040e-02 4.000e-04\n",
      "  9.000e-04 0.000e+00]\n",
      " [1.180e-02 9.484e-01 5.000e-04 4.780e-02 5.000e-04 4.280e-02 0.000e+00\n",
      "  1.900e-03 2.300e-03 2.276e-01 4.000e-04 1.140e-02 0.000e+00 1.030e-02\n",
      "  0.000e+00 6.000e-04]\n",
      " [1.140e-02 2.000e-04 9.193e-01 2.940e-02 7.000e-04 0.000e+00 4.230e-02\n",
      "  1.100e-03 2.600e-03 1.000e-04 2.192e-01 6.700e-03 1.000e-04 0.000e+00\n",
      "  1.040e-02 3.000e-04]\n",
      " [3.000e-04 5.600e-03 1.210e-02 9.053e-01 0.000e+00 4.000e-04 4.000e-04\n",
      "  4.250e-02 1.000e-04 1.000e-03 1.800e-03 2.123e-01 0.000e+00 0.000e+00\n",
      "  0.000e+00 9.400e-03]\n",
      " [4.800e-03 1.000e-04 2.000e-04 0.000e+00 9.189e-01 2.880e-02 4.540e-02\n",
      "  1.600e-03 1.100e-03 0.000e+00 0.000e+00 0.000e+00 2.238e-01 7.300e-03\n",
      "  1.150e-02 8.000e-04]\n",
      " [0.000e+00 4.600e-03 0.000e+00 2.000e-04 1.000e-02 9.091e-01 3.000e-04\n",
      "  4.710e-02 1.000e-04 1.100e-03 0.000e+00 1.000e-04 2.300e-03 2.172e-01\n",
      "  2.000e-04 1.260e-02]\n",
      " [0.000e+00 0.000e+00 4.600e-03 1.000e-04 1.190e-02 2.000e-04 8.884e-01\n",
      "  2.700e-02 0.000e+00 0.000e+00 1.300e-03 0.000e+00 2.300e-03 1.000e-04\n",
      "  2.150e-01 6.700e-03]\n",
      " [0.000e+00 0.000e+00 2.000e-04 4.200e-03 1.000e-04 6.000e-03 9.900e-03\n",
      "  8.665e-01 0.000e+00 1.000e-04 0.000e+00 1.100e-03 0.000e+00 8.000e-04\n",
      "  1.400e-03 2.084e-01]\n",
      " [1.380e-02 3.000e-04 6.000e-04 0.000e+00 7.000e-04 0.000e+00 0.000e+00\n",
      "  0.000e+00 7.434e-01 2.650e-02 3.870e-02 1.300e-03 3.460e-02 1.200e-03\n",
      "  2.100e-03 1.000e-04]\n",
      " [2.000e-04 1.050e-02 0.000e+00 5.000e-04 0.000e+00 1.000e-04 0.000e+00\n",
      "  0.000e+00 7.000e-03 7.274e-01 1.000e-04 3.840e-02 1.000e-04 3.310e-02\n",
      "  0.000e+00 9.000e-04]\n",
      " [1.000e-04 0.000e+00 1.200e-02 4.000e-04 0.000e+00 0.000e+00 7.000e-04\n",
      "  0.000e+00 9.800e-03 0.000e+00 7.166e-01 2.190e-02 5.000e-04 0.000e+00\n",
      "  3.090e-02 1.200e-03]\n",
      " [0.000e+00 2.000e-04 2.000e-04 9.800e-03 0.000e+00 0.000e+00 0.000e+00\n",
      "  9.000e-04 2.000e-04 5.000e-03 7.700e-03 7.031e-01 0.000e+00 2.000e-04\n",
      "  2.000e-04 3.300e-02]\n",
      " [0.000e+00 0.000e+00 0.000e+00 0.000e+00 1.230e-02 2.000e-04 5.000e-04\n",
      "  0.000e+00 4.000e-03 0.000e+00 0.000e+00 0.000e+00 7.100e-01 1.970e-02\n",
      "  3.480e-02 1.000e-03]\n",
      " [0.000e+00 1.000e-04 0.000e+00 0.000e+00 0.000e+00 1.060e-02 0.000e+00\n",
      "  5.000e-04 2.000e-04 3.100e-03 0.000e+00 0.000e+00 6.400e-03 7.041e-01\n",
      "  1.100e-03 3.940e-02]\n",
      " [0.000e+00 0.000e+00 2.000e-04 0.000e+00 1.000e-04 0.000e+00 1.010e-02\n",
      "  3.000e-04 0.000e+00 0.000e+00 3.200e-03 2.000e-04 9.400e-03 1.000e-04\n",
      "  6.834e-01 2.070e-02]\n",
      " [0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 1.000e-04 1.000e-04\n",
      "  1.060e-02 0.000e+00 0.000e+00 1.000e-04 3.400e-03 1.000e-04 5.500e-03\n",
      "  8.100e-03 6.649e-01]]\n"
     ]
    }
   ],
   "source": [
    "ls = []\n",
    "for qc in meas_calibs1:\n",
    "     counts = real_results_left.get_counts(qc)\n",
    "     this_array = np.array([counts.get('0000') or 0, counts.get('0001') or 0, counts.get('0010') or 0, counts.get('0011') or 0 , counts.get('0100') or 0, counts.get('0101') or 0, counts.get('0110') or 0, counts.get('0111') or 0, counts.get('1000') or 0, counts.get('1001') or 0, counts.get('1010') or 0, counts.get('1011') or 0, counts.get('1100') or 0, counts.get('1101') or 0, counts.get('1110') or 0, counts.get('1111') or 0])\n",
    "     this_array = this_array/10000\n",
    "     ls.append(this_array)\n",
    "     print(this_array)\n",
    "     # np.append(array, this_array, axis=1)\n",
    "array = np.array(ls)\n",
    "# print(array)\n",
    "array_t_left = np.transpose(array)\n",
    "print(array_t_left)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "8565a06c-3b9a-433d-9f70-96817744ffc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$\n",
       "\n",
       "\\begin{bmatrix}\n",
       "0.9576 & 0.03 & 0.0501 & 0.0023 & \\cdots & 0.0004 & 0.0009 & 0  \\\\\n",
       " 0.0118 & 0.9484 & 0.0005 & 0.0478 & \\cdots & 0.0103 & 0 & 0.0006  \\\\\n",
       " 0.0114 & 0.0002 & 0.9193 & 0.0294 & \\cdots & 0 & 0.0104 & 0.0003  \\\\\n",
       " 0.0003 & 0.0056 & 0.0121 & 0.9053 & \\cdots & 0 & 0 & 0.0094  \\\\\n",
       " \\vdots & \\vdots & \\vdots & \\vdots & \\ddots & \\vdots & \\vdots & \\vdots \\\\\n",
       " 0 & 0.0001 & 0 & 0 & \\cdots & 0.7041 & 0.0011 & 0.0394  \\\\\n",
       " 0 & 0 & 0.0002 & 0 & \\cdots & 0.0001 & 0.6834 & 0.0207  \\\\\n",
       " 0 & 0 & 0 & 0 & \\cdots & 0.0055 & 0.0081 & 0.6649  \\\\\n",
       " \\end{bmatrix}\n",
       "$$"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array_to_latex(array_t_left)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "eb22510b-f8f7-476c-afa9-66df6888f01d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 507.997x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_right0 = QuantumCircuit(n,n)\n",
    "dj_right1 = QuantumCircuit(n) \n",
    "dj_right2 = QuantumCircuit(n)\n",
    "dj_right3 = QuantumCircuit(n)\n",
    "dj_right4 = QuantumCircuit(n)\n",
    "dj_right5 = QuantumCircuit(n)\n",
    "dj_right6 = QuantumCircuit(n)\n",
    "dj_right7 = QuantumCircuit(n)\n",
    "dj_right8 = QuantumCircuit(n)\n",
    "dj_right9 = QuantumCircuit(n)\n",
    "dj_right10 = QuantumCircuit(n)\n",
    "dj_right11 = QuantumCircuit(n)\n",
    "dj_right12 = QuantumCircuit(n)\n",
    "dj_right13 = QuantumCircuit(n)\n",
    "dj_right14 = QuantumCircuit(n)\n",
    "dj_right15 = QuantumCircuit(n)\n",
    "\n",
    "dj_right0.x(1)\n",
    "dj_right0.x(2)\n",
    "dj_right0.h(1)\n",
    "dj_right0.h(2)\n",
    "dj_right0.h(1)\n",
    "dj_right0.h(2)\n",
    "dj_right0.x(1)\n",
    "dj_right0.x(2)\n",
    "dj_right0.h(0)\n",
    "dj_right0.h(0)\n",
    "dj_right0.h(3)\n",
    "dj_right0.h(3)\n",
    "\n",
    "dj_right0.measure([0,1,2,3], [0,1,2,3])\n",
    "dj_right0.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "3ec9a8d0-7112-4688-8f15-67d9bf75f612",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1502/1762657075.py:3: DeprecationWarning: The QuantumCircuit.__iadd__() method is being deprecated. Use the compose() (potentially with the inplace=True argument) and tensor() methods which are more flexible w.r.t circuit register compatibility.\n",
      "  dj_right1 += dj_right0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 507.997x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_right1.x(0)\n",
    "\n",
    "dj_right1 += dj_right0\n",
    "\n",
    "dj_right1.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "c7279204-09d7-40cd-a6ea-3a2b2ecb5093",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1502/1107241932.py:2: DeprecationWarning: The QuantumCircuit.__iadd__() method is being deprecated. Use the compose() (potentially with the inplace=True argument) and tensor() methods which are more flexible w.r.t circuit register compatibility.\n",
      "  dj_right2 += dj_right0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 507.997x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_right2.x(1)\n",
    "dj_right2 += dj_right0\n",
    "dj_right2.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c36329c0-5d16-48eb-a956-1578a6463009",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1502/2247627861.py:3: DeprecationWarning: The QuantumCircuit.__iadd__() method is being deprecated. Use the compose() (potentially with the inplace=True argument) and tensor() methods which are more flexible w.r.t circuit register compatibility.\n",
      "  dj_right3 += dj_right0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 507.997x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_right3.x(0)\n",
    "dj_right3.x(1)\n",
    "dj_right3 += dj_right0\n",
    "dj_right3.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "3cf38ce1-df6c-4bd0-9210-48201f1cf195",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1502/35910541.py:2: DeprecationWarning: The QuantumCircuit.__iadd__() method is being deprecated. Use the compose() (potentially with the inplace=True argument) and tensor() methods which are more flexible w.r.t circuit register compatibility.\n",
      "  dj_right4 += dj_right0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 568.197x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_right4.x(2)\n",
    "dj_right4 += dj_right0\n",
    "dj_right4.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "0d9dca21-9db2-435a-b423-62b0e06820ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1502/1744533513.py:3: DeprecationWarning: The QuantumCircuit.__iadd__() method is being deprecated. Use the compose() (potentially with the inplace=True argument) and tensor() methods which are more flexible w.r.t circuit register compatibility.\n",
      "  dj_right5 += dj_right0\n"
     ]
    },
    {
     "data": {
      "image/png": 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Vfz+rTtW6NdTz5qJqwUIIfftA1a2byClJLNw+6NSpU/jwww9x/PjxBqeLj4+3WHRA9aeqrF69Gmq1GoIgyf0XSZBs2dXn5MmTCAsLMxu3aNEirFu3DteuXcPZs2cxc+ZMGAwG+Pn5iZJR6K2DsDOj7vieGgi7pPuX+7iFmTaNlwI5Zpbr9kH2s2XLFqvO1X300UcYPXo0/v73v1u8j27atGn2iqdYsim7kpIS5OXlISEhwWx8cnIykpOTRUpFRNQ87ty5Y3blOTWNbMrO1dUVVVVVYscgIiIZ4gFeIiJSPJYdEREpHsuOiIgUj2VHRESKx7IjIiLFY9kREZHiseyIiEjxZHOfnVhC3Nxkue4u7ewYpIXWK1bmpq5brG1EzG2TrKfT6WyeJ7/wMgDA17uT2eOWWLdSsewsWBEYLHaERhnTV+wEtpNjZkC+2wi1jLS0NJvnmf9W9df2LJsXZ/aYGo+HMYmISPFYdkREpHgsOyIiUjyWHRERKR7LjoiIFI9lR0REiseyIyIixWPZERGR4rHsiIhI8Vh2RESkeCw7IiJSPJYdEREpHsuOiIgUj996YMGcc7k4c/u2KOsOcXNr9Cfqi5W7KZkzTgLFN+wcyEpd2jX+WxfEyt2UzHIlx+1ajmbOnIns7GxR1q3T6Rr1TRGWsOwsOHP7Ng7euC52DJvJMXfxDeD8z2KnsJ1cc8uRHLdrOcrOzobBYBA7hl3xMCYRESkey46IiBSPZUdERIrHsiMiIsVj2RERkeKx7IiISPFYdkREZBe+vr4YMmQInn76aYSGhsLZ2fmB06rVasyaNQtOTk4tko332RERUaM9+eSTSEhIwPDhw9GuXTuz5yorK3H69Gl8+OGH2LRpE0pLSwFUF93HH3+M6Oho9O3bFy+88EKz55T0np3RaERqair8/f3h4uKCkJAQGAwGBAYGIi4uTux4REQPLR8fH3z99dc4ePAgJk6ciHbt2uHKlSvIzMzEvn378P3330OlUqFfv35YvXo18vPzMXbsWLOi+/XXX/Huu++2SF5Jl93kyZORnJyMKVOmYM+ePRg/fjyio6ORn5+P0NBQsePVy1RRgYqp01D1wf+Zja/avgMV//MSTCUlIiV7MDlmBoD0peE4sWOp1eOlQq655Uau27UcPPPMMzh79iyeeuop/PLLL0hOToavry86deqEiIgIDBs2DD179kSbNm3wwgsv4MSJE+jQoQPS09Nx7ty52qIbPnw4jh071iKZJVt2mzdvxvr167Fz507MnTsXERERSEpKQv/+/VFZWYk+ffqIHbFeKkdHOMz/M4yf74YxKxsAYCoogHHtR1D/ZS5Urq7iBqyHHDMTWcLtunkMHz4cO3bsgJubG9LT09GjRw8sXrwYBQUFdaYtLS3Fpk2bEBYWhsTERFRWVsLPzw/l5eUYMWJEixUdIOGyS0lJwYgRI6DX683Gd+/eHY6OjtBqtbhx4wZGjRqFgIAAhISEYNiwYfjpp59ESvwbVVcfCC+/hKrUv8F0/Toql70N4blnIWh7iR3tgeSYmcgSbtf21bFjR2zatAmOjo5YuXIloqKicO3aNYvzCYKAQYMGwcHBASaTCU5OTtBqtS2Q+HcZWnRtVioqKkJOTg6ioqLqPFdYWAiNRgNnZ2eoVCrMnDkTeXl5OHPmDEaNGoXY2FgREtclRD4HlbcXKqckAmo1hJdixI5kkRwzE1nC7dp+3nvvPbRv3x67d+/GnDlzrJrn/nN0CxcuBACkpqbCy8urOeOakWzZAdV/Rfze3bt3YTAYag9htm3bFkOHDq19fsCAAfXuStdHpVJZNWRmZjbqNahUKqi0vYBbtyA8NQQqR0ebl5GZmWl1TnvkFjuzwWB75hOfvYH/jWtrNlzKO2zzcgwG+eVuSubGDDXuf9ySgxy3azm+1/V940GPHj3w/PPPo6SkxOoLBO8vuuHDhyMlJQUZGRlwdXXFtGnT6sxjMBga9V5ZIsmyc3d3BwDk5eWZjV++fDkuX778wItT0tLSEBkZ2dzxrGIqKIBx0xYIE6Jg3LgJpp+l/x0wcszc77kkxK++aTZ0DhgkdiyL5JpbjuS4XUvR1KlTAQAff/wxiouLLU5fX9HVnKNbtmwZgOqLEFvqPjtJlp2vry+0Wi1SUlKwYcMGfPPNN4iPj8fatWsBoN6yW7JkCX766ScsX77cqnWYTCarhvDwcJvzm8orqs8NjImEenIsVAP7o+rtlTAZjTYtJzw83OqcTc0thcx6vW2Z7Umvl1/upmRuzFDj/sctOchxu5bje33/tRIA8NRTTwGoLjtLGio6APj2229x7tw5PPbYYwgJCTGbV6/XN+q9skSSZScIArZu3QqNRoP4+HjExsbC3d0diYmJUKvVdU5sLl26FJ9//jn27t2L1q1bi5T6N8a166BycIAQU32jpDphKkxXrsK4bbvIyR5MjpmJLOF2bR+tW7dGUFAQKioqkJWV1eC0loquxvHjxwHUv/PSHCRZdgAQEBCAAwcOoLS0FIWFhUhOTsbZs2cRHByMVq1a1U63ZMkS7Nq1C/v27UPbtm3FC/xfxqxsGHfvhXr+X6ByqP6AGlXr1lDPmwvjho0wWXlOsSXJMTORJdyu7cfDwwNqtRrFxcW4d+/eA6eztugA1F4537lz52bJfD9ZfVzYyZMnERYWVvvz999/j9dffx1+fn5mhzeys7NbPtx/Cb11EHZm1B3fUwNhlzT/mpRjZgAYtzDTpvFSIdfcciPX7VqKLly4AFdXV4vn15ycnODh4WHVDeMrVqzAqlWraj9CrLnJpuxKSkqQl5eHhISE2nEajcamY7ZERGQ7k8mE0tJSi8V09+5dPPvss/D398eZM2canPbOnTv2jGiRbMrO1dUVVVVVYscgIqIG3Llzx2LRiUGy5+yIiIjshWVHRESKx7IjIiLFY9kREZHiseyIiEjxWHZERKR4LDsiIlI82dxnJ5YQNzdZrlus3E1Zb5d2dgzSgusWK7eY75dY5Lhdy5FOp2vUfPmFlwEAvt6dzB63xLotYdlZsCIwWOwIjSLH3GP6ip2gceSaW47kuF3LUVpaWqPmm//WagDAsnlxZo+lgIcxiYhI8Vh2RESkeCw7IiJSPJYdEREpHsuOiIgUj2VHRESKx7IjIiLFY9kREZHiseyIiEjxWHZERKR4LDsiIlI8lh0RESkey46IiBSP33pgQcZJoPiGOOvu0q7xn6gvVu6mZJ45cyays7PtmsdaOp2u0Z/0TkTSx7KzoPgGcP5nsVPYTo65s7OzYTAYxI5BRArEw5hERKR4LDsiIlI8lh0RESkey46IiBSPZUdERIrHsiMiIsVj2dFDp02bNmJHIKIWxvvsSJbUajWGDx+OwYMHIzQ0FB06dIDJZEJxcTFOnTqFb775pt579ubPn4+EhARERETg/PnzIiQnIjGw7EhWBEHA9OnTMXv2bHh7e9d5PiQkBM888wwWLVqE3NxcpKSk4JNPPgFQXXRvvvkmjEYj+vbty7IjeohI+jCm0WhEamoq/P394eLigpCQEBgMBgQGBiIuLk7sePVKXxqOEzuWWj1eKuSQ28/PD0eOHEFaWhq8vb3x448/Ijk5GaNHj4ZOp0Pv3r0RFRWF1NRUXLp0CcHBwdi4cSN27dqF5OTk2qKLjY3Fp59+KvbLIaIWJOk9u8mTJyMjIwOLFi1CaGgojh49iujoaFy7dg2zZ88WOx61oODgYOzfvx8eHh4oKirCtGnTsHPnTphMJrPpsrOzkZ6ejgULFiAmJgYrVqzAqFGjMGrUqNqi27Bhg0ivgojEItmy27x5M9avX4/MzEzo9XoAQEREBE6fPo2MjAz06dNH5ITUUh577DF89dVX8PDwwFdffYXx48fj1q1bDc5TWVmJdevWwd/fHwsWLAAAXLx4EVu2bGmJyEQkMZI9jJmSkoIRI0bUFl2N7t27w9HREVqtFgAQGRkJrVaL3r17o1+/fvj666/FiEvNaNWqVejSpQuOHDmC0aNHWyy6GvPnz8eCBQtgNBpx9epV+Pj4YPHixc2cloikSJJlV1RUhJycHERFRdV5rrCwEBqNBs7OzgCA9evX47vvvkNWVhY++OADjB07FlVVVRbXoVKprBoMhkyb85/47A38b1xbs+FS3mGbl2MwZFqdUyq5m5a57tWT/fv3x6RJk1BaWooXX3wRZWVlVuX4/cUosbGxGDNmDIxGI+bNmwcvL696chsanfthGWrc/5gD32sxc1tLkocxi4qKAAAdO3Y0G3/37l0YDAaMHDmydlzbtm1rH9+6dQsqlarOeZyW1u+5JPSLXGg2Ln1puDhhbCDF3ImJiQCq9+7y8/Otmuf+oqs5R/fpp58iOjoar776KvfwiB4yktyzc3d3BwDk5eWZjV++fDkuX76M0NBQs/GJiYnw9fXF2LFjsW3bNjg4WO5wk8lk1aDXh9vtddlKrw+3OqdUcjcts/kha0dHR4wdOxYA8P7771u1/gcV3e+XER0dXU9ufaNzPyxDjfsfc+B7LWZua0lyz87X1xdarRYpKSlo3749unTpgvT0dOzevRsA6pTdP/7xDwDVh6JmzZqFgwcPwtXVtcVzk3317NkTLi4uOHfuHAoLCy1O31DRAcCRI0dw9+5ddO/eHW3btsXNmzebKTkRSY0k9+wEQcDWrVuh0WgQHx+P2NhYuLu7IzExEWq1uvbilPvp9XoIgoAjR460cGJqDhqNBgBw5swZi9NaKjoAqKqqwtmzZwFU38pARA8PSe7ZAUBAQAAOHDhgNi4mJgbBwcFo1aoVAKCkpAS//PILfHx8AABZWVk4f/48evTo0eJ5a4xbmGnTeKmQYu6cnBwsWbIE3333XYPTDRgwwGLR1Vi9ejV2796NS5cu2TsuEUmYZMuuPidPnkRYWFjtz6WlpZgwYQJKSkrg4OAAFxcXbNy4sd6PkSL5yc7ORnZ2tsXpjh49ioULF+LixYsWbxhfs2aNndIRkZzIpuxKSkqQl5eHhISE2nEeHh44duyYiKlIKt544w2xIxCRhMmm7FxdXa26f46IiOh+krxAhYiIyJ5YdkREpHgsOyIiUjyWHRERKR7LjoiIFI9lR0REiseyIyIixZPNfXZi6dJOnusWK3dT1qvT6WyeJ7/wMgDA17uT2eOWWDcRyQfLzoIxfcVO0DhyzJ2WlmbzPPPfWg0AWDYvzuwxEdHv8TAmEREpHsuOiIgUj2VHRESKx7IjIiLFY9kREZHiseyIiEjxWHZERKR4LDsiIlI8lh0RESkey46IiBSPZUdERIrHsiMiIsVj2RERkeKx7IiISPFYdkREpHgsOwsSExPh4MCv/VOqYcOGQafToVevXhg3bhx+/fVXsSMpVmZmJjQaDbp3745XXnkFVVVVYkdSpBkzZsDT01NWv7cuXryIp556Cj169IBGo8GCBQvsvg6WXQMOHTqEkpISsWNQM9q6dSuys7Nx9uxZeHp6YuXKlWJHUiSj0YhXXnkFW7duxU8//YRff/0VGzduFDuWIkVFReHkyZNix7CJg4MD3nrrLfzwww/IysrC4cOH8dlnn9l1HSy7BygrK8P8+fORmpoqdhRqRm3atAFQ/cv43r17UKlUIidSpm+//RadO3dGcHAwAGDy5MnYtm2byKmUadCgQejYsaPYMWzSqVMn9O3bFwDg5OSE3r17o7Cw0K7rUJlMJpNdl6gQSUlJ8PPzw8svvwwHBwdUVlaKHYn+a9fXR5F/8TIA4PLPvwAAOnV4zOxxDV1wd+j/ENLg8p5//nkcOnQIvXr1wq5du+Dq6tpMyeXFZDLh088P4Op/bgCw/F4PDO2JvtrAepe1bds2ZGRk4JNPPgEA/PDDD5g0aRKysrKa8yXIRmVVFTZu34dbt0sBWH6vhz3ZFz26+zS4zJb4vZX1/Y84eOK72p8byv14+zaYOPopCBb+oLx+/Tp0Oh2++uorBAUF2S0r9+zq8d133+H48eOIjY0VOwrVY9ATvXD95q+1/5kA1Hl8+edfcPdeGf6g62Fxedu3b8elS5fg6emJ9PT0ZsksRyqVCoP7afHzf25YfK8BQKfp/sBl8W/qhjmo1RjYt6fZ+wnU/14/0soFgX7eYsSsQxvkB7UgWMx99dp1PNlPa7HoysvLMW7cOMyYMcOuRQew7Op15MgR5Obmolu3bujatSuqqqrQtWtXXrwgEe3auGH00IENTqMCEPXHcLg4O1m1TCcnJ0ycOBHbt2+3Q0Ll6Ozhjqef7NvgNGq1gAmjIuCgVj9wGi8vL1y8eLH258LCQnh6etotpxL4d/XEgNCeDU7j4uyEcc/oLZZGS1GrBYwfFQEHhwf/2wPAkAF94NWpQ4PTVFVVYdKkSdDpdJgzZ449YwJg2dUrPj4ely5dwoULF3DhwgWo1WpcuHABjz76qNjR6L/69PSHJqDrA58f9IQWft6dG1zG7du3cfly9eFQo9GInTt3QqPR2DOmIgzup4VPF48HPj98cD90fLx9g8vo27cvioqKkJubCwBYs2YNxowZY9ecSjBS3w+Pt2/7wOcjhw1C20eldZi9w2NtMTL8Dw983rPT44jo39vicuLi4uDm5oYVK1bYM14tlp2NeDhGGlQqFZ4f/iRcH2lV5zkP93YYNrjhvRGguuxGjx4NrVYLrVaLyspKLFy4sDniypogVP/17uTkWOe5bl6dMOiJXhaXoVar8eGHH2LcuHHw8/ODq6srYmJimiOurDk6OmDCqAgIQt09N22QL0J6+FlcxpQpU+Dp6Ymqqip4enoiMTGxOaKa6d9HA/+uXeqMd3RQY8IfI6BWN1w1R44cwdq1a3Hy5En07t0bOp0Oq1atsmtGXqBio8xj2Si+cg0Tnh3S4GEbahn/Ol+I9el7a39WCwISX4xEZw93EVMp04kz/0LG3oO1Pzs7OWLmy+PQro2biKmU6Zsjp7Hv8G+3D7i5tsasl8ehdSsXEVM17NbtUvxtzVbcKyuvHffc0wPRv480jpZwz84GZWXlOHj8DMorKll0EhHk541+Ib+dyH76yb4sumbyhDYQPbr/dmHE6KEDWXTNJLy/zuwcV9RIvaSLDgDauD2C54cNqv05oJsnwnoHi5jInOzL7uzZsxg7dizc3d3h4uICf39/JCUlNcu6jp7+HnfulWHowNBmWT41zh+H9MdjbR+FTxcPDO6nFTuOYqlUKowZMRiPtHaBJqAr+vT0FzuSYqmF6ot+HB0dENY7GAG+XmJHskpIcHeE9PBDKxdnjBupl9R9q7I+jHnq1CkMHjwYXl5emDdvHnx8fFBQUICjR49izZo1Dc47/63VLZSSiIiay7J5cVZNJ58PT6vHnDlz8Mgjj+D48eO1n4QBVH86AxERUQ3Z7tnduXMHbm5umDZtGt55551mXVdZWTneen8zvDp3QGzUyGZdFxER2Z9s9+xu3LgBo9HY6BtTG3MY81z+RR7+JCKSEGsPY8r2ApV27dpBEAQUFxeLHYWIiCROtocxASAiIgK5ubn48ccfm+3TTQ78MwtfHvwWiTGR8Orc8MfdEBGRNMl2zw4AUlNTUVJSgrCwMKxfvx4HDhzARx99hFdeecUuyy8rK8ehE98h0NeLRUdEJGOyPWcHAKGhofjnP/+JRYsWYdasWbh37x68vLwwceJEuyz/+q3baNXKmffVERHJnKwPY7YEo9EIQZD1DjAR0UOPZUdERIrHXRYiIlI8lh0RESkey46IiBSPZUdERIrHsiMiIsVj2RERkeKx7IiISPFYdkREpHgsOyIiUjyWHRERKR7LjoiIFI9lR0REiseyIyIixWPZERGR4rHsiIhI8Vh2RESkeCw7IiJSPJYdEREpHsuOiIgUj2VHRESKx7IjIiLFY9kREZHiseyIiEjxWHZERKR4LDsiIlI8lh0RESkey46IiBTv/wGdR2dRtCIbbQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 568.197x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_right5.x(2)\n",
    "dj_right5.x(0)\n",
    "dj_right5 += dj_right0\n",
    "dj_right5.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "57f3eeed-f73b-4581-bdf1-e51429c85750",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1502/1654595449.py:3: DeprecationWarning: The QuantumCircuit.__iadd__() method is being deprecated. Use the compose() (potentially with the inplace=True argument) and tensor() methods which are more flexible w.r.t circuit register compatibility.\n",
      "  dj_right6 += dj_right0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 568.197x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_right6.x(2)\n",
    "dj_right6.x(1)\n",
    "dj_right6 += dj_right0\n",
    "dj_right6.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "60388e18-acc5-4a9e-a20c-55fd100579ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1502/611483946.py:4: DeprecationWarning: The QuantumCircuit.__iadd__() method is being deprecated. Use the compose() (potentially with the inplace=True argument) and tensor() methods which are more flexible w.r.t circuit register compatibility.\n",
      "  dj_right7 += dj_right0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 568.197x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_right7.x(2)\n",
    "dj_right7.x(1)\n",
    "dj_right7.x(0)\n",
    "dj_right7 += dj_right0\n",
    "dj_right7.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "23dc9983-94fc-4c33-bcde-5aee9e9bfce1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1502/3964056886.py:2: DeprecationWarning: The QuantumCircuit.__iadd__() method is being deprecated. Use the compose() (potentially with the inplace=True argument) and tensor() methods which are more flexible w.r.t circuit register compatibility.\n",
      "  dj_right8 += dj_right0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 507.997x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_right8.x(3)\n",
    "dj_right8 += dj_right0\n",
    "dj_right8.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "1dd0397b-ba3c-4a67-aed5-2a2fb64a6904",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1502/3828651180.py:3: DeprecationWarning: The QuantumCircuit.__iadd__() method is being deprecated. Use the compose() (potentially with the inplace=True argument) and tensor() methods which are more flexible w.r.t circuit register compatibility.\n",
      "  dj_right9 += dj_right0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 507.997x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_right9.x(3)\n",
    "dj_right9.x(0)\n",
    "dj_right9 += dj_right0\n",
    "dj_right9.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "04c8bb48-072c-4635-a2a0-0dc7f9c9fef8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1502/902577939.py:3: DeprecationWarning: The QuantumCircuit.__iadd__() method is being deprecated. Use the compose() (potentially with the inplace=True argument) and tensor() methods which are more flexible w.r.t circuit register compatibility.\n",
      "  dj_right10 += dj_right0\n"
     ]
    },
    {
     "data": {
      "image/png": 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bPZYDnp4iIiJhLA0iIhLG0iAiImEsDSIiEsbSICIiYSwNIiISxtIgIiJhLA0iIhLG0iAiImEsDSIiEsbSICIiYSwNIiISxtIgIiJh/JZbG+aeP4tTxcWSbDvQza3e3+qZkQ0U3mzgQAI6tan/N6hKlRl4sNxKpNT9OjExETk5OQ0bSIBOp6vXt9U+jFgaNpwqLsaBmzekjmG3wpvAd9ekTmEfJWZWKqXu1zk5OTAYDFLHeKTx9BQREQljaRARkTCWBhERCWNpEBGRMJYGEREJY2kQEZEwlgYRPdQ6d+6M8PBwPPnkkwgLC0Pz5s3rnP/555+Hm5tbE6VTHt6nQUQPneDgYMycOROjRo1Cu3btrJ4zmUw4ffo01q5di/Xr1+Pnn3+2PJeSkoLZs2djzJgxGDRoUBOnVgZZH2mYTCYkJyejV69ecHFxQWBgIAwGA3x8fDB9+nSp4xGRzLRv3x7p6ek4fvw44uLi0K5dO1y/fh0HDhzA3r17cerUKRiNRgQGBiIlJQWXLl3CtGnTAPxWGGVlZUhOTpb4lciXrEsjLi4Oy5Ytw4wZM7B7926MHz8eEydORF5eHkJCQqSOVyNzeTnKn5sF43vvW40bt25D+eQpMJeUSJSsbpuXh+PotuXC43KgxMxKpYT9ul+/fjhz5gzGjh2L4uJirF69Gr6+vnB3d4der8fQoUOh0+ng5uaGMWPG4PPPP0fLli3x/vvv49tvv7UURlRUFDIzM6V+ObIl29JIS0vDunXrsGPHDsybNw8RERFYtGgR+vXrh4qKCgQHB0sdsUYqR0c4LHwRpl2ZMJ3MAQCY8/NhSl0Pzfx5ULm6ShuQqB7kvl/36dMHn376Kdq1a4fPPvsM/v7+mDt3Ls6dO1dt3rKyMmzbtg2DBw/GM888g9LSUvTs2RMmkwnjx49nYdgg29JISkrC8OHDodfrrcZ79uwJR0dHaLVa3Lx5EyNHjoS3tzcCAwMxdOhQXLhwQaLEv1F16wr11CkwJv8d5hs3UPH6KqhHPw21NkDqaET1Jtf92s3NDenp6XB1dcWmTZswfPhwFBQUCC3br18/uLi4wGw2Q61Wy/aXUTmRZWlcuXIFZ86cwbhx46o9V1BQAD8/Pzg7O0OlUiExMRG5ubk4deoURo4cidjYWAkSV6eOHA1Vl86omJEAaDRQT4mROhLRA5Pjfv3666+ja9euOHbsGGJjY2E0GoWWu/caxoIFC2AymfDyyy9Dq9U2cmJlk21pAICHh4fV+J07d2AwGCy/DbRu3RpDhgyxPN+/f3/k5+cLbUOlUglNWVlZ9XoNKpUKKm0AcOsW1IMHQeXoaPc6srKyhHPePxkM9uc+uv01vDO9tdX0fe4Xdq3DYFBe5gfNXZ+pyv2Pm2pS7n5t/Q237du3x7Rp02A0GhEbG4uKigqhDPcWRlRUFFatWoV33nkHjo6OmDdvXrX5DQbDQ79/iJJlabi7uwMAcnNzrcZXrlyJoqKiWi+Cp6SkIDIysrHjCTHn58P00cdQTxgH06aPYL4m/+/8Dhu9CPFrfraaOno/IXWsOikxs5LJbb+eOnUqnJycsHPnTnzzzTdCy9xfGFXXMFatWmW5rvG73/2uMWMrmixLw8vLC1qtFklJSdiwYQP27duH+Ph4pKamAkCNpbFkyRJcuHABK1euFNqG2WwWmsLDw+3Ob75bXnm+NyoSmrhYqAb0g3HVaphNJrvWEx4eLpzz/kmvtz93Q9DrlZf5QXPXZ6py/+OmmpS7X1tf4xw8eDAAYOPGjULbrq0wAODSpUswGAxwdnbGgAEDrJbT6/UP/f4hSpaloVarkZ6eDj8/P8THxyM2Nhbu7u5ISEiARqOpds5x+fLl2LVrF/bs2WPzbs+mYEpdC5WDA9QxfwQAaGY+B/MPV2HaslXiZET1J8f9uupU9ZEjR2zOW1dhVDl69CiAmn8xpUqyvSPc29sb+/fvtxqLiYmBr68vmjVrZhlbsmQJMjMzsXfvXrRu3bqJU1ZnOpkDU+YeOPzrTagcKt9eVfPm0CyYB+NLr0AdGgxV9+4SpySyjxz3axcXF7Rt2xZlZWUoLCysc16RwgBg+fRlx44dGzzvw0K2pVGT7Oxs9O3b1/LzN998g1dffRU9evSwOtyW4m8IV1EH6aDekVF93N8P6p3yPdIY+0qWXeNyoMTMSiXH/bq0tBQtWrSAi4tLnfNpNBp07NhR6Ma9TZs2IT09Hbdv327ouA8NxZRGSUkJcnNzMXPmTMuYn5+fXefiiOjhcvv2bZv/wRuNRkyaNAk6nQ7Z2dl1zltaWorS0tKGjPjQUUxpuLq6Cn/+mojoXhUVFTYLg8TI8kI4ERHJE0uDiIiEsTSIiEgYS4OIiISxNIiISBhLg4iIhLE0iIhImGLu05BKoJubIrfdqU0DBmmi7UqVWeptS0Gp+7VOp7N7mbyCIgCAV5fHrB439nYfViwNG97w8ZU6Qr1EhUqdwH5KzKxUSt2vU1JS7F5m4Yo1AIDXF0y3ekz1w9NTREQkjKVBRETCWBpERCSMpUFERMJYGkREJIylQUREwlgaREQkjKVBRETCWBpERCSMpUFERMJYGkREJIylQUREwlgaREQkjN9ya8Pc82dxqrhYkm0HurnV+9tIM7KBwpsNHEhApzb1/7ZaqTIDD5ZbiRITE5GTkyPJtnU6Xb2+rZbkgaVhw6niYhy4eUPqGHYrvAl8d03qFPZRYmalysnJgcFgkDoGKRBPTxERkTCWBhERCWNpEBGRMJYGEREJY2kQEZEwlgYREQljaRBRo2nZsqXUEaiB8T4NIqqTk5MTRo4ciQEDBiA4OBht27aFyWTCpUuXcPz4cezZswfHjh2rtlxKSgqefPJJDBo0CFevXpUgOTUGlgYR1cjJyQnz58/HrFmz0KFDh2rP63Q6jB49GkuXLkV2djaWLFmCXbt2AagsjNmzZ6OsrAz+/v4sjYeIrE9PmUwmJCcno1evXnBxcUFgYCAMBgN8fHwwffp0qePVyFxejvLnZsH43vtW48at21A+eQrMJSUSJavb5uXhOLptufC4HCgxs1IEBgbixIkTWLZsGTp06ICvv/4aixcvxogRIxAYGIiQkBBMmjQJ//znP3H9+nWEhoZi586d2LhxI95++21LYURFRWHfvn1SvxxqQLI+0oiLi0NGRgYWL16MkJAQfPnll5g4cSJ+/PFH/OUvf5E6Xo1Ujo5wWPgiKp5PhCqsD9RBOpjz82FKXQ/Na0uhcnWVOiJRnfr374/du3ejZcuWOH/+POLj47F///5q8504cQJpaWmYP38+nnvuObz22muYPHkyAFgKIzMzs6njUyOTbWmkpaVh3bp1yMrKgl6vBwBERETgxIkTyMjIQHBwsMQJa6fq1hXqqVNgTP47VP/8OypeXwX16Keh1gZIHY2oTt26dUNmZiZatmyJtLQ0TJ06FaWlpXUuU1paipSUFOh0OkyZMgUAcPbsWezevbspIlMTk+3pqaSkJAwfPtxSGFV69uwJR0dHaLVaAEBkZCS0Wi2CgoIQFhaGzz77TIq41agjR0PVpTMqZiQAGg3UU2KkjkRUJ5VKhdTUVLRq1Qrbt2/H5MmTbRZGlZSUFEyZMgVlZWW4efMmgoKCEB8f38iJSQqyLI0rV67gzJkzGDduXLXnCgoK4OfnB2dnZwDAunXr8PXXX+PkyZN47733EB0dDaPRaHMbKpVKaMrKyqrXa1CpVFBpA4Bbt6AePAgqR0e715GVlSWc8/7JYLA/99Htr+Gd6a2tpu9zv7BrHQaD8jI/aO76TFXuf9xUU03fcDtmzBhERETg2rVrmDZtGkwmk9B7d+9F76ioKMTFxQEA/va3v8HNza2G99rwSL3XSsksSpanp65cuQIA8PDwsBq/c+cODAYDRowYYRlr3bq15fGtW7egUqlgNpubJGddzPn5MH30MdQTxsG06SOofz8AqvbtpY5Vp7DRixAW+YrV2Obl4dKEEaTEzHKVkJAAAFi+fDl++uknoWXuL4yqaxhZWVkIDw/H5MmT8c477zRaZmp6sjzScHd3BwDk5uZaja9cuRJFRUUICQmxGk9ISICXlxeio6OxZcsWODjY7kKz2Sw0hYeH253ffLe88jpGVCQ0cbFQDegH46rVMAv+5lYlPDxcOOf9k15vf+6GoNcrL/OD5q7PVOX+x0013X/at3379hg0aBBu376N9evXC71ntRUGALz77rsAgIkTJ9bwXusfqfdaKZlFyfJIw8vLC1qtFklJSWjbti06deqEzZs3W3bK+0vjX//6F4DKw945c+bgwIEDcJXwU0qm1LVQOThAHfNHAIBm5nOomJEA05at0IyLliwXUW1CQyv/bOGRI0fwyy+/2Jy/rsIAYLm2GBwcDLVaLXyqi+RPlkcaarUa6enp8PPzQ3x8PGJjY+Hu7o6EhARoNBrLRfD76fV6qNVqHDp0qIkT/8Z0MgemzD3QLJwP1f+OeFTNm0OzYB5MGzbBnJ8vWTai2vj5+QEATp06ZXNeW4UBANevX8fly5fRokULdO3atcHzknRkeaQBAN7e3tU+Gx4TEwNfX180a9YMAFBSUoLr169bdsqTJ0/iu+++Q+/evZs8bxV1kA7qHRnVx/39oN65VYJEYsa+kmXXuBwoMbNcHT58GEuXLsXBgwfrnC86OtpmYVRZvXo1WrVqheLi4oaOSxKSbWnUJDs7G3379rX8/Ouvv2LChAkoKSmBg4MDXFxcsGnTJnTp0kXClETKc/DgQZuFAQBbtmxBSkoK9u7da/PGvZSUlAZKR3KimNIoKSlBbm4uZs6caRnr0KEDDh8+LGEqokfPnDlzpI5AElJMabi6ugrdf0FERI1HlhfCiYhInlgaREQkjKVBRETCWBpERCSMpUFERMJYGkREJIylQUREwhRzn4ZUAmv4ewBK2HanNg0YpIm2K1VmqbctBZ1OZ/cyeQVFAACvLo9ZPW6KbZN8sDRseMPHV+oI9RIVKnUC+ykxs1LV5ys+Fq5YAwB4fcF0q8f0aOHpKSIiEsbSICIiYSwNIiISxtIgIiJhLA0iIhLG0iAiImEsDSIiEsbSICIiYSwNIiISxtIgIiJhLA0iIhLG0iAiImEsDSIiEsbSICIiYSwNIiISxtKwISEhAQ4O/LMjJB9ZWVnw8/NDz549MW3aNBiNRqkjCRk6dCh0Oh0CAgIwduxY/PLLL1JHqtPly5cxePBg9O7dG35+fnjppZekjiRk9uzZ8PT0bLT/t1gadTh48CBKSkqkjkFkYTKZMG3aNKSnp+PChQv45ZdfsGnTJqljCUlPT0dOTg5Onz4NT09PrF69WupIdXJwcMCKFStw7tw5nDx5El988QW2b98udSybxo0bh+zs7EZbP0ujFmVlZVi4cCGSk5OljkJkcezYMXTs2BG+vpV/UTIuLg5btmyROJWYVq1aAagsvtLSUqhUKokT1e2xxx5DaGjln5N0cnJCUFAQCgoKJE5l2xNPPAEPD49GW7/KbDabG23tCrZo0SL06NEDU6dOhYODAyoqKqSORApW+MNP2LzbYPm56Np1AMBj7X9n9RgAnJ0c8afoYWju4lxtPVu2bEFGRgb+/e9/AwDOnTuHSZMm4eTJkw2e2Ww24z+79uPqTzdtZgaAASH+CNX61LnOMWPG4ODBgwgICMDOnTvh6ura4Llz8y5jt+Go5ee6cru1aIaYqKFwtHEq58aNG9DpdPj000/x+OOPN3jmCqMRm7buxa3iX21mBoChvw9F755d61xnY/2/xSONGnz99dc4cuQIYmNjpY5CD4lOHu7o1MEdRdeuW/4TAFDtcdG16wjx966xMIDK/8ibikqlwsAwLa79dNNmZgDQ+fW0uc6tW7fi+++/h6enJzZv3tzwoQH06u6Jlq7Nhd7r/iH+Ngvj7t27GDt2LGbPnt0ohQEADhoNBoT6C2Vu0cwFPj26NEoOESyNGhw6dAhnz55F9+7d0a1bNxiNRnTr1k32F+5I3kYO7oc2rdzqnKd3z651/rbeuXNnXL582fJzQUEBPD09Gyzj/Tp2cMeTvw+tcx6NRo0JIyPgoNEIrdPJyQnPPPMMtm7d2hARq1GpVIgeoa+1eKv8n643Hrfxn6/RaMSkSZOg0+kwd+7choxZTa9unugf4l/nPC7OThj7lB5qCU/tsTRqEB8fj++//x4XL17ExYsXodFocPHiRbRs2VLqaKRgLs5OGP+HcNT2z71FcxdEDx9Y57n+0NBQXLlyBWfPngUAfPjhh4iKimqEtL8ZGKZF104dan1+2MAweLRrW+c6iouLUVRUBKDymsaOHTvg5+fXoDnv1dK1OcYM+32tz/+udUs8FdHX5nqmT58ONzc3vPHGGw0Zr1Yj9GFo17Z1rc9HDn0CrVs2/Ck9e7A07MRLQPQgund+DAP/L7DG56KGD4Rri2Z1Lq/RaPDBBx9g7Nix6NGjB1xdXRETE9MYUS3UajXGj4yAk5Njtee6d34MT/QJsLmO4uJijBo1ClqtFlqtFhUVFXjllVcaI65FwONeCPLrVW1cpVJh/MgIONfweu516NAhpKamIjs7G0FBQdDpdHjzzTcbKy4AwNHRARNGRkCtrv6Lg/ZxLwT27mFzHTNmzICnpyeMRiM8PT2RkJDQoBl5IdxOWYdzUPjDj5jw9CDhw3Gie1VUGPHWhq344ccblrHQAB+MfUovYSrbjp76f8jYc8Dys7OTIxKnjrV5yk1KpWV38fcP0y0XmAEgol8Qhg3sI2Eq2/YdOoG9X/z2sVk31+aYM3UsmjdzkTBVJR5p2KGs7C4OHDmFu+UVLAyqNwcHDZ55ehA0msp/fm1bueHpwf0kTmVbH60Pevf87RrAqCEDZF0YQNUpwQjLz506uGPwgGAJE4kJ76dD58faW34eN0Ivi8IAHoLSOH36NKKjo+Hu7g4XFxf06tULixYtapRtfXniG9wuLcOQASGNsn56dHi0a4uhv+8DFYBxfwiHs7OT1JFsUqlUiBo+EC2au8DPuxuC/auf+pGjHl074onQADhoNBhvxwV7KWnUlR8ucHR0QN8gX3h7dZY6koWiT08dP34cAwcOROfOnbFgwQJ07doV+fn5+PLLL/Hhhx/WuezCFWuaKCURkfy9vmC60HyK/lKluXPnokWLFjhy5IjlblOg8i5ZIiJqeIo90rh9+zbc3Nwwa9Ys/OMf/2jUbZWV3cWKd9PQuWN7xI4b0ajbIiKSM8Ueady8eRMmk6neNzbV5/TU+bzLPK1FRA8l0dNTir0Q3qZNG6jVahQWFkodhYjokaHY01MAEBERgbNnz+Lbb79ttLu19391Ep8cOIaEmEh07tje9gJERA8xxR5pAEBycjJKSkrQt29frFu3Dvv378f69esxbdq0Bll/WdldHDz6NXy8OrMwiIig4GsaABASEoKvvvoKixcvxpw5c1BaWorOnTvjmWeeaZD137hVjGbNnHlfBhHR/yj69FRTMJlMUKsVfUBGRNRgWBpERCSMv0ITEZEwlgYREQljaRARkTCWBhERCWNpEBGRMJYGEREJY2kQEZEwlgYREQljaRARkTCWBhERCWNpEBGRMJYGEREJY2kQEZEwlgYREQljaRARkTCWBhERCWNpEBGRMJYGEREJY2kQEZEwlgYREQljaRARkTCWBhERCWNpEBGRMJYGEREJY2kQEZEwlgYREQljaRARkbD/DyosG2frQZAxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 507.997x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_right10.x(3)\n",
    "dj_right10.x(1)\n",
    "dj_right10 += dj_right0\n",
    "dj_right10.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "7912897f-f5ea-4227-a431-c2e899a12638",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1502/639014280.py:4: DeprecationWarning: The QuantumCircuit.__iadd__() method is being deprecated. Use the compose() (potentially with the inplace=True argument) and tensor() methods which are more flexible w.r.t circuit register compatibility.\n",
      "  dj_right11 += dj_right0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 507.997x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_right11.x(3)\n",
    "dj_right11.x(1)\n",
    "dj_right11.x(0)\n",
    "dj_right11 += dj_right0\n",
    "dj_right11.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "079ed6f1-fe91-4fcc-bbf3-2ba9f0c31c82",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1502/1174871471.py:3: DeprecationWarning: The QuantumCircuit.__iadd__() method is being deprecated. Use the compose() (potentially with the inplace=True argument) and tensor() methods which are more flexible w.r.t circuit register compatibility.\n",
      "  dj_right12 += dj_right0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 568.197x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_right12.x(3)\n",
    "dj_right12.x(2)\n",
    "dj_right12 += dj_right0\n",
    "dj_right12.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "91c97130-8977-46d7-b355-c06fe1ee5a01",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1502/3176146865.py:4: DeprecationWarning: The QuantumCircuit.__iadd__() method is being deprecated. Use the compose() (potentially with the inplace=True argument) and tensor() methods which are more flexible w.r.t circuit register compatibility.\n",
      "  dj_right13 += dj_right0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 568.197x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_right13.x(3)\n",
    "dj_right13.x(2)\n",
    "dj_right13.x(0)\n",
    "dj_right13 += dj_right0\n",
    "dj_right13.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "4d8f49fb-14e1-487b-93ca-fd640a36031f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1502/631145694.py:4: DeprecationWarning: The QuantumCircuit.__iadd__() method is being deprecated. Use the compose() (potentially with the inplace=True argument) and tensor() methods which are more flexible w.r.t circuit register compatibility.\n",
      "  dj_right14 += dj_right0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 568.197x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_right14.x(3)\n",
    "dj_right14.x(2)\n",
    "dj_right14.x(1)\n",
    "dj_right14 += dj_right0\n",
    "dj_right14.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "70d21efd-128b-434b-b0ad-ce5ec0780110",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1502/804146584.py:5: DeprecationWarning: The QuantumCircuit.__iadd__() method is being deprecated. Use the compose() (potentially with the inplace=True argument) and tensor() methods which are more flexible w.r.t circuit register compatibility.\n",
      "  dj_right15 += dj_right0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 568.197x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_right15.x(3)\n",
    "dj_right15.x(2)\n",
    "dj_right15.x(1)\n",
    "dj_right15.x(0)\n",
    "dj_right15 += dj_right0\n",
    "dj_right15.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "8c1caa37-5718-4f61-b5be-e5755a9f0cb6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16\n"
     ]
    }
   ],
   "source": [
    "meas_calibs2 = [dj_right0, dj_right1, dj_right2, dj_right3, dj_right4, dj_right5, dj_right6, dj_right7, dj_right8, dj_right9, dj_right10, dj_right11, dj_right12, dj_right13, dj_right14, dj_right15]\n",
    "print(len(meas_calibs2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "40dcf750-b43f-4b08-b884-7697816c366f",
   "metadata": {},
   "outputs": [],
   "source": [
    "t_qc = transpile(meas_calibs2, real_backend)\n",
    "qobj = assemble(t_qc, shots=10000)\n",
    "results_right = real_backend.run(qobj, shots=10000).result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "1cb4a28b-73d4-4a6b-bb54-8014c732983a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'0000': 9675, '0001': 93, '0010': 87, '0011': 1, '0100': 50, '1000': 91, '1001': 2, '1010': 1}, {'0000': 338, '0001': 9463, '0010': 5, '0011': 65, '0100': 3, '0101': 39, '1000': 3, '1001': 84}, {'0000': 462, '0001': 6, '0010': 9294, '0011': 92, '0110': 42, '1000': 6, '1010': 96, '1011': 1, '1110': 1}, {'0000': 16, '0001': 424, '0010': 326, '0011': 9107, '0110': 3, '0111': 48, '1011': 76}, {'0000': 520, '0001': 8, '0010': 2, '0100': 9198, '0101': 98, '0110': 78, '1000': 3, '1100': 92, '1110': 1}, {'0000': 11, '0001': 466, '0011': 1, '0100': 284, '0101': 9090, '0110': 1, '0111': 45, '1001': 6, '1100': 1, '1101': 93, '1111': 2}, {'0000': 17, '0001': 1, '0010': 470, '0011': 3, '0100': 433, '0101': 4, '0110': 8878, '0111': 92, '1010': 4, '1100': 5, '1110': 93}, {'0001': 15, '0010': 13, '0011': 443, '0100': 14, '0101': 413, '0110': 293, '0111': 8710, '1010': 1, '1011': 3, '1101': 7, '1110': 2, '1111': 86}, {'0000': 1245, '0001': 18, '0010': 11, '0100': 8, '1000': 8543, '1001': 81, '1010': 59, '1011': 1, '1100': 34}, {'0000': 45, '0001': 1223, '0011': 6, '0101': 8, '1000': 255, '1001': 8381, '1010': 2, '1011': 42, '1100': 1, '1101': 37}, {'0000': 47, '0010': 1154, '0011': 12, '0100': 2, '0110': 6, '1000': 368, '1001': 5, '1010': 8280, '1011': 83, '1100': 6, '1110': 35, '1111': 2}, {'0001': 51, '0010': 42, '0011': 1165, '0101': 1, '0111': 6, '1000': 18, '1001': 362, '1010': 301, '1011': 8011, '1101': 2, '1110': 2, '1111': 39}, {'0000': 75, '0001': 1, '0100': 1175, '0101': 12, '0110': 6, '1000': 458, '1001': 4, '1010': 8, '1100': 8103, '1101': 69, '1110': 88, '1111': 1}, {'0000': 2, '0001': 71, '0100': 39, '0101': 1183, '0111': 4, '1000': 15, '1001': 432, '1011': 1, '1100': 251, '1101': 7967, '1110': 2, '1111': 33}, {'0000': 7, '0010': 67, '0011': 1, '0100': 51, '0110': 1085, '0111': 12, '1000': 18, '1010': 407, '1011': 8, '1100': 367, '1101': 5, '1110': 7903, '1111': 69}, {'0001': 4, '0010': 1, '0011': 59, '0100': 2, '0101': 56, '0110': 57, '0111': 1052, '1001': 21, '1010': 14, '1011': 393, '1100': 7, '1101': 380, '1110': 244, '1111': 7710}]\n"
     ]
    }
   ],
   "source": [
    "result_counts_right = results_right.get_counts()\n",
    "print(result_counts_right)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "40d6a782-948a-4b25-9b4c-4e659ad6c53a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9.675e-01 9.300e-03 8.700e-03 1.000e-04 5.000e-03 0.000e+00 0.000e+00\n",
      " 0.000e+00 9.100e-03 2.000e-04 1.000e-04 0.000e+00 0.000e+00 0.000e+00\n",
      " 0.000e+00 0.000e+00]\n",
      "[3.380e-02 9.463e-01 5.000e-04 6.500e-03 3.000e-04 3.900e-03 0.000e+00\n",
      " 0.000e+00 3.000e-04 8.400e-03 0.000e+00 0.000e+00 0.000e+00 0.000e+00\n",
      " 0.000e+00 0.000e+00]\n",
      "[4.620e-02 6.000e-04 9.294e-01 9.200e-03 0.000e+00 0.000e+00 4.200e-03\n",
      " 0.000e+00 6.000e-04 0.000e+00 9.600e-03 1.000e-04 0.000e+00 0.000e+00\n",
      " 1.000e-04 0.000e+00]\n",
      "[1.600e-03 4.240e-02 3.260e-02 9.107e-01 0.000e+00 0.000e+00 3.000e-04\n",
      " 4.800e-03 0.000e+00 0.000e+00 0.000e+00 7.600e-03 0.000e+00 0.000e+00\n",
      " 0.000e+00 0.000e+00]\n",
      "[5.200e-02 8.000e-04 2.000e-04 0.000e+00 9.198e-01 9.800e-03 7.800e-03\n",
      " 0.000e+00 3.000e-04 0.000e+00 0.000e+00 0.000e+00 9.200e-03 0.000e+00\n",
      " 1.000e-04 0.000e+00]\n",
      "[1.10e-03 4.66e-02 0.00e+00 1.00e-04 2.84e-02 9.09e-01 1.00e-04 4.50e-03\n",
      " 0.00e+00 6.00e-04 0.00e+00 0.00e+00 1.00e-04 9.30e-03 0.00e+00 2.00e-04]\n",
      "[1.700e-03 1.000e-04 4.700e-02 3.000e-04 4.330e-02 4.000e-04 8.878e-01\n",
      " 9.200e-03 0.000e+00 0.000e+00 4.000e-04 0.000e+00 5.000e-04 0.000e+00\n",
      " 9.300e-03 0.000e+00]\n",
      "[0.00e+00 1.50e-03 1.30e-03 4.43e-02 1.40e-03 4.13e-02 2.93e-02 8.71e-01\n",
      " 0.00e+00 0.00e+00 1.00e-04 3.00e-04 0.00e+00 7.00e-04 2.00e-04 8.60e-03]\n",
      "[1.245e-01 1.800e-03 1.100e-03 0.000e+00 8.000e-04 0.000e+00 0.000e+00\n",
      " 0.000e+00 8.543e-01 8.100e-03 5.900e-03 1.000e-04 3.400e-03 0.000e+00\n",
      " 0.000e+00 0.000e+00]\n",
      "[4.500e-03 1.223e-01 0.000e+00 6.000e-04 0.000e+00 8.000e-04 0.000e+00\n",
      " 0.000e+00 2.550e-02 8.381e-01 2.000e-04 4.200e-03 1.000e-04 3.700e-03\n",
      " 0.000e+00 0.000e+00]\n",
      "[4.700e-03 0.000e+00 1.154e-01 1.200e-03 2.000e-04 0.000e+00 6.000e-04\n",
      " 0.000e+00 3.680e-02 5.000e-04 8.280e-01 8.300e-03 6.000e-04 0.000e+00\n",
      " 3.500e-03 2.000e-04]\n",
      "[0.000e+00 5.100e-03 4.200e-03 1.165e-01 0.000e+00 1.000e-04 0.000e+00\n",
      " 6.000e-04 1.800e-03 3.620e-02 3.010e-02 8.011e-01 0.000e+00 2.000e-04\n",
      " 2.000e-04 3.900e-03]\n",
      "[7.500e-03 1.000e-04 0.000e+00 0.000e+00 1.175e-01 1.200e-03 6.000e-04\n",
      " 0.000e+00 4.580e-02 4.000e-04 8.000e-04 0.000e+00 8.103e-01 6.900e-03\n",
      " 8.800e-03 1.000e-04]\n",
      "[2.000e-04 7.100e-03 0.000e+00 0.000e+00 3.900e-03 1.183e-01 0.000e+00\n",
      " 4.000e-04 1.500e-03 4.320e-02 0.000e+00 1.000e-04 2.510e-02 7.967e-01\n",
      " 2.000e-04 3.300e-03]\n",
      "[7.000e-04 0.000e+00 6.700e-03 1.000e-04 5.100e-03 0.000e+00 1.085e-01\n",
      " 1.200e-03 1.800e-03 0.000e+00 4.070e-02 8.000e-04 3.670e-02 5.000e-04\n",
      " 7.903e-01 6.900e-03]\n",
      "[0.000e+00 4.000e-04 1.000e-04 5.900e-03 2.000e-04 5.600e-03 5.700e-03\n",
      " 1.052e-01 0.000e+00 2.100e-03 1.400e-03 3.930e-02 7.000e-04 3.800e-02\n",
      " 2.440e-02 7.710e-01]\n",
      "[[9.675e-01 3.380e-02 4.620e-02 1.600e-03 5.200e-02 1.100e-03 1.700e-03\n",
      "  0.000e+00 1.245e-01 4.500e-03 4.700e-03 0.000e+00 7.500e-03 2.000e-04\n",
      "  7.000e-04 0.000e+00]\n",
      " [9.300e-03 9.463e-01 6.000e-04 4.240e-02 8.000e-04 4.660e-02 1.000e-04\n",
      "  1.500e-03 1.800e-03 1.223e-01 0.000e+00 5.100e-03 1.000e-04 7.100e-03\n",
      "  0.000e+00 4.000e-04]\n",
      " [8.700e-03 5.000e-04 9.294e-01 3.260e-02 2.000e-04 0.000e+00 4.700e-02\n",
      "  1.300e-03 1.100e-03 0.000e+00 1.154e-01 4.200e-03 0.000e+00 0.000e+00\n",
      "  6.700e-03 1.000e-04]\n",
      " [1.000e-04 6.500e-03 9.200e-03 9.107e-01 0.000e+00 1.000e-04 3.000e-04\n",
      "  4.430e-02 0.000e+00 6.000e-04 1.200e-03 1.165e-01 0.000e+00 0.000e+00\n",
      "  1.000e-04 5.900e-03]\n",
      " [5.000e-03 3.000e-04 0.000e+00 0.000e+00 9.198e-01 2.840e-02 4.330e-02\n",
      "  1.400e-03 8.000e-04 0.000e+00 2.000e-04 0.000e+00 1.175e-01 3.900e-03\n",
      "  5.100e-03 2.000e-04]\n",
      " [0.000e+00 3.900e-03 0.000e+00 0.000e+00 9.800e-03 9.090e-01 4.000e-04\n",
      "  4.130e-02 0.000e+00 8.000e-04 0.000e+00 1.000e-04 1.200e-03 1.183e-01\n",
      "  0.000e+00 5.600e-03]\n",
      " [0.000e+00 0.000e+00 4.200e-03 3.000e-04 7.800e-03 1.000e-04 8.878e-01\n",
      "  2.930e-02 0.000e+00 0.000e+00 6.000e-04 0.000e+00 6.000e-04 0.000e+00\n",
      "  1.085e-01 5.700e-03]\n",
      " [0.000e+00 0.000e+00 0.000e+00 4.800e-03 0.000e+00 4.500e-03 9.200e-03\n",
      "  8.710e-01 0.000e+00 0.000e+00 0.000e+00 6.000e-04 0.000e+00 4.000e-04\n",
      "  1.200e-03 1.052e-01]\n",
      " [9.100e-03 3.000e-04 6.000e-04 0.000e+00 3.000e-04 0.000e+00 0.000e+00\n",
      "  0.000e+00 8.543e-01 2.550e-02 3.680e-02 1.800e-03 4.580e-02 1.500e-03\n",
      "  1.800e-03 0.000e+00]\n",
      " [2.000e-04 8.400e-03 0.000e+00 0.000e+00 0.000e+00 6.000e-04 0.000e+00\n",
      "  0.000e+00 8.100e-03 8.381e-01 5.000e-04 3.620e-02 4.000e-04 4.320e-02\n",
      "  0.000e+00 2.100e-03]\n",
      " [1.000e-04 0.000e+00 9.600e-03 0.000e+00 0.000e+00 0.000e+00 4.000e-04\n",
      "  1.000e-04 5.900e-03 2.000e-04 8.280e-01 3.010e-02 8.000e-04 0.000e+00\n",
      "  4.070e-02 1.400e-03]\n",
      " [0.000e+00 0.000e+00 1.000e-04 7.600e-03 0.000e+00 0.000e+00 0.000e+00\n",
      "  3.000e-04 1.000e-04 4.200e-03 8.300e-03 8.011e-01 0.000e+00 1.000e-04\n",
      "  8.000e-04 3.930e-02]\n",
      " [0.000e+00 0.000e+00 0.000e+00 0.000e+00 9.200e-03 1.000e-04 5.000e-04\n",
      "  0.000e+00 3.400e-03 1.000e-04 6.000e-04 0.000e+00 8.103e-01 2.510e-02\n",
      "  3.670e-02 7.000e-04]\n",
      " [0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 9.300e-03 0.000e+00\n",
      "  7.000e-04 0.000e+00 3.700e-03 0.000e+00 2.000e-04 6.900e-03 7.967e-01\n",
      "  5.000e-04 3.800e-02]\n",
      " [0.000e+00 0.000e+00 1.000e-04 0.000e+00 1.000e-04 0.000e+00 9.300e-03\n",
      "  2.000e-04 0.000e+00 0.000e+00 3.500e-03 2.000e-04 8.800e-03 2.000e-04\n",
      "  7.903e-01 2.440e-02]\n",
      " [0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 2.000e-04 0.000e+00\n",
      "  8.600e-03 0.000e+00 0.000e+00 2.000e-04 3.900e-03 1.000e-04 3.300e-03\n",
      "  6.900e-03 7.710e-01]]\n"
     ]
    }
   ],
   "source": [
    "ls = []\n",
    "for qc in meas_calibs2:\n",
    "     counts = results_right.get_counts(qc)\n",
    "     this_array = np.array([counts.get('0000') or 0, counts.get('0001') or 0, counts.get('0010') or 0, counts.get('0011') or 0 , counts.get('0100') or 0, counts.get('0101') or 0, counts.get('0110') or 0, counts.get('0111') or 0, counts.get('1000') or 0, counts.get('1001') or 0, counts.get('1010') or 0, counts.get('1011') or 0, counts.get('1100') or 0, counts.get('1101') or 0, counts.get('1110') or 0, counts.get('1111') or 0])\n",
    "     this_array = this_array/10000\n",
    "     ls.append(this_array)\n",
    "     print(this_array)\n",
    "     # np.append(array, this_array, axis=1)\n",
    "array = np.array(ls)\n",
    "# print(array)\n",
    "array_t_right = np.transpose(array)\n",
    "print(array_t_right)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "d03803e6-1043-4188-b34f-7ccf1b63f1ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$\n",
       "\n",
       "\\begin{bmatrix}\n",
       "0.9675 & 0.0338 & 0.0462 & 0.0016 & \\cdots & 0.0002 & 0.0007 & 0  \\\\\n",
       " 0.0093 & 0.9463 & 0.0006 & 0.0424 & \\cdots & 0.0071 & 0 & 0.0004  \\\\\n",
       " 0.0087 & 0.0005 & 0.9294 & 0.0326 & \\cdots & 0 & 0.0067 & 0.0001  \\\\\n",
       " 0.0001 & 0.0065 & 0.0092 & 0.9107 & \\cdots & 0 & 0.0001 & 0.0059  \\\\\n",
       " \\vdots & \\vdots & \\vdots & \\vdots & \\ddots & \\vdots & \\vdots & \\vdots \\\\\n",
       " 0 & 0 & 0 & 0 & \\cdots & 0.7967 & 0.0005 & 0.038  \\\\\n",
       " 0 & 0 & 0.0001 & 0 & \\cdots & 0.0002 & 0.7903 & 0.0244  \\\\\n",
       " 0 & 0 & 0 & 0 & \\cdots & 0.0033 & 0.0069 & 0.771  \\\\\n",
       " \\end{bmatrix}\n",
       "$$"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array_to_latex(array_t_right)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "7d4848ea-802a-4b18-965d-276103b6529d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$\n",
       "\n",
       "\\begin{bmatrix}\n",
       "0.96255 & 0.0319 & 0.04815 & 0.00195 & \\cdots & 0.0003 & 0.0008 & 0  \\\\\n",
       " 0.01055 & 0.94735 & 0.00055 & 0.0451 & \\cdots & 0.0087 & 0 & 0.0005  \\\\\n",
       " 0.01005 & 0.00035 & 0.92435 & 0.031 & \\cdots & 0 & 0.00855 & 0.0002  \\\\\n",
       " 0.0002 & 0.00605 & 0.01065 & 0.908 & \\cdots & 0 & 0.00005 & 0.00765  \\\\\n",
       " \\vdots & \\vdots & \\vdots & \\vdots & \\ddots & \\vdots & \\vdots & \\vdots \\\\\n",
       " 0 & 0.00005 & 0 & 0 & \\cdots & 0.7504 & 0.0008 & 0.0387  \\\\\n",
       " 0 & 0 & 0.00015 & 0 & \\cdots & 0.00015 & 0.73685 & 0.02255  \\\\\n",
       " 0 & 0 & 0 & 0 & \\cdots & 0.0044 & 0.0075 & 0.71795  \\\\\n",
       " \\end{bmatrix}\n",
       "$$"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array_sum = (array_t_left + array_t_right) / 2\n",
    "array_to_latex(array_sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "2775dcea-5627-44ec-b54c-a43d47b8f09e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$\n",
       "\n",
       "\\begin{bmatrix}\n",
       "1.04288 & -0.0351 & -0.05433 & 0.00135 & \\cdots & -0.00033 & -0.00121 & 0.00003  \\\\\n",
       " -0.01158 & 1.05889 & 0.00058 & -0.05265 & \\cdots & 0.0114 & 0.00002 & -0.00092  \\\\\n",
       " -0.01134 & 0.00022 & 1.08586 & -0.03709 & \\cdots & 0 & 0.01081 & -0.00024  \\\\\n",
       " -0.00002 & -0.00704 & -0.01272 & 1.10469 & \\cdots & -0.00007 & -0.00012 & 0.01243  \\\\\n",
       " \\vdots & \\vdots & \\vdots & \\vdots & \\ddots & \\vdots & \\vdots & \\vdots \\\\\n",
       " 0 & 0.00005 & 0 & 0 & \\cdots & 1.33696 & -0.00015 & -0.07201  \\\\\n",
       " 0 & 0 & -0.00008 & 0. & \\cdots & 0.00047 & 1.3619 & -0.04277  \\\\\n",
       " 0 & 0 & 0. & 0.00014 & \\cdots & -0.00817 & -0.01423 & 1.39745  \\\\\n",
       " \\end{bmatrix}\n",
       "$$"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.linalg as la\n",
    "array_inv = la.inv(array_sum)\n",
    "array_to_latex(array_inv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "235a6ccc-1ee1-418e-bebf-2365220d3631",
   "metadata": {},
   "outputs": [],
   "source": [
    "counts = realqc_results.get_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "cee2c83f-3dcb-4a54-ad1a-190bd216aed9",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_array = np.array([counts.get('0000') or 0, counts.get('0001') or 0, counts.get('0010') or 0, counts.get('0011') or 0 , counts.get('0100') or 0, counts.get('0101') or 0, counts.get('0110') or 0, counts.get('0111') or 0, counts.get('1000') or 0, counts.get('1001') or 0, counts.get('1010') or 0, counts.get('1011') or 0, counts.get('1100') or 0, counts.get('1101') or 0, counts.get('1110') or 0, counts.get('1111') or 0])\n",
    "result_array = result_array/1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "1a5e2865-6449-48c1-b67f-668dd318d706",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.012, 0.05 , 0.013, 0.031, 0.006, 0.012, 0.006, 0.043, 0.003,\n",
       "       0.011, 0.009, 0.016, 0.045, 0.175, 0.081, 0.487])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "63546077-2538-47ba-9bff-25c3e3e304d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "mitigated = np.dot(array_inv, result_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "987a5a2b-02b5-441e-add9-1140324a8d3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9.71903459e-03,  4.98286902e-02,  1.13910188e-02,  3.35813389e-02,\n",
       "       -3.38317708e-03, -2.69024472e-02, -1.19392225e-02, -7.28724921e-02,\n",
       "        1.69313169e-04,  3.03737643e-03,  6.59951278e-03, -1.18032258e-02,\n",
       "        4.84872908e-02,  1.98158634e-01,  8.87166008e-02,  6.77211754e-01])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mitigated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "0a0b84af-1da9-46c9-af70-52150dfee357",
   "metadata": {},
   "outputs": [],
   "source": [
    "def NormalizeData(data):\n",
    "    return (data - np.min(data)) / (np.max(data) - np.min(data))\n",
    "normal = NormalizeData(mitigated)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "e7feec98-8ff4-4f86-bc89-66ced001fc55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.11010967, 0.1635832 , 0.11233873, 0.1419225 , 0.09264201,\n",
       "       0.06128651, 0.08123523, 0.        , 0.09737814, 0.10120179,\n",
       "       0.10595077, 0.08141654, 0.16179487, 0.36133425, 0.21542793,\n",
       "       1.        ])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "3cf67ff5-dd85-4549-9650-58921eb8867c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Normalize(arr):\n",
    "    return arr/sum(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "3abd200c-f7d8-4515-aee7-53bec842a8aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_normal = Normalize(mitigated)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "d3e99edd-5d77-4978-9da1-4864d2c8cc47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9.71903459e-03,  4.98286902e-02,  1.13910188e-02,  3.35813389e-02,\n",
       "       -3.38317708e-03, -2.69024472e-02, -1.19392225e-02, -7.28724921e-02,\n",
       "        1.69313169e-04,  3.03737643e-03,  6.59951278e-03, -1.18032258e-02,\n",
       "        4.84872908e-02,  1.98158634e-01,  8.87166008e-02,  6.77211754e-01])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_normal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ec97e57-3166-4ed7-8fdc-3d92b8998078",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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    "name": "ipython",
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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   "version": "3.8.13"
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  "widgets": {
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 },
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