circuit-cutting-test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d8cfdce4-3db2-4f80-9ce2-8804f28d2d5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning) \n",
    "\n",
    "from qiskit import QuantumCircuit\n",
    "from qiskit.result import ProbDistribution\n",
    "from qiskit.visualization import plot_distribution\n",
    "from circuit_knitting.cutting.cutqc import reconstruct_full_distribution, cut_circuit_wires, evaluate_subcircuits\n",
    "import matplotlib.pyplot as plt\n",
    "from utils import save_cuts, save_probs, save_reconstructed_probs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a11e0c65-d3c2-439a-8633-8174595db387",
   "metadata": {},
   "outputs": [],
   "source": [
    "FILE_PREFIX = '5-qubits-GHZ'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "22eca8f4-9421-45e9-aa10-6ac4a17a3531",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8KO0mJMQEix2XPMRiseD48eOwWCxiRyESBIucZOd8Syfe2VGJV/JPoaK2uc/1Pj9Uf+W/7xqvw1MPjcTcqXEYMIAHqohIOljkJBt2ux2vbS/Hyj8fRmt7l1PbfllsxJfFRgyL1uLNNXfgrvGRbkpJRCQs7nqQLNQZ2/CTJ/+Gn//hgNMlfrUf9K24e/FneGbdt7Bc6BYwIRGRe7DISfJOVZ/HbQt3Yt/B+uuv7KDNeSeR8dRutLRdFOw1yTtERkZi9erViIzkUReSBxY5SVpVXQum/mwX9A3tgr/2l8VGzFq+Bx0W7pnLyaBBg5CRkYFBgwaJHYVIECxykqzOi1b8NGsfDGc73DZGYUkDnnnpW7e9Pnme2WzGtm3bYDabxY5CJAgWOUnWmleP4vhp596Mi/LmoG7vAhTlzXF4mzc/rsDfvtY7G4+8VENDA9avX4+GhgaxoxAJQhFFbjKZkJOTg8TERGg0GsTExCArKwvt7e1YsmQJVCoVNm/eLHZMckLpqXPIfet7p7fThQcgekggdOEBTm23dPXXaOtw/SI6IiJ3kf3tZ6WlpcjIyIDRaERgYCBSUlJQX1+PjRs3oqqqCk1NTQCAsWPHihuUnLLhnWOwWu0eG0/f0I73PqvCsvnJHhuTiMgRst4jN5lMmD17NoxGI7Kzs2EwGFBSUgKj0Yjc3FwUFBSgqKgIKpUKaWlpYsclB51tsmDbnmqPj/tKfhnsds99eCAicoSsi3zFihXQ6/VYvnw5NmzYAK1We2VZTk4OxowZg+7ubsTFxSE4mFN0SsXWnadxscvm8XG/K29C8QmTx8clYQUEBGDixIkICHDu9AqRt5JtkZeVlSE/Px/h4eFYt25dr+uMGzcOADBmzJgrX7tc/Onp6fDz84NKpfJIXnLc10fFu0jpgIhjkzBiY2OxadMmxMbGih2FSBCyLfK8vDzYbDZkZmYiKCio13X8/f0B9Czy06dP48MPP4ROp8OECRM8kpWcc+SkeHvFR8q4Ry51VqsVbW1tsFqtYkchEoRsi3z//v0AgClTpvS5jl5/6Zaiq4v8zjvvhMFgwI4dOzBt2jT3hiSnNTV34h8G4Sd/cdTRsnOijU3CqKysxNSpU1FZWSl2FCJByPaq9cvPGh46dGivy7u7u3HgwAEAPYtcrRb+s8348eNhNBoFf10l6laHAIN/2efyorw5/d5apgv3v/Lvur0L+lzPaOrAhId3XPP1ssp/IDo62vHAXsYw+FeAehAMRoOkv4+rzZ8/36n1GxsbAQC7du3CkSNHHNpm7ty5TufyVnL8GZADnU6H4uJil7aVbZG3t1/aa+vrmcP5+fkwmUzQarWIj493axaj0YgzZ864dQzFGNgNDO578eX7xK/H10ft0Hr/ymaFtP8utVZADdisVml/H1e5/LvuqMvvCRaLxeFt5fJnBUCWPwNKJ9si1+l0MJvNKCkpwaRJk3osMxgMWLlyJQAgLS3N7Re06XQ6t76+knSrg9Hf5WZGU//TterC/eHro0a31QajqfcPef29jo/aCl1UlCNRvZLBxwc2AGofH0RK+Pu4WmCgcx/ILpe3v7+/w9tGyeTPCpDnz4Ac3EhPyLbIp02bhrKyMuTm5mL69OlISkoCABQVFWHhwoUwmS5dtOSJiWBcPVxC1+rutiH4tndhudD7hUq9HQ6/Wt3eBYgeEgijyYKY6e87Pf6UyanY+5p0p2uNnpaHM40diNRFQn9cut/H1YqKipxa/9SpU8jLy0NGRgaSkx2b4Ofll192IZl3kuPPgNLJ9mK3nJwchIWFoa6uDqmpqRg9ejSGDx+O9PR0DBs2DFOnTgXQ8/w4eT9fXzXGjggTbfxxKeGijU3CSExMxO7du5GYmCh2FCJByLbIo6OjUVhYiJkzZ0Kj0aCmpgahoaHYsmULCgoKUFFRAYBFLkXjU8UrUxa59Pn6+iIkJAS+vrI9IEkKI9siB4CRI0di586daG1tRWtrKw4dOoRly5ahvb0dNTU1UKvVGDVqlNgxyUnzp8WJMm5QwADccxvPKUqdXq9Hdnb2ldtPiaROkR9JT5w4AbvdjqSkpF6nady+fTsA4OTJkz3+Py4uDuPHj/dcUOrVHeN0SE0YjBNV5z067sJZCQgOGujRMUl4bW1tKCwsxNKlS8WOQiQIRRb5sWPHAPR9WP2BBx7o9f8XLVqEt99+263Z6PpUKhWeXpCCp174xqPj/uLBkR4dj4jIESzyXvAJV97vZ/ePwF8/LMfRU56Zae2ph0ZidFKoR8YiInKGrM+R9+V6RU7eb8AANd5eeycG+Lr/Rzju5iDkPst594nIOylyj/zyPOwkbWlJofjjsxPw7PpDDm9zeaKX600cc5nfQB9sffEuBAUMcCkjeZ+IiAhkZWUhIiJC7ChEglBkkZN8/HLhKJxr7sTa10odWv96E8ZcbeAANbZtmIrbb+XMfHISFhaGzMxMsWMQCUaRh9ZJXv6wfBz++OwEqNXCTbU7WDsQn26ajtl385nVctPS0oJ9+/ahpaVF7ChEgmCRkyysfCINB/97NlISBt/wa826MwYnPr4fP7mNT4aSo/r6eqxatQr19fViRyESBIucZGPCqAgcef8+rMsajxid8082G5cSjrzcu7Fj03TcfJPz2xMRiYHnyElWNH6++PWSMfi3RaNRUFiHvF1VKD5hQlVd6zXr+vqqkJoQgh+l3YQlc5MwYRQvfiIi6WGRkyz5+qpx35ShuG/KUADA+ZZOlNc0Y9byPTCd70REiAb/2PMQNH78FSAiaeOhdVKEwcF+mJh2E/wG+gC4dEU6S1yZ/Pz8MGLECPj5+YkdhUgQfCcjIkWJj4/H1q1bxY5BJBjukRMREUkYi5yIFKW8vByTJ09GeXm52FGIBMEiJyJFsdvt6Orq4sORSDZY5ERERBLGIiciIpIwFjkREZGE8fYzIlKUuLg45OXlISoqSuwoRIJgkRORomg0GiQkJIgdg0gwPLRORIpiMBiwdu1aGAwGsaMQCYJFTkSK0tzcjB07dqC5uVnsKESCYJETERFJGIuciIhIwljkREREEsYiJyJFUavVuOWWW6BW8+2P5IE/yUSkKDabDUePHoXNZhM7CpEgWOREREQSxiInIiKSMBY5ERGRhLHIiUhRtFotZsyYAa1WK3YUIkFwrnUiUpSoqCisWbNG7BhEguEeOREpSmdnJ+rq6tDZ2Sl2FCJBsMiJSFGqq6sxb948VFdXix2FSBAsciIiIgnjOXIvZbfbASkd+vPzg0qlEjsFEZHisMi9VWcnuh9cJHYKh/l+8A6g0Ygdg4hIcXhonYiISMK4R05EipKcnIzDhw+LHYNIMNwjJyIikjAWOREpSm1tLRYvXoza2lqxoxAJgkVORIpisVhw/PhxWCwWsaMQCYJFTkREJGEsciIiIgljkRMREUkYi5yIFCUyMhKrV69GZGSk2FGIBMH7yIlIUQYNGoSMjAyxYxAJhnvkRKQoZrMZ27Ztg9lsFjsKkSBY5ESkKA0NDVi/fj0aGhrEjkIkCBY5ERGRhCmiyE0mE3JycpCYmAiNRoOYmBhkZWWhvb0dS5YsgUqlwubNm8WO6RZfmhox8NMP8OeqU32uM/DTD/DTQ4UeTOVZdrsdXxUb8PM/HMC585ceDdvcdhEVNc0iJ/OcMw3tWPPqUTQ1X/r+zS2d+PTv/4DVahM5GRHdKNlf7FZaWoqMjAwYjUYEBgYiJSUF9fX12LhxI6qqqtDU1AQAGDt2rLhByS12H9Aje8MhnKg63+PrbR3dGDFnO6ZPuhmvPDcZibHB4gR0M5P5Ap5+8Rt8uK8GVqv9ytc7LlgxZ8VeDL05CGueuhWPzRkuYkoiuhGy3iM3mUyYPXs2jEYjsrOzYTAYUFJSAqPRiNzcXBQUFKCoqAgqlQppaWlixyWB5X1WhZnL91xT4lfb+209Ji38FMcqmjwXzEMMZzswaeGn+GB3dY8Sv1ptfRsW/eYrrHv9Ow+nE09AQAAmTpyIgIAAsaMQCULWRb5ixQro9XosX74cGzZsgFarvbIsJycHY8aMQXd3N+Li4hAcLM89MqU6fOwsFv3mqz4L7Gom8wXc+/QeNLde9EAyz7DZ7Jj9zF6c/keLQ+uv2liM7Xuq3ZzKO8TGxmLTpk2IjY0VOwqRIGRb5GVlZcjPz0d4eDjWrVvX6zrjxo0DAIwZM+bK17Zv34558+Zh6NChCAgIQHJyMp577jm0tbV5JLe7dFitMHV29vqPHK1/+3t0dTt+/lff0I53P610YyLP+tsBPY6cNDm1zQuvl8Juv/4HH6mzWq1oa2uD1WoVOwqRIGR7jjwvLw82mw2ZmZkICgrqdR1/f38APYt8w4YNiI2NxYsvvojo6GiUlpZi9erV+PLLL/HVV19BrZbmZ5815SewpvyE2DE8or6xHR/vd/4Rla/kl2H5wylQqVRuSOVZr+SXOb1N6akmHPy+EZPGDHFDIu9RWVmJxx57DO+++y6Sk5PFjkN0w2Rb5Pv37wcATJkypc919Ho9gJ5F/umnnyIiIuLK/991112IiIhAZmYmvv76a9x5551uSuxeP4sdhnk3x/S6LOPglx5O4177DtY7dEj9X52qbkZtfRviorTXX9mL2e127P5G79K2u77Wy77IieRGtkVeW3tpj2zo0KG9Lu/u7saBAwcA9Czyq0v8svHjxwMAzpw541KW8ePHw2g0OrWNv1qNk2MnuTRebxKDgvDjCPe9QSclJcFi845bmdr8JgKB97q0bfptUzDQ6tzflbexYQC6Q3/j0rZ//o9X8ea6XQIncq/58+c7tX5jYyMAYNeuXThy5IhD28ydO9fpXN7KMPhXgHoQDEYDoqOjxY5D/6TT6VBcXOzStrIt8vb2dgCAxWLpdXl+fj5MJhO0Wi3i4+P7fa0vvvgCADBy5EiXshiNRqc/BAT4+ABjXRpOFPX19ejwlnOOIQ1AoGubnjXWARcbhc3jcSog1LUt21vOob3BtQ+sYrn8u+6oy+8JFovF4W1d/RDvlbRWQA3YrFZ5fV8KJtsi1+l0MJvNKCkpwaRJPfdsDQYDVq5cCQBIS0vr95zomTNn8Nvf/hYzZsxw+V5znU7n9Db+EjsXf/PNN3vNHnmXTwcaAcBuB5w43622tUEXoYEKUW7L5imN3WfQ5ev89xHq3wL/KGl9/4GBzn1qu1ze/v7+Dm8bJbE/k/4YfHxgA6D28UGkjL4vqXOlJy6TbZFPmzYNZWVlyM3NxfTp05GUlAQAKCoqwsKFC2EyXbqit79ybmtrw3333YeBAwfizTffdDmLK4dL7BcuoPvBRS6P6WkVFRVQaTRix7jijkU78fVR5+bS/vWyyXhhRZabEnnWW59UYPHzzs3WFz0kENUle+DrK60PkUVFRU6t393djccffxxarRa+vo69Bb788ssuJPNO0dPycKaxA5G6SOiPu3YtBXkXaf3GOiEnJwdhYWGoq6tDamoqRo8ejeHDhyM9PR3Dhg3D1KlTAfQ8P341i8WC2bNno7q6Gnv27OGziyXml4+mOrW+xs8Hy+aPcFMaz3vonmEYEubv1DbPPJwiuRJ3ha+vL0JCQhwucSJvJ9vf2ujoaBQWFmLmzJnQaDSoqalBaGgotmzZgoKCAlRUVADovci7urowf/58FBcXY9euXUhJSfF0fLpB86bHI/uxUQ6tq1ar8D/r7sbQm6V9tfrVAvx9sWPjdAT6O1ZW9/84DtmLHPvzkjq9Xo/s7Owrd60QSZ2sP5KOHDkSO3fuvObrbW1tqKmpgVqtxqhRPd+8Lt97/vnnn+Ozzz5Denq6p+K6xV3hN+Hi7Af7Xed6y6VqfXY6QoL9sGbLUVzs6v38fXiIBm//4Q7MvFN+s3ylj47Al2/NxPxffY6a+t4nNFKrVXhy/gj8x/+bBB8f2X6u76GtrQ2FhYVYunSp2FGIBCHrIu/LiRMnYLfbkZSUdM18y08//TS2bduGX//61wgICMDBgwevLEtISOj19jTyTiqVCs8tG4snH0jGW59U4H8+q4LRZIGvjwoJMcFYOm8E5k+Pg8ZPvr8G41LCcbrgAXxWqMer28pw/LQZlgtWhA32w7xpcVg2Pxmxkb1PmERE0iDfd7B+HDt2DEDvh9V37bp0D+1LL72El156qceyt956C48//rjb85GwwkM0WPlEGlY+ocwH4/j4qDH77ljMvlt+Rx2IiEV+zbKamhoPpyEiInKdMk6K/Yv+ipyI5C0iIgJZWVk8TUayocg98svzsBOR8oSFhSEzM1PsGESCUeQeOREpV0tLC/bt24eWFsee1U7k7VjkRKQo9fX1WLVqFerr68WOQiQIFjkREZGEsciJiIgkjEVOREQkYSxyIlIUPz8/jBgxAn5+fmJHIRKEIm8/IyLlio+Px9atW8WOQSQY7pETERFJGIuciBSlvLwckydPRnl5udhRiATBIiciRbHb7ejq6oLdbhc7CpEgWOREREQSxovdvJWfH3w/eEfsFI7jFcBERKJgkXsplUoFaDRixyAiIi/HIiciRYmLi0NeXh6ioqLEjkIkCBY5ESmKRqNBQkKC2DGIBMOL3YhIUQwGA9auXQuDwSB2FCJBsMiJSFGam5uxY8cONDc3ix2FSBAsciIiIgljkRMREUkYi5yIiEjCWOREpCihoaFYtGgRQkNDxY5CJAgWOREpilqtxoABA6BW8+2P5IE/yUSkKCaTCa+//jpMJpPYUYgEwSInIiKSMBY5ERGRhLHIiYiIJIxFTkSKotVqMWPGDGi1WrGjEAmCD00hIkWJiorCmjVrxI5BJBjukRORonR2dqKurg6dnZ1iRyESBIuciBSluroa8+bNQ3V1tdhRiATBIiciIpIwFjkREZGEsciJiIgkjEVOREQkYbz9jIgUJTk5GYcPHxY7BpFguEdOREQkYSxyIlKU2tpaLF68GLW1tWJH8Ti73Q673X7lv0keeGidiBTFYrHg+PHjsFgsYkdxO3NLJ7btqcahY2dx5KQJJ6rM6O6+VOD1Zy1Im/cRxqWEI31UBB74STzCQzQiJyZXsMiJiGSm9NQ5bHzvBPJ2/YALndY+1ztWacaxSjPe/t9KPLv+EB78STyeeSQFE0ZFeDAt3SgeWicikgnLhW5kbziEWx/6BG99Utlvif+rzotWbN15GumP7MAz675Fe0eXG5OSkFjkREQy8H1FE8Y+8An+/O5x3Ojp7815J5E2/2MUnzgrTDhyKxY5ESlKZGQkVq9ejcjISLGjCObQ942464kCVNQ2C/aaP+hbMWXJLnxZbBDsNck9VHZeukhEElZUVOT2MSZMmOD2MVz1fUUT7nqiAOdbL7rl9YMCBmD/6xk8b+7FuEdORIpiNpuxbds2mM1msaPcsAud3Xho5X63lTgAtHV04aGVX6CN58y9FouciBSloaEB69evR0NDg9hRbtjvXinBqWrnDqcX5c1B3d4FKMqb4/A21Wda8euX3X/kg1yjiCI3mUzIyclBYmIiNBoNYmJikJWVhfb2dixZsgQqlQqbN28WOyYRkcOOVzZhwzvHnd5OFx6A6CGB0IUHOLXdf75fhsPHpH3xW+M5C/Z8o8cn+2vwxeF62VyZL/v7yEtLS5GRkQGj0YjAwECkpKSgvr4eGzduRFVVFZqamgAAY8eOFTcoEZETNr9fBpvNs5c4bco7ga2j7/bomEI49H0jNr53Etv2VKOr23bl64O0A7FodiJWZKYiISZYxIQ3RtZ75CaTCbNnz4bRaER2djYMBgNKSkpgNBqRm5uLgoICFBUVQaVSIS0tTey4REQOaW69iP/eedrj436wuxqN56Q1I96m905g0sJP8d5nVT1KHLj057jxvZMY+8DH2PONXqSEN07WRb5ixQro9XosX74cGzZsgFarvbIsJycHY8aMQXd3N+Li4hAcLN1PY0TkuICAAEycOBEBAc4dWvYmH++vQbul2+PjXuyy4YM91R4f11VvfVKBFS8dvO599W0d3bgvax8OftfomWACk22Rl5WVIT8/H+Hh4Vi3bl2v64wbNw4AMGbMmCtfKywsxLRp0xAZGQk/Pz9ER0fjoYceQllZmUdyE5F7xcbGYtOmTYiNjRU7isvEPFdddFwa58nbOrqQlXvQ4fUvdFrxzEvfujGR+8i2yPPy8mCz2ZCZmYmgoKBe1/H39wfQs8jNZjNGjx6NjRs3Ys+ePcjNzcWJEycwadIk6PXSPfRCRJdYrVa0tbXBanV8+lJvU3zSJNrYR0Qc2xn/U1CF1nbnLmYrPmGSzAeVq8n2Yrf9+/cDAKZMmdLnOpeL+eoinzNnDubM6XlbxoQJEzBixAh8+OGHyMrKckNaIvKUyspKPPbYY3j33XeRnJwsdhyXnDh9XrSxy6qbYbXa4OPj3fuBb/9vhUvbvbOjUnKT38i2yC8/a3jo0KG9Lu/u7saBAwcA9Czy3oSFhQEAfH1d++MaP348jEajS9sSUf/mz5/v1PqNjZfOg+7atQtHjhxxaJu5c+c6nctd7AA6Qlf3ubwob06/t5bpwv2v/Ltu74I+1zOaOjDh4R3XfN1msyNmaALU8O5btwyDfwWoBzm93RvvfoxPXnnQDYn6p9PpUFxc7NK2si3y9vZ2AOjzmcP5+fkwmUzQarWIj4+/ZrnVaoXNZkNtbS3+/d//HTqdDg8+6NpfrtFoxJkzZ1zaloj6d/l33VGX3xMsFovD23rd729o34su3yd+Pb4+aofW642hvh6wu282OUEEWYGBzm92wWLxvr/v65Btket0OpjNZpSUlGDSpEk9lhkMBqxcuRIAkJaWBpVKdc32d91115U99sTEROzfvx8REa4dbtHpdC5tR0TXFxjoXBldLm9/f3+Ht42KinI6lzvV2zthV/n1usxo6uh3W124P3x91Oi22mA09X0rWZ+vY7fh5ptvggre/ZiOs6o2XOzvE08fAv0uYrAIf9830hOyfWjKihUrsGnTJsTExGDfvn1ISkoCcOkBCwsXLsQPP/yArq4uPP30073O6lZeXo7z58+juroa69evR2NjIw4cOCDpK12J5MjZh6acOnXK6XPk3vbQlImP7MBhFy/Kqtu7ANFDAqFvaEfM9Ped3n5UYgiOfXS/S2N70l+3n8KyNQec3q74/fswLiXcDYncx7uvVrgBOTk5CAsLQ11dHVJTUzF69GgMHz4c6enpGDZsGKZOnQqg7/PjI0aMwMSJE7FgwQJ8/vnnaG1txR//+EdPfgtE5AaJiYnYvXs3EhMTxY7isnEpYSKOLY2Se+TeBAQHDXBqm/RREZL5/q4m2yKPjo5GYWEhZs6cCY1Gg5qaGoSGhmLLli0oKChARcWlKxqvd6EbAAwePBiJiYk4fdrzMykRkbB8fX0REhLi8sWr3mDi6JtEGzt9lDSKLjBgADb/+6Trr/hPARpfbF7l+PreRLZFDgAjR47Ezp070draitbWVhw6dAjLli1De3s7ampqoFarMWrUqOu+TmNjI8rLy5GQkOCB1ETkTnq9HtnZ2ZKeF2Luj4ci0N/zH0T8BvrgwXuGeXxcVy2cPRz/9ZvboFZfex3U1YKDBuDTTdMld9vZZdL9SHoDTpw4AbvdjqSkpGumaXz00UeRmJiIsWPHYvDgwaisrMRf/vIX+Pr64tlnnxUpMREJpa2tDYWFhVi6dKnYUVwWHDQQC2cl4tVtpzw67oM/iUd4iMajY96onz84EhNGRWDTeyfx/t9+QOfF/5sIKHSQHxb/NAlPLxiJuChtP6/i3WS9R96XY8eOAej9sPqPfvQjfPbZZ3jiiSeQkZGB9evX44477kBpaamkz6kRkbw880gKfHz639MU2orMFI+OJ5RxKeF4e+2dOLNvAcIHX7raP3ywH/R7F2B9drqkSxxQ6B55f0W+fPlyLF++3NORiIickpIQgpzH07Duje88Mt6KR1IwPlWah54vCxusgd9AHwCXThP4a+RRgdwjJyKSqN/94hakJgx2ahujqQP6hvbr3m9+tYQYLV5cMd7JdOQp8vg44qTL87ATkfJEREQgKyvL5QmevInfQB/kr5+KO58oQFNzp0Pb9Dbtan+0gQPwwfqpCAxw7lYu8hxF7pETkXKFhYUhMzPzyjMUpC41MQR/+697EBLswnyk1xEcNACf/edPcKsE761WEhY5ESlKS0sL9u3bh5aWFrGjCGbCqAh89dZMjBw2WLDXHD40GH9/417cfiunmPZ2LHIiUpT6+nqsWrUK9fX1YkcR1KjhoSjJvw+/XpJ23fum+6NSAc8uTEXpB3Nxy0juiUsBi5yISCY0fr5YlzUB3237KZ58INmpSWM0fj5YPDcJJfk/xZ9X/ggBIkw4Q67h3xQRkcyMGh6KV387Gbm/nIAP99Xg8PGzOHLShOOnzbjQeWlCFL+BPkhNGIxxKeGYkBqOedPjETqo9yeqkXdjkRMRydQg7UAsnpuExXOTrnzNarUBAHx8eEBWLljkRKQofn5+GDFiBPz8lLn3yQKXHxY5ESlKfHw8tm7dKnYMIsHwoxkREZGEsciJSFHKy8sxefJklJeXix2FSBAsciJSFLvdjq6uLtjtdrGjEAmCRU5ERCRhLHIiIiIJY5ETERFJGG8/IyJFiYuLQ15eHqKiosSOQiQIFjkRKYpGo0FCQoLYMYgEw0PrRKQoBoMBa9euhcFgEDsKkSBY5ESkKM3NzdixYweam5vFjkIkCBY5ERGRhLHIiYiIJIxFTkREJGEsciJSFLVajVtuuQVqNd/+SB74k0xEimKz2XD06FHYbDaxoxAJgkVOREQkYSxyIiIiCWORExERSRiLnIgURavVYsaMGdBqtWJHIRIE51onIkWJiorCmjVrxI5BJBjukRORonR2dqKurg6dnZ1iRyESBIuciBSluroa8+bNQ3V1tdhRiATBIiciIpIwFjkREZGEsciJiIgkjEVOREQkYbz9jIgUJTk5GYcPHxY7BpFguEdOREQkYSxyIlKU2tpaLF68GLW1tWJHIRIED60TkaJYLBYcP34cFotF7CjkATabHRW1zThy0oSSsnNoar40EVBTSyd+s6kY41LCMS4lHDG6QKhUKpHTuoZFTkREsmM424G/fliO17afwpnGjmuWWy5Y8cJfv7vy/6MSQ/DUQyPx6KwEaAMHejLqDeOhdSIiko22ji48s+5bxN7zPn73SkmvJd6b46fNeOqFbxA17X386Z1jsFptbk4qHBY5ERHJwt+LDEib9zE2551Ed7fdpddobe/Cv/3pMO54vADl1eeFDegmLHIiUpTIyEisXr0akZGRYkchAb3xUTl+vHQXqs+0CvJ6337XiPTMHSg8YhTk9dxJZbfbXfvYQkTkBYqKitw+xoQJE9w+BrnuzY8rsOR3hW557QCNL/a+NgO3jR3iltcXAvfIiUhRzGYztm3bBrPZLHYUEsBXxQYsXf21216/40I3Zi3fgzMN7W4b40axyIlIURoaGrB+/Xo0NDSIHYVuUHtHF554vhA2m+MHlovy5qBu7wIU5c1xeBtzy0U8+YcD8NYD2CxyIiKSpOc2HcEPeufOievCAxA9JBC68ACntiv4qg5bPz3t1DaeIvsiN5lMyMnJQWJiIjQaDWJiYpCVlYX29nYsWbIEKpUKmzdvFjsmERE5ofGcBa/kl3l0zDVbjjq19+8psp4QprS0FBkZGTAajQgMDERKSgrq6+uxceNGVFVVoampCQAwduxYcYMSEZFT3vi4Al3dnr3Xu6quFXu/PYN7Jkd7dNzrke0euclkwuzZs2E0GpGdnQ2DwYCSkhIYjUbk5uaioKAARUVFUKlUSEtLEzsuEXlIQEAAJk6ciIAA5w6tkvew2+3Ysv2UKGO/uk2ccfsj2yJfsWIF9Ho9li9fjg0bNkCr1V5ZlpOTgzFjxqC7uxtxcXEIDg4WMSkReVJsbCw2bdqE2NhYsaOQi6rPtKK2vk2Usb8sNnjdRW+yLPKysjLk5+cjPDwc69at63WdcePGAQDGjBnT5+tkZGRApVLh97//vTtiEpEIrFYr2traYLVaxY5CLjpy8pxoY5tbLgo26YxQZFnkeXl5sNlsyMzMRFBQUK/r+Pv7A+i7yD/44AOUlpa6KyIRiaSyshJTp05FZWWl2FHIRUfLxCtyQNwPEr2R5cVu+/fvBwBMmTKlz3X0ej2A3ou8paUFv/zlL7FhwwY8+uijN5xn/PjxMBq9f5o/IimaP3++U+s3NjYCAHbt2oUjR444tM3cuXOdzkXuYw6YA2jG9bqsKG9Ov7eW6cL9r/y7bu+Cfscxmjow4eEd13x96S9+hWc7hZ1RUKfTobi42KVtZVnktbW1AIChQ4f2ury7uxsHDhwA0HuRP/fcc0hKSkJmZqYgRW40GnHmzJkbfh0iulZ7u3Mzbl1+DrnFYnF4W/7+epnoC4Cm90WX7xO/Hl8ftUPr9aa5uRXN57znZ0KWRX75l/PyL+y/ys/Ph8lkglarRXx8fI9lxcXF+Otf/+rwJ3VH6HQ6wV6LiHoKDHTuzfjy+4O/v7/D20ZFRTmdi9zH7O+Hvh5OajT1/9hSXbg/fH3U6LbaYDT13hHXe63Bg4IQqBH2Z+JGekKWRa7T6WA2m1FSUoJJkyb1WGYwGLBy5UoAQFpaGlQq1ZVlVqsVTz75JJYvX47U1FTB8rh6uISIrs/Zh6acOnUKeXl5yMjIQHJyskPbvPzyyy4kI3d54bVS/GZz7ztbvR0Kv1rd3gWIHhIIo8mCmOnvuzR+3jubMeN277mXXJYXu02bNg0AkJubi4qKiitfLyoqwpQpU2AymQBcOxHM5s2b0dDQwKvUiWQsMTERu3fvRmJiothRyEXjUsJFHj9M1PH/lSyLPCcnB2FhYairq0NqaipGjx6N4cOHIz09HcOGDcPUqVMB9Dw/bjKZ8Nvf/hbPP/88uru7cf78eZw/fx4AcOHCBZw/fx42m2dnESIi4fn6+iIkJAS+vrI8IKkIYhZpjC4QEaH+oo3fG1kWeXR0NAoLCzFz5kxoNBrU1NQgNDQUW7ZsQUFBwZW99KuLXK/Xo7W1FU8++SRCQkKu/ANc2rMPCQnBP/7xD1G+HyISjl6vR3Z29pU7V0h6IkL9MXF0hChjz7ozRpRx+yPbj6QjR47Ezp07r/l6W1sbampqoFarMWrUqCtfT0xMxBdffHHN+lOmTMGiRYvw+OOP86I1Ihloa2tDYWEhli5dKnYUugFPPTQSh46dFWVcbyPbIu/LiRMnYLfbkZSU1GOu5aCgINx99929bhMXF9fnMiIi8rwH74nHrzYcwrnznR4b8/ZbhmDU8FCPjecoWR5a78+xY8cA9D81KxEReTeNny9eXDHeY+Op1SpsyE732HjOUNweubNF7m2T4xMR0SVL543Atj3V2Hew3u1j/duiUZiYdpPbx3EF98iJSFEiIiKQlZWFiAhxLpYi4ahUKrz++9sRHtLHNG+9MJo6oG9ov+7EMVcbmxyK1U/d6kpEj1DZuctJRBLm7IQwrpgwYYLbxyDXlZw0YerSXWhuvSj4aycNHYSv3p6JIWHedcvZ1RS3R05EytbS0oJ9+/ahpaVF7CgkkFtTwvH3N+698kAUodySHOb1JQ6wyIlIYerr67Fq1SrU17v/vCp5ztjkMBz78H4smDHshl/Lx0eFVT8bg2//e7bXlzjAIiciIpkID9Eg749T8NFffoy0JNduE5uaHomD/z0bL6wYD7+BPgIndA/FXbVORETyNvfHcfjp1KH4prQR//VBGb4oMqC+se+L2xJjg3Hv7dH4xUMjkRw/2HNBBcIiJyIi2VGpVJh8yxBMvmUIAMBwtgMlZSacNV/AxS4b/Ab4IEYXiFtHhmFwsJ/IaW8Mi5yIFMXPzw8jRoyAn5+037zJOZERAZgZESt2DLfg7WdEJGm8/YyUjhe7ERERSRiLnIgUpby8HJMnT0Z5ebnYUYgEwSInIkWx2+3o6uricxRINljkREREEsYiJyIikjAWORERkYTxPnIiUpS4uDjk5eUhKipK7ChEgmCRE5GiaDQaJCQkiB2DSDA8tE5EimIwGLB27VoYDAaxoxAJgkVORIrS3NyMHTt2oLm5WewoRIJgkRMREUkYi5yIiEjCWOREREQSxiInIkUJDQ3FokWLEBoaKnYUIkHwMaZEREQSxj1yIiIiCWORExERSRiLnIiISMJY5ERERBLGIiciIpIwFjkREZGEsciJiIgkjEVOREQkYSxyIiIiCWORExERSRiLnIiISMJY5ERERBLGIiciIpIwFjkREZGEsciJiIgk7P8DYL/7iz+BIP8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 621.941x451.5 with 1 Axes>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = QuantumCircuit(5)\n",
    "test.h(2)\n",
    "test.cx(2, 1)\n",
    "test.cx(2, 0)\n",
    "test.barrier()\n",
    "test.cx(0, 3)\n",
    "test.cx(3, 4)\n",
    "test.draw('mpl', filename=f\"{FILE_PREFIX}-circuit.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a419fee3-cb27-42cc-92e5-37d28759c6c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------\n",
      "num_cuts = 3\n",
      "subcircuit 0\n",
      "ρ qubits = 0, O qubits = 3, width = 5, effective = 2, depth = 3, size = 3\n",
      "               ┌───┐ ░ \n",
      "q_0: ──────────┤ X ├─░─\n",
      "          ┌───┐└─┬─┘ ░ \n",
      "q_1: ─────┤ X ├──┼───░─\n",
      "     ┌───┐└─┬─┘  │   ░ \n",
      "q_2: ┤ H ├──■────■───░─\n",
      "     └───┘           ░ \n",
      "q_3: ────────────────░─\n",
      "                     ░ \n",
      "q_4: ────────────────░─\n",
      "                     ░ \n",
      "subcircuit 1\n",
      "ρ qubits = 3, O qubits = 0, width = 3, effective = 3, depth = 2, size = 2\n",
      "               \n",
      "q_0: ──■───────\n",
      "     ┌─┴─┐     \n",
      "q_1: ┤ X ├──■──\n",
      "     └───┘┌─┴─┐\n",
      "q_2: ─────┤ X ├\n",
      "          └───┘\n",
      "Estimated cost = 2.048e+03\n",
      "--------------------\n",
      "Saving cuts as JSON...\n"
     ]
    }
   ],
   "source": [
    "cuts = cut_circuit_wires(circuit=test, method=\"manual\", subcircuit_vertices=[[0,1], [2,3]])\n",
    "save_cuts(cuts, FILE_PREFIX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "54edbf17-7d27-4794-b7cd-581615b84818",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving probabilities as JSON...\n"
     ]
    }
   ],
   "source": [
    "subcircuit_instance_probabilities = evaluate_subcircuits(cuts)\n",
    "save_probs(subcircuit_instance_probabilities,FILE_PREFIX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "70cf7f88-6185-43be-8a8b-2ea32fc9432a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving reconstructed probabilities as JSON...\n"
     ]
    }
   ],
   "source": [
    "reconstructed_probabilities = reconstruct_full_distribution(\n",
    "    test, subcircuit_instance_probabilities, cuts\n",
    ")\n",
    "save_reconstructed_probs(reconstructed_probabilities, FILE_PREFIX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "72164e22-a945-4ffe-8e0d-2f34827ac481",
   "metadata": {},
   "outputs": [],
   "source": [
    "reconstructed_distribution = {\n",
    "    i: prob for i, prob in enumerate(reconstructed_probabilities)\n",
    "}\n",
    "\n",
    "reconstructed_dict_bitstring = ProbDistribution(\n",
    "    data=reconstructed_distribution\n",
    ").binary_probabilities(num_bits=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "401ba517-df51-48e4-af91-8e1cbcf63d3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plot_distribution(reconstructed_dict_bitstring)\n",
    "fig.savefig(f\"{FILE_PREFIX}-circuit-cutting-test.png\")\n",
    "display(fig)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}