NSQST_with_pre_training_demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8f11af75",
   "metadata": {},
   "outputs": [],
   "source": [
    "import qiskit\n",
    "import numpy as np\n",
    "from qiskit.quantum_info import Statevector, StabilizerState\n",
    "import nqs_models\n",
    "from exact_solvers import GenericExactState\n",
    "import utils\n",
    "import torch\n",
    "\n",
    "import Helper_fun_new as helper\n",
    "import NSQST_Pre_Trainer_GHZ as shadow_trainer\n",
    "\n",
    "from qiskit import QuantumCircuit, execute, Aer\n",
    "import qiskit.providers.aer.noise as noise\n",
    "import cirq\n",
    "import json\n",
    "import pickle\n",
    "from qiskit.quantum_info import StabilizerState, Pauli"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e14c8bff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 0\n",
      "Cost function is 0.07788845493534835\n",
      "Exact infidelity is 0.847686555338967\n",
      "Iteration 1\n",
      "Cost function is 0.8244965936040166\n",
      "Exact infidelity is 0.587129176650022\n",
      "Iteration 2\n",
      "Cost function is 0.6025775604129257\n",
      "Exact infidelity is 0.01076542458526586\n",
      "Iteration 3\n",
      "Cost function is 0.0031744019885571184\n",
      "Exact infidelity is 0.2784748212274365\n",
      "Iteration 4\n",
      "Cost function is 0.19056860116112517\n",
      "Exact infidelity is 0.3945284331960802\n",
      "Iteration 5\n",
      "Cost function is 0.302003276246667\n",
      "Exact infidelity is 0.24123371552575434\n",
      "Iteration 6\n",
      "Cost function is 0.1600940876216138\n",
      "Exact infidelity is 0.04314007919389562\n",
      "Iteration 7\n",
      "Cost function is -0.001945819959812578\n",
      "Exact infidelity is 0.01920193180149865\n",
      "Iteration 8\n",
      "Cost function is 0.016216753142459517\n",
      "Exact infidelity is 0.10183954864194256\n",
      "Iteration 9\n",
      "Cost function is 0.1169821157100196\n",
      "Exact infidelity is 0.08947727444363407\n",
      "Iteration 10\n",
      "Cost function is 0.10577497664562086\n",
      "Exact infidelity is 0.022311917435912987\n",
      "Iteration 11\n",
      "Cost function is 0.02409698117135184\n",
      "Exact infidelity is 0.004974163553916666\n",
      "Iteration 12\n",
      "Cost function is -0.026463889926117368\n",
      "Exact infidelity is 0.05192957697064926\n",
      "Iteration 13\n",
      "Cost function is 0.004929892133910263\n",
      "Exact infidelity is 0.09938766500350094\n",
      "Iteration 14\n",
      "Cost function is 0.04005039098427521\n",
      "Exact infidelity is 0.10022651394489002\n",
      "Iteration 15\n",
      "Cost function is 0.03830792759858326\n",
      "Exact infidelity is 0.06144453930230276\n",
      "Iteration 16\n",
      "Cost function is 0.011307973914170155\n",
      "Exact infidelity is 0.019112202657025845\n",
      "Iteration 17\n",
      "Cost function is -0.021433943228722052\n",
      "Exact infidelity is 0.0017530345520506696\n",
      "Iteration 18\n",
      "Cost function is -0.022288319812861834\n",
      "Exact infidelity is 0.008345745351139855\n",
      "Iteration 19\n",
      "Cost function is 0.00672769434964704\n",
      "Exact infidelity is 0.01628467228552022\n",
      "Iteration 20\n",
      "Cost function is 0.013225740624450477\n",
      "Exact infidelity is 0.012235585549528305\n",
      "Iteration 21\n",
      "Cost function is 0.005713652526382895\n",
      "Exact infidelity is 0.0033893871104634377\n",
      "Iteration 22\n",
      "Cost function is -0.01471327428089464\n",
      "Exact infidelity is 0.0033169451353526336\n",
      "Iteration 23\n",
      "Cost function is -0.01714313933998657\n",
      "Exact infidelity is 0.015962034624675048\n",
      "Iteration 24\n",
      "Cost function is -0.016668762017884697\n",
      "Exact infidelity is 0.03172844049659218\n",
      "Iteration 25\n",
      "Cost function is -0.011734104949372659\n",
      "Exact infidelity is 0.03932061192749525\n",
      "Iteration 26\n",
      "Cost function is -0.0024756958470402157\n",
      "Exact infidelity is 0.03475910880813993\n",
      "Iteration 27\n",
      "Cost function is -0.010758622940375995\n",
      "Exact infidelity is 0.02225933371627975\n",
      "Iteration 28\n",
      "Cost function is -0.011473377798035589\n",
      "Exact infidelity is 0.009894082180204378\n",
      "Iteration 29\n",
      "Cost function is -0.019410106925634052\n",
      "Exact infidelity is 0.00307349807077828\n",
      "Iteration 30\n",
      "Cost function is -0.026500894916392292\n",
      "Exact infidelity is 0.0016556912466925144\n",
      "Iteration 31\n",
      "Cost function is -0.02104549971857983\n",
      "Exact infidelity is 0.002197518836416501\n",
      "Iteration 32\n",
      "Cost function is -0.002497186813728458\n",
      "Exact infidelity is 0.00209812163796097\n",
      "Iteration 33\n",
      "Cost function is -0.015076393729138937\n",
      "Exact infidelity is 0.001646340158408477\n",
      "Iteration 34\n",
      "Cost function is -0.009361398089230644\n",
      "Exact infidelity is 0.00283046415292576\n",
      "Iteration 35\n",
      "Cost function is -0.024501674822645647\n",
      "Exact infidelity is 0.006689115246221289\n",
      "Iteration 36\n",
      "Cost function is -0.02791008802956041\n",
      "Exact infidelity is 0.012088008237203907\n",
      "Iteration 37\n",
      "Cost function is -0.01584961859294065\n",
      "Exact infidelity is 0.016302972775391167\n",
      "Iteration 38\n",
      "Cost function is -0.004139508703120409\n",
      "Exact infidelity is 0.01728354492530204\n",
      "Iteration 39\n",
      "Cost function is -0.01650631497480881\n",
      "Exact infidelity is 0.014712269143898338\n",
      "Iteration 40\n",
      "Cost function is -0.013612611279104847\n",
      "Exact infidelity is 0.010365177249661683\n",
      "Iteration 41\n",
      "Cost function is -0.019794340640395536\n",
      "Exact infidelity is 0.006154080816237806\n",
      "Iteration 42\n",
      "Cost function is -0.016482295720983364\n",
      "Exact infidelity is 0.0034567713879308792\n",
      "Iteration 43\n",
      "Cost function is -0.02141675959639966\n",
      "Exact infidelity is 0.002259918275595796\n",
      "Iteration 44\n",
      "Cost function is -0.018181376292981888\n",
      "Exact infidelity is 0.0019865714931689427\n",
      "Iteration 45\n",
      "Cost function is -0.014102151836983712\n",
      "Exact infidelity is 0.0022474908673211225\n",
      "Iteration 46\n",
      "Cost function is -0.020625650248443872\n",
      "Exact infidelity is 0.003248922416050126\n",
      "Iteration 47\n",
      "Cost function is -0.017485386552369198\n",
      "Exact infidelity is 0.005139828370613886\n",
      "Iteration 48\n",
      "Cost function is -0.01778476576434973\n",
      "Exact infidelity is 0.0076097980427313194\n",
      "Iteration 49\n",
      "Cost function is -0.02023148845885836\n",
      "Exact infidelity is 0.009805026202575617\n",
      "Iteration 50\n",
      "Cost function is -0.019765585123085794\n",
      "Exact infidelity is 0.010857258483330745\n",
      "Iteration 51\n",
      "Cost function is -0.015122693163356571\n",
      "Exact infidelity is 0.010360843716774482\n",
      "Iteration 52\n",
      "Cost function is -0.024566289670210795\n",
      "Exact infidelity is 0.008690831078627559\n",
      "Iteration 53\n",
      "Cost function is -0.02961727304039763\n",
      "Exact infidelity is 0.006589580510879434\n",
      "Iteration 54\n",
      "Cost function is -0.02363367172036579\n",
      "Exact infidelity is 0.004804425636921628\n",
      "Iteration 55\n",
      "Cost function is -0.025583929840981234\n",
      "Exact infidelity is 0.003695553474342139\n",
      "Iteration 56\n",
      "Cost function is -0.018531661315492887\n",
      "Exact infidelity is 0.0032478830044742057\n",
      "Iteration 57\n",
      "Cost function is -0.014878551580413069\n",
      "Exact infidelity is 0.0033639176592606956\n",
      "Iteration 58\n",
      "Cost function is -0.019290793950835994\n",
      "Exact infidelity is 0.003941758475487167\n",
      "Iteration 59\n",
      "Cost function is -0.010038234483733088\n",
      "Exact infidelity is 0.005016888074349968\n",
      "Iteration 60\n",
      "Cost function is -0.02264592444644342\n",
      "Exact infidelity is 0.006331386397914973\n",
      "Iteration 61\n",
      "Cost function is -0.019412781676032775\n",
      "Exact infidelity is 0.007464980919453246\n",
      "Iteration 62\n",
      "Cost function is -0.026916554337780063\n",
      "Exact infidelity is 0.008101068592804128\n",
      "Iteration 63\n",
      "Cost function is -0.019137117410603866\n",
      "Exact infidelity is 0.007997272606567685\n",
      "Iteration 64\n",
      "Cost function is -0.014925896314250009\n",
      "Exact infidelity is 0.0071092292489500775\n",
      "Iteration 65\n",
      "Cost function is -0.02112262729602188\n",
      "Exact infidelity is 0.006026899199105262\n",
      "Iteration 66\n",
      "Cost function is -0.01142202364279421\n",
      "Exact infidelity is 0.0050093477422596955\n",
      "Iteration 67\n",
      "Cost function is -0.016955268449180716\n",
      "Exact infidelity is 0.004426261454543168\n",
      "Iteration 68\n",
      "Cost function is -0.018089678311351776\n",
      "Exact infidelity is 0.00415498239149803\n",
      "Iteration 69\n",
      "Cost function is -0.03082992937335205\n",
      "Exact infidelity is 0.004456588811892481\n",
      "Iteration 70\n",
      "Cost function is -0.024296382062597432\n",
      "Exact infidelity is 0.005259599595040854\n",
      "Iteration 71\n",
      "Cost function is -0.020636517319505332\n",
      "Exact infidelity is 0.006331334989366\n",
      "Iteration 72\n",
      "Cost function is -0.012530942591856498\n",
      "Exact infidelity is 0.0072543065786675465\n",
      "Iteration 73\n",
      "Cost function is -0.022816241190663167\n",
      "Exact infidelity is 0.0075784037973966045\n",
      "Iteration 74\n",
      "Cost function is -0.019921057870947223\n",
      "Exact infidelity is 0.007298005533863727\n",
      "Iteration 75\n",
      "Cost function is -0.025377800269283934\n",
      "Exact infidelity is 0.006744677159759904\n",
      "Iteration 76\n",
      "Cost function is -0.02108469670666202\n",
      "Exact infidelity is 0.006027774964788124\n",
      "Iteration 77\n",
      "Cost function is -0.027598180040870446\n",
      "Exact infidelity is 0.005464380761233456\n",
      "Iteration 78\n",
      "Cost function is -0.02153317950831135\n",
      "Exact infidelity is 0.005060633157025873\n",
      "Iteration 79\n",
      "Cost function is -0.02016095030726106\n",
      "Exact infidelity is 0.005091855992487737\n",
      "Iteration 80\n",
      "Cost function is -0.014893144322772889\n",
      "Exact infidelity is 0.005342889478037294\n",
      "Iteration 81\n",
      "Cost function is -0.02688563785777509\n",
      "Exact infidelity is 0.005693693183509763\n",
      "Iteration 82\n",
      "Cost function is -0.01883252128871704\n",
      "Exact infidelity is 0.006042752827888909\n",
      "Iteration 83\n",
      "Cost function is -0.02248827295472733\n",
      "Exact infidelity is 0.006317521990349184\n",
      "Iteration 84\n",
      "Cost function is -0.01683291093246142\n",
      "Exact infidelity is 0.006467829684545401\n",
      "Iteration 85\n",
      "Cost function is -0.015432999773034695\n",
      "Exact infidelity is 0.006486413361465493\n",
      "Iteration 86\n",
      "Cost function is -0.027866999229491984\n",
      "Exact infidelity is 0.006219202159606696\n",
      "Iteration 87\n",
      "Cost function is -0.021074713478427043\n",
      "Exact infidelity is 0.005743265325410474\n",
      "Iteration 88\n",
      "Cost function is -0.020854926993653544\n",
      "Exact infidelity is 0.005311599616503515\n",
      "Iteration 89\n",
      "Cost function is -0.02135186864010641\n",
      "Exact infidelity is 0.0050458155488848355\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 90\n",
      "Cost function is -0.016929950344330313\n",
      "Exact infidelity is 0.005095394128871833\n",
      "Iteration 91\n",
      "Cost function is -0.025434586806837745\n",
      "Exact infidelity is 0.005476174561354741\n",
      "Iteration 92\n",
      "Cost function is -0.021201219662327242\n",
      "Exact infidelity is 0.006017331970955664\n",
      "Iteration 93\n",
      "Cost function is -0.023941403112872406\n",
      "Exact infidelity is 0.006237123700327918\n",
      "Iteration 94\n",
      "Cost function is -0.0168615964951071\n",
      "Exact infidelity is 0.006203474058980474\n",
      "Iteration 95\n",
      "Cost function is -0.015940495152302114\n",
      "Exact infidelity is 0.0057819047819897\n",
      "Iteration 96\n",
      "Cost function is -0.02262981446346024\n",
      "Exact infidelity is 0.0053213798353087816\n",
      "Iteration 97\n",
      "Cost function is -0.02108628175951388\n",
      "Exact infidelity is 0.005221231179441399\n",
      "Iteration 98\n",
      "Cost function is -0.021109702448175893\n",
      "Exact infidelity is 0.0052288393860006055\n",
      "Iteration 99\n",
      "Cost function is -0.028343129205312234\n",
      "Exact infidelity is 0.005505299549726672\n",
      "Iteration 100\n",
      "Cost function is -0.023606960924146048\n",
      "Exact infidelity is 0.0056680542803033696\n",
      "Iteration 101\n",
      "Cost function is -0.014085634431915128\n",
      "Exact infidelity is 0.005965893083790363\n",
      "Iteration 102\n",
      "Cost function is -0.02611371518297778\n",
      "Exact infidelity is 0.006062344794963592\n",
      "Iteration 103\n",
      "Cost function is -0.00934810369942074\n",
      "Exact infidelity is 0.006070304713134522\n",
      "Iteration 104\n",
      "Cost function is -0.024262710654466835\n",
      "Exact infidelity is 0.005878000565682218\n",
      "Iteration 105\n",
      "Cost function is -0.022108122955270204\n",
      "Exact infidelity is 0.005571419863036775\n",
      "Iteration 106\n",
      "Cost function is -0.020677489113347214\n",
      "Exact infidelity is 0.005361186223180936\n",
      "Iteration 107\n",
      "Cost function is -0.021897985922500585\n",
      "Exact infidelity is 0.005354304279856614\n",
      "Iteration 108\n",
      "Cost function is -0.024631427518057247\n",
      "Exact infidelity is 0.005521867367091704\n",
      "Iteration 109\n",
      "Cost function is -0.02010853645032018\n",
      "Exact infidelity is 0.005740680996201619\n",
      "Iteration 110\n",
      "Cost function is -0.017171607724642657\n",
      "Exact infidelity is 0.005820877977750327\n",
      "Iteration 111\n",
      "Cost function is -0.028294835731849766\n",
      "Exact infidelity is 0.005828111553716875\n",
      "Iteration 112\n",
      "Cost function is -0.021676936738812103\n",
      "Exact infidelity is 0.00590689123238719\n",
      "Iteration 113\n",
      "Cost function is -0.020031099934030072\n",
      "Exact infidelity is 0.0059250972386231116\n",
      "Iteration 114\n",
      "Cost function is -0.016294321742785556\n",
      "Exact infidelity is 0.00605382579986391\n",
      "Iteration 115\n",
      "Cost function is -0.01556844547542302\n",
      "Exact infidelity is 0.006080710130089817\n",
      "Iteration 116\n",
      "Cost function is -0.020713384060251805\n",
      "Exact infidelity is 0.006021985328105717\n",
      "Iteration 117\n",
      "Cost function is -0.019990858051328164\n",
      "Exact infidelity is 0.005867720252263897\n",
      "Iteration 118\n",
      "Cost function is -0.016709063845510075\n",
      "Exact infidelity is 0.005816345746847307\n",
      "Iteration 119\n",
      "Cost function is -0.015936893908715666\n",
      "Exact infidelity is 0.005855039993159505\n",
      "Iteration 120\n",
      "Cost function is -0.029399977394599208\n",
      "Exact infidelity is 0.006078795498075107\n",
      "Iteration 121\n",
      "Cost function is -0.02292168732626987\n",
      "Exact infidelity is 0.0062072316694241625\n",
      "Iteration 122\n",
      "Cost function is -0.01156201818469449\n",
      "Exact infidelity is 0.006285468622139079\n",
      "Iteration 123\n",
      "Cost function is -0.02352755240048543\n",
      "Exact infidelity is 0.006297847535022272\n",
      "Iteration 124\n",
      "Cost function is -0.022806628031919685\n",
      "Exact infidelity is 0.006271321313821909\n",
      "Iteration 125\n",
      "Cost function is -0.01718813017255494\n",
      "Exact infidelity is 0.006004694871461158\n",
      "Iteration 126\n",
      "Cost function is -0.01468934436329472\n",
      "Exact infidelity is 0.005647109606426115\n",
      "Iteration 127\n",
      "Cost function is -0.02259119752020866\n",
      "Exact infidelity is 0.005332790318839042\n",
      "Iteration 128\n",
      "Cost function is -0.013553544893662561\n",
      "Exact infidelity is 0.005241807648221575\n",
      "Iteration 129\n",
      "Cost function is -0.014868058063232104\n",
      "Exact infidelity is 0.0051432052470868905\n",
      "Iteration 130\n",
      "Cost function is -0.021741212328923565\n",
      "Exact infidelity is 0.005239671142343916\n",
      "Iteration 131\n",
      "Cost function is -0.014489889906288855\n",
      "Exact infidelity is 0.005436771057328982\n",
      "Iteration 132\n",
      "Cost function is -0.016053858520000297\n",
      "Exact infidelity is 0.005926521058938983\n",
      "Iteration 133\n",
      "Cost function is -0.015998076071111145\n",
      "Exact infidelity is 0.006358301591406046\n",
      "Iteration 134\n",
      "Cost function is -0.019434109345832784\n",
      "Exact infidelity is 0.006544356559810893\n",
      "Iteration 135\n",
      "Cost function is -0.019874237263861394\n",
      "Exact infidelity is 0.006438999858383365\n",
      "Iteration 136\n",
      "Cost function is -0.017396982312700038\n",
      "Exact infidelity is 0.005900613446619185\n",
      "Iteration 137\n",
      "Cost function is -0.016553206503526763\n",
      "Exact infidelity is 0.005435235045430864\n",
      "Iteration 138\n",
      "Cost function is -0.032284510603102\n",
      "Exact infidelity is 0.005438582722256502\n",
      "Iteration 139\n",
      "Cost function is -0.027196160839332517\n",
      "Exact infidelity is 0.005311398482986784\n",
      "Iteration 140\n",
      "Cost function is -0.029854966779914838\n",
      "Exact infidelity is 0.005345791555807056\n",
      "Iteration 141\n",
      "Cost function is -0.01630298678899078\n",
      "Exact infidelity is 0.005677232458481285\n",
      "Iteration 142\n",
      "Cost function is -0.017340296532176724\n",
      "Exact infidelity is 0.005841935281479693\n",
      "Iteration 143\n",
      "Cost function is -0.028808978059793544\n",
      "Exact infidelity is 0.006145705054507067\n",
      "Iteration 144\n",
      "Cost function is -0.0188611936651073\n",
      "Exact infidelity is 0.006131475341260129\n",
      "Iteration 145\n",
      "Cost function is -0.023544749337695015\n",
      "Exact infidelity is 0.006012039276969583\n",
      "Iteration 146\n",
      "Cost function is -0.02827170329965778\n",
      "Exact infidelity is 0.005841503656291325\n",
      "Iteration 147\n",
      "Cost function is -0.02160042314904098\n",
      "Exact infidelity is 0.005639192433383178\n",
      "Iteration 148\n",
      "Cost function is -0.020551692858133297\n",
      "Exact infidelity is 0.00541133933198279\n",
      "Iteration 149\n",
      "Cost function is -0.014564263114891451\n",
      "Exact infidelity is 0.005396240184111667\n",
      "Iteration 150\n",
      "Cost function is -0.026712772540922636\n",
      "Exact infidelity is 0.0053256994966897064\n",
      "Iteration 151\n",
      "Cost function is -0.020718359294880084\n",
      "Exact infidelity is 0.005422175320743627\n",
      "Iteration 152\n",
      "Cost function is -0.02440650935171962\n",
      "Exact infidelity is 0.00578245716168857\n",
      "Iteration 153\n",
      "Cost function is -0.017564395540845323\n",
      "Exact infidelity is 0.006144111931465246\n",
      "Iteration 154\n",
      "Cost function is -0.03145238611507928\n",
      "Exact infidelity is 0.006388014340767967\n",
      "Iteration 155\n",
      "Cost function is -0.02443829826841659\n",
      "Exact infidelity is 0.006465878407752812\n",
      "Iteration 156\n",
      "Cost function is -0.017694433512605245\n",
      "Exact infidelity is 0.00658669939596368\n",
      "Iteration 157\n",
      "Cost function is -0.024510871687442037\n",
      "Exact infidelity is 0.006029045869512584\n",
      "Iteration 158\n",
      "Cost function is -0.008307916593638609\n",
      "Exact infidelity is 0.0054651377285306335\n",
      "Iteration 159\n",
      "Cost function is -0.012817101247434737\n",
      "Exact infidelity is 0.005092052984109863\n",
      "Iteration 160\n",
      "Cost function is -0.01908262898754365\n",
      "Exact infidelity is 0.005121495828505629\n",
      "Iteration 161\n",
      "Cost function is -0.02698191335040634\n",
      "Exact infidelity is 0.0058846178658363835\n",
      "Iteration 162\n",
      "Cost function is -0.012242783231612542\n",
      "Exact infidelity is 0.006347202392925411\n",
      "Iteration 163\n",
      "Cost function is -0.014582458488965333\n",
      "Exact infidelity is 0.006525377538310617\n",
      "Iteration 164\n",
      "Cost function is -0.019768654805428776\n",
      "Exact infidelity is 0.006130847516181204\n",
      "Iteration 165\n",
      "Cost function is -0.02389306057117846\n",
      "Exact infidelity is 0.005662075291772095\n",
      "Iteration 166\n",
      "Cost function is -0.027508097278035937\n",
      "Exact infidelity is 0.005391978185290247\n",
      "Iteration 167\n",
      "Cost function is -0.022132336650198647\n",
      "Exact infidelity is 0.0053326340928167015\n",
      "Iteration 168\n",
      "Cost function is -0.029869793098249664\n",
      "Exact infidelity is 0.005297443691958503\n",
      "Iteration 169\n",
      "Cost function is -0.027648290937617892\n",
      "Exact infidelity is 0.005627836327541824\n",
      "Iteration 170\n",
      "Cost function is -0.017419050129632296\n",
      "Exact infidelity is 0.005927164941609386\n",
      "Iteration 171\n",
      "Cost function is -0.02450159267975871\n",
      "Exact infidelity is 0.006395866066555622\n",
      "Iteration 172\n",
      "Cost function is -0.02200576627485984\n",
      "Exact infidelity is 0.006531485621665389\n",
      "Iteration 173\n",
      "Cost function is -0.019462714687904503\n",
      "Exact infidelity is 0.006081340063265661\n",
      "Iteration 174\n",
      "Cost function is -0.027316318531198203\n",
      "Exact infidelity is 0.005772708368936952\n",
      "Iteration 175\n",
      "Cost function is -0.026287050273036794\n",
      "Exact infidelity is 0.005535629850896284\n",
      "Iteration 176\n",
      "Cost function is -0.022214626494343914\n",
      "Exact infidelity is 0.005580795577476816\n",
      "Iteration 177\n",
      "Cost function is -0.0017182224060965368\n",
      "Exact infidelity is 0.005773468683360483\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 178\n",
      "Cost function is -0.027705661492521072\n",
      "Exact infidelity is 0.006245982717609566\n",
      "Iteration 179\n",
      "Cost function is -0.013865017016676084\n",
      "Exact infidelity is 0.006528565231749606\n",
      "Iteration 180\n",
      "Cost function is -0.01646100202958234\n",
      "Exact infidelity is 0.006279422965092651\n",
      "Iteration 181\n",
      "Cost function is -0.0306913494107115\n",
      "Exact infidelity is 0.005983626228816874\n",
      "Iteration 182\n",
      "Cost function is -0.02132226432872053\n",
      "Exact infidelity is 0.005681229256329856\n",
      "Iteration 183\n",
      "Cost function is -0.012628580201704764\n",
      "Exact infidelity is 0.005316961421632849\n",
      "Iteration 184\n",
      "Cost function is -0.025420136843164576\n",
      "Exact infidelity is 0.00490532283824785\n",
      "Iteration 185\n",
      "Cost function is -0.014924156043206294\n",
      "Exact infidelity is 0.0049039317375692\n",
      "Iteration 186\n",
      "Cost function is -0.015184629556467266\n",
      "Exact infidelity is 0.005394530699901923\n",
      "Iteration 187\n",
      "Cost function is -0.0196605882651939\n",
      "Exact infidelity is 0.006200403324499226\n",
      "Iteration 188\n",
      "Cost function is -0.014197698949680593\n",
      "Exact infidelity is 0.006793585839155791\n",
      "Iteration 189\n",
      "Cost function is -0.020337786997434604\n",
      "Exact infidelity is 0.006817938603440932\n",
      "Iteration 190\n",
      "Cost function is -0.016133917270682252\n",
      "Exact infidelity is 0.006039403966845769\n",
      "Iteration 191\n",
      "Cost function is -0.019673021309918992\n",
      "Exact infidelity is 0.0052258641540252615\n",
      "Iteration 192\n",
      "Cost function is -0.01155789710004873\n",
      "Exact infidelity is 0.0047853908987632865\n",
      "Iteration 193\n",
      "Cost function is -0.015200468061774997\n",
      "Exact infidelity is 0.004755509506891631\n",
      "Iteration 194\n",
      "Cost function is -0.01945782108517702\n",
      "Exact infidelity is 0.0053912713983904625\n",
      "Iteration 195\n",
      "Cost function is -0.020142471956422936\n",
      "Exact infidelity is 0.00645020285061404\n",
      "Iteration 196\n",
      "Cost function is -0.017839708399285392\n",
      "Exact infidelity is 0.007439049054429825\n",
      "Iteration 197\n",
      "Cost function is -0.02033964985752279\n",
      "Exact infidelity is 0.007539068967493789\n",
      "Iteration 198\n",
      "Cost function is -0.009007384672584662\n",
      "Exact infidelity is 0.006699058642707145\n",
      "Iteration 199\n",
      "Cost function is -0.0227757889213287\n",
      "Exact infidelity is 0.005876655946490894\n"
     ]
    }
   ],
   "source": [
    "N = 40\n",
    "\n",
    "max_iters_=200\n",
    "nsqst_lr=1e-2\n",
    "batch_size_=200\n",
    "num_samples_ = 5000\n",
    "K_ = 1\n",
    "\n",
    "# we first load the amplitude NN with trained parameters\n",
    "nqs_model_amp = nqs_models.TransformerWF( num_sites = N,\n",
    "                                        num_layers=3,\n",
    "                                        internal_dimension=8,\n",
    "                                        num_heads=4,\n",
    "                                        dropout=0.0,\n",
    "                                        phase_mode = 0)\n",
    "\n",
    "nqs_model_amp.load_state_dict(torch.load('data/param_GHZ_pre_amp_N40'))\n",
    "\n",
    "nqs_model_phase = nqs_models.TransformerWF_phase( num_sites = N,\n",
    "                                        num_layers=3,\n",
    "                                        internal_dimension=8,\n",
    "                                        num_heads=4,\n",
    "                                        dropout=0.0,\n",
    "                                        phase_mode = 2)\n",
    "\n",
    "\n",
    "optimizer_ = torch.optim.Adam(nqs_model_phase.parameters(), lr=nsqst_lr)\n",
    "\n",
    "state_file_name_amp =\\\n",
    "'data/param_GHZ_pre_amp_N'+str(N) \n",
    "state_file_name_phase =\\\n",
    "'data/param_GHZ_pre_phase_N'+str(N)\n",
    "\n",
    "f_name = 'data/phi_list_' + str(N) + '.json'\n",
    "phi_list = cirq.read_json(json_text=json.load(open(f_name)) )\n",
    "\n",
    "shadow_trainer_driver = shadow_trainer.ShadowTomographyTrainer(\n",
    "                    N = N,\n",
    "                    nqs_model_amp = nqs_model_amp,\n",
    "                    nqs_model_phase = nqs_model_phase,\n",
    "                    phi_list = phi_list,\n",
    "                    optimizer = optimizer_,\n",
    "                    max_iters = max_iters_,\n",
    "                    num_samples = num_samples_,\n",
    "                    batch_size = batch_size_,\n",
    "                    K = K_,\n",
    "                    state_file_name_amp = state_file_name_amp, \n",
    "                    state_file_name_phase = state_file_name_phase)\n",
    "\n",
    "exact_nsqst, loss_nsqst = shadow_trainer_driver.train()\n",
    "\n",
    "np.savetxt('data/loss_GHZ_pre_phase_N'+str(N), loss_nsqst)\n",
    "np.savetxt('data/exact_GHZ_pre_phase_N'+str(N), exact_nsqst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e38590f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Infidelity')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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PD+vkgPmz90xs7DlpKPV4JP3YPEcNxgq/0drvk+pCATMILmzWb+vSP9PLQ6PLzN8HM3XhMctDwcyyunBA9aFAyW68mTzj+djzxJi2XWrZ4Ij5WY7/nE9nnfNHiuw/qw9nt7vMtsCMmuDYjOtB87o6vl47ITN7MDC2TptaHgr4R+tzY+oUY5el65PpgMh0UHYsYSgWN4O2RxJme/j00NhrY+YzGRr9Hp5JtXHTwW2FHNPBVHu1JhTIel9j3384MPreM23acKqeEjbrJnWp7+1o3Sb9PU7VXcL+zDKvBJUnk4Z6z4yk+l2GdeL0sI73D+v46WGd6DeXnRwYVu9ATH2DMcf79cJBv+ZkBcClg+LmzKjR7BlhtcyoyfwuUhdy/Zo1FEvoyKkhHUoFsR1OBbGlA9oOnxrS6RL0hUbPmLNjvDBJn2ZawO8zAwynCYxrmVmjmTXl18cJdxHwhjG6urrU2dk5ZlkxAV2RSGTCsq1bt2rz5s0Fb9OqSnovQD4uWzZLvz7Qm3n+gcAPipvGNJepMorZKZmQju01M6ItXC299Jj9+7jqYxODxHL4yJpz9ZO9R/JuuJw/f+ZoA3f3nVIyVkAhLUjGpN1flzo+OXZ581Jn9jfOvz6V1NYTo9+z83zdavHZMz1kKYR9Cf15cJc+H3/7pOt8ZM25ZTEdpM/n0z+tu1hv/OeHdXLA+iD94VNDun7rL/S3f/QKvfs1SyuuUWEYhjpf6tXXf35AP37miBI2DLA31YX07tcs1TtfvVTNDgSNzZ5Ro1tff57ee2Wb/v2xA/r6z/cresaec8iT3VE92R2V3yetPmuWrn3FXHWsmKflLTNs2b6bkklDXSdO68nuU/r1/h49/PvjYwLBq8WJ0yN64PnjeuD50eDXOTNqdNHCRjMQblFEZ8+dodZIrWqC3g7kTUskDR3rH9LLvWZQ1MHewUyA2/4TA3md88qJYUhH+oZ0pG9Ie17szbnOjJpgJhCuNWJOo9xUH84MVKSDqtKPnezgPDMS14n+EbOTMv2/f2wH5qHooI70DTmSmc1thjHa4delgbxeG/D7MoMm6WCh+lDqZzig+tRgTHrwdkzwhiYGbsRSg2njB9kyA23xhEYSyTFBbcXe3es1iaShM6nBSTuZQXPm4Pr4AZrxgzTpgCOfT5ngmdEBGWPM86RhZAZZ04OmZ0YSGhyJp34mdCaWsKUe46b097mhJpAZTB+f9cfvG5sNaLxc7bJkKqBtODurQlaAWqUFvNshPcgWTv0fH/wkTQySMgxlgtOyg9jiSaNkg/eAV/h8GhM8Fw76M1Pd+1OBwpnzmt+nQOrc5vOZmc3Mx8rKdDb2/CdlZ3RSZjqpdCadRNbzWFYwYjogcWTc42IDF0plOJ7UcKruWIxQwFq/RvrvEfSbf4P0tWn8f0lSVpDthPqXMVr+dN3K6eD+ShL0m8FvVrqjrF5vDFlbMWmobI6PfI3Ek5m2WCHCAb9qQ/5MW6QuPBpIU5/1eDTEcmyQUXam5lgiqZ6BkUyAW8/AiGvHSNKQBkYSGrC5nZCW/tzS5/zMjSSpgG2/b9wNJr6xy/ypB9nrGobGBLANx8w2npel2zPH+0s0C00B/ErqPF+3LvJ3aYnvmGoU07BC6jbm6ulkm543FispZ/pQ4klDcQe/h7kE/b4xAf11oYDqa4JqyGqjpY/3hvDo72qCgdx3VGQzpOFEMhU8GVP/kBkc2D8uoLJ/KKbeMzFPtWtH4kkdOjWkQxb6kIN+n2algrdmzwhnbgDLvrEuO9izPvX5jT/mzceSsm44GxhOKDo4kupXSv0cHPu4d2Ck7PpAzT7dYR3rH5YOT73urluu0tlzZ5amYKgIBLxhjF27dk1YlivQKx9tbW3q6hoNgsi1DydU0nsB8vH2S5foP371kqJnYmrVCX0w+D1ndjRZRjE7RLvN4LAn7pYGrGepyltru3TFRyytet78mXrTK1v1g6cO5bWLFa2pAKlkwnw/TnriW9Lr/m7s9KytK53dZ8qPTo6d4vYiu4MsS+C6wIO6LX5dzgbsZUtn6S9fu7w0BckO9Ow9YE4XG6wxgxdbV0pzV0w5Ba8ktcys0efXvVLv+cbuvHYdTxr6zA/3aveLPdr8tldqZm0Jsjja8H6nMhJP6n+ePqS7fn5Avzl4ypYiz5lRo798bZtuuGyJGmqcr0431YX04WvP0XuuWKZv/eJFfe2RLtsatUlDevxAjx4/0KPP3f+cls1pUMcr5uraV8zT6rOay2IquGN9Q5kAvqcORvWb7lOezublphOnhycEwUnSvMYaLWqu16LmutT/ei2MmI9bI3WOT4ESTyTVNxTXqUHzrs3oYExHTw3pYCrT18vRM3o5ak+q/Up1ejiu3x09bXmK3vqwOc3NjHSwTnDsHcPh7DutgwElUxml0neID6eydqUzSqQ73HsGRmwPLKomiaSRytoVl+TdAQHIzOYRSxQ9EF+t0gNfJ8pvVvGK48YgG1BJDGP0miA5dIMjCma97UAbwwvMYGquR14zkjCDquyYIaGapD83eFerTujG4C6tCzw45Y37x41GbU9crbvjHVPODlMu4kkjE3iGwsSzg7dgu5aZhc+gg+pEwBvG2LNnz4Rls2bNKmqb44PExmddc0olvRcgH4tn1esr72jX+765Wzcmdynsc6ijYLKMYsVIxKRHvmBO++lUJrS0WW3SDd/Ja1rWv+44Rz/8zaG8spGsaG00Hxzb62zwnmRu/9heaf5Fo8vmrpAaWhzd93GjUc8bYwMfl/iOObY/p7T4+nSer1vPGmeNWT6zJqjbrrvY+akwrAZ6NrRIK280p/qdIuD0defP07suX5qZ3jAfP3r6iJ493K/b37FSKxY0OpPtzeb3O97x/mH9x69e1H/86iXb7iL0+6R3Xr5UH1lzrhpLEQw4zoyaoN5/9XK98/Kz9O1fvaRtD3fZ3rDef2JAdzyyX3c8sl9NdSG95uzZWjanQWfNatDiWfU6a3a95jfWljxt+0g8qaN9Qzp8akiHT5mZvZ55+ZSe7I5WZfY2ux3tG9bRvuFJs4fVBP2ZaerS009kT0lRHw6qNhTIZJ/I/DcMJVMZX9LZKM6MJDKBbX2DMZ0ajDHI7oJMxqvyScYKAAAAAABQlKDi+kDgB/pg8HuWxs5afH36QPAHujnwI90ef4v+LfFmxQmvABxhTsPN8YX88I3BGLkylhUzDehkOjs71d7ebvt2s1XSewHy9Zqz5+jRj71WNV9+v7M3mObKKFao/iPSPW83szw5rbXdDHabOW/6dbMsb5mhP1u5SN/tPGj5NSsWpALeSvG+JOnQk2MD3vwBM1jo0S86tsv7EldPyIpWU6Z3Nl/o369nE2MD3j7zlgu0eFa9czvNN9Bz4Lj59/zFV8wpea/4yKSBm3/3x6/QyYER/XeemQklM/Dpj7/8qCL1IZ3dMkPLW2bo7Lmj/xdG6iYEPRmpYJb+obj6hsxglv5UunKfT+aULYpr+XPbtOzZf5O/yPc7OJJQd+8ZvXTyjF7qMf93p34eODlgayao9iUR/cNbLtQF6ayNhbApm119OKj3XtmmG//gLN23u1tffXCfpXTr+To1GNOPnj4yYXk44NeiWXVaMqteZ82q1+JZ9WqsDWUyQ9UEA6mp3MZmjAr4fYolDA3HExrJmlIv+/lwPKnomREzsC1qBrcdOjWkE6eHy2aKrHDAr9ZIrWbPqNHgSEL9wzEzff9QvGynszH/NiM6md/sjAAAAAAAAIAntKhXXwvfposLmJ2mxhfXraEdujbQqfeNfFTHFbG/gECVa5lR40zyBVQ0At4wRnb2srRipwHN9frdu3c7HiRWSe8FKMSsgRekWI+zO8mVUawQ/Ueku/5Q6nF4GsxAeNoAoen89bXn6PtPvmwpaMHnk86fn5prvvdAQfvLW+/+ictW32wGCyXsmQIx27AR1H/EOyYuV+mzX9nhLN/RMc//5JUL9JZLFjq3w2ICPRMj0gOflZ6/f9IAzmDAry9df4nOnz9Tt/30+byyE6ZFz8S0+8Ve7R6X+akm6NeyOQ0KBnzqGzQD3PqH4kpMsZN0p8LyQqa8Tb3f3z2yXZ9r+ns901dnW+a2qcxqCOtv3ni+1q5aVHhWM4ey2dWGAvqLVy/V2y9dou8/+bK++tA+7TvufETSSCKpruMD6irBvrwoHPCbQZ/NdVoYMf+3Ruq0sLlOrZFazWmoyfldMVJTQqaDQE+npvDcd/y09h7q097DfXr+SD9TXgAAAAAAAAA2alGv7gt/Rsv8R6dfeQqX+Lt0X/jTum7kkwS9ATZrmVnjdhFQhgh4Q0auADE75JpGdN++fY7sK62S3gtQMLcyiuUrETMDfpwMdmtokVbeJK1+T15TIuayZHa91q1erHsef2nadZfNaVBDTepSG3c+MGfS/UQWS1d+VHrwH23f3e3xt+iQ5kxY/pIx1/Z9lUJ2ZroFTbX67Fsucu6OErsCPQ91Sne9UXr3j3MGvfn9Pv3VNWdr5ZKIPnzPEzpx2p7Ax+F4Us8d6be8vl2dCufGf6e/P36rrhv5pORgp4LPJ91w2RJtfMN5itSHC9uIg9n7soWDfq1bvVhva1+kn+49on99cJ9+c/BUYWXGBDVBv1YuiehVy2brVW2z1L6kWbWh/DOb+nw+1YYCqg0FxjTerzhn9BwaSyRHA+BSQXB7D/cpeqY8s2YCAAAAAAAAbgoqrq+Fbyu6Xzptmf+o7gj/k9aOfIrpTQEbEfCGQnAWRkY0Gq2YfVXSewEK5mZGsXw88gVngvPOuly6+B1S6yWWpwm06kOvO1vf3XNw2iw8l56VFSQbLFFFbbL9XHmL9Lv7bf2sn0y26d8Sb875u6eT9k8hXQrpzHQ+n3TbuovVVO9Qpjq7Az17uszt3fzTSQOkLl8+R//z4Sv1oW8/occPOJz9cZxy61S4cGGj/u9bLtIliyOFb8Th7H25+P0+vfHCBXrDBfP16Asn9K8P7NMvuk7mv/8qVxvya/VZs/SqZbP0qrbZunhxk2qC9l1DphIK+HX+/EadP79Rb00lEDYMQwd7B/XrAz369YFe/fpAj144drok5cFEDeGAIvVh1YUDShqGkklDCcNQMiklMo/Nn4mEof7huNtFBgAAAAAAqFofCPygoGlMp3KJv0vvD/xA/5J4q63bBaoZAW8oBAFvyHAqK5ob+6qk9wIUzM2MYlZFu83MR07o/rX0Z9uKzuiWS2ukTje9+izd+ejUwX7veNWS0SfNS20vR07Ny3IvD4SkG+41M4HZEGS1PzlP7xv56KTBRs8bi3XcaFSLr6/ofZXSi4YZWPS+K9t0+dkTM9fZxolAz0OdZnaw126cdJV5jbX69vtepc//5Hltfbh0169y6VSI1Id065pz9Y5XnaVAodOXSiXL3jcZn8+nK89p0ZXntOiJl3r1rw/u08699gQbViK/T3rlooiuOrdFV50zR69cFFE46He7WBk+n0+LZ9Vr8ax6vbV9kSSpZ2BEuw/0aPeLvXp8f4+eefmUpam2kducGTVa1FynxbPqM9PSNtWHFKkLqbkhrEhdKPU8nPd3YySe1InTwzrWP6yjfUM61j+sY31DOtY3rKP9qZ99Qzo5YP+049VgRk1QcxtrNKehRrXhgGqDftWFA6oNBlQb8meyKpr/zb/dUCyp4XhCw/GkhrMfx5MajiU0FE/q9FBMfUNx9Q3G1DcU01CMqYYBAAAAVD6/kjrP162L/F1a4jumGsU0rJBeMubq6WSbnjcWKynv9JkA02nVCX0w+D1Htv2h4Pf03cRVOWfAAbws4PdpfmOtFjTVqjVSp1kNYQX9PgX8Pvl8PgX8UsDnk9/vy/z0+aRTgzGd6B/R8dPDOt5v/j85MCzDpm7plhkEvCF/BLwho6fHmWwvkUikZPtyevtuvJfx+vr61NdnXwDJ4cOHbdsWPMbtjGJW7L7T2jR/hUjGpN1flzo+6cjmP/aG87TnxV492R3N+fsbLluii7OzQ7WudKQcE7ReMvnvZs4zA2fuebsZSFOgJ5Ntet/IR3V8iukkk/Jrd/Mf6w+j9xS8Hzc8k1ym8+fP1K2vP9e5nTgZ6PnQFuniG6YM9AwG/Pr4H71C7Wc166Pbn1L/kLPZh8qhU6GtpUHvvnyp3tq+aHQa4kK5kL1vKiuXNOuOv1it3x3t19ce6dL9Tx8h45Sk+Y21uurcObrq3BZdcfacwqetdcmshrBef8F8vf6C+ZKkwZGEnujuVeeLvXr65VN65uU+vRwddLmU3tEQDmjpnAYtSQUOLmqu0+Lmei2eVaeFkXrVhZ3L4BcO+tUaqVNrpG7K9YZiCR0+NaRD0UG93Duol6ODOhQd1KFTgzoUHdLL0UGNxKsr6GruzBq1Ruq0oKlW8xrT/2s0v7FW81LLZhR7zrZoOJ5QfyYAzvx5ajCm6JkRnRwYUc8k/wlEHRXw+1QXGhuIGAr4FfT7FAz4Mh2rQb9/zPOA3yd/anr59DTzPpnZeEcf+2QYhmJJY0wQ40g6kDGeyDweiiV0ZiShBH8bSeY5KhzwKxQwP1BDkmGY2UXTz2Wkl5vZKwkABWBFwO9TKJAaIPP5JPOfOVgm89xt3mNkDp75fZIv8zh93lfmefo1Po2ek9JncsOQDBmp85e5LJYwz/lDqesB7FET9GtmbVANNUE1hIOqCwcUCviyrunmdcW8lvsVDpo/JY1mQh6TFVmZZfGkocFYXKeH4uofiqt/2Hw8GEu4/K6BwoUDfrO+lapzhYN+1QRHl6WPnXSdd/Snee5LBxpYXS5J/cNj2y19QzH1DZqPp5qtpFUndGNwl9YFHpzyBubjRqO2J67W3fEOV4J86sMBNdQENaMmqPpwIPM5hlLnnfTPYNa5ye/zaShutgMGRxIajKUfxzPLzsRoI6TNqAmqocb8nGfWBFUfDma10VLf2TFtOHO5YRg6PRzXwHBcA8MJ8/GI+Xhg2L3z+Y3BXQr7nNl32JfQBxsf1n/MeJdqgmZbtyboV00woJqQebz7fb6stunEm+5y/Y66y6gZNUE11YUm/I/Uh9SY9byhJiCfRuuc0mjd0Xxs/uwfiuvE6WGdOD1i/uwfHvP8zEhl1DtmN4S1IFKr1qa6VL9krRZkPZ47s7a4G/6zxBNJ9ZweVN9Lv1G8u1NGzwEND51Rfzygl4y5eiq5TE8MztfR03H1TTMWNYcMbygAAW9AmfnCF76gT3/6024XA+XA7Yxi00kmpCfutrcs4z3xLel1f2frdKZptaGAvvbO1brtp8/rvt0HMw3iSH1I73z1Uv2fjnPGvmDuCqmhRRo4bntZMhpazP1MZeY8M3Dm0S+awVF5BBwOG0HdHn+L/i3x5imnkQwH/PrbPzpfb7zgIulfvmtOz1gGjhuN2h84S99/+0pnpy/0SKDnGy6Yr/Pnz9T77+7U3sPOZeJzulPhz4O79Pn42zPL8rkT9bXntujdr1mqq85pkd+mBp5b2fumc+68mdqy9mL937dcpF8f6NHOvUf1s+eOqrunOoKiwkG/XrVsll57bouuOrdF58ydkQmcqAR14YAuXz5Hly8f7Ww+eXo4Ffx2Sk+/fEpPHzylQ6eGXCylPSY7xl/WPJ1sukCa+wota2nU0jkNWjanQW1zGtQys8bzf+/aUEDLUmXOJZk0dHJgxAyCi5oBcZmguKgZKFdOWeJCAZ/ZydZUp4XNdVoYMX8uSv2c31RbsqmEragJBlQzI6A5edxpahjmlLY9p0dGB5mGzEC5MQNPQ7FMAN2ZkUTqvzko4OSAgN9nvq/0oFsmM14qU15tKDAhQC2dKa8u9bguZHbij6479vc1WY9DAe9kgTAMQ8PxpE6nBtIzgzIjcZ1ODcYMDI8un7As9ffpH4rrzEhcw/Gko4NjPp9UFwqoPhxQXTig+pAZYDCzNqjG2pAa64KaWRtSY23qZ525fGZtSDNrg6oLBRQKmoN/NYGAQsHRwb9Czo2JpJH5jp4eNj+D06lBrdHHowNb2YNc5vqjjwdHEuYU0ElDyczP4j8zv888r6a/3zXB1MBTyHycDsZIfw6hgN8crAv4FPL7FQj4FEoFbmQPXE32syZkDmSlA3jSQTuZQJ3xATtZz/0+n3xGQuGe5xQ+9huF+l6U4kNK+ms0NHOJzsx+pU43naOY4VMsYSieSGokkVQ8YWgkYQ6GjYwbOBuJm+sMx5IaSYwOomX/HIlPDA6N5RgEn+zPMRqsOjpQn/4cQ4Gxz4OpYNb05xpIDUYHxn326fXSQTNjA2LN5+kBLF/m80z9T33mGv+79N9C6UGu7OcTt5Fr++kAhMz/1PNgIOux36/xh1M68MrI+hRHl6WfG+Oej74+aRiKJ9KBQEnFk+bz9ONE6nGu2AUjx18uV9aFXH9fn5QzWCCYFUyQDmKa7PehQGHnF6ckk0Ym6HkontBQLPU4lnocT5hZXrOXp9dPLRse97rh7N/HExpO/S6WSCppjE51b9h4brMifU4LBnypgOaxj2tCfjWEzYA1M3BtNKChIfV/Rup/Q+ZnQDNrQqqvced6HkskNTCcCoJLXbf7h2I6PWwOmprX8pj5u6HUsmHz91PE9oxhGGbAXSLrf/r4SqSPudQfMfscMeackfU4FPBNWj+qC6XrW+lAndHzYfb5NHNN8vsUDvoz28m+BqWzGqcDK9ID11OdfyRlAgwz55eEkbUs+3yTej7m94ZiiWTmeTyZVCxhLoulrj+xhKGR1HUllr5WpdcZvyxrvcmuRZOxep6xejaqCflVHwqqNhxQXciv+nBQtek6WMish+X6WR9O/W3Do+um/05eOhem68DptsipVFukf+CMluzdqov2bVPAmP4GyRZfnz4Q/IFuDvzIUj/xZCL1Ic1uCGv2jBrNmRHW7IYazZ5hPp/dEFZjrRnAkgmyTQXa2hWgMZ5hmHWroZGkzsSyAuFS7bP0jTNjlsfiGso8Ti+PZ4LqhmJJGTKUTH2tk4aRCdJOGqM3mqQDuZPp51k3nCSzg7qlTFst+5xQGxxtl5nPx54zxpw3xp2Tss/7M2qDqg8F7OsjHSeRNDQwEteZ4UTqHJBIZWDPcdNSLDEmCGx0+fh1E4onjDH1vOz6eEBJ3bj/EcnBGKZ3hB7SOz64zdYxqGQyVddPjH3PY+sg5uc3OGLWbwZHspalfj84yXrD8WTmezqYCrorlbpQQDNqzXrInBk1akmdA+bMqFHLzBrNmVGjOTNHl9WGSts3dGYknslgdiL9v39k9PHpYZ08bf7e6UQCuQT9PkXqQ4rUh7WgqVYLI3WpQLbazA23C5pqS/e5RbsV3H2n5j5xt+ZOMvb555I5dnnFjRq65N06GZybyQ6X+X96SMf7h3XO3BmlKTcqCgFvmFKujGZ2iEajjmx3KpX0XgBLvJBRbCrH9job/CWZ2z+2V5p/kSObnzOjRp976yv1kY5zM5lXVi5pzj3dmT8grbzRDFpxysqbrDWsAiEzcObiG8zgqCe+NeXfIlnforvOXKE7h6+Z9s69pbPrdfs72nXhwiZzwZUflR78x3zehWu2J67WlnUrdd78mc7tpBSBno9vk1a8RZp/4bTfh7NmN+g/P3C5Pv3fv9U9j3fbXhS/kloXeND27Wa7LvCgbotfp/nqsXQn6gmjSXvnvUlL3vBhLV1+nr2FcTl7nxXhoF+vOXuOXnP2HH3yTSv0u6OntevZo9r17FE92R21Lf2425bNadDFi5p08eKILlkc0SsWNJa8g8Rts2fU6Orz5urq8+Zmlp04PaxnXj6l3x7q08HeQR3sPaOXewd1sHdwyju9vWCR/6Q21D2gNxn/q0gymnulAUnHWqSFN0rn3ixFZpeyiI7y+31qmWl2/o3JIJtlcCShQ6cGdTg6pN4zI2ZgVVaAlfkzrlOpx6cGY5nO0mKlO88bwoFUx+Ro52R2Z2VL6nlTXcixTnSv8Pl8ZjBSbf7ZOdOSSSOTfWA0CC6upDE2y9lkARxmUMJoFolMNomAX0EPBaCVms/nywz25BPEOJV4IjvAaDTQKD2Akx5A8PtGsxqlB2XSy9LBT+lAwfpwIDXQ6q3B0oDflwqmK/y7PZX0wF52EFzCMMYMVo//PLKf1QTL6Psd7TZvhnni7pztscb0g4YWsy25+uai64JANfL7fWZgioNZfaeTPrclcwT5JrMyniWNsZnQ0kGXo9NJpYIw08uylocD3rpe2CUU8CtSHy67rNyA12TXgec21poL+49I97yroBs3a3xx3Rraob9a+Hvt77hT8fq5WcHnqUDy9OPUc79PaqoLqbkh7KkbYiTz8zFvkgioSc7Uc6tdwF98GzlvR56Wvtrr7D4cGIPy+32q9ZvHq2pt2+ykYonk2H6HkYQZnJh181LMYvR+wOczA9pSwfUzas2Aypk1ZhCr19tq9eGglswOasns+mnXHYolxmQ0zL4ZzLzxa/QmukQymRXMOhqUnglSTz0w6z0hNdWH1VwfUqQubD5PnTsbwgFv1PcSMfPG/4c/by25w8Bx6dEvqvYXX9HCqz6mhVd8RApEHC8mqgMBb8iopMCtSnovQMG8klFsMnZnQJp0P086FvCWNrexdrSjYCqrb5Z+8RVnMp4FwtLq9+T3mshiMxPY6/7ObJQdelLq3S/Fh82papuXSa2XyD93hWbseVmHvvv0lJt788Wt+se3XjR2erErb5F+d3/p/t4Fiimo1Ws/qssubnVuJ8mEtPf7zgd6jpyWtl01OjDW/i5ppN/8G/QeyPr7LpVaV6p27gp97q2v1GXLZukffvisemzMEHSer3vK4DM7tPj69OngN3R94AFLmeTm+E7pqmN3S9++T7rqY9IVHyloqtCcPJK9zyqfz6fz5s/UefNn6q+uOVvH+4f1wHPHtPPZo/pl10lX7lIrxKyGsC5JBbZdvDiiixc1MRgyiTk5guAkc5DtxOlhdaeC4A6mguAO9p7RqUEzO8KZdHaekbgtgZE+nzKZidLTDzTWpn6mnrdGarVwZlDnvfA1NT7+z/IlrHeg6Bdfsf8Y97JkQnU9e7X80BNaPv5cf/5Kae4FkwZBp+9kz3U39UjqzmG/3zcmk0RNyJ/56bXMBZXE7/dlshlITOvgZcFUECGXn+L5fD4FfHIse4cnFDg4UHXXNlS2ZCLVD5G7naq5KxzJ1u+WzLlNPlXZfTgAvKr/iHTXH0o9XUVtpvbYU3rFj6+T3v1jc2YRwEsqaAzKSaGAX011fjXV0cbIRzqIeHaupGSVXNftPyLd8/bCjq/EiPTAZ6Xn75du+A7XDdiCgDcAqFReyiiWS+8BW4sy+X72l2Y/VkQWO5fx7KqPFX63vz9gNsimaJRdf+kSPXu4X9947MCE39WG/Pr0my/QdasXTxzwDoSkG+6V7npj0R0okqS6ZmnQ/ruyQtds0mWXXGz7diVNm7nBMemBsenOAanAuD9bfbNev/EaPfrCCT198JT2HT+tF46d1oGTA4olCotuuchvw9/cgpuCu/J/kd2NqzKfplmSWmbW6LpLF+u6SxfLMAwdPz2sl06e0Us9Z/TiyTPq7jmjF3vM58f7hx0pQy714YAWNJlp2ec31mpBpE6tTbWa31Sr5S0ztKi5jmCbIvn9vkzw9qqzmqdc1zDMjFOnh8dOURdLJDNTegVSmSeCfr/8fo1ZFgr41VQf0oxwcPoMX3SgTM/qNWaK7EDZd7KXjUruOARQ2bi2odrZUHcBABQpETPrI3b01Urmdr56hXTJn0uz25xrk9EORL6qcQxqOhxHzqr0uq5NwdI61GmOGRIsDRsQ8AYAlcxrGcWyxUsULFGq/VjlRMaz1nbzLn+HffJNK7RmxTxte7hLh08NSpLWrJind7566dQZ7mbOMyuu97zdrMgWqrVduu5b0n03lsfnl2/mBrdkZYxouOpjesMVH9EbLpif+XUskdRLPWe079hpvZAKgtt37LT2HR/Q6WEzA9iMVIrydLammbUhNdYG9ce9w9JRt96YRXY1rtyYptnBDgqfz6e5M2s1d2atVi+dNeH3Z0bi6u4Z1Es9Z9Q7MKLhuJkNavR/QsOxrMfxpOKJpMLBwISp9WpSy7KXz51pBrS1NtWpsS5IQJuH+Hw+1YeDqg8HJQdngKYDZRrVmh2o0jsOAVQ2rm2oZtVad0ljcBmAlzzyBfszXw0ck36eddOtnW0y2oEoVLWOQeXCceSsaqjrOhEsfc/bpZt/6v33Dk8j4A0ZkUjE7SLYppLey3i33HKL3vve99q2vcOHD+uyyy6zbXvwGK9mFJPMjr1SKNV+rLI749msNvPu/hJUCH0+n15z9hy95uw5+b945jyz4vroF6WHtuQXABYIj630l8PnV0zmBrdMkjEiFPBrecsMLW+ZoddnrW4YhobjSYUC/smnnPpJo/cD3iR7GlelTJFfG3G9g6I+HMxMh1q0XIM/fTXSmaWSb6VUu0LyMfhTVehAmVo1Zgeqho5DAOWjkMAVrm2oZuVYd7ErQM3twWUC7VBJ+D7bI9pttqucZkebrNB24GO3S6veKc27UDrVzXelmrkxBuW1c1Wl9qd46XMux7puIZwIlj7UaX7fXrvR3u2iqhDwhilFo1FHtuFGQFqlvJfGxkY1NjaWdJ8oc17NKNa81JaiTL+fZaXZTz7szHjm9UpwtkDIrLhefIO0++vm9IzTdvjeZGYSzO7wtevza1oinfN66bEv29sYsitzg1ssZozw+XyqDU3zOXkt4HQqxTauSpUi/8lvSz/8SGV0ULg9+OMWL3XIlEIh77caOlAK/R5UY3agauk4LKVKOQ9VyvtA+Sim7lIN1zbkVu3nqnKru9jVRnE7SKNa21qoTHyf7bX7ztLORFFom6yYdmAyJv36a5P/nu9K9SjlGJQXz1Wl6E+xWtetlJsJxrOzrvuVy6QL/kyqn+W9toKTwdIPbTHHDDkfo0A+wzAMtwsBb9ixY4fWrVs3YXmxX5ENGzZo27ZtY5Z1dHRo586dRW13KpX0Xpx28OBBLV488SLS3d2tRYsWuVAiOKL/qL0ZsezoYDzytPTVK4ovz3T+8tHR6f+8JhGzJ+NZuco0cp6UevdnNXKWSa2XTF+ZL/Tzm04xjaFETLpzTXlldptMa3vxGSM6vyn94EP2lclp/pD04Sfy/7snE9J/vV/6zb3OlMsuXgmSLXS633I/93mtQ8Zphb7faLf05ZXOdMAXeozbqZjvgRPXGDvO9U6yM4jcrjpsOauU81ClvI9KU8lBPcXWXS5aJ91+aeVe26rVdN/5mkZpz13eP1c5eeyWU93FzjaKkxnf7Q60S/OHrAXaVfK5vhT4/PJTrX0HTkompNvOm/q65CSrbbJS3UzMd6XylWoMavXNZj+4E9feQjndn2K1XV43S5q9XOrZJ53pmXw9p+o4Vo/zQrN4Oz0W5HZbIW3Xp8yxOKdccYvU8Unnto+cKiVGhIA3ZHR2dmrVqlUTljsRJLZ27Vpt3769qO1OpZLei9Mq5WQGC/qPeiujWCka2A0t0q3Pe7+zKNpdXMazamf188tXIR2+T94jvfSYfWVw2zWfKC5jRKk6FeyUT+PKasPeS6bqXCxF57sdgz9eCdyzqto66Yt9v8Onpce+5Fz53OpAseN78MgXnJmm/qzLzTspvTbIVk6D5F5Xbuehya5HTYvNukW+AwleP596ffC7UoJ6CmVH3WXGPOn0UfvKNF6lDg549dhwqg1g97nKC8fug5udqbsU007N9bkkY2b2kuhLhZcp3UaR4V6QhpOBdnYNVjut3M8b6c+v/V3SSL/33kcpVWPfQSl4oZ9uujaZGzcT812pXKUYg/IHpWTc/u167ab89LErFda/YJVTdZzJjvNibqhzqq6bi5v9GozlVqxKiREh4A0ZXV1dWr58+YTlxX5F1q1bpx07doxZtnHjRm3evLmo7U6lkt6L0yrlZAaLvJZRjLsCxio241m1y/X52dFxPRmrHb7lrNiMEW7fOVoIK42rQgMHvGJ856JTmXLGDzac6ZF++z1pKFr8e7AjcI8AP/vZ8X6d6ihMc6MDxY7PZe6F0onnnT/neGmQzYuD5OWonM5DTgeSe+186vVMdeUS1OOkUmUXKValDQ549dgoVRug2HOVV45dr2XtLcXNSs3LpHCDdPQZZ7afS6kD7ayqxADOQjh13nA7sNBp5Zrl2a2Ay3z265WZGKZqk5UygCRbNWQE92pQsNOcHoNyWiHXVKeOo8s/LB14pDQBqU7UcbKPcy9n8Z6KXf0a+ZwPmK2rYlVKjAgBbxjD5/NNWNbb26tIJFLwNtesWaNdu3aNWbZ582Zt3OjsIEMlvRcnVcrJDHnySkaxaLf0L+1SYsT+bQfC0oc6K7PjB9aUywCV1xUbOFqOnQpTNa6cvGu+lK75xGjGKLsz/pQq812hgXulyk5Qrp30VjkZ0Og0JztQyvlzyYfTg2xeGyQvV6U6DxU7cFLKQHIvnE+9nnGvXIJ6nOZGdpFiVMLggJePjVK3AQo5V3nt2PXKDY7lfrOSFW4E2llVKQGchSjFecPJadrcUo5Znt0K1C5kv3vukh65rfh9F8sXkN5xr7T8dWO/e062A62o1IzgXr2ZoFScHIMqJavXVLePIzs5UcdpbZeu+6Z0303ezuI9lWL6NQo5H3Q9UJpg6TffLrXf5Px+kFEpMSIEvGGM5cuXq6trbGf4vn371NbWZus2d+7cqY6OjoK3Weh+y/W9OKlSTmYokBcyipG9A04otwEqLwvPkN71I2n+hYWdD8qxU2GyxlVFBVH6pMYFUt+hwjcxvqPFjcGkYgL3rCpkEOHkPunJb0sDx+wrh1c6XstxKt/xso9xuwZ/KuFzKUSlZAEup0FAq0oxWGjHwIkbgeRunk+9nnGvHIJ6SsWt7CKFKvfBAS8dG14JXp/sXOXUlJz5mO7Y9cq0R5Vys1K5q41IF/yZVD/LetbtQqcxz5dT19RS9x04MU2bW8qpn9itQO1i9tu6Uur+Vf77dMr4754Xbpo963Lp4hvKtx2YzSs3E3ihvV1udfvJWGk/eeE48rq6Zmmw1+1SFKdhrnTJn0uz25y90TAQlhatll58rPgyT+fKW6Vr/975/SCjUmJECHjDGLmm7NyzZ4/a29sL3mZzc7Oi0eiYZcVmWrOikt6LkyrlZIYyVo537sH7KqUR6yXFdICW298jV+OKIMrc0h0tMlwaTLIhcM+qpiXS+X9kTrlZyPQ5dnAzmLuSsmNceau06t32DP5U0udSDDuDDI48LX3jT8wpVZ2SHiTvO1R+g4BTyR5IePIe6SUHOiTtzBDqZiB5MefTQgdsvJ75062/hxfbbuWYFaGQwQGvTL1Wqmnvp+PF4PXsQfdS1jmtmOrY9cK0RxV1s1IFspp1uxTsvqa61Xdg5zRtpZ52PH1deOFn0s8+LRlJ+/eRK8tzMddBtwK1KzWQNxA26zK//po3rnFp5dIOzMULNxN4Kei2kvp1p6qDleKmA3hT+jhqf5fZn+bmzTGFevUHpTd81u1SVJVKiREh4A1jbNu2TRs2bBizbPv27Vq7dm3B2xw/tWhbW5v27dtX8PasqqT34qRKOZmhzPUfle56o3cHf2Dywt1YVpTjAFU58Uq2KyflalyVW9BeKdXNloxE5U3Z6EVuTcVYaZ3qiy4z30uxgz+V9rkUq9yCDFbfnH/WELemjZpue6UMgvAFpbnnSUd/W/g2Wtul674l3Xeje8dPIYOeVj/nXB3Npcz8Wcj3z+0BoFwBiFbeh+RM+6QcsyLkMzjg9anXipXvsVGqTFKVaLLg4c5vujvtkdvnNJQfO4Ov3ew7sGuaNqcyZrpZh5VGszwXex106yYGAnnd41YwaKHcvtHGq0G3do5BuW2y9tPe70s73u1OmYBikeGt5ColRiTodgHgLbmm5hw/hWc+cr22VNN/VtJ7ASrezHlmw+met0uHOgvfjpPT+1SzfDuCct1FUsrAuN13MlDhpMSI9MBnzbuCipkyw8uCNWOfR7vNThrkNnjS7RJUj2RM2v31sVMxOq0SO9UPPp7/a8af+2RU3udSrJ4usy6Xz2Chmxnydt+Z/2vsugZaDSLx4jXViBcX7CaZ9f3bV0nxIXvKVIjs86ndn/PAcTNYyumAqUOd5j7SAx3FfP8e+YK7gSEPfM4c4Dr7WuuD36E6ST4pdmbydQoJ2EomzH2Xm+z6q12BXenv8mO3S6veKc27UDrVnX/7rtTn+kKPDeTvoS1mBrrxx1fvgdLsv3d/7uVun9NQfsafNwrldt/BoU5p65XFT9N2qNMMCimnm1ms6PymWd945Lb8roO/+MpoEI5ktnnsagdabUMlYvbuF/mZrh2YLydvKrf7uzLVd9TuadZL8Tmf83rp+R8Vl+nKH5SS8eLKVqzsOphXz7lAvpqXuV0ClCkyvGGC5cuXjwnuWr9+vbZu3VrQtjo7O7Vq1aoxy4qdVjQflfRenFIp0buoEImY2ZHw0BZv3f1TrZwamHAyTTlpu0vLjikzvGh8poByzPKBypWeirEUGTXJjpFb8zIp3CAdfcbtkniT1akiyz1DnpPTRlXSNdXL6udIl77X+qCnF/lD0l89Lj29vfDv30XrpNsvLd/PwIp8sjOWa1aEN98utV3tzmDXVO07t871xRwbyE86Y1JaMiH91/ul39zr/L5f+XbpLf9q1otLMSUiKpsd2bQrre+gYa50yZ9Ls9u8GeBcaq3t0tIrpMe+bP+2p2tDMeuAd5Qis3kxfedOfVeyv6OlCLDyYgb5QFhq/wtz6l0vuPyvzb6pSj3novqsvUta8afemEWqSlRKjAgBb5hg/FSg7e3t2rNnT0Hb2rJlizZt2pR5XuopQCvpvTilUk5mqDDRbjPjwhPfstD4u0la/Z7ST+9W6UoxMOFEoOKRp6WvXmHPtmCNXVNmeMn6h6TWS8zHBFHCi/7yUWn+Rfm9ppA7iOlURyGsTBV5pkf67ffKfypkJ6aNKvdAQJTejHnS6aOFv76mURrus688XjZZRvBKyIqw4i3Sc//j7mDX+Pad21liiz02YE36Zoy+Q+4cR3WzpNnLpZ59Zv0CKMb4AM58VEPfgRcDnCvFVG0oAnm9p1TBoPn2nUe7pS+vdKg+6Jde93dS38ulmwY+3+mmHb15PzUG1fVAaaZtt8ILmeYAuzmZLAMTVEqMCAFvyMnn84153tvbq0gkkvd21qxZo127dmWeb968WRs35pcafMuWLbr3XvOuwOuvvz7v13vpvXhRpZzMUKEyg6NPmlNVZAbnl5nBKE5PjVmtSj0wYedUtJ3f9E6js5rUNRc/ZYaXZDeshqIEUcJ7Xvs35p21Vq6Bhd5B7GhHKSpeerCwEoJIphOstWdqzllt0rpvStv/gqmKACdVaoZiL2laIp37BjNb3sAxt0uDUlh9c+kGvwEnFZNNu5puwPRagHOlqKY2VKVxOhjUat95pWWZlEqbQT6yRDrvj8xAssnGoH72GTNDOABnMatXSVRKjAgBb8hp06ZN2rJlS+b51q1btX79+ry2EY1G1dzcnHkeiUTU25vfYPi6deu0Y8eOMcvWrl2r7du3W96GV96LV1XKyQyATdyavq6YNOXZaHTCToGwdPYa6fn/cbskwETT3fFW6CC+PySteqfUs1/a9zP7yovqUglTRbohVC/FzrhdCqDyVWKGYgCAPQrJpi1V5w2Yre3Sdd+S7ruR66kdaEOVPyeDQafrO6/ULJNWppsu5ef8k09Iv7i9+P0AsMbOZBmYoFJiRAh4w6Sam5sVjUYlFTZ95/hAs+3bt2vt2rWWX9/Z2alVq1bl/N2ePXvU3t5ueVtuvxcvq5STGeBphUwj5xY3p6/LN015LjQ6AVSbXHe8MZ0MAABTq7QMxQAAe7z5dqn9pvxfV603YHI9BSZyKhh0qr7zSs4yOX666eyxlpP7pCe/bW9W4ak+52o91wNusitZBiaolBiRoNsFgHf97Gc/ywScdXV1adOmTdq8ebOl13Z1dY0JEFu/fn3eAWJdXZNH4+/evTuvgDe33wuAKlXoNHJuiXabmYDccqjTTLtuJU35ZII19pUHAMpBYkR64LPS8/ebd7zJYDoZAACmw+A8ACCX3v2jj/O5gTU+7EZp3cf1FJjoUKd0+yopPmT/dnP1nScT0rM/tHdfXvL4NmnFW6S6iLTnLuen+53qc/YztSJQcj1d5o3dxSbLQMUi4A2Tam9v1/bt27Vu3TpJ0pYtW7R8+fJppwONRqNas2ZN5nlHR4e2bt2a9/7b2toK+l0ubr8XAFUm32nkBo6bjahffMXdeel33+l+yvyHtkgX31B44F/zUluLAwBl41Cn9PU3SOEGgt0AAAAAoBDx4cJuYOUGTADZ7A52S8vuO7d6rip3I6elbVeVdp/V+DkDXmZHsgxULL/bBYC3rV27Vjt37lQkEpEkbdiwQRs2bMhMDzrejh07tGzZskx2tvXr12vnzp0F7bu9vV0dHR2Wl0/HzfcCoIr0H5HuXGNOC5pv8Fg6S8+dr5f6jzpTvskkE2ajzW3JmLT764W/vnWlfWUBgHLTu186+ozbpQAAAACA8vRyp/Tlleag6nSBDekbWP+lXTr2XGnKB6C6JWPS43dID262fq5C/vicAe95aIsZgAqM4zMMw3C7EPC+aDSqTZs2adu2bZllHR0dam9v1+zZs/XrX/9anZ2dmeCwtrY2bd26taDAtPE2bNig++67T5J03XXXafPmzZmgtUK4+V68qFLmZwY8of+IfdPIlXpe+iNPS1+9ojT7mk5Di3Tr86PTQuQjmZBuO6+yGqDzLpJip6We/dOvCwAAAAAAAACoTD6/ZCTdLkXl8welZNztUgDIdsUtUscn3S5FxaiUGBEC3pC3HTt26N5771VnZ6d6enoUjUbV1tamSCSijo4OXX/99Wpvb3e7mJZU0nspVKWczADXJWJmZrdDT9i3zdb20s1L3/lN6Qcfcn4/Vv3lo9L8iwp77a5PmXddVYJ04KMk3fN2M3UzAAAAAAAAAAAAUC2KSZaBCSolRiTodgFQftauXau1a9e6XQxbVNJ7AeCyR75gb7CbVNp56XsPOL+PfBx6svCAt9U3S7/4ijlFbDlrbZdu+M5olr+bf2p+Hx7akv90uQAAAAAAAAAAAEA5GjguHdtb+NghKpLf7QIAAFD2ot3Sw593Ztulmpc+Puz8PvLRW8T0nZHF0pUfta8spRYIS9d8wgxwy57SNhAygx8//ISZurmhxb0yAgAAAAAAAKgOfvKnAAA84NCTbpcAHkMNBQCAYu2+07mMW8mYtPvrzs9LH6xxdvv5KjYA78pbpN/db3/WPSc1tEgrb5JWv8cM2ptMZLH5fXjd35l3sxx60gwQjA+bf8fmZVLrJVJNo7TnG9IT3zLvfAEAAAAAAACAfCXj0qXvlfb8OzNPAADcU0yyDFQkAt4AAChGMiE9cbez+3jiW2Zwk5Pz0jcvdW7bhSg2AC8Qkm64V7rrjVJPlz1lcsJZl0sXv8MMUJu7Ir+/sT9gpm6eKn1zOjDuyNPSN/5EGukvusgAAAAAAAAAqsyCS6QP/x/z5mxusAUAuMFrs1XBdUxpCgBAMY7tdb5xn56X3kmtK53dfr6alxW/jZnzpHf/WGptL35bTmhtl/7iB1L7TWbQmlMBjf6AGVB32Xud2T4K07xMmneh26UAAAAAAAAApte7f3TmiVufl/58h3emOl3xZ5I/5HYpAGBqM+a5XYLy57XZquA6At4AAChGqabMdHpe+rkrzCk1vaL1Enu2M3OedPNPpWs+4a1Oj1lt0g3fMTPRlcrqm6VAuHT7K1b9HOnqj+f/d/OVQfW2tV16z0+kG//T/C4AAAAAAKoTQRoAykV2Vh1/QDpnjXTVRvfKk+3sa6UPPyFdcYu3+rgBIC0QNhM0eG2sqtzYkSwDFaUMRgQBAPCw3gMl2o/D89L7A9LKG53dh1UNLWYAnl0CIem1G73T6dHabjZsZpb4bp7IYunKj5Z2n8Vo/wvp6r+x/ndraDHXu/aTpSlfIQJhs0F780/Nv79dWQhb26XLP2xPGQFUFu4cBQAAKJzTN1Q1tEhr7yx9f8XyDunS9zLYCiA/ubLqXHmLN2YOGZ997i8fld58u7TkcrdLBoxaemX1XXvrZkmLLpPqZ7ldEvdd9TFpdpu3xqrKkV3JMlAxPJJrFgCAMlWq+eJLsZ/VN0u/+IqUGHF+X1NZeZMz03umOz1e93fmFLGHnjQ7Q+LDZodN0xLp6NPSnn+XkjH79x8Im42aKz5S2sxu2a68Rfrd/aXLTFioQFha/R7z8XR/t+ZlZiNn7grze3PkaWmXx4LeGlrM7/Xq95jvJ1s6C+GjX5Qe2pLfdy/7OyVJBx7x/t92Kq3t0tIrpMe+7HZJ4JSaJvP8d+aE2yWpDuk7R5/Zkf/5BSgH/pA0b4V0+Cm3SwIAE6XbAO3vlEb6zXbMk9+WXnrM7ZLBinRba/i09NiXnNtPuv9jfLv3hf+VfvZpyUjYv89AWHrTP5v7fM3/kXZ/XXriW9LA8clfUzdLmn221POCdKbH/jIBKA+5suoEQtIN90p3vVHq6Sp9mdLGZ5+bf5H5/+K3S3euKe/+MlSOxZdJb/k3a9feSrDyJulNXzKPyWRitG//5AtmvXjgmNslLJ3W9tE+fCn3mEc1fi75sjtZBioCAW8AABSjVPPFl2I/6QxgD/6j8/uaTHagk1OyOz1ysbvDd6pgp1LzSifUdK762MTParq/W1p6el6vdBi88u3SW/516iDOdBbCi2+w9t2b7DtVDn/bXCotcA+5tbabUzlL5fk9dYqTgZ7Zd45aPb8A5SD7OvirrxLw5rbxQT1OBmkAXlUbkS74MzNzxfgbctK8Nuiefew+dY/00P9zu0TeML6tFe2WfvVvztwYmKv/I7vdGx9ypn8mu72d7w1m2YPVvfvNvpDffk8aitpfzmqz9i5p4Sppzzeos8O7Jsuqk57B4J63S4c6S1qkjMn6zsulLxTVIT489tq79/vSjne7XSrnLH7VaH14fN/+H3zAvuOyeZkUbpCOPlP8ttLqZpttWjvqOLPazD7RXEkQnPxcKpFTyTJQ1gh4AwCgGM1LS7SfEs1L73YGsFyBTqVWbIfvZOt5hRc6oaYy/m6nfKWn5330i/aVqRhNC63//fP97o1n1982skQ65w1mp4uTd5S5FbjnlewE/qCUjLu3/1LJld3Sju9puWcEdDrQ08qdo7nOLzWNDLLBm866XLr4HROvg6W6+QQTnf16qePvcwf1OBWkAXhROqh/poVpxN0YdI8skc77I7PuOVmbotqzwPr8UsenpOWvm3hOc/LGwOn6P5zon5msvW31BrNc6139cff7F7IDOIei0jf+xAzCLicjA1LzWaN19v96v/Sbe90uFTBquqw6xcxgYIep+s693heK6pHdfvUHpJHT7pWlFKaaetKu49KJG2xntZllk+wrn5W2gsT5aiqlSJaBsuQzDMNwuxAA3HPw4EEtXjyxc6W7u1uLFi1yoURAmTnytPTVK5zfz18+On3Ho136j7pzF0lru9kx4tZ0n9UmEXOvE2oy6cak1QbgZKLd0r+0uz89ryS9+Xap/abS7rPQv+34wCQ7zwVWsl5k6z9qT+DeVIN7uYJVk3Hp+ful6IuF73c6pZoeyW3TZbe043sqeSdLilWTfS52Hm92nEuTCfNv4/VsL+Ue+Ahrpqojdn5T+sGHSl+mahcISx/qnDxQIxErv/MzkK9cQf1W2VHXnU4+5fvJJ6Rf3O5cWbzumk+YGXEn48Q5zWr/h9fqiJNxun/BH5JWvdPsF4u+NPVNYaXqp7PblbdK1/796POffUZ65Db3ygOMd8UtZkCmFdHu0mcYt9J37sW+UFSX8f3ElXyub2iRbn1++hvB7erHluypY48PULOzfPko1fkqu//cjn5xJ/vppquzI2+VEiNCwBtQ5SrlZAa4JpmQbjvP2ca71caBnUrRAZ/NyY5XTM2NTqhc8r3baToPbvZGZpFSBquOZ/VvO1VgkhMdBVZVWodCrumRvBKYaafJMv5MptjvqVtB2vkKz5De9SNp/oXOBnraeS71csdruQc+ui0QNgdVf/0172fym66OWK6D2rnMmCedPup2Kayx0tFcLudneFcgLJ29Rnr+f9wuyVjTBfVbVao6pxVeuOaH6qXYmdLvtxwCz7xWR5yK1bq91azbhXyfyzUY/tUflN7w2dHn5fo+nFROddhKM93NDpNJJsz6utNZF/PtO/dKXyiqz/h+YjdvOnC6/ZlPkKxkTz+25Fx/sl3ly5dT56vJ3q9Xb1AmWYYjKiVGhIA3oMpVyskMcNWuTzk7fWK+jQO7lOouklJ1vGJq6WxXuz4tvbCzdPstNjhpMl7ILOJGsGouxU6761bgWZrXOxSyp88Z6c/vc/ZKYKZdCu0El4r7ntoxCFjTKA33Ff766Vi9lrt9vGXzYraXUgQ+BmvN6Rgr0fjPz+k6bLEd6FbqiKW4+cRp6eP3onXSVy7zfiB0Ph3Npb6JBpUh+1w1FC19UGs+maTsUIo653TcDqyZ1SZd9y3pvptKGyRbToFnXqojWmG1bl9sWzUXLwRwFmJ8hrdKCuovVqnrsJio2Kw6Xu07zz4HnXxBevLb0sAx24uHFJ9fMpJul2Jy6QxXTsnVT+zGNasU7U+3+gezOdWf7ETdxYrp9lvTKO35hn3v10s3KJMswzGVEiNCwBtQ5SrlZAa4ysksPcU0DuxS6rtI4K5SZZ2yOzgpF7czi7gVrOoUtwLP0rzaoVDMfr0QmGknN1PLFzsI6LWOPrePN8n9wUI3pkJubU8NuN9YXsdloUEQTtdhP/Ar6Zkdzg/Ol2rQ0+rnXExHs9cDoQvpaHbqJppQnSSfOxmh0pycrqWuWRrstX+72SYL7GpaIh19Wtrz76WZ7qthrnTJn0uzl088V5UyqLUU7ZOpuFXXldwNrMkOACtlkGy5Bp55oY7odV68acOK8dPcleL853Rwx3S8WIctd83LpHCDdPQZ+7ZpR1adcuk7d7sfsVKlr4HDp6XHvuR2aSYqVfly9ROX8qaDUrY/vTT1pJt1bDfY/X7dvkGZZBmOqpQYEQLegCpXKSczwHU0Duy/iwTucer7fNbl0sXvKG1j0q3MIl4IVnVKtXUUOK1SOlS9klq+mEFAL17L3Tze3Mz24vYgdDkclz6/1PEpafnrivselOJ77/TgvJMDaMV8zoUcv04EQjfMlVa8Rfr9T6Toi4Vvp9iOZieyWEnuZyiWnJmu5fpvSffe5Ewd1u67+othJYjS6aDWfKdjr0RuZMu0exqlYvebL7cDz2iTTc7tmzYKNX6aO8n589/lfy3VzHB+Zofx7DjvOlWHvfyvpQOPlGd22nRdTfJmVh0vtrdzqbYMxf6Q1HKevUGSaeOvgV4LVi1l+SbrJy7VTQdr75JW/Glp2p9e6R+EN7h9swimVCkxIgS8AVWuUk5mgOtoHIyi47X8Vdr3uVTT82bzUrAqvK/cO1S9mFreK4Em5Xotl9zJ9uKlQWi77kR1KvuTXdeZUn7vnawjlssAmhVOTf3hlY5mJ74Hbmco9trfzO4pOXP9zZJx6fn7SxNEWS5ZYcpdqbNl2hVwWTdLmn221POCdKan+P3mi/4P73F7it5C5JrmTird+a8UAc659lsMJ+uwUuF1pva/kH79NfvKZHW/4+tqXsyqU07tbTf6Ed1yzSfM706p6pxuZrSeKqtwWqnblaW46WCya0waU0+iFNy+WQQ5VUqMCAFvQJWrlJMZ4Ak0DlBJKvH7XKpO3HIOcIF7yrVDtdJSy1fiua9QlTCNXLGD0HYECEneH9iphO99OQ2gWeHkIGWldjS7naG4Gv9mpQyirKSgVq8q92yZBJ4hzc0peguVa5q7tFKe/7KPoye/Lb30WGn2Wyin67CFXH8bW73ThvLKzQ7Zyq3dUcpgUDeMb++Uos7pRLvNCqvfFzfalU7fdDDVNSbNi0GyqEzU2T2lUmJECHgDqlylnMwAz6BxgEpSqd/n8Q2rMz3Sb78nDUWL33a5B7jAfaXsUD3rcumi66WjT0t7/t07neBuq9RzXyGYRs5UbMd/OQzsVML3vhw+53w4PUhZaR3NXgh6rNa/WbkOjnKTykQEFqISuDFFbzGmy3jm1vmvXM67pajD5nv99VobymuB8+XY7nCyH9EtU7V3nK5z2tlusyLf70up25VeyWbsxSBZAI6qlBgRAt6AKlcpJzPAU2gcoJJUy/e5HDvcUNmyOxhPvmDeYT9wzL7tu3EnbzmplnPfdLzS8eoVxXT8l8N1phK+9+XwOeeL87N1Xgl6rNa/WTkNjnohqNWLyiXABZhOqabotYOVgFC3zn/lct71Wh3Wq20oLwXOe+1vVgi72h1Lr5Ae+7J95bK6X7fbO3Z8ftMp5vtS6nall246qNa2DFCFKiVGhIA3oMpVyskM8CQaB6gk1fB9roQON1SuUg02eKkT3Auq4dw3HS91vJa7crnOlPv3vlw+53xxfrbGS0GP/M3s56W/b6UqlwAXYCpOBhzZKZ+AULfOf+V03vVSHZY2lDVe+psVwo52h1S6KT691t4p9PObjl3fl1K2K7140wFtGaDiVUqMCAFvQJWrlJMZ4Gk0DlBJquH7XO4dbqhc5TTYUGmq4dw3GS92vJa7crnOlPv3vlw+Z9ivUoMeYeLv6zzqnKgETgUc2aWQgFC3zn/ldt71Qh2WNlR+vPA3K0ax7Q6np/j0ensn38+v/Z3SSH/pvi+laldy0wGAEquUGBEC3oAqVyknMwAAbFfuHW6oTOU22IDKQMerM7jOlAafc/Ui6LGy8fd1FnVOlDsnAo4a5kor3iL9/idS9MXCt1NsQKhb5z/Ou/mhDVV9iml32BFsHlkinfdHkj9Ynu0dr7fbSlE+bjoAUEKVEiNCwBtQ5SrlZAYAAFBVGGxAqdHxCqBceX3wDMXh7+ss6pwoZ04FHHklINSt8x/nXetoQyEfXjm3wF18DwCUSKXEiBDwBlS5SjmZAQAAVCUGG1BKdLwCAFC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      "text/plain": [
       "<Figure size 2820x1410 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "matplotlib.rcParams[\"figure.dpi\"] = 470\n",
    "plt.rcParams['text.usetex'] = True\n",
    "plt.rcParams[\"figure.figsize\"] = (6,3)\n",
    "\n",
    "iters_nsqst = np.linspace(0,len(loss_nsqst)-1, len(loss_nsqst))\n",
    "plt.plot(iters_nsqst, exact_nsqst, label = 'Exact Infidelity')\n",
    "plt.plot(iters_nsqst, loss_nsqst, '.', label = 'Estimated Infidelity (Loss)')\n",
    "plt.legend()\n",
    "plt.xlabel('Iteration number')\n",
    "plt.ylabel('Infidelity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3b33302",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}