Benchmark of circuit simulation and expectation values.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Benchmark: Circuit simulation and expectation values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Circuit simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from time import time\n",
    "import copy\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from qiskit import Aer, execute\n",
    "from qiskit.quantum_info import Statevector\n",
    "from qiskit.aqua.operators import PauliExpectation, AerPauliExpectation\n",
    "from qiskit.circuit.library import ZZFeatureMap, ExcitationPreserving, RealAmplitudes\n",
    "\n",
    "AER_STATEVECTOR = Aer.get_backend('statevector_simulator')\n",
    "AER_QASM = Aer.get_backend('qasm_simulator')\n",
    "LABELS = ['Aer statevector', 'Aer shots', 'Aer snapshot', 'QI statevector']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def benchmark_circuit(n, mode='zz'):\n",
    "    if mode == 'zz':\n",
    "        circ = ZZFeatureMap(n, reps=10)\n",
    "    elif mode == 'swaprz':\n",
    "        circ = ExcitationPreserving(n, reps=10)\n",
    "    else:\n",
    "        circ = RealAmplitudes(n, reps=10)\n",
    "    return circ.assign_parameters(np.random.random(circ.num_parameters))\n",
    "\n",
    "\n",
    "def aer_statevector(circuit):\n",
    "    t0 = time()\n",
    "    result = execute(circuit, AER_STATEVECTOR).result().get_statevector()\n",
    "    return result, time() - t0\n",
    "\n",
    "\n",
    "def aer_shots(circuit):\n",
    "    t0 = time()\n",
    "    result = execute(circuit, AER_STATEVECTOR).result().get_counts()\n",
    "    return result, time() - t0\n",
    "\n",
    "\n",
    "def qi_statevector(circuit):\n",
    "    t0 = time()\n",
    "    sv = Statevector.from_label('0' * circuit.num_qubits).evolve(circuit)\n",
    "    return sv, time() - t0\n",
    "\n",
    "\n",
    "def aer_snapshot(circuit):\n",
    "    t0 = time()\n",
    "    sv = execute(circuit, AER_QASM).result().data(0)['snapshots']['statevector']['final']\n",
    "    return sv, time() - t0\n",
    "\n",
    "\n",
    "def run(nruns, circuits):\n",
    "    funs = [aer_statevector, aer_shots, aer_snapshot, qi_statevector]\n",
    "    labels = LABELS\n",
    "    means = {label: [] for label in labels}\n",
    "    stds = {label: [] for label in labels}\n",
    "\n",
    "    for circuit_ in circuits:\n",
    "        for fun, label in zip(funs, labels):\n",
    "            circuit = copy.deepcopy(circuit_)\n",
    "            if label == 'Aer shots':\n",
    "                circuit.measure_all()\n",
    "            elif label == 'Aer snapshot':\n",
    "                circuit.snapshot('final')\n",
    "            runtimes = []\n",
    "            for _ in range(nruns):\n",
    "                _, time = fun(circuit)\n",
    "                runtimes.append(time)\n",
    "            mean = sum(runtimes) / nruns\n",
    "            std = sum((time - mean)**2 for time in runtimes) / (nruns - 1)\n",
    "            print(label, 'mean', mean, 'fastest', min(runtimes))\n",
    "            means[label].append(mean)\n",
    "            stds[label].append(std)\n",
    "            \n",
    "    return means, stds\n",
    "\n",
    "\n",
    "def plot(x, means, stds, circuit_name):\n",
    "    plt.figure()\n",
    "    for label in LABELS:\n",
    "        plt.errorbar(x, means[label], yerr=stds[label],  label=label)\n",
    "    plt.title('Simulation of {} with 10 reps'.format(circuit_name))\n",
    "    plt.xlabel('number of qubits')\n",
    "    plt.ylabel('time [s]')\n",
    "    plt.legend(loc='best')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aer statevector mean 0.04555286884307861 fastest 0.041046142578125\n",
      "Aer shots mean 0.05022606372833252 fastest 0.04257798194885254\n",
      "Aer snapshot mean 0.03506753444671631 fastest 0.027051687240600586\n",
      "QI statevector mean 0.033678655624389646 fastest 0.030265092849731445\n",
      "Aer statevector mean 0.155188364982605 fastest 0.08968997001647949\n",
      "Aer shots mean 0.1417253303527832 fastest 0.05586695671081543\n",
      "Aer snapshot mean 0.07220908164978028 fastest 0.03692007064819336\n",
      "QI statevector mean 0.07265693187713623 fastest 0.04366612434387207\n",
      "Aer statevector mean 0.12310535430908204 fastest 0.07602190971374512\n",
      "Aer shots mean 0.13385824203491212 fastest 0.11483001708984375\n",
      "Aer snapshot mean 0.07911744117736816 fastest 0.06816577911376953\n",
      "QI statevector mean 0.10769449710845948 fastest 0.09860515594482422\n",
      "Aer statevector mean 0.20727049827575683 fastest 0.12965130805969238\n",
      "Aer shots mean 0.2097356700897217 fastest 0.14642000198364258\n",
      "Aer snapshot mean 0.14250317096710205 fastest 0.11803436279296875\n",
      "QI statevector mean 0.20576401710510253 fastest 0.18503808975219727\n",
      "Aer statevector mean 0.2825505495071411 fastest 0.20804095268249512\n",
      "Aer shots mean 0.30146745204925535 fastest 0.26017284393310547\n",
      "Aer snapshot mean 0.21875907897949218 fastest 0.18428492546081543\n",
      "QI statevector mean 0.39084728717803957 fastest 0.3533670902252197\n",
      "Aer statevector mean 0.40585993766784667 fastest 0.3374781608581543\n",
      "Aer shots mean 0.40820539474487305 fastest 0.3591029644012451\n",
      "Aer snapshot mean 0.31531582355499266 fastest 0.2749781608581543\n",
      "QI statevector mean 0.8876868438720703 fastest 0.8204560279846191\n",
      "Aer statevector mean 0.056613373756408694 fastest 0.05085301399230957\n",
      "Aer shots mean 0.05518864631652832 fastest 0.05164170265197754\n",
      "Aer snapshot mean 0.03826752185821533 fastest 0.03219914436340332\n",
      "QI statevector mean 0.02969141960144043 fastest 0.027888059616088867\n",
      "Aer statevector mean 0.2642342519760132 fastest 0.1787889003753662\n",
      "Aer shots mean 0.2659258651733398 fastest 0.23848223686218262\n",
      "Aer snapshot mean 0.204984827041626 fastest 0.17622780799865723\n",
      "QI statevector mean 0.1686696243286133 fastest 0.15471100807189941\n",
      "Aer statevector mean 0.5815559720993042 fastest 0.47409582138061523\n",
      "Aer shots mean 0.571722149848938 fastest 0.49771618843078613\n",
      "Aer snapshot mean 0.5339335680007935 fastest 0.4366459846496582\n",
      "QI statevector mean 0.42880184650421144 fastest 0.39018917083740234\n",
      "Aer statevector mean 1.0452611446380615 fastest 0.9794521331787109\n",
      "Aer shots mean 1.0237567472457885 fastest 0.9669559001922607\n",
      "Aer snapshot mean 0.9820241498947143 fastest 0.9051680564880371\n",
      "QI statevector mean 0.8455496311187745 fastest 0.7674901485443115\n",
      "Aer statevector mean 1.6584326696395875 fastest 1.59523606300354\n",
      "Aer shots mean 1.7156508493423461 fastest 1.597407341003418\n",
      "Aer snapshot mean 1.6065284061431884 fastest 1.537536859512329\n",
      "QI statevector mean 1.5551580047607423 fastest 1.4333932399749756\n",
      "Aer statevector mean 2.6002757120132447 fastest 2.461311101913452\n",
      "Aer shots mean 2.6376692628860474 fastest 2.488668203353882\n",
      "Aer snapshot mean 2.445125322341919 fastest 2.359985828399658\n",
      "QI statevector mean 3.2727427816390993 fastest 3.1020798683166504\n",
      "Aer statevector mean 0.016397414207458497 fastest 0.011642932891845703\n",
      "Aer shots mean 0.0184539270401001 fastest 0.016824007034301758\n",
      "Aer snapshot mean 0.01431086540222168 fastest 0.01068115234375\n",
      "QI statevector mean 0.012803030014038087 fastest 0.011439800262451172\n",
      "Aer statevector mean 0.05438659191131592 fastest 0.03497600555419922\n",
      "Aer shots mean 0.06082582473754883 fastest 0.05422472953796387\n",
      "Aer snapshot mean 0.037451467514038085 fastest 0.03260183334350586\n",
      "QI statevector mean 0.047187886238098144 fastest 0.04349017143249512\n",
      "Aer statevector mean 0.13063032150268555 fastest 0.07171201705932617\n",
      "Aer shots mean 0.12616854190826415 fastest 0.11551117897033691\n",
      "Aer snapshot mean 0.07985154151916504 fastest 0.06808590888977051\n",
      "QI statevector mean 0.10424383163452149 fastest 0.09759998321533203\n",
      "Aer statevector mean 0.19987151622772217 fastest 0.12438774108886719\n",
      "Aer shots mean 0.1982610273361206 fastest 0.1785721778869629\n",
      "Aer snapshot mean 0.14151210308074952 fastest 0.1175699234008789\n",
      "QI statevector mean 0.19686779022216797 fastest 0.18355894088745117\n",
      "Aer statevector mean 0.2769124841690063 fastest 0.19924116134643555\n",
      "Aer shots mean 0.28387300968170165 fastest 0.2514150142669678\n",
      "Aer snapshot mean 0.21297004222869872 fastest 0.1835777759552002\n",
      "QI statevector mean 0.38044792652130127 fastest 0.353405237197876\n",
      "Aer statevector mean 0.4106200838088989 fastest 0.3103911876678467\n",
      "Aer shots mean 0.43614518642425537 fastest 0.3580751419067383\n",
      "Aer snapshot mean 0.3414356231689453 fastest 0.27510499954223633\n",
      "QI statevector mean 0.8810662841796875 fastest 0.8211619853973389\n"
     ]
    }
   ],
   "source": [
    "num_qubits = [2, 4, 6, 8, 10, 12]\n",
    "nruns = 50\n",
    "means_zz, stds_zz = run(nruns, [benchmark_circuit(n, 'zz') for n in num_qubits])\n",
    "means_swaprz, stds_swaprz = run(nruns, [benchmark_circuit(n, 'swaprz') for n in num_qubits])\n",
    "means_ry, stds_ry = run(nruns, [benchmark_circuit(n) for n in num_qubits])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(num_qubits, means_zz, stds_zz, 'ZZFeatureMap')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(num_qubits, means_swaprz, stds_swaprz, 'ExcitationPreserving')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(num_qubits, means_ry, stds_ry, 'RYCX')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Expectation values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from time import time\n",
    "import copy\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "\n",
    "os.environ['QISKIT_IN_PARALLEL'] = 'TRUE'\n",
    "LABELS = ['Aer statevector', 'Aer shots', 'Aer snapshot']\n",
    "\n",
    "from qiskit.aqua.operators import PauliExpectation, AerPauliExpectation, MatrixExpectation, StateFn, H, Y, CircuitSampler, CircuitStateFn\n",
    "AER_STATEVECTOR = Aer.get_backend('statevector_simulator')\n",
    "AER_QASM = Aer.get_backend('qasm_simulator')\n",
    "\n",
    "\n",
    "def benchmark_expval(n, op='paulis', mode='zz'):\n",
    "    ops = {'paulis': Y ^ n,\n",
    "           'hadamards': H ^ n}\n",
    "    circs = {'zz': ZZFeatureMap(n, reps=10),\n",
    "             'swaprz': ExcitationPreserving(n, reps=10),\n",
    "             'ry': RealAmplitudes(n, reps=10)}\n",
    "    circ = circs[mode]\n",
    "    circ.assign_parameters(np.random.random(circ.num_parameters), inplace=True)\n",
    "    state = CircuitStateFn(circ)\n",
    "    op = StateFn(ops[op], is_measurement=True)\n",
    "    expval = op.compose(state)\n",
    "    return expval\n",
    "\n",
    "\n",
    "def aer_statevector(expr):\n",
    "    t0 = time()\n",
    "    expval = PauliExpectation().convert(expr)    \n",
    "    sampler = CircuitSampler(AER_STATEVECTOR).convert(expval)\n",
    "    result = sampler.eval()\n",
    "    return result, time() - t0\n",
    "\n",
    "\n",
    "def aer_shots(expr):\n",
    "    t0 = time()\n",
    "    expval = PauliExpectation().convert(expr)\n",
    "    sampler = CircuitSampler(AER_QASM).convert(expval)\n",
    "    result = sampler.eval()\n",
    "    return result, time() - t0\n",
    "\n",
    "\n",
    "def aer_snapshot(expr):\n",
    "    t0 = time()    \n",
    "    expval = AerPauliExpectation().convert(expr)\n",
    "    sampler = CircuitSampler(AER_QASM).convert(expval)\n",
    "    result = sampler.eval()\n",
    "    return result, time() - t0\n",
    "\n",
    "\n",
    "def run(nruns, circuits):\n",
    "    funs = [aer_statevector, aer_shots, aer_snapshot]\n",
    "    labels = LABELS\n",
    "    means = {label: [] for label in labels}\n",
    "    stds = {label: [] for label in labels}\n",
    "\n",
    "    for expr in circuits:\n",
    "        for fun, label in zip(funs, labels):\n",
    "            runtimes = []\n",
    "            for _ in range(nruns):\n",
    "                _, time = fun(expr)\n",
    "                runtimes.append(time)\n",
    "            mean = sum(runtimes) / nruns\n",
    "            std = sum((time - mean)**2 for time in runtimes) / (nruns - 1)\n",
    "            print(label, 'mean', mean, 'fastest', min(runtimes))\n",
    "            means[label].append(mean)\n",
    "            stds[label].append(std)\n",
    "            \n",
    "    return means, stds\n",
    "\n",
    "def plot(x, means, stds, expval_name):\n",
    "    plt.figure()\n",
    "    for label in LABELS:\n",
    "        plt.errorbar(x, means[label], yerr=stds[label], label=label)\n",
    "    plt.title('Expectation of {}'.format(expval_name))\n",
    "    plt.xlabel('number of qubits')\n",
    "    plt.ylabel('time [s]')\n",
    "    plt.legend(loc='best')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aer statevector mean 0.017399101257324218 fastest 0.015057802200317383\n",
      "Aer shots mean 0.020060615539550783 fastest 0.017447948455810547\n",
      "Aer snapshot mean 0.017374248504638673 fastest 0.014934062957763672\n",
      "Aer statevector mean 0.06097594261169434 fastest 0.04667520523071289\n",
      "Aer shots mean 0.06184784889221191 fastest 0.0520319938659668\n",
      "Aer snapshot mean 0.0627527618408203 fastest 0.04471015930175781\n",
      "Aer statevector mean 0.11404788017272949 fastest 0.0943002700805664\n",
      "Aer shots mean 0.15208008766174316 fastest 0.10773873329162598\n",
      "Aer snapshot mean 0.1199489688873291 fastest 0.08989691734313965\n",
      "Aer statevector mean 0.27532276153564456 fastest 0.17444109916687012\n",
      "Aer shots mean 0.2621859836578369 fastest 0.19871091842651367\n",
      "Aer snapshot mean 0.18387337684631347 fastest 0.15432214736938477\n",
      "Aer statevector mean 0.34400837898254394 fastest 0.27541494369506836\n",
      "Aer shots mean 0.42866891860961914 fastest 0.3144187927246094\n",
      "Aer snapshot mean 0.2987189674377441 fastest 0.24632716178894043\n",
      "Aer statevector mean 0.04135520935058594 fastest 0.03777503967285156\n",
      "Aer shots mean 0.04621322631835938 fastest 0.040287017822265625\n",
      "Aer snapshot mean 0.039112510681152346 fastest 0.03737211227416992\n",
      "Aer statevector mean 0.22739102363586425 fastest 0.19441580772399902\n",
      "Aer shots mean 0.7381989479064941 fastest -3.7035932540893555\n",
      "Aer snapshot mean 0.37889723777770995 fastest 0.23096704483032227\n",
      "Aer statevector mean 1.1454532432556153 fastest 0.5933749675750732\n",
      "Aer shots mean 1.2475059413909912 fastest 0.6869888305664062\n",
      "Aer snapshot mean 1.319756441116333 fastest 0.5751159191131592\n",
      "Aer statevector mean 1.1360503578186034 fastest 0.9849817752838135\n",
      "Aer shots mean 1.077573881149292 fastest 1.003352165222168\n",
      "Aer snapshot mean 1.068251314163208 fastest 0.9637279510498047\n",
      "Aer statevector mean 2.758451910018921 fastest 1.6397168636322021\n",
      "Aer shots mean 2.682426109313965 fastest 1.8786649703979492\n",
      "Aer snapshot mean 1.8960475063323974 fastest 1.782829999923706\n",
      "Aer statevector mean 0.015588102340698242 fastest 0.01327967643737793\n",
      "Aer shots mean 0.018499279022216798 fastest 0.016167879104614258\n",
      "Aer snapshot mean 0.01480916976928711 fastest 0.012871980667114258\n",
      "Aer statevector mean 0.03321027755737305 fastest 0.030606985092163086\n",
      "Aer shots mean 0.0392530632019043 fastest 0.03588390350341797\n",
      "Aer snapshot mean 0.03262674331665039 fastest 0.028647899627685547\n",
      "Aer statevector mean 0.06997398376464843 fastest 0.05623006820678711\n",
      "Aer shots mean 0.07595415115356445 fastest 0.06551694869995117\n",
      "Aer snapshot mean 0.052523412704467774 fastest 0.04665803909301758\n",
      "Aer statevector mean 0.11063852310180664 fastest 0.08723592758178711\n",
      "Aer shots mean 0.14949520111083983 fastest 0.11140799522399902\n",
      "Aer snapshot mean 0.09329253196716308 fastest 0.06621003150939941\n",
      "Aer statevector mean 0.1882334804534912 fastest 0.1297609806060791\n",
      "Aer shots mean 0.24767383575439453 fastest 0.16829586029052734\n",
      "Aer snapshot mean 0.1267673110961914 fastest 0.09682416915893555\n"
     ]
    }
   ],
   "source": [
    "num_qubits = [2, 4, 6, 8, 10]\n",
    "nruns = 25\n",
    "means_zz_paulis, stds_zz_paulis = run(nruns, [benchmark_expval(n, 'paulis', 'zz') for n in num_qubits])\n",
    "means_swaprz_paulis, stds_swaprz_paulis = run(nruns, [benchmark_expval(n, 'paulis', 'swaprz') for n in num_qubits])\n",
    "means_ry_paulis, stds_ry_paulis = run(nruns, [benchmark_expval(n, 'paulis', 'ry') for n in num_qubits])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(num_qubits, means_zz_paulis, stds_zz_paulis, 'ZZFeatureMap + Pauli op')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UFFhnkxw8eJAjR46cdn0FBQXk5uZy1VVX8dJLL/Hzzz8DEBoaSn5+ftVy+fn5xMTEUF5ezvz586umV18uLCyMrl27kpiYCFgf6FauLyQkhKFDhzJr1izGjx+Pt7f3aZe/9NJLefXVVwFwOBzk5uaeUtPo0aN57733cDgcHM3K5McfvmfYsGGNP7i10IBXSjWLBQsWMHny5BOmTZkyhQULFnDFFVdwww03MGLECPr168fUqVNPCMSa5OfnM378ePr378+oUaP4+9//DlhdPy+88ALnnXceSUlJ/OlPf2L48OGMHDmSnj17Vt3/5OXmz5/P7NmzGTBgAH369GHZsmVVy06fPp158+Yxffr0qmm1Lf/yyy/zzTff0K9fPwYPHsz27duJjIxk5MiR9O3bl4ceeojJkyfTv39/BgwYwM1TxvPIU3+iQ4cOZ3yMT9airsk6ZMgQoxf8UMo1duzYQa9evRp0n6bsolE1qzwHvlt0SJ3L1vQ3FJENxpghNS2vffBKqVppsLdu2kWjlFIeSgNeKaU8lAa8Ukp5KA14pZTyUBrwSqnavXW19aNaJQ14pVSzcvVwwTNmzGDRokX1Xj45OZl3333XJbW4mwa8UqpZuXq44IbSgFdKqSbg6uGCK61YsYILLriAhISEqtZ89SF6+/Xrx3vvvVe1jpUrVzJw4EBeeukltm3bxrBhwxg4cCD9+/dnz549rjwkLqVfdFLqbPTpo3BoS93LHdps/V+ffvgO/eDK5067iKuHC66UkZHBqlWr2LlzJxMnTmTq1KksWbKkaojerKwshg4dyoUXXshzzz3Hiy++yEcffQTA/fffz6xZs7jxxhspKyvD4XDUve8tlLbglVLNZsGCBVx33XXA8eGCgROGCx40aBA7d+6sajnXZ7jgJUuWEBQUVDXvmmuuwcvLi969e3P48GHgxCF627dvz0UXXcSPP/54ynpHjBjBs88+y/PPP8+BAwcIDAxs8uPQXLQFr9TZqI6WdpXKlvvMj894k805XLC/v3/Vcg0db+uGG25g+PDhfPzxx1x11VX897//5ZJLLmng3rYMLm/Bi4i3iPwkIh+5eltKqZaruYYLrk31IXozMzNZsWIFw4YNO2Uo33379pGQkMADDzzApEmT2Lx585nvvJs0Rwt+FrADCGuGbSmlWqgFCxbwyCOPnDCtcrjgV199lR07djBihDW4WUhICPPmzcPb27vW9eXn5zNp0iRKSkowxlQNF1ybyZMns2bNGgYMGICI8Le//Y0OHToQGRmJt7c3AwYMYMaMGZSWlvLOO+/g6+tLhw4dePzxx898593EpcMFi0gcMBf4C/BbY0zN19Sy6XDBSrlOY4YLbsouGlWz1jxc8D+Ah4HQ2hYQkbuAuwA6d+7s4nKUUg2iwd6quawPXkTGA0eMMRtOt5wx5jVjzBBjzJDo6GhXlaOUUmcdV37IOhKYKCLJwELgEhGZ58LtKaWUqsZlAW+MecwYE2eMiQeuA5YbY25y1faUUkqdSL/opJRSHqpZvuhkjPkW+LY5tqWUajozP5sJwFvj3nJzJaoxtAWvlGpWrh4u2NVa03DEGvBKqWbV0oYLdjUNeKXUWaE5hgueMWMGDzzwwCnDBRcUFHDppZcyaNAg+vXrx7Jly6q20bNnT2688UZ69erF1KlTKSoqqnX9cGbDEZ8stiKN2Iq0Mz62NdHBxpQ6Cz2/7nl2ZtfdRVK5TGVf/On0bNuTR4Y9ctpl3DlccEBAAEuXLiUsLIysrCzOP/98Jk6cCMCuXbuYPXs2I0eO5LbbbuOVV15h5syZLhmOuDlpC14p1WzcOVywMYbHH3+c/v37c9lll3Hw4MGqeZ06dWLkyJEA3HTTTaxatcplwxE3J23BK3UWqqulXakpz6Jx93DB8+fPJzMzkw0bNuDr60t8fDwlJSUAiMgJ6xYRlw1H3Jy0Ba+UahbuHi44NzeXdu3a4evryzfffMOBAweq5qWkpLBmzRoA3n33XUaNGuWy4Yibk7bglVLNwt3DBd94441MmDCBfv36MWTIEHr27Fk179xzz+Xf//43t912G7179+aee+4hNzfXJcMR/+Y3v6nrUDUZlw4X3FA6XLBSrtOY4YLPhi86JScnM378eLZu3eqW7RdnWB9kB8b0rGPJljdcsFKqFfPkYD8baB+8UuqsFh8f77bWu6tpwCt1FmlJXbKqYRrzt9OAV+osERAQwNGjRzXkWyFjDEePHiUgIKBB99M+eKXOEnFxcaSlpZGZmenuUlQ15bmHAPDNOf0Lb0BAAHFxcQ1atwa8UmcJX19funbt6u4y1Em2PXsnAL0eX9Xk69YuGqWU8lAa8Eop5aE04JVSykNpwCullIfSgFdKKQ+lAa+UUh5KA14ppTyUBrxSSnkoDXillPJQGvBKKeWhNOCVUspDacArpZSH0oBXSikP5bKAF5EAEVknIj+LyDYR+aOrtqWUUupUrhwuuBS4xBhTICK+wCoR+dQY84MLt6mUUsrmsoA31mVjCuybvvaPXkpGKaWaiUv74EXEW0Q2AUeAL40xa2tY5i4RWS8i6/VKM0op1XRcGvDGGIcxZiAQBwwTkb41LPOaMWaIMWZIdHS0K8tRSqmzSrOcRWOMyQG+AcY1x/aUUkq59iyaaBGJsH8PBC4Hdrpqe0oppU7kyrNoYoC5IuKN9ULyvjHmIxduTymlVDWuPItmM3Ceq9avlFLq9PSbrEop5aE04JVSykNpwCullButKO/Fv4rHYn03tGlpwCullBtUOJz85ePtPF98DcnOdhSVOZp8G648i0YppVQNsgpKue/djfywL5vxvhu4w/9Lgv3vbvLtaMArpVQz+inlGPfO30h2YRl/n9KH0Z/OIshRCI5y8PZt0m1pF41SSjWTBetSmP7fH/D2Ej6Y0YNrt99PtDOTUvEH42zy7WkLXimlXKyk3MHTH25j4Y+pjO4exb8vdBL24QQozOKgd0dyvNvS1se/yberLXillHKh9Jxipv93DQt/TOVXYxKY228rYQsmgJc33P4FOd5tXbZtbcErpZSLrE7K4r53f6Kswslr1/Xmiv3PwycL4JzL4NrXIch14Q4a8Eop1eSMMby+ch/PfbqTrlHBzJ4YRfxXN8PhrXDRo3DRI+Dl+g4UDXillGpChaUVPLxoMx9vyeDKvh34+8BDBC66BQS44X3ocUWz1aIBr5RSTWRfZgF3v7OBpMwCHhvXnbuc7yOLXoAO/eAX70Dbrs1ajwa8Uko1gS+3H+a3723Cx1t498YenP/TI5D0NQy8Ea7+P/ANbPaaNOCVUuoMOJyGf3y1m/+3fC/9OobzxuVetP/0Wig4BOP/AYNngIhbajttwItIXh33FyDDGNOj6UpSSqnWIaeojFkLN/Hd7kymDY7j2S4b8U18GILbwW2fQcfBbq2vrhZ8kjHmtBftEJGfmrAepZRqFban53H3vPUcyi3h2QnduT7rn8gn70DCGJjyJgRHurvEOgN+Sj3WUZ9llFLKYyz9KY3HlmwhPNCXpTfE0XfVnZCxCUY/CBc/bn2JqQU4bcAbY/YBiEgwUGyMcYpID6An8KkxprxyGaWU8nTlDid/+XgHc1YnM6xrW167IIeIjyaC0wHXLYCeV7m7xBPU90z7FUCAiHQEvgBuBua4qiillGppjuSXcOPra5mzOpnbLujCgh4riFh8PYTGwl3ftrhwh/qfRSPGmCIRuR14xRjzNxHZ5MrClFKqpdhw4Bj3zNtAXkk5r1zblav2PA0bP4f+02H8S+AX7O4Sa1TvgBeREcCNwO32tJbRyaSUUi5ijGHe2hSe+d82YsIDWTgxmITl10PuQbjqRRh6h9tOgayP+gb8LOAxYKkxZpuIJADfuK4spZRyr5JyB08s3crijWlcfG40/+6zm6Blv4PANjDzE+g0zN0l1qleAW+MWYHVD195ex/wgKuKUkopd0rNLuKX8zawLT2P317chfvL30Q+mQ3xo2HqmxDSzt0l1stpP2QVkafrWkF9llFKqdZi5Z5MJvxrFSnZRcyb1pEHUh5A1s+GCx6Amz9oNeEOdbfg76jj26wCXAc83WQVKdd762rr/5kfu7cOpVoQYwyvfpfEi5/vonu7UOaMKSLmy2uhogx+8Tb0nuTuEhusroB/HQitxzJKKdVq5ZeU82Diz3y+7TAT+sfwf7HL8fvwLxDVA6bPg6ju7i6xUer6otMfm6sQpZRyh71HCrj7nfUkHy3ij2PjuOXw88i3H0Ofa2Hi/wP/EHeX2GguG01SRDoBbwPtAQO8Zox52VXbU0qphvpsawa/e/9nAny9WXxtBANXz4RjyTD2r3D+PS36FMj6cOVwwRXA74wxG0UkFNggIl8aY7a7cJtKKVUnh9Pw4he7ePXbJAZ0imDOkGTafP4g+IfCjI+gywXuLrFJuCzgjTEZQIb9e76I7AA6AhrwSim3yS4sY9bCn1i5J4ubh8bwh4AF+Hz6GnQeAdPmQGgHd5fYZOoV8PYAY68C7Y0xfUWkPzDRGPPnet4/HjgPWFvDvLuAuwA6d+5cv6qVUqoRth7M5e53NpCZX8rLV7dn0u5HIXUtnP8ruPyP4O3r7hKbVH0HG3sd65us5QDGmM1Yp0fWSURCgMXAr40xp5xyaYx5zRgzxBgzJDo6up7lKKVUwyzakMa1r67GGMMnk4RJP1wPh7ZaX1wa96zHhTvUv4smyBizTk78wKGirjuJiC9WuM83xixpRH1KKXVGyiqcPPPRNub9kMIFCW15vfsPBH/6J2ibALd+CO16ubtEl6lvwGeJSDess2EQkanY/eu1EevVYDawwxjz9zOqUimlGuFQbgn3zt/AxpQc7h/Znt8U/ROv75ZBrwkw6RUICHNrfftz9/NOWDGHfZy85YL11zfgfwW8BvQUkYPAfuCmOu4zEmvc+C3VhhZ+3BjzSaMqVUqpBli3P5t752+kqKyCuePDuWjTPXB0L1z+jDXsgJtOgSxzlPF1ytck7k7kx0M/4h0CQ0p8KHOU4eft16Tbqu9gY/uAy+wrO3kZY/LrcZ9VWEMZKKVUszHGMGd1Mn/5eAed2wbx4SWZxH57O/gEwC3LoOuFbqkrJS+FRbsXsSxpGdkl2XQM6cisQbPo9fGbRDi9mjzcof5n0UQAtwDxgE9lX7wxRkeUVEq1GMVlDh5dspllm9K5omcU/6/dB/h/8QrEDYVpcyG8Y7PWU+4oZ3nqchJ3J7I2Yy3e4s2YTmOY1mMaI2JH4CVebPvfHJdtv75dNJ8APwBbAKfLqlFKqUZKOVrEXe+sZ9fhfJ4a05aZ6U8j61bD0Dth7LPg0/Qt5Nqk5qeyePdiPtj7AUdLjhITHMN9A+9jcvfJtAtqvtEo6xvwAcaY37q0EqWUaqRvdh1h1oKfEBEWX+3FoB9mQEkuTH4NBkxvlhrKneV8l/odibsTWZO+BhHhwrgLmdZjGiNjR+Lt1fwXwatvwL8jIncCHwGllRONMdkuqUopperB6TT865u9vPTVbnq2D2V+/020Xf5HCO8ENy2GDn1dXkN6QTqLdi/ig70fkFmcSfug9twz4B4md59Mh2D3fiu2vgFfBrwAPIF9qqT9f4IrilKqJZn52UwA3hrnihPZVGPllZTz2/c28dWOI0wf0Ja/eL+Oz4rF0ONKmPwfCIxw2bYrnBWsSFtB4u5Evj/4PQCj40bzVI+nGNVxFD5erhzmq/7qW8XvgHOMMVmuLEYppepj16F8fjlvA6nZRbx0aTDX7PktkrkTLvk9jPoteNX3S/oNc6jwEIv3LGbJniUcKTpCu8B23NX/LqZ0n0JMSIxLtnkm6hvwe4EiVxailFL18dHmdB5etJlgfx8+HZtL99V3g5eP1SXT7ZIm357D6WDVwVUk7k5k5cGVGGO4oOMFPD78cS6Ku6jFtNZrUt/KCoFNIvINJ/bB62mSSqlmUeFw8vxnO3l95X6Gdg5jTpfPCP7mXxB7HvziHYjo1KTbO1x4mCV7l7BkzxIOFR4iKjCK2/vezpQeU+gY0rynWzZWfQP+A/tHqbPO9ozTXZZYNYesglLuf/cn1uw7yj1Dwnio4G94/bgCBs+Acc+Db0CTbMfhdLA6fTWJuxNZkbYCh3EwImYEDw99mLOgEUgAACAASURBVDGdxuDr1boGJKvvN1nnuroQpZSqyabUHO6Zt4HswjLevEy4ZPPdUHQUJv0bzqtrxJT6ySzKZOnepSzevZj0wnTaBrTl1j63MrX7VDqFNe07g+Z02oAXkfeNMb8QkS0cP3umijGmv8sqU0qd9RauS+GpZdtoF+rHNxclEbv6aQiLhdu/gJgBZ7Rup3HyQ/oPJO5O5NvUb6kwFQzvMJzfDPkNl3a6FN9mGj74mcgXAHjPBeuuqwU/y/5/vAu2rZRSNSqtcPD0h9tYsC6VS7uF8ErEPPy/fx/OuRyufQ2C2jZ63VnFWXyw9wMW715MWkEabfzbcFPvm5jaYypdwro04V6432kD3r7sHsC9xphHqs8TkeeBR069l1JKNV56TjH3zN/Iz6k5PDHCnzvSH0O2bYcxj8GFDzfqFEincbLu0DoSdyWyPHU5Fc4KhrQfwv3n3c9lXS5zyUBfLUF9P2S9nFPD/MoapimlVKOtTsri/nd/orTCyeJL8hi84RFrWN8bE6H75Q1eX3ZJNsv2LmPR7kWk5KcQ7h/O9T2vZ2qPqSSEe/73NOvqg78HuBdIEJHN1WaFAt+7sjClWoou5UnuLsHjGWN4Y+V+nvtsJwlt/Xmv5yrarv4HdOgP09+BNvENWtf6w+tJ3JXIVylfUe4sZ1C7QfxywC+5Iv4K/L39XbcjLUxdLfh3gU+BvwKPVpuer+PQKKWaQmFpBQ8v3szHmzOY2iuQ5/gnPuu/gYE3wdUvgm9gvdaTU5LDsiSrtZ6cl0yoXyjTz53O1B5T6RbRzcV70TLV1QefC+QC1zdPOUqps8n+rELufmc9e48U8OIFTqYkPYAUHIYJL8OgW+u86pIxho1HNpK4O5Evk7+kzFnGwOiB/GXUX7iiyxUE+DTN+fGtVcv9jq1SyqN9tf0wv3lvEz7ewucXJtN9/dMQ3A5u+ww6Dj7tfXNLc/lf0v9YtHsRSblJhPqGMqXHFKb2mEqPNj2aZwdaAQ14pU7DaZwUiKFCDD9k/EBscCwdgjt47FkXzcHpNPzj6z388+s9nBcbwLyYRQSvfRcSLoYpsyE4ssb7GWP4OfNnEncn8nny55Q6Sukf1Z9nLniGsfFjCfINauY9afk04JWqQZmjjI/2fcTcbXNJ9bMuYnbnF3cCIAjRgdHEhMQQGxxLbIj1ExMcQ8eQjsSExBDoU79+47NNblE5v37vJ77Zlcmd/bx5LP8ZvLZtgtEPwsWPQw0Xxcgry+OjpI9I3J3I3py9BPsGc8051zC1x1R6tu3phr1oPTTglaomtzSXxN2JzN8xn6ziLHq17UVsuRDoFJ6Y+AbpBemkF6aTXpBORkEGW49u5cuUL6lwVpywnjb+bU4I/tiQ2KoXg5iQGML8wty0h+6zPT2PX87bQEZuMbNH5nDJ9icQpxOuXwjnXnnCssYYtmRtIXF3Ip/t/4wSRwl9Ivvw9IinubLrldparycNeKWAjIIM3t7+Nkv2LKGoooiRsRfw1z53MTwvh7XbVhLkdNJ/1X8gOAqCoiC4K7QdBkFROILakOUlZJQXcrAog4zCjKoXgr05e1mZtpISR8kJ2wv1Da16BxATYrf8K18IQmJp498GqeMDxtZk2aaDPLJ4MxEB3nw3/EdiN7wE7Xpbp0BGHj/DpaCsgI/3fUzi7kR2HdtFoE8g47uNZ1qPafSO7O3GPWidNODVWW1n9k7e2voWnyd/jgDjQroxo9jJuRs/h+8XApDg7UOOlzcc3GANclV64uiS3kB7oL14MzAo0noRqHohOBfT8QKOBQSR7u1FujjJcJZysDyfjNJs0gvTWX94PQXlBSesM8A7wHoBqN7yDz7+QhAdFI2XuOaiFk2p3OHk2U928Nb3yVzcxZf/BP8X/41fQf/pMP4f4Ge1xLdlbSNxdyKf7P+E4opierXtxe/P/z1XJ1xNsG+wm/ei9dKAV2cdYwxr0lbx1qZ/80P2NoLw4saCYm7OzqKDYz+06Qo9r4b40dBlJPe9PwGA9+/aZK2gotQK+sIsKMw8/ntRlv2/fTvjZyjKQkpyaQu0BU64Qqh4QWBbCI4iLyiOjMBg0v0CSPfxOf5CkJvK9iObOVaef8I++Hj5WC1++x3AyZ8FtA9u7/ahbY/kl3Df/J9Yl5zNY+eVc9ehx5HMg3DVizD0DgorivhkdyKJuxLZkb2DQJ9Arux6JdN6TKNPZB+PegfjLhrw6uzgqKD84AY+3/YOc478wC5Kia6o4Nd5+UzzjiYsfhyMHgVdRkJ4HRdz8PG3RjQMi63ntstPfRE44QUhi7DCo4RlHeDcwiwoPvU7hEUiHPLx4WBQBBlBoaT7BZJemE968U5WZ24m01F8wnCvXnjRLqhdVZ9/1QtAtc8BXPmNzg0HjnHv/A3kFpez5IJkBm1+xnoxm/kpO4JCSPzhT3y872OKKoro0aYHTwx/gqsTribUL9RlNZ2NNOCVZ3KUQ/pPkLyKwv0rWHxsM++E+HPIx4duDuGZsF5c3f1a/LpeBGE1X0uzqKyCxRsPsjn115SUR9P1sY8RQEQQwEsErH9Yvwoi1nSBavNOnC4SDkQgcg5e1e5Xuay3v4MIr3zamDzakEeEyaWNySPC5BFRlEv7wlzONXmEm2NEmDxCTT4OnBzy8SHdx5sMHx8O+viQkZ9HanYqP/r4kOUtOE9qEAc6AwhyhuFn2uBNO0Ri8KEDfkTiRyQ+Emjv5/F9FnvHrGnVaq/2u9MYvtx+mM5hPnzW7yPabJxHUfxIPhs8ncSf/87Wo1sJ8A5gbPxYpp07jf5R/bW17iIa8MozVJRB+kZIXgnJ30PqOjKdxcwPC+X98HDyI4IZEhLP7/vdwajuE07bf304r4S31yQzf20KOUXlBPuXEhvxDdeedycGgzHWxRGs/60bTnPqdGOs7qBTpmFPNzVNr7xPdNV9sw0cxdoGJ6zb/t3pIMhRQKjjGCGOHEIcuYQ5cogtz2FMSS6hjhyCnccoI5d8r0JyvUs45ONlvxAcI8PnIBnePpR5nRiywU4hosKPUGcQAY5QfJ1twBmFw9mBUtORAtpTRoC17xzf1/ScYrr75/JhxGsk7dnCv/tcxMflmRRsfJFzIs7h0WGPMqHbhLPyTKLmpgGvWqeKUkhbDwe+t0I99UeoKAZgX/uezDmnPx+VHsJhDJd1uYyZfWfSN6rvaVe5PT2PN1bt438/p1PhNFzRuz13jE7gb59fggg8OPbF5tgz13M6oSTnhM8RnIWZHM1LI73wIBlFRzhYeowMRwHpppgMrwJ2+R2l2CvlhNUEOZ3EOpzE4EOsVwCxvmHE+rclpkcU+/d/za1e3myOi8Gv5GBVa31g9EBtrTcjlwW8iLyJdaGQI8aY0z+zlKpLebEV6MmrrFBPXQeOUkCgQ1/MoFvZENmROblb+S5jDQHlTq7tMZVbe9962kuuOZ2G73Zn8saqfXy/9yiBvt7cMKwzt43qSpdI6+wNj8sjLy/rghlBbSGquzUJiLZ/TrlOkjGY4hxyc5NJz95Deu5+0vPTyCg6zMHSbDLK8/nZWUKe8wgUH4FiINyfrsEdebj3jUzsNpFw//Bm3UVlcWULfg7wL+BtF25DeaqyIkhbZ3W3JK+Cg+vBUWadedKhHwy7E7qMxNFpGF9nbmTOtjls2fkJbfzbcO+Ae7mu53W0CWhT6+pLyh0s/ekgs1ftZ++RAtqH+fPIuJ7cMKwz4UGt68LKLieCBLUhIqgNETHnUdvZ6AVlBaQXppNRkE64XzgD2mlr3d1cFvDGmBUiEu+q9SsPU1oAqWvtLpfvrXPOneVWoMcMhOF3Q5dR0Pl8CIyguKKYZXuX8fbnt5Can0qn0E48OfxJJp4z8bTDBGTml/LODweY98MBsgvL6BMbxj+mD+SqfjH4+dTcL3/A9+wcarahQvxC6OHXQwf7akHc3gcvIncBdwF07tzZzdWoZlOaDylr4cAqq4We/hM4K0C8IfY8GPEriB8FnYZDwPEP47JLslm46RUW7lzIsdJj9I/qz28G/4ZLOl2Cdw3jmFTadSif2av28cFP6ZQ5nFzWqx23j0rg/IS22spUHsvtAW+MeQ14DWDIkCGmjsVVa1WSCyk/HO9DT98ExgFePtbQsBc8cDzQ/UNOuXtKXgpvb3+bZXuXUeIoYUzcGGb0ncGgdoNqDWhjDCv3ZPHGqv2s2J1JgK8Xvxgax8yRXekWfeo2lPI0bg945aGKcyBljRXoyavg0GYwTvDyhbghMPq31peKOg0Dv9q/ir4lcwtvbXuLrw58hY+XDxO6TeDW3reSEFH79TRLKxws25TO7JX72XU4n+hQfx68ogc3DO9C22Ad5ledPTTgVdMoyoYDq4+ftnhoK2DA2x/ihsKFD1kt9LihdV6CzWmcrExbyVvb3mLD4Q2E+oVye7/buaHnDUQHRdd6v6MFpcxfm8Lbaw6QVVBKzw6hvDhtABMGxODvU3v3TV3iyx5s9H2VcidXnia5ABgDRIlIGvAHY8xsV21PNbPCo3aY210uh7cBBnwCrBAf8xjEj4SOQ8C3fpdNK3OU8fG+j5mzbQ77cvcRExzDw0Mf5tru1552wKm9RwqYvWo/SzamUVrhZMy50dwxKoGR50Rq/7o6q7nyLBq9jqsnKci0PxC1Qz1zhzXdN8jqZrn4CauF3nGQNVZLA+SV5fH+rvd5d8e7ZBZncm6bc/nr6L8yNn5srQNmGWNYk3SUN1btZ/nOI/j5eDFlUEduG9mV7u11PBPVerx39wiXrVu7aFTN8g8fP8Ml+XvI2mVN9w2GzsOh/zTrtMXY88Cncf3aGQUZvLPjHRbvXkxRRREjYkbw51F/ZkTMiFpb3mUVTj7anM4bK/ezPSOPyGA/fn1Zd246vwtRIa4bPEup1kgDXlny0q0grwz1o3ut6X6h1rnnA6+3hs+NGQDeZ/ZFoF3Zu3hr21t8vv9zDIZxXccxo8+M015+LaeojPlrU5i7Opkj+aV0bxfC81P6MWlgRwJ8G9+/rpQn04A/W1WUws8Lj/ehZ++zpvuHQZcLYNCtVh96hwHgfeYPE2MMazLWMGfrHNZkrCHIJ4jre13Pzb1uJiak5tEcAfZnFfLmqv0s2pBGcbmD0d2j+NvU/lzUI1r715Wqgwb82cIYOLIdti21viVaUQxLf4SAcOt0xaF3WP936FfjhY8bq9xZzufJnzN321x2Zu8kKjCKWYNmMa3HtFrHJzHGsG5/Nm+s2s9XOw7j6+XFpIGx3D66Kz076AiEStWXBrynO7LDCvVtSyFrt/XVf/9QCO0Av3gb2vdp0kCvVFheyOLdi5m3Yx4ZhRkkhCfwzAXPcHXC1fh519xnX+5w8smWDN5YuZ8tB3NpE+TLfRefw80jutAutH5n4iiljtOA90SZu46HeuZOK9S7jIThv4ReEyFxhrVcTP+m33RRJu/ufJf3dr1Hflk+g9sP5onhTzA6bnStY7DnFpezcF0Kc1Ynk5FbQkJUMH++pi9TBsUR6Kf960o1lga8p8jaczzUj2wHxAr1q160Qj20vUs3vy93H3O3zeV/Sf/DYRxc2vlSZvSZQf/o2l9EUo4W8eb3+3l/fSpFZQ5GJETy52v6cvG57fDy0v51pc6UBnxrdjTJDvUP4PAWQKDzCLjyBeg90eqGcSFjDBuPbGTO1jl8m/Yt/t7+XNv9Wm7pfQudw2oeOM4Yw8aUY7yxcj+fbzuElwgTB8Ry26iu9O3YMscMd+V5ykq5kgZ8a5O9zwr0bUut8V3AGqBr3HPQe1L9LwR9BhxOB8tTlzNn6xw2Z20mwj+Cewbcw3U9r6NtQNsa71PhcPLZtkO8sXI/m1JzCAvw4e6LunHriHg6hGv/ulKuoAHfGhxLPh7qGZusaXFDYeyzVqiHxzVLGSUVJdYY7NvfJiU/hbiQOJ4Y/gSTzplU6xjs+SXlvPdjKm99n8zBnGK6RAbxzKQ+TBkUR7C/PvyUciV9hrVUOSmwfdnx0xrBGlb3ij9boR7RfGPnHys5xsKdC1mwcwHHSo/RL6of/zfo/7i086W1jsGedqyIOd8ns/DHVApKKxgW35anJvTmsl7t8db+daWahQZ8S5KbdjzU0360psWeB5c/Y4V6m/hmLSc1L5W52+dWjcF+UdxFzOgzg8HtB9f6JaNNqTm8sXIfn249BMDV/WK4fVRXBnSKaM7SlVJowLtfXvrxUE9da03r0B8u/QP0uQba1j7uuatUjsH+dcrXeIs34xPGc2ufW+kWUfOl6xxOw5fbrf719QeOEervw+2junLrBfF0jDj90MBKKdfRgHeH/EPHQz1ljTWtfT+45PfQZzJENv81QJ3GyaqDq3hz65vWGOy+oczsM5Mbe91Y6xjshaUVJK5P5c3vk0nJLiKuTSBPje/NL4Z2IkT715VyO30WNpeCI3aof2CN/YKBdn3g4ietlnpUd7eUVTkG+9xtc0nKTaJDcAceHPIgU3tMrXUM9ozcYuasTmbB2hTySioY1DmCR6/syRW92+PjXfOXmZRSzU8D3pUKMmHHh1ZL/cD31iXros6FMY9C72ugXe2jJ7paHk4St8xm/o75ZBZn0qNND54d9Szjuo6rdQz2rQdzeWPlPj7anIHTGK7sG8Nto7oyuEubZq5eKVUfGvBNrfDo8VBPXmmFemR365J1fSZDu17NXpIxhsziTJJyktiXu49dcpQvKKJw4z84P+Z8/jzyz4yIrXkMdqfT8PXOI7yxch9r92cT7OfNLSPimTkynk5tg5p9X5RS9acB3xSKsmHnR7B1CexfAcYBbbvB6N/Zod4bmmFoW2MMh4sOk5STVBXmSTlJJOUmkV+WX7WcNxCOF2+Nf49ekTW/4BSXOVi0MY03V+1nf1YhseEBPHFVL6YP60RYwJmNB6+Uah4a8I1VfAx2fmy11Pd9C84KaNMVRv3aCvX2fV0W6sYYMgozqkJ8b85e9uXsIyk3icLywqrl2vi3oVtEN67qehUJ4Ql0i+hGt4huRC68BUGghnA/klfC3DXJzF+bQk5ROf3jwvnn9edxZd8O+Gr/ulKtigZ8QxTnwK5PrFBP+gac5RDRBUbcZ4V6zIAmDXWncZJekF4V4kk5SezL2ce+3H0UVRRVLRcZEEm3iG5MSJhQFeLdIrrVOmwAnFrj9vQ8Zq/az4c/H6TCabiid3vuGJ3AkC5t9MIaSrVSGvB1KcmFXZ9aob73ayvUwzvD+fdYoR573hmHusPpIL0g3Qrx3KSq1vj+3P0UVxRXLRcdGE1CRAKTu08+3iIP70ZEQOO+ROR0Gr7bnckbq/bx/d6jBPp6c8Owzswc2ZX4qJrPoFFKtR4a8DUpzYddn9mh/iU4yiAsDobfDX2uhY6DGhXqDqeD1PzUE0I8KccK8lJHadVy7YLacU7EOUzpPoVuEd04J+IcuoZ3rfUKSA1V7PThg7zuzP7HCvYeKaB9mD+PjOvJDcM6Ex6k/etKeQoN+EqlBbDbDvU9X4KjFEJjrUvZ9ZkMHYeAV/36oMud5aTmp7Iv58T+8eTcZMqcZVXLxQTHkBCRwLAOwzgn4hwSIhJICE8g1C+04eVXODhaUEZWQan1k19GZkEpmfmlx6fZ83OK7gKgd4wXL00fwNX9YvHz0f51pTzN2R3wZYWw+3M71L+AihII6QBDZlqhHjfstKFe7ignJT/lhBBPykkiOS+ZCmdF1XIdQzqSEJ7AyNiRJEQk0C28GwkRCbV+kahSaYXDCuX8U4M6s6DUmm7/n1dSUeM6Qvx9iArxIyrEn3OiQzg/oS1RexYxPCid8+99TfvXlfJgZ1/AlxVZ3S7bllrhXl4EIe1h0C1WqHc6/5RQL3OUkZyXfEKIJ+UkkZKXQoWxglUQ4kLj6BbejQvjLqz6oLNrWFeCfI+fL15S7iAzv5TdGaVkFRTYrW0ruDPtlnfl7/m1hHaovw/Rof5EhfjTs0MoUedEERXib//4ERXqT7R9u8ZL3r31qPW/hrtSHu3sCPjyYtj7lRXquz6D8kIIjoYB10Pfa62rIHl5U+ooJTlnD0k5SVar3D6PPDU/FYdxAOAlXnQK7URCeAKXdL6EbhHdiAvqSrBXDPklYod1Gfv3l/JjQSlZBTuOd4/kl5JfWnNohwX4EGWHdq+YMEaH+FWFeFSIvz3PaokH+Op1SpVSdfPcgC8vgaSv7VD/FMoKICgS+v+C4p5XkxwRw968/ezL3kDSvvfZl7uP1PxUnMYJgLd4ExMUR/vAznRrP5IgicXb0YGKkkiOFUD6oVJ+tlvfhWXJQPIpJYQH+hJlB3Wf2DCiQvzt0ParCu7oUH8iQ/zw99HQVko1Lc8K+IpSSFpuhfrOTygqL2B/SCRJ3UeS1LYjuxwlJOXu5PDq5RgMAII3wV4d8HPGEFHWl9KiaPLyI8kvbEOO8WHHCRsop03Q0apw7h8XURXW0dW6RaJC/YgM9tcPLpVSbuXSgBeRccDLWN+Of8MY81xTb8M4ytm//gMO7krkyNH1JHs52OMfyK4O7cjytgfBKtqBKdyNszQKZ1l7nKW9cJa2x1naDlMeiU9QEKGVXSLt/YnqVnN/dmSIn36bUynVargs4EXEG/g3cDmQBvwoIh8aY7Y35XYqKkqYsuOPVIhA22DE6YWjrB0+JTGEenWkrW8nYgK70Cm0E+3Cgq2wtrtJokP8aRvsp0PcKqU8kitb8MOAvcaYfQAishCYBDRpwPv6hzI1+heEtelF7+je9G7XheiQIA1tpdRZz5UB3xFIrXY7DRh+8kIichdwF0Dnzo27kPQTVz/VqPsppZQnc3sz1xjzmjFmiDFmSHR0zZeGU0op1XCuDPiDQKdqt+PsaUoppZqBKwP+R6C7iHQVET/gOuBDF25PKaVUNS7rgzfGVIjIfcDnWKdJvmmM2eaq7SmllDqRS8+DN8Z8Anziym0opZSqmds/ZFVKKeUaGvBKKeWhNOCVUspDacArpZSH0oBXSikPpQGvlFIeSgNeKaU8lAa8Ukp5KA14pZTyUBrwSinloTTglVLKQ2nAK6WUh9KAV0opD6UBr5RSHkoDXimlPJQGvFJKeSgNeKWU8lAa8Eop5aE04JVSykNpwCullIdy6UW3VQs182N3V6CUagbagldKKQ+lAa+UUh5KA14ppTyUBrxSSnkoDXillPJQGvBKKeWhNOCVUspDacArpZSH0oBXSikPJcYYd9dQRUQygQONvHsUkNWE5TQVrathtK6G0boaxhPr6mKMia5pRosK+DMhIuuNMUPcXcfJtK6G0boaRutqmLOtLu2iUUopD6UBr5RSHsqTAv41dxdQC62rYbSuhtG6Guasqstj+uCVUkqdyJNa8EopparRgFdKKQ/VqgNeRDqJyDcisl1EtonILHfXBCAiASKyTkR+tuv6o7trqk5EvEXkJxH5yN21VCciySKyRUQ2ich6d9dTSUQiRGSRiOwUkR0iMqIF1HSufZwqf/JE5NfurgtARH5jP+63isgCEQlwd00AIjLLrmmbO4+ViLwpIkdEZGu1aW1F5EsR2WP/36YpttWqAx6oAH5njOkNnA/8SkR6u7kmgFLgEmPMAGAgME5EzndzTdXNAna4u4haXGyMGdjCzlV+GfjMGNMTGEALOHbGmF32cRoIDAaKgKVuLgsR6Qg8AAwxxvQFvIHr3FsViEhf4E5gGNbfcLyInOOmcuYA406a9ijwtTGmO/C1ffuMteqAN8ZkGGM22r/nYz3xOrq3KjCWAvumr/3TIj7NFpE44GrgDXfX0hqISDhwITAbwBhTZozJcW9Vp7gUSDLGNPZb4E3NBwgUER8gCEh3cz0AvYC1xpgiY0wF8B1wrTsKMcasALJPmjwJmGv/Phe4pim21aoDvjoRiQfOA9a6txKL3Q2yCTgCfGmMaRF1Af8AHgac7i6kBgb4QkQ2iMhd7i7G1hXIBN6yu7XeEJFgdxd1kuuABe4uAsAYcxB4EUgBMoBcY8wX7q0KgK3AaBGJFJEg4Cqgk5trqq69MSbD/v0Q0L4pVuoRAS8iIcBi4NfGmDx31wNgjHHYb5/jgGH2W0S3EpHxwBFjzAZ311KLUcaYQcCVWN1tF7q7IKzW6CDgVWPMeUAhTfT2uSmIiB8wEUh0dy0Adt/xJKwXxlggWERucm9VYIzZATwPfAF8BmwCHG4tqhbGOne9Sd7xt/qAFxFfrHCfb4xZ4u56Tma/nf+GU/vc3GEkMFFEkoGFwCUiMs+9JR1nt/4wxhzB6k8e5t6KAEgD0qq9A1uEFfgtxZXARmPMYXcXYrsM2G+MyTTGlANLgAvcXBMAxpjZxpjBxpgLgWPAbnfXVM1hEYkBsP8/0hQrbdUBLyKC1Te6wxjzd3fXU0lEokUkwv49ELgc2OneqsAY85gxJs4YE4/1tn65McbtrSsAEQkWkdDK34ErsN5Wu5Ux5hCQKiLn2pMuBba7saSTXU8L6Z6xpQDni0iQ/fy8lBbwoTSAiLSz/++M1f/+rnsrOsGHwK3277cCy5pipT5NsRI3GgncDGyx+7sBHjfGfOLGmgBigLki4o31Ivq+MaZFnZLYArUHllqZgA/wrjHmM/eWVOV+YL7dHbIPmOnmeoCqF8LLgbvdXUslY8xaEVkEbMQ6y+0nWs7wAItFJBIoB37lrg/LRWQBMAaIEpE04A/Ac8D7InI71pDpv2iSbelQBUop5ZladReNUkqp2mnAK6WUh9KAV0opD6UBr5RSHkoDXimlPJQGvPJIIvKtiLh8wDIRecAeYXJ+E6xrjohMrWH6EBH5p/37GBFpEV8cUi1faz8PXqkmJyI+9oBU9XEvcJkxJs1V9Rhj1gOVwyePAQrg/7d3LyFSXGEUx//HlcbHQlwHySBEkMEHZDbqKmgWQhSVoGAQQRlBF4qgEHCTrZBlBEXcGUJQ8YEkxmdQnMEHjoIYBQfBhSRZiCYo6BwX92utKaM9SJweKt9v1V2PW7eY8dQn6QAAAttJREFU4dbXVd3ncvFDHS81R1bwqWMkTY/qd09kdP8Sv/wdVoFLmhbxCkhaK+lwZGYPStokaWsEgV2SNLVyiDWRlX5T0mex/8TI4+6Pfb6stHtE0mlKXGu9r1ujnZutLHFJu4FPgBOSttS2nyDphzi/Q5L6KufzpLLdCkn7K7t+LumypN8jO6hVtR+LQL1eYEuc1wJJK6NP1yWdf+8/RmqkrOBTp80AVtleL+lHYDnQLh9nFiU5dDxwF9hue46k74CvKYmZAB/Znh2hZftiv28oEQ3rIk6iX9Kvsf1coNv2sChXSfMov17tAQT0STpnu1fSF5QM+z9rfdwI/GN7pqRuyi87R2I6JYOnCzijSma57cG4qDyxvSv6dgNYbPtBKx4jpZas4FOn3bPdipm4Qhng2jlj+7HtP4BHwNFYfqO2/wF4lb89JQbARcCOiLY4S7lIfBzbn6wP7mE+cMj235HzfxBY0KaPC4kLle0BYGAE5wUl1mLI9h1KLMKnbba/AOyXtJ4yuUZKr2QFnzrtWeX1C2BCvH7O6wKkPuVbdZ+hyvshhv9P13M4TKnAl9u+XV0hqYcSBTwaqv2qn9u/9fntDZVPET2USVyuSJpn+6//oI+pAbKCT2PVIGUqOoA3vlkyQl8BSJpPmXjiEfAzsDmSDpE0ZwTt/AYsjYTEicCyWPYu54HVcYxZQHdl3UNJMyWNi7aqVkoaJ6mLcn//dm39Y2By642kLtt9tndSJiYZS5NYpA7LCj6NVbso6XobgOPv2cZTSdcoUyaui2XfUu7RD8QAew9Y8q5GbF+NB6H9sWiv7Wttjv09ZRaoW5S43OokKzuAY5QB+TIwqbLufhxnCtBr+2lci1qOAj/Fw+HNlAeuMyifTE4B19v0K/2PZJpkSqNA0llgW3zlMaVRkbdoUkqpobKCTymlhsoKPqWUGioH+JRSaqgc4FNKqaFygE8ppYbKAT6llBrqJScFOn9jTjxxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(num_qubits, means_swaprz_paulis, stds_swaprz_paulis, 'ExcitationPreserving + Pauli op')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(num_qubits, means_ry_paulis, stds_ry_paulis, 'RYCX + Pauli op')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aer statevector mean 0.11356609344482421 fastest 0.10116767883300781\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aer shots mean 0.11917715072631836 fastest 0.10786700248718262\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aer snapshot mean 0.038090543746948244 fastest 0.028789043426513672\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aer statevector mean 1.0052314853668214 fastest 0.8434579372406006\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
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      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
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      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
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      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
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      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
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      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
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      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
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      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
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      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
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      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
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      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
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      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
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      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
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      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
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      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
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      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
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      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
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      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
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      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
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      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
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      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
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      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
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      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
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     "output_type": "stream",
     "text": [
      "Aer statevector mean 5.059909057617188 fastest 4.863768815994263\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aer shots mean 5.615420770645142 fastest 5.425950288772583\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n",
      "Measured Observable is not composed of only Paulis, converting to Pauli representation, which can be expensive.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aer snapshot mean 1.829319248199463 fastest 1.6833980083465576\n"
     ]
    }
   ],
   "source": [
    "num_qubits = [2, 4, 6]\n",
    "nruns = 25\n",
    "means_zz_h, stds_zz_h = run(nruns, [benchmark_expval(n, 'hadamards', 'zz') for n in num_qubits])\n",
    "means_swaprz_h, stds_swaprz_h = run(nruns, [benchmark_expval(n, 'hadamards', 'swaprz') for n in num_qubits])\n",
    "means_ry_h, stds_ry_h = run(nruns, [benchmark_expval(n, 'hadamards', 'ry') for n in num_qubits])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(num_qubits, means_zz_h, stds_zz_h, 'ZZFeatureMap + H op')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(num_qubits, means_swaprz_h, stds_swaprz_h, 'ExcitationPreserving + H op')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(num_qubits, means_ry_h, stds_ry_h, 'RYCX + H op')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.5 64-bit ('latest': virtualenv)",
   "language": "python",
   "name": "python37564bitlatestvirtualenv76cfe9d7d3f749489cf2ff69593234af"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}