Circuit Library Demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Circuit library demo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.circuit.library import ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Chemistry example: VQE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# simulator\n",
    "from qiskit import Aer\n",
    "\n",
    "# the actual algorithm\n",
    "from qiskit.aqua.algorithms import VQE\n",
    "\n",
    "# for representation of the H2 molecule\n",
    "from qiskit.aqua.operators import WeightedPauliOperator\n",
    "\n",
    "# optimizer for the variational algorithm\n",
    "from qiskit.aqua.components.optimizers import SLSQP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# building the qubit operator for the H2 molecule\n",
    "h2_hamiltonian = {\n",
    "    'paulis': [{\"coeff\": {\"imag\": 0.0, \"real\": -1.052373245772859}, \"label\": \"II\"},\n",
    "               {\"coeff\": {\"imag\": 0.0, \"real\": 0.39793742484318045}, \"label\": \"IZ\"},\n",
    "               {\"coeff\": {\"imag\": 0.0, \"real\": -0.39793742484318045}, \"label\": \"ZI\"},\n",
    "               {\"coeff\": {\"imag\": 0.0, \"real\": -0.01128010425623538}, \"label\": \"ZZ\"},\n",
    "               {\"coeff\": {\"imag\": 0.0, \"real\": 0.18093119978423156}, \"label\": \"XX\"}\n",
    "               ]\n",
    "}\n",
    "h2_qubit_operator = WeightedPauliOperator.from_dict(h2_hamiltonian)\n",
    "\n",
    "# defining the backend for the simulation\n",
    "backend = Aer.get_backend('statevector_simulator')\n",
    "\n",
    "# defining the numerical optimizer\n",
    "optimizer = SLSQP()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The algorithm uses a \"guess\" or \"Ansatz\" wavefunction: this comes from our circuit library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200.56x206.4 with 1 Axes>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.circuit.library import RYRZ  # Kandala et al., Nature Cover \n",
    "varform = RYRZ(h2_qubit_operator.num_qubits)\n",
    "varform.draw(scale=1, output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# run the algorithm\n",
    "algo = VQE(h2_qubit_operator, varform, optimizer=optimizer, quantum_instance=backend)\n",
    "result = algo.compute_minimum_eigenvalue()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Energy: -1.8369679711094058\n",
      "Optimal parameters: [-3.47892620e-04 -1.24260320e+00 -3.09626868e+00  2.97334616e+00\n",
      " -9.01117678e-01 -1.35506167e-04  1.24264727e+00 -2.24067245e+00]\n"
     ]
    }
   ],
   "source": [
    "print('Energy:', result['eigenvalue'].real)\n",
    "print('Optimal parameters:', result['optimal_point'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plug-n-play: circuit library has plentiful options, e.g. `TwoLocal`, `NLocal`, `RY`, ... ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1027.87x247.68 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.circuit.library import TwoLocal\n",
    "\n",
    "# easy to implement all efficient 2-local circuits, e.g. all of Alan Aspuru-Guzik's Paper\n",
    "varform = TwoLocal(h2_qubit_operator.num_qubits, \n",
    "                    rotation_blocks='ry',\n",
    "                    entanglement_blocks='crx',\n",
    "                    entanglement='linear', \n",
    "                    reps=3)\n",
    "varform.draw(scale=1.2, output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "algo = VQE(h2_qubit_operator, varform, optimizer=SLSQP(), quantum_instance=backend)\n",
    "result = algo.compute_minimum_eigenvalue()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Energy: -1.8572749123347887\n",
      "Optimal parameters: [ 4.11779398  2.09875907 -2.64488942  5.26592202  2.004999    4.01549089\n",
      "  2.27335589  4.847305   -3.34533421  3.91904872 -5.89802565]\n"
     ]
    }
   ],
   "source": [
    "print('Energy:', result['eigenvalue'].real)\n",
    "print('Optimal parameters:', result['optimal_point'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 942.56x378.4 with 1 Axes>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "varform.num_qubits = 4\n",
    "varform.reps = 1\n",
    "varform.insert_barriers = True\n",
    "varform.entanglement = 'circular'\n",
    "varform.rotation_gates = ['z', 'ry']\n",
    "varform.draw(scale=1, output='mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Machine learning example: VQC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the algorithm\n",
    "from qiskit.aqua.algorithms import VQC\n",
    "\n",
    "# the training and test data\n",
    "from sample import training_data, test_data, feature_dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fix the random seeds\n",
    "from qiskit.aqua import aqua_globals\n",
    "from numpy import random\n",
    "\n",
    "aqua_globals.seed = 373\n",
    "random.seed = 53\n",
    "seed_simulator, seed_transpiler = 981, 74"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.circuit.library import SecondOrderExpansion\n",
    "feature_map = SecondOrderExpansion(feature_dimension=feature_dimension, depth=2)  # Havlicek et al., Nature Cover"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Neoat/45f+owyW2ZcxxcY+OtcAD7vNqSq344QVUrqR3FSQ4S4P5rC6vBgbGEzquI575XJ/tMB1g6h0nP08H+G81zjtqa7mu80bttSlsfv5uiVC+h0uhKXqUr/+EH539kDLf7SDxS15FlqSPkepBqilv3aFjwouZYjtkJUc3Gvzixz/qRO/ZjUqZ9VmlohRPUnNUQ8SKSxFXelOhzNqO4kR0KUTj4f5ZMcCXHv5OYxIYQQQgihCtLYCiGEEEIIVZDGVgghhBBCqII0tkIIIYQQQhWksRVCCCGEEKogja0QQgghhFAFaWyFEEIIIYQqSGMrhBBCCCFUQRpbIYQQQgihCtLYCiGEEEIIVZDGVgghhBBCqII0tkIIIYQQQhWksRVCCCGEEKogja0QQgghhFAFaWyFEEIIIYQq2Fk7AGFbCkZGWTuEMtl/OsDaIVgtR9XhvVua5Nr2SA0pn+zXlmONXD+IebYkOWIrhBBCCCFUQRpbIYQQQgihCnIpghDirmTnwZELkHANktNvT4/aBQ08IbgB1HS2Xnxqkp4LsTdzfcvn66CuO/jXhlYNoIaj9eIT4l4kp8PRi3Ah9fa0LzdAPQ8I9IKWfmCvs158anIhFY5fMs/1Vxuhvic09IYW9UCnskOcKns7orq6kp2Ow4yXOHj5nNn08+kpOMx4iWPXLlonMCuxxXyk58CPO2H8CvhpN+w8BWdTbs/ffRqi98KEFbBwG1zNtF6sRdlirlMyYcEfMHGFktM9Z27PO5sCO07Bkt3KtojapTTAamaL27Cq2WJOTl+BLzbAtNWw6iAcPH973snLsOU4LNim1JDfDkG+3nqx3mKLeQb48yLMWgszf4ffDisHI245kQybjsK3W2DSStj4JxiM1ou1sskRW2ER+5LP4GznQCvvBmbT9yafxs3BmWaeda0UmXXYWj72nYWf90JuQfnLGguVL6y4RHiuNXRsAhpN1cdYGlvKdWEh/BEPvx6EAkP5y+sNyg+KIxegb1to/VCVh2gVtrQNLcWWcqI3KI3sHyegsALLZ+fB+j/hYAIM6gAPeVV5iKWypTwD3ChQavXesxVbPj0Xfj0EB84rua5bq2rjswQ5YissYv/lM4TUCcBOa35+aV/SaVr7BKLVPFi7oi3lY9NRWLyjYk1tUQUG5Wjjr4eUhs1abCXXhYXwywFYvq9iTW1Rufnw/XbYfKxqYrM2W9mGlmQrOSkwKEcGt1awqS3qaqZyhPfYpSoJrUJsJc+g1IF5Gyve1BZ18TrMWQfnr1Z+XJZWfbaIULX9yWd4zLdhsel7k0/TpoTpamcr+dh/TjnScj82HVW+1KzFVnK9+bjy3/1YecD89K5a2Mo2tCRbycmSXXA86d7/Xm+A/2yFS9crL6a7YSt5NhbCf/8wvx7/buUWwPwYuJ5deXFZgzS2wiIOXD5LG99GZtOMhUYOXj7HY74NuZydTtiP4+m2ZDIdF49l0/k4K0VqGeXlA6D70inU+3IoU3eusEaIpOdA9J7yl5s9UPmvLL8egssZlRPX3bKFXCenK9cUlqUieQZYtgcyVHbNrdSP4mxhvz6coPw4LktF9usCg3J9vzWuA7WFPANsj4f45LKXqUiuc/KVeyiseZbtfkljW4TRaGTGjBk0adIEJycnQkJC2LJlC82aNeONN96wdng262JmKsnZabT2CTSbfvjKebIKbvB43SZ4ObsR0388G/qPZVGvYYzZusRK0Va9iuQD4Nsef2dal5esESIAvx+5+8sPSqM3wKoDlbOuu2EruV51APSV9KWdk69sO7WQ+lGcLezXBqNyWU1lSbwOu05X3voqwhbyDMp1tavL+WF8N44nKTef2SppbIt47bXXmDx5MkOHDuX333+nX79+DBgwgDNnztCmTRtrh2ezLmYp44zUcnQxm77k2A4er9uYBjVro9Nq0WmV3TE9L4dW3v4Wj9NSKpIPAD+32haP7ZacPNh3rnLXefQiXMuq3HWWxxZyfTWz8q8h3HdWud5ODaR+FGcL+3VcYuWP1rE93rJHEm0hzwB7z0BeJY8gsS2+ctdnSdLY3hQVFcWCBQtYtWoVI0aMICIigjFjxhAaGoper6d169bWDtFmNfesh7ujC5/uWUXajWxSc7P45tAGvj60nilhtx8teDbtCuFRE+gZPY3nmjxmxYirVkXzYU2xicpR1spUiOWv/7SFXB88f/c31ZSnwKBsQzWQ+lGcLezXB85V/jovpZmPnV3VbCHPUDW5Pp4EWTcqf72WIMN93TR16lS6d+9Oly5dzKY3btwYe3t7goODARg3bhxLlizh1KlTLF26lBdeeMEa4dqUmo4urHx+BP+3dQmB84fhYudISJ0AVv/lAzo3aGFaLrBWHTYPmMCZtMs8tXQKPRup88dERfNhTfdzA0JZig4SbgkPeq7bVZ97W+6Z1I/iHvT92lJDUtlCng1G5TKNqpB4HZpXr9HMKkQaWyAxMZG4uDj++c9/FpuXkJBAy5YtcXRUHu/TvXt3hgwZwquvvmrpMG1aR7/mbHlpQqnz8/QFONrZA1DTwZka9k4Wisw6ysuHtVXVUZHktKpZb1kk17ZP6kdx1Xm/ziuA6zlVs+4kC+/X1TnPAKlZdz88YEUlpUlja7MSE5Vzdr6+vmbTc3Nz2bJlCz169DBN69ChQ5XGorHmSPYVkD/ixypZ777k04zdthSdRkuBUc+sroPvaT3VIX+VlaPX185nT9Ip8g169iSdYmWfkWUuX5nvvf+kPfg0bGs2rby7aUua/48fzP998vQ5NJrA4gveIzXk+tU5F3Dz9DObVlauS5t3Z663btvJsCeqtl7di6qoIZVVP0BqSGVwrunNG/OumE27l/oBxffr2XO+pPfCYfcRnbnKyLU164eXfzADpx42m1ZZuf7gw3+x95cp9xFd5Sqs4AXW0tgCXl7KY03i4+N5+umnTdOnT59OUlKS3DhmAR39mrOp/zhrh1Gt/Lv7UKu9tr6gai6u0udXz3GorJlrwwOW66og9aNk1tqvq2qfhuq5X6uxVkP1zHVFSGMLNGzYkODgYKZOnYqnpyf169cnOjqaNWvWAFi0sa3oLxJrKRgZZe0QylQd8metHFXme/9pN+w8ZT7tzl/zt9z69V/a/KLC27fg+0qMUw25/vfm4kPrlJTLu8kzwIDeXfl5qvU/D3eSGlI+NezX45ebj4pQGfUD4NPJI2j/44j7C64Ia+S6MvNsMMIHP5kPF1hZuV7875kE1Z95fwFagYyKAGi1WpYtW0bLli158803eeWVV/Dy8uLtt99Gp9OZbhwT4kHhX0Wj1zTwrJr12rIGVZVr645AJB5wVbX/+UsNMaPTQj2Pqlm3rdYQaWxvatq0KTExMWRnZ5OQkMDkyZOJjY0lKCgIZ2dna4cnhEU97KcUzMr2iLqHF70nj1ZBTuy0yjYUwloeDaj8ddapabkREWxJVeS6iQ+42eg9mNLYlmHfvn3FLkMYO3Ysfn5+7Ny5k6FDh+Ln58fp0xZ+HIqwmN27d9OhQwc6depU4qgZly5domvXrnTo0IENGzZQWFhIel4Ok7ZHYzAayczPpbCwkCFr5gEwffcqIpdMInTRv1h5cq/ZugoMejr/MA6Pz1/h1HXzZyOuiN9Dw/nDylzO3d2d8PBwwsPDSU1VxtWaNm0anTp14i9/+QvZ2coDwJ955hk6d+5MZGSk6cbJ1NRU+vXrR9euXZkyZQpuTrBn0RCWjH+c6I/COb5DucFi76ppRH/UhaixbTm1V3l85IQJEwgJCSH6o3AOrJkFwLWLR1k6sSNLJ3Zk57KxAHhrE3ixd1fCwsJYunQpAAsWLDDF7OHhwaFDh9Dr9fTv35+IiAhGjRp1j1sOXvv9azouHkt6Xg56o4Eha+YRHjWB6btXAbA98TgP/2c4/zkSw5XsdEbGLAKUAf4BJm2PJu2GkrN9SaeZs/93ALoumUSXLl2IjIzkyhXzG2R+//13mjdvTqdOnYrF89xzz/Gvf/2r2HI+7sqXyNGtC1g4ohnRH4WzLUp533k56fwyoxfRH4Uze/ZsAJJP7WbpxA4sndSJLYtv75PnY9fz89SuRH8UTq0b+6nhWPI+ERYWViz+Zs2amZY7evQoAJ9//jmPP/44oaGh7Ny5E4BFixYRGhpK9+7dSU5W9r3+/fsTHh5OaGgojzzyCACHDh2iY8eOdO7cmT/++OMetyB4zXnN9BSxj3etJOCrtxi3balp/rhtS/Ge+zf0RgNfHFjL3qRTpa3Kah7UGhLSAGK+rXgN+eH/yq8hjWskEBlp+zWkpM/gLfdSQ9o1BHtd5dYQjxv7AdusIXKNbSmysrKIj4/nrbfeMps+efJkJk+ebKWobMPFzFRm7V3Niy06MCJmEVqNhsd8GzEj4uVS/2ZEzCL2J5/hUZ+HmNX1rxaMtmwBAQFs2rQJJycnBg4cSGxsLK1atTLNnzZtGpMnTyYkJIRevXopyy34gPT8XNadO0LUM++xJ+kUbX0bcSnrOuvOHqbAaGBMh+eZtmslEf4t+XzfGsaE9iFXn0907+H831bza7685rxGfVdP/Nxq8/GulXx14H/0bR5KU896gPLl/tXBdTzc5hE2b95s+rukpCT++OMPtm3bxs8//8y3337Le++9x5w5cwgMDGT9+vV89tlnzJw5k4kTJzJp0iSaN29u+ns/D2jxzg+4eDU2TWv99HDaPjua/BtZrPi4G43bPg/AzJkzWX25m2m52I1f0/HFj6nfPIzl056gICeNk7s/YcqUKbRr144ePXrQp08fhgwZwpAhQ9Dr9Tz22GNKgxwdTUhICB9++CHvvPMOhw8fJiQk5J6238Keb+Pu6MKK+D0086zHgqffovfyT0nOTqOjX3NGtXsWvdFInRrupORmkpGXQ2JmKnP3r2X7xRN4OrsyrHV3vjq0nk/DBwHwv77/h8usl1m4cCELFy5k5Mjbd0C3b9+ew4cPExkZaRbHkSNHyM3NLXW551rDqmXQpudIHo74m2m5uE3f0LzDQJp1GMAfy//CoEGDcPMKoM+Hm7BzcGLtvIFcvRBLLZ/GxG2az/Oj1+PiqOPtnsrft2rVymyfANi4cSP29vZm8Xt7exdbbsGCBezfv5+kpCSGDRvGsmXLmDdvHtu2bePQoUN88sknfPbZZyxZojSeK1asYP9+5ctw3Lhx/PTTT3h6etKnTx/Wrl1b4W1WtH4UUsj2i8cZEbOIke2eJbReEzYl/GladlKnfuxIPMGozYvZm3Sabw5t4MirMyr8WpbwINeQBp7waK8fqOVbeg1htlJDOr80E/+HS68h7ro0YqLVUUNK+gzeci81pIYjdG8Fh2Mqp4Y08dXR/+amsMUaIkdsS+Hq6orBYOCdd96xdig2Z+P5WCIDWuFf04t1/cawecAEruSkE5uSUOLyBy+fJSv/BjEDxpNv0LMvqfocAff19cXJSTkfY29vj06nM5sfGxtLhw4dcHV1xc3NDR8fH14PiaStbyOyC25wPPUiv50+QLh/Sz7dvYpJnfux5oUPmb5rFS1q+/HvwxvZdekkU3YuJz0vF58a7sViqO/mwT/b9kSr0fBqqwi+7zWMGg63zxFN6tSPEO8Ajh8/TufOnRk9ejSFhYUkJCQQFBQEwCOPPMKOHTsACAwMLPZ+4uLimDp1KhEREaZf1k4OGnYuHMyqmc+QcVV5ZJju5lih+vxcavs9DMDaIzDw9Q9Y/nE3Us4rDyz3qNuMvJx0jEZlgMUejzpy+eIZgoOD0el0+Pj4cPLkSdN72Lp1K2FhYWg0Gs6cOWO6rr1o3GX57fQBRm/+AWOhkV7R00jIuGo2f3fSKboFKPF2aRDE3hL2sfZ1m7DxfBxFB+LRoKGwsJBLWal4OrsqedMpxwNyc3Np2bKl2To8PDxMY14XNWfOHLMfyXcu5+cJDzeAQ/+bzbLJYSTEbQQgPeUMXv5KLhINQfxt+l5q1PLF7ub21+rs0Wh1JJ3ciUaj5ZfpPTj848s4oBwlOnbsmNk+Acp2vzP+1NRUwsLCGDp0KDduKHdZP/TQQ+Tl5ZGWlkbt2rW5du0afn5+6HQ6QkJCTPvJLStWrKBPnz4AXL9+HT8/P1xcXMjOzjb7Qi7NoNVzOXTlHAWRw1wAAA4ASURBVAviNrMv+Qz+Nb1o6eVnqh9XctLRUHyYpMz8XLLy8/hj4CQy829Uu6O2D3IN8a6pYdt/y64h//hBqSHbl5ReQzTAwE6OnDurkhpSwmfwlnutIREtwMv1/mvIr5/24I/vXiY3x/ZqyC3S2Ir7siXhKHXm/o1uSybTeP679Fkxk60XjhHWoAW+NWrhZOcAgL3WDp2m5N1t96VTRAYoRzC6BrRiV9LJEpcr6bUs5ciRI6SkpJiK/C0Gg8E0JqG7uzsODg78/dEnaV+vCY/UCaSWowunrifzUE0v4q5eILReUz7c8iMHr5wltF7TYl/Tg1bPJfVGFvGpl+i/SjltdCUng5eClNNSPjXcS/xyB6UAbd26levXr/Prr7/SsGFD9uzZg16vZ9OmTaSl3R7Z3GAw8NFHHzF0qDJMzY4dO/jwww9ZsmSJ6ejBzJkziT2wgzff/YA/fhhu+ttN/32LHz4Mxq9lVwAeeepdBny0n4hXvmLzQuWHoP/DT7Bl0bt8P6IZ7dqF0rONM82aNWPLli3k5OSwa9cus3iWL1/O888rR25uLQcQExNjtlxpejZqzeWcdN5c9y09G7XGv6aX2fz0vGzcHJRr5d0dXUjLyy62jsBadTiReol6rh78q0MfOtZvxsCgTqTkZOBe5FnxCRlXCQ0N5YsvvjA78laa48eP4+3tTa1aZV8cOObt3sz56Qg93/uZbVEjMBoNeNRtRuKxLRiNBi6e2Epe9u1cpCQcITcjhdr1g8hJv0x2WhLzvv+dZ5/qwPz58wE4efKk2T4BykNn7ox/27ZtbN26lYCAAL755hsAIiIiaN68OU8++STvvvsuXl5enD17luzsbGJiYrh+/fbjjgoKCoiNjTU9etzb25u4uDhSUlKIi4srdRsW/UxvTzzB08s+5ptDG/m2x1B8a9RCe7NmlFU/MvNzibzZcPjXrM26c0fKfS1L1w94cGvIn4d28Myg+6ghI5vRvWsoTf1UVENK+AyWpSI1RKuF6SN7M/zLe68hOelJrF37OxFdbKeGlJiLCi8pRAk6N2hO27qN2NB/LJ0bNGdut1fILsjDtcjRgCMpCVzNySDIq+S7WdLysqnpeKtgOJN2o+RH1pT0WpaQmprKsGHD+O6774rN02pvf4QyMjLw8PDA3dGF3k3bkZqbSdu6t0/BGQqNaDQa5j7xKr0atWHGnl/5W0gk7es1YUxoH2o5uTAzYjD7k88wffcqZnX9KzEJf+Lm4IyDrvyrhjw9PdFoNPTu3Zu4uDi8vb0ZNGgQ3bp14+TJk/j4+JiWHT58OIMHD6ZRo0aAcvNkixYt8PHxMb0nT0/l9uORQzrhbEjG/uaBpq6vzGPwp8dNA3c7uSrLefg2Ma1/Z/RYer27lJ/Wx2NIjeX8+XN8+OGHfPPNN/Tt25fmzZub4iksLGTbtm2EhYUByvV7ubm5REZG4ujoaBY3QGRkJOHh4abrs255PSSS6BO7ebVVRLHcuDu4kHlzTMaM/BxqOdYoNY+1nJR54zq+YPr/RfnX9GLnzp1MnDiRGTPKP+09a9Ys3nvvvXKX8/SoRf9QLS909sbDtyk56Zd5OPx1kk7u4JfpPahRqx4u7koubmSlsnnhMLq9ruyTNdzc6RreiZ6P6ujatSvHjh1T1nnHPgHg7+9fLP5b2/r5558nLi6OjIwMFi9ezMmTJ9m9ezejR49Gp9Mxbtw4nn76aX777TeaNm1qin3z5s2Eh4eb/j1t2jTef/99/v73vxMcHGwaKxxgw7lYui2ZzPBN35t9psP9g4gMaIWTnT1NPG4/7qi8+qE3Gk0Nh73Wjoy8ko/sWKt+wINdQxzt4NN3O6HNu/15rWgN6TtiKbsOxHMlIZZz51RUQ0r4DJalojXEz7cW7z6lpV2QN7XusoZ413bnue6daFav+teQ9evXl5kHaWzFfTmTdoVA9zoAJGamkpKbQXCd27dopuZm8Y+NC5jf/Y1S1+Hu6GL6MsrIy6WWk0uJy935WvXdqn7cF71ez6BBg5gxY0axJ9MBBAcHs3PnTrKzs8nIyKBmzZrF3nNjD1/OZVxFi4Y8fQEAOfo8ajm54O7owriOL6DTanFzcManhjuu9k7Ud/OknqsHf169QGpuFr2ip3H0aqLZjTNFGYxGDAbltP/27dtNXzavv/46mzdvJigoiJ49lQsvv/vuOzQaDYMH3346U9OmTUlKSiI7Oxu9Xg8oX7IAJ06cwL9uLT7oCS188tBqwM7BGQfnmgDk5SjL5WZexWjUowHcnAoZ1tOTp4K1uLu7k5mZiY+PDytXriQ6OhpHR0fT6cy9e/fSunVr0ylNnU7H3Llz2bhxIzqdjqeeesrsvW7cuJHNmzebbQ9joZGpO1fwr9A+TN+zqlh+Hi9ybebmhKM85tuw2DJn067Q7OY1h0V5u9Q03QxSYNCbTsfVrFmzQiOmnD9/niFDhjBq1CiioqJMR5LulJGRgVYDnRvl4pB7kkebemPvVIPuby3muVG/U1hopG6TUIwGPWu/GkTnl2bg5uFLiD9MGdqWzMvKF9GhQ4cIDAwkOzu72D5RUFBQLP78/Hzy8vLMltNqtbi4uODg4IC7u7vppqFnn32WLVu20Lt3bzp37myKfcWKFaajZaDsT+vWrWP+/Pn4+/ubTl0CdHuoFRv6j2Vm18Fmn+njqZdIzc3EUWfHidRLSr6NhnLrh51Wa2o40vKy8SulLlijfoDUEICzp0/QpEEt3ggHXzdlXyurhjjYgZdbIf94xpPm9aSG3E0NqeEIL7XNRZt1koD65dcQrzq+PNESPn6zLRfP2UYNeeKJJ8rMl9w8Ju7L0WuJBNX2w2A0otVo2Hgu1nQdknIX6Zd80uUlfGvUMk27lptldh1Y+3pN+PfhjfRt3p5N5+MY/HBYicvd+VqWsGzZMvbu3Wu6s/bjjz8mNDSUd955h7lz5zJq1CgGDx5Mbm4uEydORK/XF3vPPRo+yuaEP2nl7c/A1XO4lptFbEqC6Wk138dt4WGvBrT2bcjTyz4mMfMa125k8tXBdQxr3Z3oE7tY/cJowqMmMKlTP7YkHGVl/B7S83I4dT2Z4e16kavPp3379ri5uREYGMjEiRMB6Nu3L6mpqQQHBzNzpnLq9a233qJdu3aEh4fTpUsXJk6cyMSJExkwYAC5ubmMHz8egIEDB3L9+nU0Gg1fffUVXm5wMPo94o4e53pmPi8MGUldd4j+fiQpF+Kw0xoZMWYaf30WBjX/gHfefhmdTkeLFi1o1aoVv/32GzNmzECn0/HJJ5+YTr8Wva4K4OLFiwwcOBCtVsvgwYOpX79+udvpiwP/47kmbXk9JJIXf5nNn1cTzeb3atSa19Z+TXjUBLoHPkJd1+IDP+68FM+cEo7iaTQa6tbwIDU3i6yCG7yyZh66/f/G0dGRBQsWAJj2h3379jF69Gji4uLo1q0bq1ev5n//+x+gHJHYsGEDXbp0KXG5zz77jLVr12I0Ghk7ZjT9nrRnw9b9jBgxgjy9ho7PjcLH05mj26NIPbeX46tHkegEfT/5mGaBoXTp0oWwsDBcXFz48ccfOXnyJK+++iqurq6mfeLixYu8/PLLaLVaU/zXr1+nR48euLq64uHhweLFi3F1deXJJ58kNDQUg8HAuHHjTO/zzz//JCAggHnzlLv0CwsL2blzJ1988YUpZ9999x2LFy/G2dmZL7/8stTtduszna/XcybtMk898iR/f/RJRsYs4ufewzmZmsRvL4zGt0Yt/hsbw1cH13EtJ4u0G9mmbeXm4ExMwp88GRjMDX0B7es1qTb1A6SGFK0hQfXh80nvcTjuOOlZ+Tw3aCR1asKK70dy9UIc9jojYydM49U+8GfjD3jjNXXWkFfDw80+g1D5NWTSuNH07W3PynX7Gf8vpYaEPjcKX09nju+MIvX8Xk6uGUXSRnh22sfUr2ubNaQkmsLq8JgVYTPufErLtF0raV+vCe3qNqbjD+Oo51qLX//yAVqNliXHdvD+poUE1VZOIX4U9iJezjWZsedXvn7qdbP1vL9pIQcvnyOkTgCzI4dw6npyseXufK01L4wuVlzsPx1QNW+8gqKionj3taFm7znq6HbS83P5qPOLvLrmK3L1+Yzr+AJPPGT+4A+D0Uivn6fx36ffIt+gZ9j67/ilzygilkxk84AJAPw3NoavD63nem423Rs+Yiqi3ZZMZuPZI9jZPVi/Vct6atAHm39gd9JJfukzyuz6tlu2Jx5neMwi3m/bi/AGQUzfs6rUkTv2JZ1mx6V43m3TA7D+fmbLim6z8urHK2vmUc/Vg0D3OiXWj3HblrI8fjfdAlrx+5lDtPYJJOrZ9+65foD1t63UEMuyRg2x9j6mdtLYirtS3uMHlx3fRd/m7Uudvzx+Dx5ONYjwb1nqMnez3J2qQ8Go7Ec0Pvyf4TzXuC1TwvqXOP/Wl/vRKxeK3XGtdtZ69Gh12M9sVVnbzNr1A6rHtpUaYjnWqCHVYR9TM2lsxV2p7s95rw4FQ5oty5Fc2x6pIeWT/dpypLFVH2lshRBCCCGEKsioCEIIIYQQQhWksRVCCCGEEKogja0QQgghhFAFaWyFEEIIIYQqSGMrhBBCCCFUQRpbIYQQQgihCtLYCiGEEEIIVZDGVgghhBBCqII0tkIIIYQQQhWksRVCCCGEEKogja0QQgghhFAFaWyFEEIIIYQqSGMrhBBCCCFUQRpbIYQQQgihCtLYCiGEEEIIVZDGVgghhBBCqII0tkIIIYQQQhWksRVCCCGEEKogja0QQgghhFCF/wf0lfP6nglIwAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 900.592x144.48 with 1 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_map.draw(putput='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "varform = RYRZ(num_qubits=feature_map.num_qubits, reps=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "vqc = VQC(optimizer, feature_map, varform, training_data, test_data)\n",
    "result = vqc.run(backend, seed_simulator=seed_simulator, seed_transpiler=seed_transpiler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training loss: 0.295080504739488\n",
      "Test loss: 0.4863865293292629\n"
     ]
    }
   ],
   "source": [
    "print('Training loss:', result['training_loss'])\n",
    "print('Test loss:', result['testing_loss'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.circuit.library import QFT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "varform = QFT(2) + RYRZ(2, reps=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 911.428x144.48 with 1 Axes>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "varform.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "vqc = VQC(optimizer, feature_map, varform, training_data, test_data)\n",
    "result = vqc.run(backend, seed_simulator=seed_simulator, seed_transpiler=seed_transpiler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training loss: 0.2514647782705993\n",
      "Test loss: 1.430601507930052\n"
     ]
    }
   ],
   "source": [
    "print('Training loss:', result['training_loss'])\n",
    "print('Test loss:', result['testing_loss'])"
   ]
  }
 ],
 "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
}