Multiple Experiments for Quantum Ensemble (Simulator).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiple experiments for Quantum Ensemble of swap-test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The directory output  already exists\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from Utils import *\n",
    "\n",
    "create_dir('output')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generation of random data: each row corresponds to a possible dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x11</th>\n",
       "      <th>x12</th>\n",
       "      <th>x21</th>\n",
       "      <th>x22</th>\n",
       "      <th>x1_test</th>\n",
       "      <th>x2_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.929384</td>\n",
       "      <td>-4.277213</td>\n",
       "      <td>-5.462971</td>\n",
       "      <td>1.026295</td>\n",
       "      <td>4.389379</td>\n",
       "      <td>-1.537871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.615284</td>\n",
       "      <td>3.696595</td>\n",
       "      <td>-0.381362</td>\n",
       "      <td>-2.157650</td>\n",
       "      <td>-3.136440</td>\n",
       "      <td>4.580994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.228555</td>\n",
       "      <td>-8.806442</td>\n",
       "      <td>-2.039115</td>\n",
       "      <td>4.759908</td>\n",
       "      <td>-6.350165</td>\n",
       "      <td>-6.490965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.631027</td>\n",
       "      <td>0.636552</td>\n",
       "      <td>2.688019</td>\n",
       "      <td>6.988636</td>\n",
       "      <td>4.489106</td>\n",
       "      <td>2.220470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.448868</td>\n",
       "      <td>-3.540822</td>\n",
       "      <td>-2.764227</td>\n",
       "      <td>-5.434735</td>\n",
       "      <td>-4.125719</td>\n",
       "      <td>2.619522</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        x11       x12       x21       x22   x1_test   x2_test\n",
       "0  3.929384 -4.277213 -5.462971  1.026295  4.389379 -1.537871\n",
       "1  9.615284  3.696595 -0.381362 -2.157650 -3.136440  4.580994\n",
       "2 -1.228555 -8.806442 -2.039115  4.759908 -6.350165 -6.490965\n",
       "3  0.631027  0.636552  2.688019  6.988636  4.489106  2.220470\n",
       "4  4.448868 -3.540822 -2.764227 -5.434735 -4.125719  2.619522"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seed = 123\n",
    "np.random.seed(seed)\n",
    "\n",
    "runs = 20\n",
    "data = pd.DataFrame()\n",
    "\n",
    "for i in range(runs):\n",
    "    x1 = uniform(-10,10,2)\n",
    "    x2 = uniform(-10,10,2)\n",
    "    x_test = uniform(-10,10,2)\n",
    "\n",
    "    row = pd.Series(np.concatenate((x1, x2, x_test)))\n",
    "    data = data.append(row, ignore_index=True)\n",
    "\n",
    "data.columns = ['x11', 'x12', 'x21', 'x22', 'x1_test', 'x2_test']\n",
    "# data.to_csv('output/data.csv', index=False)\n",
    "# data = data.loc[10:20,:]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Quantum Circuit for the ensemble of quantum swap-test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 916.244x445.48 with 1 Axes>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc = quantum_ensemble(x1, x2, x_test)\n",
    "qc.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each row in data are computed the following measures:\n",
    " - The ensemble of swap test computed classically (cEnsemble)\n",
    " - quantum swap-test using x1 as training point on simulator\n",
    " - quantum swap-test using x2 as training point on simulator\n",
    " - The average of the results of the two quantum swap-test computed classically (qAVG)\n",
    " - The ensemble of swap test computed via quantum simulation (qEnsemble)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_out = pd.DataFrame()\n",
    "for index, rows in data.iterrows(): \n",
    "\n",
    "    # Extract data\n",
    "    x1 =np.array([rows.x11, rows.x12])\n",
    "    x2 =np.array([rows.x21, rows.x21])\n",
    "    x_test = np.array([rows.x1_test, rows.x2_test])\n",
    "    \n",
    "    # Compute the average classically of classical swap test\n",
    "    cAVG = classic_ensemble(x1, x2, x_test)\n",
    "    \n",
    "    # Swap test using x1 as training on simulator\n",
    "    qc_x1 = quantum_swap_test(x1, x_test)\n",
    "    m1 = exec_simulator(qc_x1)\n",
    "    r1 = retrieve_proba(m1)\n",
    "    \n",
    "    # Swap test using x2 as training on simulator\n",
    "    qc_x2 = quantum_swap_test(x2, x_test)\n",
    "    m2 = exec_simulator(qc_x2)\n",
    "    r2 = retrieve_proba(m2)\n",
    "    \n",
    "    # Compute the average classically\n",
    "    r_avg = np.mean([r1[0],r2[0]])\n",
    "    \n",
    "    # Compute the average using quantum ensemble algorithm on simulator\n",
    "    qc = quantum_ensemble(x1, x2, x_test)\n",
    "    r = exec_simulator(qc)\n",
    "    r_ens = retrieve_proba(r)\n",
    "\n",
    "    row = [cAVG, r1[0], r2[0], r_avg, r_ens[0]]\n",
    "\n",
    "    row = pd.Series(row)\n",
    "    \n",
    "    data_out = data_out.append(row, ignore_index=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cAVG</th>\n",
       "      <th>qx1_sim</th>\n",
       "      <th>qx2_sim</th>\n",
       "      <th>qAVG_sim</th>\n",
       "      <th>qEns_sim</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.741456</td>\n",
       "      <td>0.887939</td>\n",
       "      <td>0.589844</td>\n",
       "      <td>0.738892</td>\n",
       "      <td>0.741089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.521828</td>\n",
       "      <td>0.534180</td>\n",
       "      <td>0.520264</td>\n",
       "      <td>0.527222</td>\n",
       "      <td>0.524536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.911809</td>\n",
       "      <td>0.826782</td>\n",
       "      <td>0.999878</td>\n",
       "      <td>0.913330</td>\n",
       "      <td>0.914062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.948037</td>\n",
       "      <td>0.948975</td>\n",
       "      <td>0.945679</td>\n",
       "      <td>0.947327</td>\n",
       "      <td>0.944946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.759047</td>\n",
       "      <td>0.993164</td>\n",
       "      <td>0.524902</td>\n",
       "      <td>0.759033</td>\n",
       "      <td>0.761475</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       cAVG   qx1_sim   qx2_sim  qAVG_sim  qEns_sim\n",
       "0  0.741456  0.887939  0.589844  0.738892  0.741089\n",
       "1  0.521828  0.534180  0.520264  0.527222  0.524536\n",
       "2  0.911809  0.826782  0.999878  0.913330  0.914062\n",
       "3  0.948037  0.948975  0.945679  0.947327  0.944946\n",
       "4  0.759047  0.993164  0.524902  0.759033  0.761475"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_out.columns = ['cAVG', 'qx1_sim', 'qx2_sim', 'qAVG_sim', 'qEns_sim']\n",
    "# data_out.to_csv('output/data_out_simulator.csv', index=False)\n",
    "data_out.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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": [
    "avg = data_out.qAVG_sim\n",
    "ens = data_out.qEns_sim\n",
    "clas = data_out.cAVG\n",
    "\n",
    "plot_multiple_experiments(runs, avg, ens, clas, filename='Simulator')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}