Circuit library & native circuits.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Circuit library & native circuits in Aqua\n",
    "\n",
    "Development made out-of-the-box and simple"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## The circuit library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "* collection of valuable circuit families"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "* valuable? \n",
    "    * building blocks for algorithms\n",
    "    * difficult to simulate classically\n",
    "    * useful for education"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "* library contains tests, documenation and is easily extensible"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "### Goal on Aqua's side: work natively with this circuit suite."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![img](library_overview.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1095.3x467.84 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from qiskit.circuit.library import LinearPauliRotations, OR, RealAmplitudes\n",
    "\n",
    "pauli_rots = LinearPauliRotations(3, offset=1, slope=2)\n",
    "or_circuit = OR(4)\n",
    "wavefunction = RealAmplitudes(3, reps=1)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots(nrows=1, ncols=3)\n",
    "pauli_rots.draw(output='mpl', ax=ax[0])\n",
    "or_circuit.draw(output='mpl', scale=1.2, ax=ax[1])\n",
    "wavefunction.draw(output='mpl', scale=1.6, ax=ax[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### These objects are circuits!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "from qiskit.circuit.library import QFT\n",
    "qft = QFT(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit import QuantumCircuit\n",
    "isinstance(qft, QuantumCircuit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Operations: OrderedDict([('h', 3), ('cu1', 3), ('swap', 1)])\n",
      "Depth: 6 width: 3\n"
     ]
    }
   ],
   "source": [
    "print('Operations:', qft.count_ops())\n",
    "print('Depth:', qft.depth(), 'width:', qft.width())\n",
    "# etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 780.192x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qft.inverse().draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArYAAACoCAYAAAAVQK0uAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAd/ElEQVR4nO3dfVRU973v8ffMAA4GowgKFZWIgoko+JgYK6JZmpg0aXLqQ0OTtMfaajHpuuScXnubleaa2tobDvc0Z+U2ae5Jq6c5iWn0mHOa3GjrE/gYC1UQTQySkSCW8KBiBAGFmfvHCDLKw5gwew+bz2utvVzz23sz38G9Zz785rd/2+bxeDyIiIiIiPRxdrMLEBERERHpDQq2IiIiImIJCrYiIiIiYgkKtiIiIiJiCQq2IiIiImIJCrYiIiIiYgkKtiIiIiJiCQq2IiIiImIJCrYiIiIiYgkKtiIiIiJiCQq2IiIiImIJCrYiIiIiYgkKtiIiIiJiCQq2IiIiImIJCrYiIiIiYgkKtiIiIiJiCQq2IiIiImIJCrYiIiIiYgkKtiIiIiJiCQq2IiIiImIJCrYiIiIiYgkKtiIiIiJiCQq2IiIiImIJCrYiIiIiYgkKtiIiIiJiCQq2IiIiImIJCrYiIiIiYgkhZhcgxsh6w5znffExc55XRESkJ2Hbtxr+nJcX3G/4c/Yn6rEVEREREUtQsBURERERS1CwFRERERFLULAVEREREUtQsBURERERS1CwFRERERFLULAVERGRgLrUDHs/Bo/Hv+2rP4fDZQEtSSxK89iKiIhIwLg98H9zoawWzl+ChyaDzdb19tWfw693wOeN4AyFCXGGlSoWoGAr4qeGs1B9ElqawTkIYu6AsHCzq7Kmz6ug9hNovQIDIyHmdggJM7sqEfki7Da4ZwJs2Au7PvS2dRVu20LthUYYOxzGxhhbqxV5PFB3Bs6VgdsNEdEwPAkcFk2AGorQgdvtJicnh8TERJxOJ6mpqeTl5TF+/HhWrFhhdnmGeu2pERzb/ZpPm8fj4ZXv3Upp/jsmVWWOlmYo+k84uB4+2Qef5sPHu2DvK+A64P9Xa9Kzyw1Q8Bb85XXv7/bTfPjoz7DnZTh9xOzqROSLShkFf5/mDbm7PoR3C29877w+1K6YBwOCKHx5Ghu5suRR3Hv2XWtrbqYl6x9p+dkv8LjdJlbXuUvn4dC/wV/fglMfwKd/gePvez+/qkrMri4wFGw7WL58OWvXrmXlypVs3bqVpUuXkpGRgcvlYtq0aWaXZ5j6c2doqKtkWPxkn/YL1S4uN10kJmG6SZUZz+OGwnegprTzda4D3jcL+fJaL8Nf34a6ihvXuVvg451w5qjxdYlI7+gu3AZ7qAWwhYdjX7yI1jfexOPx4GltpfXnv4TQUBz/YzU2e3BFquarHQX1Z29c19IMxX+EWpfxdQVakB025tm4cSMbNmwgNzeX9PR0AObNm8fhw4fZsmULU6dONblC41S58rHZHUSNnOjTXltexMDBMQyKGmVSZcardXUetDo69QGMmgyhGpbwpVR+5B3u0Z3SPfCVCWDXO5dIn9QWbjsOS7grAV7eGdyhto396w/h3rwFz979uPML8NTWEpKTjS0s1OzSbnD6sPdbsO6c3ANRY7of89zXBNefFyZat24dCxcubA+1bcaNG0doaCgpKSkAlJWVkZ6eTlJSEpMmTWLv3r1mlBtQVa58ImOTCAlz+rTXlBcxfEz/6a0FOFMM9HDCe1rhsxOGlGNpZ47S4+/6SpM1exhE+pPre27/97a+EWoBbOFO7EsW0Zrzz3iOHiVk3Vpstww0u6xO+fMNV0MtXKwKfC1GUrAFKioqOHbsGEuWLLlhXXl5OcnJyQwYMACAlStX8s1vfpOSkhJeffVVHn30US5fvtxrtdhstoAsN6PKlU9dVSmv/iDaZ/nrey8QkzAjKF6PUcsHe45AD2No3R43P3v2BdNr7etLeWlVj79rgB8szzK9Vi1atHy5JXW0jZ3rnwTgcgtcrC3nHx66BWeosXV8YU1NOL65FFtk5E3vasTrcjhCuNLoXz1fW/AN04+H3vy/UrDFG2wBYmNjfdobGxvJy8trH4ZQW1vLvn37WL58OQCzZs1ixIgR7N6929iCA6zqVAF3fWMN3/pFoc8SEhpOTD/rsa1vPI/b3drtNnabnYamCwZVZF0NTRfweHq++OKSftcifd6Q2ERmfP2Z9seDokdz1yPPmViRf9w7duH+w9vY7ruX1nf+C0+QXj3sdrfS7GeytdrnVxB3+BsnOjoagJKSEh544IH29uzsbCorK9svHCsvLycmJqa99xZgzJgxfPrpp71WS6BOkqw3/Nuu7rNSmhvOE59yH4OiRvq2X6q76R7bYD3p/VVRCCd29Lzda5vX8WbkusAXZGGf7IdTB7vfxmaHP+5ZT1j4emOKEpFe1/FCsTZ2G0x/6Mes/vGPe5zntjeFbd/q97buv+TT+n9+jeNna7AlJtLy7WV49uzFlj7npp7TqM/F41uh8kO6/SYsNBwKTuzE7jCkJEOoxxZISEggJSWFdevW8fvf/56dO3eSmZnJ7373O4B+NSNClSufkAEDGTY61ae98uQBIqJGMXDwcJMqM0fsBAi7hW7Hfg5P9M61Kl/OyFRwhNHt73pkquYOFunLrp/9oE1PU4GZzX38Q1rX/S8c//0fsadMujbW9t83BuU0XwCjp/X8B8Jtd2KpUAsKtgDY7XY2bdpEcnIymZmZLFu2jOjoaJ588kkcDkf7hWOjR4+mqqqK5ubm9n1PnTpFfHy8WaX3uipXPjFjZmC/bubmytKD/W4YAnhvCjB1CYRdf23A1TeLyFEw4X7Dy7KkAREwZVEnN2K4+rsengSJc42uSkR6S2dTerXxZ55bs3hOldH63BocK7+P/auz2tvtX38QLlzAsyc4LyIfNBxSvg6264Pr1ffUUVNhtAU/1m2evv5dcQA98cQTFBUVcfTotUsL7733Xh555BFWrVrFgQMHWLx4MWVlZYSFBfdtkfwditDbXnzMnOftbS2X4bOP4MR27+NhiRA3CaJu8349Lr3nSiP87TiczPU+jrkd4lK8f0QY9fWkiPSuruapbftsavusOHraOxWY2+O9W1mghyXczFCE3nJ5gbG9Ic313hkSXAe8j0dM8n77dWts9/v1VfpI7kZBQcENwxB+85vf8NZbb5GUlMSKFSvYuHFj0Ida+fJCwrxvBG1SH4boBIXaQAgNh/gOvQiTHoShoxVqRfoqtwfW7/VvSq/re26Pnja2VisaEAEJ1zqamXCfdUMt6OKxLtXX11NSUsKqVat82hMSEtizZ49JVYmIiPQtdhs8djf8+Rg8NqvneWrbwm1JJUzqP/cDkl6iYNuFiIgIWlu7n+ZJREREejZyKHz3JiYPSBnlXURulr5IFRERERFLULAVEREREUtQsBURERERS1CwFRERERFLULAVEREREUtQsBURERERS9B0X/2EVe4AJiIi0luMvguYBJ56bEVERETEEhRsRURERMQSFGxFRERExBIUbEUCLDc3l2effbb98Zo1a3jzzTeZOnUqTqeTlpaWTvfLycnhyJEjXf7cM2fOkJWVxaFDh5g1axazZ8/m6aefBqCoqIjs7OzefSEiIiJBTsFWxARDhw5l586dzJw5s9P1breb/fv3M2XKlC5/xvbt21mwYAHx8fHs2rWLffv2UV1dTXFxMampqRw8eBCPxxOolyAiIhJ0FGxFTOB0OomMjOxyfVFREePGjQO8Pb6RkZHMnTuX+Ph4Hn74YQDy8vJIT08nNjYWp9MJQGhoKA6HA4DExMRue3xFRESsRsFWJAidPHmS2267DYA5c+Zw5513kpubS3p6Oi+//DIej4eGhgYiIiLa9zl69Cg1NTVMmDABgISEBE6cOGFG+SIiIqbQPLYiAeZ0Omlubm5/3NTURHh4uN/7u1wuEhISADh9+jRxcXEUFhaSmpravs25c+d46qmnePvtt3uvcBERkT5GPbYiAdY2JMDtduN2uzl8+DCJiYk97lNWVgbA8ePHSU5OprW1Fbvde8q2ja8FaGlp4fHHHycnJ4fY2Nj2n+Fyubj99tsD86JERESCkIKtSIBFRUWxaNEi0tLSSEtLY/HixQwaNIj58+dTVFTEfffdx6FDh3z2SU1NpaSkBLgWbJubm6murqayspKCggKmT58OwKZNm8jPz2f16tXMnTuXgwcPAlBSUsLkyZONfbEiIiIm0lAEEQNkZmaSmZnp07Zjx44ut7fb7aSlpXHkyBGeeeaZ9vbi4mIAFi1a1N57m5GRQUZGhs/+RUVF3H333e3biIiI9AcKtiJB6kc/+lGX65YuXdrtvqmpqT5jcEVERPoDdeeIiIiIiCUo2IqIiIiIJSjYioiIiIglaIytiIjBst4w/jlffMz45xQRMZp6bEVERETEEhRsRURERMQSFGxFRERExBIUbEVERETEEhRsRURERMQSFGxFRERExBIUbEVEREQswuOBK03+b+9uhZbLgavHaAq2IiIiIhbg8YDrAPzldWi80PP27lY49h4U/od1wq2CrcgX4G41uwKR/s3dCs0N0GqRD2OR3uBuhbOnvKH28Nvdh9u2UFt9Eupr/QvCfYGCbQdut5ucnBwSExNxOp2kpqaSl5fH+PHjWbFihdnliYnOn4bCLdce574EJ7Zb541ApK9orIOPtnvPwb2vwO6XoPAdqKswuzIR8zlCYMpiuDW2+3DbMdSGDPDuM2iY8fUGgoJtB8uXL2ft2rWsXLmSrVu3snTpUjIyMnC5XEybNs3s8sQkfzsOf/0D1J661uZugYoiOPQ61NeYV5v0D689NYJju1/zafN4PLzyvVspzX/HpKqMd7HGe86dKfKegwB4oNYFBW9B5YemlicSFEKd3YfbzkLt4K+YV29vCzG7gGCxceNGNmzYQG5uLunp6QDMmzePw4cPs2XLFqZOnWpyhWKGxjr4cNvVB54b17c0w9E/wt3fBZvN0NKkn6g/d4aGukqGxU/2ab9Q7eJy00ViEqabVJmxPB44+l9djAO8em5+uA0iR4LzVkNLEwk6beH2yGb4/DNvuG1j5VAL6rFtt27dOhYuXNgeatuMGzeO0NBQUlJSAHjuuedISkrCbrezefNmM0oVA1UcpdNA284Dl857hyqIBEKVKx+b3UHUyIk+7bXlRQwcHMOgqFEmVWasc596/9Ds7nz0uOHMUcNKEglq1/fctrFyqAUFWwAqKio4duwYS5YsuWFdeXk5ycnJDBgwAICFCxeybds25syZE5BabDabliBa/rT5EB5Pd8nW+5XwP6xYa3qtVlusfE7cjCpXPpGxSYSEOX3aa8qLGD7G/95as1/zl11Wr/olHo+7x3Px3Y37Ta9Vi/+LVY7PYF3Cwm3c8/0hnCj/S/vvuqHpAiuzZzBkhPn13cziLw1FwBtsAWJjY33aGxsbycvL4/77729vmzVrlqG1ibns9hA/TigPDpvDkHqk/6ly5VNXVcqrP4j2ab/SXM/0h35iUlXGs9sdeIDuzkabzYbDrnNRpKOmyw2c/byy/XFjcz119dUmVhRYCrZAdLT3A6OkpIQHHnigvT07O5vKykpDLxzrqXdQjPXRdu+FKt2x2eys+5dn+G3SM8YU1U/syPH+a8VzIusN/7etOlXAXd9Ywx2zv+3T/sZPJhFzEz22ff33WHUCit/rebsFD8/E81Lffq39Sdu50NePz2DV8UKxNtGD49iS/SlTl0L4YPNqCxQFWyAhIYGUlBTWrVvH0KFDiYuLY/Pmzbz//vsAmhGhHxuZ2kOwtUFYOAwba1hJ0o/UfVZKc8N54lPuY1DUSN/2S3XEJMwwsTpjDUuE0PCrd1TqJgONnNz1OpH+5PrZD1qave23xl67oMyK4VZjbAG73c6mTZtITk4mMzOTZcuWER0dzZNPPonD4Wi/cEz6n0HDYczdXay0eWdCSP4a6NtPCYQqVz4hAwYybHSqT3vlyQNERI1i4ODhJlVmPLsDJj5wdfaRLsYjJHwVIqI7XyfSn3Q2pVcbf+a57cvUY3tVUlISu3fv9ml74oknmDBhAuHh4SZVJcFg7Fe90weVfeD7BhA5EsbOhiFx5tUm1lblyidmzAzsDt+36srSgzc1DMEqosbAtKVQus/3hgzhQ2DMTBgxset9RfqLnuap7WwqMCv13CrYdqOgoICZM2f6tP30pz9l/fr11NTUUFxcTFZWFnl5eYwdq++irSxukvdDs77G+3XOgEEwcIjZVYnVzXn8nzttv2fZKwZXEjyGjITpj8KlOjhw9Z4Vs5ZrHmkR8P/mC52F22nftMYc0BqK0IX6+npKSkpuuDHD2rVrqaiooLm5mbNnz1JRUaFQ20/YbN6hCZGjFGpFzNbxHFSoFbnKBjaHf/PUdpznlm6G+PQ16rHtQkREBK2trWaXISIiIuIXux2SH/DezOSWoT1v3xZuW6+Ac1Dg6zOCgq2IiIiIRdjt/oXaNqFO72IVGoogIiIiIpagYCsiIiIilqBgKyIiIiKWoGArIiIiIpagYCsiIiIilqBgKyIiIiKWoOm+REQM9uJjZlcgImJN6rEVEREREUtQsBURERERS1CwFRERERFLULAVEcvIzc3l2WefbX+8Zs0a3nzzTaZOnYrT6aSlpaXT/XJycjhy5EiXP/fMmTNkZWW1P/7Vr37F7NmzASgqKiI7O7uXXoEEUqCPj0OHDjFr1ixmz57N008/Dej4EDGagq2IWNrQoUPZuXMnM2fO7HS92+1m//79TJkypcufsX37dhYsWABAc3MzhYWF7etSU1M5ePAgHo+ndwsXQ/Tm8REfH8+uXbvYt28f1dXVFBcX6/gQMZiCrYhYmtPpJDIyssv1RUVFjBs3DvD26EVGRjJ37lzi4+N5+OGHAcjLyyM9PR2A3/72t3znO9/x+RmJiYnd9uhJ8OrN4yM2Nhan0wlAaGgoDocD0PEhYiQFWxHp106ePMltt90GwJw5c7jzzjvJzc0lPT2dl19+GY/HQ0NDAxEREVy5coXc3Fzuuecen5+RkJDAiRMnTKheAu1mjo82R48epaamhgkTJgA6PkSMpHlsRcQynE4nzc3N7Y+bmpoIDw/3e3+Xy0VCQgIAp0+fJi4ujsLCQlJTUwF4/fXX+da3vtW7RYthAn18AJw7d46nnnqKt99+u/cKFxG/qcdWRCyj7Stft9uN2+3m8OHDJCYm9rhPWVkZAMePHyc5OZnW1lbsdu/bY8fxtR9//DGvvPIKCxcu5Pjx47z00kuAN/DcfvvtgXth0isCfXy0tLTw+OOPk5OTQ2xsbPvP0PEhYhwFWxGxjKioKBYtWkRaWhppaWksXryYQYMGMX/+fIqKirjvvvs4dOiQzz6pqamUlJQA14JLc3Mz1dXVVFZWUlBQwPTp0wF44YUX+NOf/sS2bdtITk7mhz/8IQAlJSVMnjzZ2BcrNy3Qx8emTZvIz89n9erVzJ07l4MHDwI6PkSMpKEIImIpmZmZZGZm+rTt2LGjy+3tdjtpaWkcOXKEZ555pr29uLgYgEWLFrX3znW0b98+wHtx0d13393pNhJ8Anl8ZGRkkJGR4bO/jg8RY9k8moNERILQjhzvv/N/ZG4dErx0jPR9WW94/33xMXPr6A/6y/miPyFFRERExBIUbEVERETEEhRsRURERMQSdPGYiIhIEGkbd2o0jXMVK1CPrYiIiIhYgoKtiIiIiFiCgq2IiIiIWIKCrYiIiIhYgoKtiIiIiFiCgq2IiIiIWIKCrYiIiARU3SX4f4Xgdvu3fflZyDsR2JqsquEcnPoAPB7/tj9bBpUfBrQkQ2keWxEREQkYtwdey4WK81DXCBl3gb2bbrXys/DyTmi6AkNvgUmjDCu1z2ttgSOboOkitDTDuDlgs3W9/dkyKPpPcLdA+BAYMsKwUgNGPbYduN1ucnJySExMxOl0kpqaSl5eHuPHj2fFihVmlyfSL3jcUOu69th1wPsmLdKm4RyU7r32+Pxp/3unxHh2G/zdNAhzQL4LNh7quue2Y6hNGQUT4oytta9zhEDSPWCzw6f5ULqn63OjY6iNS4XBXzG01IBRsO1g+fLlrF27lpUrV7J161aWLl1KRkYGLpeLadOmmV2eiOVdqoODG6Bwy7U21wHY9yp8sk/hpb9zu+GjP8PB30HZoWvtf/0D5L8JlxvMq81Mrz01gmO7X/Np83g8vPK9WynNf8ekqnyNjYGV87oPt9eH2u/MBodSyk0bngiTHuo+3F4fam+f333Pbl+ioQhXbdy4kQ0bNpCbm0t6ejoA8+bN4/Dhw2zZsoWpU6eaXKGItV1p8gaU5vrO15/6ABxhcNudxtYlwaNkN5w52vm6zyvhyH/AjMfA7jC2LjPVnztDQ10lw+In+7RfqHZxuekiMQnTTarsRm3h9tXd3nDbkUJt72oLt8XvesNtR1YOtaAe23br1q1j4cKF7aG2zbhx4wgNDSUlJYXz58/z4IMPkpSURGpqKvfeey+lpaUmVSxiLX87Bs0XgW56ZU8dhNbLhpUkQaTpIlQUdr/NxWqo+cSYeoJFlSsfm91B1MiJPu215UUMHBzDoKjgGqB6fc9tG4Xa3nd9z20bK4daULAFoKKigmPHjrFkyZIb1pWXl5OcnMyAAQOw2WxkZWVRUlJCUVERDz74IMuWLTOhYhHrqTzW8zatV6DG1fN2Yj2ffUS3f/QAYIPK40ZUEzyqXPlExiYREub0aa8pL2L4mODpre2oY7hto1AbGB3DbRsrh1rQUATAG2wBYmNjfdobGxvJy8vj/vvvB2DIkCHMnz+/ff2sWbPIzs7u1VpsVjzKRPzw9nOfETkopsftVn0/i3f2/YsBFUkwWfHgP7Eo7Wns3Y0z8MD+3AKmfGOGcYUFwH/7d/8Hk1e58qmrKuXVH0T7tF9prmf6Qz+5qec1+vMnee73mP+9fwXg85oyVvz9eJa36CuZQPj2vWt4YsH/BKD0zBHuXT0NTx+7aMHfevV3ERAd7X1DKCkp8WnPzs6msrKyywvHXnzxRR555JGA1yfSH1xoqMHt6XmSy88v1RpQjQSbCw213YdaoNXdyoX6aoMqCg5Vpwq46xtr+NYvCn2WkNBwYoK0xxZg+JhppGX8U/vjW4fdxj3ffRWbTbGkt01LWsDSuavbH4+Lm8Ly+39pYkWBZfP0tcgeAG63mylTplBZWUlOTg5xcXFs3ryZ999/n/Lycj744APuuusun32ef/55tm7dyq5duxg4cKBJlYtYR9kh3ymcOmMPgTmZEDLAmJokeDRegP3/2vN2E78GsXcEvp5AynrDv+3qPivl336UyKNrC4gZM+2G9u//uoqBg4f7/bwvPnazlX4x118oNjvJO8/t5VaYkdDzPLfiv+svFBsaD8fe806rGD+j53lu+yIdOoDdbmfTpk0kJyeTmZnJsmXLiI6O5sknn8ThcJCSkuKz/c9//nPee+89tm3bplAr0ktGTIKwgUA3b7LxMxRq+6vwwfCVid1sYIOBQ71jCvuLKlc+IQMGMmx0qk975ckDRESNuqlQa5TOZj9Iiu15KjC5eZ3NfhCT1PNUYH2dgu1VSUlJ7N69m4aGBsrLy1m7di3FxcVMmDCB8PDw9u2ef/553n33XbZv386QIUNMrFjEWsIGwtSlMCDiaoMNn5A7agokzDKjMgkWd8yH4eOvPrDhc4zcMhSmLvH26vcXVa58YsbMwO7wfdGVpQeDchhCd1N6+TPPrfivuym9/Jnnti/TUIRu3HHHHcycOZP169cDcPz4cSZOnMjYsWOJiIho366wsIc5aETEb+5WqD4JNSe9t4ccOATiUuCWKLMrk2Dx+Wfe6eGaLnp78GPGQ/QY3yu/+zJ/hyL0tkAORfB3ntpPqrzz3GpYwhfn7zy11Se989xabVhCP/rb9ubU19dTUlLCqlWr2tuSk5P73FWEIn2N3QGxt3sXkc7cGutdpG9we+CtD/yb0uv6mzhMGundR/zT2gIfbvNvSq/rb+IwbBwMscAtjNVjKyIiEkSs2GNbexFyT8DfTfNvntpPquCTalgw0Rq9iEa6WO2d99nfHtjqk947Po6aEvjajKBgKyIiEkSsGGxFjKKRKyIiIiJiCQq2IiIiImIJCrYiIiIiYgkKtiIiIiJiCQq2IiIiImIJCrYiIiIiYgma7ktERERELEE9tiIiIiJiCQq2IiIiImIJCrYiIiIiYgkKtiIiIiJiCQq2IiIiImIJCrYiIiIiYgkKtiIiIiJiCQq2IiIiImIJCrYiIiIiYgkKtiIiIiJiCQq2IiIiImIJCrYiIiIiYgkKtiIiIiJiCQq2IiIiImIJCrYiIiIiYgkKtiIiIiJiCQq2IiIiImIJCrYiIiIiYgkKtiIiIiJiCf8fW4IYzkS0LLcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 900.592x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qft.x(0)\n",
    "qft.cswap(0, 1, 2)\n",
    "qft.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1081.19x144.48 with 1 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_circuit = QFT(2).compose(RealAmplitudes(2)).compose(OR(1))\n",
    "my_circuit.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![img](./algorithm_flow.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### What do we need for that? \n",
    "\n",
    "Circuits as first-class citizens in Aqua."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "algorithm = Algorithm(wavefunction: QuantumCircuit, classifier: QuantumCircuit, ...)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Example: Variational Quantum Eigensolver "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "from qiskit import Aer\n",
    "from qiskit.aqua.operators import I, X, Z\n",
    "from qiskit.aqua.algorithms import VQE\n",
    "from qiskit.aqua.components.optimizers import COBYLA\n",
    "\n",
    "h2_hamiltonian = -1.05 * (I ^ I) + 0.39 * (I ^ Z) - 0.39 * (Z ^ I) - 0.01 * (Z ^ Z) + 0.18 * (X ^ X)\n",
    "h2_energy = -1.85727503\n",
    "\n",
    "optimizer = COBYLA(maxiter=100)\n",
    "backend = Aer.get_backend('statevector_simulator')\n",
    "\n",
    "vqe = VQE(operator=h2_hamiltonian, optimizer=optimizer, quantum_instance=backend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 479.192x144.48 with 1 Axes>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.circuit import QuantumCircuit, ParameterVector\n",
    "\n",
    "wavefunction = QuantumCircuit(2)\n",
    "params = ParameterVector('theta', length=8)\n",
    "it = iter(params)\n",
    "wavefunction.ry(next(it), 0)\n",
    "wavefunction.ry(next(it), 1)\n",
    "wavefunction.rz(next(it), 0)\n",
    "wavefunction.rz(next(it), 1)\n",
    "wavefunction.cx(0, 1)\n",
    "wavefunction.ry(next(it), 0)\n",
    "wavefunction.ry(next(it), 1)\n",
    "wavefunction.rz(next(it), 0)\n",
    "wavefunction.rz(next(it), 1)\n",
    "\n",
    "wavefunction.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VQE: -1.83432642048823 Reference: -1.85727503\n"
     ]
    }
   ],
   "source": [
    "vqe.var_form = wavefunction\n",
    "result = vqe.compute_minimum_eigenvalue()\n",
    "print('VQE:', result.eigenvalue.real, 'Reference:', h2_energy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 840.392x144.48 with 1 Axes>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.circuit.library import EfficientSU2\n",
    "wavefunction = EfficientSU2(2, reps=3)\n",
    "wavefunction.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960.792x144.48 with 1 Axes>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.circuit import Parameter\n",
    "wavefunction.cx(1, 0)\n",
    "wavefunction.ryy(Parameter('theta'), 0, 1)\n",
    "wavefunction.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VQE: -1.83987201315657 Reference: -1.85727503\n"
     ]
    }
   ],
   "source": [
    "vqe = VQE(h2_hamiltonian, wavefunction, optimizer=optimizer, quantum_instance=backend)\n",
    "result = vqe.compute_minimum_eigenvalue()\n",
    "print('VQE:', result.eigenvalue.real, 'Reference:', h2_energy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Example: Quantum SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "training_data = {'A': np.array([[2.95309709, 2.51327412], [3.14159265, 4.08407045]]),\n",
    "                 'B': np.array([[4.08407045, 2.26194671], [4.46106157, 2.38761042]])}\n",
    "testing_data = {'A': np.array([[3.83274304, 2.45044227]]),\n",
    "                'B': np.array([[3.89557489, 0.31415927]])}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 780.192x144.48 with 1 Axes>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.circuit.library import ZZFeatureMap\n",
    "ZZFeatureMap(2).draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2.95309709 2.51327412]\n",
      " [3.14159265 4.08407045]\n",
      " [4.08407045 2.26194671]\n",
      " [4.46106157 2.38761042]]\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.environ['QISKIT_IN_PARALLEL'] = 'TRUE'\n",
    "from qiskit.aqua.algorithms import QSVM\n",
    "\n",
    "data_preparation = ZZFeatureMap(2)\n",
    "qsvm = QSVM(data_preparation, training_data, testing_data, \n",
    "            quantum_instance=Aer.get_backend('statevector_simulator'))\n",
    "result = qsvm.run()\n",
    "print(result['svm']['support_vectors'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### But... didn't we already have this before in Aqua?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "The `VariationalForm` objects offered some trial wavefunctions as wrapped circuits.\n",
    "\n",
    "Problems:\n",
    "* parallel implementation to Terra\n",
    "* additional objects to learn\n",
    "* new class for every new wavefunction!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "# before\n",
    "from qiskit.aqua.components.variational_forms import VariationalForm\n",
    "\n",
    "class H_RX_CZ(VariationalForm):\n",
    "    def __init__(self, num_qubits, depth=3, entangler_map=None, entanglement='full', initial_state=None,\n",
    "                 skip_unentangled_qubits=False):\n",
    "        super().__init__()\n",
    "        self._num_qubits = num_qubits\n",
    "        self._depth = depth\n",
    "        self._entanglement = entanglement\n",
    "\n",
    "        if entangler_map is None:\n",
    "            self._entangler_map = VariationalForm.get_entangler_map(entanglement, num_qubits)\n",
    "        else:\n",
    "            self._entangler_map = VariationalForm.validate_entangler_map(entangler_map, num_qubits)\n",
    "\n",
    "        # determine the entangled qubits\n",
    "        all_qubits = []\n",
    "        for src, targ in self._entangler_map:\n",
    "            all_qubits.extend([src, targ])\n",
    "        self._entangled_qubits = sorted(list(set(all_qubits)))\n",
    "        self._initial_state = initial_state\n",
    "        self._skip_unentangled_qubits = skip_unentangled_qubits\n",
    "\n",
    "        self._num_parameters = len(self._entangled_qubits) * (depth + 1)\n",
    "        self._bounds = [(-np.pi, np.pi)] * self._num_parameters\n",
    "\n",
    "    def construct_circuit(self, parameters, q=None):\n",
    "        if len(parameters) != self._num_parameters:\n",
    "            raise ValueError('The number of parameters has to be {}'.format(self._num_parameters))\n",
    "\n",
    "        if q is None:\n",
    "            q = QuantumRegister(self._num_qubits, name='q')\n",
    "        if self._initial_state is not None:\n",
    "            circuit = self._initial_state.construct_circuit('circuit', q)\n",
    "        else:\n",
    "            circuit = QuantumCircuit(q)\n",
    "\n",
    "        param_idx = 0\n",
    "        for qubit in range(self._num_qubits):\n",
    "            if not self._skip_unentangled_qubits or qubit in self._entangled_qubits:\n",
    "                circuit.h(q[qubit])\n",
    "                circuit.rx(parameters[param_idx], q[qubit])  # ry\n",
    "                param_idx += 1\n",
    "\n",
    "        for block in range(self._depth):\n",
    "            for src, targ in self._entangler_map:\n",
    "                circuit.cz(q[src], q[targ])\n",
    "                \n",
    "        return circuit\n",
    "    \n",
    "h_rx_cz = H_RX_CZ(num_qubits=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### The 2-local circuits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1382.19x264.88 with 1 Axes>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now\n",
    "from qiskit.circuit.library import TwoLocal\n",
    "h_rx_cz = TwoLocal(4, ['h', 'rx'], 'cz', entanglement='circular')\n",
    "h_rx_cz.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Modifications needed? No problem, this is a circuit you can do whatever you want!  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Mutability & Pattern-circuits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900.592x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ansatz = TwoLocal(3, 'rz', 'cx', reps=1)\n",
    "ansatz.rotation_blocks = ['h', 'h']\n",
    "ansatz.reps = 2\n",
    "ansatz.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1622.99x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ansatz.num_qubits = 5\n",
    "ansatz.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1321.99x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.circuit import Parameter\n",
    "from qiskit.circuit.library import SGate\n",
    "theta = Parameter('theta')\n",
    "block = QuantumCircuit(2)\n",
    "block.swap(0, 1)\n",
    "block.ry(theta, 0)\n",
    "block.rz(theta, 1)\n",
    "\n",
    "ansatz = TwoLocal(3, SGate(), block, reps=2, insert_barriers=True)\n",
    "ansatz.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Generalization: N-local circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1683.19x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.circuit.library import NLocal\n",
    "theta, phi = Parameter('theta'), Parameter('phi')\n",
    "block = QuantumCircuit(3)\n",
    "block.rxx(theta, 0, 2)\n",
    "block.ccx(0, 2, 1)\n",
    "block.rx(theta, 0)\n",
    "block.rz(phi, 1)\n",
    "intermediate = QuantumCircuit(5)\n",
    "intermediate.h([0, 2, 4])\n",
    "ansatz = NLocal(5, intermediate, block, entanglement='linear', \n",
    "                reps=2, insert_barriers=True)\n",
    "ansatz.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Mutability in action"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VQE: -1.83962317966371 Reference: -1.85727503\n"
     ]
    }
   ],
   "source": [
    "h2_hamiltonian = -1.05 * (I ^ I) + 0.39 * (I ^ Z) - 0.39 * (Z ^ I) - 0.01 * (Z ^ Z) + 0.18 * (X ^ X)\n",
    "h2_energy = -1.85727503\n",
    "\n",
    "optimizer = COBYLA(maxiter=100)\n",
    "backend = Aer.get_backend('statevector_simulator')\n",
    "\n",
    "wavefunction = EfficientSU2()  # no number of qubits specified!\n",
    "\n",
    "vqe = VQE(h2_hamiltonian, wavefunction, optimizer=optimizer, quantum_instance=backend)\n",
    "result = vqe.run()\n",
    "print('VQE:', result.eigenvalue.real, 'Reference:', h2_energy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![img](./mutability.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "from qiskit.circuit.library import RealAmplitudes\n",
    "from qiskit.optimization import QuadraticProgram\n",
    "from qiskit.optimization.algorithms import MinimumEigenOptimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "wavefunction = RealAmplitudes(entanglement='circular')\n",
    "vqe = VQE(var_form=wavefunction, optimizer=COBYLA(maxiter=100), \n",
    "          quantum_instance=Aer.get_backend('statevector_simulator'))\n",
    "min_eigen_optimizer = MinimumEigenOptimizer(vqe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "$$\n",
    "\\text{min }\n",
    "x^T \n",
    "\\begin{pmatrix} \n",
    "2 & 0 \\\\\n",
    "0 & -1\n",
    "\\end{pmatrix}\n",
    "x \n",
    "+ \n",
    "x^T\n",
    "\\begin{pmatrix}\n",
    "1 \\\\ 2 \n",
    "\\end{pmatrix}\n",
    "$$\n",
    "\n",
    "$$\n",
    "\\text{subject to } x_1 + x_2 = 1\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0.0,1.0] / 1.0 / OptimizationResultStatus.SUCCESS)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "problem1 = QuadraticProgram()\n",
    "num_variables = 2\n",
    "_ = [problem1.binary_var() for _ in range(num_variables)]\n",
    "linear = [1, 2]\n",
    "quadratic = [[2, 0], [0, -1]]\n",
    "problem1.minimize(linear=linear, quadratic=quadratic)\n",
    "problem1.linear_constraint([1, 1], '==', 1)\n",
    "\n",
    "min_eigen_optimizer.solve(problem1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of qubits: 2\n"
     ]
    }
   ],
   "source": [
    "print('Number of qubits:', min_eigen_optimizer._min_eigen_solver._var_form.num_qubits)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "$$\n",
    "\\text{min }\n",
    "x^T \n",
    "\\begin{pmatrix} \n",
    "-1 & 0 & 0 & 1 \\\\\n",
    "0 & -1 & 0 & 0 \\\\\n",
    "0 & 0 & 3 & 0 \\\\\n",
    "1 & 0 & 0 & 2\n",
    "\\end{pmatrix}\n",
    "x \n",
    "+ \n",
    "x^T\n",
    "\\begin{pmatrix}\n",
    "1 \\\\ 2 \\\\ 0 \\\\ 1\n",
    "\\end{pmatrix}\n",
    "$$\n",
    "\n",
    "$$\n",
    "\\text{subject to } x_1 + x_2 + x_3 + x_4 = 2\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([1.0,1.0,0.0,0.0] / 1.0 / OptimizationResultStatus.SUCCESS)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "problem2 = QuadraticProgram()\n",
    "num_variables = 4\n",
    "_ = [problem2.binary_var() for _ in range(num_variables)]\n",
    "linear = [1, 2, 0, 1]\n",
    "quadratic = [[-1, 0, 0, 1], [0, -1, 0, 0], [0, 0, 3, 0], [1, 0, 0, 2]]\n",
    "problem2.minimize(linear=linear, quadratic=quadratic)\n",
    "problem2.linear_constraint([1, 1, 1, 1], '==', 2)\n",
    "min_eigen_optimizer.solve(problem2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of qubits: 4\n"
     ]
    }
   ],
   "source": [
    "print('Number of qubits:', min_eigen_optimizer._min_eigen_solver._var_form.num_qubits)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Recap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "* circuit library offers a collection of interesting (for various reasons) families of circuits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "* these circuits are ready-to-go and powerful objects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "* circuits work natively with Aqua"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![img](./what_moved.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": []
  }
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