diff --git a/All gates.ipynb b/All gates.ipynb
index 6cf1784..86363c1 100644
--- a/All gates.ipynb	
+++ b/All gates.ipynb	
@@ -2,9 +2,18 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 28,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jul/envs/latest/lib/python3.7/site-packages/ipykernel_launcher.py:3: DeprecationWarning: The module qiskit.extensions.standard is deprecated as of 0.14.0 and will be removed no earlier than 3 months after the release. You should import the standard gates from qiskit.circuit.library.standard_gates instead.\n",
+      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
+     ]
+    }
+   ],
    "source": [
     "from qiskit import QuantumCircuit\n",
     "from qiskit.circuit import Parameter\n",
@@ -13,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -26,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -74,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -88,12 +97,12 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 247.68x120.4 with 1 Axes>"
+       "<Figure size 381.046x185.231 with 1 Axes>"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -104,7 +113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -118,12 +127,12 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 247.68x120.4 with 1 Axes>"
+       "<Figure size 381.046x185.231 with 1 Axes>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -134,7 +143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -148,12 +157,12 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 247.68x120.4 with 1 Axes>"
+       "<Figure size 381.046x185.231 with 1 Axes>"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -164,7 +173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -178,12 +187,12 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAMkAAABpCAYAAACQ2gfrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAADvklEQVR4nO3cMUhjBxzH8V/eu1QPnCSD4OYpDoEIZqidxELvdCidHBwcRBBEWoqlUEotLS0ZxFIoFDrcXR1KhcampYNtry2YDk4iKE6BWyTg4CoI0vfS4eiB9Y7feRfzXi7fz/heiH8C3/fPQ14yjUajIQBPFSQ9AJB2RAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYN5IeAE/2yh+/Jj3CMzl/YyrpEa4dmwQwiAQwiAQwiAQwiAQwiAQwiAQwiAQwiAQwiAQwiAQwiAQwiAQwiAQwUhdJHMdaW1vT0NCQuru7NTIyomq1quHhYS0sLCQ9HjpQ6p4nmZ+fV6VS0crKiorFonZ2djQzM6OTkxMtLy8nPV5biX9/oOjrby6fOD+XJN34qazMzZstnqr9pCqSjY0Nra+va3t7W+Pj45KkiYkJ7e3tqVKpaHR0NOEJ20tw57aCO7cvHIurfyta/ULhO0sE8oxSFUmpVNLk5OTjQP4zODiobDarQqGQ0GQvh/jPvxR9+ZXC995V8PpE0uO0jdTck9TrdR0eHmp6evrSuaOjI+XzeXV1dSUw2csh3vrtUSAfvE8gV5SaTVKv1yVJfX19F46fnZ2pWq1qaqq1z1JnMpmW/r3/yz7Yatp7RT//ovjufYUffajgtVeb9r5S8p/T82g0Gld6fWo2SS6XkyTVarULx1dXV3V8fKxisZjEWG0v+mFT8b1vFX7ycdMD6RSp2SQDAwMqFAoqlUrq7e1Vf3+/Njc3tbX16Ira6kiuerVptmb8Wkr03feKyz8q/PxTBSPXcz+X9OfUCqnZJEEQqFwuK5/Pa3FxUXNzc8rlclpaWlIYhty0X1F0f13xZkVh6bNrC6RTZBopvxTMzs5qf39fBwcHSY/SUi+ySRoPH+qfxbelMJSy2Uvng7feVDg/9yLjPdYJv7uVmq9bT7O7u6uxsbGkx2grmVu3mnrj3+lS83XrSU5PT1Wr1fgnIhKV6k3S09OjKIqSHgMdLtWbBEgDIgEMIgEMIgEMIgEMIgEMIgEMIgEMIgEMIgEMIgEMIgEMIgEMIgGM1D+ZCCSNTQIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIYRAIY/wLKW7sySbxEwgAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 247.68x120.4 with 1 Axes>"
+       "<Figure size 381.046x185.231 with 1 Axes>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -194,7 +203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -208,12 +217,12 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAS4AAACaCAYAAAAXbKWUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAGA0lEQVR4nO3dTWiV6RnH4X/MsUZngh/VItVWsdrqMK1fg8zKjRYpQqFBsnMzLl2qoLugYDYKLgruXGTrqqNduZBgQZSisbS2I+2kCzcRM21mKtgS53QRIww10alJjvfrdUE4hPNA7uSEX573yQunq91utwNQyKJODwDwbQkXUI5wAeUIF1COcAHlCBdQjnAB5QgXUI5wAeUIF1COcAHlCBdQjnAB5QgXUI5wAeUIF1COcAHlCBdQjnAB5QgXUI5wAeUIF1COcAHlCBdQjnAB5QgXUI5wAeUIF1COcAHlCBdQjnAB5QgXUI5wAeUIF1COcAHlCBdQjnAB5QgXUI5wAeUIF1COcAHlCBdQjnAB5QgXUI5wAeUIF1COcAHlCBdQjnAB5QgXUI5wAeUIF1COcAHltDo9AM330aednqCzfv/LTk/QPHZcQDnCBZQjXEA5wgWUI1xAOcIFlCNcQDnCBZQjXEA5wgWUI1xAOcIFlCNcQDnCBZQjXEA5wgWUI1xAOcIFlCNcQDnCBZQjXEA5wgWUI1xAOY0N140bN9Lf35+1a9emp6cnmzZtyokTJ/LkyZNcvHgxXV1d2bZtW6fHBP4PjQtXu93O8ePHs3fv3ly+fDljY2NZsmRJRkdHc+7cuRw8eDD3799PkuzatavD0zIffv3x1Juw7v7uzGtO/Wxqza82LNxczJ3GhWtgYCDnz59PT09PBgcHMz4+nomJiTx69CiHDx/O8PBwhoaGkiQ7d+7s8LTMh60rksmvkz/9c+Y1H66cerw/yxreXq1ODzCXrl+/njNnzqTVauXKlSvZv3//i+fWrFmTS5cu5datW3nw4EES4WqidcuSFd9JHkwkT5+9fM2S7uRHvcm/nyV//XJh52NuNGrHdfLkybTb7Rw9evQb0ZrWarVy4MCBF58LV/NsWzH1+Md/zLJmedJaNBWtZ+2FmYu51Zhw3bx5M7dv3053d3eOHz8+47q1a9cmSTZs2JBVq1Yt1HgskA+eh2u2y8SfukwsrzGXilevXk2S7NmzJ+vXr59x3cTERBK7rabatnzq8effT3avfvmaD5/H7c/CVVZjwnXnzp0kye7du2ddd+/evSTC1VRbn0fp4++9eu39ifmdhfnTmHCNjY0lSVauXDnjmvHx8QwPDyepcytEV1dXp0d4Y7t/szAHST98L+ldnPzhi+ST3718Te/i5PovkqeTyecLdDDfhNdwrrXbb/Y70ZgzrukfxHTAXmZwcDBPnz5NYsfVRNMH83+ZZSe19fml5GdfJl/P/0jMk8bsuLZs2ZKRkZFcu3Ytk5OTabW++a1dvXo1Fy5cSDJ1a8S6des6Mea39qZ/md4GH326MF9n+mD+s9cI10KebzXhNXzbNGbH1dfXlyQZHR3NkSNH8vjx4yRTl4enT59OX19fent7k9htNdWLHdcsUfpJB8LF3GtMuPr7+7Nv374kydDQUNasWZMVK1Zk9erVGRgYyLFjx7J9+/Ykdc63eH1dmYrSf54lf/tq5nXTOy63QtTWmHAtWrQoV65cyalTp7Jx48YsXrw4y5Yty6FDhzI8PJzBwcGMjIwkseNqoo3vJ++1ks+/SiZnuDJb2p384P3kyWTy938t7HzMrcaccSXJ0qVLc/bs2Zw9e/Z/nhsdHXUPV4N98BoH8z9ennR3TZ2BOXWqrVHhms3du3eTJL29vdm8eXOHp2Gu/fbh1Mds7n2xcP8oYH415lLxVabDtWPHDvfVQHHvTLicb0FzvDPhmt5xCRfU986ccT18+IoDEKCMd2bHBTSHcAHlCBdQjnAB5QgXUI5wAeUIF1COcAHlCBdQjnAB5QgXUI5wAeUIF1COcAHlCBdQjnAB5QgXUI5wAeUIF1COcAHlCBdQTle73fZu5EApdlxAOcIFlCNcQDnCBZQjXEA5wgWUI1xAOcIFlCNcQDnCBZQjXEA5wgWUI1xAOcIFlCNcQDnCBZQjXEA5wgWUI1xAOcIFlCNcQDnCBZQjXEA5wgWUI1xAOcIFlCNcQDnCBZQjXEA5wgWUI1xAOcIFlCNcQDnCBZTzX+9jGTs3SpbPAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<Figure size 247.68x120.4 with 1 Axes>"
+       "<Figure size 381.046x185.231 with 1 Axes>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -224,7 +233,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -238,12 +247,12 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 247.68x120.4 with 1 Axes>"
+       "<Figure size 381.046x185.231 with 1 Axes>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -254,7 +263,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -262,18 +271,18 @@
      "output_type": "stream",
      "text": [
       "     ┌─────┐\n",
-      "q_0: ┤ Sdg ├\n",
+      "q_0: ┤ SDG ├\n",
       "     └─────┘\n"
      ]
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 247.68x120.4 with 1 Axes>"
+       "<Figure size 381.046x185.231 with 1 Axes>"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -291,7 +300,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -305,12 +314,12 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 247.68x120.4 with 1 Axes>"
+       "<Figure size 381.046x185.231 with 1 Axes>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -321,7 +330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -329,18 +338,18 @@
      "output_type": "stream",
      "text": [
       "     ┌─────┐\n",
-      "q_0: ┤ Tdg ├\n",
+      "q_0: ┤ TDG ├\n",
       "     └─────┘\n"
      ]
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 247.68x120.4 with 1 Axes>"
+       "<Figure size 381.046x185.231 with 1 Axes>"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -351,7 +360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -365,12 +374,12 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 333.68x120.4 with 1 Axes>"
+       "<Figure size 513.354x185.231 with 1 Axes>"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -381,7 +390,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -389,18 +398,18 @@
      "output_type": "stream",
      "text": [
       "     ┌───────┐\n",
-      "q_0: ┤ Rx(x) ├\n",
+      "q_0: ┤ RX(x) ├\n",
       "     └───────┘\n"
      ]
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 247.68x120.4 with 1 Axes>"
+       "<Figure size 381.046x185.231 with 1 Axes>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -411,7 +420,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -419,18 +428,18 @@
      "output_type": "stream",
      "text": [
       "     ┌───────┐\n",
-      "q_0: ┤ Ry(x) ├\n",
+      "q_0: ┤ RY(x) ├\n",
       "     └───────┘\n"
      ]
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 247.68x120.4 with 1 Axes>"
+       "<Figure size 381.046x185.231 with 1 Axes>"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -441,7 +450,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -449,18 +458,18 @@
      "output_type": "stream",
      "text": [
       "     ┌───────┐\n",
-      "q_0: ┤ Rz(x) ├\n",
+      "q_0: ┤ RZ(x) ├\n",
       "     └───────┘\n"
      ]
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 247.68x120.4 with 1 Axes>"
+       "<Figure size 381.046x185.231 with 1 Axes>"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -471,7 +480,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -485,12 +494,12 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 247.68x120.4 with 1 Axes>"
+       "<Figure size 381.046x185.231 with 1 Axes>"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -501,7 +510,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -515,12 +524,12 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 333.68x120.4 with 1 Axes>"
+       "<Figure size 513.354x185.231 with 1 Axes>"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -531,7 +540,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -545,12 +554,12 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 333.68x120.4 with 1 Axes>"
+       "<Figure size 513.354x185.231 with 1 Axes>"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -561,7 +570,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -570,19 +579,19 @@
      "text": [
       "     ┌─────────┐\n",
       "q_0: ┤0        ├\n",
-      "     │  Rxx(x) │\n",
+      "     │  RXX(x) │\n",
       "q_1: ┤1        ├\n",
       "     └─────────┘\n"
      ]
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 340.56x206.4 with 1 Axes>"
+       "<Figure size 523.938x317.538 with 1 Axes>"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -593,7 +602,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -602,19 +611,19 @@
      "text": [
       "     ┌─────────┐\n",
       "q_0: ┤0        ├\n",
-      "     │  Ryy(x) │\n",
+      "     │  RYY(x) │\n",
       "q_1: ┤1        ├\n",
       "     └─────────┘\n"
      ]
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 340.56x206.4 with 1 Axes>"
+       "<Figure size 523.938x317.538 with 1 Axes>"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -625,7 +634,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -641,12 +650,12 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 340.56x206.4 with 1 Axes>"
+       "<Figure size 656.246x317.538 with 1 Axes>"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -657,7 +666,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -666,19 +675,19 @@
      "text": [
       "     ┌─────────┐\n",
       "q_0: ┤0        ├\n",
-      "     │  Rzx(x) │\n",
+      "     │  RZX(x) │\n",
       "q_1: ┤1        ├\n",
       "     └─────────┘\n"
      ]
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 340.56x206.4 with 1 Axes>"
+       "<Figure size 523.938x317.538 with 1 Axes>"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -689,7 +698,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -705,12 +714,12 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 340.56x206.4 with 1 Axes>"
+       "<Figure size 523.938x317.538 with 1 Axes>"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -721,7 +730,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -739,12 +748,12 @@
     },
     {
      "data": {
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\n",
       "text/plain": [
-       "<Figure size 340.56x292.4 with 1 Axes>"
+       "<Figure size 523.938x449.846 with 1 Axes>"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -762,7 +771,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -778,12 +787,12 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 340.56x206.4 with 1 Axes>"
+       "<Figure size 523.938x317.538 with 1 Axes>"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -801,7 +810,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -817,12 +826,12 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQ8AAACqCAYAAACzicrXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAIfUlEQVR4nO3cf0ichx3H8c+dvyFNVjkSq5JUo2fnJSdoxjaTxd5YmRQ2RplJpKRFHDpxgRIYjA2zP4SDOmGhf4wEFnB/BAOKW7di8k+nR4JpwLmBso4L5oe74LIkG2tNjDb67A8TM0mb6Rf1eR59v8CITzz55MC3z/PkMOA4jiMAWKGg2wMA+BPxAGBCPACYEA8AJsQDgAnxAGBCPACYEA8AJsQDgAnxAGBCPACYEA8AJsQDgAnxAGBCPACYEA8AJsQDgAnxAGBCPACYEA8AJsQDgAnxAGBCPACYEA8AJsQDgAnxAGBCPACYEA8AJsQDgAnxAGBCPACYEA8AJsQDgAnxAGBCPACYEA8AJsQDgAnxAGBCPACYEA8AJsQDgAnxAGBCPACYEA8AJsQDgAnxAGBCPACYEA8AJsQDgAnxAGCS7vYAwC3Ts9KnD6VgQNqaI2Xy3bAiPF3YVBxHunpbupSURlMLH0tSRpr0lWLpQKmU/6K7G/2CyxYPmZ+fV2dnp0pLS5Wdna2KigolEgmVlZWpqanJ7Xm+92hOOntZ+tWHS8MhSZ/NSUNXpY5+6Y9/dW+jn3Dm4SGNjY3q6+tTW1ubqqqqNDQ0pPr6et25c0fHjx93e56vOY507oo0fP3px1/k93+W0oJSzSvrs82viIdHdHd3q6urS4ODg6qpqZEkxWIxjYyMqK+vT5WVlS4v9LfkP56GYzneH5Eqd0kv5KzdJr/jssUj4vG4amtrF8PxRElJiTIyMhSNRiVJN27cUE1NjcLhsPbu3auLFy+6Mdd3LiWlwAo+f96RPhpfszkbAvHwgFQqpbGxMdXV1T3zdxMTE4pEIsrKypIkNTc36/Dhw0omkzp9+rSOHDmi2dnZ9Z7sKw9mpbFb0nOuVD7XFeLxXMTDA1KplCQpLy9vyfHp6WklEonFS5a7d+/q0qVLamxslCRVV1crPz9fAwMD67Y1EAj47q3w5Veee4/ji0zee+D69vV8Wyni4QGhUEiSlEwmlxzv6OjQ5OSkqqqqJC2chezYsWPxLESSioqKdPPmzfUb60PO/JztcY7tcZsFN0w9oLi4WNFoVPF4XLm5uSooKFBvb6/6+/slaTEeXuBYfoS7bOaR9LMe6dH88h8TkFT00gu+/PeuF848PCAYDKqnp0eRSEQtLS1qaGhQKBRSa2ur0tLSFm+W7ty5U7dv39bMzMziY69fv65du3a5Nd0XstKlfUUru2HqSKouXatFGwPx8IhwOKyBgQHdv39fExMTam9v1+joqMrLy5WTs/D/haFQSPv379eZM2ckSUNDQ7p165ZisZib033hQHj5N0wDehycl9dw0AZAPDxseHj4mUuWU6dO6dy5cwqHw2pqalJ3d7cyMzNdWugfhbnS6xX///MCj/94+4CUlbHWq/wt4HBR50lTU1Patm2bTp48qWPHjrk9Z0NwHGngY+mDvyy8juN/BbRwZpKVLr11QIoUuLHQX4gHNp3/PJAujy+8juPf9xeOvfQlaX/pwr2RbM44loV4YFN75+zC+5NvurvDj7jnAcCEeAAwIR4ATIgHABPiAcCEeAAwIR4ATIgHABPiAcCEeAAwIR4ATIgHABPiAcCEeAAwIR4ATIgHABPiAcCEeAAwIR4ATIgHABPiAcCEeAAwIR4ATIgHABPiAcCEeAAwIR4ATIgHABPiAcCEeAAwIR4ATIgHABPiAcCEeAAwIR4ATIgHAJOA4ziO2yOA5XrnrNsLlufkm24vWHuceQAwIR4ATIgHABPiAcCEeAAwIR4ATIgHABPiAcCEeAAwIR4ATIgHABPiAcCEeAAwIR4ATIiHx8zPz6uzs1OlpaXKzs5WRUWFEomEysrK1NTU5PY8YBHx8JjGxka1t7erublZ58+f16FDh1RfX69r166pqqrK7XkbwqPZaf36R/kaOf/LJcfv/n1Up38Y0tUrPS4t85d0twfgqe7ubnV1dWlwcFA1NTWSpFgsppGREfX19amystLlhRtDemaO9n3nJ/rTBx2qeK1VaemZ+uTODf2uo1Zf/367Sr9a5/ZEX+DMw0Pi8bhqa2sXw/FESUmJMjIyFI1GJUknTpxQOBxWMBhUb2+vG1N9b883myVJH1/8jR58cke/fffb2hNrUvRbLS4v8w/i4RGpVEpjY2Oqq3v2p97ExIQikYiysrIkSbW1tbpw4YIOHjy43jM3jPSMLO377k81/Id39f4vXtfOva/pa2/83O1ZvkI8PCKVSkmS8vLylhyfnp5WIpFYcslSXV2t4uLidd33RCAQcPVtNe2J/UCzDz/Vlhfz9erR91b1a7v9PK3Hc0s8PCIUCkmSksnkkuMdHR2anJzkZukaCAbT9Wj2gb78jbcVCPKtsFLcMPWI4uJiRaNRxeNx5ebmqqCgQL29verv75ckz8TD7V+2v5q/Pf1fk3/TZw+ntL1o3+p90cfcfp7WA7n1iGAwqJ6eHkUiEbW0tKihoUGhUEitra1KS0tbvFmK1fPPa8PK2bpdW0M73Z7iS5x5eEg4HNbAwMCSY0ePHlV5eblycnJcWrVx3b4+rO1F3jij8yPOPDxueHj4mUuWtrY2FRYW6vLly2publZhYaHGx8ddWuhfr771nr734363Z/gW8fCwqakpJZPJZ14c1t7erlQqpZmZGd27d0+pVEq7d+92aSU2Ky5bPGzLli2am5tzewbwuTjzAGBCPACYEA8AJsQDgAnxAGBCPACYEA8AJsQDgAnxAGBCPACYEA8AJsQDgAnxAGBCPACYBJzN8MsWAaw6zjwAmBAPACbEA4AJ8QBgQjwAmBAPACbEA4AJ8QBgQjwAmBAPACbEA4AJ8QBgQjwAmBAPACbEA4AJ8QBgQjwAmBAPACbEA4AJ8QBgQjwAmBAPACbEA4AJ8QBg8l/9SuI09nc4ZwAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 340.56x206.4 with 1 Axes>"
+       "<Figure size 523.938x317.538 with 1 Axes>"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -840,7 +849,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -856,12 +865,12 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 340.56x206.4 with 1 Axes>"
+       "<Figure size 523.938x317.538 with 1 Axes>"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -872,7 +881,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -882,18 +891,18 @@
       "              \n",
       "q_0: ────■────\n",
       "     ┌───┴───┐\n",
-      "q_1: ┤ Rx(x) ├\n",
+      "q_1: ┤ RX(x) ├\n",
       "     └───────┘\n"
      ]
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZkAAAD+CAYAAADoI3iYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAQ2UlEQVR4nO3df1TVdZ7H8df3gvFDGfEHBoJiCibTdPBX1OwmU2laY+20LEPNH06cbHLKbaZNPOHMcUVzZT2LjdNqllPJsFs2gztnC8rdtXLIGnas+KEjlaZXSzAMSkwUFe53/2BhYxDb4vu+F+35OIdzvPf7vfe8FY5PP98fV8d1XVcAABjwhXoAAMDFi8gAAMwQGQCAGSIDADBDZAAAZogMAMAMkQEAmCEyAAAzRAYAYIbIAADMEBkAgBkiAwAwQ2QAAGaIDADADJEBAJghMgAAM0QGAGCGyAAAzBAZAIAZIgMAMENkAABmiAwAwAyRAQCYITIAADNEBgBghsgAAMwQGQCAGSIDADBDZAAAZogMAMAMkQEAmCEyAAAzRAYAYIbIAADMEBkAgBkiAwAwQ2QAAGaIDADADJEBAJghMgAAM0QGAGCGyAAAzBAZAIAZIgMAMENkAABmiAwAwAyRAQCYITIAADNEBgBghsgAAMwQGQCAGSIDADBDZAAAZogMAMAMkQEAmCEyAAAzRAYAYIbIAADMEBkAgBkiAwAwQ2QAAGaIDADADJEBAJghMgAAM0QGAGCGyAAAzBAZAIAZIgMAMENkAABmiAwAwAyRAQCYITIAADNEBgBghsgAAMwQGQCAmfBQDwDg6811XX1UuUef1h3SmZZWOWFhiogdrEuv+aaGTRob6vHQT0QGQEicbmnV/tLf652nt+oz/0fn3GdUxiSl3XWzxt6cobBLBgV5QniBw2Uws2PHDuXk5Cg+Pl6RkZEaP368Fi9erNbWVm3YsEGO4ygtLS3UYyIEDvxuh347+W7tXLpJnx1s7HO/j996TxU//oW2ZNynppr3gzghvEJk4DnXdZWXl6fMzEyVlpaqsbFRERER8vv9Kioq0ty5c1VXVydJmjp1aoinRbDtebxMry38pTpOn+18wnX73NcNdG5r+/iYtt62VA2v7QrGiPAQkYHnCgoKtGbNGkVGRqqwsFDNzc1qaWnR0aNHNW/ePFVUVKikpESSNGXKlBBPi2Dav+U1vbn8150P+m5LL27AVcfZdr2Su1rNuw/YDAcTjuue558RwJe0fft2zZw5U2FhYdq6datmzZrVY3t7e7uuuOIK7d27V5L08ssva+bMmaEYFUF26uNjKp26QIGOwHlXL+fj+HyKGXep/vr1R+U4jscTwgIrGXgqPz9frutq4cKFvQIjSeHh4ZozZ073Y1YyXx/7nn1FgfaOrxwYSXIDAR0/cEQfvfEnDyeDJSIDz1RWVmrnzp0KCwtTXl5en/vFx8dLkpKTkzV8+PDu5zs6OrR69WqlpKQoIiJCEyZM0MqVK9Xe3m4+O2wF2jv0bvF/Sh4sPhyfo3eL/6P/b4SgIDLwTHl5uSQpIyNDSUlJfe7X0tIiqfcq5v7771d+fr6uvfZarV+/Xtdff72WLl2qBQsW2A2NoDj8SpVOfvTJlzoP0xc34OqDrTvVeqS5/28Gc9wnA89UVVVJkqZNm3be/WprayX1jMzu3bv1+OOP67777tP69eslSXfffbeGDh2qRx55RPfee6+mT59uNDmsNe/y9mS9G3D1ad0hDU4Y4en7wnusZOCZxsbO+x2GDRvW5z7Nzc2qqKiQ1PPy5eeee06u6+qBBx7osX/X482bN3s9rgnHcfg6x1fRw4UKuAFP/6y/f8ttIf99fV2++oPIwDNdFyp2xeZcCgsL1dbWJqnnSuatt97SiBEjlJqa2mP/MWPGaPTo0Xr77bcNJkZweXs1mOvFsTeY43AZPJOamqqamhpt27ZN7e3tCg/v+eNVXl6utWvXSpLi4uKUmJjYva2hoaHH489LTExUfX293eAe4o6Ac6su+o1q15R6+p5bXnxBSTdwdeJAx0oGnsnKypIk+f1+zZ8/X01NTZI6D5GtWLFCWVlZiomJkdT7pP/JkycVERFxzveNjIzUqVOnDCeHtbgpqV+805fghPs04srLPH1P2CAy8ExOTk73jZUlJSWKi4tTbGysRo4cqYKCAi1atEjp6emSen+cTHR0tE6fPn3O921ra1NUVJTt8DCVeP1kDU4cKfXz+L4kyedo3K1/oai42P6/F8wRGXjG5/OprKxMS5Ys0bhx4zRo0CBFR0crOztbFRUVKiwsVE1NjaTeK5nRo0f3eUisvr6+z0NpuDA4Pp/S7rq5Xzdidgu4mnTnnC/eDwMCkYGnoqKitGrVKvn9fp05c0YNDQ0qLS3VjBkz5Pf7+7xHZtq0aWpubta+fft6PP/hhx+qoaHhCy+LxsCXcscNCosY1K/VjOPzKXbSGI3KmOThZLBEZBA01dXVkqSYmBilpKT02Hb77bfLcZzuCwO6dD2+4447gjMkzEQOj1Hm+p/qq96R6fgchQ+O1HVPLOr3ZbUIHq4uQ9B0RWby5Mm9/pJIT0/XPffco8cee0ytra2aMWOGKisr9dRTT+nOO+/UVVddFYqR4bHkudfo26sXqPKhjZ1P/D8Pnzk+n8KjIzTrX3+m2Il9f5oEBh4ig6Dp63xMl3Xr1ik5OVlPPvmkNm/erNGjR2v58uVasmRJMMeEscvn3ajoS4dpx/2P6szxk5LPkQLnjo3j88kNBDRk7CjNLH5IsZePCfK06C8+6h9Bk5SUpPr6em3atEm5ubmhHgch1n7qtA6WVeqdp7equXZ/7x0cKWnWNE3KvUmJ16XL8XF0/0JEZACEXPOuA/r03Q/0+k/XSZJmrPuJRmVMUsyYUSGeDP1FZAAMGMUJ2ZKk3CNbQjwJvML6EwBghsgAAMwQGQCAGSIDADBDZAAAZogMAMAMkQEAmCEyAAAzRAYAYIbIAADMEBkAgBkiAwAwQ2QAAGaIDADADJEBAJghMgAAM0QGAGCGyAAAzBAZAIAZIgMAMENkAABmiAwAwAyRAQCYITIAADNEBgBghsgAAMwQGQCAGSIDADBDZAAAZogMAMAMkQEAmCEyAAAzRAYAYIbIAADMEBkAgBkiAwAwQ2QAAGaIDADADJEBAJghMgAAM0QGAGCGyAAAzBAZAIAZIgMAMENkAABmiAwAwAyRAQCYITIAADNEBgBghsgAAMwQGQCAGSIDADBDZAAAZogMAMAMkQEAmHFc13VDPQSAgaE4ITvUI4Rc7pEtoR7hosJKBgBghsgAAMwQGQCAGSIDADBDZAAAZogMAMAMkQEAmCEyAAAzRAYAYIbIAADMEBkAgBkiAwAwQ2QAAGbCQz0AgK+PG59bqsTvpPd47lRTi44faFDdr17SofLKEE0GK0QGQNCMuHK83EBAtb/YIrmSE+7T0JREjb0pQ9f/Kk07lxWrbmN5qMeEh4gMgKAYMnaUIofHqOX9etUU/bbHtst/OFvfXn2Pvnn3d4nMRYZzMgCCYmT6BElS064DvbbVv1otSYocOTSoM8EekQEQFCP+NzLNu/b32hZzWbwk6di+w0GdCfaIDICg6FrJNNf2jEzE8Bhd9fc/lCTt/ud/D/pcsMU5GQBBMeLK8ZKkMbOvUsJfXiknzKfBSXEaO3u65HNU+dBGri67CBEZAOa+MT5BlwwdLEn61r1/1WPb2dZT+v2P1qh+e02v131v+yNqqKjVmwW/7n5uVMYkzX5uqXYuK9bef9lmOzj6jcNlAMx1nY/Zt/lVFSdkqzghW8+m5WrnsmINGhylzMce0CXfiO71ul2//DdNnHejIobHSJK+MWG0btj0kPZsLCcwFwgiA8Bc95Vlnzsfc+bYCdVtLJf/hT8oInaIxmd/p9fr/M//QSePfKIrFtyqyJFDdeMzP1f9q1Wq/sfNQZsd/UNkYGrHjh3KyclRfHy8IiMjNX78eC1evFitra3asGGDHMdRWlpaqMeEsf+7sqz35cv7nn1FkjT+tmt7v9B1tfvR32lS7hzNeuZn+uyDo3rjwQ2ms8JbRAYmXNdVXl6eMjMzVVpaqsbGRkVERMjv96uoqEhz585VXV2dJGnq1KkhnhamHEcjvnWZAmfb9WndwV6bj7zxJ50+dkJx01IVdemwXtv9z78hx+fTJUOitX3+Pylwtj0IQ8MrRAYmCgoKtGbNGkVGRqqwsFDNzc1qaWnR0aNHNW/ePFVUVKikpESSNGXKlBBPC0tDUxM1aEiUjr33oTpOn+213W3v0OFXq+X4fEq++epe26/+h/lywnyKGDZEbiAQjJHhISIDz23fvl0PP/ywwsPDVVZWpvz8fA0fPlySFBcXp6effloTJ07U8ePHJRGZi9357vTv8sFLf5QkJc/tGZn0B7+v5O9erZdu/bnaT55W2l032w0KE1zCDM/l5+fLdV0tXLhQs2bN6rU9PDxcc+bM0d69eyURmYvd/tIK7S+tOO8+h178bxUnZPd4LvUHN+jKv71N//WDlfpkz0Htfux5pf9dtuqefFEdp85YjgwPsZKBpyorK7Vz506FhYUpLy+vz/3i4zs/RiQ5Obl7lXPixAkVFBTolltuUXx8vBzHUW5ubjDGxgCTeMMUXVP4I+34yTod/eM7kqR9z7wstyOgy+fNDvF0+DKIDDxVXt75CboZGRlKSkrqc7+WlhZJPVcxTU1NWr58uaqqqjR9+nTbQTFgjUifoOs2Pqi3Vz3T4xMAOk6f1Z4NL+iKH98q3yUchLlQ8J2Cp6qqqiRJ06ZNO+9+tbW1knpGJiEhQYcPH1ZiYqLa2toUFRVlNygGrOba/XomZd45t+15okx7nigL8kToDyIDTzU2NkqShg3rfSlql+bmZlVUdB6j//zlyxEREUpMTLQd0JjjOKEeoV82xf9NqEcIuQv9e2jBdd2v/FoOl8FTXT+MXbE5l8LCQrW1tUnipD9wsSMy8FRqaqokadu2bWpv733TXHl5udauXSup83LmC33l8udc172gv3Dhfw8H2s8FkYGnsrKyJEl+v1/z589XU1OTpM5DZCtWrFBWVpZiYjo/7JBVDHDxIzLwVE5OjmbOnClJKikpUVxcnGJjYzVy5EgVFBRo0aJFSk9Pl8THyQBfB0QGnvL5fCorK9OSJUs0btw4DRo0SNHR0crOzlZFRYUKCwtVU9P5/4awkgEuflxdBs9FRUVp1apVWrVqVa9tfr//nPfIALg4sZJBUFVXV0uSYmJilJKSEuJpAFhjJYOg6orM5MmTz3k/wrp163Ts2LHuK9N27dqllStXSpIyMzOVmZkZvGEB9BuRQVB90fmYoqIiHTp0qPtxdXV1d5iWLVtGZIALDJFBUHUFo6/IHDx4MIjTALBGZBBUhw8fDvUIAIKIE/8AADNEBgBghsgAAMwQGQCAGSIDADBDZAAAZogMgAGhpu2IfnN8lwKuqzWfvK7mjpOhHgkeIDIABoTJkQk6Hjit4uNVmhyRoBFh0aEeCR4gMgAGjOuiL9ObbYeVGT0u1KPAI0QGwIAQcF29cOJdfW9Iml5sfS/U48AjRAbAgPDyyfc1NXK0bho8UfVnj6v+7PFQjwQP8NllAAaE2YNTu3+9cNg1IZwEXmIlAwAwQ2QAAGaIDADADJEBAJghMgAAM0QGAGCGyAAAzBAZAIAZIgMAMOO4ruuGeggAwMWJlQwAwAyRAQCYITIAADNEBgBghsgAAMwQGQCAGSIDADBDZAAAZogMAMAMkQEAmCEyAAAzRAYAYIbIAADMEBkAgBkiAwAwQ2QAAGaIDADADJEBAJghMgAAM0QGAGCGyAAAzBAZAIAZIgMAMENkAABmiAwAwAyRAQCYITIAADNEBgBghsgAAMwQGQCAGSIDADBDZAAAZogMAMAMkQEAmPkfMWN4PTQ7Q8YAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<Figure size 340.56x206.4 with 1 Axes>"
+       "<Figure size 523.938x317.538 with 1 Axes>"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -904,7 +913,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -914,18 +923,18 @@
       "              \n",
       "q_0: ────■────\n",
       "     ┌───┴───┐\n",
-      "q_1: ┤ Ry(x) ├\n",
+      "q_1: ┤ RY(x) ├\n",
       "     └───────┘\n"
      ]
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 340.56x206.4 with 1 Axes>"
+       "<Figure size 523.938x317.538 with 1 Axes>"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -936,7 +945,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -946,18 +955,18 @@
       "              \n",
       "q_0: ────■────\n",
       "     ┌───┴───┐\n",
-      "q_1: ┤ Rz(x) ├\n",
+      "q_1: ┤ RZ(x) ├\n",
       "     └───────┘\n"
      ]
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 340.56x206.4 with 1 Axes>"
+       "<Figure size 523.938x317.538 with 1 Axes>"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -968,7 +977,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -984,12 +993,12 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 340.56x206.4 with 1 Axes>"
+       "<Figure size 523.938x317.538 with 1 Axes>"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1000,7 +1009,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -1009,19 +1018,19 @@
      "text": [
       "     ┌────────┐\n",
       "q_0: ┤0       ├\n",
-      "     │  Iswap │\n",
+      "     │  ISWAP │\n",
       "q_1: ┤1       ├\n",
       "     └────────┘\n"
      ]
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 426.56x206.4 with 1 Axes>"
+       "<Figure size 656.246x317.538 with 1 Axes>"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1032,7 +1041,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -1050,12 +1059,12 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 340.56x292.4 with 1 Axes>"
+       "<Figure size 523.938x449.846 with 1 Axes>"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1066,7 +1075,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -1077,7 +1086,7 @@
       "q_0: ┤0       ├\n",
       "     │        │\n",
       "q_1: ┤1       ├\n",
-      "     │  Ms(x) │\n",
+      "     │  MS(x) │\n",
       "q_2: ┤2       ├\n",
       "     │        │\n",
       "q_3: ┤3       ├\n",
@@ -1086,12 +1095,12 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 340.56x378.4 with 1 Axes>"
+       "<Figure size 523.938x582.154 with 1 Axes>"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1102,7 +1111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -1122,12 +1131,12 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 340.56x378.4 with 1 Axes>"
+       "<Figure size 523.938x582.154 with 1 Axes>"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1145,7 +1154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -1154,19 +1163,19 @@
      "text": [
       "     ┌──────────┐\n",
       "q_0: ┤0         ├\n",
-      "     │  Unitary │\n",
+      "     │  unitary │\n",
       "q_1: ┤1         ├\n",
       "     └──────────┘\n"
      ]
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 426.56x206.4 with 1 Axes>"
+       "<Figure size 656.246x317.538 with 1 Axes>"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1194,7 +1203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1210,7 +1219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -1221,7 +1230,7 @@
       "q_0: ┤0          ├\n",
       "     │           │\n",
       "q_1: ┤1          ├\n",
-      "     │  Isometry │\n",
+      "     │  ISOMETRY │\n",
       "q_2: ┤2          ├\n",
       "     │           │\n",
       "q_3: ┤3          ├\n",
@@ -1230,12 +1239,12 @@
     },
     {
      "data": {
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       "text/plain": [
-       "<Figure size 2404.56x1668.4 with 1 Axes>"
+       "<Figure size 3699.32x2566.77 with 1 Axes>"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1248,7 +1257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -1257,19 +1266,19 @@
      "text": [
       "     ┌─────────────────────┐\n",
       "q_0: ┤0                    ├\n",
-      "     │  Diagonal(1,1,-1,1) │\n",
+      "     │  DIAGONAL(1,1,-1,1) │\n",
       "q_1: ┤1                    ├\n",
       "     └─────────────────────┘\n"
      ]
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 426.56x206.4 with 1 Axes>"
+       "<Figure size 656.246x317.538 with 1 Axes>"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1287,7 +1296,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -1296,19 +1305,19 @@
      "text": [
       "     ┌────────────────┐\n",
       "q_0: ┤0               ├\n",
-      "     │  Ucrx(0.2,0.1) │\n",
+      "     │  UCRX(0.2,0.1) │\n",
       "q_1: ┤1               ├\n",
       "     └────────────────┘\n"
      ]
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 426.56x206.4 with 1 Axes>"
+       "<Figure size 656.246x317.538 with 1 Axes>"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1319,7 +1328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -1328,19 +1337,19 @@
      "text": [
       "     ┌────────────────┐\n",
       "q_0: ┤0               ├\n",
-      "     │  Ucry(0.2,0.1) │\n",
+      "     │  UCRY(0.2,0.1) │\n",
       "q_1: ┤1               ├\n",
       "     └────────────────┘\n"
      ]
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 426.56x206.4 with 1 Axes>"
+       "<Figure size 656.246x317.538 with 1 Axes>"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1351,7 +1360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -1360,19 +1369,19 @@
      "text": [
       "     ┌────────────────┐\n",
       "q_0: ┤0               ├\n",
-      "     │  Ucrz(0.2,0.1) │\n",
+      "     │  UCRZ(0.2,0.1) │\n",
       "q_1: ┤1               ├\n",
       "     └────────────────┘\n"
      ]
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 426.56x206.4 with 1 Axes>"
+       "<Figure size 656.246x317.538 with 1 Axes>"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1399,57 +1408,288 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1201.59x264.88 with 1 Axes>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "from qiskit.circuit.library import QFT  # e.g. for the Quantum Fourier Transform"
+    "from qiskit.circuit.library import QFT  # e.g. for the Quantum Fourier Transform\n",
+    "QFT(4).draw(output='mpl')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     ┌──────┐\n",
-      "q_0: ┤0     ├\n",
-      "     │      │\n",
-      "q_1: ┤1     ├\n",
-      "     │      │\n",
-      "q_2: ┤2 Qft ├\n",
-      "     │      │\n",
-      "q_3: ┤3     ├\n",
-      "     │      │\n",
-      "q_4: ┤4     ├\n",
-      "     └──────┘\n"
-     ]
-    },
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 434.644x325.08 with 1 Axes>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from qiskit.circuit.library import OR\n",
+    "OR(4).draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 340.56x464.4 with 1 Axes>"
+       "<Figure size 533.372x325.08 with 1 Axes>"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "show(QFT(5))"
+    "from qiskit.circuit.library import LinearPauliRotations\n",
+    "LinearPauliRotations(4, offset=2, slope=0.5).draw(output='mpl')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 550.228x565.88 with 1 Axes>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from qiskit.circuit.library import PiecewiseLinearPauliRotations\n",
+    "PiecewiseLinearPauliRotations(4, breakpoints=[0, 2], offsets=[2, 1], slopes=[0.5, -0.5]).draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1141.39x264.88 with 1 Axes>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from qiskit.circuit.library import QuantumVolume\n",
+    "QuantumVolume(4).decompose().draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1683.19x866.88 with 1 Axes>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "QuantumVolume(4).decompose().decompose().draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1020.99x204.68 with 1 Axes>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from qiskit.circuit.library import EfficientSU2\n",
+    "EfficientSU2(3, entanglement='linear').draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1748.21x1167.88 with 1 Axes>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from qiskit.circuit.library import WeightedAdder\n",
+    "WeightedAdder(3, [1,2,1]).draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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R+Y1hGLYuQeyIgtVGEhISsFgs+Pv726S/vatW1YmftzzJgG5NrW2uVRzZsbgPH895BIvFdrVJxXA5/QKJ73zMysCRfOj5R2JaDmHnSws4f+iErUu7KZ3XdwcFaxnZs2cP/fv3x8vLiypVquDl5UVoaChZWVn4+voSHh6OYRjUqFEDi8VChw4dANiyZQuhoaE0atQIV1dXPD09GT58OL/88ot17OL6X7VkyRI6depEtWrVqF+/Ps888wwZGRnlegxs6WJWLrM/TGLKyPYAODhYWDn7EbKv5DF40hY0QancMk/8zKfdJ5L49goupaWDYZDzaxaHY75gXfeXOPllvK1LLJLO67uDviu4DCQmJtKpUycGDBjA/PnzcXd3JyUlhdjYWKpWrcoHH3zAkCFD8PLyYsaMGQB4eHgAsHfvXkJCQhgxYgTu7u4cOHCAiIgIAKKjowGK7Q8wZswYoqKiCA8PZ+rUqZw6dYpXXnmFtLQ0Nm7cWJ6HwqbmLU/mxbB7GdCtKb06eeFVtxqdn/2MKzn5ti5NbMgwDGKHvc3F0+n/ayj0IPk5ecQOf5sBu/6BW91aNqmxODqvKz4FaxlYsmQJHh4eLF261Nr28MMP88wzzwDQvn17UlNTGTx4MB07dizUd+zYsdY/5+fnExwczN69e/nyyy+t7cX1X7ZsGfPmzWPRokWEhYVZ293c3Bg0aBDHjh2jadOmZr7cCutSVi5vL9rHohkP8VN6FsFDPiXzYo6tyxIbO7PnEBn7Um6+g2GQdzmH75f+F79xA8qvsFLSeV3x6VJwGahbty5paWmEh4cTH3/jJaWDBw9y6dIlAgICCrXn5eURExNDSEgI9erVw9HREWdnZ+bPn0/16tVL7A8wffp0AgICGDx4MLm5udatTZs2AKSkpHDu3Dn69OmDj48Pfn5+dO/enSNHjpj2+i0WS5ltt6O6mzN/XfgdP2dcrnCvpzSv1VbPb6/byEcGlLhYyTAMVs6YW65/z7dK57Xtai+JgrUMjBs3jhkzZrBp0yYCAwNp0qQJs2bNIj+/4FJNXFwcUDDzvFZYWBgjRowgODiY6Ohodu7cye7du3F3d8fX19e63836nzhxgoMHDxIfH4+zs3OhrV27dgDWe7Ljxo3j8OHD7N27lz59+lhn0/ZkcO8WTB7mx/trDjF2cFtblyMVhFMpfu1ZLJZS7WcLOq8rvop55tzlXFxciIiI4PDhw3z//fd07dqViIgIVq1aBUB8fDz169enQYMG1j5JSUnExMQwd+5c3nrrLR599FEeeOABrly5QmZmJoGBgdZ9i+oPcOrUKQAWLFjAt99+W+Tm6+tLzZo16dq1q7VfcHAwKSnFXBq7RYZhlNlWWr1CvPjHq8GEjvuCF978hrq1XXm8e7MK93pK81pt9fz2uv114bySZx8OFroNGVCuf8+lofPatltpKVjLmLe3N5MmTQIgNzcXgOTkZOul2atSU1MBaN26tbUtJyeH8ePHAxQK1qL6A3h6egLg5OREUFBQkZuzs/MN/d59911CQ0Pv5GVWKA/41WX5W114+tWtbIs7bb0nNWVUe+7gypvYiSZ9OuJyjxvFngz5Bq2e7lZ+RZWCzuu7h4LVZM8//zxDhw4lJiaGr776io8++oj+/fvj7e1N3759AahVqxbJycls2LCBb775htOnT+Pn54erqyuTJk1i06ZNLFu2jODgYNLT03F0dCz0edWi+gM0atSIbt26MWHCBGbPnk1sbCwbN27kX//6F4899hjp6ek31Dtt2jSOHDnCW2+9VT4HqIy19a7F+nndGT97F5/EHre2v7c8md/Vuv1392I/nKpW4YG3RgLGTcPVZ0g36rRvWb6FFUPn9d3FYtzqtQgp1ty5c1mxYgWHDh0iMzOTRo0aERoayssvv0ydOnWAgsVHzz77LAkJCVy+fJm1a9cSGhrK2rVrmThxIidPnqRt27ZMnDiR9evXExcXx/79+63PcbP+AOfOnWPGjBmsW7eOkydP4u7ujre3Nz179mTq1KmFap05cyaffPIJmzdvpmbNmuV3kO6Apd375f6cxnfDyv05ARY1GAjA0LRVNnl+e5e6cTffTl9MZsppa5tLjWq0HdWXdi88hsWhfOYdtjinQed1WVKwVlLTpk1jw4YN/Oc//7lrQhUUrGIuwzA4s+cQG/oWfFb8qZSlOLm6lGsNClb7o8+xVkL79+/ntddeo0WLFjz88MPW9sTERNsVJWIDFouFuvf9tq6hvENV7JOCtRJq27btLa9GFBGR0tHiJRERERMpWEVEREykYBURETGRglVERMREClYRERETKVhFRERMpGAVERExkT7HKncVW31bjEhZ0TltfzRjFRERMZGCVURExEQKVhERERMpWEVEREykYBURETGRglVERMREClYRERETKVhFRERMpGAVERExkYJVRETERApWEREREylYRURETKRgFRERMZGCVURExEQKVhERERPp/2OVu4ql3fvl/pz6/zJF5FZoxioiImIiBauIiIiJdClYRCqd/Nw8Tu9I4kzCEc4dOG5t3zNrCXXaNadhZz9c7qlmwwrlbqZgFZFKIzcrm/0L1nNo8X+4lJZxw+NJ8/4NgJNbFZoPeAi/cQOp1tCjvMuUu5yCVUQqhTPxh9n2wjwu/PBjifvmXsrm8EebSfn3du6f/gwtBz1SDhWKvdA9VhGxeyc27+Hz0CmlCtVr5WRmseMv/yDu9RgMwyij6sTeKFhFxK6diT9M7LDZ5Ofk3vYY++auJfmf602sSuyZgtVOXLlyxdYliFQ4uVnZbHthXomhOjRtFUPTVhW7T9ysGM4fOmFmeWKn7D5Y161bR9euXalZsyZubm74+vqycOFCAAzDICoqioCAANzc3GjcuDHjxo3j119/LTRGhw4d6N+/P8uWLcPf35+qVavi5+fHt99+S05ODjNnzqRZs2ZUr16dRx99lIyMwosiOnfuTO/evXnzzTdp1aoVrq6u+Pj4sHTp0hvq3bJlC6GhoTRq1AhXV1c8PT0ZPnw4v/zyS6F6BgwYwEcffYSfnx8uLi68//5vX5ywZMkSOnXqRLVq1ahfvz7PPPPMDTXZu2pVnfh5y5MM6NbU2uZaxZEdi/vw8ZxHsFhsV5uUn/0L1t/y5d+byc/JZVfkQlPGEvtm14uXIiMjmTVrFmFhYYwePZqqVauyfft2MjIyyM/P56mnnmLdunW8+uqrdOzYkUOHDjFp0iTS0tJYsWIFAHl5eezbt4/Tp0+TmZnJ1KlTycvLY/To0Tz77LM0btyYJk2aEBUVxYEDBxg/fjxvv/02b7zxBlAQ3gkJCeTn55ORkcHrr7+Oq6srM2fO5Omnn8bPz4+2bdtaa967dy8hISGMGDECd3d3Dhw4QEREBADR0dHWek6ePMmJEyeYPHkyderUwcfHB4AxY8YQFRVFeHg4U6dO5dSpU7zyyiukpaWxcePGcv4bsJ2LWbnM/jCJKSPbs3rzMRwcLKyc/QjZV/IYPGkLul1m//Jz8zi0+D+mjpm2bR/nD5+kpo+XqeOKfbHbYF2zZg0zZ85k0aJFhIWFWdt79eoFwNy5c1mxYgVbtmzhwQcfBKBLly6cOXOGKVOmsGDBAmrWrMnBgwfJysqibdu2fPbZZ1j+N9XZsWMH7777Lk899RQvv/wyAD169CAmJobk5GTr8x0+fJjMzEzuu+8+vvrqK1xcXABo27YtzZo1Y8OGDYWCdezYsdY/5+fnExwczN69e/nyyy8BrPX4+vqyfft2nJ2drfsvW7aMefPm3fCa3dzcGDRoEMeOHaNp06amHN+7wbzlybwYdi8DujWlVycvvOpWo/Ozn3ElJ9/WpUk5OL1zf5EfqblTP6z6isBXBps+rtgPu70UHBkZSZcuXQoFzLXmzJlDnz59rKF6lbe3NwAnT54EID4+HoDp06dbQxXg4sWL1KpVi/Hjxxfqf+HCBTw8fvvcW1xcHAAzZsywhipA06ZNcXV1JT093dqWl5dHTEwMISEh1KtXD0dHR5ydnZk/fz7Vq1cvVM/bb79dKFSv1hgQEMDgwYPJzc21bm3atAEgJSUFgGPHjtG5c2d8fHzw9fVl27ZtNz+Qd7FLWbm8vWgfi2Y8xMP3NaDnnzeSeTHH1mVJOTmb8H3ZjJt4pEzGFfthl8GamppKcnIygwYNKvLxH374gePHj9O7d+8bHjtxomBxQsOGDQFISEigdu3aBAUFFdovPj6eLl26FAq3X3/9le+//x5/f39rW1xcHDVr1qR79+6F+mdkZHD58mW8vH67pBQWFsaIESMIDg4mOjqanTt3snv3btzd3fH19bXW4+HhQUhIyA11Hzx4kPj4eJydnQtt7dq1A6BGjRoAjBw5kj/96U8cPnyYBQsWMGjQILte/FTdzZm/LvyOnzMu27oUKUcZ13yjkpnOJZfNuGI/7PJS8I8/FixW8PT0LPLxM2fOAL+F57U2b96Mv78/tWvXBgqCLDAwsNA+OTk5JCUl0b9//0LtiYmJGIZBQECAtS0uLo4GDRoUmu0C1nu4PXr0ACApKYmYmBiio6MZNuy3/01lx44dZGZmWmtISEggKCgIB4fC74lOnToFwIIFCwo9/7V8fX05e/Ys27dvZ926dQAEBwfTsGFDYmNjrbXcqetfq6l8o0u96+DeLZg8zI/31xxi7OC2RK8+dFtPWaavpxgf1B9g0+e/242tGYy/a4NCbSWt/L3Z44saDLT++ZezGfo7uQN383ld2s8y22WwXg3MpKSkImelV+8zHjlS+JLOhg0b+OKLL1i8eLG1LTExkVGjRhXab//+/WRnZ98QuAkJCTg4OFhnrFcXLgFcvnwZV1dXoOBy8V//+lcGDhxIy5YtgYJZNkDr1q2t4+Xk5FgvNV99rsTEREaPHn3Da7r6JsLJyemG2fW1UlNTqVevHlWqVLG2NWvWjOPH7etdeK8QL/7xajB9xmwmLvksKZ//kce7N+PjTSm2Lk3KSR5lcy89z9A9eimeXQZr48aN6dKlC7NmzcLR0ZHAwEAyMjLYvHkzgwcP5sEHH6RPnz68/vrr3HPPPTRv3pytW7fy5ptvMnLkSIYMGQLA0aNHOX/+/A0BevU+Z1HB6uPjY70f+v3333PhwgUaN27M4MGDGTVqFOnp6bzxxhvWe6dX+fn54erqyqRJk4iMjCQ9PZ133nmHc+fO4ejoiL+/v7WeomakjRo1olu3bkyYMIGMjAwCAwPJzs7mxIkTbNiwgejo6EL3fstSWX5DTWn+P9YH/Oqy/K0uPP3qVrbFnQbg7UX7mDKqPas2p9zyimBbfePO1VmSvvHn9sTNimHfvLWF2q6deV7r6kz1Zo9fq3FgG4wN6+68wEqqMpzXdnmPFWDlypU89dRTzJs3j549ezJmzBjS09OtC3kWL15Mv379iIiIoHfv3nz66afMmzePqKgo6xhXZ5tFBWjjxo2pU6fODe3XXwaGgs/SGobBY489xvPPP09AQAA7duwo1N/T05OlS5dy+vRp+vXrx5w5c3jppZcIDg6mVatWuLm5Weu52aXeFStWMHToUKKioujVqxdDhgxh4cKF+Pv7W0O1cePG/PTTT2RnZ1v7paSk0KRJk1s7wBVUW+9arJ/XnfGzd/FJ7G+z8PeWJ/O7Wq483r2ZDauT8uTh17xsxm3XokzGFfthMez5bYONTZgwgSVLlnD69Glbl1JI9+7dCQ0NZfTo0ezcuZOBAwdy7NixQquWK6rSzFjNZnw3rOSdysDVd/Yl3ReUol3JvMRK/xHkXsoucd9bmbF2XzmFhg+2u+P6KqvKcF7b7Yy1IoiLi7thtlsRREVFsXz5cnx8fHjuuedYtmzZXRGqIrfCxd2N5gMeMnXMe1o0pEGIr6ljiv2xy3usFYFhGMTHxzNu3Dhbl3KDq/eUReyd37iBpHyyg5wLl0wZLyhyyF25mlXKl4K1jFgslkLf7ysi5a9aQw/un/4MO8a9d8djtRj4EI173GdCVWLvFKwiYtda/qkLmUfT+O7va266T0n3Vut3upcH3hppdmlip3SPVUTsXvtJT3DftKE4uDiXvPN1Wgx8iK4fTcapapWSdxZBM1YRqQQsFgttn+uDZ2c/dk35gLSt35XY568hrDAAABPiSURBVJ4WDQmKHKLLv3LLFKwiUmnUbNWIHiumcP7wSY6u3sqZxCOcSz7O5bMF6yHqBLSkTrvmNOndkfqd7tVCJbktClYRqXRq+ngRMPlJ689X77H2+ewNW5UkdkT3WEVEREykYBURETGRLgXLXcVWXy8oIlJamrGKiIiYSMEqIiJiIgWriIiIiRSsIiIiJlKwioiImEjBKiIiYiIFq4iIiIkUrCIiIiZSsIqIiJhIwSoiImIiBauIiIiJFKwiIiImUrCKiIiYSMEqIiJiIgWriIiIiRSsIiIiJlKwioiImEjBKiIiYiIFq4iIiIkUrCIiIiZSsIqIiJhIwSoiImIiBauIiIiJFKwVWFJSEhaLhQ0bNgCQkpKCxWJh5cqV1n2KahMREdtxsnUBcnMNGjTg66+/JiAgAIC4uDgAgoKCrPsU1SZ3v/ycXFuXIGK63MtXbF1CudCMtQLz8PCgY8eOuLi4AAUhWqtWLZo3b27dp6g2uXtlnTnPt9M+ZFnbZ61tGwdM5eSX8TasSuTOnDt0gm0vzGWpz9PWti2j3iH9u6M2rKrsKFhLacuWLYSGhtKoUSNcXV3x9PRk+PDh/PLLL9Z9goKCeOKJJ1i9ejUdOnTAzc2NZs2asWLFiiLHXLduHV27dqVmzZq4ubnh6+vLwoULrY936NCBxx9/3PpzXFycdfZ6s7bS1CkV068nz/Bpz5fZv+BTcjIvWdt/+iaZL556naSodTasTuT2/PRNMut7vswPq7cWuhJz7NOv+azPK3b5plHBWkp79+4lJCSEqKgoNm3axJQpU/jkk0948cUXAcjNzSUpKYldu3Yxd+5cJkyYwNq1a6lbty5Dhw7lwoULhcaLjIy0BuDChQtZvXo1ffv2JSMjA4C8vDz27dtXKDTj4uJuuOR7fVtJdUrFtW3MXC6dzgCjcLuRX9CwZ9piziYesUFlIrcn9/IV/jvsbfKu5ED+dSd2vkF+Xh5bnpvDlQsXbVNgGdE91lIaO3as9c/5+fkEBwezd+9evvzySwCSk5PJzs6mZcuWfP755zg4FLxnyc7Opl+/fhw/fhxfX18A1qxZw8yZM1m0aBFhYWHWcXv16mX988GDB8nKyqJ9+/YAHDt2jIyMDAIDA637FNVWUp1SMZ07mMpP3yQXv5ODhYOL/kPIu97lU5TIHTr+6ddkZ2TefId8g9xL2fzw8Vf8ftj/lV9hZUwz1lLIy8sjJiaGkJAQ6tWrh6OjI87OzsyfP5/q1asDkJCQAMD06dOtoQpYZ6ANGza0tkVGRtKlS5dCoXq9+PiCyyNXg/XqIqVrQ/T6ttLUCQWB3LlzZ3x8fPD19WXbtm23eWSKZrFYtN3i9njgIyUf2HyDXUs/s3mt9rjp3C2b7fXhL5Jn5Bd7WucZ+bw3YYbNa72V86QkCtZSCAsLY8SIEQQHBxMdHc3OnTvZvXs37u7u1lloQkICtWrVokOHDoX6JiYm4uXlhYeHBwCpqakkJyczaNCgYp8zISGBhg0bUq9ePaAgRGvXrn3DwqVr20pTJ8DIkSP505/+xOHDh1mwYAGDBg3iypXKsVqvonKgdP9oS7ufSEXgUIowsmB/57UuBZcgKSmJmJgYoqOjGTZsmLV9x44dZGZmWmeLCQkJhWaTV12/uOjHH38EwNPTs9jnTUhIsM5Wixrn+rbS1nn27Fm2b9/OunUFC2GCg4Np2LAhsbGx9OjRo+QDUgqGYZS8kxTy0+6DfN4voth9LA4W2vV8COODz8qpqspjUYOBgM5dsx14fwO7IhYWu4+DxYFhkX/h7+N3llNVZU8z1hKkpqYC0Lp1a2tbTk4O48ePBwouwxqGQWJi4g3Bmp+fT2JiYqGAvHpJOCkpqdjnvb5fXFzcDeNf21aaOq/uV69ePapUqWLdr1mzZhw/frzYeqRs1b2vFTVbNcLicPN37ka+Qeswc978iJSH5gM74+jqTHETUoujAy0Hdy2/osqBgrUEfn5+uLq6MmnSJDZt2sSyZcsIDg4mPT0dR0dH/P39OXr0KBcuXLhhxe7hw4f59ddfC800GzduTJcuXZg1axazZ88mNjaW1atXM2rUKOu9zqNHj3L+/Hlrv+PHj5Oenl5o/OvbSlOnVFwWi4WQv4Xj6OpyY7j+73Kaz1PdaNjZzwbVidyeKjWq0WnO6IIfrj+v//dzh1nDcKtXq5wrK1u6FFwCT09Pli5dysSJE+nXrx9t27Zl4sSJrF+/nipVquDm5mZduFTUjBK44RLuypUrmTJlCvPmzSMtLQ0PDw86depEmzZtgN8WQt3KwqXS1AkFwf7TTz+RnZ1tnbWmpKTQpEkTk46Y3K46fi3ovf514l6PKfhs3/+uSrrVr0XbkY/SZkTvW1pAIVIRNO//IC41q5P49nLOJv5gba/VqjH+Lz5Ok94dbVhd2bAYuqlQ6XTv3p3Q0FBGjx7Nzp07GThwIMeOHbN+w5PY3sUf0/k4cCQAT59cgYOjo40rsm9X77EOTVtl40rs2y9HTrH2wYKPBIb9+LHdvlHUjLUSioqKYujQobz77ru4uLiwbNkyhWoFU62hh/XPClWxFzW8f1u0aa+hCgrWSql58+Zs3brV1mWIiNglLV4SERExkYJVRETERApWEREREylYRURETKRgFRERMZGCVURExEQKVhERERMpWEVEREykYBURETGRglVERMREClYRERETKVhFRERMpGAVERExkYJVRETERApWEREREylYRURETKRgFRERMZGCVURExEQKVhERERMpWEVEREykYBURETGRglVERMREClYRERETOdm6ABGpeCzt3rfJ8xrfDbPJ89qSjrX90YxVRETERApWERERE+lSsEgFkZuVzYlNezgTd5iM/ces7V+NfhePds1p1DWQGt6etitQ5DZczsgk9fNdnE38gfOHTljbd7w4H492zWnyfx2o+ruaNqzQfBbDMAxbFyFSmeVczOK7d9dwaMkmrpy/WOy+DR70pf3EQdQNalWmNVW2+36LGgwEYGjaqnJ/bns91hfT0kl4czlH124n/0rOTfdzcHakSe+OBEx+EvfG9cq0pvKiGauIDf206wDbXpjLr6k/l2r/tG37SNueRNuRfQh8ZTAOzvonLBXPD6u28s2r0eRcuFTivvk5eaT8ewcnNu0haMrTtHq6OxaLpRyqLDu6xypiIyc27+E/j08rdahaGQb7oz7lv8PeJq+YmYCILeybt5ZtY/5eqlC9Vu6lbL6Z9C/i31jK3X4hVcFagSUlJWGxWNiwYQMAKSkpWCwWVq5cad2nqDap+NK/O0rs8Dnk5+Te9hgnN8fx9cv/NLEqkTvzw6qtxM2KuaMx9s1dy8EPNppUkW0oWCuwBg0a8PXXX9O1a1cA4uLiAAgKCrLuU1SbVGx52TlsGzuv2PtOUHC/r6R7fkeWx3Ji0x4zyxO5LRfT0vnm1egS9yvNeb1nxkdcOJpmVmnlTsFagXl4eNCxY0dcXFyAghCtVasWzZs3t+5TVJtUbIeXbOb8wVTTxtsVsZD8vDzTxrtV1ao68fOWJxnQram1zbWKIzsW9+HjOY9QkW+XXTiaRvwbS60/n965v0JfhqzIxzrhzeW3fPn3ZvIuX2HPjI9MGcsWFKyltGXLFkJDQ2nUqBGurq54enoyfPhwfvnlF+s+QUFBPPHEE6xevZoOHTrg5uZGs2bNWLFiRZFjrlu3jq5du1KzZk3c3Nzw9fVl4cKF1sc7dOjA448/bv05Li6OgICAQmNc31aaOsV2DMPggMmXuX498TOnYhNNHfNWXMzKZfaHSUwZ2R4ABwcLK2c/QvaVPAZP2kJFzKn83Dx2TlzAmk5j+G7uGmv7xgFT+az3K2SdOW/D6m6uoh7ryxmZHF273dQxT2z6lounzpo6ZnlRsJbS3r17CQkJISoqik2bNjFlyhQ++eQTXnzxRQByc3NJSkpi165dzJ07lwkTJrB27Vrq1q3L0KFDuXDhQqHxIiMjrQG4cOFCVq9eTd++fcnIyAAgLy+Pffv2FQrNuLi4Gy75Xt9WUp1iWxlJKVz44UfTxz26ZpvpY96KecuTqV+nKgO6NeWfUzrhVbca/cZ+wZWcfJvWdTPfvvYhhz/aXPDDdWF0NvEImwbNqLALwyrisU79fFeJtzZulZFvkLJuh6ljlhet1S+lsWPHWv+cn59PcHAwe/fu5csvvwQgOTmZ7OxsWrZsyeeff46DQ8F7luzsbPr168fx48fx9fUFYM2aNcycOZNFixYRFhZmHbdXr17WPx88eJCsrCzaty94Z3rs2DEyMjIIDAy07lNUW0l1im2dTfyhTMZN31s245bWpaxc3l60j0UzHuKn9CyCh3xK5sWKGUyXTmdwcFExVw0Mg3PJx0nd+C3N+gaXX2GlVBGPdVmd12dtfF7fLs1YSyEvL4+YmBhCQkKoV68ejo6OODs7M3/+fKpXrw5AQkICANOnT7eGKmCdgTZs2NDaFhkZSZcuXQqF6vXi4+MBrMF6dZHStSF6fVtp6gSYMmUKPj4+ODg4sGpV+X8gvjK79ptnzHThaBq5l6+Uydi3orqbM39d+B0/Z1y2dSk3dfTf2zHySpjdOVj44eMt5VLP7apIx7qszuvzB8tm3LKmGWsphIWFsWbNGsLDw3n55ZepU6cOTk5O/OEPf7DOQhMSEqhVqxYdOnQo1DcxMREvLy88PDwASE1NJTk5udDMsigJCQk0bNiQevUKvokkLi6O2rVr37Bw6dq20tQJ0LNnT4YOHcqzzz575wenCHf7h7vL0tB7Aujs1qxwWwkrJG/2+NVvC7rqHrfqZBkmzVx8S17dea3BvVsweZgf7685xNjBbYlefei2nrY8zp3Hq99Lz2o+OBT3XPkGOz7/km6WV8u8Hns41lM9HqGpc61CbbdzXl9/Th/ef6BC/T4p7cI2BWsJkpKSiImJITo6mmHDfvsKsB07dpCZmWmdLSYkJBSaTV51/eKiH38suL/m6Vn8d74mJCRYZ6tFjXN9W2nrBAgOrniXtyqLHMP81buGYWCxWMpk7NLoFeLFP14Nps+YzcQlnyXl8z/yePdmfLwpxSb1lORX40rxoQrkGwYX8rPLqaLSq6jHuqzOvRyjYt6jL4mCtQSpqQUfi2jdurW1LScnh/HjxwMFl2ENwyAxMZE///nPhfrm5+eTmJhYaOHQ1UvCSUlJ9O7d+6bPm5iYSHh4uPXnuLg4hg8fXmifa9tKU2d5qcgfV7C1gx/+h28m/atQ2/Xv0q+6+o7+Zo9fZbFYcG9Sj5w08365lfb7ax/wq8vyt7rw9Ktb2RZ3GoC3F+1jyqj2rNqccsurVMvj3Pn15BlW3T+a4opzsFgY/c/XmTPwoTKvxx6O9Y4X5/P90sLrOO70vAYIfqw7xr823XmB5Uz3WEvg5+eHq6srkyZNYtOmTSxbtozg4GDS09NxdHTE39+fo0ePcuHChRtW7B4+fJhff/210EyzcePGdOnShVmzZjF79mxiY2NZvXo1o0aNYtu2gpWdR48e5fz589Z+x48fJz09vdD417eVpk6xvTp+LcpkXI8yGrc4bb1rsX5ed8bP3sUnscet7e8tT+Z3tVx5vHuzYnrbTnWv39HyiUduvoODhXuaN6BJn47lV1QJKvqx9mhXNp+jL6t/L2VNM9YSeHp6snTpUiZOnEi/fv1o27YtEydOZP369VSpUgU3NzfrwqXrZ4VXFxddfwl35cqVTJkyhXnz5pGWloaHhwedOnWiTZs2wG8LoW5l4VJp6hTb82jXHPcm9cg8/pOp49pi9er+I+fweHDJDe1Zl/Oo32VpET0qjo6vDyfn4mWOfbIDHAouC1uwYOTnU8Pbk+5LI3BydbFxlb+p6Me6ca/72R25kPwcEy8JWyw06fOAeeOVI/23cZXYww8/THh4OAMHlnxJRsyTNP8T9kwv+VtlSnvJzK1BbQbuno+Dk6Mp9YH9/ldm1zu79weOLP8vF39Mx9ndjWZ9g/H8Q3scHM07liWxl2P91Z//Hyn/Lvlzp6U9rz0faU+3mHJYPFYGNGOthCIjI/nggw84c+YM+/btY9y4cXz11Ve0aHF3Xna527QO68nhJV+Y9l2o97021NRQrUzq+LW4ay83VjQBk5/kxKY95F6680VfDs5OBE152oSqbEMzVhEb+Pnbg3zefypG7p1dOmvaN5iHF4w3qarf2Mss6m5gT8f60OJNpvyPS4GvDsY3/DETKrINLV4SsYG697XmwbljsDje/j/B+p3uJeT/PW9iVSJ3ptXT3Wn3Qv87GqP10B7c+3yoSRXZhoJVxEaah4bQ9aNXqFq35i339Rncla4fTcbJrUoZVCZy+wImP0nHN4bjeIuLvxycnQh8dTAdXh9eob4U4nYoWEVsyLOLP6Fb/h+twnrgWLXkkKzj7023ZREEzx6FUyn2F7GF1kN70u+/c2jc634sDiWEpMWC1x8CeHTz2/iGP3bXhyroHqtIhZH9y0WOf7qTM/Hfk5F0jCsXLmJxcuSeZvXxaNeCRt0CqePvXS612NN9v4rO3o/1xVNnSfl0J+l7f+DcwRPkZWXj6OpCTR8vPNo1p+mjwbg3qVcutZQXBauI3MDef9lXJDrW9keXgkVEREykYBURETGRLgWLiIiYSDNWEREREylYRURETKRgFRERMZGCVURExEQKVhERERMpWEVEREykYBURETGRglVERMREClYRERETKVhFRERMpGAVERExkYJVRETERApWEREREylYRURETKRgFRERMZGCVURExEQKVhERERMpWEVEREykYBURETHR/weV2lHUqOW3DAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 604.408x385.28 with 1 Axes>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from qiskit.circuit.library import IntegerComparator\n",
+    "IntegerComparator(3, 4).draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 719.992x445.48 with 1 Axes>"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from qiskit.circuit.library import MCXVChain\n",
+    "circuit = QuantumCircuit(7)\n",
+    "circuit.mcx([0, 1, 2, 3], 4, ancilla_qubits=[5, 6], mode='v-chain')\n",
+    "circuit.decompose().draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1683.19x927.08 with 1 Axes>"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "circuit.decompose().decompose().draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 780.192x325.08 with 1 Axes>"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from qiskit.circuit.library import GMS\n",
+    "GMS(5, [[0.2] * 5] * 5).decompose().draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1683.19x686.28 with 1 Axes>"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "GMS(5, [[0.2] * 5] * 5).decompose().decompose().draw(output='mpl')"
+   ]
   },
   {
    "cell_type": "code",
diff --git a/Benchmark of circuit simulation and expectation values.ipynb b/Benchmark of circuit simulation and expectation values.ipynb
index 4f09dd9..ce0bdd7 100644
--- a/Benchmark of circuit simulation and expectation values.ipynb	
+++ b/Benchmark of circuit simulation and expectation values.ipynb	
@@ -329,14 +329,6 @@
     "    return result, time() - t0\n",
     "\n",
     "\n",
-    "def matrix(expr):\n",
-    "    t0 = time()\n",
-    "    expval = MatrixExpectation().convert(expr)\n",
-    "    sampler = CircuitSampler(AER_STATEVECTOR).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",
diff --git a/Circuit Library Demo (working).ipynb b/Circuit Library Demo (working).ipynb
index 67c79fb..2fde9bc 100644
--- a/Circuit Library Demo (working).ipynb	
+++ b/Circuit Library Demo (working).ipynb	
@@ -445,13 +445,6 @@
     "print('Training loss:', result['training_loss'])\n",
     "print('Test loss:', result['testing_loss'])"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/Circuits in Quantum Algorithms.ipynb b/Circuits in Quantum Algorithms.ipynb
index 03e9e48..e68dad4 100644
--- a/Circuits in Quantum Algorithms.ipynb	
+++ b/Circuits in Quantum Algorithms.ipynb	
@@ -42,6 +42,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "slideshow": {
@@ -51,7 +52,7 @@
    "source": [
     "## Recap: The circuit library\n",
     "\n",
-    "Library of interesting families of circuits. \n",
+    "The circuit library is a collection of valuable families of circuits. These circuits are are easily accessible, tested building blocks that work natively with the rest of Qiskit.\n",
     "\n",
     "Interesting?\n",
     "For algorithms, eduction and complexity analysis\n",
@@ -59,21 +60,18 @@
     "Further material:\n",
     "* `qiskit.circuit.library` \n",
     "* qiskit.org/documentation/apidoc/circuit_library.html\n",
-    "* Interactive overview: qisk.it/circuits\n",
+    "* Interactive overview: http://qisk.it/circuits\n",
     "* See also previous session with Ali Javadi"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Seen how to import circuits from the library:"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
+   "execution_count": 83,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -82,7 +80,7 @@
        "<Figure size 742.777x204.68 with 1 Axes>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 83,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -95,14 +93,18 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "source": [
     "Seen they might have a couple of options."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -112,7 +114,7 @@
        "<Figure size 742.777x264.88 with 1 Axes>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -123,15 +125,23 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "source": [
     "Seen that they are normal circuits."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -148,7 +158,7 @@
        "<Figure size 742.777x204.68 with 1 Axes>"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -162,7 +172,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "source": [
     "It's easy to stack them together. Remember the `compose` method:\n",
     "```\n",
@@ -172,17 +186,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
+   "execution_count": 84,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 923.377x204.68 with 1 Axes>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 84,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -192,7 +210,7 @@
     "from qiskit.circuit.library import Diagonal\n",
     "\n",
     "circuit = QuantumCircuit(3)\n",
-    "circuit.compose(Diagonal([1, 1, -1, -1]), [0, 1], inplace=True)\n",
+    "circuit.compose(Diagonal([1, 1, -1, -1]), [1, 2], inplace=True)\n",
     "circuit.barrier()  # barrier for visualization purposes\n",
     "circuit.compose(QFT(3), inplace=True)\n",
     "circuit.draw(output='mpl')"
@@ -200,7 +218,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "source": [
     "## Overview\n",
     "\n",
@@ -243,13 +265,19 @@
    "source": [
     "### Example: Variational Quantum Classifier\n",
     "\n",
+    "Supervised learning with quantum enhanced feature spaces, Havlicek et al. [arXiv:1804.11326](https://arxiv.org/abs/1804.11326)\n",
+    "\n",
     "![](quantum_algo_vqc.png)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -258,14 +286,13 @@
        "<Figure size 1344.63x144.48 with 1 Axes>"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "from qiskit.circuit.library.data_preparation import ZZFeatureMap  # can also drop .data_preparation\n",
-    "from qiskit.circuit.library.n_local import RealAmplitudes  # can also drop .n_local\n",
     "\n",
     "data_preparation = ZZFeatureMap(2)\n",
     "data_preparation.draw(output='mpl')"
@@ -273,8 +300,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -283,12 +314,14 @@
        "<Figure size 562.032x144.48 with 1 Axes>"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "from qiskit.circuit.library.n_local import RealAmplitudes  # can also drop .n_local\n",
+    "\n",
     "classifier = RealAmplitudes(2)\n",
     "classifier.draw(output='mpl')"
    ]
@@ -296,52 +329,89 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [],
    "source": [
     "from qiskit.aqua.algorithms import VQC\n",
     "from qiskit.aqua.components.optimizers import SPSA\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.patches as mpatches\n",
     "from qiskit.ml.datasets import ad_hoc_data"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
+   "execution_count": 141,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'ad_hoc_data' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-1-351bc0edef11>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraining_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mad_hoc_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0moptimizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSPSA\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaxiter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mvqc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mVQC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_preparation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclassifier\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraining_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'ad_hoc_data' is not defined"
-     ]
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
     "_, training_data, test_data, _ = ad_hoc_data(10, 10, 2, 0.2)\n",
+    "\n",
+    "markers = ['o', '^']\n",
+    "plt.figure(figsize=(12,6))\n",
+    "plt.title('Training (full) and test (hollow) data')\n",
+    "for marker, (label, data) in zip(markers, training_data.items()):\n",
+    "    plt.scatter(data[:,0], data[:,1], color='tab:blue', label=label, s=60, marker=marker)\n",
+    "for marker, (label, data) in zip(markers, test_data.items()):\n",
+    "    plt.scatter(data[:,0], data[:,1], facecolor='', edgecolor='tab:blue', s=60, marker=marker)\n",
+    "plt.legend(loc='best');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
     "optimizer = SPSA(maxiter=100)\n",
     "vqc = VQC(optimizer, data_preparation, classifier, training_data)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
+   "execution_count": 143,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Loss: 0.23919843187372664\n"
+      "Loss: 0.3044436195870143\n"
      ]
     }
    ],
    "source": [
     "from qiskit import Aer\n",
+    "from qiskit.aqua import QuantumInstance\n",
     "backend = Aer.get_backend('qasm_simulator')\n",
     "\n",
     "result = vqc.run(backend)\n",
@@ -349,164 +419,712 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 144,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "fragment"
     }
    },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Score: 19 / 20\n"
+     ]
+    }
+   ],
    "source": [
-    "## Circuits in Algorithms\n",
+    "points_a = test_data['A']\n",
+    "points_b = test_data['B']\n",
+    "_, predicted_a = vqc.predict(points_a)\n",
+    "_, predicted_b = vqc.predict(points_b)\n",
     "\n",
-    "![](algo_overview.png)"
+    "print('Score:', sum(predicted_a == 0) + sum(predicted_b == 1), '/', (len(predicted_a) + len(predicted_b)))"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 145,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
     }
    },
-   "source": [
-    "## Parameterized quantum circuits"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Give example for which ones used... Maxcut -> real amplitudes, chemistry -> complex. But chemistry also others... like classically motivated ones such as UCCSD."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<matplotlib.legend.Legend at 0x13109add0>"
       ]
      },
+     "execution_count": 145,
      "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "import networkx as nx\n",
-    "\n",
-    "graph = nx.Graph()\n",
-    "graph.add_nodes_from([0, 1, 2, 3])\n",
-    "elist = [(0, 1, 1.0), (0, 2, 1.0), (0, 3, 1.0), (1, 2, 1.0), (2, 3, 1.0)]\n",
-    "graph.add_weighted_edges_from(elist)\n",
+    "plt.figure(figsize=(12,6))\n",
+    "plt.title('Correctly predicted')\n",
+    "plt.scatter(points_a[:,0], points_a[:,1], s=60, marker='o',\n",
+    "            color=['tab:green' if label == 0 else 'tab:red' for label in predicted_a])\n",
+    "plt.scatter(points_b[:,0], points_b[:,1], s=60, marker='^',\n",
+    "            color=['tab:green' if label == 1 else 'tab:red' for label in predicted_b])\n",
+    "for marker, (label, data) in zip(markers, training_data.items()):\n",
+    "    plt.scatter(data[:,0], data[:,1], color='tab:blue', label=label, s=60, marker=marker)\n",
     "\n",
-    "nx.draw(graph, with_labels=True)"
+    "train = mpatches.Patch(color='tab:blue', label='training data')\n",
+    "correct = mpatches.Patch(color='tab:green', label='correctly labeled')\n",
+    "wrong = mpatches.Patch(color='tab:red', label='wrongly labeled')\n",
+    "plt.legend(handles=[train, correct, wrong])"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "source": [
-    "Expect 0101 (or 1010, since symmetric problem)"
+    "## Circuits in Algorithms\n",
+    "\n",
+    "![](algo_overview.png)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "nx.draw(graph, with_labels=True, node_color=[0, 1, 0, 1])"
+    "Notebook link: github.com/Cryoris/notebooks"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
-    "Quantum optimization using variational algorithms\n",
-    "on near-term quantum devices, Moll et al. [arXiv:1710.01022](https://arxiv.org/pdf/1710.01022.pdf)"
+    "* QSVM, VQC: Supervised learning with quantum enhanced feature spaces, Havlicek et al. [arXiv:1804.11326](https://arxiv.org/abs/1804.11326)\n",
+    "* VQE: Moll\n",
+    "* Variational QTE: Ardle\n",
+    "* QGAN: Zoufal\n",
+    "* QBM: Zoufal\n",
+    "* QAOA: Farhi\n",
+    "* QSBO: Gacon\n",
+    "* QPE: Textbook\n",
+    "* QAE: , Woerner\n",
+    "* HHL: .."
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "source": [
-    "from qiskit.aqua.operators import Z, I\n",
+    "## Building a quantum algorithm\n",
     "\n",
-    "# ising = (Z ^ Z ^ I ^ I) + (Z ^ I ^ Z ^ I) + (Z ^ I ^ I ^ Z) + (I ^ Z ^ Z ^ I)\n",
-    "ising = (I ^ I ^ Z ^ Z) + (I ^ Z ^ I ^ Z) + (Z ^ I ^ I ^ Z) + (I ^ Z ^ Z ^ I)"
+    "Let's see how easy it is to build the quantum phase estimation (QPE) algorithm. Provided with $U$ and $|u\\rangle$, we want to find $\\varphi$.\n",
+    "\n",
+    "$$\n",
+    "U|u\\rangle = e^{2\\pi i \\varphi}|u\\rangle\n",
+    "$$\n",
+    "\n",
+    "This example:\n",
+    "$$\n",
+    "U = X \\otimes X \\cong \n",
+    "\\begin{pmatrix} \n",
+    "0 & 0 & 0 & 1 \\\\\n",
+    "0 & 0 & 1 & 0 \\\\\n",
+    "0 & 1 & 0 & 0 \\\\\n",
+    "1 & 0 & 0 & 0\n",
+    "\\end{pmatrix}\n",
+    "\\text{ and }\n",
+    "|u\\rangle = \\frac{|00\\rangle - |11\\rangle}{\\sqrt{2}} \n",
+    "\\cong\n",
+    "\\frac{1}{\\sqrt{2}} \n",
+    "\\begin{pmatrix}\n",
+    "1 \\\\ 0 \\\\ 0 \\\\ - 1\n",
+    "\\end{pmatrix}\n",
+    "\\text{ with }\n",
+    "e^{2\\pi i\\varphi} = - 1\n",
+    "$$"
    ]
   },
   {
-   "cell_type": "markdown",
-   "metadata": {},
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<qiskit.circuit.instructionset.InstructionSet at 0x131127390>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "Which Ansatz? Ideally, we have full access to the subspace of possible solutions. Here, we e.g. don't need complex amplitudes."
+    "unitary = QuantumCircuit(2, name='U')\n",
+    "unitary.x(0)\n",
+    "unitary.x(1)\n",
+    "\n",
+    "# (|00> - |11>) / sqrt(2)\n",
+    "bell1 = QuantumCircuit(2, name='|u>')\n",
+    "bell1.x(0)\n",
+    "bell1.h(0)\n",
+    "bell1.cx(0, 1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Before applying the unitary:\n",
+      " Statevector([ 0.70710678+0.j,  0.        +0.j,  0.        +0.j,\n",
+      "             -0.70710678+0.j],\n",
+      "            dims=(2, 2))\n",
+      "After:\n",
+      " Statevector([-0.70710678+0.j,  0.        +0.j,  0.        +0.j,\n",
+      "              0.70710678+0.j],\n",
+      "            dims=(2, 2))\n",
+      "Factor of -1?\n",
+      " True\n"
+     ]
+    }
+   ],
    "source": [
-    "from qiskit.circuit.library import RealAmplitudes\n",
+    "from qiskit.quantum_info import Statevector\n",
     "\n",
-    "ansatz = RealAmplitudes(num_qubits=3)\n",
-    "ansatz.draw(output='mpl')"
+    "before = Statevector.from_instruction(bell1)\n",
+    "after = before.evolve(unitary)\n",
+    "print('Before applying the unitary:\\n', before)\n",
+    "print('After:\\n', after)\n",
+    "print('Factor of -1?\\n', all(after.data * -1 == before.data))"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "source": [
-    "Why is it called `RealAmplitudes`? It has only real amplitudes! RY does not apply complex amplitudes and neither does CX."
+    "The circuit we want to use is the following (see also [Nielsen]):\n",
+    "\n",
+    "![](qpe_circ.png)\n",
+    "\n",
+    "[Nielsen]: Quantum Computation and Quantum Information, Michael A. Nielsen & Isaac L. Chuang. [Online](http://mmrc.amss.cas.cn/tlb/201702/W020170224608149940643.pdf)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bound_ansatz = ansatz.assign_parameters(np.random.random(ansatz.num_parameters))\n",
-    "Statevector.from_instruction(bound_ansatz)"
-   ]
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<qiskit.circuit.instructionset.InstructionSet at 0x1314bcc10>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from qiskit.circuit import QuantumRegister, ClassicalRegister\n",
+    "\n",
+    "num_eval_qubits = 3\n",
+    "\n",
+    "qr_eval = QuantumRegister(num_eval_qubits, 'eval')\n",
+    "qr_unitary = QuantumRegister(unitary.num_qubits, 'state')\n",
+    "cr = ClassicalRegister(num_eval_qubits, 'bits')\n",
+    "\n",
+    "circuit = QuantumCircuit(qr_eval, qr_unitary, cr)\n",
+    "\n",
+    "# start with eigenstate\n",
+    "circuit.append(bell1, qr_unitary[:])\n",
+    "\n",
+    "# hadamards\n",
+    "circuit.h(qr_eval)\n",
+    "\n",
+    "# controlled powers\n",
+    "for i in range(num_eval_qubits):\n",
+    "    controlled_power = unitary.power(2 ** i).control()\n",
+    "    circuit.compose(controlled_power, qubits=[i] + qr_unitary[:], inplace=True)\n",
+    "    \n",
+    "# inverse qft \n",
+    "iqft = QFT(num_eval_qubits, do_swaps=False).inverse()\n",
+    "circuit.append(iqft, qr_eval[:]) # could also use compose but append wraps it so it's nicer to read\n",
+    "\n",
+    "circuit.measure(qr_eval, cr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "scrolled": true,
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 842.348x385.28 with 1 Axes>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "circuit.draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from qiskit import Aer, execute\n",
+    "backend = Aer.get_backend('qasm_simulator')\n",
+    "counts = execute(circuit, backend, shots=1024).result().get_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'001': 1024}"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "counts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "Binary fraction: \n",
+    "$$\n",
+    "    0.b_0 b_1 \\dots = \\frac{b_0}{2^1} + \\frac{b_1}{2^2} + \\cdots\n",
+    "$$\n",
+    "Means \n",
+    "$$\n",
+    "    |001\\rangle \\rightarrow 0.100 = \\frac{1}{2} + \\frac{0}{4} + \\frac{0}{8} = \\frac{1}{2} = \\varphi\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "Thus we have the eigenvalue\n",
+    "$$\n",
+    "    \\lambda = e^{2\\pi i \\varphi} = e^{\\pi i} = -1\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "Could also just do this with the `PhaseEstimation` circuit from the library:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 653.095x325.08 with 1 Axes>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from qiskit.circuit.library import PhaseEstimation\n",
+    "\n",
+    "pec = PhaseEstimation(3, unitary)\n",
+    "pec.draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 802.977x385.28 with 1 Axes>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "circuit = QuantumCircuit(5, 3)\n",
+    "\n",
+    "# start with eigenstate\n",
+    "circuit.append(bell1, [3, 4])\n",
+    "\n",
+    "# phase estimation\n",
+    "circuit.compose(PhaseEstimation(3, unitary), inplace=True)\n",
+    "\n",
+    "# measure\n",
+    "circuit.measure([0, 1, 2], [0, 1, 2])\n",
+    "\n",
+    "circuit.draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Parameterized quantum circuits"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Max-cut problem\n",
+    "\n",
+    "Goal: Find the largest possible cut in a given graph.\n",
+    "\n",
+    "Further references: [Qiskit textbook](https://qiskit.org/textbook/ch-applications/qaoa.html) or [James Weaver's Medium article](https://medium.com/qiskit/the-variational-quantum-eigensolver-43f7718c2747) or [Moll].\n",
+    "\n",
+    "[Moll]: Quantum optimization using variational algorithms\n",
+    "on near-term quantum devices, Moll et al. [arXiv:1710.01022](https://arxiv.org/pdf/1710.01022.pdf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import networkx as nx\n",
+    "\n",
+    "graph = nx.Graph()\n",
+    "graph.add_nodes_from([0, 1, 2, 3])\n",
+    "elist = [(0, 1, 1.0), (0, 2, 1.0), (0, 3, 1.0), (1, 2, 1.0), (2, 3, 1.0)]\n",
+    "graph.add_weighted_edges_from(elist)\n",
+    "\n",
+    "nx.draw(graph, with_labels=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "![](maxcut_line.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "Maximum cut at labels 0101 (or 1010, since symmetric problem)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "Objective function must be encoded in a Hamiltonian (see Ref. [Moll]):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from qiskit.aqua.operators import Z, I\n",
+    "ising = (I ^ I ^ Z ^ Z) + (I ^ Z ^ I ^ Z) + (Z ^ I ^ I ^ Z) + (I ^ Z ^ Z ^ I)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "Which Ansatz? Ideally, we have full access to the subspace of possible solutions. Here, we e.g. don't need complex amplitudes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1344.78x264.88 with 1 Axes>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from qiskit.circuit.library import RealAmplitudes\n",
+    "\n",
+    "ansatz = RealAmplitudes(num_qubits=4)\n",
+    "ansatz.draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "Why is it called `RealAmplitudes`? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Statevector([0.35223618+0.j, 0.19504538+0.j, 0.3413613 +0.j,\n",
+       "             0.00225788+0.j, 0.07074963+0.j, 0.11899706+0.j,\n",
+       "             0.33085966+0.j, 0.24249931+0.j, 0.23170059+0.j,\n",
+       "             0.26273251+0.j, 0.41993687+0.j, 0.02834874+0.j,\n",
+       "             0.09823054+0.j, 0.10132007+0.j, 0.34313451+0.j,\n",
+       "             0.31047391+0.j],\n",
+       "            dims=(2, 2, 2, 2))"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from qiskit.quantum_info import Statevector\n",
+    "\n",
+    "bound_ansatz = ansatz.assign_parameters(np.random.random(ansatz.num_parameters))\n",
+    "Statevector.from_instruction(bound_ansatz)"
+   ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "source": [
     "Let's run the algorithm!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 52,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Minimal energy at: {'0010': 1, '0101': 1, '1010': 741, '1100': 202, '1110': 79}\n"
+     ]
+    }
+   ],
    "source": [
     "from qiskit.aqua.algorithms import VQE\n",
-    "from qiskit.aqua.components.optimizers import SPSA, COBYLA\n",
+    "from qiskit.aqua.components.optimizers import SPSA\n",
     "\n",
-    "optimizer = SPSA()\n",
-    "ansatz = RealAmplitudes(reps=1)\n",
+    "optimizer = SPSA()  # choosing Simultaneous Perturbation Stochastic Approximation optimizer\n",
+    "ansatz = RealAmplitudes(4, reps=4)  # ansatz on 4 qubits\n",
     "\n",
-    "vqe = VQE(ising, ansatz, optimizer)#, include_custom=True)\n",
+    "vqe = VQE(ising, ansatz, optimizer)\n",
     "result = vqe.run(Aer.get_backend('qasm_simulator'))\n",
     "print('Minimal energy at:', result.eigenstate)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 53,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x360 with 1 Axes>"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from qiskit.visualization import plot_histogram\n",
     "plot_histogram(result.eigenstate)"
@@ -514,62 +1132,93 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "source": [
     "We can also choose others, but they have more parameters and are more difficult to optimize since they have more parameters!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1585.58x264.88 with 1 Axes>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from qiskit.circuit.library import EfficientSU2\n",
     "\n",
-    "ansatz = EfficientSU2(3)\n",
+    "ansatz = EfficientSU2(4)\n",
     "ansatz.draw(output='mpl')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bound_ansatz = ansatz.assign_parameters(np.random.random(ansatz.num_parameters))\n",
-    "Statevector.from_instruction(bound_ansatz)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 32,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x360 with 1 Axes>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "vqe = VQE(ising, EfficientSU2(), optimizer)\n",
+    "vqe = VQE(ising, ansatz, optimizer)\n",
     "result = vqe.run(Aer.get_backend('qasm_simulator'))\n",
     "plot_histogram(result.eigenstate)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
    "source": [
-    "### The `TwoLocal` circuit\n",
+    "### Creating your own Ansatz: The `TwoLocal` circuit\n",
     "\n",
-    "Hardware efficient, quantum-native.\n",
+    "Template for hardware efficient, quantum-native circuits.\n",
     "\n",
-    "    TwoLocal(num_qubits, rotation_blocks, entanglement_blocks, reps, entanglement)\n",
-    "    \n",
-    "    \n",
-    "[arXiv:1905.10876](https://arxiv.org/pdf/1905.10876.pdf)"
+    "    TwoLocal(num_qubits, rotation_blocks, entanglement_blocks, reps, entanglement)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
+   "execution_count": 33,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -578,7 +1227,7 @@
        "<Figure size 1344.78x264.88 with 1 Axes>"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -592,8 +1241,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -602,34 +1255,97 @@
        "<Figure size 1043.78x264.88 with 1 Axes>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "two = TwoLocal(4, ['rx', 'ry'], 'cx', entanglement='linear')\n",
+    "two = TwoLocal(4, ['rx', 'ry'], 'cx', entanglement='linear') # other options: 'full', 'circular', 'sca'\n",
     "two.draw(output='mpl')"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "Convenient access to standard ansatz circuits, but complex custom circuits are easily constructed, e.g.:\n",
+    "* custom single- or two-qubit circuits\n",
+    "* custom entanglements\n",
+    "* different numbers of repetitions\n",
+    "* coupling of parameters\n",
+    "* etc.\n",
+    "\n",
+    "$n$-local version: `qiskit.circuit.library.NLocal`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 983.577x204.68 with 1 Axes>"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "Circuit 6 in [arXiv:1905.10876](https://arxiv.org/pdf/1905.10876.pdf)"
+    "from qiskit.circuit import Parameter\n",
+    "\n",
+    "p = Parameter('p')\n",
+    "ent = QuantumCircuit(2)\n",
+    "ent.swap(0, 1)\n",
+    "ent.rx(p, 0)\n",
+    "ent.ry(p, 1)\n",
+    "\n",
+    "single = QuantumCircuit(1)\n",
+    "single.t(0)\n",
+    "single.y(0)\n",
+    "\n",
+    "two = TwoLocal(3, [single, 'rx'], ent, entanglement='circular', reps=1, insert_barriers=True)\n",
+    "two.draw(output='mpl')"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "source": [
-    "![](circuit6.png)"
+    "Which Ansatz works well? Active field of research! See e.g. [Sim].\n",
+    "\n",
+    "Circuit 6 in [Sim] is proposed to work well for one repetition already.\n",
+    "\n",
+    "![](circuit6.png)\n",
+    "\n",
+    "[Sim]: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms, Sim et al. [arXiv:1905.10876](https://arxiv.org/pdf/1905.10876.pdf)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
+   "execution_count": 54,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -638,7 +1354,7 @@
        "<Figure size 1224.38x264.88 with 1 Axes>"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -654,33 +1370,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 89,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [],
    "source": [
-    "vqe = VQE(ising, two, SPSA())\n",
-    "result = vqe.run(Aer.get_backend('qasm_simulator'))\n",
-    "plot_histogram(result.eigenstate)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you come up with your own good ansatz, add it to the library!"
+    "vqe = VQE(ising, two, optimizer)  # then run algorithm..."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "source": [
-    "E.g. we want our own circuit, say:"
+    "Any arbitrary circuit is supported as algorithm input."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [],
    "source": [
     "from qiskit.circuit import QuantumCircuit, ParameterVector\n",
@@ -702,8 +1421,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
+   "execution_count": 39,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -712,7 +1435,7 @@
        "<Figure size 441.777x264.88 with 1 Axes>"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -723,48 +1446,89 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [],
    "source": [
-    "vqe = VQE(ising, ansatz, optimizer)\n",
-    "result = vqe.run(Aer.get_backend('qasm_simulator'))\n",
-    "plot_histogram(result.eigenstate)"
+    "vqe = VQE(ising, ansatz, optimizer)  # then run algorithm..."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "If it turns out to be good, add it to the library! "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "source": [
     "### Summary\n",
     "\n",
     "* Choose the right Ansatz for your application\n",
-    "* Circuit library offers a set of standard varforms (like `RealAmplitudes`) and tools to build new ones (`TwoLocal`)\n",
-    "* Algorithms can use individual circuits as input"
+    "* Qiskit's algorithms run on any custom circuit\n",
+    "* Circuit library offers a set of standard ansatz circuits (like `RealAmplitudes`) and tools to build new ones (`TwoLocal`)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "source": [
     "## Chemistry applications\n",
     "\n",
-    "Need varform with complex amplitudes, since we now represent wave functions of molecules which might have complex amplitudes. So `RealAmplitudes` is not the right. \n",
-    "Could use `EfficientSU2` -- > complex states."
+    "The ansatz tries to approximate the ground state of a molecule. `RealAmplitudes` from before is not the right, we want complex amplitudes!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 56,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Statevector([ 0.77658928-0.18547926j, -0.02534895+0.21396543j,\n",
+       "              0.25962842+0.22161376j,  0.06110877+0.4425256j ],\n",
+       "            dims=(2, 2))"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "ansatz = EfficientSU2()"
+    "from qiskit.circuit.library import EfficientSU2\n",
+    "\n",
+    "ansatz = EfficientSU2(2)\n",
+    "bound_ansatz = ansatz.assign_parameters(np.random.random(ansatz.num_parameters))\n",
+    "Statevector.from_instruction(bound_ansatz)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -774,16 +1538,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 183,
-   "metadata": {},
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "-1.062946587529937"
+       "-1.5598794124645186"
       ]
      },
-     "execution_count": 183,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -798,7 +1566,7 @@
     "        + 0.18093119978423156 * (X ^ X)\n",
     "h2_energy = -1.85727503\n",
     "\n",
-    "optimizer = L_BFGS_B()\n",
+    "optimizer = L_BFGS_B()  # Broyden–Fletcher–Goldfarb–Shanno\n",
     "\n",
     "backend = Aer.get_backend('qasm_simulator')\n",
     "\n",
@@ -809,124 +1577,68 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "source": [
     "### Another approach: Classically motivated\n",
     "\n",
     "In chemistry, the UCCSD(T) method is considered the \"gold\" standard to compute the ground state energy.\n",
     "This also uses the variational principle with a certain wavefunction ansatz. \n",
-    "We can also use this wavefunction here! It looks like this\n",
-    "[panos paper]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's compare circuit sizes though...."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "* number of parameters increases significantly with the number of elections (bad scaling to larger systems)\n",
-    "* long circuits\n",
-    "* circuits ignorant of hardware structure"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from qiskit.chemistry.components.variational_forms import UCCSD"
+    "We can also use this wavefunction here! \n",
+    "\n",
+    "[Barkoutsos]: Quantum algorithms for electronic structure calculations: particle/hole Hamiltonian\n",
+    "and optimized wavefunction expansions, Barkoutsos et al. [arXiv:1805.04340](https://arxiv.org/pdf/1805.04340.pdf)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 186,
-   "metadata": {},
+   "execution_count": 57,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "39 92 10\n",
-      "     ┌─────────────────────┐┌───────────────────┐┌──────────────────────┐»\n",
-      "q_0: ┤ U3(pi/2,-pi/2,pi/2) ├┤ U3(0,0,-2.0*p[0]) ├┤ U3(-pi/2,-pi/2,pi/2) ├»\n",
-      "     ├─────────────────────┤└┬──────────────────┤├──────────────────────┤»\n",
-      "q_1: ┤ U3(pi/2,-pi/2,pi/2) ├─┤ U3(0,0,2.0*p[1]) ├┤ U3(-pi/2,-pi/2,pi/2) ├»\n",
-      "     └─────────────────────┘ └──────────────────┘└──────────────────────┘»\n",
-      "«     ┌─────────────────────┐                               »\n",
-      "«q_0: ┤ U3(pi/2,-pi/2,pi/2) ├──■─────────────────────────■──»\n",
-      "«     └──┬───────────────┬──┘┌─┴─┐┌───────────────────┐┌─┴─┐»\n",
-      "«q_1: ───┤ U3(pi/2,0,pi) ├───┤ X ├┤ U3(0,0,-1.0*p[2]) ├┤ X ├»\n",
-      "«        └───────────────┘   └───┘└───────────────────┘└───┘»\n",
-      "«     ┌──────────────────────┐   ┌───────────────┐                            »\n",
-      "«q_0: ┤ U3(-pi/2,-pi/2,pi/2) ├───┤ U3(pi/2,0,pi) ├─────■──────────────────────»\n",
-      "«     └──┬───────────────┬───┘┌──┴───────────────┴──┐┌─┴─┐┌──────────────────┐»\n",
-      "«q_1: ───┤ U3(pi/2,0,pi) ├────┤ U3(pi/2,-pi/2,pi/2) ├┤ X ├┤ U3(0,0,1.0*p[2]) ├»\n",
-      "«        └───────────────┘    └─────────────────────┘└───┘└──────────────────┘»\n",
-      "«             ┌───────────────┐    ┌─────────────────────┐┌───────────────────┐»\n",
-      "«q_0: ──■─────┤ U3(pi/2,0,pi) ├────┤ U3(pi/2,-pi/2,pi/2) ├┤ U3(0,0,-2.0*p[3]) ├»\n",
-      "«     ┌─┴─┐┌──┴───────────────┴───┐├─────────────────────┤└┬──────────────────┤»\n",
-      "«q_1: ┤ X ├┤ U3(-pi/2,-pi/2,pi/2) ├┤ U3(pi/2,-pi/2,pi/2) ├─┤ U3(0,0,2.0*p[4]) ├»\n",
-      "«     └───┘└──────────────────────┘└─────────────────────┘ └──────────────────┘»\n",
-      "«     ┌──────────────────────┐┌─────────────────────┐                          »\n",
-      "«q_0: ┤ U3(-pi/2,-pi/2,pi/2) ├┤ U3(pi/2,-pi/2,pi/2) ├──■───────────────────────»\n",
-      "«     ├──────────────────────┤└──┬───────────────┬──┘┌─┴─┐┌───────────────────┐»\n",
-      "«q_1: ┤ U3(-pi/2,-pi/2,pi/2) ├───┤ U3(pi/2,0,pi) ├───┤ X ├┤ U3(0,0,-1.0*p[5]) ├»\n",
-      "«     └──────────────────────┘   └───────────────┘   └───┘└───────────────────┘»\n",
-      "«          ┌──────────────────────┐   ┌───────────────┐        »\n",
-      "«q_0: ──■──┤ U3(-pi/2,-pi/2,pi/2) ├───┤ U3(pi/2,0,pi) ├─────■──»\n",
-      "«     ┌─┴─┐└──┬───────────────┬───┘┌──┴───────────────┴──┐┌─┴─┐»\n",
-      "«q_1: ┤ X ├───┤ U3(pi/2,0,pi) ├────┤ U3(pi/2,-pi/2,pi/2) ├┤ X ├»\n",
-      "«     └───┘   └───────────────┘    └─────────────────────┘└───┘»\n",
-      "«                                 ┌───────────────┐    ┌─────────────────────┐»\n",
-      "«q_0: ──────────────────────■─────┤ U3(pi/2,0,pi) ├────┤ U3(pi/2,-pi/2,pi/2) ├»\n",
-      "«     ┌──────────────────┐┌─┴─┐┌──┴───────────────┴───┐├─────────────────────┤»\n",
-      "«q_1: ┤ U3(0,0,1.0*p[5]) ├┤ X ├┤ U3(-pi/2,-pi/2,pi/2) ├┤ U3(pi/2,-pi/2,pi/2) ├»\n",
-      "«     └──────────────────┘└───┘└──────────────────────┘└─────────────────────┘»\n",
-      "«     ┌───────────────────┐┌──────────────────────┐┌─────────────────────┐     »\n",
-      "«q_0: ┤ U3(0,0,-2.0*p[6]) ├┤ U3(-pi/2,-pi/2,pi/2) ├┤ U3(pi/2,-pi/2,pi/2) ├──■──»\n",
-      "«     └┬──────────────────┤├──────────────────────┤└──┬───────────────┬──┘┌─┴─┐»\n",
-      "«q_1: ─┤ U3(0,0,2.0*p[7]) ├┤ U3(-pi/2,-pi/2,pi/2) ├───┤ U3(pi/2,0,pi) ├───┤ X ├»\n",
-      "«      └──────────────────┘└──────────────────────┘   └───────────────┘   └───┘»\n",
-      "«                               ┌──────────────────────┐   ┌───────────────┐   »\n",
-      "«q_0: ───────────────────────■──┤ U3(-pi/2,-pi/2,pi/2) ├───┤ U3(pi/2,0,pi) ├───»\n",
-      "«     ┌───────────────────┐┌─┴─┐└──┬───────────────┬───┘┌──┴───────────────┴──┐»\n",
-      "«q_1: ┤ U3(0,0,-1.0*p[8]) ├┤ X ├───┤ U3(pi/2,0,pi) ├────┤ U3(pi/2,-pi/2,pi/2) ├»\n",
-      "«     └───────────────────┘└───┘   └───────────────┘    └─────────────────────┘»\n",
-      "«                                      ┌───────────────┐    \n",
-      "«q_0: ──■────────────────────────■─────┤ U3(pi/2,0,pi) ├────\n",
-      "«     ┌─┴─┐┌──────────────────┐┌─┴─┐┌──┴───────────────┴───┐\n",
-      "«q_1: ┤ X ├┤ U3(0,0,1.0*p[8]) ├┤ X ├┤ U3(-pi/2,-pi/2,pi/2) ├\n",
-      "«     └───┘└──────────────────┘└───┘└──────────────────────┘\n"
-     ]
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1645.63x505.68 with 1 Axes>"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
+    "from qiskit.chemistry.components.variational_forms import UCCSD\n",
+    "\n",
     "ucc = UCCSD(4, 2, reps=3)\n",
     "p = ParameterVector('p', ucc.num_parameters)\n",
     "ansatz = ucc.construct_circuit(p).decompose()\n",
-    "print(ansatz.decompose().depth(), circ.num_parameters, circ.num_qubits)\n",
-    "print(ansatz.decompose())"
+    "ansatz.draw(output='mpl')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 187,
-   "metadata": {},
+   "execution_count": 49,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "-1.1032215948736686"
+       "-1.869270947305635"
       ]
      },
-     "execution_count": 187,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -937,16 +1649,33 @@
     "res.eigenvalue.real"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "This worked well for this small system... what about larger systems? "
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 173,
-   "metadata": {},
+   "execution_count": 63,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [],
    "source": [
     "from qiskit.chemistry.drivers import PySCFDriver\n",
+    "from qiskit.chemistry import FermionicOperator\n",
+    "from qiskit.aqua.operators import Z2Symmetries\n",
     "\n",
-    "H2 = 'H 0.0 0.0 0.0; H 0.0 0.0 0.735'\n",
-    "LiH = 'Li .0 .0 .0; H .0 .0 1.3'\n",
+    "H2 = 'H 0.0 0.0 0.0; H 0.0 0.0 0.735'  # molecular coordinates in Angstrom\n",
+    "LiH = 'Li 0.0 0.0 0.0; H 0.0 0.0 1.3'\n",
     "\n",
     "def define_chemistry_problem(atom, basis, reduction=True):\n",
     "    molecule = PySCFDriver(atom=atom, basis=basis).run()\n",
@@ -954,18 +1683,23 @@
     "    num_orbitals = 2 * molecule.num_orbitals\n",
     "\n",
     "    ferOp = FermionicOperator(h1=molecule.one_body_integrals, h2=molecule.two_body_integrals)\n",
-    "    qubitOp = ferOp.mapping(map_type=mapping)\n",
-    "\n",
     "    if reduction:\n",
+    "        qubitOp = ferOp.mapping(map_type='parity')\n",
     "        qubitOp = Z2Symmetries.two_qubit_reduction(qubitOp, num_particles)\n",
-    "        \n",
+    "    else:\n",
+    "        qubitOp = ferOp.mapping(map_type='jordan_wigner')\n",
+    "\n",
     "    return qubitOp.to_opflow(), num_particles, num_orbitals"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 174,
-   "metadata": {},
+   "execution_count": 64,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -987,16 +1721,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 177,
-   "metadata": {},
+   "execution_count": 65,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(12, 4)"
+       "(4, 2)"
       ]
      },
-     "execution_count": 177,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1007,8 +1745,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 175,
-   "metadata": {},
+   "execution_count": 66,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1639,433 +2381,231 @@
       "0.06025354250147995 * IZIZIIIIII,\n",
       "0.060253542501479976 * ZZZZIIIIII,\n",
       "0.054078128864330104 * ZIZZIIIIII,\n",
-      "-0.003470877943035282 * XYYZIIIIII,\n",
-      "0.003470877943035282 * YYXIIIIIII,\n",
-      "-0.003470877943035282 * YYXZIIIIII,\n",
-      "0.003470877943035282 * XYYIIIIIII,\n",
-      "0.06558452315458396 * ZIZIIIIIII,\n",
-      "0.06305846439752381 * ZZZIIIIIII,\n",
-      "0.0630584643975238 * IZIIIIIIII])\n"
-     ]
-    }
-   ],
-   "source": [
-    "qubitOp, num_particles, num_orbitals = define_chemistry_problem(LiH, 'sto3g')\n",
-    "print(qubitOp)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 178,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "#Qubits: 10 Depth: 8467 #Params: 92\n"
-     ]
-    }
-   ],
-   "source": [
-    "ansatz = UCCSD(num_orbitals, num_particles, two_qubit_reduction=reduction)\n",
-    "p = ParameterVector('p', ucc.num_parameters)\n",
-    "ansatz = ucc.construct_circuit(p).decompose()\n",
-    "\n",
-    "print('#Qubits:', ansatz.num_qubits, 'Depth:', ansatz.depth(), '#Params:', ansatz.num_parameters)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Middle ground?\n",
-    "\n",
-    "Or: think in more detail about the application. We're working in second quantization meaning we want to know which orbitals are occupied and which are free. This get's mapped onto qubits e.g. with the Jordan-Wigner mapping. \n",
-    "There a 0 state is associated with the electron not being in the orbital and 1 with it being in the orbital.\n",
-    "Since we cannot lose electrons, the sum of 0 and 1s in a state must stay the same.\n",
-    "\n",
-    "Hence: Excitation-preserving.\n",
-    "\n",
-    "$$\n",
-    "\\begin{pmatrix}\n",
-    "    1 & 0 & 0 & 0 \\\\\n",
-    "    0 & \\cos & -\\sin & 0 \\\\\n",
-    "    0 & \\sin & \\cos & 0 \\\\\n",
-    "    0 & 0 & 0 & 1\n",
-    "\\end{pmatrix}\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 182,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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L9L/aAGDVv59DdPgI9AkbbHbbdW35X20y9gOwbe7m9gNoux1RWj8A8fsQ9kMZeOSHyEoXSnLx1w2j4O7q1bCTamobqRP7QeawH8rCj7oTWelWn8742+TtFreROrEfZA77oSw88kNERESqwuGHiIiIVIXDDxEREakK3/NDNpWRdxxrd81BeVUJ+vUYjq+Pf4yZj7+Pnt10+Cn7f1izYxYcHDQIu60v4kesQF5RBl7ZNBr9w4djypC/M7cKmFvrXy5mYcaqfgjq0APtHJ3xeuxXilhrGTPLytxaV9VUIHHzGFTVlMPD1RsLJn2KCyU5ilhrWXOrheOiRYsWiQ5BypWfBlSVtuy+tcarWPbp01j01Dbc6nMbLpdfQO/ug3HvXY8BABwd2mF4/zgM6x+LfUc3o7O2O4I69kBQhztQcDETvUIaX6DG1QsIuNN2mW2R25rMsrL1WpdVluD8pXNIfHoHHuw9CQDg5e5702tty143N3NzcsvG1v349scv0M7RGbPHfYj8i5morCpFRNcYxe9Dmpu7rfZDJL7sRTZz1PAVIoN1cHPxhJ+XP7Ynv2Vyu69XJzg7uQIAHDVO0GgcRcRsRNbcMrK01gDw/ZkDmPXuvfjXNysEJGxMxsyysrTWAX7dUFVTDgAoryyBl4efiJiNyJpbTfiylwDpXwNXzot57PYd7HfF35Nn9DiWsQ9HDV+htKIYEx9Y0OTPZeafxOXyC+jSMdw+wSwQnVst/QAsr7Wvlz8+nGOAs6MLXt4wEr1CBiE4INJ+AZughMyiOqK0fgRqQ/HTuW/xp2UR8PHsgD8Nfd1+4cwQnVtN+5CW4vAjwJXzyjvrqS0Ul+YjccpOaL0D8P7O2aivr2v0M6UVF/H29uew4MlPBSRsmujcaukHYHmtndu5AHABAPTvMRxZhWnChx8lZFZLRyyt9X9SN6J/+CMYO2A2th5chv3HPsKDff4oKO2vROdWSz9uBl/2IpvxcPPBlYpilJYXI/eCAR6uPia3G421WJL0JGKHL4OvVydBKRuTNbeMLK11RdWVhj//kPVf+Pt1s3fERmTMLCtLa12PerR39wUAeHloUV51WUTMRmTNrSY88kM2M6xfLJZvnQovDy2eG7kKp84mm9yuP7kVhpwUrN31ZwDA1IdfQ3jXaBFRTciaW0aW1vrU2WRs3LsQTu1ccOft96JHUD9BSX8lY2ZZWVrrgb0m4u8fjcO+o5vRztEJ85/8RFBSU7LmVhMOP2QzwQGRePv57xq+z8g/ji0HliBQG4qe3XQY2GsCBvaaYHKfvKIMrNs9F/dFjrF33Aay5paRpbXu12Mo+vUYanIf0WstY2ZZWVprTzcfLHlmr8l9lLDWsuZWE17VXYDmXDF4XGIAyiouQaNxhEbjiPCgaMwasxYdfG5r0WM39+rASrsqs+grMreELdec/TClhKu6t4QsHZG9HwD3Ib8neh8iCt/zo2BFl/NwsbQA7848hi9eLcPmuZkoKT+PdbvmiI5GCsB+kCXsCJmj5n5w+FGw9JwUuLt6IajDHQAALw8/hARGoaRM0GcYSVHYD7KEHSFz1NwPDj8Klp6TgrDOfeHg4ACjsRYpP++B/sQnGNhrouhopADsB1nCjpA5au4H3/CsYOk5KUjLOoRRC31QWVMGVyd3JIz5ALqeY0VHIwVgP8gSdoTMUXM/FH3kp66uDsuWLUNoaChcXV3Rs2dP6PV6hIWFITY2VnQ8mzudm4p5E5OwPbEE/5yfA613Z2Sf/1l0LFII9oMsYUfIHDX3Q9FHfqZOnYpt27Zh4cKF6N27Nw4fPowJEybgwoULePHFF0XHs6m8ogxcqbyE0M5RAAA/L3+M1iVgw96FeGLQAmg0GhjrjJi7djDq6owoqyxBXb0RaxNO2Tzbge+3YMfhd5CZfwJVVyuw9/Vamz9ma5A1d1OU3A9AzrWWMbM5Su6IrGsta+6mKLkf9qDYIz9JSUnYsGEDduzYgZdeegn3338/5s+fj+joaNTW1iIqKkp0RJtKz0lBe3dfdLylS8O2mIiRKLlSiLSsQwAAR40j3ojbj3lPJKGTb1e8MmWHXbJ5ut2CR6KnIX7ESrs8XmuRNXdTlNwPQM61ljGzOUruiKxrLWvupii5H/ag2OFn8eLFGDJkCHQ6ncn2kJAQODk5ITLy2rVyXn75ZXTv3h0ajQafffaZiKg2YchJQUhAL5Nt3h5ahHeNQfLJX/+eBRfPYtW2aXjhsTXw973dLtn6hj2Egb0mwN8v2C6P11pkzd0UJfcDkHOtZcxsjpI7Iutay5q7KUruhz0o8mWv3NxcpKWlYdasWY1uy87ORkREBFxcrl04cMiQIZg8eTKefvppe8e0qWdHvNnk9hXTfj1N+rnCH7Fx78tIGLse7d1vsVc0UgD2gyxhR8gctfdDkUd+cnOvnZqyUyfTi0ZWVlZCr9ebvOQVExOD4GDbTOEODg42+dLrD7ZKvrlrB6Pw0jks2vgoElYPQGnFRYv30esPCsnaWqzJL2Nm9qN1WJtf1tyiOyJ7PwDuQ5rDHvuQ1vxqDkUe+dFqtQAAg8GAoUN/vUbO0qVLUVBQgN69e4uKpihJCxR0/nVSHPaDLGFHyJy23A9FHvkJDg5GZGQkFi9ejE2bNmH//v2Ij4/H+vXrAcBuw099fb1NvnS6AXbJ3xSdboA0WZtiTX4ZMyslv0xZm2Jtfllzi84vS05zuA9RVtbW/GoORQ4/Go0GW7duRUREBOLj4zFlyhRotVpMnz4djo6ODW92JjGMdUbUXK3C1doaAEDN1SrUXK1qdvnsTdbcMpJxrWXMLCtZ11rW3NSYIl/2AoDu3bvjwIEDJtsmTZqE8PBwuLm5CUpFALDv6GYs+3RKw/fD5l3777H5L2fRyberoFSWyZpbRjKutYyZZSXrWsuamxpT7PDTlNTUVPTv399k28KFC/Hhhx/iwoULOHXqFGbOnAm9Xo9u3boJStn2PdR3Mh7qO1l0jGaTNbeMZFxrGTPLSta1ljU3NabIl72aUlZWBoPB0OjkhomJicjNzUV1dTWKi4uRm5vbJgafjLzjmPP+YDz3jz9g839ewZSlYThxRg8AKLqcj/iVURj6F1cYjdfOMJpXlIG4N+/Gh3sWiIxNdmKuH9f965sVmPnOPQDYDzUy15GUn/cgYfUAJKwegHGv+OO/advZEZVo7nMLAKzeMQuz3r0X73z+AoC2sT+RZvjx9PSE0WjEjBkzREexuVrjVazbPReLntqGiYMWQKNxxBjdbPTsdu2Ej17uvlgaux89gn49ChaoDcG0kfKfdZQss9QPAKiprcaZ/O8bvmc/1MVSR/reMQTL4w9iefxBdPAJQlToA+yICrTkueV07jFUVpdhxbRk1NbWID0npU10RZrhR02OGr5CZLAObi6e8PPyx/bkt0xud3ZybXMnnCLrWeoHAOz57gM82OcpAelICazpCAAUFGfCp31HuLl42jkhidCS55afso+gd/cHAQBRoQ/gx3Pf2i2vLUn1nh+1OHlGj2MZ+3DU8BVKK4ox8QF5Dy1S67PUj1rjVZw4cxAjYqZh496XBaUkkazdhxw6tQ3/d+ejdk5HorTkuaWssgT+vtdOJOzh6o2swh9sHdMuOPwoUHFpPhKn7ITWOwDv75yN+vo60ZFIQSz1Y9/RzRjYa6KgdKQE1u5Dvv3pCyz64zY7pyNRWvLc4uHqjYrqUgBAeXUpPN18bB3TLviylwJ5uPngSkUxSsuLkXvBAA/XtlE2ah2W+pFzIR1ffLsaf1k7BOcKf8D2Q6sEJSVRrNmHXCz9BU6OzvDy8BOQkERoyXNLeJdoHD+9HwBw/PQ+k/cDyYxHfhRoWL9YLN86FV4eWjw3chVOnU02ub3WeBXz1j2MzIITmLvuITz98GL0COonKC3Zm6V+PDPs9YY/z3znHoy6p+1/SIBMWeoIABz+4XNER4wUkI5Eaelzi5OTK2a9ey+6BdyNO4L+ICh96+Lwo0DBAZF4+/nvGr7PyD+OLQeWIFAbip7ddGjn6ISlcftM7pNXlIF1u+fivsgx9o5LdmapH7+1cvohAOyH2ljTkeHRcSb3YUfavpY8twDA9JGmb4xuC13h8COB+yJH477I0WZ/JlAbglUzjtgpESkJ+0GWsCPUFGt60ZS20BUOPwK07yDPY4vM2hRr8siY+WZ+vjXJlLUp1uaRNXdLf761yJLTHO5DbEtpa3cjDvW8IhsRERGpCD/tRURERKrC4YeIiIhUhcMPERERqQqHHyIiIlIVDj9ERESkKhx+iIiISFU4/BAREZGqcPghIiIiVeHwQ0RERKrC4YeIiIhUhcMPERERqQqHHyIiIlIVDj9ERESkKhx+iIiISFU4/BAREZGqcPghIiIiVeHwQ0RERKrC4YeIiIhU5f8BVqmUt6cRw2YAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 742.632x144.48 with 1 Axes>"
-      ]
-     },
-     "execution_count": 182,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from qiskit.circuit.library import ExcitationPreserving\n",
-    "\n",
-    "ansatz = ExcitationPreserving(2)\n",
-    "ansatz.draw(output='mpl')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Problem: JW mapping means no reduction, so Hamiltonian is larger"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 181,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "#Qubits: 12 Depth: 94 #Params: 246\n"
-     ]
-    }
-   ],
-   "source": [
-    "qubitOp, num_particles, num_orbitals = define_chemistry_problem(LiH, 'sto3g', reduction=False)\n",
-    "ansatz = ExcitationPreserving(qubitOp.num_qubits)\n",
-    "print('#Qubits:', ansatz.num_qubits, 'Depth:', ansatz.depth(), '#Params:', ansatz.num_parameters)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Quantum Fourier Transform\n",
-    "\n",
-    "Used e.g. in Shor's algorithm or phase estimation. Let's see how easy it is to build the QPE algorithm."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 191,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<qiskit.circuit.instructionset.InstructionSet at 0x139438a90>"
-      ]
-     },
-     "execution_count": 191,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from qiskit.circuit.library import QFT\n",
-    "\n",
-    "unitary = QuantumCircuit(2, name='U')\n",
-    "unitary.x(0)\n",
-    "unitary.x(1)\n",
-    "\n",
-    "bell1 = QuantumCircuit(2, name='psi')\n",
-    "bell1.x(0)\n",
-    "bell1.h(0)\n",
-    "bell1.cx(0, 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 192,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Before applying the unitary:\n",
-      " Statevector([ 0.70710678+0.j,  0.        +0.j,  0.        +0.j,\n",
-      "             -0.70710678+0.j],\n",
-      "            dims=(2, 2))\n",
-      "After:\n",
-      " Statevector([-0.70710678+0.j,  0.        +0.j,  0.        +0.j,\n",
-      "              0.70710678+0.j],\n",
-      "            dims=(2, 2))\n",
-      "Phase of -1?\n",
-      " True\n"
+      "-0.003470877943035282 * XYYZIIIIII,\n",
+      "0.003470877943035282 * YYXIIIIIII,\n",
+      "-0.003470877943035282 * YYXZIIIIII,\n",
+      "0.003470877943035282 * XYYIIIIIII,\n",
+      "0.06558452315458396 * ZIZIIIIIII,\n",
+      "0.06305846439752381 * ZZZIIIIIII,\n",
+      "0.0630584643975238 * IZIIIIIIII])\n"
      ]
     }
    ],
    "source": [
-    "from qiskit.quantum_info import Statevector\n",
-    "\n",
-    "before = Statevector.from_instruction(bell1)\n",
-    "after = before.evolve(unitary)\n",
-    "print('Before applying the unitary:\\n', before)\n",
-    "print('After:\\n', after)\n",
-    "print('Phase of -1?\\n', all(after.data * -1 == before.data))"
+    "qubitOp, num_particles, num_orbitals = define_chemistry_problem(LiH, 'sto3g')\n",
+    "print(qubitOp)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 193,
-   "metadata": {},
+   "execution_count": 90,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<qiskit.circuit.instructionset.InstructionSet at 0x138394dd0>"
+       "(12, 4)"
       ]
      },
-     "execution_count": 193,
+     "execution_count": 90,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from qiskit.circuit import QuantumRegister\n",
-    "\n",
-    "num_eval_qubits = 3\n",
-    "\n",
-    "qr_eval = QuantumRegister(num_eval_qubits, 'eval')\n",
-    "qr_unitary = QuantumRegister(unitary.num_qubits, 'state')\n",
-    "\n",
-    "circuit = QuantumCircuit(qr_eval, qr_unitary)\n",
-    "\n",
-    "# start with eigenstate\n",
-    "circuit.append(bell1, qr_unitary[:])\n",
-    "\n",
-    "# hadamards\n",
-    "circuit.h(qr_eval)\n",
-    "\n",
-    "# controlled powers\n",
-    "for i in range(num_eval_qubits):\n",
-    "    controlled_power = unitary.power(2 ** i).control()\n",
-    "    circuit.compose(controlled_power, qubits=[i] + qr_unitary[:], inplace=True)\n",
-    "    \n",
-    "# inverse qft \n",
-    "iqft = QFT(num_eval_qubits, do_swaps=False).inverse()\n",
-    "\n",
-    "circuit.append(iqft, qr_eval[:])\n",
-    "# could also use compose but append wraps it so it's nicer to read\n",
-    "# circuit.compose(iqft, qubits=qr_eval, inplace=True)"
+    "num_orbitals, num_particles"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 194,
-   "metadata": {},
+   "execution_count": 82,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "<qiskit.circuit.instructionset.InstructionSet at 0x1384a1510>"
-      ]
-     },
-     "execution_count": 194,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "#Qubits: 10 Depth: 8467 #Params: 92\n"
+     ]
     }
    ],
    "source": [
-    "# add measurements\n",
-    "from qiskit.circuit import ClassicalRegister\n",
+    "ucc = UCCSD(num_orbitals, num_particles)\n",
+    "p = ParameterVector('p', ucc.num_parameters)\n",
+    "ansatz = ucc.construct_circuit(p).decompose()\n",
     "\n",
-    "bits = ClassicalRegister(num_eval_qubits)\n",
-    "circuit.add_register(bits)\n",
-    "circuit.measure(qr_eval, bits)"
+    "print('#Qubits:', ansatz.num_qubits, 'Depth:', ansatz.depth(), '#Params:', ansatz.num_parameters)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 195,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 842.348x385.28 with 1 Axes>"
-      ]
-     },
-     "execution_count": 195,
-     "metadata": {},
-     "output_type": "execute_result"
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
     }
-   ],
-   "source": [
-    "circuit.draw(output='mpl')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 196,
-   "metadata": {},
-   "outputs": [],
+   },
    "source": [
-    "from qiskit import Aer, execute\n",
-    "backend = Aer.get_backend('qasm_simulator')\n",
-    "counts = execute(circuit, backend, shots=1024).result().get_counts()"
+    "## Compromise?\n",
+    "\n",
+    "In the Jordan-Wigner mapping, a qubit state of $|1\\rangle$ can be associated with an existing particle. Since the total number of particles should be conserved, the Ansatz wavefunction should conserve the number of $|1\\rangle$ states, e.g.:\n",
+    "\n",
+    "$$\n",
+    "\\begin{matrix}\n",
+    "    & \\mid & |00\\rangle & |10\\rangle & |01\\rangle & |11\\rangle \\\\\n",
+    "    \\hline\n",
+    "    \\langle 00| & \\mid & 1 & 0 & 0 & 0 \\\\\n",
+    "    \\langle 10|& \\mid & 0 & \\cos(\\theta/2) & -i\\sin(\\theta/2) & 0 \\\\\n",
+    "    \\langle 01|& \\mid & 0 & -i\\sin(\\theta/2) & \\cos(\\theta/2) & 0 \\\\\n",
+    "    \\langle 11|& \\mid & 0 & 0 & 0 & 1\n",
+    "\\end{matrix}\n",
+    "$$\n",
+    "\n",
+    "The `ExcitationPreserving` circuit does that.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 197,
-   "metadata": {},
+   "execution_count": 98,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [
     {
      "data": {
+      "image/png": "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\n",
       "text/plain": [
-       "{'001': 1024}"
+       "<Figure size 742.632x144.48 with 1 Axes>"
       ]
      },
-     "execution_count": 197,
+     "execution_count": 98,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "counts"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Binary fraction: \n",
-    "$$\n",
-    "    0.b_0 b_1 \\dots = \\frac{b_0}{2^1} + \\frac{b_1}{2^2} + \\cdots\n",
-    "$$\n",
-    "Means \n",
-    "$$\n",
-    "    |001\\rangle \\rightarrow 0.100 = \\frac{1}{2} + \\frac{0}{4} + \\frac{0}{8} = \\frac{1}{2} = \\varphi\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Thus we have the eigenvalue\n",
-    "$$\n",
-    "    \\lambda = e^{2\\pi i \\varphi} = e^{\\pi i} = -1\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you ever get confused by Qiskit's ordering..."
+    "from qiskit.circuit.library import ExcitationPreserving\n",
+    "\n",
+    "ansatz = ExcitationPreserving(2)\n",
+    "ansatz.draw(output='mpl')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 198,
-   "metadata": {},
+   "execution_count": 80,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 411.859x204.68 with 1 Axes>"
-      ]
-     },
-     "execution_count": 198,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "#Qubits: 12 Depth: 94 #Params: 246\n"
+     ]
     }
    ],
    "source": [
-    "test = QuantumCircuit(2)\n",
-    "test.x(0)\n",
-    "test.measure_all()\n",
-    "test.draw(output='mpl')"
+    "qubitOp, num_particles, num_orbitals = define_chemistry_problem(LiH, 'sto3g', reduction=False)\n",
+    "ansatz = ExcitationPreserving(qubitOp.num_qubits)\n",
+    "print('#Qubits:', ansatz.num_qubits, 'Depth:', ansatz.depth(), '#Params:', ansatz.num_parameters)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 199,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'01': 1024}"
-      ]
-     },
-     "execution_count": 199,
-     "metadata": {},
-     "output_type": "execute_result"
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
     }
-   ],
+   },
    "source": [
-    "counts = execute(test, backend).result().get_counts()\n",
-    "counts"
+    "# Contributing!\n",
+    "\n",
+    "* Add new circuits\n",
+    "    * more arithmetic circuits\n",
+    "    * more basis changes\n",
+    "    * circuits that you'd like to share! \n",
+    "    * ?\n",
+    "* More tests\n",
+    "    * Find efficient ways to test circuits\n",
+    "* Optimized versions of circuits\n",
+    "* More options for circuits"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "source": [
-    "Could also just do this with the `PhaseEstimation` circuit from the library:"
+    "# Summary\n",
+    "\n",
+    "1. Circuits are algorithm primitives\n",
+    "2. The circuit library...\n",
+    "    1. offers a building blocks for algorithms\n",
+    "    2. has a selection for different algorithm inputs\n",
+    "    3. is extensible!"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 39,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 833.695x445.48 with 1 Axes>"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "from qiskit.circuit.library import PhaseEstimation\n",
-    "\n",
-    "pec = PhaseEstimation(3, unitary)\n",
-    "pec.draw(output='mpl')"
+    "# Supplementary material"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Contributing!\n",
+    "## QAE\n",
+    "\n",
+    "Estimate $a$ in\n",
+    "\n",
+    "$$\n",
+    "\\mathcal{A}|0\\rangle = \\sqrt{1 - a}|\\Psi_0\\rangle + \\sqrt{a}|\\Psi_1\\rangle \n",
+    "$$\n",
+    "\n",
+    "If we set $a$ to $\\mathbb{E}[f(X)]$ we can get a quadratic speedup compared to Monte-Carlo simulations [Woerner]!\n",
+    "Works by creating a circuit\n",
     "\n",
-    "Missing:\n",
-    "* arithmetic circuits, like add "
+    "$$\n",
+    "\\mathcal{A}|0\\rangle_{n+1} = \\sum_{i=0}^{N - 1} \\sqrt{p_{x_i} (1 - f(x_i))} |x\\rangle_n |0\\rangle + \n",
+    "\\sum_{i=0}^{N - 1} \\sqrt{p_{x_i} f(x_i)} |x\\rangle_n |1\\rangle\n",
+    "$$\n",
+    "\n",
+    "Here\n",
+    "\n",
+    "$$\n",
+    "f_y(x) = \\sin^2(2x - y)\n",
+    "$$"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 109,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "## From Phase Estimation to Amplitude Estimation"
+    "y = 0.5"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 110,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2074,38 +2614,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 111,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 431.769x264.88 with 1 Axes>"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 111,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "LinearPauliRotations(3, slope=1, offset=0).draw(output='mpl')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Can be used to map a function to qubit amplitudes. E.g.\n",
-    "$$\n",
-    "    f(x) = 2x - 1\n",
-    "$$"
+    "LinearPauliRotations(3, slope=2*2, offset=-2*y).draw(output='mpl')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 207,
+   "execution_count": 112,
    "metadata": {},
    "outputs": [
     {
@@ -2114,7 +2644,7 @@
        "array([0.48021114, 0.51978886])"
       ]
      },
-     "execution_count": 207,
+     "execution_count": 112,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2132,21 +2662,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 168,
+   "execution_count": 115,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.08870967741935484"
+       "0.10714285714285714"
       ]
      },
-     "execution_count": 168,
+     "execution_count": 115,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "num_probability_qubits = 3\n",
     "y = 0.5\n",
     "M = 2 ** num_probability_qubits\n",
     "1 / M * sum((i / (M - 1) - y) ** 2 for i in range(M))"
@@ -2154,7 +2685,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 172,
+   "execution_count": 116,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2173,7 +2704,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 173,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [
     {
@@ -2183,7 +2714,7 @@
        "<Figure size 381.577x264.88 with 1 Axes>"
       ]
      },
-     "execution_count": 173,
+     "execution_count": 117,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2201,16 +2732,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 175,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<qiskit.circuit.instructionset.InstructionSet at 0x133151e90>"
+       "<qiskit.circuit.instructionset.InstructionSet at 0x1352ed0d0>"
       ]
      },
-     "execution_count": 175,
+     "execution_count": 118,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2224,16 +2755,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 176,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<qiskit.circuit.instructionset.InstructionSet at 0x1325b5590>"
+       "<qiskit.circuit.instructionset.InstructionSet at 0x13534d1d0>"
       ]
      },
-     "execution_count": 176,
+     "execution_count": 119,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2249,7 +2780,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 198,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2265,7 +2796,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 199,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
@@ -2275,7 +2806,7 @@
        "<Figure size 1224.38x264.88 with 1 Axes>"
       ]
      },
-     "execution_count": 199,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2286,7 +2817,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 200,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2299,16 +2830,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 201,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<qiskit.circuit.instructionset.InstructionSet at 0x1326c9310>"
+       "<qiskit.circuit.instructionset.InstructionSet at 0x134dd4810>"
       ]
      },
-     "execution_count": 201,
+     "execution_count": 123,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2323,17 +2854,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 202,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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U7d+/XwMHDiz22tGjR9WuXTu5u7tLknr37q3Nmzfr9ttvr+4yq016Sqzq+QfLxc3Dpv300QQ1vIlePgA1V2Z2hk5lHFXrgF+H7DfxayUvj9pKPp5gx8oA1GQrvnhBB4/+oH+OjVH7VuFydnaRl4ePHr8rSo3qt9SnOxdKkga/3FTb9q7W+h3z1edZT/V51lMZWac1fVk/9XvOW59//7ad98QxBAQEaP78+ZKkUaNGaePGjeVa7so5fEFBQUpISFBaWprCwsI0cSK9qNXNYUOfJPn7+9u0Z2dnKyYmxmZoZ/fu3RUYWPwvPJXFZDJV+hQTE12hGtJTYpWRnqTFY/xspt0bZ6tRYFiF1hUTE10l+8TEZDJV/NhGzVEZnx0lHR+Xcy9Jkmp51rFp9/aoq8s5F6tj11BJ+H5hqsqpIt8vF7LOaN3X8/RorxdV26u+zWsmk0lN/YJ1+sIxSdKKKUlyMjlr0dPx+uyVbL3w2Hr512upT2dl6tNZmbq7y8hqP7avrvXqx9X3XscUe08nT56sOnXqaMOGDVq5cmW5/h2uvmjLnj17FBERoREjRkiSpk2bJi8vL5v5Y2Ji7H6c1cSpvBwy9Pn5+UmSEhMTbdrnzJmjEydO3HAXcUk/HKcu/WdoyKx4m8nF1VON6OkDUIN5uRcOp8rKth3qk5mTIS+Psu/1BADX2p34pfLNuQpv/0CJr5/KOKr6PoUdC8knEuTu6qmmDYIlSYnH4tS66Y31O7MsXl5eGjp0qKTC06rK49rAd+Ucvs2bN+u7775T3bp1NWTIkCqsGtdyyNAXGBiokJAQRUVFaeXKldq6davGjh2rpUuXSlK1hj6r1VrpU3h4j3JvP+NkknKzzqtFSKR8fJsWTeb8HOVezqhwT194eI8q2ScmJqu1Ysc2apbK+Owo6fjw9qyrhnWbKyltT1HbibMpupxzUYGNQ6pxD/F78f3CVJVThX47ZZ6Sj2d9ebh5FXst9XSiUk4kKKzNXZKkQ6m71SqgY1GPSWJq8dBX3cf2Fdc+rr732vZ6Grfddpvq1KmjuLg4JSSUPey+tMB3xTvvFF6Nvk+fPte8z+F2P85q4lReDhn6nJyc9OGHH6pdu3YaO3ashg0bJj8/P40fP17Ozs5FF3G5EaSnxMrF3UsNmre3aT9xaKe8fZvJq05DO1UGAJXj7q6j9H70bJ04d1hZORf19udT1Dk4Uv71W9q7NAAOzGwxKy8/R/kFeZKkvPwc5eXnqHH9QF3KPqeLl8/ZzG+1WrVk42Td3Lybure7V1Jh6Au+6pziQ2m7bc4xxq+dLd9++22Z85YV+K5ez402cs/eHPaWDcHBwdq+fbtN2yOPPKK2bdvK09PTTlVVv/SUWDW6KUxOzrb/VCeSvmNoJwBDGBQxVZmXz+uJBWHKN+eqU+s7NXXIe/YuC4CD+2r3Ks39YFjR8z7TCn8fLp38k1o0bKt/fvC4/t5/oer7+Ovoqf9p2abnlH7+iF4bs01OToX9Hompu/VAj8lF67iQeVpWlb/35EbQqlUrSYVX4bye8gQ+qfD0rYKCAjVr1kwuLi4qKCiokrphy2FDX0ni4uLUtWtXm7bp06dr2bJlOn36tP773//qqaeeUkxMTNEBWtPd/vC8Ett7DltYzZUAQNVwdnLW6H5zNbrfXHuXAqAGiQwbqsiwoSW+9srIL7R8y/P625tddP7SSRWY8zWyzxxNe3it3FwKrwCfV5CrI+kHFHTV7RkG9XxWr6werHxznmYO26COQT2rY1cc2uzZs7V69WodPHjwuvP16dOnXDdet1qtioyMVF5eniwWS1WUjBLUmNCXmZmpxMREjRs3zqb9pZde0ksvvWSnqgAAAOBo/Oo00aQHCq8FcenyeY15vYPyC3KLAp8kubm4a/PsfJvlHu01Q4/2mlGdpTq8Q4cO6dChQ2XO9/bbbys/P1+ffPJJmTde37ZtW2WVh3KqMaHP29tbZrPZ3mUAAACgBvHxqqcXHluv2J82KS8/R26uHmUvhN9k+fLl9i4BpagxoQ8AAAD4LYKbhiqYWzHgBuaQV+8EAAAAAFQOQh8AAAAAGBihDwAAAAAMjNAHAAAAAAZG6AMAAAAAAyP0AQAAAICBEfoAAAAAwMC4T58dBNS7MbcNAAAAx9ahQ4cKL5Ny9IQkKbB5Y5vHVb1dlB+hzw76d7Z3BQAAAEBx8+fPr/AyU2cvkSS9OmWUzWM4DoZ3AgAAAICBEfoAAAAAwMAIfQAAAABgYIQ+AAAAADAwQh8AAAAAGBihDwAAAAAMjNAHAAAAAAZG6AMAAAAAAyP0AQAAAICBEfoAAAAAwMAIfQAAAABgYIQ+AAAAADAwQh8AAAAAGJiLvQu4Ea2Pk9LO22fbAfWk/p3ts20AAK7n4Dbp0il7V4Gq4NNQatPT3lXAqJ566inFx8fbZdsdOnTQ/Pnz7bLtiiD02UHaeSmZLzUAAGxcOiVlpNq7CgA1TXx8vGJiYuxdhkNjeCcAAAAAGBihDwAAAAAMjNAHAAAAAAZG6AMAAAAAAyP0AQAAAICBEfoAAAAAwMC4ZQMAAACAG0adOnV02223KTQ0VC1atJCTk5POnj2rvXv3aufOnTp8+HCxZYKDg7V69WoNGTJEhw4dskPVv49Dhz6LxaJ58+Zp8eLFOnbsmNq0aaM33nhDo0aNUnh4uJYsWWLvEgEAAADUAEFBQXrmmWf00EMPycvLq9T5vvrqK82bN0+bNm2SVBj4oqOj1bhxY73wwgt6+OGHq6vkSuPQoW/48OFav369pk+frtDQUO3cuVODBw/W6dOnNWHCBHuXV63eeaKJut4/U7dEjChqs1qtWjSyju4cvUJBYX+1Y3UAAFQds8Wsdz+fqi/iliuvIEehrXvpqQGLVaeWn71Lgx29/dkUff+/jTqdcUwe7t7q8oc+GtFntmp71bd3aXAwJpNJTz/9tF5++WV5enpKkr755hvt2rVLBw8elNlsVpMmTRQaGqpevXrpjjvu0B133KG1a9dq/vz5+vjjj9W4cWNt27ZNI0eOtPPe/DYOG/rWrFmj5cuXKzo6WuHh4ZKkiIgI7dmzR+vXr1enTp3sXGH1yTyXpqyME2rQooNN+4VTKcrLuaRGgZ3tVBkAAFVv7fZXtfPAJ3rzb9+rtpev5n7wuGaveURRIzbZuzTYkZOTs6YOfk8t/W9RZnaG5qx9VK+9P1QvDdtg79LgQJydnbVixQo99NBDkqSVK1dq1qxZSkxMLHH+OnXqaOTIkXrhhRc0aNAgDRgwQC4uLtq2bZv69u2r7Ozs6iy/0jjshVyioqLUu3fvosB3RVBQkFxdXRUSEqLz58+rb9++Cg4OVvv27dWrVy8lJSXZqeKqk54SK5OTs3yb3mLTfuZogrzqNJKPbzM7VQYAQNX7fNcSPRgxRY19A1XLs45G9pmj2IOblX7+iL1Lgx0NvytKQQEd5eLsqrreDfTX257UvuRoe5cFB/Ovf/1LDz30kC5duqR+/frpscceKzXwSdKFCxc0d+5c3XvvvcrPz5eLi4suX76sIUOG1NjAJzlo6EtNTdX+/fs1cODAYq8dPXpU7dq1k7u7u0wmk5566iklJiYqISFBffv21bBhw+xQcdVKT4lVPf9gubh52LSfPpqghjfRywcAMK7M7Aydyjiq1gGhRW1N/FrJy6O2ko8n2LEyOJq9SVsV2KS9vcuAA+nbt6/GjBmj7OxsRUZGauPGjeVaLjg4WO+9955cXV116dIleXl56eWXX67iaquWw4Y+SfL397dpz87OVkxMTNHQzrp16+qOO+4oer179+4lXm3n9zCZTJU+xcREV6iG9JRYZaQnafEYP5tp98bZahQYVqF1xcREV8k+MTGZTBU/tlFzVMZnB8eHsVXVMXI595IkqZZnHZt2b4+6upxzsTp2DZXg9x4fZX1+7Ni3Tht3LdK4exY4XO0Vna649rEjT/auOSYmpti/m6urqxYuXChJmjZtmr777rty/XtffdGWbdu26fbbb1dOTo5GjBihbt26lXB8xDjEe18Whwx9fn6FJ2Zf2/U6Z84cnThxQqGhoSUtpvnz5+u+++6r8vqqW/rhOHXpP0NDZsXbTC6unmpETx8AwMC83H0kSVnZF2zaM3My5OVR2x4lwcHEJHyo1z8aqZlDN6h10xvnmg+4vr/+9a9q2rSp9u/frzfeeKNcy1wb+Pr27av4+HjNmzdPkvS3v/2tKkuuUg4Z+gIDAxUSEqKoqCitXLlSW7du1dixY7V06VJJKjH0vfjii0pKStKcOXMqtRar1VrpU3h4j3JvP+NkknKzzqtFSKR8fJsWTeb8HOVezqhwT194eI8q2ScmJqu1Ysc2apbK+Ozg+DC2qjpGvD3rqmHd5kpK21PUduJsii7nXFRg45Bq3EP8Hr/3+Cjt82Nz7DItWDdaM4d9qg5BEQ5Ze0WnK6597MiTvWu+9hogkjR06FBJ0ltvvSWLxVLmv3NJge/KOXwLFy6U2WzW/fffLx8fn2uOj3CHeO/L4pChz8nJSR9++KHatWunsWPHatiwYfLz89P48ePl7OyskBDbD/mXX35ZGzdu1ObNm697z42aKD0lVi7uXmrQ3HaM+olDO+Xt20xedRraqTIAAKrH3V1H6f3o2Tpx7rCyci7q7c+nqHNwpPzrt7R3abCjj795Q0s2TtIrI7folptutXc5cDBdunSRJH3yySdlznu9wCcVnnq2Z88eubm5qWPHjlVWc1Vy2Fs2BAcHa/v27TZtjzzyiNq2bVt0fw2psIfv888/15dffqm6detWd5lVLj0lVo1uCpOTs+0/1Ymk7xjaCQC4IQyKmKrMy+f1xIIw5Ztz1an1nZo65D17lwU7e+uTJ+Xs5KJJi2x7+D6dlWmniuAomjVrpvr16ys9PV3Hjx+/7rxlBb4r9uzZo7CwMHXo0EFff/11VZVeZRw29JUkLi5OXbt2LXp+4MABzZgxQ61atVKPHj2K2uPj4+1QXdW4/eF5Jbb3HLawmisBAMA+nJ2cNbrfXI3uN9fepcCBfPla+Ye24cazYcMGnTx58rrz1KtXr1yBT5K+++47BQQElBkiHVWNCX2ZmZlKTEzUuHHjitratWtXobGsAAAAAIzt2LFjuvfee8uc7/z581qwYIHuvPNO9evX77r34VuxYoVWrFhRmWVWqxoT+ry9vWU2m+1dBgAAAACDmD17tubOnWv4nOGQF3IBAAAAgOpg9MAnEfoAAAAAwNAIfQAAAABgYIQ+AAAAADAwQh8AAAAAGBihDwAAAAAMjNAHAAAAAAZG6AMAAAAAA6sxN2c3koB6N+a2AQAAfgufhjfmtlE+HTp0+E3LpRw9IUkKbN7Y5nF1bLu6EfrsoH9ne1cAAABQc7Tpae8K4Mjmz5//m5abOnuJJOnVKaNsHhsRwzsBAAAAwMAIfQAAAABgYIQ+AAAAADAwQh8AAAAAGBihDwAAAAAMjNAHAAAAAAZG6AMAAAAAAyP0AQAAAICBEfoAAAAAwMAIfQAAAABgYIQ+AAAAADAwQh8AAAAAGBihDwAAAAAMzMXeBdyI1sdJaefts+2AelL/zvbZNgAAAIDqR+izg7TzUvIpe1cBAAAA4EbA8E4AAAAAMDBCHwAAAAAYGKEPAAAAAAyM0AcAAAAABkboAwAAAAADI/QBAAAAgIER+gAAAADAwAh9AGqcKUt6adMP79bY9QMAAFQnh745u8Vi0bx587R48WIdO3ZMbdq00RtvvKFRo0YpPDxcS5YssXeJ1eadJ5qo6/0zdUvEiKI2q9WqRSPr6M7RKxQU9lc7Vle6vGzpTJKUnyt5+Eh+rSRnhz7qUBW2x6/Vhp3/VsrxBOXkX9aW2QWSCv8f3xy7VJt/eFfHTv8kk0wK8GutXmHD1K/bmFLXN3vUF1Va77Xrn/p2pEKDe2lg+ERJ0tufTdH3/9uo0xnH5OHurS5/6KMRfWartld9SdKIue2Ufv5I0fIWi1l5BTl668ndat20k6TS35Or7Un8Ssu2PK+fT+6Xm4uHwts/oL/3f6vEmk+e+1krv5ihZwYtL3P/ypq3PLVVpureHmoWjg8A+P0c+uf38OHDtX79ek2fPl2hoaHauXOnBg8erNOnT2vChAn2Lp8r5b4AACAASURBVK/aZJ5LU1bGCTVo0cGm/cKpFOXlXFKjwM52qqx0FrN0KEZKjZesll/bXdylVrdJzTrarzZUP2/PeurXbZzy8rP1+rpRkiSzxaxZ7z2owyf/q/H3vqkOQREyyaSE5Git2/H6dUPf9ZjNBXKu5L8sJKft1aCIqUXPnZycNXXwe2rpf4syszM0Z+2jeu39oXpp2AZJ0juTDtgsv3TTc9p54P+KAp9U8ntytYTkaM1cNUATBr6jrm37SVarjqT/WGy+bXv/o8zsDIW16S1JOn4mWau3vqwJA94u9j6Ud96yaqts1b091CwcHwDw+zls6FuzZo2WL1+u6OhohYeHS5IiIiK0Z88erV+/Xp06dSpjDcaRnhIrk5OzfJveYtN+5miCvOo0ko9vMztVVjKrVTrwuZR+sPhrBbnSwa2FobCF42VVVJGwNpGSCoPMFf/56mUdOLJTi56OVz3vhkXtnYLvUKfgO0pdV3zSds1670F9OONU0fOXVg3Uw3f+Qx/vWKDcgmy9P/249h/+Viu++IcSj8XKZHJSp+A79Y9HPtRnu5boi7jlWvDEzqJ1zv3gcXl71NWYe+bZrP9i1lk9NKu5cvIva/qyfpKksfe8ruF3RRUtW9e7gf5625N6+b0HSqzXbC7QltilGtTz2TLfk6u9+/mz6tttjG4PGVDUdnVovKJH+0H6as8qvf7RKB0/m6Q125z18J3/KDH4lnfesmqrbNW9PdQsHB8A8Ps5bOiLiopS7969iwLfFUFBQXJ1dVVISIgk6b777lNKSoqcnZ3l6uqqqKgo3XFH6T8Ya6L0lFjV8w+Wi5uHTfvpowlqeJPjJacLaSUHvqsl75Ca3CK5elx/PhhTVvYFfRDzmkb2mWMT+MrjUOputW4aavM8MydDF7POavHEfXJ2ctGOfev0xvqx+nv/heryeB8VmPN14MjOEpe/0jYwfHKx12vX8tX0Rz/Sm+vHadW0w6XWtDdpqwKbtC/xtW8P/J+yci7oztBHy72P2XlZOnjsB7VreavGzu+kU+ePqqX/LRrVd67aNCv+/7xJpl8fm5zkZCr9dO2KzAsAAIzBIb/tU1NTtX//fg0cOLDYa0ePHlW7du3k7u4uSVq+fLn27dunvXv3avHixbr//vtlNpuru+QqlZ4Sq4z0JC0e42cz7d44W40Cw+xdXjFp+6SrfleWyGKWTv6vWsqBA9qTtFU5eVnq2XFIhZdNTLMNbYlpu9U5OFKPRb4oT7daMlsKNH/daD01YIn+HHK/3Fw95OXhU9RbkJgap6CAX3vMcvOzdST9x6JetGLrPxZXLCRebce+ddq4a5HG3bOgxNc/27VY4e0flLdn3XLvY+bl87JYLdoev0aTH1iutdOPKzS4l55/925lZmfYzBudsFaXcy/p6QFLFBLYQw9GTNGqL1+U2Vz8vKeKzAsAAIzDYUOfJPn7+9u0Z2dnKyYmxmZoZ926v/6QunDhgkwmk6xWa6XVYjKZKn2KiYmuUA3ph+PUpf8MDZkVbzO5uHqqUQV7+mJioqtkn66etm76Tirjn8BiMWvWC/OqvBam6p3Ke2yfuZAqL4/aFQpCVxxK3a3WAbY9fX/p9FDR833JMTKZTOre7t5iy+YX5Onnk/sVfNXyyccT5OrirmYN/lDi+hNTSw99MQkf6vWPRmrm0A0lDr08fiZZe5O2qm8Fz0/0cveRJEWGDVNgkxC5urhpcM9nVWDJ14Gfd9rM27PjEN1763iZfumxC/AL0qQHlpY4vLMi8xbb10r47KjoZx9qFo4RXE91/P4wynTFtY8deaqJNdfkukvah7I45PBOPz8/SVJiYqLuvvvuovY5c+boxIkTCg21/QE2fvx4bdq0SRcuXNC6devk4uKQu/WbZJxMUm7WebUIiZSPb1Pb9ssZDtnTl52bKYvFLCcn51LnMZlMys27XI1VwZHU92ms7NxLunj5XNEVL0uSkByj976aKYvFLN/aTfTk/Yt0/GySgn8JYVk5F3X8bJJubt61aJmLWWfk7VmvxA/CY6d+klVWtWjUtqjth58+V2Dj9nJycipaX/DVw0fTduue7uOLrWtz7DIt+XSiZg77VLfcdGuJ9X+2a7ECG7fXzc27lP2mXKWWZx3512tpMxRTKhyaWdoHvH/9luW6cmdF5wUAADWfQ/b0BQYGKiQkRFFRUVq5cqW2bt2qsWPHaunSpZJULPT9+9//VkpKitavX6/JkycrMzOz0mqxWq2VPoWH9yj39tNTYuXi7qUGzW3PFzpxaKe8fZvJq07FzocKD+9RJft09TRkzJ3XDXxS4blEC5Y/X+W1MFXvVNqxbbaYlZefo/yCPElSx6C/qGHdFpr7/jCdOn9UUmGA27FvnVZ9ObPwGD+bosUbJ+qFR9fpn2Oj9cygFUpK26PaXr5qWK+5JCkpbY+8PeupsW9g0baCmnbSyXOH9d2Pn8pisSgzO0OxB7dIkqyyyiSTsnIvSpL2pXytdV/PK+qlu3b9knQh87Ss13Rdf/zNG1qycZJeGbml1MCXX5CnL+KWq2/Xknv5rn1P8vJzlJefI6u1cFv9uo/TlrhlOpL+o8zmAn0Q/ZpcXdzVrkX3EtdXmUqqrWu3rrJYLNVyfFz7XqBmqIzvl5KOEY4PY6iO3x9Gma649rEjTzWx5ppcd0n7UBaH7BJzcnLShx9+qNGjR2vs2LHy9fXVY489pvHjx2vatGlFF3G5Vnh4uJycnPTtt98qMjKymquuGukpsWp0U5icrhl+dSLpuwoP7awujdtJyTsLr9RZ2jDPOk2k2v4lvwbj+Wr3Ks39YFjR8/tn+EqSbm7RVRMXhuvC5TPydPdRcECoBvxyL7xte/+j+279W9EQUBdnVyWm7rY5Hy/xmqGYknST/y2aMPAdLdk4SVGrB8vL3UeRYY8rrE2kWjVpr790elhj5rWXf/2bdHOLbgrwa120zmvXL0mDej6rV1YPVr45TzOHbVDHoJ5665Mn5ezkokmLImzm/XTWr39w+mb/euUV5NgMPb3ee9JnmqckadWzh+Vfv6UGhk/S5dxLmryop/IKchQU0FGzhm9SLc865XjHf5/Sajt8+2G1bNmy2rZ35b3AjY3jAwB+P5O1IhHRzh555BElJCRo3759kqTMzEydPXtWLVq0kCTt3btXPXv2VEJCgpo3b369VdnVm19Kyafss+1WDaW/3Vn127lwQtr70S/B7wqTJKtUy1fq9IDkXqvq60D1ilsrZaRWzrqWbX5ejesHqvefHi/sYZJVzmX0IKPq1G0qdR70+9ZRmccHHA/HCK6nMo6PG8XU2UskSa9OGWXz2JHVxJqlmlv3b+GQPX2liYuLU9euv567k5WVpQcffFCZmZlycXGRh4eH3nvvPYcOfDeKOo2lbo8XXskz5dvCttqNpCZ/lBrfLDm72bc+OL67u4xU1Ooh+mL3CplkUtSITXJ28rR3WQAAADVOjQl9mZmZSkxM1Lhx44raGjVqpF27dtmxKlyPey0psNuvoe9PD9u3HtQsjeq10IInvrV3GQAAADVejQl93t7ehrv/HgAAAABUNYe8eicAAAAAoHIQ+gAAAADAwAh9AAAAAGBghD4AAAAAMDBCHwAAAAAYGKEPAAAAAAyM0AcAAAAABlZj7tNnJAH1bsxtAwAAAKh+hD476N/Z3hUAAAAAuFEwvBMAYChTlvTSph/erbHrBwCgstHTBwBwSBaLRZtjl2rzD+/q2OmfZJJJAX6t1StsmPp1G1PqcrNHfVGldV27/qlvRyo0uJcGhk+UJL392RR9/7+NOp1xTB7u3uryhz4a0We2anvVlySNmNtO6eePFC1vsZiVV5Cjt57crdZNO0mStsev1Yad/1bK8QTl5F/WltkFxerYk/iVlm15Xj+f3C83Fw+Ft39Af+//Vok1nzz3s1Z+MUPPDFpe5v6VNW9p+1dX9ctcNwDAPgh9AACHY7aYNeu9B3X45H81/t431SEoQiaZlJAcrXU7Xr9u6Lvues0Fcnau3K++5LS9GhQxtei5k5Ozpg5+Ty39b1FmdobmrH1Ur70/VC8N2yBJemfSAZvll256TjsP/F9R4JMkb8966tdtnPLys/X6ulHFtpmQHK2ZqwZowsB31LVtP8lq1ZH0H4vNt23vf5SZnaGwNr0lScfPJGv11pc1YcDbxd6H8s5b2v69OX1DRd86AEA1YXgnAMDh/Oerl3XgyE7NG7dDndv0kouzq5ydXdQp+A7NGv5ZqcvFJ23XwBkNbZ7f/4KfPv7mDT36SisNiWouSdp/+FtNXvwX3ft8bd03va5mrhooSfps1xI9+a/uNuuc+8HjWrRhQrH1X8w6q37Taikj67SmL+unfs956/Pv39bwu6IUFNBRLs6uquvdQH+97UntS44usV6zuUBbYpeqT9fRNu1hbSLVs+NgNfYNLHG5dz9/Vn27jdHtIQPk5uIuN1cPm9B4RY/2g+ThVkuvfzRK+1KitWZblB6+8x8lBt/yzluR/QMAOAZ6+gAADiUr+4I+iHlNI/vMUT3vhmUvcJVDqbvVummozfPMnAxdzDqrxRP3ydnJRTv2rdMb68fq7/0XqsvjfVRgzteBIztLXP5K28DwycVer13LV9Mf/Uhvrh+nVdMOl1rT3qStCmzSvsTXvj3wf8rKuaA7Qx8t9z5m52Xp4LEf1K7lrRo7v5NOnT+qlv63aFTfuWrTrPiVwkwy/frY5CQnU+l/763IvFdcb/8AAI6Bnj4AgEPZk7RVOXlZ6tlxSIWXTUyzDW2JabvVOThSj0W+KE+3WjJbCjR/3Wg9NWCJ/hxyv9xcPeTl4aOwNpGF86fGKSjg1x6z3PxsHUn/sagXrdj6j8UVC4lX27FvnTbuWqRx9ywo8fXPdi1WePsH5e1Zt9z7mHn5vCxWi7bHr9HkB5Zr7fTjCg3upeffvVuZ2Rk280YnrNXl3Et6esAShQT20IMRU7TqyxdlNhc/R7Ai85Z3/wAAjoHQBwBwKGcupMrLo3aFgtAVh1J3q3WAbU/fXzo9VPR8X3KMTCaTure7t9iy+QV5+vnkfgVftXzy8QS5urirWYM/lLj+xNTSQ19Mwod6/aORmjl0Q4lDL4+fSdbepK3qW8HzE73cfSRJkWHDFNgkRK4ubhrc81kVWPJ14OedNvP27DhE9946XqZfeuwC/II06YGlJQ7vrMi85dk/AIDjYHgnAMCh1PdprOzcS7p4+VzRFS9LkpAco/e+mimLxSzf2k305P2LdPxskoJ/CWFZORd1/GySbm7etWiZi1ln5O1ZTyaTqdj6jp36SVZZ1aJR26K2H376XIGN28vJyalofcFXDx9N2617uo8vtq7Nscu05NOJmjnsU91y060l1v/ZrsUKbNxeNzfvUvabcpVannXkX6+lzVBMqXBoZkn7JUn+9VuW68qd5Z23PPsHAHAc9PQBABxKl5v7qFG9lpr7/jCdOn9UUmGA27FvnVZ9OVOSdOJsihZvnKgXHl2nf46N1jODVigpbY9qe/mqYb3Ci7Ukpe2Rt2c9m4uhBDXtpJPnDuu7Hz+VxWJRZnaGYg9ukSRZZZVJJmXlXpQk7Uv5Wuu+nlfUi3Xt+iXpQuZpWWW1qf/jb97Qko2T9MrILaUGovyCPH0Rt1x9u5bcy2e2mJWXn6P8gjxJUl5+jvLyc2S1Fm6rX/dx2hK3TEfSf5TZXKAPol+Tq4u72rXoXuL6KlN59g8A4Fjo6QMAOBQPNy/NH/+tVn7xgiYuDNeFy2fk6e6j4IBQDfjlXnjb9v5H9936t6IhoC7OrkpM3W1zPl7iNUMxJekm/1s0YeA7WrJxkqJWD5aXu48iwx5XWJtItWrSXn/p9LDGzGsv//o36eYW3RTg17pondeuX5IG9XxWr6werHxznmYO26COQT311idPytnJRZMWRdjM++mszKLH3+xfr7yCHJuhp1f7avcqzf1gWNHzPtM8JUmrnj0s//otNTB8ki7nXtLkRT2VV5CjoICOmjV8k2p51qnQe/1blLZ/O1ZklrIEAMDeCH0AAIfjW7uxnh6wpNTX8wpyZLGYJRXexN0qqwaGTyy6QbqkYs+v6NX5MfXq/FiJ6534wLs2z0f2mX3d9T3aa4Ye7TXDpu3L12x7/koS0WGQIjoMKvX1yLChigwbWurrJpNJQyNnamjkzDK3VdnKs38AAMfC8E4AQI1zd5eR2vTDu5qwMFyTF/dUgTnP3iUBAOCw6OkDANQ4jeq10IInvrV3GQAA1AiEPjtYHyelnbfPtgPqSf2L37sXAAAAgEER+uwg7byUfMreVQAAAAC4EXBOHwAAAAAYGKEPAAAAAAyM0AcAAAAABkboAwAAAAADI/QBAAAAgIER+gAAAADAwAh9AAAAAGBghD4AAHDDmLKklzb98G6NXT8A/BYOfXN2i8WiefPmafHixTp27JjatGmjN954Q6NGjVJ4eLiWLFli7xJhYFarZDLZuwoAQEksFos2xy7V5h/e1bHTP8kkkwL8WqtX2DD16zam1OVmj/qiSuu6dv1T345UaHAvDQyfKEl6+7Mp+v5/G3U645g83L3V5Q99NKLPbNX2qi9JGjG3ndLPHyla3mIxK68gR289uVutm3aSJG2PX6sNO/+tlOMJysm/rC2zC4rVsSfxKy3b8rx+Prlfbi4eCm//gP7e/60Saz557met/GKGnhm0vMz9K++8FotFT791m3488p3+89wx1W3atMx1A6g6Dh36hg8frvXr12v69OkKDQ3Vzp07NXjwYJ0+fVoTJkywd3nV6p0nmqjr/TN1S8SIojar1apFI+voztErFBT2VztWZxyWAun4ASl1r5R5VjI5Sb4tpeahUv3m9q6uZirrBw7AMYLrKen4GHZXlN5cP1aHT/5X4+99Ux2CImSSSQnJ0Vq34/Xrhr7rMZsL5OxcuT+NktP2alDE1KLnTk7Omjr4PbX0v0WZ2Rmas/ZRvfb+UL00bIMk6Z1JB2yWX7rpOe088H9FgU+SvD3rqV+3ccrLz9br60YV22ZCcrRmrhqgCQPfUde2/SSrVUfSfyw237a9/1FmdobC2vSWJB0/k6zVW1/WhAFvF3sfKjKvJK3b8brcXb3K+zYBqGIOO7xzzZo1Wr58uTZs2KBJkyYpIiJCzz33nLp166aCggJ16tSp7JUYROa5NGVlnFCDFh1s2i+cSlFeziU1Cuxsp8qMxZwv7Vkn/fSllHlGklWymqUzKdKeD6Sfv7d3hTXTlR846148q8VPJ+jMhVS99v5Qe5cFB8Ixgusp6fiY8NZtOnBkp+aN26HObXrJxdlVzs4u6hR8h2YN/6zUdcUnbdfAGQ1tnt//gp8+/uYNPfpKKw2JKvzr3v7D32ry4r/o3udr677pdTVz1UBJ0me7lujJf3W3WefcDx7Xog0Tiq3/YtZZ9ZtWSxlZpzV9WT/1e85bn3//tobfFaWggI5ycXZVXe8G+uttT2pfcnSJ9ZrNBdoSu1R9uo62aQ9rE6meHQersW9gicu9+/mz6tttjG4PGSA3F3e5uXrYhMYrerQfJA+3Wnr9o1HalxKtNdui9PCd/ygxxFVk3tTTifp051sa1XduifUBqH4OG/qioqLUu3dvhYeH27QHBQXJ1dVVISEhNu1LliyRyWTSRx99VJ1lVov0lFiZnJzl2/QWm/YzRxPkVaeRfHyb2akyYzkUI2UcK+EFa+F/knZIZ4+U8DquqyI/cHBj4hjB9Vx7fNz1pxFKPZ2oh/7yvOp5Nyx7BVc5lLpbrZuG2jzPzMnQxayzWjxxn1Y9e1g79q3Tiyv+qnu6jdOHL5zSf547prv+NKLE5a+0BQV0KvZ67Vq+mv7oR/Kv11KfzsrUp7MydXeXkcVq2pu0VYFN2pdY77cH/k9ZORd0Z+ij5d7H7LwsHTz2g8zmAo2d30n3v+CniQt76OCxuBLnN+nX8xhMJic5mUr/aVieeS0Wi+Z+8LhG9Z0rb8+65a4bQNVyyNCXmpqq/fv3a+DAgcVeO3r0qNq1ayd3d/eitkOHDmnZsmXq2rVrdZZZbdJTYlXPP1gubh427aePJqjhTfTyVYaCXOn4f8uYySQd210t5Rja9X7gABLHCK7vy90rJUk9Ow6p8LKJabahLTFttzoHR+qxyBfl6VZLZkuB5q8bracGLNGfQ+6Xm6uHvDx8FNYmsnD+1LiigCdJufnZOpL+Y1EvWrH1H4srFhKvtmPfOm3ctUjj7llQ4uuf7Vqs8PYPVig8ZV4+L4vVou3xazT5geVaO/24QoN76fl371ZmdobNvNEJa3U595KeHrBEIYE99GDEFK368kWZzcXPESzvvB9/s0D1ffx12x857QRwJA4b+iTJ39/fpj07O1sxMTE2QzsLCgr0+OOPa+HChTZBsLKYTKZKn2JioitUQ3pKrDLSk7R4jJ/NtHvjbDUKDKvQumJioqtkn643VeV7WVnTrR36yGIu482zSicP5du9Vkeeyjq2y/qBA8dVGZ8d5fns4xipuarjGNmxb53iDm6Wh1ut39SLdCh1t1oH2Pb0/aXTQ0XP9yXHyGQyqXu7e4stm1+Qp59P7lfwVcsnH0+Qq4u7mjX4Q4nrT0wtPfTFJHyo1z8aqZlDN5Q49PL4mWTtTdqqvhU8P9HL3UeSFBk2TIFNQuTq4qbBPZ9VgSVfB37eaTNvz45DdO+t42X6pccuwC9Ikx5YWuKQzfLMm3YmSR99/U89cd+/iu+vHX5/1NTpimsfO/JUE2uuyXWXtA9lccgLufj5+UmSEhMTdffddxe1z5kzRydOnFBo6K8foC+99JLuuusudejQodh6jCL9cJy69J+hm2+zHd6x+tk/qhE9fZXC1cWtXPO5OLvKyeQki9VSxRUZT0zCh1qwbnSpP3AAjhFcz5Xj48GIqVq99SVdvHzuuhf7SUiO0XtfzZTFYpZv7SZ68v5FOn42ScG/hLCsnIs6fjZJNzf/dZTQxawz8vasV+IPqWOnfpJVVrVo1Lao7YefPldg4/ZycnIqWl/w1cNH03brnu7ji61rc+wyLfl0omYO+1S33HRrifV/tmuxAhu3183Nu5T95lyllmcd+ddraTMUUyocmlnaD0T/+i3LdeXOsubdf/gbXcg8rZH/LDwdxfrLd+XoeSG6owJDVAFUPofs6QsMDFRISIiioqK0cuVKbd26VWPHjtXSpUslqSj0ff/999q2bZumTJlSZbVYrdZKn8LDe5R7+xknk5SbdV4tQiLl49u0aDLn5yj3ckaFe/rCw3tUyT5db6rK97Kypi0xH5fr/fOqJ5ktZrvX66hTacf25thlhT/mh32qDkERFTpm4Rgq47Pjep99HCM1X1UeI1cfHw9GTFGjei019/1hOnX+qKTCALdj3zqt+nKmJOnE2RQt3jhRLzy6Tv8cG61nBq1QUtoe1fbyVcN6hRdrSUrbI2/PejYXQwlq2kknzx3Wdz9+KovFoszsDMUe3CJJssoqk0zKyr0oSdqX8rXWfT2v6A8U165fki5knpZVv34PStLH37yhJRsn6ZWRW0oNfPkFefoibrn6di25l89sMSsvP0f5BXmSpLz8HOXl58hqLdxWv+7jtCVumY6k/yizuUAfRL8mVxd3tWvRvcT1VZbw9g9oxdRkLXo6XouejtfLwz+XJL0y8gs9Nfxlu39H1ZTpimsfO/JUE2uuyXWXtA9lcciePicnJ3344YcaPXq0xo4dK19fXz322GMaP368pk2bVnQRl+3btys5OVmtWrWSJJ08eVI//vijjhw5ookTJ9pzFypNekqsXNy91KC57fktJw7tlLdvM3nVqdhJ7ChZrfpS3WZSRqqk6/z/07RjtZVkGB9/84ZWffmiXhm5RW2aVeyPFLgxcIzgeko6PuaP/1Yrv3hBExeG68LlM/J091FwQKgG/HIvvG17/6P7bv1b0RBQF2dXJV51wRVJSrxmKKYk3eR/iyYMfEdLNk5S1OrB8nL3UWTY4wprE6lWTdrrL50e1ph57eVf/ybd3KKbAvxaF63z2vVL0qCez+qV1YOVb87TzGEb1DGop9765Ek5O7lo0iLbP258Oiuz6PE3+9crryDHZujp1b7avUpzPxhW9LzPNE9J0qpnD8u/fksNDJ+ky7mXNHlRT+UV5CgooKNmDd+kWp51yv/G/wYebl7ycPv1Ng1mS+H5fvV9/OXl4V2l2wZwfQ4Z+iQpODhY27dvt2l75JFH1LZtW3l6Fn64TZ06VVOn/nrvmx49euiJJ57QgAEDqrXWqpSeEqtGN4XJ6Zrx9SeSvmNoZyX7Q08p9j+SuUAlBr/ajaWAP1Z7WTVeeX7g4MbGMYLr+S3HR15Bjiy/nKhtsVhklVUDwycW3SBdUrHnV/Tq/Jh6dX6sxPVOfOBdm+cj+8y+7voe7TVDj/aaYdP25Wtl/2U+osMgRXQYVOrrkWFDFRk2tNTXTSaThkbO1NDImWVuqyr5129Zrv0FUPUcNvSVJC4uzrBX6CzN7Q/PK7G957CF1VyJ8Xk3kDoPlg5ulTLSfm03OUuN20rBEZKzq/3qq6n4wkdZOEZwPb/l+Li7y0hFrR6iL3avkEkmRY3YJGcnzyqoDgBqhhoT+jIzM5WYmKhx48aVOk90dHT1FQRD8mlYGPwyz0i7lhe2/XmM5MZvBQCoMRrVa6EFT3xr7zIAwGHUmNDn7e0ts7msa+oDlcPb79fHBD4AAADUZA559U4AAAAAQOUg9AEAAACAgRH6AAAAAMDACH0AAAAAYGCEPgAAAAAwMEIfAAAAABgYoQ8AAAAADIzQBwAAAAAGVmNuzm4kAfVuzG0DAAAAqH6EPjvo39neFQAAAAC4UTC8EwAAAAAMjNAHAAAAAAZG6AMAAAAAAyP0AQAAAICBEfoAAAAAwMAIfQAAAABgYIQ+AAAAADAwQh8AAAAAGBihDwAAAAAMjNAHAAAAAAZG6AMAAAAAAyP0AQAA6eXUFwAAGV9JREFUAICBEfoAAAAAwMBc7F3AjWh9nJR23j7bDqgn9e9sn20DAAAAqH6EPjtIOy8ln7J3FQAAAABuBAzvBAAAAAADI/QBAAAAgIER+gAAAADAwAh9AAAAAGBghD4AAAD8f3v3Hld1ne97/A2LOwIqiKiApiAIggLmNUNJy5kxs8J22niso8GgM9vMuVRme5/pjKN5qWY8mWfKHKfGjuYl2+1qyoQ02wlpXkolo0KUlLwBKhIL9h+cmFBYeIH1u/h6Ph7r8Vh8fz9c78X6xIN3v9/6LQA2RukDAAAAABuj9AEAAACAjVH6AAAAAMDGTF36amtrtWjRIsXGxsrPz0/9+vVTXl6e4uLilJWVZXQ8t3rhl121b8sLjdbq6uq0bFqwDuVvMCgVzKTiuFS0XfoiTyrZLdVcMDrRP614a44mz7tBdzwerAn/Hq7fr8rU8VPFRseCiTAjcIX5AIBr42V0AFemTp2q9evXa+7cuUpLS9P27ds1ceJElZWV6eGHHzY6nttUnjyis6dL1al7/0brZ44XqbqqQp17DjAoGcyg+py09z+ki//+KdwixQyXotOMyfVjo9Im619G/FaB/iGqqj6nlW8/rj+8cq+e/eV2o6PBJJgRuMJ8AMC1MW3pW716tVauXKnc3Fylp6dLkkaOHKmdO3dq/fr1Sk1NNTih+xwrypeHp0OhkX0brX9XvFsBIZ0VFBplUDIYzVkj7VwrVZZduq22pr74eXhKUSnuz/Zj0eHxDffrVCcPD08dLjtoYCKYDTMCV5gPALg2pi198+bN05gxYxoK3w9iYmLk7e2t5ORkSdKIESP0zTffKCQkRJI0ZswYzZ8/3+1529Kxonx1iOgtLx+/RutlxbsVfgNH+a5nxw40Xfh+7NBWqWtfyeHtnkzNeX/X3/Xs+hydqyqXw9NLv7h9ibGBYDrMCFxhPgDg6pmy9JWUlGjfvn2aNWvWJduKi4uVmJgoX1/fhrWFCxcqMzPTnRHd6lhRvk4fO6TlvwhrtP79hUoNuP1Rg1LBDI7sleQhqa75fZzVUtmXUkR88/u4Q0bKJGWkTNLJ8m/1Vv6LuqFLkrGBYDrMCFxhPgDg6pnyQi4lJSWSpIiIiEbr58+fV15enltP7fTw8Gj1W15e7hVlOPZVgQbd9e+a9IdPG928vP3V+QqP9OXl5bbJc3J1a8uf5fWe+4vPil0Wvh/MzPmtW/Jczmx3DI7QTwc9qMdXjFX5uZNXNL8wTmv87rjc333MiDW5a0aYD2sy4u8Pq95+cPF9M9+smNnKuZt6Di0xZekLC6s/olVYWNho/amnnlJpaanS0hpfmWLOnDlKSkrSHXfcoT179rgtpzuc/vaQLpw9pe7JtykoNLLh5vy+ShfOnVbnnjcaHREGOnv+jOrqalver+qMG9JcPqezRlXVZ3Wi/KjRUWBSzAhcYT4A4MqYsvT17NlTycnJmjdvnlatWqXNmzcrJydHK1askKRGpW/VqlU6cOCA9u7dq4kTJ+q2227T2bNnWy1LXV1dq9/S00dc9uMfK8qXl2+AOkX3a7Re+sV2tQuNUkBI+BU9n/T0EW3ynFzd2vJneb3nHjUhSR4erv8z9vCU1r233C15mprt2tpabfxwqU5VHpcklZ0u0dINMxTRoYeiOxl8zikuW2v87mjudx8zYg9tNSPMhz0Y8feHVW8/uPi+mW9WzGzl3E09h5aYsvR5enpq7dq1SkxMVE5Ojh544AGFhYVpxowZcjgcDRdxkaTo6OiGQ5v33nuvfHx8dPCgfa7odawoX51vuFGejsZvvyw99NEVn9oJ++maJHn5qv59fS728Q10W6Qm7Tjwn8pa1Fe3PxaoX/15kHx9ArQg6z05HKZ8WzEMwIzAFeYDAK6NaX9b9u7dW1u2bGm0NnnyZCUkJMjf31+SVFVVpcrKyobTQTdv3qyKigrFxMS4PW9bufnnTV+dLOOBZW5OAjPyDZRSMqVd66Saqh9t+P8XdwnrJcWNNCpdPU9PT82b+p/GhoCpMSNwhfkAgGtn2tLXlIKCAg0ePLjh6/Lycv3kJz9RdXW1PD09FRwcrE2bNik4ONjAlIB7hXSRhk2TSj+XCt+vX+vcW+qWLHWIlq7gPb4AAACwIcuUvsrKShUWFmr69OkNa+Hh4frkk08MTAWYg7efFJ36z9KXdLuxeQAAAGAelil97dq1k9PpNDoGAAAAAFiKKS/kAgAAAABoHZQ+AAAAALAxSh8AAAAA2BilDwAAAABsjNIHAAAAADZG6QMAAAAAG6P0AQAAAICNWeZz+uykW4fr87EBAAAAuB+lzwB3DTA6AQAAAIDrBad3AgAAAICNUfoAAAAAwMYofQAAAABgY5Q+AAAAALAxSh8AAAAA2BilDwAAAABsjNIHAAAAADZG6QMAAAAAG6P0AQAAAICNUfoAAAAAwMYofQAAAABgY5Q+AAAAALAxSh8AAAAA2JiX0QGuRwfflyqOG/PYQeFSXMbVfe+15i549eq+z8jMkjVzG5EZAAAA5kTpM0DFcel0idEprty15jbiObfGz9qKua04XwAAAGgbnN4JAAAAADZG6QMAAAAAG6P0AQAAAICN8Z4+k/uXJ7uq8twpeXo65OnpUEL0EM2a8BeFt48yOlqzrJhZsmZuK2YGAACAe3Gkz8S+O3NEJ8tL9dxDO/XGHyr1t0eKdPrscb3w5u+MjtYsK2aWrJnbipkBAADgfpQ+Ezt4OF8BfsGKDo+XJAUHhiqmW6pOVxr0eQ+XwYqZJWvmtmJmAAAAuB+lz8QOHs5XXOSN8vDwkNNZo/wDbytv9/9TRsoko6M1y4qZJWvmtmJmAAAAuB/v6TOxg4fzte/rbRo/t73OV1fKzztAsye8qPR+9xgdrVlWzCxZM7cVMwMAAMD9TH2kr7a2VosWLVJsbKz8/PzUr18/5eXlKS4uTllZWUbHa3NflBTosUmrtfHJ0/r7nMMKC4lU8fEDRsdyyYqZJWvmtmJmAAAAuJ+pS9/UqVP15JNPKjs7W2+99ZbuueceTZw4UUVFRUpLSzM6Xps68t0hVZw/pdjIVElSaHAXZabP1n/81/Oqra2VJO376kPNXz254Xv++Pf7dKB4hyF5JWtmlqyZ24qZpfr/kTNz6VCN/o2Hyk6XGJoF5sN8oCXMCABcHdOWvtWrV2vlypXatGmTfv3rX2vkyJGaM2eOhgwZopqaGqWmphodsU0dPJyvoICO6tyhe8Pa0MQ7dLrimPZ9vU2S1DsyTUVHd0uS9hZtlbeXr+KjBxqSV7JmZsmaua2YWZLWbX1avt4BhmaAeTEfaAkzAgBXx7Slb968eRozZozS09MbrcfExMjb21vJycmSpOrqaj388MOKjY1VUlKSbr75ZiPitrrCw/mK6ZrSaC0kMEwJPYZq657XJEk+3n7y922n8rMn9NI7j2vqT/5oRNQGVswsWTO3FTOXlBXqje3PKWvsIkNzwJyYD7SEGQGAq2fKC7mUlJRo3759mjVr1iXbiouLlZiYKF9fX0nSY489poqKCh04cEAOh0OlpaXujtsmfjFuSZPrT0/f2ujrPtGD9cy6bA1JGKcOQZ3dEa1ZVswsWTO31TLX1tZq0Zr/qayxi9TOv71hOWBOzAdawowAwLUx5ZG+kpL68/QjIiIarZ8/f155eXkNp3aeO3dOy5cv18KFC+VwOCRJXbp0adUsHh4erX7Ly8tttXwJPYbq62/36c6b/vWy9s/LyzU8txUzWzW3OzO7yr1h27PqGBShm5LubJXnBfe71tlgPuyPGYErrTEf18vtBxffN/PNipmtnLup59ASU5a+sLAwSVJhYWGj9aeeekqlpaUNF3E5dOiQQkJCtGTJEg0cOFCDBw/WmjVr3J7XSLu/zNX0O/4kL4e30VEumxUzS9bMbYbMR747pNc+WKxfjl9qWAaYF/OBljAjAHDtTFn6evbsqeTkZM2bN0+rVq3S5s2blZOToxUrVkhSQ+mrqanRkSNH1KVLF+3YsUOvvvqqHnroIe3atavVstTV1bX6LT19xDXnKjtdon9bOV4BfsEaEHfrZX9fevoIw3JbMbNVcxuRubnc+77apjOVZXpwcV/d/W9hmv5M/ZH67CXJ2rT9uWt6nnCfa50N5sP+mBG40hrzcb3cfnDxfTPfrJjZyrmbeg4tMeV7+jw9PbV27VplZ2crJydHoaGhmjJlimbMmKHHHnus4SIu0dHRkqQpU6ZIknr06KFhw4Zpx44dSklJafbft4NO7SP1v+7faHSMK2LFzJI1c5spc3q/e5QaO6rh67IzJZq5dIj++OA/FB0eb2AymAHzgZYwIwBw7UxZ+iSpd+/e2rJlS6O1yZMnKyEhQf7+/pLqTwMdM2aM3nzzTU2YMEEnTpzQjh07NHv2bCMiA2iCn0+A/Hz+eYl1Z22NJKljUIT8fdsZFQsmwXygJcwIAFw705a+phQUFGjw4MGN1p5//nlNnTpVv//971VXV6dHHnnkkn0AmEdExx56d+Hln46A6wvzgZYwIwBw5SxT+iorK1VYWKjp06c3Wu/evbvee+89g1IBAAAAgLlZpvS1a9dOTqfT6BiG++7MUc19aay+Ofa53vjflXI4jH0Jl22apcKSAsV0S9WMO55tWN9f/LGe3zRLHh6eiou6UTnjnlZV9Tk9+bcJqqo+q0C/ED0+eY18vHz1bsEq/eOTv6q21qlHJ72isJBuhuX+9uTX+tWfByk6vI+8HD5akPUP5R94W69umS9JKik7qH+9a5niogYa9jq4mgFX29Z98LS27l2nZ2Zsc1tWAAAAGM+UV+9E84IDOuqprM3qE238KaxflOzU+QuVenr6VtXUVOvg4fyGbZ3bd9fC7Pf1zIxtOl15XF+V7lX+wbcVHz1Ii3NyFRc9UAUH3tZ3Z45oT1GeFmZv1uKcXLcUPle5JSktdrQW5+RqQdY/JEk3xo/R4pxcLc7JVXj7aKXGjjL0dXD12M1tq665oC+PfuquiAAAADARSp/F+Hj7KSigg9ExJEn7i/9Lab1HS5JSY0fp828+atjWMThCPt5+kiSHp7c8PR3qGtpLVdVnJUlnz59WcGCoCg6+I2etU79ZfouWbvyVnLVtfzTXVW5J+vTLLZr13HCt++DpRuulJ4rUPqiz/H3bGfo6uHrs5ra9veNFjR4wpa2jAQAAwIQofbhqledPK8A3WJIU6BeiyvOnL9mn6OgenTlbpu6dE9QtLFb7v/lI0xYlqrCkQAndh+pU5THVOKu1MHuzfL0DtP2z1w3N3TG4i176XaEWZW/Rzi/eU9HRPQ3btu1dr2F972zzfK2txvm9dn+Zq5SYDKOjAAAAwACUPotYk7tQs5eN0PFTxUZHaRDoF6JzF8olSWcvlKudf/tG28vPndTSjb/U7AkvSpLeLfirBifcrhd+/ZkGxf9Mm3e+rEC/ECX3TJck9Y/JUPHx/Ybm9vHylb9PoBwOLw3uM1ZfH9vXsO2j/W9oaMK4Ns/X2t775G/KSJlkdAwAAAAYhNJnEfeM+E39e8o6RBsdpUFC9yHa9cVmSdKuL95r9D4yp7NG81f/XFljF6ljcIQkqU51CgroKEkKDgzT2aozSug+VEWl9UfTvjz6qbp0vMHQ3OeqKhruf/b1h+oS2kuSdLL8W3k7fBQcGNrm+Vrb4bKDeuOjZXr0L2P0zbHPtHHbn42OBAAAADei9FlMjfN7/Xb5KBWV7tYjL9ym/cUfG5YlNjJV3t5+mvXccHl6OhQfPVAny7/VK5v/oLw9a1V4OF9/efO3mr1shD7/+iNlpExS3u41mr1shN7f9YoyUu9TTLf+8vX21+xlI1R4OF/DkzINzb33q62a/kyaZi4dqtCQbuoTPUiStP2z1zUk8Y6Gf8PI16Gpx/4hf1PbHvzZAs1/8B398cG31b1zosbf9Cu3ZQUAAIDxLPORDajn5fDWU9nm+VzCH3/cgVR/AZf7bpkjScpImXjJ/vMffOeStezbF7VNOBdc5R7U56eX7D92SHajr418HZp77B/yu8rFxzUAAABcfzjSBwAAAAA2xpE+AwSFW/OxjcptxczX+thW/Fm3xvfDvFrjtWU+7I0ZgSu8toCxKH0GiLPolfOtmNuKmSVyw56YD7SEGQGAtsHpnQAAAABgY5Q+AAAAALAxSh8AAAAA2BilDwAAAABsjNIHAAAAADZG6QMAAAAAG6P0AQAAAICNUfoAAAAAwMYofQAAAABgY5Q+AAAAALAxSh8AAAAA2BilDwAAAABsjNIHAAAAADZG6QMAAAAAG6P0ucmtt96q/v37KykpSZmZmSovLzc6EgAAAGwkNzdXiYmJiomJ0bRp0+R0Oo2O1KKZM2cqMjJSXl5eRke5bIcPH9Ytt9yiPn36KDExUY8++qjRkVpE6XOTtWvX6tNPP9XevXsVGRmpJUuWGB0JAAAANlFbW6tp06Zp7dq1OnTokMrLy/Xyyy8bHatFEyZMUEFBgdExroiXl5cWLFig/fv3a9euXdq2bZtef/11o2O5ROlzk5CQEEn1/0FWVVXJw8PD4EQAAACwi/z8fHXt2lUJCQmSpKlTp2rdunUGp2rZTTfdpIiICKNjXJEuXbpowIABkiQfHx+lpKSouLjY4FSuUfrc6M4771R4eLgOHjyo2bNnGx0HAAAANlFSUqKoqKiGr6Ojo3X48GEDE10fTp48qY0bN2r06NFGR3HJOifP2sCGDRtUXV2tqVOn6rXXXtP9999vdCQAAAAYaPfnh5T78e5L1p99ad0l9wP9/XT/hDHycjgu2b+urq7tQl6k+vsavbT2LVVdqG603lRmSbplWKr69r7Bbfma8+En+1Sw5+Al603l7tg+SD8fP9rl2XnV1dXKzMzUzJkzFR8f3/qBWxFH+tzMx8dH9957rzZs2GB0FAAAABgsIbaHzlddUOnxEyo9fqJh/eL7pcdPKLF3jyYLnyRFRUU1OrJXXFysyMjINsns4+2l+F7Rl5X5+5oaxfeKbpMcV6p/QoxOnam4rNwpibEuC5/T6dSkSZPUv39/S5zBR+lzg4qKCpWWlkqqf0/fpk2blJiYaHAqAAAAGM3b20s/HTGoxf06h3XQwP59mt0+YMAAlZSU6PPPP5ckvfjii7rrrrtaLefFhqX1VWj74Bb3G5sxpNmi6m6B/n4adVNai/v1jO6qxNgeLvfJyspSUFCQFi9e3Erp2halzw0qKio0btw4JScnKzk5WTU1NXr88ceNjgUAAAATSIrvqR6Rri9mMjZjiByezf/p7nA49MILLygzM1O9evVSu3btNHny5NaO2sDLy6Gfjhzscp/YHpGK6xnlcp/s7GxFRkbK6XQqMjJSM2bMaM2YlxiSkqhOHUOa3e4haewtQ1we5fvwww+1YsUKFRQUKCUlRf3799ef/vSnNkjbejzq3HkCMC5RV1fHlTwBAACucyXflun//HWDmvrDPL5XtO7PHOP2TC2pq6vTX159U0XFRy/Z5uHhoYceuFudO3U0IJlrB74s1srX3m5y28B+8bprzM1uTtT2ONJnsLyPd+uVje+qxgIfngkAAIC2ERnRSal9e1+y7unpoZ+1cETNKB4eHs0eFRvUv48pC58kxfWMUu8bLn2/o6+Pt0YPH2BAorZn+dK3adMmjRo1Su3bt1dAQICSkpK0YsUKo2NdlgsXqvXBx7v1fY3TNOc6AwAAwBi33XyjfLwbX1x/aGpfdQptb1CilnUND9WNyXGN1vx8fTT6JvOWJw+P+iLteVFZzRiaqqDAAINStS1Ll765c+dq/PjxioqK0ooVK7Ru3TqNGzdOJ0+eNDraZdm+8zOdq7qgW4alGh0FAAAABgsOCtSIwf0bvg7w91WGBf5OvHX4jfL18W74etSwNAUG+BmYqGWdO3XUoJR/XhinY/sgDUvra2CitmXZ9/StX79ed999t1auXKkpU6a02eM8suD/ttm/DQAAAABXa/7vsi5rP8se6Zs7d65GjhzZpoUPAAAAAKzOkkf6iouL1b17dy1fvlxZWZfXbs3kwoVqLXh+taK7dTbllZgAAABgnLq6OpWdOK3wsA5GR7kix747pfDQ9pa6Mr3TWatTZyoU5uJjHOzAq+VdzOfo0frLwnbr1q3NH6stT+888GUxp48CAAAAuCq2Pr2za9eukqR9+/YZnAQAAAAAzM2Sp3dKUkZGhgoKCvTEE08oLS1NJ0+e1Lvvvqv77rtPw4cPNzpes7Z8tEvvfJCvGf9jvKK6hBsdBwAAAIDNWfL0Tklas2aNnnjiCS1dulSlpaUKDQ3VsGHDlJCQYHS0Zl24UK2tO/Yovlc0hQ8AAACAW1j2SJ8VlR4/oZc3vKt7x2VQ+gAAAAC4BaXPzWpra+Xpacm3UgIAAACwIEofAAAAANgYh5wAAAAAwMYofQAAAABgY5Q+AAAAALAxSh8AAAAA2BilDwAAAABsjNIHAAAAADZG6QMAAAAAG6P0AQAAAICNUfoAAAAAwMYofQAAAABgY5Q+AAAAALAxSh8AAAAA2BilDwAAAABsjNIHAAAAADZG6QMAAAAAG6P0AQAAAICNUfoAAAAAwMYofQAAAABgY5Q+AAAAALAxSh8AAAAA2BilDwAAAABsjNIHAAAAADZG6QMAAAAAG6P0AQAAAICNUfoAAAAAwMYofQAAAABgY5Q+AAAAALCx/wYg2uT8kSZKjAAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 1164.18x505.68 with 1 Axes>"
       ]
      },
-     "execution_count": 202,
+     "execution_count": 124,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2344,7 +2875,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 181,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2355,14 +2886,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 191,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'000': 52, '001': 7, '010': 14, '011': 13, '100': 455, '101': 4, '110': 4, '111': 475}\n"
+      "{'000': 44, '001': 6, '010': 14, '011': 12, '100': 456, '101': 8, '110': 5, '111': 479}\n"
      ]
     }
    ],
@@ -2374,17 +2905,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 192,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 504x360 with 1 Axes>"
       ]
      },
-     "execution_count": 192,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2393,23 +2924,44 @@
     "plot_histogram(counts)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Accumulate states with same outcome and get most likely"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "accumulated = {}\n",
+    "for state, count in counts.items():\n",
+    "    y = int(state[::-1], 2)\n",
+    "    if y > M / 2:\n",
+    "        y = M - y\n",
+    "    accumulated[y] = accumulated.get(y, 0) + count"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 193,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "111\n"
+      "1\n"
      ]
     }
    ],
    "source": [
     "most_likely = ''\n",
     "max_count = 0\n",
-    "for state, count in counts.items():\n",
+    "for state, count in accumulated.items():\n",
     "    if count > max_count:\n",
     "        most_likely = state\n",
     "        max_count = count\n",
@@ -2419,16 +2971,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 194,
+   "execution_count": 130,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x360 with 1 Axes>"
+      ]
+     },
+     "execution_count": 130,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "M = 8"
+    "plot_histogram(accumulated)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 196,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
@@ -2437,18 +3001,18 @@
        "0.1464466094067263"
       ]
      },
-     "execution_count": 196,
+     "execution_count": 131,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "np.sin(np.pi * int(most_likely[::-1], 2) / 2 ** num_eval_qubits) ** 2"
+    "np.sin(np.pi * most_likely / 2 ** num_eval_qubits) ** 2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 139,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -2460,7 +3024,7 @@
     }
    ],
    "source": [
-    "print([np.sin(np.pi * i / M) ** 2 for i in range(int(M/2 + 1))])"
+    "print([np.sin(np.pi * i / 2 ** num_eval_qubits) ** 2 for i in range(2 ** (num_eval_qubits - 1) + 1)])"
    ]
   },
   {
diff --git a/Faster Amplitude Estimation.ipynb b/Faster Amplitude Estimation.ipynb
new file mode 100644
index 0000000..c999154
--- /dev/null
+++ b/Faster Amplitude Estimation.ipynb	
@@ -0,0 +1,782 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from qiskit import Aer, QuantumCircuit\n",
+    "from qiskit.aqua.utils import CircuitFactory\n",
+    "from qiskit.aqua.algorithms import AmplitudeEstimation, FasterAmplitudeEstimation\n",
+    "from qiskit.aqua.components.uncertainty_models import NormalDistribution\n",
+    "from qiskit.quantum_info import Statevector\n",
+    "\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from scipy.stats import norm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class BernoulliAFactory(CircuitFactory):\n",
+    "    r\"\"\"Circuit Factory representing the operator A in a Bernoulli problem.\n",
+    "\n",
+    "    Given a probability $p$, the operator A prepares the state $\\sqrt{1 - p}|0> + \\sqrt{p}|1>$.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, probability=0.5):\n",
+    "        #\n",
+    "        super().__init__(1)\n",
+    "        self._probability = probability\n",
+    "        self.i_state = 0\n",
+    "        self._theta_p = 2 * np.arcsin(np.sqrt(probability))\n",
+    "\n",
+    "    def build(self, qc, q, q_ancillas=None, params=None):\n",
+    "        # A is a rotation of angle theta_p around the Y-axis\n",
+    "        qc.ry(self._theta_p, q[self.i_state])\n",
+    "\n",
+    "    def value_to_estimation(self, value):\n",
+    "        return value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class BernoulliQFactory(CircuitFactory):\n",
+    "    r\"\"\"Circuit Factory representing the operator A in a Bernoulli problem.\n",
+    "\n",
+    "    Given a probability $p$, the operator A prepares the state $\\sqrt{1 - p}|0> + \\sqrt{p}|1>$.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, probability=0.5):\n",
+    "        #\n",
+    "        super().__init__(2)\n",
+    "        self._probability = probability\n",
+    "        self.i_state = 0\n",
+    "        self._theta_p = 2 * np.arcsin(np.sqrt(probability))\n",
+    "\n",
+    "    def build(self, qc, q, q_ancillas=None, params=None):\n",
+    "        # A is a rotation of angle theta_p around the Y-axis\n",
+    "        qc.ry(2 * self._theta_p, q[self.i_state])\n",
+    "        \n",
+    "    def build_power(self, qc, q, power, q_ancillas=None):\n",
+    "        qc.ry(2 * power * self._theta_p, q[self.i_state])        \n",
+    "\n",
+    "    def value_to_estimation(self, value):\n",
+    "        return value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit.circuit.library import RYGate\n",
+    "\n",
+    "class BernoulliS11QFactory(CircuitFactory):\n",
+    "    r\"\"\"Circuit Factory representing the operator A in a Bernoulli problem.\n",
+    "\n",
+    "    Given a probability $p$, the operator A prepares the state $\\sqrt{1 - p}|0> + \\sqrt{p}|1>$.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, probability=0.5):\n",
+    "        #\n",
+    "        super().__init__(2)\n",
+    "        self._probability = probability\n",
+    "        self._theta_p = 2 * np.arcsin(np.sqrt(probability))\n",
+    "        \n",
+    "    def required_ancillas(self):\n",
+    "        return 1\n",
+    "\n",
+    "    def build(self, qc, q, q_ancillas=None, params=None):\n",
+    "        # A is a rotation of angle theta_p around the Y-axis\n",
+    "        self.build_power(qc, q, 1, q_ancillas)\n",
+    "        \n",
+    "    def build_power(self, qc, q, power, q_ancillas=None):\n",
+    "        qc.ccx(0, 1, 2)\n",
+    "        #qc.cry(2 * power * self._theta_p, 2, 0)\n",
+    "        qc.cry(2 * power * self._theta_p, 2, 1)\n",
+    "        qc.ccx(0, 1, 2)\n",
+    "#         qc.append(RYGate(2 * power * self._theta_p).control(2), [0, 1, 2])\n",
+    "        \n",
+    "\n",
+    "    def value_to_estimation(self, value):\n",
+    "        return value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.030975999999999997\n"
+     ]
+    }
+   ],
+   "source": [
+    "n = 2\n",
+    "#X = NormalDistribution(n, mu=1, sigma=1, low=0, high=2)\n",
+    "y = 0\n",
+    "# a_factory = SingleRV(X, y, 1)\n",
+    "p = (0.176)**2\n",
+    "a_factory = BernoulliAFactory(p)\n",
+    "q_factory = BernoulliQFactory(p)\n",
+    "backend = Aer.get_backend('qasm_simulator')\n",
+    "\n",
+    "N = [1000, 1000]\n",
+    "delta = 1 - 0.01\n",
+    "maxiter = 3\n",
+    "\n",
+    "print(p)\n",
+    "a = np.sqrt(p)\n",
+    "\n",
+    "faster = FasterAmplitudeEstimation(N, delta, maxiter, a_factory, q_factory, i_objective=[0])\n",
+    "#faster._x_factory = a_factory\n",
+    "# faster = FasterAmplitudeEstimation(N, delta, maxiter, a_factory, q_factory)\n",
+    "#faster._x_factory = None\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0308891684730608"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result = faster.run(backend)\n",
+    "result['estimation']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.03224264518583157"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0576"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.05763189814265775"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.arcsin(p)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reps = 1000\n",
+    "results = []\n",
+    "maxiters = [2]\n",
+    "for maxiter in maxiters:\n",
+    "    faster = FasterAmplitudeEstimation(N, delta, maxiter, a_factory)\n",
+    "    inside = 0\n",
+    "    for _ in range(reps):\n",
+    "        result = faster.run(backend)\n",
+    "        if result['value_ci'][0] <= p and p <= result['value_ci'][1]:\n",
+    "            inside += 1\n",
+    "    print(N, delta, maxiter, 'confidence:', inside/reps)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 -1.9153754392602926\n",
+      "[0.38323556853974877, 1.6793026100265211] True\n",
+      "1 -1.9457796655360402\n",
+      "1 1.8983435390644192\n",
+      "3 -3.070674878630545\n",
+      "5 -0.1305617798610741\n",
+      "10 -0.545211727683385\n",
+      "[0.8698663142448932, 0.9603221347121461] True\n",
+      "1 -1.9656214028218646\n",
+      "1 1.9478723981115798\n",
+      "3 -2.891106491815511\n",
+      "5 0.09990989706871115\n",
+      "10 -0.39667283080928845\n",
+      "20 -1.0973323672040916\n",
+      "40 -2.684324050745464\n",
+      "79 0.44034012848574555\n",
+      "158 0.45192656221409666\n",
+      "[0.9187078852956052, 0.9244233120614351] True\n",
+      "1 -1.9238026095357599\n",
+      "1 1.948718623201206\n",
+      "3 -2.9235098479769226\n",
+      "5 -0.02042991137445622\n",
+      "10 -0.47152712307554084\n",
+      "20 -1.078074129969064\n",
+      "40 -2.6933611551201544\n",
+      "79 0.4125836506259257\n",
+      "158 0.37548068648157323\n",
+      "316 0.39884177383933533\n",
+      "632 0.3960235154840463\n",
+      "1264 0.32913768458642023\n",
+      "2528 0.2215186869294156\n",
+      "[0.9214145854385846, 0.9217719685768764] True\n",
+      "1 -1.9129365048968887\n",
+      "1 1.9495315943515537\n",
+      "3 -2.866566049168211\n",
+      "5 0.04304300967375341\n",
+      "10 -0.3263590248237056\n",
+      "20 -1.0937015806828392\n",
+      "40 -2.6547967721233765\n",
+      "79 0.46000874276787235\n",
+      "158 0.4485584554455757\n",
+      "316 0.451125120848994\n",
+      "632 0.4233131158523193\n",
+      "1264 0.34212438320550365\n",
+      "2528 0.21919574776989334\n"
+     ]
+    }
+   ],
+   "source": [
+    "maxiters = list(range(3, 20, 4))\n",
+    "results = []\n",
+    "for maxiter in maxiters:\n",
+    "    faster = FasterAmplitudeEstimation(N, delta, maxiter, a_factory)\n",
+    "    faster._x_factory = None\n",
+    "    result = faster.run(backend)\n",
+    "    results.append(result)\n",
+    "    print(result['value_ci'], result['value_ci'][0] <= p and p <= result['value_ci'][1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "theta 0.05632978476122883 sin(theta) 0.0563 a/4 0.05\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('theta', np.arcsin(0.0563), 'sin(theta)', 0.0563, 'a/4', 0.2 / 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = np.sqrt(0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.8660254037844386,\n",
+       " 0.22360679774997896,\n",
+       " 0.43301270189221935,\n",
+       " 0.11180339887498948)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sqrt(15 * (1 - a**2)) / 4, np.sqrt(1 - a**2) / 4, np.sqrt(15) * a /4, a / 4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.11180339887498948\n",
+      "1 0.32982002668121896\n",
+      "2 0.5313456531533876\n",
+      "3 0.7063039969678867\n"
+     ]
+    }
+   ],
+   "source": [
+    "p = 0.2\n",
+    "a = np.sqrt(p)\n",
+    "theta = np.arcsin(a / 4)\n",
+    "print(np.sin(theta))\n",
+    "for m in range(1, 4):\n",
+    "    print(m, np.sin((2*m + 1) * theta))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "zero = Statevector.from_int(0, dims=(2,2))\n",
+    "three = Statevector.from_int(3, dims=(2,2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Statevector([0.       +0.j, 0.       +0.j, 0.       +0.j, 0.1118034+0.j],\n",
+       "            dims=(2, 2))"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "circuit = QuantumCircuit(2)\n",
+    "circuit.ry(2 * np.arcsin(np.sqrt(0.2)), 0)\n",
+    "circuit.ry(2 * np.arcsin(0.25), 1)\n",
+    "Statevector.from_int(0, dims=(2,2)).evolve(circuit) @ three"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Statevector([0.8660254+0.j, 0.4330127+0.j, 0.2236068+0.j, 0.1118034+0.j,\n",
+       "             0.       +0.j, 0.       +0.j, 0.       +0.j, 0.       +0.j],\n",
+       "            dims=(2, 2, 2))"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "circuit = QuantumCircuit(3)\n",
+    "circuit.ry(2 * np.arcsin(np.sqrt(0.2)), 0)\n",
+    "circuit.ry(2 * np.arcsin(0.25), 1)\n",
+    "Statevector.from_int(0, dims=(2,2,2)).evolve(circuit)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Statevector([0.        +0.j, 0.        +0.j, 0.        +0.j,\n",
+       "             0.06708204+0.j, 0.        +0.j, 0.        +0.j,\n",
+       "             0.        +0.j, 0.        +0.j],\n",
+       "            dims=(2, 2, 2))"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zero = Statevector.from_int(0, dims=(2,2,2))\n",
+    "three = Statevector.from_int(3, dims=(2,2,2))\n",
+    "\n",
+    "\n",
+    "q = QuantumCircuit(3)\n",
+    "q.ccx(0, 1, 2)\n",
+    "q.ry(2 * 2 * np.arcsin(np.sqrt(p)), 2)\n",
+    "# q.cry(2 * 2 * np.arcsin(np.sqrt(0.2)), 2, 1)\n",
+    "q.ccx(0, 1, 2)\n",
+    "zero.evolve(circuit).evolve(q) @ three"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Statevector([0.6846532+0.j, 0.6846532+0.j, 0.1767767+0.j, 0.1767767+0.j,\n",
+       "             0.       +0.j, 0.       +0.j, 0.       +0.j, 0.       +0.j],\n",
+       "            dims=(2, 2, 2))"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "circuit = faster.construct_circuit(0)\n",
+    "circuit.draw()\n",
+    "zero = Statevector.from_int(0, dims=(2,2,2))\n",
+    "three = Statevector.from_int(3, dims=(2,2,2))\n",
+    "zero.evolve(circuit) \n",
+    "#faster.run(backend)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">         ┌──────────┐       ┌───────────┐  ┌───┐     ┌───┐  ┌──────────┐      »\n",
+       "  q_0: ──┤ RY(pi/2) ├───■───┤ RY(-pi/2) ├──┤ X ├──■──┤ X ├──┤ RY(pi/2) ├───■──»\n",
+       "       ┌─┴──────────┴┐  │  ┌┴───────────┴─┐├───┤  │  ├───┤┌─┴──────────┴┐  │  »\n",
+       "  q_1: ┤ RY(0.50536) ├──■──┤ RY(-0.50536) ├┤ X ├──■──┤ X ├┤ RY(0.50536) ├──■──»\n",
+       "       └────┬───┬────┘┌───┐└──────────────┘└───┘┌─┴─┐├───┤└────┬───┬────┘┌───┐»\n",
+       "aux_0: ─────┤ X ├─────┤ H ├─────────────────────┤ X ├┤ H ├─────┤ X ├─────┤ X ├»\n",
+       "            └───┘     └───┘                     └───┘└───┘     └───┘     └───┘»\n",
+       "«        ┌───────────┐  ┌───┐     ┌───┐  ┌──────────┐       ┌───────────┐  »\n",
+       "«  q_0: ─┤ RY(-pi/2) ├──┤ X ├──■──┤ X ├──┤ RY(pi/2) ├───■───┤ RY(-pi/2) ├──»\n",
+       "«       ┌┴───────────┴─┐├───┤  │  ├───┤┌─┴──────────┴┐  │  ┌┴───────────┴─┐»\n",
+       "«  q_1: ┤ RY(-0.50536) ├┤ X ├──■──┤ X ├┤ RY(0.50536) ├──■──┤ RY(-0.50536) ├»\n",
+       "«       └────┬───┬─────┘└───┘┌─┴─┐├───┤└────┬───┬────┘┌───┐└────┬───┬─────┘»\n",
+       "«aux_0: ─────┤ H ├───────────┤ X ├┤ H ├─────┤ X ├─────┤ X ├─────┤ H ├──────»\n",
+       "«            └───┘           └───┘└───┘     └───┘     └───┘     └───┘      »\n",
+       "«       ┌───┐     ┌───┐  ┌──────────┐       ┌───────────┐  ┌───┐     ┌───┐»\n",
+       "«  q_0: ┤ X ├──■──┤ X ├──┤ RY(pi/2) ├───■───┤ RY(-pi/2) ├──┤ X ├──■──┤ X ├»\n",
+       "«       ├───┤  │  ├───┤┌─┴──────────┴┐  │  ┌┴───────────┴─┐├───┤  │  ├───┤»\n",
+       "«  q_1: ┤ X ├──■──┤ X ├┤ RY(0.50536) ├──■──┤ RY(-0.50536) ├┤ X ├──■──┤ X ├»\n",
+       "«       └───┘┌─┴─┐├───┤└────┬───┬────┘┌───┐└────┬───┬─────┘└───┘┌─┴─┐├───┤»\n",
+       "«aux_0: ─────┤ X ├┤ H ├─────┤ X ├─────┤ X ├─────┤ H ├───────────┤ X ├┤ H ├»\n",
+       "«            └───┘└───┘     └───┘     └───┘     └───┘           └───┘└───┘»\n",
+       "«         ┌──────────┐ \n",
+       "«  q_0: ──┤ RY(pi/2) ├─\n",
+       "«       ┌─┴──────────┴┐\n",
+       "«  q_1: ┤ RY(0.50536) ├\n",
+       "«       └────┬───┬────┘\n",
+       "«aux_0: ─────┤ X ├─────\n",
+       "«            └───┘     </pre>"
+      ],
+      "text/plain": [
+       "         ┌──────────┐       ┌───────────┐  ┌───┐     ┌───┐  ┌──────────┐      »\n",
+       "  q_0: ──┤ RY(pi/2) ├───■───┤ RY(-pi/2) ├──┤ X ├──■──┤ X ├──┤ RY(pi/2) ├───■──»\n",
+       "       ┌─┴──────────┴┐  │  ┌┴───────────┴─┐├───┤  │  ├───┤┌─┴──────────┴┐  │  »\n",
+       "  q_1: ┤ RY(0.50536) ├──■──┤ RY(-0.50536) ├┤ X ├──■──┤ X ├┤ RY(0.50536) ├──■──»\n",
+       "       └────┬───┬────┘┌───┐└──────────────┘└───┘┌─┴─┐├───┤└────┬───┬────┘┌───┐»\n",
+       "aux_0: ─────┤ X ├─────┤ H ├─────────────────────┤ X ├┤ H ├─────┤ X ├─────┤ X ├»\n",
+       "            └───┘     └───┘                     └───┘└───┘     └───┘     └───┘»\n",
+       "«        ┌───────────┐  ┌───┐     ┌───┐  ┌──────────┐       ┌───────────┐  »\n",
+       "«  q_0: ─┤ RY(-pi/2) ├──┤ X ├──■──┤ X ├──┤ RY(pi/2) ├───■───┤ RY(-pi/2) ├──»\n",
+       "«       ┌┴───────────┴─┐├───┤  │  ├───┤┌─┴──────────┴┐  │  ┌┴───────────┴─┐»\n",
+       "«  q_1: ┤ RY(-0.50536) ├┤ X ├──■──┤ X ├┤ RY(0.50536) ├──■──┤ RY(-0.50536) ├»\n",
+       "«       └────┬───┬─────┘└───┘┌─┴─┐├───┤└────┬───┬────┘┌───┐└────┬───┬─────┘»\n",
+       "«aux_0: ─────┤ H ├───────────┤ X ├┤ H ├─────┤ X ├─────┤ X ├─────┤ H ├──────»\n",
+       "«            └───┘           └───┘└───┘     └───┘     └───┘     └───┘      »\n",
+       "«       ┌───┐     ┌───┐  ┌──────────┐       ┌───────────┐  ┌───┐     ┌───┐»\n",
+       "«  q_0: ┤ X ├──■──┤ X ├──┤ RY(pi/2) ├───■───┤ RY(-pi/2) ├──┤ X ├──■──┤ X ├»\n",
+       "«       ├───┤  │  ├───┤┌─┴──────────┴┐  │  ┌┴───────────┴─┐├───┤  │  ├───┤»\n",
+       "«  q_1: ┤ X ├──■──┤ X ├┤ RY(0.50536) ├──■──┤ RY(-0.50536) ├┤ X ├──■──┤ X ├»\n",
+       "«       └───┘┌─┴─┐├───┤└────┬───┬────┘┌───┐└────┬───┬─────┘└───┘┌─┴─┐├───┤»\n",
+       "«aux_0: ─────┤ X ├┤ H ├─────┤ X ├─────┤ X ├─────┤ H ├───────────┤ X ├┤ H ├»\n",
+       "«            └───┘└───┘     └───┘     └───┘     └───┘           └───┘└───┘»\n",
+       "«         ┌──────────┐ \n",
+       "«  q_0: ──┤ RY(pi/2) ├─\n",
+       "«       ┌─┴──────────┴┐\n",
+       "«  q_1: ┤ RY(0.50536) ├\n",
+       "«       └────┬───┬────┘\n",
+       "«aux_0: ─────┤ X ├─────\n",
+       "«            └───┘     "
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m = 4\n",
+    "circuit = faster.construct_circuit(m)\n",
+    "circuit.draw()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Statevector([0.        +0.j, 0.        +0.j, 0.        +0.j,\n",
+       "             0.99959107+0.j, 0.        +0.j, 0.        +0.j,\n",
+       "             0.        +0.j, 0.        +0.j],\n",
+       "            dims=(2, 2, 2))"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zero.evolve(circuit) @ three "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sin(2m + 1)theta 0.8459471409339914\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('sin(2m + 1)theta', np.sin((2 * m + 1) * np.arcsin(a / 4)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "widths = [np.diff(result['value_ci'])[0] / 2 for result in results]\n",
+    "oracle_calls = [result['num_oracle_calls'] for result in results]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "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": [
+    "plt.loglog(oracle_calls, widths)\n",
+    "plt.grid()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<qiskit.circuit.instructionset.InstructionSet at 0x134521150>"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from qiskit import transpile\n",
+    "from qiskit.circuit import QuantumCircuit, Parameter\n",
+    "circuit = QuantumCircuit(3)\n",
+    "a, t = Parameter('alpha'), Parameter('theta')\n",
+    "\n",
+    "circuit.ry(2 * t, 0)\n",
+    "circuit.ry(2 * a, 1)\n",
+    "circuit.x([0, 1, 2])\n",
+    "circuit.h(2)\n",
+    "circuit.ccx(0, 1, 2)\n",
+    "circuit.h(2)\n",
+    "circuit.x([0, 1, 2])\n",
+    "circuit.ry(-2 * t, 0)\n",
+    "circuit.ry(-2 * a, 1)\n",
+    "circuit.cz(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transpiled = transpile(circuit, optimization_level=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">     ┌─────────────┐┌───┐     ┌───┐┌──────────────┐   \n",
+       "q_0: ┤ RY(2*theta) ├┤ X ├──■──┤ X ├┤ RY(-2*theta) ├─■─\n",
+       "     ├─────────────┤├───┤  │  ├───┤├──────────────┤ │ \n",
+       "q_1: ┤ RY(2*alpha) ├┤ X ├──■──┤ X ├┤ RY(-2*alpha) ├─■─\n",
+       "     └────┬───┬────┘├───┤┌─┴─┐├───┤└────┬───┬─────┘   \n",
+       "q_2: ─────┤ X ├─────┤ H ├┤ X ├┤ H ├─────┤ X ├─────────\n",
+       "          └───┘     └───┘└───┘└───┘     └───┘         </pre>"
+      ],
+      "text/plain": [
+       "     ┌─────────────┐┌───┐     ┌───┐┌──────────────┐   \n",
+       "q_0: ┤ RY(2*theta) ├┤ X ├──■──┤ X ├┤ RY(-2*theta) ├─■─\n",
+       "     ├─────────────┤├───┤  │  ├───┤├──────────────┤ │ \n",
+       "q_1: ┤ RY(2*alpha) ├┤ X ├──■──┤ X ├┤ RY(-2*alpha) ├─■─\n",
+       "     └────┬───┬────┘├───┤┌─┴─┐├───┤└────┬───┬─────┘   \n",
+       "q_2: ─────┤ X ├─────┤ H ├┤ X ├┤ H ├─────┤ X ├─────────\n",
+       "          └───┘     └───┘└───┘└───┘     └───┘         "
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "transpiled.draw()"
+   ]
+  },
+  {
+   "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
+}
diff --git a/Grover op.ipynb b/Grover op.ipynb
new file mode 100644
index 0000000..6b337b4
--- /dev/null
+++ b/Grover op.ipynb	
@@ -0,0 +1,1039 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "from qiskit import Aer\n",
+    "from qiskit.circuit import QuantumCircuit\n",
+    "from qiskit.quantum_info import Operator, Statevector\n",
+    "from qiskit.aqua.algorithms import IterativeAmplitudeEstimation as IQAE\n",
+    "from qiskit.aqua.algorithms import AmplitudeEstimation as QAE\n",
+    "\n",
+    "from qiskit.aqua.components.uncertainty_problems.grover_operator import GroverOperator\n",
+    "from qiskit.aqua.components.uncertainty_problems.bit_oracle import BitOracle\n",
+    "from qiskit.aqua.utils import CircuitFactory\n",
+    "from qiskit.circuit.library import LinearPauliRotations\n",
+    "from qiskit.aqua.components.uncertainty_models import NormalDistribution"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating a bit-oracle \n",
+    "\n",
+    "This oracle is used to mark objective qubits."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">         ┌───┐          ┌───┐     \n",
+       "state_0: ┤ X ├───────■──┤ X ├─────\n",
+       "         └───┘       │  └───┘     \n",
+       "state_1: ────────────┼────────────\n",
+       "         ┌───┐┌───┐┌─┴─┐┌───┐┌───┐\n",
+       "state_2: ┤ X ├┤ H ├┤ X ├┤ H ├┤ X ├\n",
+       "         └───┘└───┘└───┘└───┘└───┘</pre>"
+      ],
+      "text/plain": [
+       "         ┌───┐          ┌───┐     \n",
+       "state_0: ┤ X ├───────■──┤ X ├─────\n",
+       "         └───┘       │  └───┘     \n",
+       "state_1: ────────────┼────────────\n",
+       "         ┌───┐┌───┐┌─┴─┐┌───┐┌───┐\n",
+       "state_2: ┤ X ├┤ H ├┤ X ├┤ H ├┤ X ├\n",
+       "         └───┘└───┘└───┘└───┘└───┘"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s_f = BitOracle(3, [0, 2])  # 3 qubits, with objectives 0 and 2\n",
+    "s_f.draw()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Grover operator\n",
+    "\n",
+    "Constructs the Grover operator based on the oracle $\\mathcal{S}_f$:\n",
+    "$$\n",
+    "\\mathcal{Q} = \\mathcal{A}\\mathcal{S}_0\\mathcal{A}^\\dagger\\mathcal{S}_f\n",
+    "$$\n",
+    "where $\\mathcal{A}$ defaults to $H^{\\otimes n}$, $\\mathcal{S}_0 = \\mathbb{I} - 2 |0\\rangle\\langle 0|$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "q = GroverOperator(s_f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">         ┌───┐          ┌───┐┌───┐┌───┐               ┌───┐┌───┐┌───┐     \n",
+       "state_0: ┤ X ├───────■──┤ X ├┤ H ├┤ X ├────────────■──┤ X ├┤ X ├┤ H ├─────\n",
+       "         ├───┤┌───┐  │  └───┘└───┘└───┘            │  ├───┤├───┤├───┤     \n",
+       "state_1: ┤ H ├┤ X ├──┼─────────────────────────────■──┤ X ├┤ X ├┤ H ├─────\n",
+       "         ├───┤├───┤┌─┴─┐┌───┐┌───┐┌───┐┌───┐┌───┐┌─┴─┐├───┤├───┤├───┤┌───┐\n",
+       "state_2: ┤ X ├┤ H ├┤ X ├┤ H ├┤ X ├┤ H ├┤ X ├┤ H ├┤ X ├┤ H ├┤ X ├┤ X ├┤ H ├\n",
+       "         └───┘└───┘└───┘└───┘└───┘└───┘└───┘└───┘└───┘└───┘└───┘└───┘└───┘</pre>"
+      ],
+      "text/plain": [
+       "         ┌───┐          ┌───┐┌───┐┌───┐               ┌───┐┌───┐┌───┐     \n",
+       "state_0: ┤ X ├───────■──┤ X ├┤ H ├┤ X ├────────────■──┤ X ├┤ X ├┤ H ├─────\n",
+       "         ├───┤┌───┐  │  └───┘└───┘└───┘            │  ├───┤├───┤├───┤     \n",
+       "state_1: ┤ H ├┤ X ├──┼─────────────────────────────■──┤ X ├┤ X ├┤ H ├─────\n",
+       "         ├───┤├───┤┌─┴─┐┌───┐┌───┐┌───┐┌───┐┌───┐┌─┴─┐├───┤├───┤├───┤┌───┐\n",
+       "state_2: ┤ X ├┤ H ├┤ X ├┤ H ├┤ X ├┤ H ├┤ X ├┤ H ├┤ X ├┤ H ├┤ X ├┤ X ├┤ H ├\n",
+       "         └───┘└───┘└───┘└───┘└───┘└───┘└───┘└───┘└───┘└───┘└───┘└───┘└───┘"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "q.draw()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">         ┌─────────────┐                 ┌─────────────┐                ░ »\n",
+       "state_0: ┤ U3(pi,0,pi) ├──────────────■──┤ U3(pi,0,pi) ├────────────────░─»\n",
+       "         └─────────────┘              │  └─────────────┘                ░ »\n",
+       "state_1: ─────────────────────────────┼─────────────────────────────────░─»\n",
+       "         ┌─────────────┐┌──────────┐┌─┴─┐  ┌──────────┐ ┌─────────────┐ ░ »\n",
+       "state_2: ┤ U3(pi,0,pi) ├┤ U2(0,pi) ├┤ X ├──┤ U2(0,pi) ├─┤ U3(pi,0,pi) ├─░─»\n",
+       "         └─────────────┘└──────────┘└───┘  └──────────┘ └─────────────┘ ░ »\n",
+       "«         ┌──────────┐ ░ ┌─────────────┐                                       »\n",
+       "«state_0: ┤ U2(0,pi) ├─░─┤ U3(pi,0,pi) ├───────────────────────────────■───────»\n",
+       "«         ├──────────┤ ░ ├─────────────┤                               │       »\n",
+       "«state_1: ┤ U2(0,pi) ├─░─┤ U3(pi,0,pi) ├───────────────────■───────────┼───────»\n",
+       "«         ├──────────┤ ░ ├─────────────┤┌──────────┐┌───┐┌─┴─┐┌─────┐┌─┴─┐┌───┐»\n",
+       "«state_2: ┤ U2(0,pi) ├─░─┤ U3(pi,0,pi) ├┤ U2(0,pi) ├┤ H ├┤ X ├┤ TDG ├┤ X ├┤ T ├»\n",
+       "«         └──────────┘ ░ └─────────────┘└──────────┘└───┘└───┘└─────┘└───┘└───┘»\n",
+       "«                                ┌───┐             ┌─────────────┐»\n",
+       "«state_0: ──────────────■────■───┤ T ├──────■──────┤ U3(pi,0,pi) ├»\n",
+       "«               ┌───┐   │  ┌─┴─┐┌┴───┴┐   ┌─┴─┐    ├─────────────┤»\n",
+       "«state_1: ──■───┤ T ├───┼──┤ X ├┤ TDG ├───┤ X ├────┤ U3(pi,0,pi) ├»\n",
+       "«         ┌─┴─┐┌┴───┴┐┌─┴─┐├───┤└┬───┬┘┌──┴───┴───┐├─────────────┤»\n",
+       "«state_2: ┤ X ├┤ TDG ├┤ X ├┤ T ├─┤ H ├─┤ U2(0,pi) ├┤ U3(pi,0,pi) ├»\n",
+       "«         └───┘└─────┘└───┘└───┘ └───┘ └──────────┘└─────────────┘»\n",
+       "«         ┌─────────────┐ ░ ┌──────────┐\n",
+       "«state_0: ┤ U3(pi,0,pi) ├─░─┤ U2(0,pi) ├\n",
+       "«         ├─────────────┤ ░ ├──────────┤\n",
+       "«state_1: ┤ U3(pi,0,pi) ├─░─┤ U2(0,pi) ├\n",
+       "«         ├─────────────┤ ░ ├──────────┤\n",
+       "«state_2: ┤ U3(pi,0,pi) ├─░─┤ U2(0,pi) ├\n",
+       "«         └─────────────┘ ░ └──────────┘</pre>"
+      ],
+      "text/plain": [
+       "         ┌─────────────┐                 ┌─────────────┐                ░ »\n",
+       "state_0: ┤ U3(pi,0,pi) ├──────────────■──┤ U3(pi,0,pi) ├────────────────░─»\n",
+       "         └─────────────┘              │  └─────────────┘                ░ »\n",
+       "state_1: ─────────────────────────────┼─────────────────────────────────░─»\n",
+       "         ┌─────────────┐┌──────────┐┌─┴─┐  ┌──────────┐ ┌─────────────┐ ░ »\n",
+       "state_2: ┤ U3(pi,0,pi) ├┤ U2(0,pi) ├┤ X ├──┤ U2(0,pi) ├─┤ U3(pi,0,pi) ├─░─»\n",
+       "         └─────────────┘└──────────┘└───┘  └──────────┘ └─────────────┘ ░ »\n",
+       "«         ┌──────────┐ ░ ┌─────────────┐                                       »\n",
+       "«state_0: ┤ U2(0,pi) ├─░─┤ U3(pi,0,pi) ├───────────────────────────────■───────»\n",
+       "«         ├──────────┤ ░ ├─────────────┤                               │       »\n",
+       "«state_1: ┤ U2(0,pi) ├─░─┤ U3(pi,0,pi) ├───────────────────■───────────┼───────»\n",
+       "«         ├──────────┤ ░ ├─────────────┤┌──────────┐┌───┐┌─┴─┐┌─────┐┌─┴─┐┌───┐»\n",
+       "«state_2: ┤ U2(0,pi) ├─░─┤ U3(pi,0,pi) ├┤ U2(0,pi) ├┤ H ├┤ X ├┤ TDG ├┤ X ├┤ T ├»\n",
+       "«         └──────────┘ ░ └─────────────┘└──────────┘└───┘└───┘└─────┘└───┘└───┘»\n",
+       "«                                ┌───┐             ┌─────────────┐»\n",
+       "«state_0: ──────────────■────■───┤ T ├──────■──────┤ U3(pi,0,pi) ├»\n",
+       "«               ┌───┐   │  ┌─┴─┐┌┴───┴┐   ┌─┴─┐    ├─────────────┤»\n",
+       "«state_1: ──■───┤ T ├───┼──┤ X ├┤ TDG ├───┤ X ├────┤ U3(pi,0,pi) ├»\n",
+       "«         ┌─┴─┐┌┴───┴┐┌─┴─┐├───┤└┬───┬┘┌──┴───┴───┐├─────────────┤»\n",
+       "«state_2: ┤ X ├┤ TDG ├┤ X ├┤ T ├─┤ H ├─┤ U2(0,pi) ├┤ U3(pi,0,pi) ├»\n",
+       "«         └───┘└─────┘└───┘└───┘ └───┘ └──────────┘└─────────────┘»\n",
+       "«         ┌─────────────┐ ░ ┌──────────┐\n",
+       "«state_0: ┤ U3(pi,0,pi) ├─░─┤ U2(0,pi) ├\n",
+       "«         ├─────────────┤ ░ ├──────────┤\n",
+       "«state_1: ┤ U3(pi,0,pi) ├─░─┤ U2(0,pi) ├\n",
+       "«         ├─────────────┤ ░ ├──────────┤\n",
+       "«state_2: ┤ U3(pi,0,pi) ├─░─┤ U2(0,pi) ├\n",
+       "«         └─────────────┘ ░ └──────────┘"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "GroverOperator(s_f, insert_barriers=True).decompose().draw()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Bernoulli example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<qiskit.circuit.instructionset.InstructionSet at 0x12ea31850>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p = 0.2\n",
+    "angle = np.arcsin(np.sqrt(p))\n",
+    "\n",
+    "a_operator = QuantumCircuit(1)\n",
+    "a_operator.ry(2 * angle, 0)\n",
+    "a_operator.value_to_estimation = lambda x: x  # TODO remove that this is necessary\n",
+    "\n",
+    "q_operator = QuantumCircuit(1)\n",
+    "q_operator.ry(4 * angle, 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings('ignore', category=DeprecationWarning)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.5 0.20000000018626451\n"
+     ]
+    }
+   ],
+   "source": [
+    "backend = Aer.get_backend('statevector_simulator')\n",
+    "ae = QAE(2, state_in=a_operator, grover_operator=q_operator)\n",
+    "result = ae.run(backend)\n",
+    "print(result['estimation'], result['mle'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">           ┌──────────┐                                                ┌───┐\n",
+       "eval_0: ───┤ U2(0,pi) ├──────────■──────────────────────────────■──────┤ H ├\n",
+       "           ├──────────┤          │                        ┌───┐ │-pi/2 └───┘\n",
+       "eval_1: ───┤ U2(0,pi) ├──────────┼───────────────■────────┤ H ├─■───────────\n",
+       "        ┌──┴──────────┴──┐┌──────┴───────┐┌──────┴───────┐└───┘             \n",
+       "   q_0: ┤ R(0.9273,pi/2) ├┤ circuit54**1 ├┤ circuit54**2 ├──────────────────\n",
+       "        └────────────────┘└──────────────┘└──────────────┘                  </pre>"
+      ],
+      "text/plain": [
+       "           ┌──────────┐                                                ┌───┐\n",
+       "eval_0: ───┤ U2(0,pi) ├──────────■──────────────────────────────■──────┤ H ├\n",
+       "           ├──────────┤          │                        ┌───┐ │-pi/2 └───┘\n",
+       "eval_1: ───┤ U2(0,pi) ├──────────┼───────────────■────────┤ H ├─■───────────\n",
+       "        ┌──┴──────────┴──┐┌──────┴───────┐┌──────┴───────┐└───┘             \n",
+       "   q_0: ┤ R(0.9273,pi/2) ├┤ circuit54**1 ├┤ circuit54**2 ├──────────────────\n",
+       "        └────────────────┘└──────────────┘└──────────────┘                  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ae.construct_circuit().decompose().draw()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using the default $\\mathcal{Q}$ operator:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         ┌───┐┌───┐┌───┐┌─────────────┐┌───┐┌───┐┌───┐┌───┐┌────────────┐\n",
+      "state_0: ┤ X ├┤ Z ├┤ X ├┤ RY(-0.9273) ├┤ X ├┤ Z ├┤ X ├┤ X ├┤ RY(0.9273) ├\n",
+      "         └───┘└───┘└───┘└─────────────┘└───┘└───┘└───┘└───┘└────────────┘\n"
+     ]
+    }
+   ],
+   "source": [
+    "flag_last_qubit = BitOracle(1, [0])  # 0th qubit is objective qubit\n",
+    "q_default = GroverOperator(flag_last_qubit, a_operator)\n",
+    "print(q_default.draw())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0 0.019677546808452455\n"
+     ]
+    }
+   ],
+   "source": [
+    "ae = QAE(2, a_operator, q_default)\n",
+    "result = ae.run(backend)\n",
+    "print(result['estimation'], result['mle'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Idle qubits \n",
+    "\n",
+    "Idle qubits are ignored in the creation $\\mathcal{S}_0$ reflection in the Grover operator."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                   \n",
+      "q_0: ──────■───────\n",
+      "     ┌─────┴──────┐\n",
+      "q_1: ┤ RY(0.9273) ├\n",
+      "     └────────────┘\n"
+     ]
+    }
+   ],
+   "source": [
+    "p = 0.2\n",
+    "angle = np.arcsin(np.sqrt(p))\n",
+    "\n",
+    "a_with_idle = QuantumCircuit(2)\n",
+    "a_with_idle.cry(2 * angle, 0, 1)\n",
+    "a_with_idle.value_to_estimation = lambda x: x\n",
+    "\n",
+    "print(a_with_idle.draw())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that qubit 0 is not included in the $\\mathcal{S}_0$ reflection:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                                                              »\n",
+      "state_0: ─────────────────────────────────────────────────────────────────────»\n",
+      "         ┌─────────────┐┌──────────┐┌─────────────┐┌──────────┐┌─────────────┐»\n",
+      "state_1: ┤ U3(pi,0,pi) ├┤ U2(0,pi) ├┤ U3(pi,0,pi) ├┤ U2(0,pi) ├┤ U3(pi,0,pi) ├»\n",
+      "         └─────────────┘└──────────┘└─────────────┘└──────────┘└─────────────┘»\n",
+      "«                                                                         »\n",
+      "«state_0: ──────────────────────■───────────────────────■─────────────────»\n",
+      "«         ┌──────────────────┐┌─┴─┐┌─────────────────┐┌─┴─┐┌─────────────┐»\n",
+      "«state_1: ┤ U3(-0.46365,0,0) ├┤ X ├┤ U3(0.46365,0,0) ├┤ X ├┤ U3(pi,0,pi) ├»\n",
+      "«         └──────────────────┘└───┘└─────────────────┘└───┘└─────────────┘»\n",
+      "«                                                                              »\n",
+      "«state_0: ─────────────────────────────────────────────────────────────────────»\n",
+      "«         ┌──────────┐┌─────────────┐┌──────────┐┌─────────────┐┌─────────────┐»\n",
+      "«state_1: ┤ U2(0,pi) ├┤ U3(pi,0,pi) ├┤ U2(0,pi) ├┤ U3(pi,0,pi) ├┤ U3(pi,0,pi) ├»\n",
+      "«         └──────────┘└─────────────┘└──────────┘└─────────────┘└─────────────┘»\n",
+      "«                                                          \n",
+      "«state_0: ─────────────────────■────────────────────────■──\n",
+      "«         ┌─────────────────┐┌─┴─┐┌──────────────────┐┌─┴─┐\n",
+      "«state_1: ┤ U3(0.46365,0,0) ├┤ X ├┤ U3(-0.46365,0,0) ├┤ X ├\n",
+      "«         └─────────────────┘└───┘└──────────────────┘└───┘\n"
+     ]
+    }
+   ],
+   "source": [
+    "s_f = BitOracle(2, [1])  # ignore first qubit\n",
+    "q_with_idle = GroverOperator(s_f, a_with_idle, idle_qubits=[0])\n",
+    "print(q_with_idle.decompose().draw())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# initial_state = QuantumCircuit(2)\n",
+    "# initial_state.x(0)\n",
+    "# ae = QAE(2, a_factory=a_with_idle, q_factory=q_with_idle, initial_state=initial_state)\n",
+    "# print(ae.construct_circuit().draw())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# result = ae.run(backend)\n",
+    "# print(result['estimation'], result['mle'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's turn the `X` gate off and then the rotation in the $\\mathcal{A}$ operator does not act."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# initial_state = QuantumCircuit(2)\n",
+    "# ae = QAE(2, a_factory=a_with_idle, q_factory=q_with_idle, initial_state=initial_state)\n",
+    "# result = ae.run(backend)\n",
+    "# print(result['estimation'], result['mle'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Expectation value of a quadratic function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class ContinuousQuadratic(QuantumCircuit):\n",
+    "    def __init__(self, x_dist, y, rescaling_factor=0.1, name='A'):\n",
+    "        num_qubits = x_dist.num_target_qubits + 1\n",
+    "        super().__init__(num_qubits, name=name)\n",
+    "        self.c = rescaling_factor\n",
+    "        self.x_dist = x_dist\n",
+    "        self.y = y\n",
+    "        self.ub = x_dist.high\n",
+    "                \n",
+    "        indices = list(range(num_qubits - 1))\n",
+    "        self.i_x = indices[:self.x_dist.num_target_qubits]\n",
+    "        self.i_f = num_qubits - 1\n",
+    "        \n",
+    "        self._build()\n",
+    "        \n",
+    "    def _build(self):\n",
+    "        q = self.qubits\n",
+    "        beta_x = self.c / (self.x_dist.num_values - 1)\n",
+    "        gamma_y = -self.c * self.y / self.ub\n",
+    "\n",
+    "        lin_x = LinearPauliRotations(self.x_dist.num_target_qubits, offset=2 * gamma_y, slope=2 * beta_x)\n",
+    "\n",
+    "        q_x = [q[i] for i in self.i_x]\n",
+    "        q_f = q[self.i_f]\n",
+    "        self.x_dist.build(self, q_x)\n",
+    "        \n",
+    "        self.compose(lin_x, qubits=q_x[:] + [q_f], inplace=True)\n",
+    "        \n",
+    "    def value_to_estimation(self, value):\n",
+    "        return value * (self.ub / self.c) ** 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = NormalDistribution(2, mu=1, sigma=1, low=0, high=2)\n",
+    "a_X = ContinuousQuadratic(X, y=1)\n",
+    "bad_state_reflection = BitOracle(a_X.num_qubits, [a_X.i_f])\n",
+    "q_X = GroverOperator(bad_state_reflection, a_X, insert_barriers=False, mcx='v-chain')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">           ┌───┐           ┌───┐     \n",
+       "  state_0: ┤ X ├───────■───┤ X ├─────\n",
+       "           ├───┤       │   ├───┤     \n",
+       "  state_1: ┤ X ├───────■───┤ X ├─────\n",
+       "           ├───┤       │   ├───┤     \n",
+       "  state_2: ┤ X ├───────■───┤ X ├─────\n",
+       "           ├───┤       │   ├───┤     \n",
+       "  state_3: ┤ X ├───────■───┤ X ├─────\n",
+       "           ├───┤       │   ├───┤     \n",
+       "  state_4: ┤ X ├───────■───┤ X ├─────\n",
+       "           ├───┤┌───┐┌─┴──┐├───┤┌───┐\n",
+       "  state_5: ┤ X ├┤ H ├┤0   ├┤ H ├┤ X ├\n",
+       "           └───┘└───┘│    │└───┘└───┘\n",
+       "ancilla_0: ──────────┤1   ├──────────\n",
+       "                     │  X │          \n",
+       "ancilla_1: ──────────┤2   ├──────────\n",
+       "                     │    │          \n",
+       "ancilla_2: ──────────┤3   ├──────────\n",
+       "                     └────┘          </pre>"
+      ],
+      "text/plain": [
+       "           ┌───┐           ┌───┐     \n",
+       "  state_0: ┤ X ├───────■───┤ X ├─────\n",
+       "           ├───┤       │   ├───┤     \n",
+       "  state_1: ┤ X ├───────■───┤ X ├─────\n",
+       "           ├───┤       │   ├───┤     \n",
+       "  state_2: ┤ X ├───────■───┤ X ├─────\n",
+       "           ├───┤       │   ├───┤     \n",
+       "  state_3: ┤ X ├───────■───┤ X ├─────\n",
+       "           ├───┤       │   ├───┤     \n",
+       "  state_4: ┤ X ├───────■───┤ X ├─────\n",
+       "           ├───┤┌───┐┌─┴──┐├───┤┌───┐\n",
+       "  state_5: ┤ X ├┤ H ├┤0   ├┤ H ├┤ X ├\n",
+       "           └───┘└───┘│    │└───┘└───┘\n",
+       "ancilla_0: ──────────┤1   ├──────────\n",
+       "                     │  X │          \n",
+       "ancilla_1: ──────────┤2   ├──────────\n",
+       "                     │    │          \n",
+       "ancilla_2: ──────────┤3   ├──────────\n",
+       "                     └────┘          "
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "BitOracle(6, [0, 1, 2, 3, 4, 5], mcx='v-chain').draw()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Running canonical QAE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def post(x):\n",
+    "    return a_X.value_to_estimation(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0 8.651370895929562\n"
+     ]
+    }
+   ],
+   "source": [
+    "ae = QAE(3, state_in=a_X, grover_operator=q_X, objective_qubits=[a_X.i_f], post_processing=post)\n",
+    "result = ae.run(backend)\n",
+    "print(result['estimation'], result['mle'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "             ┌───┐                                                   »\n",
+      "eval_0: ─────┤ H ├───────────────────────────────────────────────────»\n",
+      "             ├───┤                                                   »\n",
+      "eval_1: ─────┤ H ├───────────────────────────────────────────────────»\n",
+      "             ├───┤                                                   »\n",
+      "eval_2: ─────┤ H ├───────────────────────────────────────────────────»\n",
+      "        ┌────┴───┴─────┐┌───┐┌─────────────────┐┌───┐                »\n",
+      "   q_0: ┤ U3(pi/2,0,0) ├┤ X ├┤ U3(0.22042,0,0) ├┤ X ├───────■────────»\n",
+      "        ├──────────────┤└─┬─┘└─────────────────┘└─┬─┘       │        »\n",
+      "   q_1: ┤ U3(pi/2,0,0) ├──■───────────────────────■─────────┼────────»\n",
+      "        └─┬──────────┬─┘                             ┌──────┴───────┐»\n",
+      "   q_2: ──┤ RY(-0.1) ├───────────────────────────────┤ RY(0.066667) ├»\n",
+      "          └──────────┘                               └──────────────┘»\n",
+      "«                       ┌─────────┐                      ┌──────┐\n",
+      "«eval_0: ───────────────┤0        ├──────────────────────┤0     ├\n",
+      "«                       │         │┌─────────┐           │      │\n",
+      "«eval_1: ───────────────┤         ├┤0        ├───────────┤1 qft ├\n",
+      "«                       │         ││         │┌─────────┐│      │\n",
+      "«eval_2: ───────────────┤         ├┤         ├┤0        ├┤2     ├\n",
+      "«                       │  c_Q**1 ││         ││         │└──────┘\n",
+      "«   q_0: ───────────────┤1        ├┤1 c_Q**2 ├┤1        ├────────\n",
+      "«                       │         ││         ││  c_Q**4 │        \n",
+      "«   q_1: ───────■───────┤2        ├┤2        ├┤2        ├────────\n",
+      "«        ┌──────┴──────┐│         ││         ││         │        \n",
+      "«   q_2: ┤ RY(0.13333) ├┤3        ├┤3        ├┤3        ├────────\n",
+      "«        └─────────────┘└─────────┘└─────────┘└─────────┘        \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(ae.construct_circuit())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# post(result['mle'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Iterative QAE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.4580526785717565\n"
+     ]
+    }
+   ],
+   "source": [
+    "ae = IQAE(0.01, 0.01, state_in=a_X, grover_operator=q_X, objective_qubits=[a_X.i_f], post_processing=post)\n",
+    "result = ae.run(backend)\n",
+    "print(result['estimation'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Quadratic expecation value with qubit-encoded offset parameter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class DiscreteQuadratic(QuantumCircuit):\n",
+    "    def __init__(self, x_dist, k, y_max, rescaling_factor=0.1, name='A'):\n",
+    "        num_qubits = x_dist.num_target_qubits + k + 1\n",
+    "        super().__init__(num_qubits, name=name)\n",
+    "        self.c = rescaling_factor\n",
+    "        self.x_dist = x_dist\n",
+    "        self.k = k\n",
+    "        self.y_max = y_max\n",
+    "                \n",
+    "        indices = list(range(num_qubits - 1))\n",
+    "        self.i_x = indices[:self.x_dist.num_target_qubits]\n",
+    "        self.i_y = indices[self.x_dist.num_target_qubits:]\n",
+    "        self.i_f = num_qubits - 1\n",
+    "        \n",
+    "        self._build()\n",
+    "        \n",
+    "    def _build(self):\n",
+    "        beta_x = self.c / (self.x_dist.num_values - 1) * self.x_dist.high\n",
+    "        beta_y = -self.c / (2**self.k - 1) * self.y_max\n",
+    "        \n",
+    "        lin_x = LinearPauliRotations(self.x_dist.num_target_qubits, offset=0, slope=2 * beta_x)\n",
+    "        lin_y = LinearPauliRotations(self.k, offset=0, slope=2 * beta_y)\n",
+    "\n",
+    "        q = self.qubits\n",
+    "        q_x = [q[i] for i in self.i_x]\n",
+    "        q_y = [q[i] for i in self.i_y]\n",
+    "        q_f = q[self.i_f]\n",
+    "        self.x_dist.build(self, q_x)\n",
+    "        \n",
+    "        self.compose(lin_x, qubits=q_x[:] + [q_f], inplace=True)\n",
+    "        self.compose(lin_y, qubits=q_y[:] + [q_f], inplace=True)   \n",
+    "        \n",
+    "    def value_to_estimation(self, value):\n",
+    "        return value / self.c ** 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "__init__() got an unexpected keyword argument 'initial_state'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-26-55fde4ccc6f3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mq_XY\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGroverOperator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms_f\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma_XY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midle_qubits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mae\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mQAE\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma_factory\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0ma_XY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mq_factory\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mq_XY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitial_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minit_y\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: __init__() got an unexpected keyword argument 'initial_state'"
+     ]
+    }
+   ],
+   "source": [
+    "a_XY = DiscreteQuadratic(X, k=2, y_max=3)\n",
+    "init_y = QuantumCircuit(5)\n",
+    "init_y.x(2)\n",
+    "# X.build(init_y, init_y.qubits[:2])\n",
+    "s_f = BitOracle(5, [4])  # ignore first qubit\n",
+    "q_XY = GroverOperator(s_f, a_XY, idle_qubits=[2, 3])\n",
+    "\n",
+    "ae = QAE(3, a_factory=a_XY, q_factory=q_XY, initial_state=init_y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "result = ae.run(backend)\n",
+    "print(result['estimation'], result['mle'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ae = IQAE(0.01, 0.01, a_factory=a_XY, q_factory=q_XY, initial_state=init_y, i_objective=4)\n",
+    "result = ae.run(backend)\n",
+    "print(result['estimation'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Bernoulli with ancillas "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit.circuit import AncillaRegister, QuantumRegister\n",
+    "\n",
+    "p = 0.2\n",
+    "angle = np.arcsin(np.sqrt(p))\n",
+    "\n",
+    "qr, ar = QuantumRegister(1), AncillaRegister(1)\n",
+    "\n",
+    "a_operator = QuantumCircuit(qr)#, ar)\n",
+    "a_operator.ry(2 * angle, qr[0])\n",
+    "a_operator.value_to_estimation = lambda x: x  # TODO remove that this is necessary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a_operator.draw()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a_operator.num_ancillas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_state_qubits = a_operator.num_qubits - a_operator.num_ancillas\n",
+    "flag_last_qubit = BitOracle(num_state_qubits, [num_state_qubits - 1])  # 0th qubit is objective qubit\n",
+    "q_default = GroverOperator(flag_last_qubit, a_operator)\n",
+    "print(q_default.draw())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "backend = Aer.get_backend('qasm_simulator')\n",
+    "ae = IQAE(0.01, 0.01, a_factory=a_operator, q_factory=q_default)\n",
+    "result = ae.run(backend)\n",
+    "print(result['estimation'])#, result['mle'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ae.construct_circuit(2).draw()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ae.construct_circuit().decompose().draw()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class BernoulliAFactory(CircuitFactory):\n",
+    "    r\"\"\"Circuit Factory representing the operator A in a Bernoulli problem.\n",
+    "\n",
+    "    Given a probability $p$, the operator A prepares the state $\\sqrt{1 - p}|0> + \\sqrt{p}|1>$.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, probability=0.5):\n",
+    "        #\n",
+    "        super().__init__(1)\n",
+    "        self._probability = probability\n",
+    "        self.i_state = 0\n",
+    "        self._theta_p = 2 * np.arcsin(np.sqrt(probability))\n",
+    "\n",
+    "    def build(self, qc, q, q_ancillas=None, params=None):\n",
+    "        # A is a rotation of angle theta_p around the Y-axis\n",
+    "        qc.ry(self._theta_p, q[self.i_state])\n",
+    "\n",
+    "    def value_to_estimation(self, value):\n",
+    "        return value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a_factory = BernoulliAFactory(0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "backend = Aer.get_backend('qasm_simulator')\n",
+    "ae = IQAE(0.01, 0.01, a_factory=a_factory)\n",
+    "result = ae.run(backend)\n",
+    "print(result['estimation'])#, result['mle'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ae.construct_circuit().decompose().draw()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit.aqua.algorithms import VQE\n",
+    "from qiskit.aqua.operators import *\n",
+    "from qiskit.circuit import ParameterVector\n",
+    "from qiskit.circuit.library import RealAmplitudes\n",
+    "\n",
+    "op = (X ^ X) + (Z ^ Z) + (Y ^ Y)\n",
+    "ansatz = RealAmplitudes(2)\n",
+    "vqe = VQE(op, ansatz, expectation=MatrixExpectation())\n",
+    "exp = vqe.construct_circuit(ParameterVector('p', ansatz.num_parameters))\n",
+    "print(exp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(exp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "exp.oplist[0].to_circuit()"
+   ]
+  },
+  {
+   "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
+}
diff --git a/IQX Colors Big Blue.ipynb b/IQX Colors Big Blue.ipynb
index 067571a..57702c8 100644
--- a/IQX Colors Big Blue.ipynb	
+++ b/IQX Colors Big Blue.ipynb	
@@ -27,9 +27,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 1683.19x325.08 with 1 Axes>"
+       "<Figure size 418.992x144.48 with 1 Axes>"
       ]
      },
      "execution_count": 2,
@@ -37,6 +37,37 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "from qiskit.circuit import QuantumCircuit, Gate\n",
+    "\n",
+    "my_gate = Gate('abc', 2, [])\n",
+    "\n",
+    "circuit = QuantumCircuit(2)\n",
+    "circuit.iswap(0, 1)\n",
+    "circuit.dcx(0, 1)\n",
+    "circuit.append(my_gate, [0, 1])\n",
+    "circuit.draw(output='mpl', style='legacy')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1683.19x686.28 with 1 Axes>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from qiskit.circuit import QuantumCircuit\n",
     "\n",
@@ -66,12 +97,15 @@
     "\n",
     "circuit.ch(3, 4)\n",
     "\n",
-    "circuit.rx(0, 0)\n",
-    "circuit.ry(0, 1)\n",
-    "circuit.rz(0, 2)\n",
+    "circuit.crx(0, 0, 1)\n",
+    "circuit.cry(0, 1, 2)\n",
+    "circuit.crz(0, 2, 3)\n",
     "circuit.r(0, 0, 3)\n",
     "circuit.t(4)\n",
     "circuit.tdg(4)\n",
+    "circuit.rx(0, 0)\n",
+    "circuit.ry(0, 1)\n",
+    "circuit.rz(0, 2)\n",
     "\n",
     "circuit.rxx(0, 0, 1)\n",
     "circuit.ryy(0, 1, 2)\n",
@@ -83,6 +117,9 @@
     "circuit.rcccx(0, 1, 2, 3)\n",
     "circuit.ms([[0] * 5] * 5, [0, 1, 2, 3, 4])\n",
     "\n",
+    "circuit.reset(0)\n",
+    "circuit.barrier()\n",
+    "\n",
     "circuit.unitary(np.eye(4), [0, 1])\n",
     "#circuit.initialize(np.eye(4)[0,:], [2, 3])\n",
     "#circuit.isometry(np.eye(4), [], [1, 2])\n",
@@ -92,7 +129,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1683.19x686.28 with 1 Axes>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "circuit.draw(output='mpl', style=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -102,7 +160,7 @@
        "<Figure size 1201.59x264.88 with 1 Axes>"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -114,7 +172,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -124,7 +182,7 @@
        "<Figure size 434.644x325.08 with 1 Axes>"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -136,7 +194,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -146,7 +204,7 @@
        "<Figure size 533.372x325.08 with 1 Axes>"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -158,17 +216,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 550.228x565.88 with 1 Axes>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -180,17 +238,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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3vKJ6fu3eIWE6V1CqxI2Ha2V7zu711YReOw69dhx67Rj02XHqqte5lmJlHMtXt47NbcvaB/nK39ddKalnHHV49Updvg6s7X8XhAhJZrNZy5cvV1RUlKZOnapJkyYpMDBQ06dPl4uLi+2m6qtVRDt/3XlLkLw8XVRSWq7c/GJZrVJ2TpEOZ1l0/7AImc0mdQ4P0OQRkbb1PlqfptDBH+mGUSt1w6iV+v30/0qSBsat05JVh6rdr6urSR7uLnJ1Mctsrvh/d7eqQzbunkgtWpmq0tL6/QvhwjG4SJI83F3k4e7i5KoaJ3qNxohxjcbo7Y8P6n/vj1a7IB/5ervpxZk9tG5rpjKO5Tu7NFSDj3j9WUREhDZt2lRpWUxMjDp16iQvLy8nVVU/uLuZ9UxcV0WFBkiSfjiapxGPblRRcZkmPLVZC57sreljOmr7/pNamJiqiUPDJUkFhWXKKrzwfRCuLhUB4ET2eZ0rqP7jXv/5f300cWiE7efCPRN1OMui9nd9ZFt2c3QLXR/eTMMe3lArx1qXYoaEKeH5CzfkF+6ZKElqN2gZk2Uto9dojBjXaIzmLkxRgJ+Hdn8wVB7uZn22/ZjufTzJ2WXBDiYr5/MuqWPHjurZs6cWL77whT5PP/20Fi9erFOnTsnHx0deXl5KTk5WaGioEyu1jyl6obNLaBCsKbFXvA16bR967Tj02nHotWPQZ8eh145TG712FC5nuoT8/HylpqZW+ZK5OXPmKDMzU0VFRcrOzlZmZmaDCBAAAABAbeFypkvw8fFRWVn9/NKyxuJA4nAFt676ZTIZx/LVeXiiEyoCAACAPQgRcBqCAgAAQMPE5UwAAAAADCFEAAAAADCEEAEAAADAEEIEAAAAAEMIEQAAAAAMIUQAAAAAMISPeAUAoJ5pSN9aC+DqxJkIAAAAAIYQIgAAAAAYQogAAAAAYAghAgDsZDJJW5cMkTUlVkEtm1T7/Otaecuy475Kf4r2TlTutpiLPv/Def1lTYnVLV1bVloeN7KDvl91jyw77tO+ZcPU98ZWtXI89ZnRXkvSTde3UPLiwTr7xb06sWmclrzQV838PWq07bkP95A1JVbjB4de0XEAl1OTcX59eIA2/PMunfniXh3bOFazp3Wr9HjL5l76cF5/nUwapzNf3KuN/7pL0RHN6qL8eq0mvU1fO0oFuydUmrM7hwfYHrentyFtfJX4/25XztYY5WyN0fb3/iBXV1OtHlt9QYgAADvNjOms84Wldj//6Ilz8u25pNKf3QdO671Pf6zy3P+5PVjNm1Z9wXvPgHaaM72bRv35c/n3fldvrTioT/8xUNe18r6iY6nvjPbabDZp9WsDtPXLn9Si3/vqOHSFWl/TRK8+1tPwtnt0DtRdfdro2MlzNaodsJfRce7n46Z1b9yp/27LVIu+7+t3D6zRxKHhenRCZ9tzFjzZW838PRTxhxVq2e8D7fnmtFa/PqAuyq/XjPb2Fw88+0WlOfvAobO2x6rrbWCAp7YkDNb+1DNqe+eHanbre3oofrvKyqy1ckz1DSECwFXjj+M6KW3tKOVtj1HmZ2P0wp+6K7i1T5V3qibcHa5Dq0dWWjc82E/TRnfUrJd21Xj/UWEBuqVrS7350XeVljfz99D8R2/S5NlfVFln5MD2eu/TH7X/+zMqL7fqreUHdfJMoSYODa9xHY7g6F77+7ipRTMvLf4kVaWlVp3NK9ZH/01Tl8jmhrbt7mbWwtm3Ku65rSouKTdwxLgaOXqc33JDS3l6uOpvi79WWZlVB9NztXBlqqaN7mh7TlhbPy1fn64cS7FKSsu1cGWqrmvlc9E3KeozZ8/XF1Ndbx+J6awjJ85p9htfKi+/ROXlVu399rSsjTND8BGvAK4O4cF+mjujh3qM+0Tf/pgjf193dWjvb9e6JpO0aPatmvXSLuVYimtcw4MjO2jbVz/p61+9syVJrz/RS6998K0OZ+VfZN8mmX5zJtwk6YbfvDiuT5zR67N5xXrzo+80eUSknnptr/x93TVmUIhWbjxsaNvPTu2mz3cd146Uk3bvG1cnZ4zzi80HZrMU0sZPvt5uspwr0d8Svta9g0O18vMMWc6VaMqISG3Zd0LZOUVGDs+pnDlfv/znm/XqY7105Hi+3vjoO7294nvbY9X1tv9N1+roiXNa/fpA9epyjTJ/OqcXF6XogzVVzz43BpyJAHBVKC21ymSSokID5O3lqlxLsXamnLJr3Rnjo3Qiu0D//jyjxvv38nTRvYND9daKg5WWD+0frJA2vnrl/W8uut7qzUd07+Awde8UKFdXk6aP6ai21/rIz8etxrXUNWf1evln6fqf37XTuZ0TdDJpvMqt0l8X7rd72907BWrkwPZ68tU9hveNq48zxvn2/T+pvNyqxx/oIjdXs6LCAnT/sAhJss0JW7/8SS4uJp1KHq/8Hfdp+B3tNPnZqmc56zNnzSETntqskLs+Usv+7+vPL+9S/J9u1JR7Im2PV9fbwKaeGn57sBZ/kqpr+r2vR+fv1MLZfarc59ZYECIAXBXSsywa/3iSJo+I1LGNY7UlYbAG9Aqqdr3Q63z16ITr9VD89iva/5hBISq3SsvWpduWBfi569XHeuqBZ7+45OnuJat+0N8Svtb7c/vqxOfj1LVDc23YcUynz9bfdxWd0euwtn5au+BOPf/2V/K6KUH+vZfox6N5WvfGILu27eZq1uI5t2p6/DadKzB+HTWuPs4Y52fzijX4ofUadEsbHf98rJa8cJsW/TtVZWXlOptXLJNJ2vDPQUrNyJNfryVqctM7euGfX2lLwmBd08yzJofpFM6arzfvPaFzBaUqLbVqw45jevndA7p3SJgk2dVby7kSbd9/Uh9/dlhlZRXbWLc1S3f3a1ujeuo7LmcCcNVYuTFDKzdmyM3VrAdHddAnr9yhsCHLJUneXhfe2W99zYXrbft0baUWAZ46kDhcUsUNvJKUsmK4nnp9r95YVvn+hkt5cGRHvbPqkIqKy2zLoiOaqXWLJtr0r99Xeu7q1wbozeUH9fgrFe+Iz1uconmLUyRVvNhNXztKc97+yujhO5Sje90lspnO5hXrnVWHJEl5+SV6bem3+vrj4fL3da9222u2HFVUaIDe/2s/2zYD/Nz1xlO9dVefNrr38eRa6AoaG2fMKTtTTqnvpE9tP897pId2f3Na5wtK1byph0La+Om1D9bLcq5EkrQwMVUvPtxDvbq01Ceban421dGcOV//orzcql+uHmvmX31vv/o+W2Ft/apsh3siAKABi2jnr/ZBPtq894QKCsuUm18sq1XKzinS4SyL7h8WoSde3aNOoU01eUSk7dM0Plqfpg07s2zbadPSWzveu1sD49bpYHqOXfu+oUNz3XR9C8U8WfmF6Pb9J9XurmWVlmV+NlaTntmiTbuPS6q4RKF1iyY6mJ6rwABPzZ1xo3LzL7xYro+c0eu9356Wv4+bxg8O1dK1aWri6aqHxnTSj0fzlGsprnbbBUVlanvnh5W2uf3dP2je4q8b7fXMuDLOmlO6dmyub3/MkdVq1d392mrKiA4a/sgGSRX7/v5wjqaN7qjHXtmjouIy3feHMPl6uykl9UzdNKIOOKO3ba/1UUgbX23ff1IlpeXq07WlZsZ01py3Kt6wsae3b604qC2Lh2ho/2CtSspQ3xuv1cBeQXpxUUoddcq5CBEArgrubmY9E9dVUaEVn/n9w9E8jXh0o4qKyzThqc1a8GRvTR/TUdv3n9TCxFTbpx8VFJYpq/C8bTuuLhVXgZ7IPm/3ZS9x90Rq065jSj2cW2l5cUm5sn46X+X5p84WKvfnGwL9vN21/KXb1a61j4pLyvXp5qPqH7tGhUVlVdarL5zR68NZ+RrxyOd6dmpXvf54L5WVW7X7m9MaOmOD3dv+7d9FWZlVZ/OKdCa3/l46Budx1pzy4MgOGjmwvdzdzDrww1mNnPW5Pt953Pb4sBkb9LdHblLGutFyczXrh6N5Gvno50rPstTm4dcpZ/TW28tVL8+6WWFtfWW1SkdOnNNzb36pf3x44exFdb3dmXJK4x7bpBdn3qj35/ZVela+JjyV3Gg/qMFktTbWkyz4LVP0QmeX0CBYU2KveBv02j702nHotePURq/hGIxp+zB/OE5Dmj+4sRoAAACAIVzOBABX4EDicAW39qmyPONYvjoPT3RCRY0XvcbVgHFed+ht7SJEAMAV4BeP41xNvebSj+o1pMs+jLiaxrmj0dvaxeVMAAAAAAwhRAAAAAAwhBABAAAAwBBCBAAAAABDCBEAAAAADCFEAAAAADCEEAEAAADAEEIEAAAAAEMIEb9SXl6u+fPnKzw8XJ6enurSpYuSk5MVGRmpKVOmOLs8AAAAoF7gG6t/JTY2VomJiXr66afVvXt3bdu2TWPHjtWpU6f0yCOPOLu8OjF6UIimj+moLhHN1MTTVW7dFju7JOCKmc0mzX34Rk28O1yeHi5avz1Lcc9tVXZOkbNLA2qMcY3GiNchDRdnIn62dOlSJSQkaNWqVZo1a5b69++vJ598Ur169VJpaam6devm7BLrxNm8Ii1Y9p0enrfD2aWghkwmaeuSIbKmxCqoZRO713tobCd9v+oe5e+8T0fWj9akYeG2x0YPCtHmhMHK3Rajkn2TLrudB0d1kDUlVk9OvqHGx1DbHouN1tB+wbp5/H/UZsCHkqR34/s6uSrgyjCu0RjxOqTh4kzEz+Lj4zVo0CD17Vt5Qg4LC5Obm5uio6N19uxZxcTEKDU1VV5eXmrZsqUWLFigsLAwJ1V95dZvy5Ik9b2xlZMrQU3NjOms84WlhtZ5cvINivlDqMY9lqR9351WgJ+HAgM8bY//Mql7ebjo7Wf6XHI7ba/10aP3Xa+U1DM1rr8uTBnRQc+99aXSsyySpL+8vFs/rhmlttf66MjxfCdXB9QM4xqNEa9DGi7OREjKzMzUgQMHNHLkyCqPHTlyRFFRUfLw8JDJZNLDDz+s1NRU7d+/X0OGDNGkSZd/lxaozh/HdVLa2lHK2x6jzM/G6IU/dVdwa58qZxYm3B2uQ6srj9HwYD9NG91Rs17aZff+/H3d9cQDXTTjxR3a++1pWa3SmdwipR7OtT1n/bYsfbg2TWmZlstua+HsPnrytT06k1t/Lqfw93VXcGsf7f32tG1ZWqZFuZZidYls5sTKgJpjXAOobwgRqggRktSqVeUUXFBQoOTkZNulTE2bNtUdd9xhe7x3795KT0+v1VpMJlOd/YF9HNnr8GA/zZ3RQ0MeWi+/Xu8qaniiViUdsbNOadHsWzXrpV3KsRTbvc+e0S3UxMtVXSKaK23tKB3bOFYfvNhP1zTzrH7lX5lyT6TOFZTqo//W/N9AXfTat4mbJCk3v3JPcizF8vN2q3GtDR1ziOMwrh2DMe049Npx6vJ1YG3/XREiJAUGBkqSUlNTKy2fN2+ejh8/ru7du190vb///e8aNmxYndeHxqu01CqTSYoKDZC3l6tyLcXamXLKrnVnjI/SiewC/fvzDEP7DGxaERbuvCVIN49fpY7DPpaXh6ve+2s/u7dxXStvPTXlBk17YZuhfTuC5XyJJMnfx73S8qa+7so7V+KMkoArxrgGUN8QIiSFhIQoOjpa8fHxWrJkiTZu3KipU6dq0aJFknTREDF79mz98MMPmjdvXq3WYrVa6+wP7OPIXqdnWTT+8SRNHhGpYxvHakvCYA3oFVTteqHX+erRCdfrofjtho/P8vMLjvh/7tepM4XKtRTr2Tf26fabW6uJl323Sf3r2Vv1/Ntf6djJ84b3/2t10etcS7EyjuWrW8fmtmXtg3zl7+te7+7dcCTmEMdhXDsGY9px6LXj1OXrwNr+uyJESDKbzVq+fLmioqI0depUTZo0SYGBgZo+fbpcXFwUHR1d6fnPP/+8Vq9erXXr1qlJE/s/Dac+MptN8nB3kbubiyTJw91FHu4uTq7q6rJyY4YGxq1T4G3v66P16frklTtUUlouSfL2unCZQutrLoy1Pl1bqUWApw4kDtep5PHat6zijFjKiuGaOrrjZff31fcVLzisqvmkPrB3kOL/dKNOJY/XqeTxuuWGlnr8gWhtThhc423Wprc/Pqj/vT9a7YJ85Ovtphdn9gGnib4AAAr6SURBVNC6rZnKOMbNp2i4GNdojHgd0nDx6Uw/i4iI0KZNmyoti4mJUadOneTl5WVbNnv2bK1Zs0afffaZmjZt6ugya13MkDAlPH+b7efCPRMlSe0GLeMXkwNEtPNX+yAfbd57QgWFZcrNL5bVKmXnFOlwlkX3D4vQE6/uUafQppo8IlJlZRUv/D9an6YNO7Ns22nT0ls73rtbA+PW6WB6zmX3eeR4vj7dfFSPx3bRl99lq6TUqqfjbtB/t2XqfEHFpzyZzSa5uZorTeqSVFRcVrG/AUsrbXP5/Nu1Zd8JvfTO17XTmCs0d2GKAvw8tPuDofJwN+uz7cd07+NJzi4LuCKMazRGvA5puAgRl7Fnzx717NnT9vM333yjZ599VqGhoerXr59t+VdffeWE6mrHO6sO6Z1Vh5xdxlXL3c2sZ+K6Kio0QJL0w9E8jXh0o4qKyzThqc1a8GRvTR/TUdv3n9TCxFRNHFrxXQ4FhWXKKrxwKZGrS8VJxRPZ53WuoPqPe415IkmvP9Fbh9eNVkFRmdZvy9KDcy7c31DdpJ71U+XLmIqKy5SXX6KTZwpr1ohaVl5u1Z9f2qU/G/jUKqC+Y1yjMeJ1SMNFiLiE/Px8paamatq0abZlUVFRXNeHWnXg0Fndct/qiz62ee8JdR6eWGnZC/+8eGDNOJYvU/RCu/d7Nq9Y4x9LuuTjRif1/rFr7H4uAABo+AgRl+Dj46OysjJnlwEAAADUO4QIoBE6kDhcwa19qizPOJZf5ewGAACAUYQIoBEiKAAAgLrER7wCAAAAMIQQAQAAAMAQQgQAAAAAQwgRAAAAAAwhRAAAAAAwhBABAAAAwBA+4vUqYk2JdXYJQK1jXANA/cdc3fhwJgIAAACAIYQIAAAAAIYQIgAAAAAYQogAAAA2JpO0dckQWVNiFdSySbXPb9HMU++8cJsOrxsty477dGj1SD0WG13pOaMHhWhzwmDlbotRyb5JdVU6AAfixmoAAGAzM6azzheW2v18Hy83fftjjv5vwT4dzspXVFiAVr82QEXF5fp/7x6QJJ3NK9KCZd/Jy8NFbz/Tp65KB+BAnIkAAKAR+eO4TkpbO0p522OU+dkYvfCn7gpu7VPlzMKEu8N1aPXISuuGB/tp2uiOmvXSLrv3l55l0YuLUnQ4K1+S9M0PZ/XhujT169HK9pz127L04do0pWVarvDoANQXnIkAAKCRCA/209wZPdRj3Cf69scc+fu6q0N7f7vWNZmkRbNv1ayXdinHUlzjGkwmqV+Pa/XZ9qwabwNA/ceZCAAAGonSUqtMJikqNEDeXq7KtRRrZ8opu9adMT5KJ7IL9O/PM66ohpf/fLMC/Nw1/52vr2g7AOo3QgQAAI1EepZF4x9P0uQRkTq2cay2JAzWgF5B1a4Xep2vHp1wvR6K335F+39p1s26q08b3T55rfLyS65oWwDqNy5nAgCgEVm5MUMrN2bIzdWsB0d10Cev3KGwIcslSd5ebrbntb7mwv0Rfbq2UosATx1IHC5JMptNkqSUFcP11Ot79cay7y67T5NJeuuZPurV5Rr1nbRGP2UX1PZhAahnCBEAADQSEe381T7IR5v3nlBBYZly84tltUrZOUU6nGXR/cMi9MSre9QptKkmj4hUWZlVkvTR+jRt2HnhHoY2Lb214727NTBunQ6m51x2ny4uJr0b31cd2jVVv/s/VXZOUZXnmM0mubma5e7mIknycK/4b1FxWW0dOgAHI0QAANBIuLuZ9UxcV0WFBkiSfjiapxGPblRRcZkmPLVZC57sreljOmr7/pNamJiqiUPDJUkFhWXKKjxv246rS8XVzieyz+tcweU/7vWWG1pq7F2hKiwq1eF1o23Lt+w7od9PWy9JihkSpoTnb7M9VrhnoiSp3aBlyjiWf+UHDsDhTFar1ersIoDGxhS90NklNAjWlFhnlwADGNf2qY1xTa+rR58dh7kaF8ON1QAAAAAM4XImAABwWQcShyu4tU+V5RnH8tV5eKITKgLgbIQIAABwWQQFAL/F5UwAAAAADCFEAAAAADCEEAEAAADAEEIEAAAAAEMIEQAAAAAMIUQAAAAAMIQQAQAAAMAQQgQAAAAAQwgRv1JeXq758+crPDxcnp6e6tKli5KTkxUZGakpU6Y4uzw0cGazSfMe6aGTSeOUtz1GK17+nZo39XB2WcAVYVw7zuhBIdqcMFi522JUsm+Ss8tp1Og1UD1CxK/ExsZqzpw5iouL09q1azVq1CiNHTtWaWlp6t69u7PLQwP3WGy0hvYL1s3j/6M2Az6UJL0b39fJVQFXhnHtOGfzirRg2Xd6eN4OZ5fS6NFroHquzi6gvli6dKkSEhKUlJSkvn0rfgH2799f+/btU2Jiorp16+bkCtHQTRnRQc+99aXSsyySpL+8vFs/rhmlttf66MjxfCdXB9QM49px1m/LkiT1vbGVkytp/Og1UD3ORPwsPj5egwYNsgWIX4SFhcnNzU3R0dGSpGHDhik6Olpdu3bVTTfdpA0bNjijXDQw/r7uCm7to73fnrYtS8u0KNdSrC6RzZxYGVBzjGsAuHpxJkJSZmamDhw4oJkzZ1Z57MiRI4qKipKHR8U1vgkJCWratKkk6csvv1S/fv105swZubi4OLRmNCy+TdwkSbn5xZWW51iK5eft5oySgCvGuAaAqxchQhUhQpJatap82rKgoEDJycm66667bMt+CRCSlJubK5PJJKvVWmu1mEymWtsWnOj6f1X60XK+RJLk7+NeaXlTX3flnStxWFn1DeO9gWFc26VWxvVveo2q6LPjMFdfXex9XcvlTJICAwMlSampqZWWz5s3T8ePH69yU/X06dMVEhKiESNG6OOPP5arK1kMl5drKVbGsXx169jctqx9kK/8fd2VknrGiZUBNce4BoCrF69+JYWEhCg6Olrx8fFq1qyZgoKCtGLFCq1Zs0aSqoSIf/zjH5Kk5ORkzZw5U5s3b5aPj0+t1FKbZzXgPKbohVWWvf3xQf3v/dHatPu4snOK9OLMHlq3NVMZx67em08Z7w0L49o+tTGuL9Zrs9kkN1ez3N0qLp/1cK/4b1Fx2RXvryGqqz5L9Pq3mKtxMZyJkGQ2m7V8+XJFRUVp6tSpmjRpkgIDAzV9+nS5uLjYbqr+rb59+8psNmvr1q0OrhgN0dyFKfpP8lHt/mCosjaMkYvZpHsfT3J2WcAVYVw7TsyQMBXumaj1bw2Sq6tZhXsmqnDPRAW3rp03sXABvQaqZ7ISLy8pJiZG+/fvV0pKiiQpPz9f2dnZCg4OllRxY/Xvfvc77d+/X23btnVmqahnLvXuFiqzpsQ6uwQYwLi2T22Ma3pdPfrsOMzVuBguZ7qMPXv2qGfPnrafz507p9GjRys/P1+urq7y9PTUe++9R4AAAADAVYUQcQn5+flKTU3VtGnTbMtatmypHTv49koAAABc3QgRl+Dj46OysqvzBioAAADgcrixGgAAAIAhhAgAAAAAhhAiAAAAABhCiAAAAABgCCECAAAAgCGECAAAAACGECIAAAAAGGKyWq1WZxcBAAAAoOHgTAQAAAAAQwgRAAAAAAwhRAAAAAAwhBABAAAAwBBCBAAAAABDCBEAAAAADCFEAAAAADCEEAEAAADAEEIEAAAAAEMIEQAAAAAMIUQAAAAAMIQQAQAAAMAQQgQAAAAAQwgRAAAAAAwhRAAAAAAwhBABAAAAwBBCBAAAAABDCBEAAAAADCFEAAAAADDk/wOBFHxDhAlloQAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<Figure size 900.592x264.88 with 1 Axes>"
+       "<Figure size 1020.99x264.88 with 1 Axes>"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -202,17 +260,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1683.19x866.88 with 1 Axes>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -223,7 +281,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -233,7 +291,7 @@
        "<Figure size 1020.99x204.68 with 1 Axes>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -245,7 +303,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -255,7 +313,7 @@
        "<Figure size 1748.21x1167.88 with 1 Axes>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -267,7 +325,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -277,7 +335,7 @@
        "<Figure size 604.408x385.28 with 1 Axes>"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -289,17 +347,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 719.992x445.48 with 1 Axes>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -313,17 +371,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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nSZKk9BPKav3760ezrSRJUjL42wlQWFhIcXEx5eXldO3alR49ejB79mzmzZsHcMRQ8NZbb2XFihU8+eSTca0lGo22vJGURs6dG/vTfTex7HPy2GtJrVUXgVH/gPf2NL9dThjWL11Ix5zk1CVJktqW0tJSACoqKlrc5mi4UhAIh8PMmjWLoqIixo8fz5gxYygoKGDixIlkZWVRXFzcaPvbb7+dhx9+mEcffZQTTjghRVVLkiQplbLDcNkZzW8TAsp6YCAoSZLSjisF9+nXrx/z589vNHb11VczcOBA2rdvf2Ds1ltvZd68eTz22GN06dIl2WVKkiQpjVx9Fjy1HlZsa7jj8H4h4NT2MHFAKiqTJElqnisFm7Fo0aJGpw4vXbqUW265hc2bN1NaWsqQIUMYMmRICiuUJElSKuXnwK8ugE/1hnYH/WadFYKP9YAZH4SCvNTVJ0mS1BRXCjahpqaGqqoqJkyYcGCsqKjIa0xJkiSpkfwcuGkwXD8APvRobGzeR+Ekw0BJkpTGDAWbkJ+fT319farLkCRJUobo1K7h7waCkiQp3Xn6sCRJkiRJkhQwhoKSJEmSJElSwBgKSpIkSZIkSQFjKChJkiRJkiQFjKGgJEmSJEmSFDCGgpIkSZIkSVLAGApKkiRJkiRJAWMoKEmSJEmSJAWMoaAkSZIkSZIUMIaCkiRJkiRJUsAYCkqSJEmSJEkBYygoSZIkSZIkBYyhoCRJkiRJkhQwhoKSJEmSJElSwBgKSpIkSZIkSQFjKChJkiRJkiQFjKGgJEmSJEmSFDCGgpIkSZIkSVLAGApKkiRJkiRJAWMoKEmSJEmSJAWMoaAkSZIkSZIUMIaCkiRJkiRJUsAYCkqSJEmSJEkBYygoSZIkSZIkBYyhoCRJkiRJkhQwhoKSJEmSJElSwBgKSpIkSZIkSQFjKChJkiRJkiQFjKGgJEmSJEmSFDCGgpIkSZIkSVLAGApKkiRJkiRJAWMoKEmSJEmSJAWMoaAkSZIkSZIUMIaCB4lEIkyfPp2+ffuSl5fH4MGDqayspH///owbNy7V5UmSJEmSpOPwTi389nW442W4ayksfhei0VRXJaVGdqoLSCdjx45lzpw5TJ06lZKSEhYsWMDo0aPZuHEjkydPTnV5kiRJkiTpGESi8PNl8LsVEAHCoVgY+PuVMKQr/OD9cGJuqquUkstQcJ+ZM2cyY8YMKioqGDFiBAAjR47kxRdfZM6cOQwdOjTFFUqSJEmSpGNx72tw/4qGzyMHrQ58+V2Y+CzM+CC0y0p+bVKqePrwPuXl5ZSVlR0IBPfr06cPOTk5FBcXA/CpT32K4uJizjnnHD7wgQ/w+OOPp6JcSZIkSZLUClv3NA4EDxUFqrbBk+uSVpKUFlwpCKxZs4YlS5YwadKkwx6rrq6mqKiI3NzYOuIZM2bQpUsXAP79739TWlrKu+++S1aWbydIkiRJkpRuHnsb9kaa3yYMzK2Gsp5JKUlKC4aCxEJBgG7dujUar62tpbKyklGjRh0Y2x8IAmzdupVQKEQ0jlclDYVCcZtLSoaSh2L7v/tuYtnn5LHXko6HzyGSlH66f/ZWTrvq5ma3iQCV/15G6IKBySlKOgbx/v3C04eBgoICAKqqqhqNT5s2jXXr1lFSUtJofOLEiRQWFnL55Zfz5z//mexss1VJkiRJktJR/fZ3W9wmGqmnbtumJFQjpQ/TLKCwsJDi4mLKy8vp2rUrPXr0YPbs2cybNw/gsFDw5z//OQCVlZVMmjSJp556ivz8/LjUEs9Vh1IynDs39qf7bmLZ5+Sx15KOh88hkpR+3qmFSx6LrQZsSiicxR2jP8gV3/L5W+mntLQUgIqKiha3ORquFATC4TCzZs2iqKiI8ePHM2bMGAoKCpg4cSJZWVkHbjJyqBEjRhAOh3nmmWeSXLEkSZIkSWqNU9rDpb2afjwMnJIHo7yeoALGlYL79OvXj/nz5zcau/rqqxk4cCDt27cHoKamhs2bN9O7d28gdqORlStXMmDAgKTXK0mSJEmSWuebg2D7XnhiXSwEPHjV4Cnt4efnQwcTEgWMu3wzFi1axLBhww58vmPHDq666ipqamrIzs4mLy+P3//+9/Tq1cxbDpIkSZIkKaXaZcH3zoUl78Ffq+Gh6tj47UPhQ91jj0tBYyjYhJqaGqqqqpgwYcKBsVNPPZXnnnsuhVVJkiRJkqRjEQrBoK6xj/2hYJmnDCvADAWbkJ+fT319farLkCRJkiRJkuLOG41IkiRJkiRJAWMoKEmSJEmSJAWMoaAkSZIkSZIUMIaCkiRJkiRJUsAYCkqSJEmSJEkBYygoSZIkSZIkBYyhoCRJkiRJkhQwhoKSJEmSJElSwBgKSpIkSZIkSQFjKChJkiRJkiQFjKGgJEmSJEmSFDCGgpIkSZIkSVLAGApKkiRJkiRJAWMoKEmSJEmSJAWMoaAkSZIkSZIUMIaCkiRJkiRJUsAYCkqSJEmSJEkBYygoSZIkSZIkBYyhoCRJkiRJkhQwhoKSJEmSJElSwBgKSpIkSZIkSQFjKChJkiRJkiQFjKGgJEmSJEmSFDDZqS5Ame/cuan5vosuTc33lSRJkqS2JlT8m5R83+grY1PyfVPJXiePvW6eKwUlSZIkSZKkgDEUlCRJkiRJkgLG04clSZIkSUqhbXugahvU7IVwCHqcAGd0hKxQqitre97dDa9va/h8dQ2c3iHWd8VPNAobd8HK7bCrHtqF4Yx8OO0ECNnrtGEoKEmSJElSkm3ZDXPfgoeqY8HUofKyYPgpcOUZcG6BQcrxeKcW/rIaHlkDb+9s/NjlT0KHbBjRLdbr951or4/Hmh3w51Xw6NpYKHiozu3gI93hijOgb+dkV6dDGQpKkiRJkpQk0SjMWQ0/Xgo765veblc9PLku9nFuAdw8JLbKSq1XF4Hfr4R7XoO9kaa321EH89bEPkZ2gynFcFJe8upsC3bXw72vwe9WQDOtZuse+PPq2MelvWByEeTnJK1MHcJrCkqSJEmSlAS76uEbz8N3X2k+EDzUok1w1Xx4ZkPiamtrtu+FCc/Cz5Y1Hwgeav56uKoCFr+bsNLanI274ItPw/0tBIKHmlsNn6mAN7cnqjK1xFBQkiRJkqQEq4vAN5+HivXH9vW19fD1hfDcO/GtK956n5bPb2/7YEprqK2D65+DFzcf29dv2RMLFF/dEt+62qL3dsN/LWh8ncajsb4WrnsGqo9wCn06SYf9OhEMBSVJkiRJSrAZr8OCFgK9RZfGPppSF4X/92LsZhlq2o9fhSXvNb9NS72urYdvLYoFjDqyaBTuePnI18Q8WEu9fndPbL+uO5plhooLQ8GDRCIRpk+fTt++fcnLy2Pw4MFUVlbSv39/xo0bl+ryJEmSJEkZaOU2+HVVfObasgd+sDg+c8XbT6YM44/TRlJ2QU/m/+Zihg44Kek1vLAJZq+Kz1xrd8Ivlsdnrrbo8bePfeXroV7dAg+ujM9c8ZYO+3WieKORg4wdO5Y5c+YwdepUSkpKWLBgAaNHj2bjxo1Mnjw51eW1Ga/dVEqnwR+h+6f/X6vGJUmSJCmTPfhGbJVfvDz+NkzcAT07xG/Olmx/7gsH/p6bkwXA7r0NF0bsOOwBvvq95+h9Wj63jD+HMVOfTl5xB7l/RXzn+/MquLYfdGoX33lbqzV9T4VoFH77enznfHAlfPYsyEni8rVM2a8TxVBwn5kzZzJjxgwqKioYMWIEACNHjuTFF19kzpw5DB06NMUVSpIkSZIyTc1e+Pua+M4ZBf6yGq4fGN95m3Nw+PSrWy4kOyuUdgHJ2zvh2Thfc3FPBB5ZA6ML4ztva6Vr35dugapjvI5gU97dAxXr4KM94jtvc9K1v8ni6cP7lJeXU1ZWdiAQ3K9Pnz7k5ORQXFzcaPzee+8lFAoxe/bsZJYpSZIkScogS96D3Qm4VtqiTfGfM9O9uCkWmMabvT5conpir5PLUBBYs2YNS5Ys4corrzzsserqaoqKisjNzT0w9vrrr/Pb3/6WYcOGJbNMSZIkSVKGWb41MfO+vs0bMxwqUb1e7l2ID5OoXi9L0Lw6Mk8fJhYKAnTr1q3ReG1tLZWVlYwaNerAWF1dHV/60pf45S9/yQ033BD3WkKhUNznTLSSh47+vZh1s+5gw1+nNxqrr62h0+CPtHqOTOxVW7T/5+/PI7Hsc/LYa0nHw+cQSYfq+eW7OPWTX2s01tydWJt7/Ny5DX/fE4G8LgXUb998nBXuM+jXcZlm1+56lr/Z+mQnns+XZ37jT3S98NONxuLR6/U1ewmF4nhRwTj1+mjFs9f9yp+iY9EHG40dS68P7jPAS69XExrR+zirO0gb2K8TOa+hIFBQUABAVVUVF1988YHxadOmsW7dOkpKSg6M3XbbbYwaNYohQ4Ykvc62pPuVNx3xRiOSJEmS1JYk8k2CUDj9Tv7bsLmW79/3Skq+d8J6HUq/Pqda0Hqdyv06kQwFgcLCQoqLiykvL6dr16706NGD2bNnM2/ePIADoeC//vUvnnzySSoqKhJWSzSaiCsgJNahyX6yZGKv2qL9P39/Holln5PHXks6Hj6HSDrUfVXwi+WNx5o6htq/kqo1x1g5Ydj57jtxu1NrqPg38ZnoKMXz+XLaYvjfNxuPxaPXp3TIimudbaHX33wenlzXeCwevR58Vk9esdeHKS0tBWg2j9q/zdFIzwg2ycLhMLNmzaKoqIjx48czZswYCgoKmDhxIllZWQduMjJ//nxWrlzJWWedxRlnnMFzzz3HhAkT+OEPf5jif4EkSZIkKR0N6JKYeft2Im6BYFtxdufMmjeTJazXCfr/oiNzpeA+/fr1Y/78+Y3Grr76agYOHEj79u0BmDJlClOmTDnweGlpKV/5yle44oorklqrJEmSJCkzvO9EaBeOXQMwnoaeFN/52oJE9aTEXh+mpCBB89rrpDIUbMaiRYu8w3AC9L+j4qjGJUmSJClTdcyBj/WAh9+K77z/Gcd7MRyta2/5Z+q+eTN6doDzToZ/bYzfnO3CcMnp8ZvveKRT34tPhLM6wsrt8ZuzSzsY2T1+8x2tdOpvsrjYuAk1NTVUVVUxdOjQJrepqKhwlaAkSZIkqVmfK4RwHO/LMLI79M6P33xtyTV94jvff/SCLrnxnbMtCIXi3+vRhZCbFd851TxDwSbk5+dTX1/P9ddfn+pSJEmSJEkZrG9nGNM3PnN1zIH/HhSfudqiD5wcC/LioVt7mDggPnO1RaN6wgWnxGeufp3gC3EOGdUyTx+WJEmSJCnBvtwPXnkXnt/U9DYt3Z01HILvnAMFefGtra2ZVATLtkDVtqa3aanXeVlQXgL5OfGtrS0JhWDqEBj7T1i7s+ntWup15xy4o8Qb56SCLZckSZIkKcFywnDnB+D8k4/t69uFYdq58MFu8a2rLcrPgV+cD0XHeCfb/Gz48XlQ3DW+dbVFBXlwz/BjP539pFz45XA4s2N861LrGApKkiRJkpQE7bPhx8NgchHkHsXRePGJ8IdSKE3hTRgyTZdc+PWFcG0/yDqK6zkOPwX+NDJxd9dti7qdAL+/CD5z5tF93cd7xHrdr3Ni6lLLPH1YkiRJkqQkCYfgs2fBR3vAX1fDQ9Wwvvbw7bJDsevjffpMOP+Uowu2FJMThuvOhk+cDnNWwyNvwebdh2/XLgwfPBWuPBNKToqdFquj0z4bbhwEl50Bs1fBo2tg294jbJcFHz4NrjgD3ndikovUYQwFJUmSJElKspPz4Nr+sY9Nu+C1rfC1f8Ue+91FcFZHaOedWOOiZwf46kC4fgBsqIXXt8GOOsgOQ+8OsVNXsz2PMi4KO8I3B8E33he7zuCKbXDj87HHZo2EXvkG3OnEUFCSJEmSpBQqyGt885ABx3gtPDUvFIqd6trthFRX0vaFQrEwtmeHhjGvG5h+zMIlSZIkSZKkgDEUlCRJkiRJkgLG04d13BZdmuoKJEmSJEnHI/rK2FSXEBj2OnnsdfNcKShJkiRJkiQFjKGgJEmSJEmSFDCGgpIkSZIkSVLAGApKkiRJkiRJAWMoKEmSJEmSJAWMoaAkSZIkSZIUMIaCkiRJkiRJUsAYCkqSJEmSJEkBYygoSZIkSZIkBYyhoCRJklDMpocAACAASURBVCRJkhQwhoKSJEmSJElSwBgKSpIkSZIkSQFjKChJkiRJkiQFjKGgJEmSJEmSFDCGgpIkSZIkSVLAZKe6AGW+xQ/kpuT7DvrC7pR835bYj+Sx18ljr5PHXjdmPyRJkqTEcKWgJEmSJEmSFDCGgpIkSZIkSVLAePqwpKOyoRYeWwtLt8Ab2xvGv/k8DOwCHzkNenZIXX1tSXUNPLEOXt3SMHbVfDirU6zXHzsNTmmfuvrakte3QsX6xr3+bAX06QTvOzHW6y6pOYtVkiRJkhLClYJKirKv7+HhZ+pbPd7WZWI/1uyAbzwPn3wM7noVHnsbVh4UCj65Dn62DD71BHz1OVixLXW1HiwTe121Fb7yLFz2JPx8Gcxf1/DYyu3wj7Vw11K45HH47+dh7Y7U1XqwTOz1S5vhy/+E0ZVwz2vw9IaGx6q2wbw1MG0xXPwY3Ppv2LQrdbUeLBN7nUj2Q5IkSTp6rhSU1KLZb8aCwF2tPLZe8A4s3AjXnQ1f7AOhUGLraysiUbjvdfjVa1Afbd32T6yL9XtSEVx2RsJLbDPqIvCTV2HmG9CKVrMnAn97CyrXw7cHx1bESpIkSVImc6WgpGb9cjl8b3HrA8H96qKxVW7fewWirUldAi4ShfKX4e7lrQsED1ZbD+WvwL2vJaa2tqYuAv+9CP7QykDwYNv2wpRFMHtVIiqTJEmSpOQxFJTUpL+uht9UHd8cf14NM1bEp5627L4q+Gv18c1x72vwt+OcIwimL4mt+Dse338FntnQ8naSJEmSlK4MBZUW3toQ5Yt37OWaO/Zyze17WfpmJNUlpdy139/LiK/s4d6HUnM9rLd3wp1LWt5u0aWxj+bcszx2I4d0lepev7YVftVC+NqaPkMs8FpfG5+6EiHVvX7unZZX+bWm11Hg9pdh2554VRZ/qe51OvE1RpIkSTqcoeBBIpEI06dPp2/fvuTl5TF48GAqKyvp378/48aNS3V5GS07C+qOcFxaVwfZ2dCxA9z1tWzuvymHm8dkM+0PbfsgtqV+ANx2bTaTr8pKbmEH+eUy2BmnH0NdFH78anzmOlqZ0Ou7lh79KcNN2VEXOwU5FdK919Eo3Lk0fvNt3AUPpGgVbLr3Otl8jWnazjqYsyp2h/jJC+EXy2D9zlRXJR2fzbtiK+xvXAjfWAgPrkzvN2kkSUpX3mjkIGPHjmXOnDlMnTqVkpISFixYwOjRo9m4cSOTJ09OdXkZ7bSCENXvNE49du6Ksmkr9Dw5RJf8hjtRtMuGrDYeV7fUD4BuXVN3d453d8Pj61re7mg8txGqa6BXfnznbUm69/rN7fD8pvjO+Y+1cEMRdGkX33lbku69fmEzvLG95e2Oxl+rYVx/aJfk7C3de51svsYc2UubYdJC2L634V3gp9bDb1+HiQPgi31TWp50TOZWx67BWxdt2K/nr48F3neUQGn3lJYnSVJGCcivxS2bOXMmM2bMYO7cudx4442MHDmSm266ifPPP5+6ujqGDh2a6hIz2n9cGObPFRFeeC1CfSTKth1Rvv9gPX1PD3F274aDtfpIlO89WMeXPtG2V7e0th+pUrke9ibg7LrH3o7/nC1J914/noCe7Ikc/zXzjkW69/qxtfGfc8ue+Ie6rZHuvU42X2MOV10D1z8HO/bGPo/s+4DY6e8/Wxa7bqyUSf65Ab7zUiwQhMb79Z59N5Fa/G6qqpMkKfO4UnCf8vJyysrKGDFiRKPxPn36kJOTQ3FxMQClpaWsXr2azp07A1BWVsb3vve9pNebaT4xPIvaPVD+QD3rNkc5IRdKzg7z0xuyyc6KHbBFo1Fu/nU9Fw0Oc2Fx286rW9OPVHp1S2LmXZ6geZsT5F7/R6/EzN2UdO/1sgRd13LZVrjg1MTM3ZR073Wy+RpzuJlvxO4a39yVCX71GnyyFwRwl1GGuve12IqGI71vGSV2mYgZK+CHH0hyYZIkZShDQWDNmjUsWbKESZMmHfZYdXU1RUVF5ObmHhj7wQ9+wBVXXJGQWkKhzPvN/JX7W3eO4hWlWVxR2vTqjO/+rp5ep4a46sOtW8GRrr2KVz+OVjz70a/8KToWfbDRWEs3XjjS4+fObfz5vOeXMf28gcdZXYO20Ouiu18nr3ufRmPN9bqpxw7t9W/mPsmUwR8+zuoatIVeD/lTDVl5HQ58fiz7NBze6zvueZBr7/z8cVbXoC30Op58jTk2Q2ZuJeuETs1us2EXdHnfRdS8+nSSqgqGkodiUWym70Pppl23Qgbds7LZbSJAxdv1ZJ1wIpHaOF8vQkoSn0PUFrlfx0+8e2goSCwUBOjWrVuj8draWiorKxk1alQqygqU55dFmF0RYUjfEP96NULnDvCjr+akuqyUuvnXdSxeGWVPXZRX3ojws0nJ60e4XV6C5m2fkHmPV0p7nWOvk8VeB/c5NXCvMeGsFgPB/bI7FSS4GCk+WruvhsJZZOefyB5DQUmSWmQoCBQUxH7JqKqq4uKLLz4wPm3aNNatW0dJSUmj7W+66SZuvfVWCgsLue222w6cWhwP0WicbkGaRIsfyG15oxa8f0CYF+87ursipGuv4tEPgO98+ej+e8azH+OegRc3Nx47dHXUfvtXUzX1+MHOPusMFsexzrbQ68uegOodjceO1Muj6TPAyAvPZ7m9buSiRxrfUTse+zTAZ6+4jNu+a68TxdeYY1M6D2rqWt6u4pE5DO6a+HqCZP9zR6bvQ+nm7Z1w6eMtbxcOwbtvr6aDRznKUD6HqC1yvz5+paWlAFRUVLS4zdHw5RIoLCykuLiY8vJyunbtSo8ePZg9ezbz5s0DaBQKPvDAA5x++umEQiH++Mc/8vGPf5wVK1bQoUOHpqaXMk5hx8NDwXjNq8YKOx4eCsZrXjVW2AmWvJeYeaV088le8Mc3mr6mYAg47QQYdGIyq5KO3WknwJCu8Mq7R76mIMSuN/jh7hgISpLUSm3/StutEA6HmTVrFkVFRYwfP54xY8ZQUFDAxIkTycrKarQSsFevXgfO4f7MZz5Du3bteO2111JVupQQA7skZt4BCZo3k9nr5ElYrzsnZl7peHy2MBaMNPWLXhQYf3ZsVZWUKa7rH/vzSLttCMgOwxf7JrMiSZIym6HgPv369WP+/Pns2LGD6upqbrvtNhYvXszAgQNp3z52vahdu3axadOmA1/zxBNPsH37dvr06dPUtFJGKu0GuXF+dggBH+8R3znbgo/1OPLBzfHIy4r9DNVYWQL2v4JcONdLsikNdT8B7h4OXY9w9nV2CKYMgrKeya9LOh7vPxnKz4XcI9wvKD8H7joP+vtGjSRJrebi+mYsWrSIYcOGHfh827ZtjBo1ij179hAOh+nUqRNz586lUyfPHVPb0qld7GDxoer4zXnhqbGDVDXWswMMPwWeeSd+c47qGTs4UmODTowdLL62NX5z/mfv2MoUKR2d3QUe/ihUrIcpi2JjXxsInzwdusTn0pVS0n3kNBh2MvzfGvj+4tjYzUNib7Llxe+G7JIkBYKHMk2oqamhqqqKoUOHHhg75ZRTeOGFF1i8eDEvv/wyTz/9NBdeeGEKq8wM0x6s45o79vK937fiiucBtuHdKN9/sKFHv3u0nmtu35uyev7rbOgYp2CpXRhuKIrPXImQ6t7fUBTrUTx0ymk4vSodpLq3BwuF4Bvvi9/KzNNOgKvTdKF4OvU90XyNaV52OBai7Hd1HwNBZb78HLjyzIbPL+1lIChJ0rEwFGxCfn4+9fX1XH/99akuJaO9uirCzt1w/0057K2DJW80dWloPbc0wvlFsf+Se/ZGWV6d2jsznZwH/z2o5e3OndvyXVq/OhB658enrkRIde/P7AgTBjS/TWv6DPCtYijIi09d8ZDq3h5qyEnw+bOa36Y1vQ6HYitTTkjT9fbp1vdE8TVGkiRJOnZpejijtuKVlVHOL4qtyxlWFOblFVHeV3j4ds8vi3DDT+ro3yvE2o1R+vcK8ZMb2u75j0f693buEOJbV8cO4uc8FeHSC8P8Yk59Suss6wkbd8GPXz32Ob7QB646s+XtkqGp/WzR8mjKe/+5Qti0C36/8tjnmFQEH03hdRszZb/+ykDYtDt26tmxCIfg1nPS41qC6bxPJ4OvMZIkSdKxc6WgEmr7TujQPnbA1vGE2OdHUtI/xKDCEPd9K4eS/mFu+kLbzquP9O+t3Q0n5IXYWxdl0bIo5w1Mj/+eV/eB24ce/anEeVmxlYbXD4idtpkOjtT3aDSaFr0PhWLX+vrGoKO/yUunHLijBD7Xwgq4RMuU/TprX6h3Xf/Y34/GyXnw4/Ni121MB+m8TyeDrzGSJEnSsfO3YiVUx/awozZ22lpNbeyg7UjWbIQeJ8cO7Da8G+XUrmmSIiXIof/e97ZH6dcr9vnDCyJcfH56HcCX9YSSAvjFMvj7WtjTzBl62SH40Gkw4ezYTTRSYdOWKN/8ZePrixV0DvGVy7MO28+Wr46kTe9DodiqyuEnwy+Ww5ProL6Zsz7bhWPh1Piz0+OU4Uzar8MhuLY/XNQN7l4O/9wAzZ1g2yE7ds2qa/vFbsSTbJm6TyearzGSJEnSsTMUVEIV9wkxe36Ej58Xu8bVf1wYpq4+ytYaOKlzw0HZyrVR+vQIUR+JEmq7x68HHPrvfXZpwylwq9ZFea06wv/Or2fF2ih/eKyez3409VfPPjkP/uec2E0x5q+DV7fAyu2wqz62su3MjjCgC5R2S31AVdAltiLoUPNfjBy2n6Vj70/Ph++eGzuduGIdvLoVVm2H3RFonwVndYSBJ8LIbqkJqJqSift1/87wo/Ng7Q54agMs2wLVO2BvJBYE9u0ERV2gtHtqrx+Y6ft0ovgaI0mSJB07Q0El1MAzwrTLiXDNHXs5u1eIQWeFqd4Q5b5H6rnlSw2734q1UQb3CbFnL7y7DTZuiXJyl7a7kuPQf++/lka4pizWj0lXNfTlmtv3pt0BfOd28KnesY9Mc6T97NU3o1xTFksJ0q33BXlwRZpcj7E1Mnm/7tEBRh/hWnTpLtP26XjzNUaSJEk6doaCSrgpn2+8m71WHWXUsMZLNa79ZMOB6pw72v7F3w/99/79X/WEw4cfoN7//9p+L5LpSPvZR84N2fs4cb9OPvdpX2MkSZKkY2UoqKT76Ps9d+tQHz+vba3eyST2PnHsbWoEve++xkiSJEmtYyio4zboC7tTXUJasR/JY6+Tx14nj71uzH5IkiRJieHb6ZIkSZIkSVLAGApKkiRJkiRJAWMoKEmSJEmSJAWMoaAkSZIkSZIUMIaCkiRJkiRJUsAYCkqSJEmSJEkBYygoSZIkSZIkBYyhoCRJkiRJkhQwhoKSJEmSJElSwBgKSpIkSZIkSQFjKChJkiRJkiQFjKGgJEmSJEmSFDCGgpIkSZIkSVLAGApKkiRJkiRJAWMoKEmSJEmSJAVMdqoLUOZb/EBuSr7voC/sTsn3bYn9kCSpsVS8Ngb1ddFeJ4+9bsx+SEpHoeLfpOT7Rl8Zm5Lve7RcKShJkiRJkiQFjKGgJEmSJEmSFDCGgpIkSW3Uu7vhmQ0Nnz+2FqprIBJNXU1t1cZdULm+4fMn34a3d0LUXsdVNArrdsb6u1/lenin1l5LknS0DAWVFGVf38PDz9S3erytsx+SpETZVQd/WQ2fq4SP/R2+9q+Gx771Alz2JIz6B/x8GayvTV2dB8vU18WavTDzDbhiX0+/vrDhsW8ugksfh08+DvdVxQLadJCpvX5vd6yP+3v6zUUNj319IVz8GFwxH/6wMvZzSQeZ2utEsR+SlH680YgkSVIb8cIm+M5LsHZn89tt3g2/fT0WoEwcAJ8phHAoOTW2FZXrofzlWC+bs74WfrEcHlgBN74PPnE6hOx1q0Wj8Mhb8MOlsL2FsG91Ddy5FGasgG8XQ2n35NQoSVKmcqWgJElSG/D7lXDdgpYDwYPtjsRClMkLYZcLdVolGoWfvBpbndZSIHiwmjq45SW49SWoiySuvrakLhILuW95qeVA8GDv7oYbn4cfL/WUYkmSmmMoKEmSlOH+9CbctfTYv/6fG+C/nzesao39q/6O1cNvwR0vG1a1JBqN9elvbx37HL9bGft5SZKkIzMUVFp4a0OUL96xl2vu2Ms1t+9l6ZselVz7/b2M+Moe7n3IpRuSpKZVbYU7lzS/zaJLYx/NeeYdeHBl/OqKt3R4XfzXxthp181pTa//9hb835r41RVv6dDrR9e2HAi2pte/fT32c0tX6dDrdOHxgCQln6HgQSKRCNOnT6dv377k5eUxePBgKisr6d+/P+PGjUt1eRktOwvqjvC7Tl0dZGdDxw5w19eyuf+mHG4ek820P7TtX4xa6gfAbddmM/mqrOQWJknKKNFo7PTK+jitOrvnNVizIz5zHY1MeF3cUw+3vxS/+aYvgS174jdfa2VCr7fsgR8sjt98t70Eu1Pwq2Um9DqZPB5o2TsH3fzJldtqK1bXNPzdVfLpxxuNHGTs2LHMmTOHqVOnUlJSwoIFCxg9ejQbN25k8uTJqS4vo51WEKL6ncbPADt3Rdm0FXqeHKJLfsMVt9tlQ1Ybj6tb6gdAt65ehVyS1LxFm2D51vjNtycSOxX56++L35ytkQmvi0+sg3VxvFvztr0wtxq+0Cd+c7ZGJvT6b9Wx/sTL+lp44m24+PT4zdkamdDrZPJ4oGlvbIefvhq7lMN+n3gMRhfGniO8EZQy0XPvwC+Xw9ItDWOfng9f7g8f75G6upqz/bkvHPh7bk7sDZvdexveoOg47IGk15RoAXqqbd7MmTOZMWMGc+fO5cYbb2TkyJHcdNNNnH/++dTV1TF06NBUl5jR/uPCMH+uiPDCaxHqI1G27Yjy/Qfr6Xt6iLN7N7zK1UeifO/BOr70ibb9jmlr+yFJUnPmrI7/nA+/BXuTvEIlE14X/5KAXs9ZFf85WxLYXidgzpZkQq+TyeOBI3t9K3zxaXhmAxwcmW7eDT9bBrf+29VVyjxPvA3XPwfLtjQeX1UDN70AM99ITV0t6TjsgQMf9//tdR6ct6LRWFvkSsF9ysvLKSsrY8SIEY3G+/TpQ05ODsXFxQDs2bOHKVOm8Le//Y28vDxOPPFEnnrqqVSUnFE+MTyL2j1Q/kA96zZHOSEXSs4O89MbssnOiv0SEI1GufnX9Vw0OMyFxW07r25NPyRJasm/N8d/zu17YeU2OLtL/OduSrq/Lu6NwOL34j/vmp2waRcU5MV/7qake6837YLqBJzCvuS92Cng7ZKYM6V7r5PN44Ej+85LsKsOmnov5pE18KHTYES3pJYlHbOddbEwGw7fr/fn23cugdJu0P2EZFamIzEUBNasWcOSJUuYNGnSYY9VV1dTVFREbm4uAN/+9rfZvn07y5cvJysri3Xr1iW73Ix1RWkWV5Q2/ZvYd39XT69TQ1z14WC8K9hSPyRJas6mXbBpd2LmXrY1uaEgpPfr4srtiVs9uWwLfDDJB/vp3Ot4ng5/sLpo7Oc4wP06pTweaOzVLbHn2+aEgdlvGgoqczy6Bna2cEnQKLEV3BMHJKUkNcNQkFgoCNCtW+Nn2traWiorKxk1ahQAO3fu5J577uGtt94iKyv2QtW9e/e41hIKZd67hq/c3+6453h+WYTZFRGG9A3xr1cjdO4AP/pqTrNfk669ikc/AG7+dR2LV0bZUxfllTci/GxSZvZDkpQY7XsPYuBPXmk01tKdWJt6/Ny5jT+fcOO3uWz2d4+jusbi8dqYytfFTud8jL63/L3RWLx6/Z+fH8PmJ2Yce3GHyPRen/ShazjjazMajcWr18M+VMa2f//9yBsfg0zvdbwdbz/a0vFAaxV8fBy9J9zT7DYR4Ok3NhMaXpCcoqTj1Gv83RR8dCyhrKbjpmgkwl1/+gdfuXVU4gsa9OvEf48jSNTzU7znNRQECgpiT7BVVVVcfPHFB8anTZvGunXrKCkpAWDFihV07tyZO++8k0cffZRwOMzkyZP59Kc/nZK625L3Dwjz4n3xCdPaiu982f+ekqSmRUngRabS8AJWqXxdjCayH/a6kYT2OpH/Z46Rv+81COLxQDTSujsst3Y7KR1EI/XQYnAVBffrtOCrEFBYWEhxcTHl5eV07dqVHj16MHv2bObNmwdwIBSsq6tj7dq1dO/enYULF7Jq1SqGDx9O3759Oeecc+JSS2J/EUqMxQ/kpuT7pmuv7IckKRm27oEPP9p47NCVUfvtX0nV1OOH+tWPvsslcVwpmIrXxni+Lr6xPXbHxIPFq9cPz5zBsFNmHHNth8r0Xi/cCBOebTwWr16/UPF3+nQ69toOlem9jjf7cfSqa+CyJ5vfJgx8YuApvJzh/1YFx+Nvw5RFzW8TCmdx8xc+wdXfSfx+HSr+TcK/x5FkyvNTMK7e2oJwOMysWbMoKipi/PjxjBkzhoKCAiZOnEhWVtaBm4z06tULgGuuuQaAM844gwsuuICFCxemrHZJkhRMndtBjwRdoHtgkq+7lu5650P7BF3iLNnXuEt3Z3dOzLx5WXBmx8TMLR2rXvkw7GRobk1VBPj0GUkqSIqD0m5wUm7TYVMIyA3DJ3slsyo1xVBwn379+jF//nx27NhBdXU1t912G4sXL2bgwIG0b98eiJ1mXFZWxiOPPALA5s2bWbhwIYMHD05l6ZIkKaA+cHL85zw5LxaCqUFWCM5NwOW8+neOhbtq0KkdDEhAMFhyUuznKKWb/zkHurc/fHz/gfr4s2HISUktSTou2WGY/n7IzTo88A4D4RDcUQJdfP1LC4aCzVi0aNGBU4f3u/vuu7nnnnsYNGgQI0aMYMqUKQwbNixFFUqSpCC7vHf857yst+HJkVx+RvznvDIBc7YFCen1mfGfU4qHk/Pg/ovgmj7Q8aD7qgw5CX74ARjbL3W1ScdqUFf43YjYasCcfalTVgg+1B3uuxBK43u/1oS49pZ/Mmbq06kuI+G8pmATampqqKqqYsKECY3Ge/fuzeOPP56iqjLTtAfrWLoqyoDeIaZ83l1OkqR4ObsLDD8FFrwTn/k65SQmkGkLzj8ldmrr8q3xma97eyjrEZ+52pqyHnDf6/D2zvjM179z7Oen1PKYoGkn5sL1A2OrArftja2w6mCLlOHOyIebh8CUQbB9L3TIiV3KQenFlYJNyM/Pp76+nuuvvz7VpWS0V1dF2Lkb7v//7N17vFV1nTfwzz7ncFO8hJgoiooIKnpQqESzAbuC020qK5sxQybLSzPqNDM2RqOZzETW09MzM93Leioriaes7B6cNLTCO6biHTFUREVRBM45+/ljK3C4HmBfzjnr/X699kvOb//O2l9/e5291vrs31rrwn5Z254svK+z0SX1WI8+Uc4nv92+7uf/+/OOnPaJtQ2sCIDe4N/GVe/g8Z+PSoY05n5Z29To7WRzKbnomKSlSrMoP3Z0MrCHHPQ3emw3NrClMj7V0FJKLjq6585+7WljXyuOCbqnpanyGSwQpC/p35zsNVAg2FP5uKGmbr23nOPGVvbCJo5tyi33lHPkyE37/emOzpz7ufaMGVHKw8vKGTOilM+d22/Tjn3Y9bd35rixlZx+zdpy7lzcO+5WBEBjDRuUfGJ88k9/Sjq3sOnozt1ZTz6oZ89c6wnbyVG7Jx8Zl1xy85b7dGesP3hY8vIaXA9yR/WEsd3Yy4YmZx2W/M+dW+nTjbG+oDU5tEY3L6mGnjj2teCYAKBnMlOQmnrmuWTXQZUdgN12qfy8ORPGlHLUyFK+9pF+mTCmKRe+t2/n1X+6ozOvPHNNTv+PtXnD+WvyD59dmwV3lvOywypjNed3nXnzCf48AeieVw1LPvXyHb9D7t+OrMwSLPWA2VSb20Ym6THbybeMqMxi67cDY1VKcs7hyd838BphvWkf5PTRyYcO37EDlpZSMuPo5K01uO7mjujp63WtOSYA6Jn65laHHmO3QcmzqyrfeK5cVdkJ2Jwly5Lhe1d2FB59opx9hvSAo5Ia2twOz6rVyS4DS1nbXs6CO8o59gh/ngB036RhyXcnb99dcl86MPncxOS8Iyt3A+wJNreNLJfLPWo7+eYRlQuob89dcg8cnHz1hOR9h9auru7obfsgpx2afOWE7bsj9uF7JN+aVAlwe4resF7XkmMCgJ7JVy/UVOuoUmbP7cwbjq2cHvGWE5rS3lHOipXJXnus38jf+3A5o4aX0tFZTqlv7gt1sfEOz5PPlDN6ROXnn8zvzEnHFWAQAKi64bsmnz8uueWJ5MoHkuuXJSvWdO3Tvyk58iXJW0ckr9mvckH7Rnj8qXL+5fPtXdqG7lHKOW9v3iQUuPPBzh63nRy1e+WOoX9clsx+IFnweLKy6/9OBjUnRw9J3nZQ8qp9KtcLa7TeuA/SOiT53uTkmkeTOQ8kNz+RrOro2mfXlkog/o6DkmP3blzI3dvX61pxTADQMwkFqakjDmpK/36dOe3StTlsRClHHdKUxY+W87WfduSi09evfvc8XM64UaWsWZs88XSy7Kly9t6z734zuPEOz3W3r7/OygNLy7lrcWe+P7cj9zxcznd+1ZH3vM5VWQHonlIpOXqvyqNcTh5ZVXl0lJM9+1fuBtgTwqmhe1ZmTW1s7o2dm4QCPXU72VRKJr608ugsJw8/myxbXRn3IQOSEYN73g0ueus+SEtTcuK+lUdHOXloZbJ8dWV933tAJRDvCbNd+8J6XQuOCQB6JqEgNXfB33Vdze5aXM7UiV2PRt7/pvU7PXMu7fsXE954h+cPt3fmtCmVcTrvXevH67RPrO1TO4QA1FeplOy7S+XRW2wuFPjz/eWcNqWy79BTt5NNpeSAwZVHT9YX9kGaS8lBu1UevUVvXa+ryTEBQM9TKpfLffMWV9TNbd8c0JDXPeq9qxvyutuyI+Pxiz905A3H7tzOX08dDwDY2X2FHdlOFnW7uL1jbR9kx1mvu2rEMUFPHg+gZyi1frUh1+6/6wAAIABJREFUr1u+dXpDXnd79YCTR4Cd3RkHgL7MdrJ2jG3jGHsAGs1MQQAAAAAoGDMFAQAAAKBghIIAAAAAUDBCQQAAAAAoGKEgAAAAABSMUBAAAAAACkYoCAAAAAAFIxQEAAAAgIIRCgIAAABAwQgFAQAAAKBghIIAAAAAUDBCQQAAAAAoGKEgAAAAABSMUBAAAAAACkYoCAAAAAAFIxQEAAAAgIIRCgIAAABAwQgFAQAAAKBghIIAAAAAUDBCQQAAAAAoGKEgAAAAABSMUBAAAAAACkYoCAAAAAAFIxQEAAAAgIIRCgIAAABAwQgFAQAAAKBghIIAAAAAUDBCQQAAAAAoGKEgAAAAABSMUBAAAAAACkYoCAAAAAAFIxQEAAAAgIIRCgIAAABAwQgFAQAAAKBghIIAAAAAUDBCQQAAAAAoGKEgAAAAABSMUBAAAAAACkYoCAAAAAAFIxQEAAAAgIIRCgIAAABAwQgFAQAAAKBghIIAAAAAUDBCQQAAAAAoGKEgAAAAABSMUBAAAAAACkYoCAAAAAAFIxQEAAAAgIIRCgIAAABAwQgFAQAAAKBghIIAAAAAUDBCQQAAAAAoGKEgAAAAABSMUBAAAAAACkYoCAAAAAAFIxQEAAAAgIIRCgIAAABAwQgFAQAAAKBghIIAAAAAUDBCQQAAAAAoGKEgAAAAABSMUBAAAAAACkYoCAAAAAAFIxQEAAAAgIIRCgIAAABAwQgFAQAAAKBghIIAAAAAUDBCQQAAAAAoGKEgAAAAABSMUBAAAAAACkYouIHOzs5cdtllOfTQQzNw4MCMGzcubW1tGTNmTM4444xGlwcAAEAfdeeK5JcPJ22PJCvXNroaoAhaGl1ATzJ9+vTMmTMnM2bMyIQJEzJ//vyccsopWbZsWc4///xGlwcAAEAfc/Py5LKFlVDwRQOakrcdlHzo8KR/c8NKA/q4UrlcLje6iJ7giiuuyHve857MmzcvkyZNWtf+9re/PXPmzMkf//jHvPzlL29ghQAAAPQlNy1PzpyfdJaTzs08f/xLk8+8Imlxjh9QAz5aXjBz5sxMmTKlSyCYJKNGjUq/fv3S2tqaJHnggQcyadKkjB49OkcddVSuueaaRpQLAABAL1YuJ5fesuVAMEnmP5b8ZmldywIKRCiYZMmSJVm4cGFOPvnkTZ5bvHhxxo4dmwEDBiRJPvCBD+Rd73pXFi1alC9+8Yt597vfnTVr1tS7ZAAAAHqxW55IHli55UAwqRywz76/XhUBReOagqmEgkkybNiwLu2rVq1KW1tbpk6dmiR5/PHHc+211+aqq65Kkhx//PHZb7/9Mnfu3LzhDW+oSi2lUqkqywEAAKDnGvqGM3LgWV/cap/OJH968ImUXrVXfYoCer3tuUqgmYJJhg4dmiRZtGhRl/ZZs2Zl6dKlmTBhQpLKrMF99tln3azBJDn44IPz4IMP1q9YAAAAer1ye/fOOOtuP4DtZaZgkpEjR6a1tTUzZ87MkCFDMnz48MyePTtXX311kqwLBevhxWsazps3b4t9Jk+evM0+AAAA9FyPrEre9Ktka3N6SkneMW5YbnF/UKAGzBRM0tTUlCuvvDJjx47NmWeemWnTpmXo0KE5++yz09zcvO4mIyNGjMijjz6a1atXr/vd+++/PwceeGCjSgcAAKAXGjYoec2+leBva04+uC7lAAUkFHzB6NGjM3fu3Dz77LNZvHhxLrnkktx222054ogjMmjQoCSV04xf+cpX5qtf/WqSZP78+Xn44Ydz4oknNrJ0AAAAeqF/G5ccunvl3xuGg00v/HzhuOSwPRpQGFAITh/eigULFmTixIld2r7whS/kfe97Xz772c+mf//+ueKKK9K/f/8GVQgAAEBvtXv/5KsnJD9cXLnL8IPPVtpfNzw5ZWRy5EsaWx/QtwkFt2DlypVZtGhRzjrrrC7tI0eOzO9+97sGVQUAAEBfMqilEgCeMjJ52VWVtkvrd1l7oMCEglswePDgdHR0NLoMAAAAAKg61xQEAAAAgIIRCgIAAABAwQgFAQAAAKBghIIAAAAAUDBCQQAAAAAoGKEgAAAAABSMUBAAAAAACkYoCAAAAAAFIxQEAAAAgIIRCgIAAABAwQgFAQAAAKBghIIAAAAAUDBCQQAAAAAoGKEgAAAAABSMUBAAAAAACkYoCAAAAAAFIxQEAAAAgIIRCgIAAABAwQgFAQAAAKBghIIAAAAAUDBCQQAAAAAoGKEgAAAAABSMUBAAAAAACkYoCAAAAAAFIxQEAAAAgIIRCgIAAABAwQgFAQAAAKBghIIAAAAAUDBCQQAAAAAoGKEgAAAAABSMUBAAAAAACkYoCAAAAAAFIxQEAAAAgIIRCm6gs7Mzl112WQ499NAMHDgw48aNS1tbW8aMGZMzzjij0eUBAADURWc5+d0jyYeuT97wi+SNv0ouvSW55+lGVwY7bnVHctXiZNo1yet/kfzNb5L/uSN5dFWjK4PGaGl0AT3J9OnTM2fOnMyYMSMTJkzI/Pnzc8opp2TZsmU5//zzG10eAABAzbV3Jh+7MfnlXyqzSDpfaP/hg5XHheOStx7YyAph+61Yk5x9XXLnivXr9ROrk6/dnVxxX/K/Jybj92p0lVBfQsEXXHHFFbn88sszb968TJo0KUly4okn5sYbb8ycOXMyfvz4BlcIAABQe19ZVAkEk/WBYJKUX/jvpbckBw1Ojhag0It87MbkrhWVf3du9NzqjuQfr09++Jpkr4F1Lw0axunDL5g5c2amTJmyLhB80ahRo9KvX7+0trYmST72sY9l9OjRaWpqyuzZsxtRKgAAQE0835F8976t9ymlMrMKeov7n0l+/9j6YHtjnUlWdSRXPVTPqqDxzBRMsmTJkixcuDDnnXfeJs8tXrw4Y8eOzYABA5IkU6ZMyfve976cfvrpNamlra0tSVIqlbbZtzt9AAAAumu3ca/N6I//aqt9OpP8+qG1Kb2if32KKpAJP6rEVo71qmuft/1L9j/tk1vtU+7szKyrFuT0Dx9bp6qgNsrlLcXfmxIKphIKJsmwYcO6tK9atSptbW2ZOnXqurbjjz++rrUBAADUS9PAXbvVr9TSL2luSTraa1wR7LzurNelpqY0DRxch2qg5xAKJhk6dGiSZNGiRTnppJPWtc+aNStLly7NhAkT6lbLi6cvz5s3b4t9Jk+evM0+AAAA2+vep5N3zdt2v5cOTMrta2teT9G87KrKf7dnpg/b9tOHkn+/aet9mpJMffkRud3YUyBCwSQjR45Ma2trZs6cmSFDhmT48OGZPXt2rr766iSpaygIAADQKIfsnhy5Z/Lnpza9GcOG3n5QvSqCnfeafZNP3Zas3MrE1s4kf+Ou2hSMG40kaWpqypVXXpmxY8fmzDPPzLRp0zJ06NCcffbZaW5uXneTEQAAgL7u/COTptLmDxZLSUbsmrzz4HpXBTtuYEty7tit9zlhn+S4l9anHugpzBR8wejRozN37twubaeeemqOOOKIDBo0qEFVAQAA1FfrkOR/jk8uuTl56Nmuz03cO7nomGS3fo2pDXbUWw9MmkvJ//5z8tSa9e3NpeTNI5J/fiEMhyIxU3ArFixYsMmpwzNmzMj++++f6667Lh/4wAey//775957721QhQAAANU3fq9kzquTL2xwn8UfvDr5P8clew1sXF2wM940IvnZ65NPvXx9289en1w4Lunf3Li6oFGEgluwcuXKLFq0KOPHj+/Sfskll2TJkiVZvXp1li9fniVLluSQQw5pUJUAAAC1USolLxu6/ucD3ZiVPqBfU3Livut/HjKgcbVAozl9eAsGDx6cjo6ORpcBAAAAAFVnpiAAAAAAFIxQEAAAAAAKRigIAAAAAAUjFAQAAACAghEKAgAAAEDBCAUBAAAAoGCEggAAAABQMEJBAAAAACgYoSAAAAAAFIxQEAAAAAAKRigIAAAAAAUjFAQAAACAghEKAgAAAEDBCAUBAAAAoGCEggAAAABQMEJBAAAAACgYoSAAAAAAFIxQEAAAAAAKRigIAAAAAAUjFAQAAACAghEKAgAAAEDBCAUBAAAAoGCEggAAAABQMEJBAAAAACgYoSAAAAAAFIxQEAAAAAAKRigIAAAAAAUjFAQAAACAghEKAgAAAEDBCAUBAAAAoGCEggAAAABQMEJBAAAAACgYoeAGOjs7c9lll+XQQw/NwIEDM27cuLS1tWXMmDE544wzGl0eANBA7Z1J2yPJt+9N/t+DyePPN7oiAADYcS2NLqAnmT59eubMmZMZM2ZkwoQJmT9/fk455ZQsW7Ys559/fqPLAwAa5OdLks8sTJ5Ys76tqZSctH9ywVHJQHtUAAD0MnZhX3DFFVfk8ssvz7x58zJp0qQkyYknnpgbb7wxc+bMyfjx4xtcIQDQCD9fknz0xqS0UXtnOfnJQ8kjq5L/mpi0OP8CAIBexO7rC2bOnJkpU6asCwRfNGrUqPTr1y+tra158skn88Y3vjGjR4/OuHHj8vrXvz733HNPgyoGAGptbWdy2cJKIFjeQp8Fj1dOKwYAgN5EKJhkyZIlWbhwYU4++eRNnlu8eHHGjh2bAQMGpFQq5dxzz82iRYtyyy235I1vfGOmTZvWgIoBgHr43SPJU2u2HAgmlZ2pOQ/WqyIAAKgOpw+nEgomybBhw7q0r1q1Km1tbZk6dWqSZM8998xrX/vadc8ff/zxmTVrVlVraWtrS5KUShufpLSp7vQBAHbcPm//1+z/3v/cap/OJL+7/YGUjj+4PkUB1NmEH1W+GnH8UXvGun6MNX1Vuby1r7O7MlMwydChQ5MkixYt6tI+a9asLF26NBMmTNjs7332s5/NW9/61prXBwA0Rufzz26zT7lc7lY/AADoScwUTDJy5Mi0trZm5syZGTJkSIYPH57Zs2fn6quvTpLNhoIXX3xx7rnnnvz2t7+tai0vXtNw3rx5W+wzefLkbfYBAHbeX55L3vLrrZ8+XCqVcs5rx+bM7fhWFqA3edlVlf9uz+wTdoyxrh9jDWYKJkmamppy5ZVXZuzYsTnzzDMzbdq0DB06NGeffXaam5vT2trapf8nPvGJ/OQnP8nPf/7z7LLLLg2qGgCotf12SV6976Z3Hn5RU5IBTcnfHFjPqgAAYOeZKfiC0aNHZ+7cuV3aTj311BxxxBEZNGjQuraLL744V199dX71q19lzz33rHeZAECdzTg6eez55LYnKyFg5wbP9WtKPv2KZNigLf02AAD0TELBrViwYEEmTpy47ufbb789F110UQ455JB1p/Amyc0339yA6gCAehjcL/ni8cmv/5L84MHklicq7e8blbz9oGRfJw0AANALCQW3YOXKlVm0aFHOOuusdW1jx451vQEAKKD+zclJB1QeL16D6JwjGlsTAADsDKHgFgwePDgdHR2NLgMAAAAAqs6NRgAAAACgYISCAAAAAFAwQkEAAAAAKBihIAAAAAAUjFAQAAAAAApGKAgAAAAABSMUBAAAAICCEQoCAAAAQMEIBQEAAACgYISCAAAAAFAwQkEAAAAAKBihIAAAAAAUjFAQAAAAAApGKAgAAAAABSMUBAAAAICCEQoCAAAAQMEIBQEAAACgYISCAAAAAFAwQkEAAAAAKBihIAAAAAAUjFAQAAAAAApGKAgAAAAABSMUBAAAAICCaWl0AUDPUGr9at1fs3zr9Lq/JgAAAGCmIAAAAAAUjlAQAAAAAApGKAgAAAAABSMUBAAAAICCEQoCAAAAQMEIBQEAAACgYISCAAAAAFAwQkEAAAAAKBihINtULje6gmIol411vRhrAAAAiq6l0QX0JJ2dnfnMZz6TL37xi3nooYcyZsyYfO5zn8sZZ5yRSZMm5Utf+lKjS6ybG5cnV9yb/P6xZG1ncvBuyckHJW8ekQxobnR1fcu1jybfuy9Z8HjSmWT07sk7D06m7p+09NDY/pnr37vu3wP6VVaI1Ws71rXtNvGbda9pW8rl5Nd/Sb53f3Lbk5W21pck7xqZvGbfpFRqbH0AAABQT0LBDUyfPj1z5szJjBkzMmHChMyfPz+nnHJKli1blvPPP7/R5dXNd+5NPnN7ZRpp5wtt9z+TfPK25GdLkv86LtnFmrPTyuXkv+5IvnFP17G+a0Vy8c3Jb5cmn3p5zwwGNwz9vnzRCWlpLmXajGsaWNHWdZaTj9+c/OShrmN9yxPJTU9Uwu6PjkuaBIMAAAAURA+MGxrjiiuuyOWXX56rrroqH/7wh3PiiSfmwgsvzHHHHZf29vaMHz++0SXWxU3LK4Fgsj44SZIXz7S89cnk0wvrXVXf9JullUAw6TrWL/77mkeTLy+qd1V90/fvrwSCyebH+qrFyQ8eqHNRAAAA0EBCwRfMnDkzU6ZMyaRJk7q0jxo1Kv369Utra2uS5K1vfWtaW1tzzDHH5BWveEV+/etfN6LcmvnefdteKX76UPLU6rqU06d9595tj/WV9yerO7bRia3qLFfGemuTAEtJvnOf6wwCAABQHE4CTbJkyZIsXLgw55133ibPLV68OGPHjs2AAQOSJJdffnn23HPPJMlNN92UyZMn54knnkhzc3UutNfW1pYkKXXjAmfd6bO9jv7eM2keOHirfdrLycGvfkeemv+Dqr9+UTQN3DXHfG/lNvs9vTbZ68hX5tk759e+qKO+UvvX2Egt1uGNDdh3VI78wt1b7VNO8tCzycBhB2fNYw/UvCagd5vwo8o3CPX4DAPoCXzu1Y+xrh9jTV9V3o7ZLmYKphIKJsmwYcO6tK9atSptbW1dTh1+MRBMkhUrVqRUKm3XgPd0peZ+Ve3H5m3P+JVajPXOKLX0737fft3vCwAAAL2ZmYJJhg4dmiRZtGhRTjrppHXts2bNytKlSzNhwoQu/c8+++z87Gc/y4oVK/KDH/wgLS3VG8YXT1+eN2/eFvtMnjx5m3121Cnzknuf7nrdtc25/sdXZPQeV1T99Yuis5xM/WWyfBunYTclue9P87L3wNrXVGr9au1fZCP1CNSfa09e9/Nk9TZW6kHNyVMP3JWBPhWBbXjZVZX/9qUvBQG2xude/Rjr+jHWIBRMkowcOTKtra2ZOXNmhgwZkuHDh2f27Nm5+uqrk2STUPC///u/k1RO9T3vvPPyu9/9LoMHb/2U297i5IOSmbdu+fmmJEfsmYzeo14V9U1NpeTtByVfumsrfZJMGpa6BIJ92S4tyV8fkPy/B9ffMGdjpSRvPCACQQAAAArD6cNJmpqacuWVV2bs2LE588wzM23atAwdOjRnn312mpub191kZGOTJk1KU1NTfv/739e54tp54wHJ+L02/1xTkn5Nyb9ufjjYTu8ZmRyy2+ZvgNGUZLd+yT8cUe+q+qYzxlTC1c2NdSnJPoOSvx9d76oAAACgccyLecHo0aMzd+7cLm2nnnpqjjjiiAwaNChJsnLlyixfvjwHHnhgksqNRu69994cfvjhda+3Vvo3J587Nvlftyc/fihZs8Epl0e+JPnno5LD99zy79N9g/slX3plctltyS//knRsMI1twtDkgtbkgF4wAfX9F13b6BK2aejA5PJXJf95a3LNo+tnDJZSmY35r63JXmZkAgAAUCBCwa1YsGBBJk6cuO7nZ599Nu9617uycuXKtLS0ZODAgfnWt76VESNGNLDK6hvYknxkXHL24cmrf15p++7kZNTuDS2rT9qjf3LJhOTcsckbfllpm/PqZEQvCAN7m5cOSj5zbLL0ueRNv660/fi1ybBdGlsXAAAANIJQcAtWrlyZRYsW5ayzzlrXts8+++T6669vYFX1tfsGN2IVCNbWhrPUBIK1te8GIaBAEAAAgKISCm7B4MGD09HR0egyAAAAAKDq3GgEAAAAAApGKAgAAAAABSMUBAAAAICCEQoCAAAAQMEIBQEAAACgYISCAAAAAFAwQkEAAAAAKJiWRhcA9AzlW6c3ugQAAACgTswUBAAAAICCEQoCAAAAQMEIBQEAAACgYISCAAAAAFAwQkEAAAAAKBihIAAAAAAUjFAQAAAAAApGKAgAAAAABSMUBAAAAICCEQoCAAAAQMEIBQEAAACgYISCAAAAAFAwQkEAAAAAKBihIAAAAAAUjFAQAAAAAAqmpdEFwNaUWr9a99cs3zq97q/ZE7zsqvq/5oI31/81AQAAADMFAQAAAKBwhIIAAAAAUDBOH2YTy55P/rAsueOp9W0zbkzG7JGMe0ly5EuSUqlx9QEAAACwc4SCrHPXiuSri5K2R5KOctfnfrak8kiSkbsl7xmZvHlE0iQcBAAAAOh1hIKkvbMSBn7t7k3DwM2575nkE7ckVy9J/v3oZPiuta8RAAAAgOpxTcGCW9uZXLAg+fKi7gWCG7pxefK+a5J7nq5NbQAAAADUhlCw4C69JZn3yI7//pNrkrOuSx5/vno1AQAAAFBbQsEC++1fkp88tPU+C95ceWzNE6sr4WJ5O2caAgAAANAYQsENdHZ25rLLLsuhhx6agQMHZty4cWlra8uYMWNyxhlnNLq8qlrbmXxqYfWWd82jye8fq97ydsQz17933WPNDdOy5oZpXdqonrsunJyl3/9Et9sBAACAnsWNRjYwffr0zJkzJzNmzMiECRMyf/78nHLKKVm2bFnOP//8RpdXVXOXJsuqfMrv9+9PTtinusvcHrtN/Oa6f3/5ohPS0lzKtBnXNK4gAAAAgB5KKPiCK664IpdffnnmzZuXSZMmJUlOPPHE3HjjjZkzZ07Gjx/f4Aqr66fbOG14R8x/LFn+fLLXwOovGwAAAIDqcfrwC2bOnJkpU6asCwRfNGrUqPTr1y+tra1d2r/0pS+lVCpl9uzZ9SyzKsrl5PanarPsO1bUZrkAAAAAVI+ZgkmWLFmShQsX5rzzztvkucWLF2fs2LEZMGDAura77747X//61zNx4sR6llk1y1cnT62pzbLvfrqxpxBTP0uvvDSP/vCyLm0dq1Zm93GvbVBFAAAAQHcJBVMJBZNk2LBhXdpXrVqVtra2TJ06dV1be3t7Tj/99Hz+85/PueeeW/Va2trakiSlUmmbfbvTZ3MG7DsqR37h7i5t27rD8Jaef9lVXX/+6Mdn5vRvXbhDdW3WUV+p3rK6aUfHdWdN+FG5R7x+d+178oXZ950f7dJ214WTt2sZjf5/bdTrA72bzxCgaHzu1Y+xrh9jTV9VLnf/2N7pw0mGDh2aJFm0aFGX9lmzZmXp0qWZMGHCurZLLrkkU6dOzdFHH13XGqup3F6jaYJJymtX12zZAAAAAFSHmYJJRo4cmdbW1sycOTNDhgzJ8OHDM3v27Fx99dVJsi4U/MMf/pDf/va3mTdvXs1qefGahlt7jcmTJ2+zz9a0dyaTf5Y837G+beMZfy96cYbglp7f2Nc/fXHe8N2Ld6iuzSm1frVqy+qu7UnVq+nFMW7069dTo/9fG/X6QO/mMwQoGp979WOs68dYg5mCSZKmpqZceeWVGTt2bM4888xMmzYtQ4cOzdlnn53m5uZ1NxmZO3du7r333hxyyCE56KCDcv311+ess87Kpz/96Qb/H2yflqZk9O61Wfbhe9RmuQAAAABUj5mCLxg9enTmzp3bpe3UU0/NEUcckUGDBiVJLrjgglxwwQXrnp88eXLOOeecvOMd76hrrdXw6v2SW5+s7jJH7ZYcsGt1l7mj3n/RtY0uoU8bc+m87WoHAAAAehYzBbdiwYIFXa4n2Je86YCkf5Xf/XcclLhGKwAAAEDPJxTcgpUrV2bRokUZP378FvvMmzevV84STJI9+ienH1q95Y3cLXnTiOotDwAAAIDacfrwFgwePDgdHR3b7tiLve/QpO2R5I4VO7ecplLysaOTAc3VqQsAAACA2jJTsMBampLPHJvsv8uW+7zsqq3flbYpycePSY58SdXLAwAAAKBGhIIFt/fA5CsnJOP32v7f3aNf8qlXJFP2r35dAAAAANSOUJAMHZh84fjkn49K9hqw7f7NpWTq8OT7JyaThtW+PgAAAACqyzUFSVK5LuC7Dk7edmAyb2ky/7HkjqeSpauSjnJlVuDoPZJxQ5K/PqAywxAAAACA3kkoSBf9mpLXDa88AAAAAOibnD4MAAAAAAUjFAQAAACAgnH6MD1a+dbpjS6hMBa8udEVAAAAAPVipiAAAAAAFIxQEAAAAAAKRigIAAAAAAUjFAQAAACAghEKAgAAAEDBCAUBAAAAoGCEggAAAABQMEJBAAAAACgYoSAAAAAAFIxQEAAAAAAKRigIAAAAAAUjFAQAAACAghEKAgAAAEDBCAUBAAAAoGCEggAAAABQMC2NLgAAAIrktm8OaMjrHvXe1Q153UZqxFgXcZxrzftYHz6b6sdYb8rfeWOYKQgAAAAABSMUBAAAAICCEQoCAACwifbO5J6n1//8l+eScrlx9fRlazqSO1es//mxVca6Vp5vT/781Pqfn3AGKQXmmoIAAAAkSVZ3JL/5S/LDxcnCJ5M1neufe/Ovkz36Jce+NDn5oOToIUmp1LBSe73n2pOfL0mueii586mkfYMQ8KRfJXsNSF65T2WsD9+zYWX2CU+vSX78UPLThypB9wardV7/i+SlA5PJ+ybvOCgZuVujqoT6M1MQAAB6mCn/tCY/+X1Ht9vZMca5q+seS972m+RjNyU3Lu8aCL5oxdrklw8n7/998sH5yZJn61/nxnrb+1guV8bwzb9OZt5aCV/bNzMrcPnq5KrFyam/Sz78x+Tx5+tf68Z641jPfiB546+S/3V7smijQPBFjz2ffP/+5J1zk4tvSp5ZW+9KN9XbxrrWjEdtCAUBAAAKrLOcfPb25EPXJ49uR/B0w/LklHnJvKU1K63Pae9MLrop+bcbkqfWdP/35j1SCaxuWl672vqaVe3JeX9M/vPW5LntyIx+/FDyrrnJ3Su23Rd6O6EgAABAQZXLyaduS7517479/qolIZ7IAAAgAElEQVSO5F8WCAa7o6OcfPTG5KdLduz3n16bnHNdcrNgcJtWd1QCwWsf3bHff+z55APzk3uf3nZf6M2EggAAAAX1i4eTKx/Yep8Fb648tqSzXDnl+JHnqlpan3PFvcmv/7L1Ptsa69WdyQULKtfIY8s+f2ey4PGt99nWWD+9tjLWq52ZSh8mFNxAZ2dnLrvsshx66KEZOHBgxo0bl7a2towZMyZnnHFGo8sDgM1q76zcOW9lD7j+DVTLmo5k+fOV07/Y1EOPlvO+S9fmtEvX5rRPrM3t92/uKllUw/s/uTaTzlmTL/2o7yUDT65OZt1WnWU9155cekt1llULjX4fH1qZ/M+d1VnW46sr18frqRo91gufTL69gzNfN3b/yuQri6qzrFpo9Fj3JLaLO8bdhzcwffr0zJkzJzNmzMiECRMyf/78nHLKKVm2bFnOP//8RpcHAF2sWFM53WvOg5V/J0nrS5L3HJK8dr/G1gY7asmzyTfuSa5+qDIjppRk0rDk1FHJuCGNrq5+WpqT9s0c47W3Jy0tyW67Jp/9x5bsObiUex8u5+OXt+cbF/q+f3tta5yT5JL3t+QPt3fm0SfqW1s9/OCBymyoarluWXLHU/W/U25veB+/fd/mb9yyo376UPKBw5Jhg6q3zO7oDWN9+d3JZu7bssO+d19y2qhkcL8qLrQbesNY15PtYm0YoRdcccUVufzyy3PVVVflwx/+cE488cRceOGFOe6449Le3p7x48c3ukQAWOeJ1cm0a5Kv370+EEwq345fsCD57zsaVxvsqDtXJH/XlvzwwUogmFQO7H73SPL+ayt36yyK/YaWsvixroe1zz1fzuMrkv33LmXPwZVHkvRvSZrt1e+QbY1zkgwbUmpEaTXXUa58qVRtsx+o/jK3pae/j8+1V77oqKbOJP+vBu/ftvT0sX50VWWbUU3PdSQ/28HrQO6Mnj7W9Wa7WBuG6QUzZ87MlClTMmnSpC7to0aNSr9+/dLa2pokmTx5cg4++OAcffTROfroo3PBBRc0olwACu4/bkkeenbT9hcnIXz97uT6x+paEuyUjnLyz3+sHDxvPMOjM5W2j92YLNuOO6P2Zm85oSk/mNeZG+7qTEdnOU8/W84nv92RQw8o5bAD1x8EdnSW85/fbs/pf93cwGp7r+6Oc1+0eGXlZgrV9qdtXMetFnr6+3j7k9t399vu+tOy6i9zW3r6WN/w+Pp9oWr6o/W64WwXa8Ppw0mWLFmShQsX5rzzztvkucWLF2fs2LEZMGDAurZPfepTecc73lHPEgFgnUdWJfMe2fqpMU1Jvn9/MvGl9aoKds51jyVLV235+XKS9nJlFuH7x9StrIb56+Obs2pNMvObHVm6vJxdBiQTDmvK/zm3JS3NlYOfcrmcj32lI381rikntPquf0d0Z5z7qjueqs1y//Jc8tSaZM/+tVn+5vT09/GOFbVZ7l0rKtcVbqnjn39PH+s7azTWtfp72ZqePtb1ZrtYG0LBVELBJBk2bFiX9lWrVqWtrS1Tp06tWy1tbW1JklJp23/k3elD7zHhR5XDe+9r7RlreruX/NUpGflP39lqn84kcx94JqWJu9enqALxGVIbw9/3qezzlvNTatryTny53JnLZrfljI++uo6VVd+t3+heWvKOyc15x+Qtz3T4j//bkRH7lPKu13RvNkQR19nujPW2xnl79ZZx3uft/5r93/ufXdq2difWrT3/squ6/rzf6NaserBKdzBJ738f9//7z2afN/1jl7ZqjPXqzmTgnkPT8czynaywolqfTdurmmN98D9/L0NOeGeXtmqM9dJn1qZUql7S3RfGutqq8Xduu1hRLnf/qppCwSRDhw5NkixatCgnnXTSuvZZs2Zl6dKlmTBhQpf+F154YS6++OKMHDkyl1xyybpTiwGgHkpN3dzR6WY/6AlKzc3Z9qXhS4n1Oknypzs6M3teZ44+tJQ//Lkze+ya/K9/qPNV8AviY19pz233lrOmvZxb7+vMf53XN8a5lBoeDPfAA+1Gvo+1DB629kVKo/TJsS71vHFO+u7n046wXdwxpfL2RIh9VGdnZ4455pgsXbo0l112WYYPH57Zs2fn6quvzuLFi3P99dfn2GOPTVI5nfiAAw5IqVTKd7/73Zx33nm55557suuuu1allsmTJydJ5s2bt1N96H1e/CZqW99ksfOMNb3dfc8k75y79T5NSVqHJF85oS4lFYrPkNr42ZJkxo1b71NK8u6RyT8dWZeSaua2bw7YdqcaOOq9qxvyuo3UiLHuLeP8wweTT9zSvb4vft5tPCNwS372+mTvgTtW1+b09vfxK4uSL9zZvb7bM9bNpeSak5L+VfqupC98Nn3y1uTKB7rXd3vGeq8ByS/esMNlbaIvjHW19fa/896qZ8bdddbU1JQrr7wyY8eOzZlnnplp06Zl6NChOfvss9Pc3NxlJuCIESPWffvw7ne/O/37989dd93VqNIBKKCRuyXHDNn6RrwzyckH16si2Hmv3jfZvV+2OnepnOTtB9WpIOjjDtuzNsvda0B1A8G+4LA9arPcUbtXLxDsK8bUaKxr9R5CowkFXzB69OjMnTs3zz77bBYvXpxLLrkkt912W4444ogMGjQoSfL888/n8cfX33boN7/5TZ555pmMGjWqUWUDUFAXjksG99vyhvy1+yWv26+uJcFOGdCcfHx85azDjdfrF4PCcw5PDhpc78qgbzpkt0oQX23H7FX9ZfZ2R74kaanBWa3HDKn+Mnu7o2u0/lmv6atcU3ArFixYkIkTJ677+emnn87UqVOzZs2aNDU1Zffdd89VV12V3Xd3EXcA6uug3ZLLX5X81x3JvKWVmYFJ8pL+ldMrTxuVNPW8SzrBVp2wT/KF45P/uSO5+Yn17SN2TU4fnfz1AY2rDfqafk3JGw9IvnNfdZf7tgOru7y+YM/+yav3S375cHWX+zfGehMHDU7G75XcWJ17rySpBLpvtP2hjxIKbsHKlSuzaNGinHXWWevaXvrSl+aGG25oYFUAsN6IwcmslyePP59M+WWl7WevT1qcB0AvNn6vyrUwlzybvPU3lbbZr+6R9y2omVnfbs/tD5Rz+IGlXPB3dtepnXcenMx+IFnTuc2u3TJmj+TlQ6uzrL7mb0cmv3p427dT6q7jX5ocYm7KZp16SHVDwZMOSIY6Jb7hbBtrw2HDFgwePDgdHR350Ic+1OhSAGCrNtxRFQjSV+y/wT3cihQI/vmBzjy3OvnGhf2ytj1ZeF+V0hq2y6NPlPPJb7ev+/n//rwjp31ibQMrqo39d00+eFh1ltVcSj52dM/9e230ezr2JckpI6uzrF2ak4+0brtfvTR6bDf2qmHJ64dXZ1l7DUj+8YjqLKsWetrY14ptY+2IVwEAoIe49d5yjhtbSVUmjm3KLfeUc+RmgoQ/3dGZcz/XnjEjSnl4WTljRpTyuXNrcIG4grr+9s4cN7byLcuateXcubha87t6nr89JLnh8eT3j225T3fuznre2Nrd5KEaesJ7etbhyW1PVh5bsq2xLiX56NHJvrtUtbSd0hPGdmP/elSyaEXywMot99nWWPcrJZeMT/boX93aqqknjn0t2DbWjvkEAADQQzzzXLLroMqBz267VH7enAljSjlqZClf+0i/TBjTlAvf67v+HfWnOzrzyjPX5PT/WJs3nL8m//DZtVlwZzkvO6zyPsz5XWfefELfPWxqLiWffHnyqn12fBnnHlG5nm1PsLn3M0mPeE8HNiefm5iM28EbhDSXkouOqd4suB3RW/5e9uiffP74yg11dsSApuRTr0hesXd169pRPXm9rgfbxtrpm2sMAAD0QrsNSp5dVZnpsXJV5eBnc5YsS4bvXTlAevSJcvYZ0kPP2ewFNncQuWp1ssvAUta2l7PgjnKOPaJvHzYNbE4+/Yrkw0dWwpDuGr5L8sXjk78bVbvattfm3s9yudxj3tPd+lXG7IwxlZCvuw7dPfnGXzX+hku96e9l74HJN15VOW17ez4hxw1JrphcuflVT9HT1+tas22sHbEpAAD0EK2jSpk9tzNvOLZyWthbTmhKe0c5K1Yme+2x/uDm3ofLGTW8lI7Ockp98xiwbjY+iHzymXJGj6j8/JP5nTnpuGIMcFOpMtvvxH2TOQ8mP3wwWb56833H7JGcfFAyZXgysEFHlI8/Vc6/fL69S9vQPUo55+3Nm4QCdz7Y2aPe05amSig4dXjlRi8/fih5eguXgWt9SeWGMK/Zr3LH6EbrbX8vA1uSfzoyecuI5Pv3Jz9fkjzXsWm/UiqzAk8+qHJNwu0JbKupN6/XtWTbWDtCQQAA6CGOOKgp/ft15rRL1+awEaUcdUhTFj9aztd+2pGLTl+/637Pw+WMG1XKmrXJE08ny54qZ+89zYjYERsfRF53+/prVz2wtJy7Fnfm+3M7cs/D5XznVx15z+uaG1xxbe0zKDnzsOSDYyp3Ab9zRbJibeUUs2G7JIfvkbxkQKOrTIbuWZk1tbG5N3ZuEgr01Pf0gMHJeUcm/zA2WbwyuWtFsnJt0txUmYV52B7J7j3sena99e9l1O7Jv41L/uWo5P5nkrufTp5rrwS0IwZXgu5de0A60hfW61qwbaydHrDaAwAAL7rg77ruot+1uJypE7tOeXj/m9Yf7M251EXUd8bGB5F/uL0zp02pvAfnvWv9e3HaJ9b2qYPsbSmVKqHVAYMbXcn22Vwo8Of7yzltSuVvqCe+p82l5ODdKo+errf/vbQ0JYfuUXn0Jr1xva4228baEAoCAEAP9rqXOweqljY+iPzFHzrS1LTpzJJvfNQBZm+wuVDgtS8reU+rxN9LY1ivN2XbWB1GEQAA4AVvOLbvzbApOu9p7RjbxjH2VIOZggAAUEdHvXcLd2+g6ox13+B9rA/jXD/GelPGpDHMFAQAAACAghEKAgAAAEDBCAUBAAAAoGCEggAAAABQMEJBAAAAACgYoSAAAAAAFIxQEAAAAAAKRigIAAAAAAUjFAQAAACAghEKAgAAAEDBCAUBAAAAoGCEggAAAABQMEJBAAAAACgYoSAAAAAAFIxQEAAAAAAKpqXRBQA9Q6n1q3V/zfKt0+v+mn3Zbd8c0JDXPeq9qxvyut3RiDHpyeNRK9a9+jHWfYP3sX5sBwBgy8wUBAAAAICCEQoCAAAAQME4fRgapFxOFj6Z/OHx5I6n1rd/6LrksD2TY/ZKjt07aS41rsa+olxOblie3PB4cseK9e3n/iE5fI9kwtBkwl5JyVjTi6ztTK55JLn1yWTRBuv1BQuSw/ZITtgnGbV74+rrS55vT+Y9UvnMXvT0+vaP3lD5vP6rfZIRgxtXX1+ycm0yd2ly+1PJPRuM9UU3JYfvmUwalgwb1Lj6AAD6EjMFoQF+85fkb9uSadcmX7gzaXtk/XPXLUu+fnfyD9cnb/118t37ko5y42rtzcrl5EeLk7f/Nvng/OTLi5JrH13//LWPVto+OD85eW7y48WV3+nLpvzTmvzk9x3dbu/reuN4rO5IvnxX8te/TP5lQfKte5M/Pr7++V//JfmvO5J3z0v+/trkj8saVmoXvXGsV65N/vftydRfJR+9Mfnu/cmNy9c///OHk8/enrztt8k511VCw56gN471k6uT/7glmfLL5OKbk9kPJDc/sf75nzyUfOq25M2/Sj78x+T+ZxpWal30xvewtzLWABSZmYJQRyvWJDNvSX6ztHv9l65KLluY/OLh5OPHJAeYidJtj62qzCzZMCzZmgdWVg5Ef/Fw8u/HJHsPrG19sCPufCqZcWNy/8ru9b/5ieSs65K3HZicf2QysLm29fUlCx6vfIY8sqp7/a9fVglg3zsq+eBhSYuvXbtt3tLk0luSJ9dsu29nKrM25z9WGedTDzHLGwBgR9llhTp5cnXygd93PxDc0G1PJqdf2/VUKrbsL88l06/tfiC4oeuXVWZXLX2u+nXBzrjh8eT9v+9+ILihOQ9WZh+vaq9+XX3R3KWVmX/dDQRf1Jnk8nuSj9yQtHfWpLQ+Z84DyYf/1L1AcENrOpPP/Tn5z1v7/gxvAIBaEQpCHbR3Juf9MblnJ053enJN5SD1ydXVq6vaDtxvcL5+yasaWsNz7ZVxWrqdB/Mbevi55JzrK9cRg57gwZWVz5BVO3Em243LK6fAClC2buGTyUcWJO07MU5zl1ZOdWXrrnkk+Y9bd24ZP3gw+eqi6tQDAFA0QkGog2/cs+1rTS14c+WxNY+vTmY50Nyq//PnZPGzW+/TnbF+cGXy33dWr67e4qFHy3nfpWtz2qVrc9on1ub2+013ev8n12bSOWvypR815tpSHeXk4psqgffWdGe9bnsk+emS6tVWbY0e6+c7kn+/aduBYHfG+gcPJtc/Vr3aqq3RY71iTfKJW5JtZa/dGesvL0ruXLH1Pn1Ro9/DorBdBKAvEwpuoLOzM5dddlkOPfTQDBw4MOPGjUtbW1vGjBmTM844o9Hl0Ustfz75ShVnMfzqL10vdN9TfO6CifnurBMz5ZX7Z+5XT8r4w/eqew33PZNc+UD1lvfd+yrXGuxLWpqT9s0cP7a3Jy0tyW67Jp/9x5Z848J++di0lsz6Tt8+2NzWeCTJJe9vyfnvatzF+H75cOUOw9Xy2dsrNyupt94w1lfeX/lCoFpm3ZZ0NmBmZm8Y668tSpZXaeZ7Rzn5zMLqLKun6A3vYV9huwhAkbnRyAamT5+eOXPmZMaMGZkw4f+3d+/BVZznHcd/50hCVxASAiQESMYSyJIlzC2IiwtMii1DHI9jiuu0QYObpEMcXKO6LTHj1KkdfMnUaRq3MRPs2DjETsDtFBeKE1KCk6G2jD0TxM1UGEUgq4BlwBISSOec7R9rLkcgdDm72j2738+MZtAKvXrPs+/ue/Y572Wadu/erfvuu0+nTp1STU2N09VDnPqPRqnL4g+VNx2Vpg5izq317WWX/p2cZD6AXOi6/KZ4aOUGPfjU2yoYk6HHVkzR8kd/O3iVu8LmBmvLMyS93iD99c3WluukMTkBNZ6MzlK0nzf08Vlp7MiAhmdcXrF/SKKU4PGPjnqLhyTlZju7i8Gmo9aWd6bT3KF48Thry+2N22MdMay/hzSek979WJo50tpye+P2WJ8PSVuOWVvm+y3murtFw6wt1yluP4deQr8IAPAzurXPvPrqq3rppZe0ZcsWPfzww1qwYIHWrFmjWbNmKRQKaerUqU5XEXFqe5P1Ze5sNqe5DZahlRsufb38xv9q47b6qGNuYBjSmzZMi9x+3FtrsN01N6jXfxPRex9EFI4Y+vScoac3hlU8LqCSgssPPuGIoac2hnT/Ym+PQulrPJzyUbu1owQv2u7AFGK3x3rfaXM9UasR66vtPim1dllf7ps29LdOcfs59BL6RQCAnzFS8DNr165VVVWV5s2bF3W8qKhISUlJqqiokCR1dnZq9erVeuONN5SSkqKsrCy99dZbTlQZcaA9JB2NYXORnoQMc0TEzVnWlx2vmtqlszY8ZJ7ulE50SLlp1pfthMWzE9TRKa3dEFZzi6G0ZGlaSVA/fChRiQnmw49hGPr2+rD+aHJQcyu8/dlRX+LhpANnbCr3rJnsDgziS/RtrG0q93qIdfxz+zn0EvpFAICfkRSUdPz4ce3bt0+rVq266meNjY0qKytTcnKyJOmRRx5Ra2urDh06pISEBDU3Nw92dRFHGtp6X0R9oEgKRvvQhuTrRfWt3kkKStKS+QlaMr/nkQ5PvhLW+NEB3ft5f4yG6C0eTjryqT3lnu0013PLSbGn/J64OtY23UOOtppTk4ODnMfxY6ztul6c4uZz6DX0iwAAvyIpKDMpKEm5ublRxzs6OrRr1y7dcccdkqT29natW7dOx44dU0KC+aYgLy/P0rrs2rVLkhTow/CNvvwfOCuj9FZNejJ6JGlvuyj29PPpW6K///oDK3X31udiqF035etjLuL8hbAOHe37FpBWtuGsuUs14W9+HnXMqljf+aUlOrP79RhqNzj2vjwk5jLePRjR5t9EdEtxQO8ciCgzXfr+g0nX/R0334usiMm314dUd8RQZ8jQ3g8jem7V4MUjf9lTyr3n76KOWdWu8wtuUOfJhoFX7gpWxFlyNtaFD72sEQuWRR2zItYRSYnJKTK6rNlVwwuxLv7OLzXsloVRx6yI9f+1nFYgkB1j7QaHE/cmyd33a7vEGmuv9YsAAO8z+rH+FUlBSTk5OZKkw4cPa9GiRZeOP/PMM2pubta0adMkSfX19crMzNSzzz6r7du3KxgMqqamRkuXLnWk3nC/SNd5+8ru7LCt7IE60dKhp1/c68jfjnTaGOsL7ou1XWbcFNT7L1qTdPCKf/iqc12lrfcQG8seKEdjbdM9xIhEZIRtWNsgRp6MtY39gBs5eQ79hH4RAOBlvJuQNGHCBFVUVGjt2rXKzs5Wfn6+Nm/erG3btknSpaRgKBRSU1OT8vLyVFtbq4aGBs2ePVvFxcWaMmWKJXXpT0YX7tfWJc3/r+hj3UfrXHRxFERPP+/u16+t1+Ts2Ef3XRSoeMGysvrKyvbe2CZ96b+jj1kV6wO7tio/feB1Gyx1G5Id+btuvm85ERMr4/Fmk7TmvehjVrTrYUlSZ0uzZWsKeqHtbTwifX9/9DErYj1hWFDvha3bGcoLsf7BfumVI9HHrIj17Il5+r2L70dX8sJ5jBfx3g8AAGAnVsqVFAwGtWnTJpWVlWnFihVavny5cnJy9MADDyghIeHSJiPjx4+XJFVXV0uSCgsLNWfOHNXW1jpWd7hbRpJUkGF9uQkBaeIw68uNZ2PTpQwbPubITJLGeGg9QcSX0kx7yi3JHNxNRuJB6XB7yr3JpnLjGbEGAABwB5KCn5k4caJ27typc+fOqbGxUY8//rjq6upUWlqq1NRUSeY046qqKm3dulWS1NLSotraWk2ePNnJqsPlbhtjfZm3jpZSGecbJRiQFuZbX+7CfJIncM7YdDOBZ7XbbLhW4l15ljTaho1XiPXVZo+W0mzYr8GO/hYAAMDLSApex549ey5NHb7o+eef17p161ReXq558+Zp9erVqqysdKiGiAd3F5gj+6y0pNDa8vrja4/9Tssf/a1zFbgOO+LiZKyBQMD6NpiRKN1OouoqiUHp7kJryxyTJs0aZW2ZXpCeKC0eZ22ZN2dJJYwUBAAA6BeSgj1oa2vT4cOHNXXq1KjjBQUF2rFjh+rq6rRv3z6tWLHCoRoiXoxKlZYVWVferaOlmSOtK89LJmVKd1r4oHl3gVTksWnaz2wMqfq7XXrqpyGnq+JaJz4x9PTGy/F5ZXtY1U84t1HEF8ZZO1rwm6XuHWnsdOy/PEHKt3C5gJoy6z8UGiinY9vdVyeayzNYISgz1n7jtnMar+gXAQB+RlKwBxkZGQqHw1q5cqXTVYEHfG2iVNxLcmn6lt4XUs9Mkh6ZzHTW66m5WcpNvf7/6Uusx6RJf1VqXb3c4EBDRO0XpJfXJKkrJO37MOJ0lVzp7f0RzSozu8fOLkOHGp1dMD4xKD02RRrSS4/dl3Y9c6R0T4F1dbOa07FPS5QevaX3N0d9ifWisdL8PMuqFjOnY9vdiBTpW31YfaUvsf5KkVSRbU294onbzmk8ol8EAPgdSUFgEAxJkH4wUxoXww62GYnSP1dKI21Y88pLhiZJz1VKI2LYbDAnWfphpblRjJfsPWJoVpmZUa4sC+r39dd+gHz3YERzVnTq/ie7dHtNpx78J++OPLnWa91zyND0EjNO//ZWRF+c63xXWTRM+t4MKSmGqpQOl56e7o4PFXpqY26I/fQc6e+nxPYGqXKktMbB5YbjpV3/8ZjYP3ypype+cZM19XErN18v8Y5+EQDgd7x7AAbJqFTpx3PMh8X+unGotH6uVJZlfb28qHCo9MJcc+OA/qrIMn/Xjl2jndbaLqWnmg8/Q9PM769l2qSAyicE9OK3kjRtUlBrlrl0rqkFrvVaOy5IaSkBdYUM7TloaGapO7rKOaOlf50l5fUyEvZabss3f9ctie5rxd0wDNfEfvE46R8/J2UP6f/v/kmh9OznpGQbNtLoq3hq118pMkfCpvfzNhMMSPcXS9+Z6p4p2nZx+/USz+gXAQB+R48GDKKcFHME2pZj0k8OS8d7ePN5UdYQ6d4bpOri2EYI+dHYdDOR+tqH0sYj0snz1///o1OkPy+Slt7g3QfMoanSuQ5zFERbh/kAdC3HT0n5I80gnPjE0OhsjwZEV7/W062GJo43v//P3REtmuWuC2/KCOm1BdKPP5D+/Q/SuV6WwCoaKv1libTAoWmsH58x9Lc/iq5kTmZA37wn4ao2dugPEVfF/tZc6RcLpH85JG07Jl3oZVZheZb0jRJphgvWfI23dv2FceYIzecOSDs+kkK9zIKdkSOtLDVHv3pJPF8v8Yp+EQDgdyQFgUEWCEh3jTc3xKg9Jb1zSjp4VjrZIUUMKTvZ3DBjyghpfq459RgDkxCQ/uxGM7H6uxPSey3SwTNSywUpIHOKcclw82F0zihz7TYvqygKaPPOiG6faa5FddfcoEJhQ2fbpBGZlx9wjjQZKsoPKBwxFPB4TLq/1v/Zf3kqWUOzoQ8aI/rFzrDqmwz97FdhfXmh8xdkeqL0UJn09UnSrz+S9p6WPjgrne0023x+mtmu54ySJmc7O104Z7g5sqa7ne9Hrmpjboz98GRzGvDKm6RffiTtPy0d/lRq6zLvF+PTzU1g5uW6a+fbeGzXuanSE9PMtr3js1jXt0rtIXM9zcIMM8afzzNHg3tRvF8v8Yh+EQDgdyQFAYcEA1LlKPML9koMmgv+u2nRfyeUFgY1JCmi6u92qWR8QOU3BtV4wtCLW8N67P7L3UF9k6HJRQF1dkmffCqdOmNo5HBvjoro/lrf2R9RdZUZi1X3Xo5J9RNdrnvITkuU7hxvfsWba7WxA0cNVQgQGbYAAAF4SURBVFeZT9tui/2wIdKSQvMrHsRzu85Jkf50gtO1cJd4u17iCf0iAMDvAoZhsFUZAAUqXhj0v2ns/YtB/5teVreh/7ur/OrdiIalK6a1qMqXXRjw79qtvzF5852wbp8Z2wO1m+Nhl4G0ve4GEnti3Te0a/eJ9ZoZ6Dn143nsb6y93i8CAHAlRgoCgI8tnME8qCvFmjjBwBF7+xBb7+Gc2od+EQDgJ4wUBAAAAAAAAHyGj8IAAAAAAAAAnyEpCAAAAAAAAPgMSUEAAAAAAADAZ0gKAgAAAAAAAD5DUhAAAAAAAADwGZKCAAAAAAAAgM+QFAQAAAAAAAB8hqQgAAAAAAAA4DMkBQEAAAAAAACfISkIAAAAAAAA+AxJQQAAAAAAAMBnSAoCAAAAAAAAPkNSEAAAAAAAAPAZkoIAAAAAAACAz5AUBAAAAAAAAHyGpCAAAAAAAADgMyQFAQAAAAAAAJ8hKQgAAAAAAAD4DElBAAAAAAAAwGf+Hwtt1hnwXiTDAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 1683.19x927.08 with 1 Axes>"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -334,17 +392,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1502.59x204.68 with 1 Axes>"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -356,17 +414,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1502.59x204.68 with 1 Axes>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -378,17 +436,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 719.992x204.68 with 1 Axes>"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -400,17 +458,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 780.192x325.08 with 1 Axes>"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -422,17 +480,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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YBk7oBH1aRxdfNllbAc+8B4s3wYot+8q/8y8Y1BZOPBK6tIguPkmSJEmSVDeTgspo67fDz98MEoK74gc+//cPgscvl8Cn2sE1g2Bou9THub8x/7mTa87K5/TP5terPB2s3go/exPmrIFP2NU8vyZ43P0WHHc4fGuQiVhJkiRJktKVtw8rYz39HpwzC/66+pMTgvt7fSNc/iL8bDFU1mN77TNjJXxtNsw6SEJwfy9/CBfOgd+8DYlEsqOTJEmSJEkNZVJQGelPK+F7r8KWXQ17XQL47XL47qsmBuvrnn/DjxbC9qqGva4yAf/7FvxogYlBSZIkSZLSjUlBZZwXPoApCw+tjllr4KeLw4knmz36Dvzf0kOr4+F3YNqycOKRJEmSJEnhMCmojLJ5J9w2v+7t5o0NHrX540r417pw4kqGK+7YxahrdnLvYw0coheS97fBTxbVvV199vUv/w1vbw4nLkmSJEmSdOhcaKSaeDzOT37yE375y1/y7rvv0r9/f+666y7Gjx/PqFGjuPfee6MOMedNexs27Aivvh8vgodGQywWXp31UZAPlZ+Q66ushILdn8rJVxTwyuI4azemNrY97nkLtoWUj6xMBIuU3D0ynPokSfWzrRL+thrmrgv64j6t4Kzu0LF51JEpHSQS8OoGeGI1fLQDDmsKp3aB4e1Tf24khaUyDrM/CBbAq6iEzs3hzO4ugKd9lmyGx8pgzTZoUQBf6ASf7wgFDplSDrLZV3PZZZcxefJkrrzySp588knOPfdczj//fFasWMHw4cOjDi+llkwazZo/3Vbv8lTYXhV03mFatgXmR5B0O7I4RtmHNSfa27Y9wfrN0KVDcBbesV10Z+Mbd8Cza8Ktc+46KCsPt86GSsd2XZdMjFlSenhjA5z2DJQugNlr4MUP4Ndvw5efDb5kU27bsgu++XLweLwMXlob/PvNl+HKlxo+b7Nql6nH80yLu6wcvvo83DgPnn0vaNd/3L1g3m1vQJXzXOe0yjj892vw9TkwfWXQPp5+D74zD86dBe9tjTrC7JJp/ccemRp3Y5kU3O2hhx5i2rRpzJw5kxtuuIETTjiBSZMmMXLkSCorKxk2bFjUIea8f62Dj5NwgvrUe+HXWZczjs/j4dlxXl0Spyqe4OOtCe54sIq+XWMM6B79V/NzPqjfis4N9cz74dcpSTpQWTlcOxe27j5uxtm3enwCuPutYN5Y5aZEAr7zL3htQ/BznJpt5PWNcMM/XShMmaV8d6J7zbbg5z1tek8zfrQM7n4zouCUFn6yGP66Ovh/gpr93uqtcNU/gtGlUi4xKbhbaWkpY8aMYdSoUTXK+/TpQ2FhIUOGDAHglltuoV+/fuTl5TFjxowoQs1Zb25KTr3/jmCuu9OOy+fas/MpfaCKz121i7O+t4vtO+Hn1xVQkB99UjBp+zpJ9UqSanpoRTDCvrbvd361xFEzuWr+RvjX+n3Jkv0lCG4rjuJuCqmxHn8XPtxee7/3h5WwaWfKQlIaWb8dZqw6+PNx4L1t0QwYkaLknILA6tWrWbRoERMnTjzgubKyMkpKSmjatCkAY8aM4ZJLLuHSSy9NSiyxJE3gMvyxaM76w/x9en77D7Q7/rwaZXUtcHGw54+Zue//b6zeTCzW9hCj22fB/U3qtd3Zo/M5e3R+aO8b5r7uV/p3WpV8rkZZY/Z19f0M8MS/3mLqsYMOMbp9MrVdRxF3svqWuuz5XaN6/1zivlZ1Rz+0mfzmtU+gtXY7tD3q85S/+UKKomo423VydL3yf+kw5kpieQc/D0nEqzjj5l9Rds+EFEbWMFG3j0w8nmfquVN9DPifV2jeZ3it7XpXHPp+eTzrn/5V0uNprKjbdbbqcPq1dLvirlq3ScSr+K8HX+Qrk0anJqhGiLp92O/VX7L+RmHXa1KQICkI0LFjxxrlFRUVzJkzh1NOOWVv2XHHHZfS2KK0ZvrtrH10ao2yqopyWg89MZJ48gqbJaXeWJPk1HuobrmvkoXLE+ysTLBgRZy7Jxam7L3zkrRP8poUJaXehki3dl0fmRizpAjl5deZENyjoHVxkoNROipoXVz3vcGJhO0jZJl6PM+UuAvadKg1Ibh3O9t1TqrP3z2Wl09Bm8NTEE3uyJT+Y3+ZGndjmBQEiouDDmLp0qWceuqpe8unTJnCmjVrUrrISCJJk7fsP2KrPjqdM4lO595Uo2xJA781CfP3+e68A+ekO9jvtWfUWn1+79ZFTUONc+EDTUOp59bLG/bxDPN3GP/SvnmG9ghjXw/o3YOFIcaZqe26oXGnQ8yNted3jer9c4n7WtWNfgLK6zEv0uy/PsLQdsmPp7Fs18kxZWEwyX5tezUvv4DxF5zNf/0offd91O0jE4/nmXruVB/jXoBFH9XergF+8eNSzphemvR4Givqdp2t/rQy6Ptqkwd87lMDWZzG+z7q9mG/V39h/41Gjw7ef/bs2XVu0xDOKQj06tWLIUOGUFpaygMPPMBzzz3HhAkT+PWvfw2QcysPp6verZJUb/0GU+SUXkna18mqV5JU05e7QW03l8SAzs1h8GGpikjp5PSudSdOEru3kzJFfdp1kzz4YqeUhKM0c9KRUFDHXZdx7PeUe0wKAnl5eUyfPp2SkhImTJjAuHHjKC4u5uqrryY/P3/vIiOK1qAkXbgMCm86wayRrH0y0H0tSSlxQS9oUXDwE70EMGEA5DllVU4a1BY+d0Tt23zuCCgxaawMckoX6Nqi9gvcb/SBlqmbkUdppG1TuKD3wZ/PA3q2hC8dmbKQpLRgUnC3fv36MWvWLLZu3UpZWRmTJ09m4cKFDBo0iKKi6OdBE3y6GNrVbw2PBhnTOfw6M93ojtA05N4hBpzsvpaklOjUHH5xHLT7hBktCmJw42AY0yX1cSl9lA6HUbun094/N/z5I+B2b5RRhmleAPcc98l3psSAi3rDFf1THpbSyNUD4Ws9P3kkfb828P+Og6bhrQMpZQTnFKzFvHnzGDFiRI2ym2++md/85jesW7eOhQsXct111zFnzhx6967la4cM1P/22Q0qT4XCPPhKD/i/peHVOahtdN+CT3mwksWrEgzsHuPGC/d9FG/9TSXLVieIxWDSxfn065b63H3rJsHF4mNl4dV5/BHBRWqU0rFd1yUTY5aUHga0hce/BLM/gBvnBWX/MQi+3DUYMaHcVlQAP/4MLNkMT7wLD64Iyn83Cga0iTa2bJSpx/NMi7tjEfx+NPxzHVwzNyi7oh+M7Rb9eaiilx+DGwbD13rBX97dd135i+NgeHtwwedwZVr/sUemxt1YjhQ8iPLycpYuXcqwYcNqlE+ePJnVq1ezY8cONmzYwOrVq7MuIZjOLuodHOzDkAfccFQ4dTXUm6vibNsB908qZFclLFoR3/vcpafn88DNhdx6eQH3PFYVTYDANwdAq5Bur2iSB9eVhFOXJKn+CvLgxGq3Ql3Ux4SgaurfBiZWOx8yIahMlxeDEdUWkL1ygAlB1dSlRTCFxh7HFJsQVO5ypOBBtGzZkqqq6BIy+mQtC+GWo+GafwQTwR5MfVYYurgPDIloxcUFyxOMLAmOPCNK8pi/LMFRvYLnunQIygvyIT/Co1OHZvBfg+Gm12rfrj77+luDoHvLcOKSJEmSJEmHzpGCyjif6QC3fOrQGu/pXeGqgaGF1GBbtkGLoiDh16p58PP+fja9igtOivYjOqZLcKvZobi4D5zXM5x4JEmSJElSOBwpqIx0eldo2wQmvwEbdtT/dQUxuLw/XNo32hUXWxXB1ooEAOUVQWKwut8+VUXvzjGG9Ys+b39Rn2DU4B0LYcuu+r+uWX6QUDy7h8PxJUmSJElKN9FnHKRGOv4I+NMJwSi05nWkt2PA546A334eLu8XbUIQYEifGK+8GSQF5y6OM6T3voBeXhhn/tsJxo9Nn4/nmC7Bvv5y12B+wNoUxOCkzvCH0XBOTxOCkiRJkiSlI0cKKqO1aQLfHhzcCjx7DSzeBMs+hq2VwWrF3VvAwLbw+Y5wZBpNMDyoRx5NCuN84/ZdDOgWo1P7GPfOrGL82Hx++LtKWhbFuOxHlfToGOOWcenxMe3QDP77U8GCIbPWwJubYPkW2F4FTfOgZ6tgX4/uCMXNoo5WkiRJkiTVJj2yDdIhalEAp3UNHpnixgtrfvzGj80H4C93NIkinHpr0wTO7B48JEmSJElSZkqf+xMlSZIkSZIkpYRJQUmSJEmSJCnHePtwjpg3NuoIcsfgixuwHLIOSaa260yNW5Ik7ZOJx/NMjFlS+sjEPiQTY04lRwpKkiRJkiRJOcakoCRJkiRJkpRjTApKkiRJkiRJOcakoCRJkiRJkpRjTApKkiRJkiRJOcakoCRJkiRJkpRjTApKkiRJkiRJOcakoCRJkiRJkpRjTApKkiRJkiRJOcakoCRJkiRJkpRjTApKkiRJkiRJOcakoCRJkiRJkpRjTApKkiRJkiRJOcakoCRJkiRJkpRjTApKkiRJkiRJOaYg6gCUGsfMjOZ9542N5n0lCWDhA01T/p6DL96R8veMWqYeY6KI2+Ni5oii/4Dc7EMyVSb2IZnaX0uSksORgpIkSZIkSVKOMSkoSZIkSZIk5RiTgpKkUCUSsHorPPv+vrIX18L67dHFlK3iCVi1BZ56b1/Z3A9h087oYlJ62bgDXlq77+dn3oOy8qDtSDuqYNFHMLNsX9mij2B7VXQxSZKk1HFOQUlSKNZvh4dXwaNlsG6/BOB1rwT/9m4FZ/eA07pCc49AjfbeVpixCma+C5v3SwBeMzf4t6QtnNMDvtQZmuanOkJFaXslPPle0EaWbK753HdfDf5t3xTGdoOv9oCORamOUFFKJOC1DUH7mLUGKvdLEF/yAhTEYHSnoL8e3h5isSgilSRJyeZIQUnSIYknYMZK+Mpz8KulByYEq1u+Be5YCOfMCka0RW3Mf+7k8ZcOHBJzsPKoVcbhN2/D2bPgt8sPTAhWt3gTfP8N+PqcYOSPcsOr6+G82XD7/AMTgtVt2BG0pa8+B79f7sjBxsq0PmTjDviveXDly/DM+wcmBPeoTASjvb/5MnxnHmxwpLckSVnJpKAkqdF2VsF358GPFkJFA65/11YEI9ruWxKMWlHdtlYG++x/34Jd8fq/blU5XPoCPLIqaaEpTfxueZDseW9b/V+zIw4/WQzX/9NbRrPd0s3wtdnw/JqGvW7WmuB1tSWZJUlSZjIpKElqlHgCbnoNnmvgBWZ1v1gC9y8LL6ZstbMKJr4C89Y37vVxoHQB/KWszk2Vof64Eu5c3PjXv7gW/utfwWhUZZ93ymHCy8FIwcb4aCdc9XLwJYMkScoeJgUlSY0yY1XdI07mjQ0etfl/b8HCjaGFlZXuWxrMAVab+uzrHy4IFplQdlm6GX6yqPZt6tM+XvoQHlweXlxKD5VxuPk12Lyr9u3qaiObd8HNr5o4liQpm5gUrCYejzN16lT69u1Ls2bNGDp0KHPmzKF///6MHz8+6vAkKW2srYCfvxlOXXHg1jegKk1vI77ijl2MumYn9z4Wzb2Vyz6GaSGNptwZh9vmh1OX0kMiEe7n55dLgtXDFZ6o+5CHVsCbm8Kp663N8PsV4dQlSZKiZ1Kwmssuu4zJkydz5ZVX8uSTT3Luuedy/vnns2LFCoYPHx51eCm1ZNJo1vzptnqXS8otD69q2ByCdVlZDi+tDa+++irIh8pP+D0qK6Fg9+rIk68o4Przolu+9/crwl0E4rUNsDjihUcy8RiTrjHPWw//DnGut53x4FZk1U+69yGV8fCTeA+tcLRgY6RrH1KXTI1bklQ/JgV3e+ihh5g2bRozZ87khhtu4IQTTmDSpEmMHDmSyspKhg0bFnWIkpQW4gl4LAlz0z3yTvh11uXI4hhlH9bMuG3bnmD9ZujSIQZAx3ax1Ae229ZK+Nvq8Ov9cwT7WsmRjM/N4+82bDGbXJbufchLH9a+InxjrNsezEEpSZIyn0nB3UpLSxkzZgyjRo2qUd6nTx8KCwsZMmQIH330Eaeffjr9+vVj6NChnHTSSSxb5gz5knJL2VbY0MjJ6mvzxobUr0R8xvF5PDw7zqtL4lTFE3y8NcEdD1bRt2uMAd2ju5Df461NwcitsL3uHKA/2Y8AACAASURBVI5Z4/U65ppsjC27YPnH4debjdK9D0lG+0hmvZIkKbUKog4gHaxevZpFixYxceLEA54rKyujpKSEpk2bUlFRwXXXXceJJ54IwF133cW4ceN44YUXUh2yJEXm3yHNTbW/8spgLrOuLZNT/yc57bh8KnZC6QNVrNmQoHlTGD4gj59fV0BBfvQX9GHeFlpdWXkwCrGFZwEZbf12WJ+EBD0Ec8cNaJucurNJrvYhyapXkiSllpcDBElBgI4dO9Yor6ioYM6cOZxyyikAtG3bdm9CEOC4445jypQpocYSiyXnBHL4Yw0ffrNm+u2sfXRqjbKqinJaDz3xIK84ULJ+H0nROfz0b9H1ip/VKKtrVdODPX/MzJo/DzxmJFuXzD2E6GpacH+TOrc5e3Q+Z48Ob76vMPu9zhf/kI5fvbFGWRj7OgG069yDnR+Gc+9pph5jGhp3OsRcXVH3wQy6a0GNsrA+i1fd8D3OmvHDQ4guHHv+RlGcT9Sn/4D07kMG/WwBRT0G1yhrTBvZv328+NoiYscPPnDDFIuyfVR///pKhz4kU/vrxoi6fTRWpsadiTJxX2dizApX2H97k4JAcXExAEuXLuXUU0/dWz5lyhTWrFlz0EVG7rzzTs4888yUxBiFTudMotO5N9UoWzJpdDTBSEobCZJ4j2+q7x+uh1vuq2Th8gQ7KxMsWBHn7omFKXvvRBL3RyIe7aRxmXiMSbeYc+2zmKki7UOS1UZsH42Sbn1IfWVq3JKkupkUBHr16sWQIUMoLS2lXbt2dO7cmRkzZvDEE08AfGJS8Ac/+AHLli3j+eefDzWWZF0A7v8Nb6ok84JWUjSefx++M69m2cH6mD0jTurbBy17Yy4dixof2/4WPtD0kOu49fKGHSrD7Pf+tBKmLKxZFsa+zo/Bxx+U0SykwU2ZeoyJIu4w28fmnfDFv9UsC+uz+Kuf/pDT02Ck4J54ozifCKP/gGj7kG/NhZc/rFkWRhv5wqcHszgNzvGibB/V3z+VMrHfg2j+RlG3j8bK1LgzUSbu60yMWeEYPXo0ALNnz65zm4ZwoREgLy+P6dOnU1JSwoQJExg3bhzFxcVcffXV5OfnM2TIkBrb33bbbTz++OP87W9/o3nz5hFFLUnRGJikecbaNYEjmiWn7kw1oE1y6u3ditASgopOmybQOUmnIYOcTzArJKsPcb5JSZKygyMFd+vXrx+zZs2qUXbRRRcxaNAgior2DVv5wQ9+wBNPPMEzzzxD27aeEUnKPR2LoGsLeHdruPV+ugM4PUpN/dtA60L4eFe49X66ONz6FJ3PdIA/hzM15F4dmkH3FC74o+T5TAf49dtJqNc+RJKkrGBSsBbz5s1jxIgRe39evHgx3//+9+ndu3eNYZlvvPFGBNElV//bZzeoXFLuiMXgq93hzjfDrfecHuHWlw2a5sPYbvC75eHW+9Ue4dbXUJl4jEnXmL/aPfyk4Fndg1vMlfmGt4ceLWFVeXh1dm8Jx5gUbLB07UPqkqlxS5Lqx6TgQZSXl7N06VKuuuqqvWUlJSXeuy9JwBnd4YHlsHFHOPUNaw9D24VTV2NMebCSxasSDOwe48YL9x0ab/1NJctWJ4jFYNLF+fTrlvpZN77WEx5eBRVV4dT3pSOhm6PAssaAtnDc4QfOG9dYrQujTxpnmnTuP2IxuLQv3PJ6eHWO6+uobkmSsoVzCh5Ey5Ytqaqq4tprr406FElKO60K4XtD6t6uPprmwc1Do7vIfHNVnG074P5JheyqhEUr9q3Ke+np+TxwcyG3Xl7APY+FlJVroI7N4T8GhVNX2ybw7cHh1KX08b2h0CKkr3m/PRjahbO+Rk5I9/4D4JQucPwR4dR1/BFwWpdw6pIkSdEzKShJapTRneDiPrVvc8zM2lc6jAHf/xR0jXDk2oLlCUaWBBnJESV5zF+2b0R4lw5BeUE+5Ec4NOasHnVfiNe1rwtj8MPhJnyyUcciuG0Y5NXSROtqHxDcwj+mc6ihZb1M6D9iMfjvo+ueJ7KuNtKtBdxytKMEJUnKJiYFJUmNdu1AuKxf417bJA9uHw5fijgJsWUbtCgKrnJbNQ9+3t/PpldxwUnRHTLzYnDz0cFcb43RsgDuHBEs5qLs9LmO8D+fhqJGrir99V7BKEETPg2TCf0HwGFN4ZfHQb/WjXt9v9Zw72f9UkGSpGxjUlCS1GixGEwYAL84Djo3r//rhraDP4yGk9JgVFKrIthaEYzuKa8ILuyr++1TVfTuHGNYv2gPmQV5wW2iUz/dsAvzzx4OfzoBjjUhmPVGdQw+Vw1ZBOLwZnDXCJh4VO0jDfXJMqX/AChuBvd/Pvgip74LyeTvnpNw2ueC10uSpOziQiOSpEN2TDHM+ALMWhOshLpgI+yI19ymdSF8pgOc3SNYETNdRiQN6RNjxqw4Jx8LcxfHOeP4fRfvLy+MM//tBP9zdSOHXyXB6E4wogM8/T48+g68tRl27bevD2sSLD5xTk8oaZs++1rJ17kF3DMS5m+E6atg7jrYvLPmNk3y4KjD4Mxu8MUjg1Wu1TiZ1n8U5gVf5JzVPeg/nnoPyrYeuF23FnByZzizOxxRlPo4JUlSapgUlCSFojAvGPl3UmeojMM75fDRzmD00RHN4Mjm6ZmcGtQjjyaFcb5x+y4GdIvRqX2Me2dWMX5sPj/8XSUti2Jc9qNKenSMccu49DhsNiuAsd2Cx644rNwCH+8KRvUc2TwY/ZWO+1qpEYvB0e2DRyIBH1QEj6pEsNhMj5bByFMdukzsPyBI9F05IHiU7wr6kB3xYOGnnq2gZWHUEUqSpFRIn7MTSVLWKMiD3o2cuyoKN15Y83A4fmwwsucvdzSJIpwGKcyDfm2ijkLpKhaDTs2Dh5Ijk/sPCBKAg9tFHYUkSYqC3xNLkiRJkiRJOcakoCRJkiRJkpRjvH04R8wbG3UEkpR6gy/eEXUIOSFTjzGZGrdSw/5DdcnEPiQTY5YkJY8jBSVJkiRJkqQcY1JQkiRJkiRJyjEmBSVJkiRJkqQcY1JQkiRJkiRJyjEmBSVJkiRJkqQcY1JQkiRJkiRJyjEmBSVJkiRJkqQcY1JQkiRJkiRJyjEmBSVJkiRJkqQcY1JQkiRJkiRJyjEmBSVJkiRJkqQcY1JQkiRJkiRJyjEmBSVJkiRJkqQcY1JQkiRJkiRJyjEmBSVJkiRJkqQcUxB1AEqNY2ZG877zxkbzvpIkSYdi4QNNI3nfwRfviOR91XBRnF8f6rm11wSqjf2e6mK/V3+Z0u85UlCSJEmSJEnKMSYFJUmSJEmSpBzj7cOSJElKC8s+hidXw+JNwf/3uOQFGNgGvngkDG8PsVh0MSo6H++EJ1bD6xvg35v3lZ/1HAxoC58phpM6Q3OvcHJSVQL+8SHM+QDe2rSv/OSnoG9rOOowOK0LdG0ZXYyK1qpyeOJdWPRRzWPMxX+HAW3ghE5wbAfI8xijHOIhU5IkSZF6ezP8zyJ4bcMnP7/oo+AxfRX0aAnXlcDxR6Q0xE805j93cs1Z+Zz+2fx6latxtlXC/3sL/vwO7Igf+HzZ1uDx9Hvw08XwtV5wWV9o4u7PCYkEPPUe/L9/w/vbDnx+ww7YsA7mroP7lgZ9xw1HQZcWqY81G2Riv/dOOUxdCP9Y98nPv7kpeDzyDnRpDtcMghOPTG2MUlS8fViSJEmRSCRg2ttw0d8PnhDc36pyuO4VuPUN2FGV3PgUvUUfwddmwx9WfnJCcH9bK+H/lsKFf4cVW5IeniJWvgu+8y+46bVPTgh+khfXBm3q0XeSGprSxPSVcP7sgycE97d6G9w4D747DyoqkxqalBZMCkqSJCnlEgm4czHc/RZUJhr++pllcP0/YaeJwaz12gb45sv1T/ZUt2ILXP5iMApV2al8F1z1D5j1QcNfu70KbpsP978dflxKH79aAncshJ31+EJhf8+8D1f/IxipLGUzk4KSJElKuUfegQdX1L7NvLHB42BeWRfcdqzs80EFXP9KkLw5mLrax8e74FuvBHMRKrskEnDza8EtnwdTV/sA+PlbMHtNuLEpPTz1HvxySe3b1NVGFnwUjEqXsplJQUmSJKXU+9uCUYJh+PM7weIC6eqKO3Yx6pqd3PuYQxrrK5GA29+A8hBG6KzbDj82cZx1/vouvLA2nLpKF8AmE8ehirrfW78d7lgQTl3Pvg/PvBdOXVI6MilYTTweZ+rUqfTt25dmzZoxdOhQ5syZQ//+/Rk/fnzU4aXUkkmjWfOn2+pdLkmSVF+/WgIVIV4r3rk4SCSlWkE+VH7C71FZCQW7l/ObfEUB15+XfhPvp7N/rq///F/18dfVNVcajUKmnlunY9yV8WDagbBs3AG/XRZefdkuE/q9+5cFI4XDctebwerWaph07D/qI1PjbiyTgtVcdtllTJ48mSuvvJInn3ySc889l/PPP58VK1YwfPjwqMOTJEnKeJt2Brd1hWn5Fpi/Mdw66+PI4hhlH9a8Uty2PcH6zdClQwyAju1iqQ8sw81YmYQ6V4Vfp6Ix+wNYvyPcOh8rc37S+kr3fq+iEv5SFm6dayrSe0S6dChMCu720EMPMW3aNGbOnMkNN9zACSecwKRJkxg5ciSVlZUMGzYs6hAlSZIy3strGzfpe12ei2BesDOOz+Ph2XFeXRKnKp7g460J7niwir5dYwzobjKwMXZWhXdbaHXPvx/NaFKF7/kkfNY37YTXI/hiIROle7/3r/XhTD2wv+feD79OKR0URB1AuigtLWXMmDGMGjWqRnmfPn0oLCxkyJAhAJx55pmsWLGC/Px8CgsLKS0t5cQTT4wiZEmSpIzzVpJWg/13LQsOJMtpx+VTsRNKH6hizYYEzZvC8AF5/Py6Agryo784zkTLtjRuNeq6bNwJH26HI4rCr1uplazP+lub4NgOyak7m6R7v/dWktrHv13JXFnKpCCwevVqFi1axMSJEw94rqysjJKSEpo2bQrAtGnTaNu2LQCvv/46o0ePZuPGjeTnhzNnQiyWnI50+GMNP7taM/121j46tUZZVUU5rYfWPwmarN9HkiRlpj43P06bY06rUVbXCqEHe/6Ymfv+/88Va4l9ruMhRrfPgvub1Gu7s0fnc/bo8ObOyvVzp3ajLqDn9Q/WKAujfQD0HfFFtix4/hCiq6mh59fpcG6d8dcEsRjDH6051Dis9vH9u/6PcXdffgjBhWPP3yiKviAb+r2e1z9Iu1EX1CgLo40sWb+dWCz6bxWibB/V37++0qH/yPh+L8n1mhQkSAoCdOxY80SyoqKCOXPmcMopp+wt25MQBNi8eTOxWIxElt6L0OmcSXQ696YaZUsmjY4mGEmSlBViBfW76EyXeg/VLfdVsnB5gp2VCRasiHP3xMKoQ0pryfw7xgqbJq3u+sjUc+t0ijuWn7zPT9TtI5tE2e/FCnPrGJPu0qn/aIhMjbsxTAoCxcXFACxdupRTTz11b/mUKVNYs2bNAYuMXH311Tz55JNs3ryZhx9+mIKC8HZjshKM+38TlirZmjCVJEmNc8M/g4UCqjvYecqe0Rv1OY/pXHwYb4R43rHwgXASBLde3rDzxFw/d3r2fbhxXs2yMNoHwOynnuBT7Rsf2/6iOL8+1PaR6dcEiQSMfLzmLeZhtY/x37iQ7/7kwkMLMAR74o2iL8iGfu/7r8Pj79YsC6ONtGySlxb9c5Tto/r7p1Ku93t7jB49GoDZs2fXuU1DmBQEevXqxZAhQygtLaVdu3Z07tyZGTNm8MQTTwAckBT83//9XwDmzJnDxIkT+fvf/07Lli1THrckSVKm6dP6wKRgWPUq8yXz72gbyXyxWPB3TMb8braP7NCnVZLqtX0oS7n6MJCXl8f06dMpKSlhwoQJjBs3juLiYq6++mry8/P3LjKyv1GjRpGXl8dLL72U4oglSZIyU0nburdpVL2HJadepVa3FtAqCXcaJqtepZ59iGqTrL9jstqdFDVHCu7Wr18/Zs2aVaPsoosuYtCgQRQVBROKlpeXs2HDBrp37w4EC40sX76cgQMHpjzeZOt/++wGlUuSJNXHiMOhXZNgNdgwndol3PoUjbwYnNIF/rQy3HpP6xpufQ2VqefW6Rj3aV3h4XfCrbNnSxjYJtw6FY2h7aBzc3hvW7j1Rt2HZKJ07D/qI1PjbiyTgrWYN28eI0aM2Pvz1q1bOe+88ygvL6egoIBmzZrxu9/9jm7dukUYpSRJUuYozIOzesB9S8Or87OHQ5cW4dXXEFMerGTxqgQDu8e48cJ9p9a3/qaSZasTxGIw6eJ8+nXzBp36OqcHTF8JYc3G1CQPzvB0PWsMPgwGtAn3FuJzewa3Jqt+0rnfy4sFfcidb4ZX59B20N+ksbKUZycHUV5eztKlSxk2bNjesiOOOIK5c+eyaNEi3njjDebOnctpp50WYZSSJEmZ5xt9gpEcYWiaB/95VDh1NdSbq+Js2wH3TypkVyUsWhHf+9ylp+fzwM2F3Hp5Afc8VhVNgBmqZyu4oFd49X1zABQ3C68+RSsWgxuHhHchO7ANfKV7SJXlgEzo987tCb1DmlswPwbfGRxOXVI6cqTgQbRs2ZKqKk/gJEmSwlZUAD/4FHzz5ZqriO6vPisGXlcC3SJa723B8gQjS4LhRSNK8pi/LMFRu5NZXToE5QX5kO8QpAabMADmroPlWw6+TX3ax7D28PXe4cWl9HDUYXBpv9pHHNenfRTlw39/CgocKlNvmdDvNcmHHwyDy16AHfGDb1efNvLNAY4SVHaz+5MkSVLKHd0eSodDwSFcN47vD+f0DC+mhtqyDVoUBb9Aq+bBz/v72fQqLjjJU+6GalYAd4+EHoeQ8C1pCz/+TDDSR9nnyv7BbaKNVZQPPz3WVWUbKlP6vQFtgs9/00MI4+u94JI+4cUkpSPPUCRJkhSJLxwJvzgOujZwPsBWhXDrp4KkYJRaFcHWimCoY3lFcIFc3W+fqqJ35xjD+nnK3RgdmsF9x8OXjmz4a7/SHe45zhWHs1ls922dNxwFzfIb9tp+reH/jodjipMTWzbLpH5vxOFBH9KrgbcSN8+H7w4JRqI70FvZLvpPqiRJknLW0e3hoVHwzf5BEqg2zQt2L0JxApyaBitBDukT45U3g4vjuYvjDOm97+rx5YVx5r+dYPxYT7cPRdsm8MNjghE/Q9vVvf2xHeCekTBpaNBelN1iMfhaL/jDaPhy12BRmdp0bh4keu7/PPTzltBGybR+b2Bb+N3n4dqB0LGo9m2b5sGZ3eCPJ8BXe5gQVG7wUClJkqRINSuAy/vDJX1h3npYtAmWfwzbKoOL/B6tglvBRhwOLdLo7HVQjzyaFMb5xu27GNAtRqf2Me6dWcX4sfn88HeVtCyKcdmPKunRMcYt49Io8Aw0qmPwWPYxvL4hWHl2447guQ7Nggv/Y9pD14jml1S0urQI5ga8riSYi/Lfm6BsK1TGoWUh9G0dzEM4rH2wOq0aLxP7vSb58I2+cGEfeG33Mebtj2HrLijMC+alHbj7GOPoYuWa9PiUSpIkKecV5AUXZSMOjzqS+rvxwpqn0+PHBvcx/uWOJlGEk/X6tHYOOB1cmyZwcufgoeTJ1H4vPwaf7hA8JAXSZ1yvJEmSJEmSpJQwKShJkiRJkiTlGG8fzhHzxkYdgSRJUuYYfPGOqENQmsvE8+tMjFmpY7+numRiH5KJMaeSIwUlSZIkSZKkHGNSUJIkSZIkScoxJgUlSZIkSZKkHGNSUJIkSZIkScoxJgUlSZIkSZKkHGNSUJIkSZIkScoxJgUlSZIkSZKkHGNSUJIkSZIkScoxJgUlSZIkSZKkHGNSUJIkSZIkScoxJgUlSZIkSZKkHGNSUJIkSZIkScoxJgUlSZIkSZKkHGNSUJIkSZIkScoxJgUlSZIkSZKkHFMQdQBKjWNmRvO+88ZG876SJEmSpPpb+EDTSN538MU7InlfNVwUeYVDzSmYC6mdIwUlSZIkSZKkHGNSUJIkSZIkScox3j4sSZIkSVJEtuyCtzbB0s37yv76LgxsCz1aQl4sutgUvco4LN8StJE9/rACBrSB/m2gyKyODoHNR5IkSZKkFFuwEf6wEp5/HyoTNZ/779eDf7s0h6/2gK90h5aFKQ/xAGP+cyfXnJXP6Z/Nr1e5Gm/Ddpi+Cv78DmzYb9rFqYuCf5vlwyld4Gs9oXfrlIeoLGBSUJIkSZKkFCnfBT97M0j21GX1tmDbP6yAm46GkYcnPz5FK5GAJ1YHib8tu2rfdntV0I4eK4NL+8Jl/aDQSeLUADYXSZIkSZJSYN12GPdC/RKC1a3dDtfOhQeWJScupYd4AqYsDEaK1pUQ3P919y2Fq/8RJJ2l+jIpKEmSJElSkm3ZBVe9DCvLG1/HXW/C9JXhxaT08vM3g1uGG+u1DXD9P4N5CKX6MCkoSZIkSVKS/WRR3QnBeWODR631LIYVW8KLK2xX3LGLUdfs5N7HqqIOJaPM/RB+u7z2berTPl7bANPeDi8uZTeTgtXE43GmTp1K3759adasGUOHDmXOnDn079+f8ePHRx2eJEmSJCkDzVsPf3k3nLp2xeH2+eHU1VAF+VD5Cbm+ykoo2L1iweQrCrj+PBccaYiw/6b3LYWyQxiRqtxhUrCayy67jMmTJ3PllVfy5JNPcu6553L++eezYsUKhg8fHnV4KbVk0mjW/Om2epdLkiRJkj7Z71eEW9/8jbD4o3DrrI8ji2OUfVhzqeRt2xOs3wxdOsQA6NgulvrAMtzsNbCmIrz6KhOHdhtyGDI1p5CpcTeWqw/v9tBDDzFt2jRmz57NqFGjADjhhBN47bXXeOSRRxg2bFjEEUqSJEmSMs367fDiB+HX+2gZlBwWfr21OeP4PKb8vorPDo5zdN8YWyvgx3+oom/XGAO6mwxsrEfLwq/z8XfhPwZBgUPBVAubx26lpaWMGTNmb0Jwjz59+lBYWMiQIUNqlN97773EYjFmzJiRyjAlSZIkSRlk0UeQjHUfFmxMQqV1OO24fK49O5/SB6r43FW7OOt7u9i+E35+XQEF+SYFGyOegIVJ+Ftu2QWrvIVYdXCkILB69WoWLVrExIkTD3iurKyMkpISmjZturfs7bff5je/+Q0jRoxIZZiSJEmSpAyzZHNy6l25BXZUQdMUT9939uh8zh7tnIFheXcrbEvSmiz/3gR9WienbmUHk4IESUGAjh071iivqKhgzpw5nHLKKXvLKisrufTSS7nnnnu47rrrQo8lFkvOtyvDH0vUvdF+1ky/nbWPTq1RVlVRTuuhJ9a7jmT9PpIkSZKUCbpecReHn35tjbK6VpA92PPHzNz3/zjQsv3hVG5ed2gB7rbg/iah1HPLfZUsXJ5gZ2WCBSvi3D2xsNbtc/2asUX/EQyY8o8aZWG0D4DLrp3IhzPvPIToampoXiEdcgrZlgsJu16TgkBxcTEAS5cu5dRTT91bPmXKFNasWVNjkZHJkydzyimncPTRR6c8zlTrdM4kOp17U42yJZNGRxOMJEmSJGWgRDxJw8CARDwZNyYfmlsvN83QEMltH8mruz4yNaeQqXE3hp9WoFevXgwZMoTS0lLatWtH586dmTFjBk888QTA3qTgK6+8wvPPP8/s2bOTFksi0fAsdn3s/41BqiTr95EkSZKkTPD75fCTxTXLDnZ9tmcEWH2u35rnw87N68kLaeDQwgea1r1REuT6NeOG7XDy0zXLwmgfANN/dRejOt7V+OD2E0Ve4VDbh7mQ2rnQCJCXl8f06dMpKSlhwoQJjBs3juLiYq6++mry8/P3LjIya9Ysli9fTu/evenRowdz587lqquu4sc//nHEv4EkSZIkKR0NbJucege0JbSEoKLTvhkc0Sw5dQ9sk5x6lT0cKbhbv379mDVrVo2yiy66iEGDBlFUVATAjTfeyI033rj3+dGjR3PNNddw9tlnpzRWSZIkSVJmGNQWWhfCx7vCrXfk4eHWp+iMPBweLQu3zt6toEOSko3KHiYFazFv3rycXWG4/+2zG1QuSZIkSTpQ03wY2w1+tzy8OgticEa38OpTtM7uEX5S8OweEOUaLpmaU8jUuBvLpOBBlJeXs3TpUq666qqDbpPMuQUlSZIkSdnhgl7w53dga2U49Z3TE9pFMwUgUx6sZPGqBAO7x7jxwn0phVt/U8my1QliMZh0cT79ujlbWX0NaAuf7wh//yCc+o5sDqd1DacuZTc/pQfRsmVLqqqquPbaa+veWJIkSZKkgzi8CK4/Kpy6ujSHqweEU1dDvbkqzrYdcP+kQnZVwqIV+1Y/vvT0fB64uZBbLy/gnseiXfU2E31vCLQqDKeuW46G5g4BUz2YFJQkSZIkKcnGdoVzetS+zTEza18ttXUhTP0MNIso4bNgeYKRJcE9qSNK8pi/bN8Kq106BOUF+ZAf5X2rGaq4GdxxDDSpJUtTV/sA+I9BcExxuLEpe5kUlCRJkiQpyWIx+PZguKRP415/ZHO497PQp3W4cTXElm3QoihI+LVqHvy8v59Nr+KCk0w1NMZnOsDPjoU2jRgxWLC7fV3UyPal3OQnVZIkSZKkFMiLwTWD4JfHQdcW9XwNwaIRfxgdbUIQoFURbK0IRgeWVwSJwep++1QVvTvHGNbPVENjfboDTP8CfLFT/V8zqC389vNwXs/kxaXs5F3mkiRJkiSl0PBimPEFeGktzCyDhR/Bhh37ns+PBQnAzx4OZ/WAjkWRhVrDkD4xZsyKc/KxMHdxnDOO35f8e3lhnPlvJ/ifq/MjjDA7tGsKd3waVm6Bh1fBP9fDqi0Qr7ZNxyI4uh18pTsMax/tSsPKXCYFJUmSJElKsfxYsOLs5zsGP2/YDlsqg9tAD28GTdIwtzaoRx5NCuN84/ZdDOgWo1P7GPfOrGL82Hx++LtKWhbFuOxHlfToGOOWcaYbDlXPVnDD4OD/FZWwbnuQGGzbJHhIh8pPSzIZwQAAIABJREFUqSRJkiRJEWvfDNpHHUQ93HhhzTTC+LFB9vIvd5ilSqaiAujWMuoolG280V+SJEmSJEnKMSYFJUmSJEmSpBwTSyQSiaiDkCRJkiRJkpQ6jhSUJEmSJEmScoxJQUmSJEmSJCnHmBSUJEmSJEmScoxJQUmSJEmSJCnHmBSUJEmSJEmScoxJQUmSJEmSJCnHmBSUJEmSJEmScoxJQUmSJEmSJCnHmBSUJEmSJEmScoxJQUmSJEmSJCnHmBSUJEmSJEmScoxJQUmSJEmSJCnHmBSUJEmSJEmScoxJQUmSJEmSJCnHmBSUJEmSJEmScoxJQUmSJEmSJCnHmBSUJEmSpP/f3t3HR1Xe+f9/TW4gYBDEWG4F5CYI0aDEtmhtAX/Wglrrtqi1ra2IYpV2q+i36y5iV1FaKXbbtbb2bqWtLduCrKKL2nYlUYvaUrzhRomCgGBERUAC4SaZ+f1xgBAg9zNzJjmv5+ORh+aak2s+OVznTOY917mOJEkRYygoSZIkSZIkRYyhoCRJkiRJkhQxhoKSJEmSJElSxBgKSpIkSZIkSRFjKChJkiRJkiRFjKGgJEmSJEmSFDGGgpIkSZIkSVLEGApKkiRJkiRJEWMoKEmSJEmSJEWMoaAkSZIkSZIUMYaCkiRJkiRJUsQYCkqSJEmSJEkRYygoSZIkSZIkRYyhoCRJkiRJkhQxhoKSJEmSJElSxBgKSpIkSZIkSRFjKChJkiRJkiRFjKGgJEmSJEmSFDGGgpIkSZIkSVLEGApKkiRJkiRJEWMoKEmSJEmSJEWMoaAkSZIkSZIUMYaCkiRJkiRJUsQYCkqSJEmSJEkRYygoSZIkSZIkRYyhoCRJkiRJkhQxhoKSJEmSJElSxBgKSpIkSZIkSRFjKChJkiRJkiRFjKGgJEmSJEmSFDGGgpIkSZIkSVLEGApKkiRJkiRJEWMoKEmSJEmSJEWMoaAkSZIkSZIUMYaCkiRJkiRJUsQYCkqSJEmSJEkRYygoSZIkSZIkRYyhoCRJkiRJkhQxhoKSJEmSJElSxBgKSpIkSZIkSRFjKChJkiRJkiRFjKGgJEmSJEmSFDGGgpIkSZIkSVLEGApKkiRJkiRJEWMoKEmSJEmSJEWMoaAkSZIkSZIUMYaCkiRJkiRJUsQYCkqSJEmSJEkRYygoSZIkSZIkRYyhoCRJkiRJkhQxhoKSJEmSJElSxBgKSpIkSZIkSRFjKHiIeDzO7NmzGTJkCHl5eYwYMYKysjKGDh3K5MmTwy5PkiRJkiRJSoqcsAvIJJMmTWLBggVMnz6dkpISlixZwuWXX857773H1KlTwy5PkiRJkiRJSgpDwf3mzp3LnDlzKC0tZfTo0QCMHTuWZcuWsWDBAkaOHBlyhZIkSZIkSVJyePnwfjNnzmTcuHEHA8EDBg8eTG5uLsXFxQCsW7eO0aNHU1hYyKmnnsozzzwTRrmSJEmSJElSixkKAhs3bmTFihVccsklRzy2YcMGioqK6NixIwDXXnstl112GeXl5fzsZz/ji1/8Inv37k13yZIkSZIkSVKLefkwQSgI0LNnzzrtVVVVlJWVMX78eADef/99nn32WRYuXAjAWWedRe/evVm8eDGf+cxnklJLLBZLSj+SJEmSJEmKlkQi0eRtnSkIFBQUAFBeXl6nfdasWVRUVFBSUgIEswZ79OhxcNYgwEknncT69evTV6wkSZIkSZLUSs4UBAYOHEhxcTEzZ86ke/fu9OnTh/nz57No0SKAg6FgOhxY07C0tLTebcaMGdPoNpIkSZIkSVJ9nCkIZGVlMW/ePIqKirjuuuuYOHEiBQUFTJkyhezs7IM3GenXrx+bN29mz549B3/2zTffpH///mGVLkmSJEmSJDWbMwX3KywsZPHixXXarrjiCoYPH06nTp2A4DLjT3ziE/zqV7/i+uuvZ8mSJWzatImxY8eGUbIkSZIkSZLUIoaCDVi6dCmjRo2q03b//fdz5ZVX8sMf/pAOHTowd+5cOnToEFKFkiRJkiRJUvMZCtajsrKS8vJyrr/++jrtAwcO5Omnnw6pKkmSJEmSJKn1DAXrkZ+fT01NTdhlSJIkSZIkSUnnjUYkSZIkSZKkiDEUlCRJkiRJkiLGUFCSJEmSJEmKGENBSZIkSZIkKWIMBSVJkiRJkqSIMRSUJEmSJEmSIsZQUJIkSZIkSYoYQ0FJkiRJkiQpYgwFJUmSJEmSpIgxFJQkSZIkSZIixlBQkiRJkiRJihhDQUmSJEmSJCliDAUlSZIkSZKkiDEUlCRJkiRJkiLGUFCSJEmSJEmKGENBSZIkSZIkKWIMBSVJkiRJkqSIMRSUJEmSJEmSIsZQUJIkSZIkSYoYQ0FJkiRJkiQpYgwFJUmSJEmSpIgxFJQkSZIkSZIixlBQkiRJkiRJipicsAuQpKg5Y2H6n3PpRel/TkmSJElS5nKmoCRJkiRJkhQxhoKSJEmSJElSxBgKSpIkSZIkSRFjKChJkiRJkiRFjKGgJEmSJEmSFDGGgpIkSZIkSVLEGApKkiRJkiRJEWMoKEmSJEmSJEWMoaDapUQi7AqUyRIJx4gkSZIkKdoMBQ8Rj8eZPXs2Q4YMIS8vjxEjRlBWVsbQoUOZPHly2OWpEdv2wi/L4YI/wccehdGL4K6XYc2HYVemTJBIwJ83wdXPwqjHgq9rnoW/vJ3ZAeHqaWOo+OOdTW6XJEmSJKkpcsIuIJNMmjSJBQsWMH36dEpKSliyZAmXX3457733HlOnTg27PDXgnV1w9V/hnaratp3V8PB6eHQDfP+j8Mme4dWncMUTcMdL8NhbwSch8f3tL38AL34AF/WDW0dAVizMKiVJkiRJSh9Dwf3mzp3LnDlzKC0tZfTo0QCMHTuWZcuWsWDBAkaOHBlyhapPIgHfXgrvVh3lMaAmAf+yFB45F07IS3t5ygB/fDMIBKE2EDz0/xdugGFd4ZKT0l2ZJEmSJEnh8PLh/WbOnMm4ceMOBoIHDB48mNzcXIqLiwG47bbbKCwsJCsri/nz54dRqg6zchus2lY37DlUAtgbD2YNKnriCfj9GmhoEmAM+P3azL6MWJIkSZKkZHKmILBx40ZWrFjBjTfeeMRjGzZsoKioiI4dOwIwbtw4rrzySq666qqU1FJWVgZALNb4dYxN2SYKel12G72/dHuD2yQSCe5Z+AKTv31mmqpSpujYazCn3P96g9skgLd2Ql7Pk9j77rqU11TySPPSx4p5d7H54dl12mqqKjl2xLlN7sPzhSRJkiS1f4lmzHYxFCQIBQF69qy76FxVVRVlZWWMHz/+YNtZZ52V1trUuFhuBxKJRIOhRywWIyu3YxqrUqaI5XRo+ra5Td82nXpdMo1el95ap231tDHhFCNJkiRJahcMBYGCggIAysvLOf/88w+2z5o1i4qKCkpKStJWy4HLl0tLS+vdZsyYMY1uEyVPbIRblzW8TRYw4VOnc4fXh0bOrmr49BOwp77ry/frlA3b1q0mLw1nxTMWpv45DtecT4skSZIkSe2foSAwcOBAiouLmTlzJt27d6dPnz7Mnz+fRYsWAaQ1FFTzje0Fx+bCjn3BZaBHEwcmDEhjUcoYnXPgghPhf9bXPz5iwIUnkpZAUJIkSZKkTOCNRoCsrCzmzZtHUVER1113HRMnTqSgoIApU6aQnZ198CYjykwds+HfRgT/X98FxP/UH049Lm0lKcNMHhrcefpo4yMG9OgEVxemuypJkiRJksLjvJj9CgsLWbx4cZ22K664guHDh9OpU6eQqlJTndsbOnwMfrQS1u+sbc/Pga8MgqsKwfssRFdBHsz5JHzvFXhmc+2MwRgwuif8SzEcnxdmhfUbeldps9olSZIkSWoKZwo2YOnSpUdcOjx9+nT69u3Lc889x7XXXkvfvn1Zs2ZNSBXqUJ/qCfPPgf86u7btyc/A1UMhy0Aw8j7SCX7wcVh4yA17Hz0XZn8smEUoSZIkSVKUGArWo7KykvLyckaOHFmnfcaMGWzcuJE9e/awZcsWNm7cyKBBg0KqUoeLxaC4e+33HbPDq0WZqVfn2v/v2bn+7SRJkiRJas+8fLge+fn51NTUhF2GJEmSJEmSlHTOFJQkSZIkSZIixlBQkiRJkiRJihhDQUmSJEmSJCliDAUlSZIkSZKkiDEUlCRJkiRJkiLGUFCSJEmSJEmKGENBSZIkSZIkKWJywi5AkqJm6UVhVyBJkiRJijpnCkqSJEmSJEkRYygoSZIkSZIkRYyhoCRJkiRJkhQxhoKSJEmSJElSxBgKSpIkSZIkSRFjKChJkiRJkiRFjKGgJEmSJEmSFDGGgpIkSZIkSVLEGApKkiRJkiRJEWMoKEmSJEmSJEWMoaAkSZIkSZIUMYaCkiRJkiRJUsQYCkqSJEmSJEkRYygoSZIkSZIkRYyhoCRJkiRJkhQxOWEXIElRc8bC9D/n0ota9/Nh1Aytr1tqSFs8FiVJkqRkcaagJEmSJEmSFDGGgpIkSZIkSVLEGApKkiRJkiRJEWMoKEmSJEmSJEWMoaAkSZIkSZIUMYaCkiRJkiRJUsQYCkqSJEmSJEkRYygoSZIkSZIkRYyh4CHi8TizZ89myJAh5OXlMWLECMrKyhg6dCiTJ08Ou7y0SiTglQ/g/tfgP1fBYxtgd3XYVSmTvL8bHlwDP1oJc16Hil1hVyS13ps74Jflwbj+45vw4d6wK2q/Xt0GP9v/GvPwetjla4wkSZKUVjlhF5BJJk2axIIFC5g+fTolJSUsWbKEyy+/nPfee4+pU6eGXV7aVOyCb/8dXt0efB8DEsDsFfAvxTC+b5jVKWzVcfjRKvjDmxBP1I6P+16FC06Efy2GjtlhV9l+rJ42hmNHnEuvS29tUnumaGt1V+6D6cvgmc3B9wfG9X+shKsL4aohEIuFWWH78f5uuGUpvPRB8P2BfX3PCrihCL4wIMTiGtDWxrQkSZLUGEPB/ebOncucOXMoLS1l9OjRAIwdO5Zly5axYMECRo4cGXKF6bFtL1zzV3i3qrYtsf+/O6uDN80ds+Cc3qGUpwxwzwqYt672+8Qh/33sLaiqhrs/GkJhUgtVx+Fbz8MrW2vbDozrfXH46WuQHYMrh4RSXruysxq+vgTWV9a2HdjXVTXw3VegQxZ8tl8o5UmSJEmR4uXD+82cOZNx48YdDAQPGDx4MLm5uRQXF7N161YuvPBCCgsLGTFiBOeddx5vvPFGSBWnxkPr4J0qiB/lsQTBjI4frQpmiCl6Nu6E+esa3ub/KmDl1oa3kTJJ2Tvw8tbacOpofrE6mE2o1nnsLVhX2fC+vndVENRKkiRJSi1DQWDjxo2sWLGCSy655IjHNmzYQFFRER07diQWi3HDDTdQXl7Oyy+/zIUXXsjEiRNDqDh1Hl4fBH/1SQCbdtVe9qVoefStht/MQzB+HtmQjmqk5Fi4ofEXwz1x+NOmtJTTrjX2GgPwwV5Y8m5aypEkSZIizcuHCUJBgJ49e9Zpr6qqoqysjPHjxwPQrVs3zj333IOPn3XWWcyaNSuptZSVlQEQa8LiVU3ZprlGPrSXWE5uo9t9+gtf5oOy3yf9+ZOl5JEgukrFPoqy/v/8AMeP+Qqx7PpPHfF4DQ889CTTTrsgjZU1T9jj48DzN1XFvLvY/PDsOm01VZUcO+Lcen7iSK39XZtbM2RG3U0x/N4VdOpX1OA2iUSCb956F1/43fSU19OejXhwCzlduje63YRJU3hv0U9SXk9bPBYlSZKkhiQSTf8b11AQKCgoAKC8vJzzzz//YPusWbOoqKigpKTkqD/3wx/+kIsvvjgtNaZLTdWOJr1hq9m5PQ3VKNPU7Nre+N0WEolgOyVNr0umHfXmBpmurdRdU7mVRDxOLKv++YKxWMxxnQQ1u7aTnX9co8FYpr7GtJUxLUmSJDWFoSAwcOBAiouLmTlzJt27d6dPnz7Mnz+fRYsWARw1FLz99tt54403eOqpp5Jay4E1DUtLS+vdZsyYMY1u01J3vxKsGddQrpyfA+88/xh5GXyH2TMWBv9tTkKuxr20Ba7+a8PbxLJz+K+bLmfM7MvTU1QLhD0+Djx/OrX2dw2jZkjPv9F/rw3urt6QGPCP336fPsd8P+X1tGf3vQoPvN7wNh2yYN2fH6RL7oMpr6ctHouSJElSsrimIJCVlcW8efMoKiriuuuuY+LEiRQUFDBlyhSys7MpLi6us/2dd97JY489xhNPPEHnzp1Dqjo1LhsIuVkNr/n05UFkdCCo1BnRPfiqb3xkAQPy4ewe6axKap0LT4TuHRp+Qfx0H+hzTNpKare+MAA6ZTe8ry8ZAF0aX8VCkiRJUisZCu5XWFjI4sWL2blzJxs2bGDGjBksX76c4cOH06lTp4Pb3X777Tz66KP8+c9/plu3biFWnBoD8uGejwUzNQ514Nt/6g+TCtNeljJELAazPwqFxwbfH34C6XMM/HgU5HhmURuSnws/OQuO61i3/cAw/vgJMH1E2stql3p2gv8cBZ0Ou07hwL4+rzd8c3jay5IkSZIiKZbwOpZ6DRs2jFGjRvHAAw8AsHLlSk455RQGDRpEfn7+we1eeumlpD1nUy4NTuXlwwds2R3cQfYnrwXfX3giTBgARd0aX1IuExy4JGzpReHW0V5Vx6H0Hfjft+CZzUHb7afDub2hYxuYRRr2+AjjksXW/q5hXT6czn+jndXw+FvwveXB9+f0gov7w6gTIKsNnPfakm174bEN8MNVwffj+sDnB8Dp3dP7GtMWj0VJkiQpWVxTsB6VlZWUl5dz/fXXH2wrKiqKzFpAx+fBVYW1oeC/nx5uPcosOVlBAHhu79o31RecGG5NUmsdkwMTTqoNBWd9NNx62rNuHeArg2tDwTuPfj8vSZIkSSlkKFiP/Px8ampqwi5DkiRJkiRJSjpX/pIkSZIkSZIixlBQkiRJkiRJihhDQUmSJEmSJCliDAUlSZIkSZKkiDEUlCRJkiRJkiLGUFCSJEmSJEmKGENBSZIkSZIkKWJywi5AkqJm6UVhV9B8bbFmqTGOa0mSJEWZMwUlSZIkSZKkiDEUlCRJkiRJkiLGUFCSJEmSJEmKGENBSZIkSZIkKWIMBSVJkiRJkqSIMRSUJEmSJEmSIsZQUJIkSZIkSYoYQ0FJkiRJkiQpYgwFJUmSJEmSpIgxFJQkSZIkSZIixlBQkiRJkiRJihhDQUmSJEmSJCliDAUlSZIkSZKkiDEUlCRJkiRJkiLGUFCSJEmSJEmKmJywC5Dam+W/6RjK85761T2hPK+a74yF6X/OpRe17ufDqBlaX7ckKfP5GiNJUjicKShJkiRJkiRFjKGgJEmSJEmSFDGGgjqqRALe2VX7ffl22BcPrx5lnsp9sGJr7fcf7g2vFkmSJEmS1DyuKaiDEglYvhXmrYPn3oVth4Q8XyqD3CwY1hUu7g/n9YG87NBKVUje3w0Pr4cnNsG6yrqPnfME9OkcjI3P94dencOpUZIkSZIkNc6ZggLgnSr45+fhqmfh8Y11A8ED9sXhla1wx0vwub9AaUX662wvxt20l8f+WtPk9rBVx+GXq+HCP8P9q48MBA/YtAseeD0YHz9aCbsz71eRJEmSJEkYCopgVuBli+G595r+M1v2wM1/h++9AjWJ1NWm8G3dA5OeDcLA6ib+W8eB366Brz5d9zJ0SZIkSZKUGQwFI+75d2HqC7CzumU/P38d3PlScOmx2p/te+HrS2Dltpb9/NodMHkJvFuV3LokSZIkSVLrGApG2Pu74d/+AfsaCPSWXhR8NeTRt+CRDcmtTZnhu6/Amh0Nb9PYGHl7F9z2IsQNjiVJkiRJyhiGghE2azl8uC85ff1gpbPBku2au/cx+ht7+fkj4SzM99Tb8Je3k9PX0vdhwfrk9CVJioa9NbBlN1S18GoGNd2e/fvatYAlSYoWQ8FDxONxZs+ezZAhQ8jLy2PEiBGUlZUxdOhQJk+eHHZ5SfXmDngqiTcK2VUNf3wzef21dznZUH2UP7yrqyFn/z3BZ1yTw9TLwrnFcyIBv3o9uX3++nXXn2yJ1dPGUPHHO5vcninaat2SwrdxJ9z1Mox9HD7zJ/jUIrj5b/DyB2FX1v68vh2m/wNGLwr29ehFcOs/gvZM5muMJEnJYSh4iEmTJjFjxgyuvfZaHn/8cS699FIuv/xy1q5dS0lJSdjlJVUqZm09vCG4Q7Ea17sgxoZ36yZku3YneH879D0hBkDP7rEwSgOCNQRXJ/kNQUUVLNmc3D4lSe3La9vhK2Xw8HrYs/9vigTw9DtwzbPwp02hlteu/P09+Noz8OSm2huJ1SSCffy1Z+BvzbgBnSRJapsMBfebO3cuc+bMYeHChdx8882MHTuWadOmceaZZ1JdXc3IkSPDLjGpXkjBH3rb9sLrHya/3/boc2dn8VBpnH+sjlMTT/DhzgR3/66GISfGOLl/eGHgAal6I/C391PTrySp7atJwP/7W3D1weETy+MEbbctg/d2h1BcO1NVDd/+O1THg317qDjBh7zf/ruXbkuS1N7lhF1Appg5cybjxo1j9OjRddoHDx5Mbm4uxcXFAFx88cWsXbuW7OxscnNzmTlzJueee24YJbdYVTWsa+TmES21ahsM75aavtuTC87KpmovzPxNDRVbEnTuCCUnZ3HvDTnkZIcfCr6aosuGXmvhXYwlSe3fc+8Gs8rrkyCY0fbwerhmaNrKapee3AQ7Ggj8EkBlNTyxCf6pf9rKkiRJaWYoCGzcuJEVK1Zw4403HvHYhg0bKCoqomPHjgDMmTOHbt2C1OvFF19kzJgxfPDBB2RnJ2ftt7KyMgBiscaDoaZsczQdeg7k1J+tqdPW2B2G63v8jIV1v//WtBlc8vvbWlRXMpU8EswxaOk+ao1Xft2hSdtNGJPNhDHJWzMwmb/r0FnPkT90VJ22loyRw8fH8yvXEPvk4FZW13phjo9Dn7+pKubdxeaHZ9dpq6mq5NgRTf9AorW/a3NrhsyouyXCHh9R4r7Wofpc+X16fG4qsaz6L2RJJOLMnl/G5FvPSWNl7c+AqQ/S/ezLiGXX/1YgUVPNTf/5Rz7/gy+nvJ4ovcZIkpRqiUTTX1cNBQlCQYCePXvWaa+qqqKsrIzx48cfbDsQCAJs376dWCzWrB2eCWKk8A8g/7hKmtt+Wc3yNQn2Vid4ZW2cH9+Ym7bnTtkfyY6PFul1yTR6XXprnbbV08aEU0wztNW6JYUjlp3NkRcOH7EVZIVzE672JNbUfZikD71TwdcYSZJaz1AQKCgoAKC8vJzzzz//YPusWbOoqKg44iYjU6ZM4fHHH2f79u089NBD5OQkbzceuHy5tLS03m3GjBnT6DYNqdwHYx6v23b4jK4DDsz+qu/xw82+41Yuf/DWxjdMsQP1hhHYLv9Nx6T0c8fVzRtXyfxdb3gBnj3spiDJGCMjTx7I8gwI0cMcH4c+fzq19ncNo2YI598o7PERJe5rHerxjTB9WcPbZMViTP7sp7jpXx0zrfHbN+BHqxreJpadw3euuYyvzbos5fVE6TVGkqRMYigIDBw4kOLiYmbOnEn37t3p06cP8+fPZ9GiRQBHhIL33XcfEFzqe+ONN/L000+Tn5+f9rpbKj8X+naGjbuS3/ewrsnvU+l3ctcjQ8Fk9StJ0tGc0wu+nws79tU/XzABfGFAGotqpz57Ivzk1WCNxvr2dW4MPtcvrWVJkqQ08+7DQFZWFvPmzaOoqIjrrruOiRMnUlBQwJQpU8jOzj54k5HDjR49mqysLP7617+mueLWO/345PfZKRuGGvq0C6kYH6nsV5LU9nXMhjtGBitNHP4H6oHFJ74xDAa0nc9hM1a3jvBvI4JA8PCFPQ58/68jgu0kSVL75UzB/QoLC1m8eHGdtiuuuILhw4fTqVMnACorK9myZQv9+we3YXvxxRdZs2YNw4YNS3u9rfX5/vDoW8ntc3xf6OSIahc+WgB9OsOmJM4m7ZoLY3slr7+oGHpXabPaM0VbrVtSuM7uAfefFcxie+mD2vZ+x8BVhXDBieHV1t58th906wD3vwarP6xtH3IsfP1k+FTP+n82bL7GSJKUHEY4DVi6dCmjRtXegXXnzp1cdtllVFZWkpOTQ15eHg8++CD9+rW9aytOOQ5GdIeXP2h826bIjsEXByanr6iY9btqVq5LMKx/jFu+Unso3vFANW9sTBCLwbSvZlPYL/0TerNi8JVBcPfy5PV52cBgFogkSQ0ZeTz88mzYuBMu/r+gbf453qsqFT7ZM/h6cwdcsv+z8d+Ndl9LkhQVhoL1qKyspLy8nOuvv/5gW48ePXj++edDrCp5YjGYPgK+VAZ7463vb1IhDOzS+n6iYtW6OLv2wK+n5TJjTjUr1sY5ZWAQ/l11YTZ9T4ix/p0EP5xXzX98M5yr/L8wAJ7cVHemRksN6gJXDm59P5Kk6Oh7TO3/G1Kl1kmH/A3nvpYkKToMBeuRn59PTU1N2GWk1IAu8O1T4c6X69+mKXeD+2gBTBySvLqi4JU1Cc4sCv7qHlWUxctvJDhl/0zLvicE7TnZkB3iX+ZZMbj9dLjqWdiyp/7tGhsj+TlwVwl0cJagJEmSJEkZwxuNRNzF/eGW4pYPhI8VwD0fg1xHUrPs2AXHdAoCvy6dg+8P96N5NXzpvHB3bJ9jgrWdPpLXsp/vmgv3nQmDj01uXZIkSZIkqXWMcsSEAcHaPf2bcTe/Dlnwz8Ph3jOhs/NNm61LJ9hZlQCgsioIBg/12ydrGNQnxsjC8A/Rk7rA3DEwvk/zfu6TPeAPY6HouJSUJUmSJEmSWiH8xEEZobg7/H40/PtpMLxb/dt16wBfGxws+P3VwcENRtR8xYNjvLAqCAWfXxmneFDtjlyyPM7LryeYfFHmHJ5dO8CMEvjV2UE4WN/M0OxYcIfhn5wJP/gYFLRwhqEkSZIkSUot53jpoI7ZcGG/4GvbXnhtG7zdltx2AAASNklEQVS9C+LAsbkwtCuceEyw1pxaZ/iALDrkxvnaXfs4uV+MXsfH+PnCGiZflM13H6wmv1OMSd+rZkDPGLdNzJzDdET34Gt6DbyxA9Z+CLtrIDcbBubDkK6Q59qBkiRJkiRlvMxJG5RRunWAUR8Ju4r27Zav1D38Jl8UpGmP3t0hjHKapUN2MKO0oVmlkiRJkiQpc2XO9YmSJEmSJEmS0sJQUJIkSZIkSYoYLx+WkuzUr+4JuwRluKUXhV1B87XFmiVJbYOvMZIkhcOZgpIkSZIkSVLEGApKkiRJkiRJEWMoKEmSJEmSJEWMoaAkSZIkSZIUMYaCkiRJkiRJUsQYCkqSJEmSJEkRYygoSZIkSZIkRYyhoCRJkiRJkhQxhoKSJEmSJElSxBgKSpIkSZIkSRFjKChJkiRJkiRFjKGgJEmSJEmSFDGGgpIkSZIkSVLEGApKkiRJkiRJEWMoKEmSJEmSJEVMTtgFSO3N8t90DOV5T/3qnlCeV813xsL0P+fSi1r382HUDK2vuy3yHJI+HotN57GYPh6L6RPFcS1J0qGcKShJkiRJkiRFjKGgJEmSJEmSFDFePqx2YWc1lFbAiq2wZkdt+23LYFg3GN0TencOrz6FK5GAf2yBf7wPr26vbb/hBRjWFUoKoOR4iMXCq1Hh2rQTnt4Mq7bVtk3+Kww+Fk7pBmN6QWdfMZPizR3w7GH7+utLoPBYKO4On+wBHbPDq0/hqtwHiytg5TZ448Pa9n9/sfb1vGen8OprT7buCfb1qm2w9pC/ne58KdjXY3tB93CuoJYkSWniWxy1adv3ws9Xw6MbYFfNkY8v2hh8/WAFnN0Drj8ZhnRNf52HG3fTXr7x+Wwu/ER2k9rVMokELHwLfv06bNh55OPPbg6+flEOA/Lha4PhwhMNB6Nk9Xb46Wvw182QOOyxZVuCrz8CnZfD5/rBNYVwbIcwKq2rLZ5DXtwC978WBPSHW/p+8PX7tdA1F74wAK4aAnn+lRIZW/cE4+N/N8Luo7yeP/ZW8HXPcvhUT5gyDE7qkv46D9cWj8V3dsF9r8Ff3oZ98SMff3hD8DV7BZzbC64fBr38YFWSpHbJP7fVZj27GWa8BFuasB53AnhmMzz3LlxdCFcVQpbBT7v2blUws+Rv7zdt+3WVcPtL8OQm+M7pcEJeautTuOIJ+GU5/Kocag5PA49iVzXMXRu8ib7tNDjzI6mvsb3YWwP/uQr+8OaRwevRbN8H//U6/PltuON0OLV7yktUyEor4K6XYevexreNA6XvwJJ34esnwxWD/CCnOR7ZAPesCM5pjdkXh8c3QdlmmFoEF/dPfX2SJCm9XFNQbdJjb8HUF5oWCB6qOgH3r4Y7XgpCAbVPb++CSc82PRA81PPvwdXPQsWu5NelzFCTgO8sC2YZNyUQPNR7u+Fbz8PjG1NTW3uzpwZu+hv8dxMDwUO9tROuXRJ8mKP2a8E6uPnvTQsED7U3HoTN33slmBWuxv1idfBhalMCwUPtqoY7Xw7OmZIkqX0xFFSb87f34I4Xg9kCLfXYW/CTV5NWkjLIrmr4xnNQUdXyPjbtgm88D7ub+cZJbcOPVwWzX1oqDnznxeByVzXszpfgufda/vN743Dz3+quLaf245l34LuvtK6Ph9YHM37VsIfXw89aGer9fHXQjyRJaj8MBdWmVO4LLvFsLBBcelHw1ZBfvwGvfJC00pLumrv3Mfobe/n5I0dZXEn1unfV0dcPPFRTxsf6ymDNJbUvL26BB9c0vE1Txkc8Ecw4bu6Mm3QK+xzyf283Hr42ZV/viQchbHVrPglSxtm+N5h91tgkv6aMkV+Uw2vbG94mTGEfi2/vCtZWbkxT9vU9K4L+JElS+2AoeIh4PM7s2bMZMmQIeXl5jBgxgrKyMoYOHcrkyZPDLk/Ab9fA5lbMADtUgmAR7TDkZEP1Ud4bVFdDzv6VPmdck8PUyzJvgfJMtnYHzFuXvP7+e22w1qDah0QiOOaTdaXh27vgt28kqbNmyvRzSHU8CA+SZfX2YC00tR//Vd78JUDqU5NoWuiVCpl+LALc9+rRb8bWElU1wYdvkiSpfTAUPMSkSZOYMWMG1157LY8//jiXXnopl19+OWvXrqWkpCTs8iJvXxz+Z11y+1y1DVZuTW6fTdG7IMaGd+tGE7t2J3h/O/Q9IVgxvWd3V05vrvnrkttfAngoyX021+ppY6j4451Nbs8UmVj38q1BuJRM/7M+nBlsmX4OefodeHd3cvucvy7cteMycUw3RSbWvbs6uDN8Mi3bEs5l5pl+LL6/O5i1m0xPVQT9hikTx7UkSW2RoeB+c+fOZc6cOSxcuJCbb76ZsWPHMm3aNM4880yqq6sZOXJk2CVG3t/fhw+auRB5UzzRirXFWupzZ2fxUGmcf6yOUxNP8OHOBHf/roYhJ8Y4ub9hYEskEvBkCm7+8MRGF7FvL1JxrL+/J5y1BTP9HJKKff36h8FsYLV9S96FHfuS3++Tvp4f4amK4CZryVSTCO7ELkmS2r6csAvIFDNnzmTcuHGMHj26TvvgwYPJzc2luLi4TvvPf/5zrr32WubNm8eECRPSWWpkrUrRjL5V21LTb0MuOCubqr0w8zc1VGxJ0LkjlJycxb035JCTHf6biLZo0y7YnoI3mVv3Bpes9+yc/L6VXqk61l/dDqM+kpq+65Pp55CU7ettMOjY1PSt9EnV+PD1/EipPBYlSVLbZygIbNy4kRUrVnDjjTce8diGDRsoKiqiY8eOB9tef/11HnjgAUaNGpXOMiNvTYpmiKwJ6a6WE8ZkM2GMawYmSypnEL2xw1CwPVibomM9Vf02JlPPIbuq4Z0krf16uFS9Dii9fD1Pn1S9NjprV5Kk9sFQkCAUBOjZs2ed9qqqKsrKyhg/fvzBturqaq666ip++tOfcsMNNyS9lrKyMgBiscY/XW7KNu3JoH97mG4f/1ydtsbuklff42csrP3/D6v2EIvltbK6Wq/8ukNS+rntl9UsX5Ngb3WCV9bG+fGNuQ1uH7XxcLjjzr6Ugf/vD3XakjE+AD77+QlsW/JQK6qrq+SR5l3LVTHvLjY/PLtOW01VJceOOLfJfbR2fDS3ZsiMug81ckE1sezaN+7JGh+/n7+AO8/4Qiurq9XWzyE5XU9gxG/erdOWrH19z733ccPPvtGK6uryWAznWBxy+5849rRP12lLxhh5Z8tWYrHurayuVls/FgGG37uCTv2K6rS1ZF8ffiwuW7GK2JiiIzdsofYwriVJyhSJZqx/ZSgIFBQUAFBeXs75559/sH3WrFlUVFTUucnIjBkzGD9+PKeddlra64y6+L7UrGqd2Bvyatn1uONqD8/miKfw3zG+J0XTnpqo1yXT6HXprXXaVk8bE04xzZBpdcf37SY7+5jk97s33PFRn7DOIR6LmSfT6k7VGPH1/Eip2tdhH4uQeeNakqS2yNQBGDhwIMXFxcycOZPu3bvTp08f5s+fz6JFiwAOhoIvvPACTz31FKWlpSmrpTmJbtT87DX4RXndtsM/uT7gwKfc9T1+qNP6duXFJO735b/p2PhGKRD1sbOhEj7/VN22ZIwPgFVl/0ufJGZJTX3eZGrt+AijZkjuuP7q03XX10rW+Jh27Ze5+p4vt664Q7SHc8j5f6p79+Fk7ev7br+Zzz1wc+uKO4THYtMlc3z8aCX8dk3dtmSMkbMKe/Gyr+d1TF8Gjx92E65k7OsJo0uYkcQ628O4liSpLfLuw0BWVhbz5s2jqKiI6667jokTJ1JQUMCUKVPIzs4+eJORxYsXs2bNGgYNGsSAAQN4/vnnuf7667nnnntC/g2iYXi31PQ7LEX9Kr36HgP5KfiYo2su9HY9wXZhWNfU9Juqc1Nblqrz6vAU/RsqvXw9Tx/3tSRJaoih4H6FhYUsXryYnTt3smHDBmbMmMHy5csZPnw4nTp1AuCWW27h7bffZt26daxbt45Ro0bxk5/8hJtuuink6qPhoyfAsQ0vw9Mi5/VOfp9Kv6wYfLpP8vv9dB9wyaH2IRXjo1sHOOP45Pfb1n06BefVAfkw2DsPtwtn9YDOKbgvh6/nRzqnV/D6mExZwDnua0mS2gUvH27A0qVLvcNwBsnLhs/1O/KSo9YY3AVGJG9N8maZ9btqVq5LMKx/jFu+Unso3vFANW9sTBCLwbSvZlPYz+y+qSYMgP9Zn/w+wzT0rtJmtWeKTKy75HgY2CW5d828uB90COmmo5l8DjmnF3TvAB/sTV6fEwaEG9Bn4phuikys+5gcuOBEmLcueX2echycHNLstUw+Fnt0gtE9YPE7yevzUz2hZ6fk9dcSmTiuJUlqi0wb6lFZWUl5eTkjR46sd5vS0lImTJiQxqp05RA4PolL/Nx8ajhvMleti7NrD/x6Wi77qmHF2vjBx666MJvfTM/ljqtz+OkjNekvrg0b2hU+e2Ly+vun/s5Mak9iMZiavJtlckIefG1I8vprjkw/h3TIhm8lcV8P7AKf75+8/hS+qwuD5RmSIYvkHtvNkenHIsA3hkOHJP3F3yELvjk8OX1JkqTwOVOwHvn5+dTUGMhkmq4d4NYRcOPfGt6uKQtWX3YSnFGQnLqa65U1Cc4sCtLIUUVZvPxGglMGBo/1PSFoz8mGbK9bbbapp8Df34d3GrgxYlPGR+/O8C3f+LQ7oz4SzDibv67+bZoyPmIE56IuKVjSoCnawjnk/L6wuAJKG5ih1JR9nROD75wW3oxMpcbxefCvI+CWpQ1v15QxcsVgKA5p1n9bOBb758M/D4fZKxrerin7+pvDg/4kSVL74ExBtTmf7AnfPrV1fYztBTeGNKsAYMcuOKZT8AahS+fg+8P9aF4NXzrPQ7S5uuTCj0e1bkZpQUe4dxTkhxT4KLVuPgVG92xdH7cUwyd6JKeelmgL55BYDGaMhNNaEdZkx2BmCRQdl7y6lDnO7d36D1/G9YHrhyWnnpZoC8ciBB+EfmVQ6/r40kD44knJqUeSJGUGEwe1SZeeFLxRbO6NR2LAFYPguyWQE+Lo79IJdlYlAKisCt5IHOq3T9YwqE+MkYUeoi0xoAv86mw4tQVBQvFxwc86E6L9ysmCu8+ALw8MzgnNcWwufO8M+MKAVFTWdG3lHNIpJwjpW3JZ/0fygnDeGxq0b1cMhn8/PVhnsDmyYnDVELh9ZBAeh6WtHIuxWBDATi1q/qXEHbKCD1JvLPLGW5IktTcmDmqzzusDfxwLF/SF3CaM5NO7B2HPt4rCDQQBigfHeGFV8Cbi+ZVxigfV/pW9ZHmcl19PMPkiD8/W6HsM/PLs4E3MR/Ia375HHtx0CvzibOhzTOrrU7hysuDGU+AXnwjODY3pkBUEW/PGBrObwtaWziF5OfCd0+GHH4eTuza+fafsYFbTH8bCx05IfX0K34UnBv/e4/oEl4s35qMFMOeTwQzBMANBaFvHYiwGXxoEvx8Dn+zR+IciMYLtfj8avjzIQFCSpPbINQXVphXkBbMEbiiCP70NK7fCGx/CzuogKOyfD8O6wphemXXDiOEDsuiQG+drd+3j5H4xeh0f4+cLa5h8UTbffbCa/E4xJn2vmgE9Y9w20cO0pbJjwRuZy06CZzfDP7bAq9tgy57gzc7xHYO7VZ5RAJ/4SPhhsdLvtOODIPiND4P1717dDhsqYV88mLk05Njg0tXzekO3JN7kqLXa4jnk7B7BcbZyW3A8rtoGm3ZBTTy47L+wazC799zeXrofRT07wZ0lwev5Xw68nu+AXdVBKD8gPzhf/3+9gtngmaItHosD8uE/Pg4Vu+DPbwevi2t2wJ4a6JgNg7rAsG7Bsdi7c+P9SZKktiuWSCQSYRchtSfLfxNOcnDqV/eE8rxqvqYs5p5sSy9q3c+HUTO0vu62yHNI+ngsNp3HYvp4LKZPFMe1JEmHcl6MJEmSJEmSFDGGgpIkSZIkSVLEePmwJEmSJEmSFDHOFJQkSZIkSZIixlBQkiRJkiRJihhDQUmSJEmSJCliDAUlSZIkSZKkiDEUlCRJkiRJkiLGUFCSJEmSJEmKGENBSZIkSZIkKWIMBSVJkiRJkqSIMRSUJEmSJEmSIsZQUJIkSZIkSYoYQ0FJkiRJkiQpYgwFJUmSJEmSpIgxFJQkSZIkSZIixlBQkiRJkiRJihhDQUmSJEmSJCliDAUlSZIkSZKkiDEUlCRJkiRJkiLGUFCSJEmSJEmKGENBSZIkSZIkKWL+fwlG2pqCQWK0AAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 1683.19x686.28 with 1 Axes>"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -440,6 +498,65 @@
    "source": [
     "GMS(5, [[0.2] * 5] * 5).decompose().decompose().draw(output='mpl')"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import qiskit.visualization.qcstyle as s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "issubclass() arg 1 must be a class",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-22-389e75a4b414>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m \u001b[0;32min\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__dict__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0missubclass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMatplotlibDrawerStyle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-22-389e75a4b414>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m \u001b[0;32min\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__dict__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0missubclass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMatplotlibDrawerStyle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: issubclass() arg 1 must be a class"
+     ]
+    }
+   ],
+   "source": [
+    "c = dict([(name, cls) for name, cls in s.__dict__.items() if issubclass(cls, s.MatplotlibDrawerStyle)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "issubclass(c['BWStyle'], s.MatplotlibDrawerStyle)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for name, cls in s.__dict__.items():\n",
+    "    if isinstance(cls, type):\n",
+    "        if issubclass(cls, s.MatplotlibDrawerStyle):\n",
+    "            print(name, cls)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/IQX Colors Big Red-Copy1.ipynb b/IQX Colors Big Red-Copy1.ipynb
new file mode 100644
index 0000000..40bfd20
--- /dev/null
+++ b/IQX Colors Big Red-Copy1.ipynb	
@@ -0,0 +1,814 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Circuit library\n",
+    "\n",
+    "They are all rendering as block."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1645.78x686.28 with 1 Axes>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from qiskit.circuit import QuantumCircuit\n",
+    "\n",
+    "circuit = QuantumCircuit(5)\n",
+    "circuit.i(0)\n",
+    "circuit.x(1)\n",
+    "circuit.y(2)\n",
+    "circuit.z(3)\n",
+    "\n",
+    "circuit.h(0)\n",
+    "circuit.s(1)\n",
+    "circuit.sdg(2)\n",
+    "circuit.cx(0, 1)\n",
+    "circuit.cy(2, 3)\n",
+    "circuit.cz(1, 2)\n",
+    "circuit.swap(2, 3)\n",
+    "circuit.iswap(2, 3)\n",
+    "circuit.dcx(0, 1)\n",
+    "\n",
+    "circuit.u1(0, 0)\n",
+    "circuit.u2(0, 0, 1)\n",
+    "circuit.u3(0, 0, 0, 2)\n",
+    "\n",
+    "\n",
+    "circuit.cu1(0, 0, 1)\n",
+    "circuit.cu3(0, 0, 0, 2, 3)\n",
+    "\n",
+    "circuit.ch(3, 4)\n",
+    "\n",
+    "circuit.rx(0, 0)\n",
+    "circuit.ry(0, 1)\n",
+    "circuit.rz(0, 2)\n",
+    "circuit.r(0, 0, 3)\n",
+    "circuit.t(4)\n",
+    "circuit.tdg(4)\n",
+    "\n",
+    "circuit.rxx(0, 0, 1)\n",
+    "circuit.ryy(0, 1, 2)\n",
+    "circuit.rzz(0, 2, 3)\n",
+    "circuit.rzx(0, 0, 1)\n",
+    "circuit.ccx(0, 1, 2)\n",
+    "circuit.cswap(0, 1, 2)\n",
+    "circuit.rccx(0, 1, 2)\n",
+    "circuit.rcccx(0, 1, 2, 3)\n",
+    "circuit.ms([[0] * 5] * 5, [0, 1, 2, 3, 4])\n",
+    "\n",
+    "circuit.unitary(np.eye(4), [0, 1])\n",
+    "circuit.initialize(np.eye(4)[0,:], [2, 3])\n",
+    "circuit.isometry(np.eye(4), [], [1, 2])\n",
+    "\n",
+    "circuit.draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1164.18x264.88 with 1 Axes>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from qiskit.circuit.library import QFT  # e.g. for the Quantum Fourier Transform\n",
+    "QFT(4).draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 392.767x325.08 with 1 Axes>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from qiskit.circuit.library import OR\n",
+    "OR(4).draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 491.969x325.08 with 1 Axes>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from qiskit.circuit.library import LinearPauliRotations\n",
+    "LinearPauliRotations(4, offset=2, slope=0.5).draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Ju/qi/uOr+bKeMq8vDHPW/mx7q7yoqEirVq1SaWmpXC6XJCk7O1vl5eUqLi7W0KFnfns9cuSI0tPTm20fFxcXWI9P7dz9slIuHyxJete3Q/0Sh6hTp+a/v73ne0OpSZmB/645eUSn6+tUePdbOn7ysGb9YpDe8P5F37jym2f2VbVDA5KulsPhUGNjg8rfe0met36vOd/6ZWCff972pNa88p/66fQ/aMDXrtb2dzbqgf/6jn5337vq2b2PFt62Vr8qnqOnF+xth05Y47P9R2h8vqfM69BiztqPba+48/PzlZOTEwjts/r3768uXbooIyPjnPd19OhRjR8/XmlpaRo8eLDGjh2r3bt3h7Reh8Nh6eLxlH5pja/97Tlt2LYi8EPHW1WmAUlXt/ha7/7P/IDbX65Rg76jW0bfrbAuEUq4NFFdIy6V3+8PvP7dqh2q2LdFNy+M1U0LIvTg6smaN2mlcq65Q5J08lSNVm78ieZN/q0GfO3M97xm4DhdGt1D7/rKAvWc/Z5neTyllvc2VOfg8/23SkfvKfO6483ZULFL31tbzpUtg9vn86miokKTJk1qtq6yslJOp1Ph4eGSzlxZHz16tNnrzl5px8XFyeFw6Ec/+pG8Xq/eeustjR8/XjNmzGjbg7AZz1v/o8fWfl8/v329UpOGyu/36739byjtay2/D/ie7w2lJp75YbN7f7lGOicG1tXWVeuj6g90Re9Bn3l9mRZ8t0h/XFStZ/OqlHBpkio//vRtijd3v6JLHJfo6/2zA2N+v1/HTxwKPPTi9TX/AddRfL7/uHAt9ZR5HTrMWfuybXBLUq9evYLG6+rq5PF4ArfJJcnpdOrtt98O+i1Zknbt2qXOnTtr4MCBio2N1ZgxYwLrRo4cqb17Q3vbyu/3W7q4XFmt1vbCjt/pl8+59fMZfwr8gNl/aLdq66pbfIDnxKnj+vDwbqUlZepE3TEdOPK+Bn7tmsB6r+8NXRqVoMu69w3sq6buaOAfd3y33rrVNU8btq1QU1PTmX1+clzRUd2Dvs/rb/9JYV0idWWf4ZKk9/Z/+kP1LJcry/LeXug5aKn/VurIPWVed8w5Gyp26Xtry7myZXAnJCRIkrxeb9B4QUGBDhw4EHgwTZImTpyoQ4cO6cUXXwyM1dfXa82aNRozZoy6du3abP+PP/64br755jaq3l6e37JUT264V//5/Rd11RWjAuNe3w51jbhUPS5N0un6U4GlsalRu/eXq1tUvHp276P39pcrOrK7esenfGbbMqV+5orm3aodiomKC/zAk6SRzomqrvlIFfvO/CGcK/t8Qx8d2ae/vf+qGpsatXP3K/pl8Wy5x/9CYV0iJEnHag/Kr3OfvCZorf/46r6op8zrC8ectT9bPpyWkpKijIwM5efnKy4uTomJiVq7dm3gM9mfDe4JEybouuuu04wZM1RQUKDevXvriSeeUGVlpYqKiprt+4EHHtDu3bv1yiuvtNvxWGnZuh/qkk6dde+K4N+av/mNWTpx6pi+uSAyaPzH01brSM0/1P+fH115b395s6uF93xvBF3ReKt2qP/lQ4Jec2nXBKUnj9Rrf1urjJTRSuqRprsnPaXFv5+hozX/UGKPNN01calGZ9wa2Gbq9T/Rfz4zTfWNp/XzGes1pP/1IemBlVrr/58eqrWoIvN9UU+9VWXM6wvEnLU/h/98rs/bkdfrldvt1vbt2xUfH6/c3FzFxMRowYIFqqmpUWTkp/8wq6urNX/+fBUXF6u2tlZDhgzRww8/rNGjRwft88EHH9S6deu0adMmxcbGtvchtamyNVK1z+oqQi82SRo21eoqzo0p54CeWs8u56Cj9rc1dun7hbLlFbckpaWlafPmzUFj06dPV3p6elBoS1JsbKwKCwtVWFjY6v4eeOABlZSUdMjQBgBcPGwb3C0pKyvT8OHDz3u7Xbt26f7771e/fv2UlZUVGH/zzTdDWB0AAG3PmOCura2V1+vVnDlzzntbp9N5Xk/sAQBgV8YEd3R0tBobG60uAwAAS9ny42AAAKBlBDcAAAYhuAEAMAjBDQCAQQhuAAAMQnADAGAQghsAAIMQ3AAAGITgBgDAIAQ3AAAGIbgBADAIwQ0AgEEIbgAADEJwAwBgEIIbAACDENwAABiE4AYAwCAENwAABiG4AQAwCMENAIBBCG4AAAxCcAMAYBCCGwAAgxDcAAAYhOAGAMAgBDcAAAYhuAEAMAjBDQCAQQhuAAAMYnxwHzt2TLNnz1bPnj0VFRWlUaNGacuWLVaXBQBAmzA6uP1+vyZOnKjnn39eixcv1vr165WQkKAbbrhBO3futLo8AABCrrPVBVyIDRs2yOPxqKSkROPGjZMkjR49Wk6nU3l5eSopKbG4Quv9dmOeNu98VsdPHlZY5wgNShmt2ROWqGf3PlaXdtHgHIQePW1b9NfebHvF3dTUpMWLFys1NVUREREaPHiwPB6PBgwYoFmzZkmS1q1bp/j4eOXk5AS2CwsL09SpU7Vp0yadOHHCqvJtY0zmdK24+02te/C4nl6wTz1j++ihZ6ZaXdZFhXMQevS0bdFfe7PtFffMmTNVXFyshQsXKjMzU1u3btW0adN08OBB3XPPPZKkiooKOZ1OORyOoG2vuuoqNTQ06J133lFmZqYV5dtGn54DA//tl18ORydVHXzXwoouPpyD0KOnbYv+2pstg7uoqEirVq1SaWmpXC6XJCk7O1vl5eUqLi7W0KFDJUlHjhxRenp6s+3j4uIC6yG9svNZ/bL4Bzp56rgu6dRZsycssbqkiw7nIPToaduiv/Zly1vl+fn5ysnJCYT2Wf3791eXLl2UkZFxXvu7+eablZGRoSFDhuiaa67RSy+9FMpyJUkOh8PSxeMpbbW264d8V+sWHdPvFx7Q9LH364reg0J+/G3F4ym1vLcd7RzQU+vZ5Rx01P62xi59b205V7YLbp/Pp4qKCk2aNKnZusrKSjmdToWHh0s6c2V99OjRZq87e6V99sp71apV+tvf/qadO3eqsLBQt9xyixobG9vwKOwprlsv3fSN7+unvx2v4ye5G2EFzkHo0dO2RX/tx5bBLUm9evUKGq+rq5PH4wncJpckp9Opt99+W36/P+i1u3btUufOnTVw4Jn3aWJjYwPrjh07JofD0WybC+X3+y1dXK6sc6qzsbFBp06f0OHjH4b0+NuKy5VleW872jmgp9azyznoqP1tjV363tpyrmwX3AkJCZIkr9cbNF5QUKADBw4EPWw2ceJEHTp0SC+++GJgrL6+XmvWrNGYMWPUtWvXwPhdd92llJQU3XLLLXruuefUubMt394PqaamJv3xr0/oaO3HkqSD1T498fxd6tU9WX16DPySrREKnIPQo6dti/7an+3SKyUlRRkZGcrPz1dcXJwSExO1du3awGeyPxvcEyZM0HXXXacZM2aooKBAvXv31hNPPKHKykoVFRUF7ffXv/61JMnj8ejuu+/Wq6++qujo6PY7MItsf6dEz2z6uU6dPqGukbEa3C9Lj8x6SZdcYrtT32FxDkKPnrYt+mtvDv/5XJ+3E6/XK7fbre3btys+Pl65ubmKiYnRggULVFNTo8jIyMBrq6urNX/+fBUXF6u2tlZDhgzRww8/rNGjR7e6/2HDhumhhx7SjTfe2B6H0y7K1kjVPqurCL3YJGmYIR8fNeUc0FPr2eUcdNT+tsYufb9Qtvz1KS0tTZs3bw4amz59utLT04NCWzrz/nVhYaEKCwtb3Fdtba0OHz6svn37SpJ27typPXv26Morr2yb4gEAaEO2DO6WlJWVafjw4ee93YkTJzRlyhTV1taqc+fOioiI0OrVq9WnD3+6DwBgHiOCu7a2Vl6vV3PmzDnvbS+77DJt27atDaoCAKD9GRHc0dHRF+XnrgEA+DzbfRwMAAC0juAGAMAgBDcAAAYhuAEAMAjBDQCAQQhuAAAMQnADAGAQghsAAIMQ3AAAGITgBgDAIAQ3AAAGIbgBADAIwQ0AgEEIbgAADEJwAwBgEIIbAACDENwAABiE4AYAwCAENwAABiG4AQAwCMENAIBBCG4AAAxCcAMAYBCCGwAAgxDcAAAYhOAGAMAgBDcAAAYhuAEAMAjBDQCAQQhuAAAMYnRw+3w+/du//ZtGjhypqKgoORwOVVRUWF0WAABtprPVBVyI3bt36/e//70yMzPlcrn0wgsvWF0ScN6ampr0wo7f6oXtK1V18B055FBiQqrGXj1DE0bMtro8oEXMW+sYHdyjR4/WRx99JElatWoVwf0FmpqadPeya/X2B6/r2bwq9YhNsrokSGpsatRDq6do7z/+rrsm/kpf758thxx6a0+pnnvtMX4AfgnmtTWYt9aybXA3NTVpyZIlKiwsVFVVlQYMGKClS5dq1qxZcrlcevLJJ9Wpk9F3+tvVc689pvAuUVaXgc959qUHteuDrVpx95vqHt0zMD40bYyGpo2RJL25e7MWPT1JuTf+XM+9ukRHjh9Qzjfu1Hf/JU/L1/1I298pUUxkd82ftlpXXTFKb+97XT9ZmaNvjZij1/7+nI7UHNDQ1Bs0f9rTigzratWhtgnmtTXsOG9f2fmsfvfCT1Vd+7GuHjhOvbon6+Axn/K+V9RmfbCKbZNv5syZWrRokdxutzZu3KjJkydr2rRpev/995WZmWl1eUbxHfTqT1uXadb4xVaXgs84UXdMf/A8qu/9y0+Dfvh93nu+N3Tyk+NqaKzXb+7dpfw7X9AftyzVw89+T5Nc9+q5Bw7rqiuuU9Er+ZKk3R/u1MlTx+WXX0/e8zc9NW+X3vO9oT9tXdZeh9YumNfWsOO83bh9pVaW/EQ/+e6z+uOiYxp0xXV6fssv1e/yr4fsuO3ElsFdVFSkVatWaf369br33nuVnZ2tvLw8jRgxQg0NDRo6dKjVJRqjqalJi/9wh2aNX6zoyFiry8FnlO9+WadOn9D1Q777ha/z7n9D1w66Rd+57ocK6xyu/olDJEnTrl+g1KShuqTTJUq5fLAamxokSbv379Tgflm686aHFdYlQj2799E1A2/SBx+93ebH1F6Y19axw7w9duKQ5j85VpJ06vRJ/ebP9+n/ffvXSu87XJd0ukTjrrlTDY316n/5kBAfvT3YMrjz8/OVk5Mjl8sVNN6/f3916dJFGRkZ57W/ffv2yeVyKS0tTYMGDdJrr70WynIlSQ6Hw9LF4yltsa7nt/xScTG9dO2gb4f8mNuDx1NqeW8v9By05tAxn6Iiun1p8Lzne0OjnDcHvt7z4ZuKiuimjJRP/33sPfB3pfQ+8+9i94c7df2Q7wXt40jNAcX+8+qoI/SUeW3NnJWsm7eftefDtwK/CLy1p1SXdOqs4enjA+uPnzgkSeqXGHzFbZe+t7acK9sFt8/nU0VFhSZNmtRsXWVlpZxOp8LDw89rn263W1OmTJHX61VhYaGmTp2q06dPh6pk29p/aLfWvvoLzb35CatLQQviYnqr7pMaHT95pNXXnDh1XB8e3q0BX7s6MOb1vaG0xMygZzze2/+GUpMy1djYoH3/qFDCpYmBdUdrPlK5d1PQD1GTMa+tZdW83bprvf7tVyN074pslb65JhDc1bUfN/sl4rW/P6f4bpd/4a18k9kyuCWpV69eQeN1dXXyeDznfZv80KFD2rJli2bOnClJGjlypC6//HJt3rw5NAX/k9/vt3RxubKa1VSxd4uO1R7U939xlW75jwTNefxM79xLMrTekPc7Xa4sy3t7Iefgi3zjym/qsu7JWvz7Gfr4aKWkMz/wXvvbc3p6088lSbv3lys6srt6x6cEtvP6ypSWNCzw9anTJ1X58f8pLWmY9n20S01NjXq5fLVON3yij45+oIeemapRg76j9OQRHaKnzGvr5qxkzbz9x5F9Kn7tMRW4X9Yjs15S+XubArfBr+g9SB8e2q2du19RQ2O9tvz9eT296YEW39+2S99bW86V7Z4qT0hIkCR5vV7ddNNNgfGCggIdOHDgvB9Mq6ys1GWXXRZ0lX7FFVfogw8+CE3BNuYaPFlDU8cEvj54zKcfPjFC//n9v6hPz4EWVgZJigiL0uN3/VX//Zf/0LzlLh07eUiR4TFKS8zUra55ks5cpaQmBs/593xvaMTY+6RYp1sAABfkSURBVANf7/nwTUWGRevy+H76S9l/KSPFpe4xvTT5gcsUFR6jG4bl6l/H/Kw9D61NMa+tZcW8fbl8tXKunqmIsDOfIAjrEqnEhFRJUlpSpv51zM+06OlJ6uTopDFDp8uZPEoD+3yjHbphDdsFd0pKijIyMpSfn6+4uDglJiZq7dq1KikpkaRmwb127VpJUllZmSRp06ZNeuedd9S1a1eNGzeufYu3mYiwqMBElxR4CCQuppciw6OtKgufEd+tt+6+9clW109yzdOkf/4wPGvlvwc/rONMHqk/LqqWdOZ9wit6D9LsCb/Q7Am/CH3BNsC8tl57z9tTp0+oofHM25svlz+jmMi4oFvu08f+h6aP/Q9JUs3Jo/rX/L6686aHv9rBGcB2wd2pUyf9z//8j9xut37wgx8oPj5eubm5uuuuu7RgwYJmD6Z9/r3we+65R5LUt29f7du3T3369NFHH32kTz75JHDVvXfvXvXt27d9DshGesUla9Oj5347BubZs3+nxg673eoy2hXz2nxfNm/HZN6mn//3Ldr8ZpEkR+D9bUl6t2qHIsKi1afnQH14eI8eXztLI50364reg9q+cIvYLrglKS0trdl70NOnT1d6eroiIyODxr/sfYGEhASNGjVKK1eu1Jw5c7R161bt379f2dnZIa8bsJLf79eeA28puddVVpcCnLNzmbd9L7uy2RX7Wfv+sUsrS36suk9qFHfp5br+69/VtOt/0lbl2oItg7slZWVlGj58+FfadsWKFbr99tv1+OOPKywsTEVFRQoLCwtxhYC1HA6H1i06ZnUZwHm50Hl749W368arbw9dQQYwIrhra2vl9Xo1Z86cr7R9SkqKXn311RBXBQBA+zMiuKOjo9XY2Gh1GQAAWM52n+MGAACtI7gBADAIwQ0AgEEIbgAADEJwAwBgECOeKseXi+mY/yc4Hfa4cG7a6/xXn/n/NlJsUvt8P+Y1LgTB3UEMuN7qCoDQa695/dLiM/87bGr7fD/gQnCrHAAAgxDcAAAYhOAGAMAgBDcAAAYhuAEAMAjBDQCAQQhuAAAMQnADAGAQghsAAIMQ3AAAGITgBgDAIAQ3AAAGIbgBADAIwQ0AgEEIbgAADEJwAwBgEIIbAACDENwAABiE4AYAwCAENwAABiG4AQAwCMENAIBBjA7ul19+Wbm5uUpNTVVUVJSSk5N12223ae/evVaXBgBAm+hsdQEXYsWKFaqurtZ9992ntLQ0VVVVadGiRcrMzFR5ebmSk5OtLhEAgJAyOriXLVumHj16BI1de+21SklJ0fLly/XII49YVBnQXFNTk+5edq3e/uB1PZtXpR6xSVaXdNGrOyZVlX/6tefX0uVO6WtDpYhu1tVlF8xZe7LtrfKmpiYtXrxYqampioiI0ODBg+XxeDRgwADNmjVLkpqFtiQlJycrISFBPp+vvUsGvtBzrz2m8C5RVpeBfzr+D2nbf0mVb3w6Vl8nfVB2Zvz4R9bVZhfMWXuybXDPnDlTixYtktvt1saNGzV58mRNmzZN77//vjIzM1vdrqKiQgcPHpTT6WzHaoEv5jvo1Z+2LtOs8YutLgWSGhukncVSY33L6xtOS28WS02N7VuXnTBn7cuWt8qLioq0atUqlZaWyuVySZKys7NVXl6u4uJiDR06tMXt6uvr5Xa7lZCQILfb3Z4lA61qamrS4j/coVnjFys6MtbqciDp43el+pNf8AK/dPqE9PF7Uq+B7VaWbTBn7c2WV9z5+fnKyckJhPZZ/fv3V5cuXZSRkdFsG7/frzvvvFM7duzQM888o/j4+MC6ffv2yeVyKS0tTYMGDdJrr70W8podDgfLRb54PKUtzo3nt/xScTG9dO2gb4d83n0VHk+p5b2yeln60DNqbGr4wj41NjVoyf3/ZXmtzNnQsfvcP1e2C26fz6eKigpNmjSp2brKyko5nU6Fh4c3Wzd37lytXr1aTz/9tMaOHRu0zu12a8qUKfJ6vSosLNTUqVN1+vTpNjsG4Kz9h3Zr7au/0Nybn7C6FHxGl0vCvvxF/nN8XQfDnLU/h9/v91tdxGdt27ZNI0aM0J///GfddNNNgfG6ujr169dP48aN08qVK4O2mTdvnh577DE99dRTuuOOO4LWHTp0SH379tWRI0cCgX/11VfrwQcf1I033tj2B4SLRtkaqfpzz0S+uGOVfvmcW5HhMZIkv79JNXVHFRPZXbfnPKhvjZzT7nXGJknDprb7t7WVPX+V9r7+5a9LGSWljGj7eqxiypwNlY4y9233HndCQoIkyev1BgV3QUGBDhw40OzBtLy8PC1ZskS/+tWvmoW2dOYq/bLLLgu6Sr/iiiv0wQcftNERAJ9yDZ6soaljAl8fPObTD58Yof/8/l/Up+dF+OapTSQOkvZuk/RFly2OM6+72DBn7c92wZ2SkqKMjAzl5+crLi5OiYmJWrt2rUpKSiQpKLgfffRR5efna8qUKRo2bJi2bdsWWNetWzelp6e3e/3AZ0WERSki7NOP05x9XzUuppciw6OtKuuiF9FN6n+ttPsLHndJHS1djKeIOWt/trtVLp252na73dq+fbvi4+OVm5urmJgYLViwQDU1NYqMjJQkZWVlyePxtLgPl8ul0tJSbpWj3bR029GOOsrtwlDwvSW9v/XME+RnhUefuUV+MVxtmzJnQ6WjzH3bXXFLUlpamjZv3hw0Nn36dKWnpwdCW5JKS0u/dF8JCQkaNWqUVq5cqTlz5mjr1q3av3+/srOzQ102AMMkDZYuHyRVV0mn66SwKKl7kuSw3WO7wKdsGdwtKSsr0/Dhw7/StitWrNDtt9+uxx9/XGFhYSoqKlJY2MX3tCiA5jp1kuL6Wl0FcO6MCO7a2lp5vV7NmfPVnmZMSUnRq6++GuKqAABof0YEd3R0tBobL+K/PQgAwD/xTg4AAAYhuAEAMAjBDQCAQQhuAAAMQnADAGAQghsAAIMQ3AAAGITgBgDAIAQ3AAAGIbgBADAIwQ0AgEEIbgAADEJwAwBgEIIbAACDENwAABiE4AYAwCAENwAABiG4AQAwCMENAIBBCG4AAAxCcAMAYBCCGwAAgxDcAAAYhOAGAMAgBDcAAAYhuAEAMAjBDQCAQQhuAAAMQnADAGAQghsAAIMYHdwvv/yycnNzlZqaqqioKCUnJ+u2227T3r17rS4NAIA20dnqAi7EihUrVF1drfvuu09paWmqqqrSokWLlJmZqfLyciUnJ1tdIgAAIWV0cC9btkw9evQIGrv22muVkpKi5cuX65FHHrGoMuCMgjW365Wdz6hL5/DA2Pe/WaBvjZxjYVVA65iz9mfb4G5qatKSJUtUWFioqqoqDRgwQEuXLtWsWbPkcrn05JNPNgttSUpOTlZCQoJ8Pp8FVQPN3TAsV/MmPWV1GcA5Y87am22De+bMmSouLtbChQuVmZmprVu3atq0aTp48KDuueeeVrerqKjQwYMH5XQ627FaAADahy2Du6ioSKtWrVJpaalcLpckKTs7W+Xl5SouLtbQoUNb3K6+vl5ut1sJCQlyu93tWTLQqi1/f05//XuxunVN0EjnRE2/4T8UGR5tdVlAq5iz9ubw+/1+q4v4vEGDBikpKUkbN24MGp8/f74ee+wx1dTUKDw8PGid3+/X7bffrqKiIm3YsEFjx44NrPvZz36mNWvWaPfu3frDH/6gW2+9NeQ1OxyOkO8TZlk8e7MG98sKGvP63lCPS5N0adceqvz4/7T4DzPUO76f8r5XZE2Rkt7aU6p7V2Rb9v1hH6bM2VCx+9w/1zi23cfBfD6fKioqNGnSpGbrKisr5XQ6m4W2JM2dO1erV6/W008/HRTakpSTk6MXXnhBo0ePbrO6gZakJWWqe8xl6tSpk5J7OTV7wmN67W9rdbrhE6tLA1rEnLU/290qP/tQWa9evYLG6+rq5PF4NG7cuGbbzJs3T8uXL9dTTz2lKVOmNFs/cuTItin2M2x44wLtrGyNVP0lz0R2cvzzd2UL54vLlSX/cuYrzJmzodJR5r7trrgTEhIkSV6vN2i8oKBABw4cUGZmZtB4Xl6elixZoqVLl+qOO+5otzqBc7H5zTWqrauWJPkOvqfCDfM0Iv1bCusSYXFlQMuYs/ZnuyvulJQUZWRkKD8/X3FxcUpMTNTatWtVUlIiSUHB/eijjyo/P19TpkzRsGHDtG3btsC6bt26KT09vd3rBz5rw+sr9KviOapv+ESx0T016qpva/rY+60uC2gVc9b+bPlwmtfrldvt1vbt2xUfH6/c3FzFxMRowYIFqqmpUWRkpCQpKytLHo+nxX24XC6VlpYGjWVlZWnu3Llt8nAacC63He0gNkkaNtXqKmAHpszZUOkoc992V9ySlJaWps2bNweNTZ8+Xenp6YHQltQsmAEA6Ohs9x53a8rKypq9v32uFi5cqKSkJL3++utyu91KSkrSnj17QlwhAABtz4jgrq2tldfrbfUPr3yZRYsWyefz6ZNPPtHhw4fl8/nUr1+/EFcJAEDbs+Wt8s+Ljo5WY2Oj1WUAAGA5I664AQDAGQQ3AAAGIbgBADAIwQ0AgEEIbgAADEJwAwBgEIIbAACDENwAABiE4AYAwCAENwAABiG4AQAwCMENAIBBCG4AAAxCcAMAYBCCGwAAgxDcAAAYhOAGAMAgBDcAAAYhuAEAMAjBDQCAQQhuAAAMQnADAGAQghsAAIMQ3AAAGITgBgDAIAQ3AAAGIbgBADAIwQ0AgEEIbgAADGJ8cL/88svKzc1VamqqoqKilJycrNtuu0179+61ujQAAEKus9UFXKgVK1aourpa9913n9LS0lRVVaVFixYpMzNT5eXlSk5OtrpEAABCxvjgXrZsmXr06BE0du211yolJUXLly/XI488YlFlwKfKvS/pdy/+VPv+UaGwzhFyDZ6sf/vOMqvLAlrFnLUvWwd3U1OTlixZosLCQlVVVWnAgAFaunSpZs2aJZfLpSeffLJZaEtScnKyEhIS5PP5LKgaCPbWnlL9/Olbdc+kpzQ8fYLk9+uDj962uiygVcxZe7N1cM+cOVPFxcVauHChMjMztXXrVk2bNk0HDx7UPffc0+p2FRUVOnjwoJxOZztWC7RsZclPNH7EbI3OuDUwlpo01MKKgC/GnLU32wZ3UVGRVq1apdLSUrlcLklSdna2ysvLVVxcrKFDW55E9fX1crvdSkhIkNvtbs+SgWbqTp/Qu1Xb5UwepR88PlQfH61Ucq+rNGv8Yg342jCrywOaYc7an22fKs/Pz1dOTk4gtM/q37+/unTpooyMjGbb+P1+3XnnndqxY4eeeeYZxcfHS5KOHj2q8ePHKy0tTYMHD9bYsWO1e/fukNbrcDhYLvLF4yltNi9qTx5Vk79Jm98s0r9PXqU1Cz9UZtpY/XTlTaqtqw7pHDxXHk+p5b1iscdiypwNFbvP/XNly+D2+XyqqKjQpEmTmq2rrKyU0+lUeHh4s3Vz587V6tWr9fTTT2vs2LGBcYfDoR/96Efyer166623NH78eM2YMaNNjwGQpKjwGEnSjVfPUMrlGerSOUzTrv+JGprqtWvfVourA5pjztqfLW+Vn32orFevXkHjdXV18ng8GjduXLNt5s2bp+XLl+upp57SlClTgtbFxsZqzJgxga9HjhypgoKCkNbs9/tDuj+Yp2yNVP255yG7Rl6qXt2T5VDwb9MOnd9v2KHkcmXJv5z5CnPmbKh0lLlvyyvuhIQESZLX6w0aLygo0IEDB5SZmRk0npeXpyVLlmjp0qW64447vnT/jz/+uG6++ebQFQx8gQkj5+jFst/pg4/eVmNjg/5Q+qi6dA6Xs+9Iq0sDWsSctTdbXnGnpKQoIyND+fn5iouLU2JiotauXauSkhJJCgruRx99VPn5+ZoyZYqGDRumbdu2BdZ169ZN6enpQft+4IEHtHv3br3yyivtczC46E1y3auTn9To31dcr9MNp9Q/cYgemrlRXSMvtbo0oEXMWXtz+G16j9fr9crtdmv79u2Kj49Xbm6uYmJitGDBAtXU1CgyMlKSlJWVJY/H0+I+XC6XSktLA18/+OCDWrdunTZt2qTY2Nj2OAxcRFq67WhHsUnSsKlWVwE7MGXOhkpHmfu2vOKWpLS0NG3evDlobPr06UpPTw+EtqSgYP4iDzzwgEpKSghtAIDRbBvcLSkrK9Pw4cPPe7tdu3bp/vvvV79+/ZSVlRUYf/PNN0NYHQAAbc+Y4K6trZXX69WcOXPOe1un08lT3wCADsGY4I6OjlZjY6PVZQAAYClbfhwMAAC0jOAGAMAgBDcAAAYhuAEAMAjBDQCAQQhuAAAMQnADAGAQghsAAIMQ3AAAGITgBgDAIAQ3AAAGIbgBADAIwQ0AgEEIbgAADEJwAwBgEIIbAACDENwAABiE4AYAwCAENwAABiG4AQAwCMENAIBBCG4AAAxCcAMAYBCCGwAAgxDcAAAYhOAGAMAgBDcAAAYhuAEAMAjBDQCAQQhuAAAMYnRwv/zyy8rNzVVqaqqioqKUnJys2267TXv37rW6NAAA2kRnqwu4ECtWrFB1dbXuu+8+paWlqaqqSosWLVJmZqbKy8uVnJxsdYkAAISU0cG9bNky9ejRI2js2muvVUpKipYvX65HHnnEosqAM+5c7NRHRz8IfN3U1KjTDae07IdvKDVpqIWVAa1j3tqbbYO7qalJS5YsUWFhoaqqqjRgwAAtXbpUs2bNksvl0pNPPtkstCUpOTlZCQkJ8vl8FlQNBHvq3l1BX/92Y5627vojP/xga8xbe7Pte9wzZ87UokWL5Ha7tXHjRk2ePFnTpk3T+++/r8zMzFa3q6io0MGDB+V0OtuxWuDLNTY26MUdv9U3h7utLgU4Z8xb+7HlFXdRUZFWrVql0tJSuVwuSVJ2drbKy8tVXFysoUNb/q2vvr5ebrdbCQkJcruZZLCXv+76o06cOqYbMm+zuhTgnDFv7ceWV9z5+fnKyckJhPZZ/fv3V5cuXZSRkdFsG7/frzvvvFM7duzQM888o/j4+MC6m2++WRkZGRoyZIiuueYavfTSSyGv2eFwsFzki8dT+oVz5M/bCuUaPEXRkbEhn3/nw+MptbxXLPZYvmzOSvaZt6Fg97l/rmx3xe3z+VRRUaG777672brKyko5nU6Fh4c3Wzd37lytXr1azz77rMaOHRu0btWqVYqNPTPpdu7cqaysLB05ckSXXHJJ2xwE8DkfHtqjnbtf1i/nvm51KcA5Y97aky2DW5J69eoVNF5XVyePx6Nx48Y122bevHlavny5nnrqKU2ZMqXZ+rOhLUnHjh2Tw+GQ3+8Pad2h3h/MU7ZGqm7lmcg/bytUSu/BurLPN9q3qBa4XFnyL2e+4ovnrGSveRsKHWXu2+5WeUJCgiTJ6/UGjRcUFOjAgQPNHkzLy8vTkiVLtHTpUt1xxx2t7veuu+5SSkqKbrnlFj333HPq3Nl2v7Ogg6pvOK2/lK3S+OGzrS4FOGfMW/uyXXqlpKQoIyND+fn5iouLU2JiotauXauSkhJJCgruRx99VPn5+ZoyZYqGDRumbdu2BdZ169ZN6enpga9//etfS5I8Ho/uvvtuvfrqq4qOjm6no8LFbEtFsU43nNK/DP2e1aUA54x5a18Ovw3v8Xq9Xrndbm3fvl3x8fHKzc1VTEyMFixYoJqaGkVGRkqSsrKy5PF4WtyHy+VSaWlpi+uGDRumhx56SDfeeGNbHQIuQl9229EuYpOkYVOtrgJ2YMqcDZWOMvdtd8UtSWlpadq8eXPQ2PTp05Wenh4IbUmtBvNn1dbW6vDhw+rbt6+kMw+n7dmzR1deeWVIawYAoD3YMrhbUlZWpuHDh5/3didOnNCUKVNUW1urzp07KyIiQqtXr1afPn3aoEoAANqWEcFdW1srr9erOXPmnPe2l112WdB73wAAmMyI4I6OjlZjY6PVZQAAYDnbfRwMAAC0juAGAMAgBDcAAAYhuAEAMAjBDQCAQYx4qhwwQUxPqys4N6bUibZ3sc2FjnK8tvyTpwAAoGXcKgcAwCAENwAABiG4AQAwCMENAIBBCG4AAAxCcAMAYBCCGwAAgxDcAAAYhOAGAMAgBDcAAAYhuAEAMAjBDQCAQQhuAAAMQnADAGAQghsAAIMQ3AAAGITgBgDAIAQ3AAAG+f/+flh7HGqbjAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 635.649x565.88 with 1 Axes>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from qiskit.circuit.library import PiecewiseLinearPauliRotations\n",
+    "PiecewiseLinearPauliRotations(4, breakpoints=[0, 2], offsets=[2, 1], slopes=[0.5, -0.5]).draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 983.577x264.88 with 1 Axes>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from qiskit.circuit.library import QuantumVolume\n",
+    "QuantumVolume(4).decompose().draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1645.78x866.88 with 1 Axes>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "QuantumVolume(4).decompose().decompose().draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 983.577x204.68 with 1 Axes>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from qiskit.circuit.library import EfficientSU2\n",
+    "EfficientSU2(3, entanglement='linear').draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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MFSVJkiRJkiRFxFBRkiRJkiRJUkQMFSVJkiRJkiRFxFBRkiRJkiRJUkQMFSVJkiRJkiRFpEmGikeOHOGRRx6hoKAgJstLkiRJkiRJiaxJhoqrVq1ixowZFBcXx2R5SZIkSZIkKZGlxLqAWCgoKCAQCJCTkxOT5SVJkhJFKASHdsHxw9AsA1p1hkAC/CxdfBS27Q9f79YWMjNiW09jduAwfHYAkpKgRxY0T4t1RbULlsPBHVB+HNLPg8wOsa6obo4cgMP7ISkZWneB5NRYV9R4fbkXjh4Mj3HrLpCUAN+cj5XC5j0QDEKn1pDVMtYVSWrsEuClMXIrVqzg8ccfZ9myZezZs4f27dszfPhwFi1axCWXXMKaNWsAaNWqFQCXXHIJS5cu5e2332b27NmsXLmSPXv2kJWVxdixY3n66acr+g4cOLDa5U+aP38+c+bMYdWqVWRmZlaso23bttEcBkmSpHPy+cfwybvhIOOktEw4Pxe6DoxdXTUpPgp/WgmriyAYCrclBSCnO4wfDpnpsa2vMdlbDP+5EtbtgBNDTUoSfO18uH4opDeLaXlVCoVg6/LwpfToqfbMDtDnKmjbI3a11aR4D2z8OxzYdqotuRl0HQy9r0iMwCtRHNwOG98O/5hyUko6dB8GvS6Nzx9VjpfBfxfAB59Aafmp9v6dYfww6NgqdrVJatwa3dvPqlWrGDFiBBMnTmTOnDlkZmayefNm3nrrLTIyMvjNb37D5MmTyc7OZtasWQBkZWUBsHr1aq644gruvPNOMjMzWb9+PXl5eQDMmzcPoMblAaZOncrcuXOZMmUKDz/8MDt27ODBBx9k586dvPbaa9EcCkmSpK9sx0ew/q9nt5cUh9uPHwl/wY4nXx6D2a/D/i9PhVwQDhcLtkLRPpg2GloaLJ6zfV/C7L/C4ZLKY10WhCWFsH0/TBkFaXH0bSMUgo/fhO2rzr6veA/kvwyDx0H7C6JfW02Kd8OKRVBeVrm9vBSKVsDhvTB4fHj2os7N/iIoeDm8rZyu7Bh8+l545uKAMRAIxKa+qpSVw3NvwSe7z77v413h5+m00QaLkhpGHL3N14/58+eTlZXFwoULK9pGjhzJbbfdBsCQIUMoKiri5ptv5rLLLqu07H333VdxPRgMkpuby+rVq3nzzTcr2mtaftGiRTz77LO88MIL3HLLLRXtzZs358Ybb2TLli307NmzPh+uJElSvSs9Bhv+VnOfT96BTv0hI46+qP71o3DYVZUQsPdLeH0NTBge1bIapT/nnx0onm7bfnjnY/jWRVEtq0Zf7Kw6UAQqHsi61+Dr/yu+Arr1b5wIFKsZ7H1bYNcG6BJHY52IQqHw/38oRLVjvXMtdLoQsnpGs7KaLSmsOlCE8GM5VgZ/XAH3fDO6dUlqGuJw8va56dChAzt37mTKlCnk5+efdf+GDRs4cuQIQ4cOrdReXl7OggULuOKKK+jYsSPJyck0a9aMOXPm0LJly1qXB5g5cyZDhw7l5ptvpqysrOIyYMAAADZv3gzAT3/6U/r27UtSUhIvv/xyfT58SZKkc7ZzLYTKa++348OGr6WuSspg6ae191v6SXhXQX11XxyBj7ZXHyie9O7Gs2d8xdL2VUAtM8xKj8KewqiUUyfFe+DQTmoe7ABsL4hWRY3X/i1w7BC1j3V1wXSMvLux5s06FIKNu8KHK5Ck+tboZipOmzaN48eP89vf/pZf/vKXdO/enbvuuouf/OQnJCUlsXLlSiA84/B0t9xyC3/605+YMmUKP/7xj2nXrh0pKSl885vfZODAUwcNqm75bdu2sWHDBgCaNav6ADInj8E4ZswYbr31Vm6//fb6edCnCcTTXPwT3ngq/M4cj7VV5775iVdzIo5zokrUsU7UuhORYx09jnXDuP9787hm2C0kJ1f/UTEYLOelF14j78rvRLGy6rXtciGTn1xXa7+SMujUvT8Hdn4chaoap24XfYMJP3mz1n4Hj0B6i1YcP3ooClXV7rn7V3N+50E19gmFQvz4f8/iP15/OEpV1eybQ29m+k3za+4Ugr3bjhEIeDaic/G9qx7gru88VXOnEKxZtoWc8b2iU1QtAoEk/veLdfgFCLj06nF8uvLPDVyRpERQn5+bG12omJqaSl5eHnl5eRQWFvLEE0+Ql5dHnz59mDRpEvn5+XTq1InOnTtXLLNmzRoWLFjAvHnzuOOOOyra33vvPYqLixk2bFhFW1XLA+zYsQOA5557rspZjEBFOJmbm1tvj1eSJKm+lZeX1nrQsBAhyspLo1RR7YIR1BJJX52tvCwxx7qsvJRQKFTjl6lAIEBZML5qrlO/OKo5UdV5rONomw4RIhgsI6kOZ+qJp+eipMaj0YWKp+vduzfTp0/n+eefp6wsvJ/LunXrKnZHPqmoqAiA/v37V7SVlpZy//33A1QKFataHqBr164ApKSkMHx47A7UE4qnfUxO+NvPw3/jsbbqTFsQ/ptINSfiOCeqRB3rRK07ETnW0eNYN4zPN8JHf6m5T3JSCrffN46HX4iPsQ8GYdaf4cCRmvu1bQH7d31CkpNbv7LjZfDQH8OzPqsTCECPLCgtqeU/JIoK34EtS2vv98vfzmJ+51kNX1AdlByGd+ZS6y65PQdm+jp4jr7cAx/8R+39LrumD6Gfxc9Yz3kzvHtzTRWlJMHapa/QPDVqZUmKQyNHjgTg7bffrrVPXTWqUPHee+/l8OHDjBo1iuzsbIqKinjyySfp3bs31113HQBt2rRh8eLFvPrqq7Rt25aePXsyePBg0tPTmT59Og899BD79u3jmWee4cCBAyQnJ5OTk1Pxb1S1fKdOnejWrRujRo3igQceYP/+/QwbNoySkhK2bdvGq6++yrx58yqdJVqSJCletb8A0lqGA40qv6kGILkZdD77d9aYSUqCK/uHTyBSkyv7Y6B4jlJTILcPvLW++j6hEFzZL3o11UXXQbB1OYSC1XQIQGYHOK9TVMuqUVqL8AmRdtUw1oSg25Aa7ledtGwPrbvBwe1Un9AFIDunmvti5Kr+4bM81+SS8zFQlNQgGtWJWvr3709hYSHTpk1j9OjRzJw5kzFjxrBkyZKKk63MmDGDXr16MXHiRC6//HI++OADunbtysKFC9m1axfXX389Tz/9ND/84Q/Jzc2lX79+NG/evOLfqGr5k1566SVuvfVW5s6dy9ixY5k8eTLPP/88OTk5BoqSJClhJCXD4PGQUs2X0KRkGDwOUtKiW1dtruoHQ7qHr5++h+vJq0N7wJV9o15Wo/TtwdCnY/j66RntyXG/qj8M6RH1smqU0Qou/jbhgqsIltNawMBra93zP+r6fTMceJ3lRJ29r4Q23aJaUqN18bch/bwq7jgx1gNGQ4s4+1o3oCtcc3H4elWvez3awfVVH51Lks5Zo5qpOHXqVKZOnVpjn/79+/P++++f1T5+/HjGjx9fqW3SpEl1Xh7CsxifeeYZnnnmmQiqliRJij/ndYRL/wWKVsJna6D8eLi96yDoPiz+vlhDeLbi5Cug/6fwzsew/UC4PbstfL0fDO/lLMX60iwZ7r4alhSGzz77+YlzsZzfPjxDcVC3+AvnADr2h4zW4e365Oy/Zhnh7brb0HCwGG+apcPwm2DHathWcOIMxUBWz/BzMatnLKtrXNIz4ZLvh8+mvX0VHD+x936HPtB9OLTuEtv6qvPtweHw8B8bwrtCA2S1hCv6QW7v8OxiSWoIjWqmYqJ46KGHyM7OZsmSJdx9991kZ2fzySefxLosSZKkSjJaQb9vwMjTfrO98Jr4DBRPSgrApRfAA98+1faDseHd/wwU61dKcjis/cm1p9qmjoLB3eMzUDzpvE5w8T+dun3VvdD76/EZKJ6Ukgo9vgZX3HWqbchEA8WGkJoB5+fClfecaht0XfwGiidd1BXu+eap23nXw8j+BoqSGpahYgzMmjWL7du3U1JSwr59+9i+fTsXXHBBrMuSJEmqUjwHRJIkSYoNQ0VJkiRJkiRJETFUlCRJkiRJkhQRQ0VJkiRJkiRJETFUlCRJkiRJkhQRQ0VJkiRJkiRJETFUlCRJkiRJkhQRQ0VJkiRJkiRJETFUlCRJkiRJkhQRQ0VJkiRJkiRJETFUlCRJkiRJkhQRQ0VJkiRJkiRJETFUlCRJkiRJkhQRQ0VJkiRJkiRJETFUlCRJkiRJkhQRQ0VJkiRJkiRJETFUlCRJkiRJkhQRQ0VJkiRJkiRJETFUlCRJkiRJkhQRQ0VJkiRJkiRJETFUlCRJkiRJkhQRQ0VJkiRJkiRJETFUlCRJkiRJkhQRQ0VJkiRJkiRJETFUlCRJkiRJkhQRQ0VJkiRJkiRJETFUlCRJkiRJkhQRQ0VJkiRJkiRJETFUlCRJkiRJkhQRQ0VJkiRJkiRJETFUlCRJkiRJkhQRQ0VJkiRJkiRJEWmSoeKRI0d45JFHKCgoiMnykiRJkiRJUiJrkqHiqlWrmDFjBsXFxTFZXpIkSZIkSUpkKbEuIBYKCgoIBALk5OTEZHlJkiRJkpR4ivfA9lVwoAhCIWjVBbJzoHWXWFdWvZJSWLEZlm+GQ0chMx2+dj4M7wXpzWJdXfW++Ay2rQr/JQBtu4XHOrNDrCvTSY1ypuKKFSuYMGEC2dnZpKWlkZ2dzbhx4zh69CgDBw5kypQphEIhWrVqRSAQ4NJLLwXg7bffZty4cXTr1o309HS6du3Kv/7rv/LFF19UrLum5U+aP38+I0aMoEWLFnTq1InbbruN/fv3R3UMJEmSJElS/SlaCUv/A3ashiMH4OhB2LUeViyETf8Ih4zxZt+X8OQr8IflsHUv7D8MRfvg5eXh9n1fxrrCs4VCUPgOLF8YHt+jB+HoAdjxISz9LWxdEesKdVKjm6m4atUqRowYwcSJE5kzZw6ZmZls3ryZt956i4yMDH7zm98wefJksrOzmTVrFgBZWVkArF69miuuuII777yTzMxM1q9fT15eHgDz5s0DqHF5gKlTpzJ37lymTJnCww8/zI4dO3jwwQfZuXMnr732WjSHQpIkSZIk1YO9n8LGt6q440SQuHU5NG8DXQdFtawaBYPwf94KB4lQUWrF3wOHYe7f4SffgaQ4mnL22RrYsvTEjSqC2k1vh8e6/QXRrEpVaXSh4vz588nKymLhwoUVbSNHjuS2224DYMiQIRQVFXHzzTdz2WWXVVr2vvvuq7geDAbJzc1l9erVvPnmmxXtNS2/aNEinn32WV544QVuueWWivbmzZtz4403smXLFnr27FmfD1eSJEmSJDWwLcuBAFWGXBV9lkGXgRAIRKuqmq3fCZ8fqv7+ELCnGNZ9BhdnR62sGoVCsHVZLZ0C4T6GirHX6ELFDh06sHPnTqZMmcLtt9/O0KFDK92/YcMGjhw5clZ7eXk5v/vd75gzZw6bNm1i9+7dFfed3re65QFmzpzJ0KFDufnmmykrK6toHzBgAACbN2+mVatWTJ48mY0bN5KRkUHHjh351a9+Re/evevl8Qfi5dXrNG88FX7VjcfaqnPf/MSrORHHOVEl6lgnat2JyLGOHsc6ehJxrBPx/TxRJeJYJ+I2DYlbdyJKxLFOxOdiImiZ0Zr/nHmg1n5HD8L5nS9my+dro1BV7b71r/O48MpbSUpKrrZPsLyM+2fN543/c1sUK6te9w4X8usfrqu5UwgO7oBWLdpx6Mi+6BTWiNTn60McTXCtH9OmTWPWrFm8/vrrDBs2jB49evDYY48RDAYBWLlyJRCecXi6W265hTvvvJPc3FzmzZvH+++/z7Jly8jMzGTgwIEV/apbftu2bWzYsIH8/HyaNWtW6TJoUHj+88ljME6bNo2NGzeyevVqvvOd71TMopQkSZIkSfElI7Vl3fum1b1vQ2uW3rJOB3pslh4/NUcyfumpLRqwEtVFo5upmJqaSl5eHnl5eRQWFvLEE0+Ql5dHnz59mDRpEvn5+XTq1InOnTtXLLNmzRoWLFjAvHnzuOOOOyra33vvPYqLixk2bFhFW1XLA+zYsQOA5557rspZjBA+yUuzZs341re+VdGWm5vLk08+WS+PHSAUh0eG/dvPw3/jsbbqTFsQ/ptINSfiOCeqRB3rRK07ETnW0eNYR08ijnUivp8nqkQc60TcpraTyrAAACAASURBVCFx605EiTjWifhcTATlZfCPZyFYVnvfgrUfEC+54n8XwJu1TPpLSk7hrlu+yyv/Hh/bTMlheGdO7f2SkuGz3VtJjuOzV8ebkSNHAuGTFNfWp64aXah4ut69ezN9+nSef/75it2R161bV7E78klFRUUA9O/fv6KttLSU+++/H6BSqFjV8gBdu3YFICUlheHDh9e5xtmzZzNu3Lg695ckSZIkSdGTnAKdLwqf9blaAWjXi7gJFAEuu6D2UBHgsvo5Glu9SGsB7S4Inxin2uNXBsL/HwaKsdeoQsV7772Xw4cPM2rUKLKzsykqKuLJJ5+kd+/eXHfddQC0adOGxYsX8+qrr9K2bVt69uzJ4MGDSU9PZ/r06Tz00EPs27ePZ555hgMHDpCcnExOTk7Fv1HV8p06daJbt26MGjWKBx54gP379zNs2DBKSkrYtm0br776KvPmzat0lmiAGTNmUFhYyN///veojpMkSZIkSaq7XpfC7o1Qeoyzw65AeObcBVfEorLqtT8Pvt4P3vm4+j5X9IGO50WvprrofQUcKArPEK1qrJulQc9LY1GZztSojqnYv39/CgsLmTZtGqNHj2bmzJmMGTOGJUuW0LJl+OeCGTNm0KtXLyZOnMjll1/OBx98QNeuXVm4cCG7du3i+uuv5+mnn+aHP/whubm59OvXj+bNm1f8G1Utf9JLL73Erbfeyty5cxk7diyTJ0/m+eefJycn56xA8dFHH+V//ud/eO211yqtX5IkSZIkxZf082D4TZDZ/uz7MlrBsBsgs0P066rN+KHwzQGQfEb6k5wUbp9Q9x0to6Zlexg6KTyuZ93XLvz/UNV9ir5GNVNx6tSpTJ06tcY+/fv35/333z+rffz48YwfP75S26RJk+q8PIRnMT7zzDM888wzNdYwY8YMXn31Vd544w1at25dY19JkiRJkhR7LdrCJZPh0C5YfuL4lUO/B226Q7yecDspCa4dAt8YAB9ug5eWhttnToAWabGtrSatOkPuHXBgG+T/Ptw2/J/D7fE61k1Ro5qpmAjWrl3LI488wr59+xg5ciQ5OTmVdq+WJEmSJEnxKRAIB1snte2RGCFXizS4vHfl2/EuEIC23U/dbt0lMca6KWlUMxUTwUUXXeSZuCRJkiRJkpTQnKkoSZIkSZIkKSKGipIkSZIkSZIiYqgoSZIkSZIkKSKGipIkSZIkSZIiYqgoSZIkSZIkKSKGipIkSZIkSZIiYqgoSZIkSZIkKSKGipIkSZIkSZIiYqgoSZIkSZIkKSKGipIkSZIkSZIiYqgoSZIkSZIkKSKGipIkSZIkSZIiYqgoSZIkSZIkKSKGipIkSZIkSZIiYqgoSZIkSZIkKSKGipIkSZIkSZIiYqgoSZIkSZIkKSKGipIkSZIkSZIiYqgoSZIkSZIkKSKGipIkSZIkSZIiYqgoSZIkSZIkKSKGipIkSZIkSZIikhLrAiQlvh98vI7VxcUx+bcHZ2bydL8BMfm3JTVdvu5JkiQ1frH6zJcon/cMFSWds9XFxSw+sD/WZUhS1Pi6J0mS1Pj5ma9m7v4sSZIkSZIkKSKGipIkSZIkSZIi4u7P0gnHSqFoH2zbd6rtjTWQ3RZ6tIPmqbGrTZIkSZKkSIVC8Pmh8HfdXV+cal/2KXRrC51aQSAQu/qU2AwV1eR9fgjeXg8rN8Px8sr3vbI6/DclCYb0hJH9oWubqJcoSZIkSVKdlQdh6Sfw7kb47ODZ9y9cEv7buTVc0QcuvQBSkqNboxKfoaKarPIg/H0dvPZR+HpNyoKw/NNw8PjNATB6oC+4kiRJkqT4s+NAODTccaD2vjsPwh+Ww3uF8M+XhffUk+rKYyqqSSorh+cXh2ci1hYoni4YgjfWwty3oKS04eqTJEmSJClS63bA//fXugWKp/vsAMz+K6zd3jB1qXEyVFSTEwrBi+/D2h1ffR2Fn8OvF0MwgkBSkiRJkqSG8unu8OSZsvLa+1alLAjPvwOf7K7futR4GSqqyVn6KawuqrnP7JvDl5ps3AX/+Lj+6pIkSZIk6asoKYUFS8LBYE1q+65bHoT574dPZCrVpkmFiitWrGDChAlkZ2eTlpZGdnY248aN4+jRo5SXl9OiRQtmzJhRaZmysjLS09P52c9+VtF26aWXMmHCBBYtWkROTg4ZGRkMHjyY5cuXU1payqOPPkqvXr1o2bIl1157Lfv374/2Q40LwXLY88mp22XHY1fLSUdK4L9W1t/6XlkNB4/U3/qaitDRo5R+70aCi9891VZSQtm0H1A28zFCTgGVVItgEPZ+eup26bHY1VIXvu5JktR4HD7tK35xnMzqe30N7PuyftZ14DD89aP6WVdT09Q+8zWZE7WsWrWKESNGMHHiRObMmUNmZiabN2/mrbfeIiMjg7Vr13LkyBGGDBlSabm1a9dSUlJS0V5eXs5HH33Erl27KC4u5uGHH6a8vJx77rmH22+/ne7du9OjRw/mzp3L+vXruf/++3nqqad44oknYvGwY2bHh1D4DpQePdX2zhzoPgzOz4VAjOLsZZ/W7y8uZeXw/ib49uD6W2dTEMjIIOm7EylfsJDA10dAMEj5o09As2YkT/8RgaQm9XuHpAjtWg8b34bjh0+1vTMHsnOg95WQFIcn0vJ1T5KkxHesGNa9Bvu3nmpb+ls4rzMMGA0t28WmruNl4e+l9WlJIYwZBGlNJjWqH03tM1+T2Tzmz59PVlYWCxcurGgbOXIkt912GwD5+fkAZ4WKBQUFldo3bNjA0aNHueiii3jllVcIBAIAvPfee8yePZvvf//7/PjHPwZg9OjRLFiwgHXr1jXsg4szRfmw8e9nt5eXwuYP4PgRuPCa6NcFsPST2vt8lXWOHQQnNgXVUdJ11xJ8+U+E3nmP4PIVhPbuJeXnTxJIbRbr0iTFsc/WhD/MnylYDkUr4diXMPA78fma7OueJEmJq+QwLF8IJVXMBjy0C1Ysgq/dDC1icPbkj7bB0XreXflYaXi9w3vV73qbgqb0ma9xRaQ16NChAzt37mTKlCkVAeLp8vPzycrKolu3bme1d+3alfbt21fcBpg5c2ZFoAhw+PBh2rRpw/33319p+UOHDpGVlVVxe8uWLVx11VX07duXgQMH8s4779TbY4wHpcdg0z9q7rPjQzj0eXTqOd2xUtj5Rf2v94uj7gL9VQQy0kn63kTKf/4MoQ8/JOXxWQRaNI91WZLiWHkpfFzFj1an2/0xHNgWnXoi5eueJEmJa+syKCkGQlXcGQof7uuTd6u4Lwq27G2g9e5pmPU2dk3pM1+TCRWnTZvGrFmzeP311xk2bBg9evTgscceI3hif/b8/PyzZilCeKbi6e0FBQW0bduW4cOHV+qXn5/P1VdfTbNmp5LnL7/8kk2bNpGTk1PRdvfdd3PDDTewceNGnnvuOW688UaOH4+Dgw3Wk53rIFTbmaYCsGN1VMqp5LMDDbfuHQ247kbv2DGSb5hEoE2bWFciKc59vhHKa3vLjNF7TER83ZMkKaEEy2BHbccYDMHuTZUPzxItDfV9dLvfc89NE/jM12R2f05NTSUvL4+8vDwKCwt54oknyMvLo0+fPkyaNInVq1dz1113VVrm0KFDLF++nB/96EcVbQUFBQwbNqxSv9LSUtasWcOECRMqta9atYpQKMTQoUMB2Lt3L++++y5/+ctfAMjNzaVLly689dZbjB49ul4eZyDG+3tNGfcLvnP5/yI5qfpNKxQM8ep//oMBo6+OYmXQK+efuO6B/6nUVtsZnqu7f9qCyre/d9O/sOHdF8+hunP3xlPhn8xisQ0kP/VvJA0eFNEywb/9neBLvycw+hrK//PPBMaO/kq1v/322wS+dlnEy52LWI71uUjUuhORY90wbhvzKDd948GaxzUES95azaDrcqrvUw+a2useJOZ2fd/8xKs5USXiWCfiNg2JW3ciSsSxTsTnYiJo16ori/K2194xBDkDLmN90dKGL+o0Nz/xEe26XVyp7at81z3ze+7K1esIjLnoHKs7d03xu25Dft6rz3FsMjMVT9e7d2+mT58OhM/uXFxczBdffEF2dnalfrNnz650khYIB4VnhoonT+ZyZntBQQFJSUkVMxWLioro2LEjaWlpFX169erF1q1baSxKSo8SqOUsLKFQkJLTz+ASJcFgWYOtO1TecOtujILLllP+7C9Jfvghku/5X3DwIKHFjetQAJLqX/g9puYPQcFQkGOl8XdMCl/3JElKXJF8f43Fd91QA33X9Xtu5JraZ74mMVPx3nvv5fDhw4waNYrs7GyKiop48skn6d27N9dddx0tW7akS5cuvPzyy4wfP55gMMgLL7zAr371K+DUSVo+/fRTDh48eFZ4ePI4i1WFin379qVly5ZReJRhoVBVB3iIngPbYOVLNfdJSkpm8r1jeXBedGv9/BA88d+V2878Jeakk7/aVHf/mV77y0J6tltYe8cG9Lefh//GYhv41oqlLD6wv059g2vXUf74v5H8wx+QNGggQPh4E/MXEfj6FRGfDWvkyJH8LcqPOZZjfS4Ste5E5Fg3jOLd4TMs1iQpkMS137+c//2Lhh37pva6B4m5XZ98H0+kmhNVIo51Im7TkLh1J6JEHOtEfC4mimXzT5wboIahTcuEwu2rqWWeTb17fjF8eMYxpevju+7I3EHMj4NtqSl+122Iz3sjR44EwrMga+tTV01ipmL//v0pLCxk2rRpjB49mpkzZzJmzBiWLFlSEfi9+OKL7N27lz59+nDNNdeQlpbGHXfcQevWrenZsydw6kzQVYWH3bt3p127dme1n9z1GaB79+58/vnnlJSUVLRt3ryZHj16NMTDjonW2dCyPVDdRJIApKRD5xjMoG6fCWkNEKMnBaBL6/pfb2MU2ryF8p8+QvLdd5I0IreiPem678AXXzTqX3AknbvMDuH3mZreY5KbQdeLq7k/BnzdkySpcejxNWoMFAG6DyPqgSJAdgOdcbpbDM5knaia6me+JjFTcerUqUydOrXGPt/4xjdYt27dWe2PP/54xfWJEydWmYz/4he/4Be/+MVZ7SdDyJPatWvHiBEj+PWvf80999zD+++/z44dO7j66ugeW7AhBQIweFx4tuKxQ2ffn9wMhkyElNTo15YUgAu7wKqi+l1v746Q2iSeSecu0Ksnzf74+7Pb09Np9vvYzvSUlBgGXht+jzlSxQ/GSckweDyktoh+XdUJ9PJ1T5KkxqBjP/hyD2z+gPAPnCejgRPXuwwMh4qxMKALvNoAJ6ob0LX+19lYBXo1zc98RiFRNnfuXG699VZmz55NamoqixYtIjU1BglbA8poBZf+C3z2UfgMWSe/+PW8DLIHQ3pm7Gq7om/9h4pX9K3f9UmSqpfWAi75PuxcA9s/hMN7w+09hkN2DmQ4c1ySJDWQC66ANt1h+yrYvTHcltUDsodAu/PDk2xiIbst9GwHW/bW3zp7ZDlTUbVrErs/x5Pzzz+fxYsXs3HjRtasWcNVV10V65IaRLP08PTw3NtPtfW+IraBIsAFHaBvp/pbX/csuMhfbyQpqlJSodtQuPzWU219RhooSpKkhte2Owy67tTtId+F9hfELlA86duD63d9Y+t5fWqcDBXVpAQCcOOl9XNsxeQk+OfLw38lSZIkSYqVvp0gt3f9rOuyC6B/5/pZlxo3d39Wk9O2Jdz6dZj3DygPVt2ntjNhBYDJudCpVb2XJ0mSJElSxMYNg93FUPh59X1q+657QQeYMLx+61Lj5RwrNUkXdoG7RkKLtMiXTW8Gt10JOY3npN2SJEmSpASXmhL+nju421dbflA3uOtqT0SqunNTUZPVrzP85DvwxxWwauupk3fV5KKu8L1LoHXzBi9PkiRJkqSIpKaE98zL3wL/lQ/Fx2pfpmU6jBsKw3rG/tiQSiyGimrSWqbDLVfAPw2GJYWw6XP47ACUndgtOjkJOrcOTwG/vLe7O0uSJEmS4lsgAMN6weDu8OE2yN8K2/bBF0dP9WmVAd2yYEiP8MzGlOTY1avEZagoAe0y4doh4evlQThyHAhBRqovrpIkSZKkxJOSDEN7hi8Q/p5bWg7NkqF5aiwrU2NhqCidITkJMtNjXYUkSZIkSfXHIFH1zRO1SJIkSZIkSYqIoaIkSZIkSZKkiLj7s6RzNjgzs0n+25KaLl/3JEmSGr9Yfe5KlM97hoqSztnT/QbEugRJiipf9yRJkho/P/PVzN2fJUmSJEmSJEXEUFGSJEmSJElSRAwVJUmSJEmSJEXEUFGSJEmSJElSRAwVJUmSJEmSJEXEUFGSJEmSJElSRAwVJUmSJEmSJEXEUFGSJEmSJElSRAwVJUmSJEmSJEXEUFGSJEmSJElSRAwVJUmSJEmSJEXEUFGSJEmSJElSRFJiXYAkqfH7wcfrWF1cHPV/d3BmJk/3GxD1fzdWYjXO4FhHU1Mb60Tl654kSWrsDBUlSQ1udXExiw/sj3UZjZ7jHD2OtWrjNiJJkho7d3+WJEmSJEmSFBFDRUmSJEmSJEkRMVSUpAiEQqeulx6NXR2SJMWLkrJT18vKY1dHJCq9nx+LXR2SJCUyj6koSbUoK4Gd62D3Rijefar9H7+EtJZwXifoNADaXwBJybGrU5KkaCnaB0sK4ZPdsOfQqfYf/x46t4Z+nSC3D2S1jF2NZyo9BjvXwu5NZ7yfPwtpmeH3884XQfvzIeDUC0mSamWoKEnVCAWhaCV8+j6Ul1bdp+RL2FMYvqRlQv9vQvve0a1TkqRo2X0IXloaDhOrUh6E7fvDl7+vg2E9YfxwaJEW1TIrCQZh6zLY/AEEy6ruU1IMe4phzyZIPw8uHAVZvaJbpyRJicZQUZKqcPwwrP4LfLGj7suUFMPq/4IuA6H/t5y1KElqXJZ+An9YXvddnEPAii3w8S647etwfoeGrK5qJV+G35sP7ar7MscOQcEfITsH+n3DWYuSJFXHt0hJOsPxI7DypcgCxdN99hF89D/hmRGSJDUG726ERR98tWMmFh+DOX+HTz6v/7pqUvIlrPhdZIHi6bavgrX/r/LxFyVJ0imGipJ0mlAI1rwKh/dX3+dbD4QvNdmzCTYvqd/aJEmKhc174I/La+4z++bwpTql5fD8YiiO0knOQqHwD3xHD1bfpy7v57vWw9ZaHrskSU1VkwoVV6xYwYQJE8jOziYtLY3s7GzGjRvH0aNHKS8vp0WLFsyYMaPSMmVlZaSnp/Ozn/2sou3SSy9lwoQJLFq0iJycHDIyMhg8eDDLly+ntLSURx99lF69etGyZUuuvfZa9u+vIZ2QFFc++wj2b6mfdW1ZWvlA8JIkJZrjZbBwSXhX5nN1+Hh49+lo2FYAB7fXz7o+eQ8O76ufdUmS1Jg0mVBx1apVjBgxgvT0dObMmcNf//pXZs2axXnnnUdGRgYbNmzgyJEjDBkypNJya9eupaSkpKK9vLycjz76iJUrV/L888/z8MMP8+KLL7Jz505uv/12xo0bx2effcbcuXN59NFHeeWVV3jqqadi8ZDVRJR8CZ+8e+r2u8+FP/yWHI5dTYkqGIRP63F2YSjobMWvInT0KKXfu5Hg4lMbdqikhLJpP6Bs5mOE4nS/8vLS8K5yH/zHqbZ1f4XiPbGrqTaJOtaJKFHH+viR8Oviu//nVFvhO3CsOHY1NVbxuo2s3BI+gUl9+XBb+CQuDSlYVr/vv6Fy2Ly0/tan+FZ+HLblw5LfnGpb/wZ8uTd2NUlNUSgEuzfByt+falv1J9i3JWYlqQpN5kQt8+fPJysri4ULF1a0jRw5kttuuw2A/Px8gLNCxYKCgkrtGzZs4OjRo1x00UW88sorBAIBAN577z1mz57N97//fX784x8DMHr0aBYsWMC6desa9sGpySreHX6RLTt2qu1YcfiD9PZVMOwGaNkudvUlmr2fhE+2Up92F4b/T9Iz63e9jVkgI4Ok706kfMFCAl8fAcEg5Y8+Ac2akTz9RwSS4u/3sLKS8HOx+IzjhX32EXy2Bi7+J+jUPza11SQRxzpRJeJYH94fPr7s8TN+pNqyNPweM/R7cF6n2NTWGMXrNvLexgZY5ya44dL6X+9JuzdBaT3vZv35x9B3JKQ2r9/1Kr4cPwr5L50dIO5YHX5PH3gtdOgTm9qkpiQUgnWvwc61QOBU+97NsPdT6PE16H0lBALVrkJREn+fYBtIhw4d2LlzJ1OmTKkIEE+Xn59PVlYW3bp1O6u9a9eutG/fvuI2wMyZMysCRYDDhw/Tpk0b7r///krLHzp0iKysrIrbP/3pT+nbty9JSUm8/PLL9fb41PQEy8JnJiwrqfr+0mOw6o8Q/AoHVG+q9m1ugJWGYP/WBlhvI5d03bWw/wChd96jfPYvCO3dS/IjPyWQ2izWpVVp3V/PDhQrhGDNK/E7wyHRxjqRJdJYh4Lh2QDHj1R9f9nx8HtQeWl062rs4m0bOXQUth+o//Wu/6z+13m6vZ/W/zpD5XCgqP7Xq/iy9v9V/34dCsJH/w1HajhOp6T6UbTyRKAIlY+/ceL61uXhY94q9ppMqDht2jRmzZrF66+/zrBhw+jRowePPfYYwRO7kuTn5581SxHCMxVPby8oKKBt27YMHz68Ur/8/HyuvvpqmjU79aHvyy+/ZNOmTeTk5FS0jRkzhtdee40rr7yyvh+impjdm07MHqnuIEeh8Ay5PZ9Es6rEdqiBzkrZUOttzAIZ6SR9byLlP3+G0IcfkvL4LAIt4nN6yLFDsLu2mTyh8MyueJRIY53oEmms920+cYKLGt5jSo/Crg3RrKrxi7dtZFsD7aZ88EjDnrDF93N9FUcOwL5aAulQMDxrUVLDCQXDoWKNAlC0IirlqBZNZvfn1NRU8vLyyMvLo7CwkCeeeIK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\n",
+      "text/plain": [
+       "<Figure size 1704.91x1167.88 with 1 Axes>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from qiskit.circuit.library import WeightedAdder\n",
+    "WeightedAdder(3, [1,2,1]).draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 580.436x385.28 with 1 Axes>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from qiskit.circuit.library import IntegerComparator\n",
+    "IntegerComparator(3, 4).draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 682.577x445.48 with 1 Axes>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from qiskit import QuantumCircuit\n",
+    "circuit = QuantumCircuit(7)\n",
+    "circuit.mcx([0, 1, 2, 3], 4, ancilla_qubits=[5, 6], mode='v-chain')\n",
+    "circuit.decompose().draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1645.78x927.08 with 1 Axes>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "circuit.decompose().decompose().draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1645.78x686.28 with 1 Axes>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from qiskit.circuit.library import ZZFeatureMap\n",
+    "ZZFeatureMap(3).draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1465.18x204.68 with 1 Axes>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from qiskit.circuit.library import ExcitationPreserving\n",
+    "ExcitationPreserving(3).draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 682.577x204.68 with 1 Axes>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from qiskit.circuit.library import IQP\n",
+    "IQP([[6, 5, 3], [5, 4, 5], [3, 5, 1]]).decompose().draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 742.777x325.08 with 1 Axes>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from qiskit.circuit.library import GMS\n",
+    "GMS(5, [[0.2] * 5] * 5).decompose().draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1645.78x686.28 with 1 Axes>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "GMS(5, [[0.2] * 5] * 5).decompose().decompose().draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit.circuit import QuantumCircuit\n",
+    "from qiskit.aqua.operators import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Pauli'}"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "op = (0.2 * (X ^ Y ^ Z) + 3 * (I ^ Z ^ I))\n",
+    "op.primitive_strings()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "exp = PauliExpectation().convert(~StateFn(op) @ state)\n",
+    "sampler = CircuitSampler(backend).convert(exp)\n",
+    "result = sampler.eval()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "exp = PauliExpectation().convert(~StateFn(op) @ state)\n",
+    "result = exp.eval(backend)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "z = MatrixOp([[1, 0], [0, -1]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "op = (z + Z + z)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SummedOp([MatrixOp(Operator([[ 1.+0.j,  0.+0.j],\n",
+       "          [ 0.+0.j, -1.+0.j]],\n",
+       "         input_dims=(2,), output_dims=(2,)), coeff=2.0), PauliOp(Pauli(z=[True], x=[False]), coeff=1.0)], coeff=1.0, abelian=False)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "op.reduce()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "((z + Z) @ (Z + z)) == ((z + Z) @ (z + Z))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Process ForkProcess-2:\n",
+      "Process ForkProcess-1:\n",
+      "Traceback (most recent call last):\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
+      "    self.run()\n",
+      "  File \"/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
+      "    self.run()\n",
+      "  File \"/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/lib/python3.7/concurrent/futures/process.py\", line 233, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "  File \"/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/lib/python3.7/concurrent/futures/process.py\", line 233, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "  File \"/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/queues.py\", line 93, in get\n",
+      "    with self._rlock:\n",
+      "  File \"/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/queues.py\", line 94, in get\n",
+      "    res = self._recv_bytes()\n",
+      "  File \"/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "  File \"/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/connection.py\", line 216, in recv_bytes\n",
+      "    buf = self._recv_bytes(maxlength)\n",
+      "KeyboardInterrupt\n",
+      "  File \"/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/connection.py\", line 407, in _recv_bytes\n",
+      "    buf = self._recv(4)\n",
+      "  File \"/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/connection.py\", line 379, in _recv\n",
+      "    chunk = read(handle, remaining)\n",
+      "KeyboardInterrupt\n"
+     ]
+    },
+    {
+     "ename": "TypeError",
+     "evalue": "can't pickle PyCapsule objects",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31m_RemoteTraceback\u001b[0m                          Traceback (most recent call last)",
+      "\u001b[0;31m_RemoteTraceback\u001b[0m: \n\"\"\"\nTraceback (most recent call last):\n  File \"/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/queues.py\", line 236, in _feed\n    obj = _ForkingPickler.dumps(obj)\n  File \"/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/reduction.py\", line 51, in dumps\n    cls(buf, protocol).dump(obj)\nTypeError: can't pickle PyCapsule objects\n\"\"\"",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-1-6bcb6c324f75>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mlist_op\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mListOp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mops\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mexpectation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPauliExpectation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mStateFn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist_op\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m@\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0msampler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCircuitSampler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mAer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'statevector_simulator'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpectation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0msampler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Work/Qiskit/qiskit-aqua/qiskit/aqua/operators/converters/circuit_sampler.py\u001b[0m in \u001b[0;36mconvert\u001b[0;34m(self, operator, params)\u001b[0m\n\u001b[1;32m    192\u001b[0m         \u001b[0mcircs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_circuit_ops_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_transpiled_circ_cache\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    193\u001b[0m         sampled_statefn_dicts = self.sample_circuits(circuit_sfns=circs,\n\u001b[0;32m--> 194\u001b[0;31m                                                      param_bindings=param_bindings)\n\u001b[0m\u001b[1;32m    195\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    196\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mreplace_circuits_with_dicts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Work/Qiskit/qiskit-aqua/qiskit/aqua/operators/converters/circuit_sampler.py\u001b[0m in \u001b[0;36msample_circuits\u001b[0;34m(self, circuit_sfns, param_bindings)\u001b[0m\n\u001b[1;32m    270\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    271\u001b[0m         results = self.quantum_instance.execute(ready_circs,\n\u001b[0;32m--> 272\u001b[0;31m                                                 had_transpiled=self._transpile_before_bind)\n\u001b[0m\u001b[1;32m    273\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m         \u001b[0;31m# Wipe parameterizations, if any\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Work/Qiskit/qiskit-aqua/qiskit/aqua/quantum_instance.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, circuits, had_transpiled)\u001b[0m\n\u001b[1;32m    270\u001b[0m         \u001b[0;31m# maybe compile\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    271\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhad_transpiled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 272\u001b[0;31m             \u001b[0mcircuits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcircuits\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    273\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m         \u001b[0;31m# assemble\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Work/Qiskit/qiskit-aqua/qiskit/aqua/quantum_instance.py\u001b[0m in \u001b[0;36mtranspile\u001b[0;34m(self, circuits)\u001b[0m\n\u001b[1;32m    232\u001b[0m         \"\"\"\n\u001b[1;32m    233\u001b[0m         transpiled_circuits = compiler.transpile(circuits, self._backend, **self._backend_config,\n\u001b[0;32m--> 234\u001b[0;31m                                                  **self._compile_config)\n\u001b[0m\u001b[1;32m    235\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtranspiled_circuits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    236\u001b[0m             \u001b[0mtranspiled_circuits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mtranspiled_circuits\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Work/Qiskit/qiskit-terra/qiskit/compiler/transpile.py\u001b[0m in \u001b[0;36mtranspile\u001b[0;34m(circuits, backend, basis_gates, coupling_map, backend_properties, initial_layout, layout_method, routing_method, translation_method, seed_transpiler, optimization_level, pass_manager, callback, output_name)\u001b[0m\n\u001b[1;32m    217\u001b[0m     \u001b[0;31m# Transpile circuits in parallel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m     circuits = parallel_map(_transpile_circuit, list(zip(circuits, transpile_args)),\n\u001b[0;32m--> 219\u001b[0;31m                             task_args=(faulty_qubits_map, backend))\n\u001b[0m\u001b[1;32m    220\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    221\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcircuits\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Work/Qiskit/qiskit-terra/qiskit/tools/parallel.py\u001b[0m in \u001b[0;36mparallel_map\u001b[0;34m(task, values, task_args, task_kwargs, num_processes)\u001b[0m\n\u001b[1;32m    135\u001b[0m             \u001b[0;31m# Otherwise just reset parallel flag and error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    136\u001b[0m             \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menviron\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'QISKIT_IN_PARALLEL'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'FALSE'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 137\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    138\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\u001b[0m         \u001b[0mPublisher\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpublish\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"terra.parallel.finish\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Work/Qiskit/qiskit-terra/qiskit/tools/parallel.py\u001b[0m in \u001b[0;36mparallel_map\u001b[0;34m(task, values, task_args, task_kwargs, num_processes)\u001b[0m\n\u001b[1;32m    125\u001b[0m                 \u001b[0mfuture\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexecutor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_task_wrapper\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 127\u001b[0;31m             \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfuture\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    128\u001b[0m             \u001b[0mPublisher\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpublish\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"terra.parallel.done\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/lib/python3.7/concurrent/futures/process.py\u001b[0m in \u001b[0;36m_chain_from_iterable_of_lists\u001b[0;34m(iterable)\u001b[0m\n\u001b[1;32m    481\u001b[0m     \u001b[0mcareful\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mto\u001b[0m \u001b[0mkeep\u001b[0m \u001b[0mreferences\u001b[0m \u001b[0mto\u001b[0m \u001b[0myielded\u001b[0m \u001b[0mobjects\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    482\u001b[0m     \"\"\"\n\u001b[0;32m--> 483\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0melement\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    484\u001b[0m         \u001b[0melement\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreverse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    485\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0melement\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/lib/python3.7/concurrent/futures/_base.py\u001b[0m in \u001b[0;36mresult_iterator\u001b[0;34m()\u001b[0m\n\u001b[1;32m    596\u001b[0m                     \u001b[0;31m# Careful not to keep a reference to the popped future\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    597\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 598\u001b[0;31m                         \u001b[0;32myield\u001b[0m \u001b[0mfs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    599\u001b[0m                     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    600\u001b[0m                         \u001b[0;32myield\u001b[0m \u001b[0mfs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mend_time\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmonotonic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/lib/python3.7/concurrent/futures/_base.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    426\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mCancelledError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    427\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mFINISHED\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 428\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    429\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    430\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_condition\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/lib/python3.7/concurrent/futures/_base.py\u001b[0m in \u001b[0;36m__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    382\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    383\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 384\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    385\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    386\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_result\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/queues.py\u001b[0m in \u001b[0;36m_feed\u001b[0;34m(buffer, notempty, send_bytes, writelock, close, ignore_epipe, onerror, queue_sem)\u001b[0m\n\u001b[1;32m    234\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    235\u001b[0m                         \u001b[0;31m# serialize the data before acquiring the lock\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 236\u001b[0;31m                         \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_ForkingPickler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdumps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    237\u001b[0m                         \u001b[0;32mif\u001b[0m \u001b[0mwacquire\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    238\u001b[0m                             \u001b[0msend_bytes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/reduction.py\u001b[0m in \u001b[0;36mdumps\u001b[0;34m(cls, obj, protocol)\u001b[0m\n\u001b[1;32m     49\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdumps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprotocol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m         \u001b[0mbuf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m         \u001b[0mcls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprotocol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mbuf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetbuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: can't pickle PyCapsule objects"
+     ]
+    }
+   ],
+   "source": [
+    "from qiskit import Aer\n",
+    "from qiskit.aqua.operators import *\n",
+    "\n",
+    "ops = [X, X ^ X, X ^ 5]\n",
+    "state = Zero\n",
+    "\n",
+    "list_op = ListOp(ops)\n",
+    "expectation = PauliExpectation().convert(~StateFn(list_op) @ state)\n",
+    "sampler = CircuitSampler(Aer.get_backend('statevector_simulator')).convert(expectation)\n",
+    "sampler.eval()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ListOp([MatrixOp(Operator([[0.+0.j, 1.+0.j],\n",
+       "          [1.+0.j, 0.+0.j]],\n",
+       "         input_dims=(2,), output_dims=(2,)), coeff=1.0), MatrixOp(Operator([[0.+0.j, 0.+0.j, 0.+0.j, 1.+0.j],\n",
+       "          [0.+0.j, 0.+0.j, 1.+0.j, 0.+0.j],\n",
+       "          [0.+0.j, 1.+0.j, 0.+0.j, 0.+0.j],\n",
+       "          [1.+0.j, 0.+0.j, 0.+0.j, 0.+0.j]],\n",
+       "         input_dims=(2, 2), output_dims=(2, 2)), coeff=1.0)], coeff=1.0, abelian=False)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ListOp([X, X^X]).to_matrix_op()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit.circuit import Parameter, QuantumCircuit, Instruction\n",
+    "from qiskit.circuit.library import MCMT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "th = Parameter('θ')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<qiskit.circuit.instructionset.InstructionSet at 0x12b1c2cd0>"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qc = QuantumCircuit(5)\n",
+    "# qc.mcmt('h', [0, 1], [2, 3])\n",
+    "qc.append(MCMT('h', 3, 2, label='gate'), [0, 1, 4, 2, 3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<PIL.Image.Image image mode=RGB size=292x172 at 0x12B1188D0>"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qc.decompose().draw(output='latex')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit import QuantumRegister, transpile\n",
+    "from qiskit.circuit.library import RXGate, RYGate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mcrx: OrderedDict([('cx', 92), ('u3', 62), ('u1', 1)])\n",
+      "RXGate: OrderedDict([('cx', 92), ('u3', 62), ('u1', 1)])\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_ctrl = 5\n",
+    "q_ctrl = QuantumRegister(num_ctrl)\n",
+    "q_target = QuantumRegister(1)\n",
+    "function = QuantumCircuit(q_ctrl, q_target)\n",
+    "function.mcrx(0.2, q_ctrl[:], q_target[0], None)\n",
+    "\n",
+    "gate = QuantumCircuit(q_ctrl, q_target)\n",
+    "gate.append(RXGate(0.2).control(num_ctrl), q_ctrl[:] + q_target[:])\n",
+    "basis = ['u1', 'u3', 'cx']\n",
+    "print('mcrx:', transpile(function, basis_gates=basis).count_ops())\n",
+    "print('RXGate:', transpile(gate, basis_gates=basis).count_ops())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "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
+}
diff --git a/Playground/Untitled.ipynb b/Playground/Untitled.ipynb
new file mode 100644
index 0000000..20e34c3
--- /dev/null
+++ b/Playground/Untitled.ipynb
@@ -0,0 +1,518 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit import *  # this is evil"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "CircuitError",
+     "evalue": "'Expression cannot bind non-real or non-numeric values ({Parameter(a): 1j}).'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mCircuitError\u001b[0m                              Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-2-e133cca380c8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mqc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mQuantumCircuit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mqc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mqc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind_parameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mp\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0;36m1.0j\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/Work/Qiskit/qiskit-terra/qiskit/circuit/quantumcircuit.py\u001b[0m in \u001b[0;36mbind_parameters\u001b[0;34m(self, value_dict)\u001b[0m\n\u001b[1;32m   1207\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1208\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mparameter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0munrolled_value_dict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1209\u001b[0;31m             \u001b[0mnew_circuit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_bind_parameter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparameter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1210\u001b[0m         \u001b[0;31m# clear evaluated expressions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1211\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mparameter\u001b[0m \u001b[0;32min\u001b[0m \u001b[0munrolled_value_dict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Work/Qiskit/qiskit-terra/qiskit/circuit/quantumcircuit.py\u001b[0m in \u001b[0;36m_bind_parameter\u001b[0;34m(self, parameter, value)\u001b[0m\n\u001b[1;32m   1229\u001b[0m         \u001b[0;34m\"\"\"Assigns a parameter value to matching instructions in-place.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1230\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0minstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam_index\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parameter_table\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mparameter\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1231\u001b[0;31m             \u001b[0minstr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mparam_index\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minstr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mparam_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mparameter\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1232\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1233\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_substitute_parameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameter_map\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Work/Qiskit/qiskit-terra/qiskit/circuit/parameterexpression.py\u001b[0m in \u001b[0;36mbind\u001b[0;34m(self, parameter_values)\u001b[0m\n\u001b[1;32m     66\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raise_if_passed_unknown_parameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparameter_values\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 68\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raise_if_passed_non_real_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparameter_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     69\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m         symbol_values = {self._parameter_symbols[parameter]: value\n",
+      "\u001b[0;32m~/Work/Qiskit/qiskit-terra/qiskit/circuit/parameterexpression.py\u001b[0m in \u001b[0;36m_raise_if_passed_non_real_value\u001b[0;34m(self, parameter_values)\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnonreal_parameter_values\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m             raise CircuitError('Expression cannot bind non-real or non-numeric '\n\u001b[0;32m--> 142\u001b[0;31m                                'values ({}).'.format(nonreal_parameter_values))\n\u001b[0m\u001b[1;32m    143\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_raise_if_parameter_names_conflict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother_parameters\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mCircuitError\u001b[0m: 'Expression cannot bind non-real or non-numeric values ({Parameter(a): 1j}).'"
+     ]
+    }
+   ],
+   "source": [
+    "p = circuit.Parameter('a')\n",
+    "qc = QuantumCircuit(1)\n",
+    "qc.ry(p, 0)\n",
+    "qc.bind_parameters({p : 1.0j})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qc.to_instruction().definition"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit.extensions.standard import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "qiskit.extensions.standard.ccx.ToffoliGate"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ToffoliGate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(CCXGate(), ToffoliGate)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jul/Envs/latest/lib/python3.7/site-packages/ipykernel_launcher.py:1: DeprecationWarning: ToffoliGate is deprecated, use CCXGate instead!\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(ToffoliGate(), CCXGate)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class A:\n",
+    "    @classmethod\n",
+    "    def __instancecheck__(cls, inst):\n",
+    "        print(\"checking instance A\")\n",
+    "        return True\n",
+    "\n",
+    "class B(A):\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n",
+      "False\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(isinstance(B(), A))\n",
+    "print(isinstance(A(), B))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "isinstance() arg 2 must be a type or tuple of types",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-1-8c4e9f64ddfb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: isinstance() arg 2 must be a type or tuple of types"
+     ]
+    }
+   ],
+   "source": [
+    "isinstance(1, {float, int})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit import QuantumCircuit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method x of <qiskit.circuit.quantumcircuit.QuantumCircuit object at 0x1344a3890>>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qc = QuantumCircuit(4)\n",
+    "qc.x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method mct of <qiskit.circuit.quantumcircuit.QuantumCircuit object at 0x137b17850>>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qc.mct"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'QuantumCircuit' object has no attribute 'mcmt'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0mTraceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-4-6e27c0695f48>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mqc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmcmt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m: 'QuantumCircuit' object has no attribute 'mcmt'"
+     ]
+    }
+   ],
+   "source": [
+    "qc.mcmt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<qiskit.circuit.quantumcircuit.QuantumCircuit at 0x1344a3890>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qc.cry(0, 0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(<qiskit.extensions.standard.u3.U3Gate object at 0x1344bb9d0>, [Qubit(QuantumRegister(4, 'q'), 1)], []), (<qiskit.extensions.standard.cx.CnotGate object at 0x1344bb6d0>, [Qubit(QuantumRegister(4, 'q'), 0), Qubit(QuantumRegister(4, 'q'), 1)], []), (<qiskit.extensions.standard.u3.U3Gate object at 0x1344bba10>, [Qubit(QuantumRegister(4, 'q'), 1)], []), (<qiskit.extensions.standard.cx.CnotGate object at 0x1344bb8d0>, [Qubit(QuantumRegister(4, 'q'), 0), Qubit(QuantumRegister(4, 'q'), 1)], [])]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qc.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tempfile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "__init__() got an unexpected keyword argument 'name'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0mTraceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-13-44da25a3da1b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtempfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTemporaryDirectory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'./'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'hello'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: __init__() got an unexpected keyword argument 'name'"
+     ]
+    }
+   ],
+   "source": [
+    "d = tempfile.TemporaryDirectory(dir='./', name='hello')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tempfile.TemporaryDirectory"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'name': '/var/folders/w4/sd0wnrdd0bb5wpqc5gfqs17m0000kp/T/tmp_5s2au24',\n",
+       " '_finalizer': <finalize object at 0x10e7fc2b0; for 'TemporaryDirectory' at 0x1344af2d0>}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d.__dict__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'finalize' object has no attribute '__dict__'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0mTraceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-11-7e14d132414f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0md\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_finalizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__dict__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m: 'finalize' object has no attribute '__dict__'"
+     ]
+    }
+   ],
+   "source": [
+    "d._finalizer.__dict__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/w4/sd0wnrdd0bb5wpqc5gfqs17m0000kp/T/tmp_5s2au24\n"
+     ]
+    }
+   ],
+   "source": [
+    "with d as name:\n",
+    "    print(name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Awda-Kaa'"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "'-'.join([name.capitalize() for name in 'aWda-kaa'.split('-')])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def capitalize(author):\n",
+    "    splitchars = [' ', '-']\n",
+    "    \n",
+    "    # only capitalize if not a github username\n",
+    "    if not any(splitchars in authors):\n",
+    "        return author\n",
+    "    \n",
+    "    capitalized_author = author\n",
+    "    for splitchar in splitchars:\n",
+    "        capitalized_author = splitchar.join([name.capitalize() for name in capitalized_author.split(splitchar)])\n",
+    "    \n",
+    "    return ca"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "authors = ['tigerjack', 'Christa Zoufal', 'ajack']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Christa Zoufal', 'ajack', 'tigerjack']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# authors = sorted(set(authors), key=lambda x: (x.split()[-1], x.split()[:]))\n",
+    "authors = sorted(set(authors), key=lambda x: x.split()[:])\n",
+    "\n",
+    "print(authors)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[['Christa', 'Zoufal'], ['ajack'], ['tigerjack']]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print([x.split()[:] for x in authors])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "latest",
+   "language": "python",
+   "name": "latest"
+  },
+  "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
+}
diff --git a/Playground/a.pdf b/Playground/a.pdf
new file mode 100644
index 0000000..d8864b5
Binary files /dev/null and b/Playground/a.pdf differ
diff --git a/QFT madness.ipynb b/QFT madness.ipynb
index 806be25..31a8d95 100644
--- a/QFT madness.ipynb	
+++ b/QFT madness.ipynb	
@@ -134,7 +134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -213,6 +213,28 @@
     "print(one.evolve(textbook))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Statevector([ 5.00000000e-01+0.j ,  7.85046229e-17+0.5j,\n",
+       "             -5.00000000e-01+0.j , -7.85046229e-17-0.5j],\n",
+       "            dims=(2, 2))"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "one @ rswap @ QFT(2) @ rswap"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -352,6 +374,148 @@
     "np.round(Operator(QFT(2, do_swaps=False)).data * 2, 2)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = [1, -1, -1, 1]\n",
+    "# x /= np.sqrt(np.linalg.norm(x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0. +0.j , 0.5-0.5j, 0. +0.j , 0.5+0.5j])"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.fft.ifft(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Statevector([0.+0.j, 1.-1.j, 0.+0.j, 1.+1.j],\n",
+       "            dims=(2, 2))"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Statevector(x) @ rswap @ QFT(2) @ rswap"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(Statevector(x) @ rswap @ QFT(2) @ rswap).equiv(np.fft.ifft(x) * 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">     ┌──────┐\n",
+       "q_0: ┤1     ├\n",
+       "     │  qft │\n",
+       "q_1: ┤0     ├\n",
+       "     └──────┘</pre>"
+      ],
+      "text/plain": [
+       "     ┌──────┐\n",
+       "q_0: ┤1     ├\n",
+       "     │  qft │\n",
+       "q_1: ┤0     ├\n",
+       "     └──────┘"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "circuit = QuantumCircuit(2)\n",
+    "circuit.append(QFT(2).to_instruction(), [1, 0])\n",
+    "circuit.draw()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit import Aer, execute"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.5+0.j, 0.5+0.j, 0.5+0.j, 0.5+0.j])"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "execute(circuit, Aer.get_backend('statevector_simulator')).result().get_statevector()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/Untitled.ipynb b/Untitled.ipynb
new file mode 100644
index 0000000..3217fbe
--- /dev/null
+++ b/Untitled.ipynb
@@ -0,0 +1,150 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit import QuantumCircuit, Aer\n",
+    "from qiskit.circuit.library import LinearPauliRotations\n",
+    "from qiskit.aqua.algorithms import AmplitudeEstimation\n",
+    "from qiskit.aqua.components.uncertainty_models import NormalDistribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class DiscreteQuadratic(QuantumCircuit):\n",
+    "    def __init__(self, x_dist, k, y_max, rescaling_factor=0.1, name='A'):\n",
+    "        num_qubits = x_dist.num_target_qubits + k + 1\n",
+    "        super().__init__(num_qubits, name=name)\n",
+    "        self.c = rescaling_factor\n",
+    "        self.x_dist = x_dist\n",
+    "        self.k = k\n",
+    "        self.y_max = y_max\n",
+    "                \n",
+    "        indices = list(range(num_qubits - 1))\n",
+    "        self.i_x = indices[:self.x_dist.num_target_qubits]\n",
+    "        self.i_y = indices[self.x_dist.num_target_qubits:]\n",
+    "        self.i_f = num_qubits - 1\n",
+    "        \n",
+    "        self._build()\n",
+    "        \n",
+    "    def _build(self):\n",
+    "        beta_x = self.c / (self.x_dist.num_values - 1) * self.x_dist.high\n",
+    "        beta_y = -self.c / (2**self.k - 1) * self.y_max\n",
+    "        \n",
+    "        lin_x = LinearPauliRotations(self.x_dist.num_target_qubits, offset=0, slope=2 * beta_x)\n",
+    "        lin_y = LinearPauliRotations(self.k, offset=0, slope=2 * beta_y)\n",
+    "\n",
+    "        q = self.qubits\n",
+    "        q_x = [q[i] for i in self.i_x]\n",
+    "        q_y = [q[i] for i in self.i_y]\n",
+    "        q_f = q[self.i_f]\n",
+    "        self.x_dist.build(self, q_x)\n",
+    "        \n",
+    "        self.compose(lin_x, qubits=q_x[:] + [q_f], inplace=True)\n",
+    "        self.compose(lin_y, qubits=q_y[:] + [q_f], inplace=True)   \n",
+    "        \n",
+    "    def value_to_estimation(self, value):\n",
+    "        return value / self.c ** 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jul/Work/Qiskit/qiskit-terra/qiskit/circuit/instruction.py:232: DeprecationWarning: instruction.mirror() is deprecated. Use circuit.reverse_ops()to reverse the order of gates.\n",
+      "  'to reverse the order of gates.', DeprecationWarning)\n",
+      "/Users/jul/Work/Qiskit/qiskit-terra/qiskit/circuit/instruction.py:232: DeprecationWarning: instruction.mirror() is deprecated. Use circuit.reverse_ops()to reverse the order of gates.\n",
+      "  'to reverse the order of gates.', DeprecationWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = NormalDistribution(2, mu=1, sigma=1, low=0, high=2)\n",
+    "a_XY = DiscreteQuadratic(X, k=2, y_max=3)\n",
+    "\n",
+    "ae = AmplitudeEstimation(3, a_factory=a_XY)\n",
+    "backend = Aer.get_backend('statevector_simulator')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'DiscreteQuadratic' object has no attribute 'num_target_qubits'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-4-2036b5dfbb45>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mae\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbackend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/Work/Qiskit/qiskit-aqua/qiskit/aqua/algorithms/quantum_algorithm.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, quantum_instance, **kwargs)\u001b[0m\n\u001b[1;32m     68\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquantum_instance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mquantum_instance\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mabstractmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Work/Qiskit/qiskit-aqua/qiskit/aqua/algorithms/amplitude_estimators/ae.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    387\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    388\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_quantum_instance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_statevector\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 389\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconstruct_circuit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmeasurement\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    390\u001b[0m             \u001b[0;31m# run circuit on statevector simulator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    391\u001b[0m             \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_quantum_instance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_circuit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Work/Qiskit/qiskit-aqua/qiskit/aqua/algorithms/amplitude_estimators/ae.py\u001b[0m in \u001b[0;36mconstruct_circuit\u001b[0;34m(self, measurement)\u001b[0m\n\u001b[1;32m    129\u001b[0m             \u001b[0miqft\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iqft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_ancillae\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_m\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m             \u001b[0mstate_in_circuit_factory\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0ma_factory\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m             \u001b[0munitary_circuit_factory\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mq_factory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m         )\n\u001b[1;32m    133\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Work/Qiskit/qiskit-aqua/qiskit/aqua/algorithms/amplitude_estimators/ae_algorithm.py\u001b[0m in \u001b[0;36mq_factory\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    113\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_a_factory\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 115\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mQFactory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_a_factory\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mi_objective\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Work/Qiskit/qiskit-aqua/qiskit/aqua/algorithms/amplitude_estimators/q_factory.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, a_factory, i_objective)\u001b[0m\n\u001b[1;32m     27\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma_factory\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi_objective\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma_factory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_target_qubits\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m         \u001b[0;31m# store A factory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'DiscreteQuadratic' object has no attribute 'num_target_qubits'"
+     ]
+    }
+   ],
+   "source": [
+    "ae.run(backend)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "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": 4
+}
diff --git a/ae tests.ipynb b/ae tests.ipynb
index 43cee19..7f2067e 100644
--- a/ae tests.ipynb	
+++ b/ae tests.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -45,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -95,7 +95,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -109,18 +109,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1e-30\n",
-      "[1e-30, 1.600340544152679e-05]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "ae = MaximumLikelihoodAmplitudeEstimation(3, *get_bernoullis(0.0))\n",
     "result = ae.run(backend)\n",
@@ -130,7 +121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -141,373 +132,259 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "q = QFactory(integral, 3)\n",
+    "print(q.required_ancillas_controlled())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "circs = ae.construct_circuits()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "circs[2].draw()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result = ae.run(backend)\n",
+    "print(result['estimation'])\n",
+    "print(ae.confidence_interval(0.05, kind='lr'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit import QuantumCircuit, Aer, execute"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "4\n"
+      "/Users/jul/Work/Qiskit/qiskit-terra/qiskit/circuit/measure.py:81: DeprecationWarning: The module qiskit.extensions.standard is deprecated as of 0.14.0 and will be removed no earlier than 3 months after the release. You should import the standard gates from qiskit.circuit.library.standard_gates instead.\n",
+      "  from qiskit.extensions.standard.h import HGate\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<qiskit.circuit.instructionset.InstructionSet at 0x121c98c10>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "q = QFactory(integral, 3)\n",
-    "print(q.required_ancillas_controlled())"
+    "qc = QuantumCircuit(3, 3)\n",
+    "qc.measure([0, 1, 2], [0, 1, 2])\n",
+    "qc.measure_pauli('y', [0, 1, 2], [0, 1, 2])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'000': 126,\n",
+       " '001': 148,\n",
+       " '010': 107,\n",
+       " '011': 124,\n",
+       " '100': 124,\n",
+       " '101': 132,\n",
+       " '110': 118,\n",
+       " '111': 145}"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "circs = ae.construct_circuits()"
+    "execute(qc, Aer.get_backend('qasm_simulator')).result().get_counts()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">              ┌───┐                                                 »\n",
-       "  q_0: |0>────┤ H ├───────────────────────■──────────────────────■──»\n",
-       "              ├───┤                       │                      │  »\n",
-       "  q_1: |0>────┤ H ├───────────────────────┼──────────────────────┼──»\n",
-       "              ├───┤                       │                      │  »\n",
-       "  q_2: |0>────┤ H ├───────────────────────┼──────────────────────┼──»\n",
-       "          ┌───┴───┴───┐┌───────────────┐┌─┴─┐┌────────────────┐┌─┴─┐»\n",
-       "  q_3: |0>┤ Ry(0.125) ├┤ U3(0.125,0,0) ├┤ X ├┤ U3(-0.125,0,0) ├┤ X ├»\n",
-       "          └───┬───┬───┘└─────┬───┬─────┘└───┘└────────────────┘└───┘»\n",
-       "aux_0: |0>────┤ X ├──────────┤ H ├──────────────────────────────────»\n",
-       "           ┌──┴───┴───┐   ┌──┴───┴───┐                              »\n",
-       "aux_1: |0>─┤ U2(0,pi) ├───┤ U1(pi/4) ├──────────────────────────────»\n",
-       "           ├──────────┤   ├──────────┤                              »\n",
-       "aux_2: |0>─┤ U2(0,pi) ├───┤ U1(pi/4) ├──────────────────────────────»\n",
-       "           └──────────┘   └──────────┘                              »\n",
-       "«                                                                      »\n",
-       "«  q_0: ───────────────────────────────────────────────────────────────»\n",
-       "«                                                                      »\n",
-       "«  q_1: ──────────────────■─────────────────────■──────────────────────»\n",
-       "«                         │                     │                      »\n",
-       "«  q_2: ──────────────────┼─────────────────────┼───────────────────■──»\n",
-       "«       ┌──────────────┐┌─┴─┐┌───────────────┐┌─┴─┐┌─────────────┐┌─┴─┐»\n",
-       "«  q_3: ┤ U3(0.25,0,0) ├┤ X ├┤ U3(-0.25,0,0) ├┤ X ├┤ U3(0.5,0,0) ├┤ X ├»\n",
-       "«       └──────────────┘└───┘└───────────────┘└───┘└─────────────┘└───┘»\n",
-       "«aux_0: ───────────────────────────────────────────────────────────────»\n",
-       "«                                                                      »\n",
-       "«aux_1: ───────────────────────────────────────────────────────────────»\n",
-       "«                                                                      »\n",
-       "«aux_2: ───────────────────────────────────────────────────────────────»\n",
-       "«                                                                      »\n",
-       "«                                                                    »\n",
-       "«  q_0: ─────────────────────────────────────────────────────────────»\n",
-       "«                                                                    »\n",
-       "«  q_1: ─────────────────────────────────────────────────────────────»\n",
-       "«                                                                    »\n",
-       "«  q_2: ──────────────────■───────────────────■───────────────────■──»\n",
-       "«       ┌──────────────┐┌─┴─┐┌───┐     ┌───┐┌─┴─┐┌─────────────┐┌─┴─┐»\n",
-       "«  q_3: ┤ U3(-0.5,0,0) ├┤ X ├┤ X ├──■──┤ X ├┤ X ├┤ U3(0.5,0,0) ├┤ X ├»\n",
-       "«       └──────────────┘└───┘└───┘┌─┴─┐├───┤├───┤└────┬───┬────┘├───┤»\n",
-       "«aux_0: ──────────────────────────┤ X ├┤ H ├┤ X ├─────┤ X ├─────┤ H ├»\n",
-       "«                                 └───┘└───┘└───┘     └───┘     └───┘»\n",
-       "«aux_1: ─────────────────────────────────────────────────────────────»\n",
-       "«                                                                    »\n",
-       "«aux_2: ─────────────────────────────────────────────────────────────»\n",
-       "«                                                                    »\n",
-       "«                                                                       »\n",
-       "«  q_0: ─────────────────────────────────────────────────────────────■──»\n",
-       "«                                                       ┌───┐        │  »\n",
-       "«  q_1: ──────────────────■────────────────────■────────┤ H ├────────┼──»\n",
-       "«            ┌───┐        │       ┌───┐        │        └───┘        │  »\n",
-       "«  q_2: ─────┤ H ├────────┼───────┤ X ├────────┼─────────────────────┼──»\n",
-       "«       ┌────┴───┴─────┐┌─┴─┐┌────┴───┴─────┐┌─┴─┐┌───────────────┐┌─┴─┐»\n",
-       "«  q_3: ┤ U3(-0.5,0,0) ├┤ X ├┤ U3(0.25,0,0) ├┤ X ├┤ U3(-0.25,0,0) ├┤ X ├»\n",
-       "«       └──────────────┘└───┘└──────────────┘└───┘└───────────────┘└───┘»\n",
-       "«aux_0: ────────────────────────────────────────────────────────────────»\n",
-       "«                                                                       »\n",
-       "«aux_1: ────────────────────────────────────────────────────────────────»\n",
-       "«                                                                       »\n",
-       "«aux_2: ────────────────────────────────────────────────────────────────»\n",
-       "«                                                                       »\n",
-       "«                                                ┌───┐           ┌───┐     »\n",
-       "«  q_0: ────────────────────────────■────────────┤ H ├───────────┤ X ├─────»\n",
-       "«             ┌───┐                 │            └───┘           └───┘     »\n",
-       "«  q_1: ──────┤ X ├────────■────────┼──────────────────────────────────────»\n",
-       "«             └───┘        │        │                                      »\n",
-       "«  q_2: ───────────────────┼────────┼──────────────────────────────────────»\n",
-       "«       ┌───────────────┐  │      ┌─┴─┐    ┌────────────────┐┌────────────┐»\n",
-       "«  q_3: ┤ U3(0.125,0,0) ├──┼──────┤ X ├────┤ U3(-0.125,0,0) ├┤ Ry(-0.125) ├»\n",
-       "«       └───────────────┘  │      └───┘    └────────────────┘└────────────┘»\n",
-       "«aux_0: ───────────────────┼───────────────────────────────────────────────»\n",
-       "«                        ┌─┴─┐┌───────────┐                                »\n",
-       "«aux_1: ─────────────────┤ X ├┤ U1(-pi/4) ├────────────────────────────────»\n",
-       "«                        └───┘└───────────┘                                »\n",
-       "«aux_2: ───────────────────────────────────────────────────────────────────»\n",
-       "«                                                                          »\n",
-       "«                                                                             »\n",
-       "«  q_0: ───────■──────────────────────────────────────────────────────────────»\n",
-       "«              │                                                              »\n",
-       "«  q_1: ───────┼────────────────■─────────────────────────────────────────────»\n",
-       "«              │                │                                             »\n",
-       "«  q_2: ───────┼────────────────┼─────────────────────────────────────────────»\n",
-       "«       ┌───┐  │                │                                             »\n",
-       "«  q_3: ┤ X ├──┼────────────────┼─────────────────────────────────────────────»\n",
-       "«       └───┘  │                │                                             »\n",
-       "«aux_0: ───────┼────────────────┼─────────────────────────────────────────────»\n",
-       "«            ┌─┴─┐┌──────────┐┌─┴─┐┌───────────┐┌──────────┐                  »\n",
-       "«aux_1: ─────┤ X ├┤ U1(pi/4) ├┤ X ├┤ U1(-pi/4) ├┤ U2(0,pi) ├──■───────────────»\n",
-       "«            └───┘└──────────┘└───┘└───────────┘└──────────┘┌─┴─┐┌───────────┐»\n",
-       "«aux_2: ────────────────────────────────────────────────────┤ X ├┤ U1(-pi/4) ├»\n",
-       "«                                                           └───┘└───────────┘»\n",
-       "«                                                                       »\n",
-       "«  q_0: ────────────────────────────────────────────────────────────────»\n",
-       "«                                                                       »\n",
-       "«  q_1: ────────────────────────────────────────────────────────────────»\n",
-       "«                                                                       »\n",
-       "«  q_2: ──■─────────────────────────────────────────────────────────────»\n",
-       "«         │                                                    ┌───┐    »\n",
-       "«  q_3: ──┼──────────────────────────────────────────────■─────┤ X ├────»\n",
-       "«         │                                            ┌─┴─┐   ├───┤    »\n",
-       "«aux_0: ──┼────────────────────────────────────────────┤ X ├───┤ H ├────»\n",
-       "«         │                                            └─┬─┘   └───┘    »\n",
-       "«aux_1: ──┼────────────────■─────────────────────────────┼──────────────»\n",
-       "«       ┌─┴─┐┌──────────┐┌─┴─┐┌───────────┐┌──────────┐  │  ┌──────────┐»\n",
-       "«aux_2: ┤ X ├┤ U1(pi/4) ├┤ X ├┤ U1(-pi/4) ├┤ U2(0,pi) ├──■──┤ U2(0,pi) ├»\n",
-       "«       └───┘└──────────┘└───┘└───────────┘└──────────┘     └──────────┘»\n",
-       "«                                                                        »\n",
-       "«  q_0: ─────────────────────────────────────────────────────────────────»\n",
-       "«                                                                        »\n",
-       "«  q_1: ─────────────────────────────────────────────────────────────────»\n",
-       "«                                                          ┌───┐    ┌───┐»\n",
-       "«  q_2: ─────────────────────────────────────────────■─────┤ X ├────┤ H ├»\n",
-       "«       ┌───────────┐┌───────────────┐               │     └───┘    └───┘»\n",
-       "«  q_3: ┤ Ry(0.125) ├┤ U3(0.125,0,0) ├───────────────┼───────────────────»\n",
-       "«       └───┬───┬───┘└─────┬───┬─────┘    ┌───┐      │                   »\n",
-       "«aux_0: ────┤ X ├──────────┤ X ├──────────┤ H ├──────┼───────────────────»\n",
-       "«           └───┘          └───┘          └───┘      │                   »\n",
-       "«aux_1: ─────────────────────■───────────────────────┼────────────────■──»\n",
-       "«        ┌──────────┐      ┌─┴─┐      ┌───────────┐┌─┴─┐┌──────────┐┌─┴─┐»\n",
-       "«aux_2: ─┤ U1(pi/4) ├──────┤ X ├──────┤ U1(-pi/4) ├┤ X ├┤ U1(pi/4) ├┤ X ├»\n",
-       "«        └──────────┘      └───┘      └───────────┘└───┘└──────────┘└───┘»\n",
-       "«                                                                 ┌───┐    »\n",
-       "«  q_0: ────────────────────────────────────────────────────■─────┤ X ├────»\n",
-       "«                                                           │     └───┘    »\n",
-       "«  q_1: ──────────────────────────────■─────────────────────┼──────────────»\n",
-       "«                                     │                     │              »\n",
-       "«  q_2: ──────────────────────────────┼─────────────────────┼──────────────»\n",
-       "«                                     │                     │              »\n",
-       "«  q_3: ──────────────────────────────┼─────────────────────┼──────────────»\n",
-       "«                                     │                     │              »\n",
-       "«aux_0: ──────────────────────────────┼─────────────────────┼──────────────»\n",
-       "«        ┌──────────┐┌──────────┐   ┌─┴─┐    ┌───────────┐┌─┴─┐┌──────────┐»\n",
-       "«aux_1: ─┤ U2(0,pi) ├┤ U1(pi/4) ├───┤ X ├────┤ U1(-pi/4) ├┤ X ├┤ U1(pi/4) ├»\n",
-       "«       ┌┴──────────┤├──────────┤┌──┴───┴───┐└┬──────────┤└───┘└──────────┘»\n",
-       "«aux_2: ┤ U1(-pi/4) ├┤ U2(0,pi) ├┤ U2(0,pi) ├─┤ U1(pi/4) ├─────────────────»\n",
-       "«       └───────────┘└──────────┘└──────────┘ └──────────┘                 »\n",
-       "«       ┌───┐                                                       »\n",
-       "«  q_0: ┤ H ├──────────────────■─────────────────────────────■──────»\n",
-       "«       └───┘    ┌───┐         │            ┌───┐            │      »\n",
-       "«  q_1: ──■──────┤ X ├─────────┼────────────┤ H ├────────────┼──────»\n",
-       "«         │      └───┘         │            └───┘            │      »\n",
-       "«  q_2: ──┼────────────────────┼─────────────────────────────┼──────»\n",
-       "«         │                  ┌─┴─┐    ┌────────────────┐   ┌─┴─┐    »\n",
-       "«  q_3: ──┼──────────────────┤ X ├────┤ U3(-0.125,0,0) ├───┤ X ├────»\n",
-       "«         │                  └───┘    └────────────────┘   └───┘    »\n",
-       "«aux_0: ──┼─────────────────────────────────────────────────────────»\n",
-       "«       ┌─┴─┐┌───────────┐┌──────────┐   ┌──────────┐   ┌──────────┐»\n",
-       "«aux_1: ┤ X ├┤ U1(-pi/4) ├┤ U2(0,pi) ├───┤ U2(0,pi) ├───┤ U1(pi/4) ├»\n",
-       "«       └───┘└───────────┘└──────────┘   └──────────┘   └──────────┘»\n",
-       "«aux_2: ────────────────────────────────────────────────────────────»\n",
-       "«                                                                   »\n",
-       "«                                                                      »\n",
-       "«  q_0: ───────────────────────────────────────────────────────────────»\n",
-       "«                                                                      »\n",
-       "«  q_1: ──────────────────■─────────────────────■──────────────────────»\n",
-       "«                         │                     │                      »\n",
-       "«  q_2: ──────────────────┼─────────────────────┼───────────────────■──»\n",
-       "«       ┌──────────────┐┌─┴─┐┌───────────────┐┌─┴─┐┌─────────────┐┌─┴─┐»\n",
-       "«  q_3: ┤ U3(0.25,0,0) ├┤ X ├┤ U3(-0.25,0,0) ├┤ X ├┤ U3(0.5,0,0) ├┤ X ├»\n",
-       "«       └──────────────┘└───┘└───────────────┘└───┘└─────────────┘└───┘»\n",
-       "«aux_0: ───────────────────────────────────────────────────────────────»\n",
-       "«                                                                      »\n",
-       "«aux_1: ───────────────────────────────────────────────────────────────»\n",
-       "«                                                                      »\n",
-       "«aux_2: ───────────────────────────────────────────────────────────────»\n",
-       "«                                                                      »\n",
-       "«                                                                    »\n",
-       "«  q_0: ─────────────────────────────────────────────────────────────»\n",
-       "«                                                                    »\n",
-       "«  q_1: ─────────────────────────────────────────────────────────────»\n",
-       "«                                                                    »\n",
-       "«  q_2: ──────────────────■───────────────────■───────────────────■──»\n",
-       "«       ┌──────────────┐┌─┴─┐┌───┐     ┌───┐┌─┴─┐┌─────────────┐┌─┴─┐»\n",
-       "«  q_3: ┤ U3(-0.5,0,0) ├┤ X ├┤ X ├──■──┤ X ├┤ X ├┤ U3(0.5,0,0) ├┤ X ├»\n",
-       "«       └──────────────┘└───┘└───┘┌─┴─┐├───┤├───┤└────┬───┬────┘├───┤»\n",
-       "«aux_0: ──────────────────────────┤ X ├┤ H ├┤ X ├─────┤ X ├─────┤ H ├»\n",
-       "«                                 └───┘└───┘└───┘     └───┘     └───┘»\n",
-       "«aux_1: ─────────────────────────────────────────────────────────────»\n",
-       "«                                                                    »\n",
-       "«aux_2: ─────────────────────────────────────────────────────────────»\n",
-       "«                                                                    »\n",
-       "«                                                                       »\n",
-       "«  q_0: ─────────────────────────────────────────────────────────────■──»\n",
-       "«                                                       ┌───┐        │  »\n",
-       "«  q_1: ──────────────────■────────────────────■────────┤ H ├────────┼──»\n",
-       "«            ┌───┐        │       ┌───┐        │        └───┘        │  »\n",
-       "«  q_2: ─────┤ H ├────────┼───────┤ X ├────────┼─────────────────────┼──»\n",
-       "«       ┌────┴───┴─────┐┌─┴─┐┌────┴───┴─────┐┌─┴─┐┌───────────────┐┌─┴─┐»\n",
-       "«  q_3: ┤ U3(-0.5,0,0) ├┤ X ├┤ U3(0.25,0,0) ├┤ X ├┤ U3(-0.25,0,0) ├┤ X ├»\n",
-       "«       └──────────────┘└───┘└──────────────┘└───┘└───────────────┘└───┘»\n",
-       "«aux_0: ────────────────────────────────────────────────────────────────»\n",
-       "«                                                                       »\n",
-       "«aux_1: ────────────────────────────────────────────────────────────────»\n",
-       "«                                                                       »\n",
-       "«aux_2: ────────────────────────────────────────────────────────────────»\n",
-       "«                                                                       »\n",
-       "«                                                ┌───┐           ┌───┐     »\n",
-       "«  q_0: ────────────────────────────■────────────┤ H ├───────────┤ X ├─────»\n",
-       "«             ┌───┐                 │            └───┘           └───┘     »\n",
-       "«  q_1: ──────┤ X ├────────■────────┼──────────────────────────────────────»\n",
-       "«             └───┘        │        │                                      »\n",
-       "«  q_2: ───────────────────┼────────┼──────────────────────────────────────»\n",
-       "«       ┌───────────────┐  │      ┌─┴─┐    ┌────────────────┐┌────────────┐»\n",
-       "«  q_3: ┤ U3(0.125,0,0) ├──┼──────┤ X ├────┤ U3(-0.125,0,0) ├┤ Ry(-0.125) ├»\n",
-       "«       └───────────────┘  │      └───┘    └────────────────┘└────────────┘»\n",
-       "«aux_0: ───────────────────┼───────────────────────────────────────────────»\n",
-       "«                        ┌─┴─┐┌───────────┐                                »\n",
-       "«aux_1: ─────────────────┤ X ├┤ U1(-pi/4) ├────────────────────────────────»\n",
-       "«                        └───┘└───────────┘                                »\n",
-       "«aux_2: ───────────────────────────────────────────────────────────────────»\n",
-       "«                                                                          »\n",
-       "«                                                                             »\n",
-       "«  q_0: ───────■──────────────────────────────────────────────────────────────»\n",
-       "«              │                                                              »\n",
-       "«  q_1: ───────┼────────────────■─────────────────────────────────────────────»\n",
-       "«              │                │                                             »\n",
-       "«  q_2: ───────┼────────────────┼─────────────────────────────────────────────»\n",
-       "«       ┌───┐  │                │                                             »\n",
-       "«  q_3: ┤ X ├──┼────────────────┼─────────────────────────────────────────────»\n",
-       "«       └───┘  │                │                                             »\n",
-       "«aux_0: ───────┼────────────────┼─────────────────────────────────────────────»\n",
-       "«            ┌─┴─┐┌──────────┐┌─┴─┐┌───────────┐┌──────────┐                  »\n",
-       "«aux_1: ─────┤ X ├┤ U1(pi/4) ├┤ X ├┤ U1(-pi/4) ├┤ U2(0,pi) ├──■───────────────»\n",
-       "«            └───┘└──────────┘└───┘└───────────┘└──────────┘┌─┴─┐┌───────────┐»\n",
-       "«aux_2: ────────────────────────────────────────────────────┤ X ├┤ U1(-pi/4) ├»\n",
-       "«                                                           └───┘└───────────┘»\n",
-       "«                                                                       »\n",
-       "«  q_0: ────────────────────────────────────────────────────────────────»\n",
-       "«                                                                       »\n",
-       "«  q_1: ────────────────────────────────────────────────────────────────»\n",
-       "«                                                                       »\n",
-       "«  q_2: ──■─────────────────────────────────────────────────────────────»\n",
-       "«         │                                                    ┌───┐    »\n",
-       "«  q_3: ──┼──────────────────────────────────────────────■─────┤ X ├────»\n",
-       "«         │                                            ┌─┴─┐   ├───┤    »\n",
-       "«aux_0: ──┼────────────────────────────────────────────┤ X ├───┤ H ├────»\n",
-       "«         │                                            └─┬─┘   └───┘    »\n",
-       "«aux_1: ──┼────────────────■─────────────────────────────┼──────────────»\n",
-       "«       ┌─┴─┐┌──────────┐┌─┴─┐┌───────────┐┌──────────┐  │  ┌──────────┐»\n",
-       "«aux_2: ┤ X ├┤ U1(pi/4) ├┤ X ├┤ U1(-pi/4) ├┤ U2(0,pi) ├──■──┤ U2(0,pi) ├»\n",
-       "«       └───┘└──────────┘└───┘└───────────┘└──────────┘     └──────────┘»\n",
-       "«                                                                        »\n",
-       "«  q_0: ─────────────────────────────────────────────────────────────────»\n",
-       "«                                                                        »\n",
-       "«  q_1: ─────────────────────────────────────────────────────────────────»\n",
-       "«                                                          ┌───┐    ┌───┐»\n",
-       "«  q_2: ─────────────────────────────────────────────■─────┤ X ├────┤ H ├»\n",
-       "«       ┌───────────┐┌───────────────┐               │     └───┘    └───┘»\n",
-       "«  q_3: ┤ Ry(0.125) ├┤ U3(0.125,0,0) ├───────────────┼───────────────────»\n",
-       "«       └───┬───┬───┘└───────────────┘               │                   »\n",
-       "«aux_0: ────┤ X ├────────────────────────────────────┼───────────────────»\n",
-       "«           └───┘                                    │                   »\n",
-       "«aux_1: ─────────────────────■───────────────────────┼────────────────■──»\n",
-       "«        ┌──────────┐      ┌─┴─┐      ┌───────────┐┌─┴─┐┌──────────┐┌─┴─┐»\n",
-       "«aux_2: ─┤ U1(pi/4) ├──────┤ X ├──────┤ U1(-pi/4) ├┤ X ├┤ U1(pi/4) ├┤ X ├»\n",
-       "«        └──────────┘      └───┘      └───────────┘└───┘└──────────┘└───┘»\n",
-       "«                                                          ┌───┐    ┌───┐»\n",
-       "«  q_0: ─────────────────────────────────────────────■─────┤ X ├────┤ H ├»\n",
-       "«                                                    │     └───┘    └───┘»\n",
-       "«  q_1: ───────────────────────────■─────────────────┼────────────────■──»\n",
-       "«                                  │                 │                │  »\n",
-       "«  q_2: ───────────────────────────┼─────────────────┼────────────────┼──»\n",
-       "«                                  │                 │                │  »\n",
-       "«  q_3: ───────────────────────────┼─────────────────┼────────────────┼──»\n",
-       "«                                  │                 │                │  »\n",
-       "«aux_0: ───────────────────────────┼─────────────────┼────────────────┼──»\n",
-       "«        ┌──────────┐┌──────────┐┌─┴─┐┌───────────┐┌─┴─┐┌──────────┐┌─┴─┐»\n",
-       "«aux_1: ─┤ U2(0,pi) ├┤ U1(pi/4) ├┤ X ├┤ U1(-pi/4) ├┤ X ├┤ U1(pi/4) ├┤ X ├»\n",
-       "«       ┌┴──────────┤├──────────┤└───┘└───────────┘└───┘└──────────┘└───┘»\n",
-       "«aux_2: ┤ U1(-pi/4) ├┤ U2(0,pi) ├────────────────────────────────────────»\n",
-       "«       └───────────┘└──────────┘                                        »\n",
-       "«                                                                            »\n",
-       "«  q_0: ──────────────────■──────────────────────────■───────────────────────»\n",
-       "«           ┌───┐         │            ┌───┐         │                       »\n",
-       "«  q_1: ────┤ X ├─────────┼────────────┤ H ├─────────┼────────────────────■──»\n",
-       "«           └───┘         │            └───┘         │                    │  »\n",
-       "«  q_2: ──────────────────┼──────────────────────────┼────────────────────┼──»\n",
-       "«                       ┌─┴─┐    ┌────────────────┐┌─┴─┐┌──────────────┐┌─┴─┐»\n",
-       "«  q_3: ────────────────┤ X ├────┤ U3(-0.125,0,0) ├┤ X ├┤ U3(0.25,0,0) ├┤ X ├»\n",
-       "«                       └───┘    └────────────────┘└───┘└──────────────┘└───┘»\n",
-       "«aux_0: ─────────────────────────────────────────────────────────────────────»\n",
-       "«       ┌───────────┐┌──────────┐                                            »\n",
-       "«aux_1: ┤ U1(-pi/4) ├┤ U2(0,pi) ├────────────────────────────────────────────»\n",
-       "«       └───────────┘└──────────┘                                            »\n",
-       "«aux_2: ─────────────────────────────────────────────────────────────────────»\n",
-       "«                                                                            »\n",
-       "«                                                                      \n",
-       "«  q_0: ───────────────────────────────────────────────────────────────\n",
-       "«                                                                      \n",
-       "«  q_1: ───────────────────■───────────────────────────────────────────\n",
-       "«                          │                                           \n",
-       "«  q_2: ───────────────────┼───────────────────■────────────────────■──\n",
-       "«       ┌───────────────┐┌─┴─┐┌─────────────┐┌─┴─┐┌──────────────┐┌─┴─┐\n",
-       "«  q_3: ┤ U3(-0.25,0,0) ├┤ X ├┤ U3(0.5,0,0) ├┤ X ├┤ U3(-0.5,0,0) ├┤ X ├\n",
-       "«       └───────────────┘└───┘└─────────────┘└───┘└──────────────┘└───┘\n",
-       "«aux_0: ───────────────────────────────────────────────────────────────\n",
-       "«                                                                      \n",
-       "«aux_1: ───────────────────────────────────────────────────────────────\n",
-       "«                                                                      \n",
-       "«aux_2: ───────────────────────────────────────────────────────────────\n",
-       "«                                                                      </pre>"
+       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">     ┌─┐      ┌──────────────┐\n",
+       "q_0: ┤M├──────┤0             ├\n",
+       "     └╥┘┌─┐   │              │\n",
+       "q_1: ─╫─┤M├───┤1             ├\n",
+       "      ║ └╥┘┌─┐│              │\n",
+       "q_2: ─╫──╫─┤M├┤2             ├\n",
+       "      ║  ║ └╥┘│  MEASURE_YYY │\n",
+       "c_0: ═╩══╬══╬═╡0             ╞\n",
+       "         ║  ║ │              │\n",
+       "c_1: ════╩══╬═╡1             ╞\n",
+       "            ║ │              │\n",
+       "c_2: ═══════╩═╡2             ╞\n",
+       "              └──────────────┘</pre>"
       ],
       "text/plain": [
-       "<qiskit.visualization.text.TextDrawing at 0x13342bc90>"
+       "     ┌─┐      ┌──────────────┐\n",
+       "q_0: ┤M├──────┤0             ├\n",
+       "     └╥┘┌─┐   │              │\n",
+       "q_1: ─╫─┤M├───┤1             ├\n",
+       "      ║ └╥┘┌─┐│              │\n",
+       "q_2: ─╫──╫─┤M├┤2             ├\n",
+       "      ║  ║ └╥┘│  MEASURE_YYY │\n",
+       "c_0: ═╩══╬══╬═╡0             ╞\n",
+       "         ║  ║ │              │\n",
+       "c_1: ════╩══╬═╡1             ╞\n",
+       "            ║ │              │\n",
+       "c_2: ═══════╩═╡2             ╞\n",
+       "              └──────────────┘"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "circs[2].draw()"
+    "qc.draw()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.2720783397764194\n",
-      "[0.2720783397764194, 0.2720783397764194]\n"
-     ]
+     "data": {
+      "text/html": [
+       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">     ┌─┐┌─────┐ ┌───┐             ┌─┐┌───┐┌───┐          \n",
+       "q_0: ┤M├┤ SDG ├─┤ H ├─────────────┤M├┤ H ├┤ S ├──────────\n",
+       "     └╥┘└─┬─┬─┘┌┴───┴┐ ┌───┐      └╥┘└┬─┬┘├───┤┌───┐     \n",
+       "q_1: ─╫───┤M├──┤ SDG ├─┤ H ├───────╫──┤M├─┤ H ├┤ S ├─────\n",
+       "      ║   └╥┘  └─┬─┬─┘┌┴───┴┐┌───┐ ║  └╥┘ └┬─┬┘├───┤┌───┐\n",
+       "q_2: ─╫────╫─────┤M├──┤ SDG ├┤ H ├─╫───╫───┤M├─┤ H ├┤ S ├\n",
+       "      ║    ║     └╥┘  └─────┘└───┘ ║   ║   └╥┘ └───┘└───┘\n",
+       "c_0: ═╩════╬══════╬════════════════╩═══╬════╬════════════\n",
+       "           ║      ║                    ║    ║            \n",
+       "c_1: ══════╩══════╬════════════════════╩════╬════════════\n",
+       "                  ║                         ║            \n",
+       "c_2: ═════════════╩═════════════════════════╩════════════\n",
+       "                                                         </pre>"
+      ],
+      "text/plain": [
+       "     ┌─┐┌─────┐ ┌───┐             ┌─┐┌───┐┌───┐          \n",
+       "q_0: ┤M├┤ SDG ├─┤ H ├─────────────┤M├┤ H ├┤ S ├──────────\n",
+       "     └╥┘└─┬─┬─┘┌┴───┴┐ ┌───┐      └╥┘└┬─┬┘├───┤┌───┐     \n",
+       "q_1: ─╫───┤M├──┤ SDG ├─┤ H ├───────╫──┤M├─┤ H ├┤ S ├─────\n",
+       "      ║   └╥┘  └─┬─┬─┘┌┴───┴┐┌───┐ ║  └╥┘ └┬─┬┘├───┤┌───┐\n",
+       "q_2: ─╫────╫─────┤M├──┤ SDG ├┤ H ├─╫───╫───┤M├─┤ H ├┤ S ├\n",
+       "      ║    ║     └╥┘  └─────┘└───┘ ║   ║   └╥┘ └───┘└───┘\n",
+       "c_0: ═╩════╬══════╬════════════════╩═══╬════╬════════════\n",
+       "           ║      ║                    ║    ║            \n",
+       "c_1: ══════╩══════╬════════════════════╩════╬════════════\n",
+       "                  ║                         ║            \n",
+       "c_2: ═════════════╩═════════════════════════╩════════════\n",
+       "                                                         "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "result = ae.run(backend)\n",
-    "print(result['estimation'])\n",
-    "print(ae.confidence_interval(0.05, kind='lr'))"
+    "qc.decompose().draw()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">     ┌───┐\n",
+       "q_0: ┤M_Y├\n",
+       "     └─╥─┘\n",
+       "q_1: ──╫──\n",
+       "       ║  \n",
+       "q_2: ──╫──\n",
+       "       ║  \n",
+       "c_0: ══╩══\n",
+       "          \n",
+       "c_1: ═════\n",
+       "          \n",
+       "c_2: ═════\n",
+       "          </pre>"
+      ],
+      "text/plain": [
+       "     ┌───┐\n",
+       "q_0: ┤M_Y├\n",
+       "     └─╥─┘\n",
+       "q_1: ──╫──\n",
+       "       ║  \n",
+       "q_2: ──╫──\n",
+       "       ║  \n",
+       "c_0: ══╩══\n",
+       "          \n",
+       "c_1: ═════\n",
+       "          \n",
+       "c_2: ═════\n",
+       "          "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qc = QuantumCircuit(3, 3)\n",
+    "qc.measure_pauli('y', 0, 0)\n",
+    "qc.draw()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
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