cirq-maxcut.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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dO3dCFEW5oxCRl2DxedA333yD2bNn4/3338eAAQPkjuMXoqOjERoaitzcXLmjEJGXYPF5SHZ2NubOnYt58+bh2muvlTuOX+FwJxFdjMXnZpIkYe7cuVixYgUWLlyI7t27yx3J73CCCxFdjMXnRqIoYs6cOfjPf/6DhQsXIjY2Vu5IfiktLQ179+6F3W6XOwoReQEWn5s4HA689NJLOHHiBObNm4fw8HC5I/mtkJAQdOvWDQcOHJA7ChF5ARafG1itVvzv//4vzGYzPvjgAwQFBckdye9xuJOI6rH4XKympgaPP/44goODudm0F+EEFyKqx+JzofrNpnv27ImXX34ZKpVK7kj0q5SUFBw/fhy1tbVyRyEimbH4XKS4uBiTJk3CkCFDMH36dG427WV0Oh2uvfZa7NmzR+4oRCQzfnd2gfz8fEycOBGjRo3C5MmTudm0l7r++us53ElELL6rdeTIEUyePBmPPvooxowZI3ccagEnuBARwOK7Krt27cLUqVPx3HPP4Y477pA7Dl1G3759UVhYiPPnz8sdhYhkxOK7Qj/++COeffZZzJ49G0OHDpU7DrWCUqnEddddx6s+Ij/H4rsC69atw6uvvor33nsPaWlpcsehNuBwJxGx+Npo2bJl+OijjzBv3jwkJibKHYfaiOv5iIjF10qSJGH+/Pn44osvsHDhQsTHx8sdia7ANddcg7q6OhQWFsodhYhkwuJrBVEU8dZbb2Hz5s1YuHAh4uLi5I5EV0gQBKSlpXG4k8iPsfguw+FwYObMmTh27Bg3m/YRXM9H5N9YfC2wWq2YMWMGKisr8eGHHyI4OFjuSOQC9RNcJEmSOwoRyYDF1wyz2YwnnngCAQEBePvtt6HT6eSORC7SsWNHBAQEIC8vT+4oRCQDFl8Tzp8/j8mTJyMhIQGzZs2CWq2WOxK5GGd3EvkvFt9vlJSUYNKkSbjxxhvxzDPPcLNpH2UwGFh8RH6K39Uvcvr0aUycOBEjR47Eo48+ys2mfZjBYMDu3bvhdDrljkJEHsbi+9XRo0fx8MMP4+GHH8YDDzwgdxxys7CwMHTs2BGHDh2SOwoReRiLD8Du3bvxxBNP4Nlnn8Wdd94pdxzyEG5fRuSf/L74/vOf/+CZZ57B3//+d9x0001yxyEP4no+Iv/k18X37bffYtasWXj33XdhNBrljkMedt111+HIkSOwWCxyRyEiD/Lb4vviiy/wwQcfYO7cuejbt6/ccUgGgYGB6NmzJ/bt2yd3FCLyIL8rPkmSsHDhQixduhQLFy5EQkKC3JFIRlzPR+R//Kr4RFHEO++8g02bNmHhwoXo2LGj3JFIZlzPR+R/VHIH8BSn04lZs2ahoKAA8+fP576bBADo168fTp8+DZPJhJCQELnjEJEH+MUVn81mw4wZM1BRUYGPPvqIpUcN1Go1+vfvj507d8odhYg8xOeLr7a2FlOnToVWq8Xbb7+NgIAAuSORl+F6PiL/0q6KzylKKKysw+lzZhRW1sEptnysTGVlJR555BF07doVr776KjebpiZxPR+Rf/H6e3wmix0bDpdg7YEinCozA4IEAQIkSJAkICEqCCP6xSE9MQbBuv8WW2lpKR577DHcdNNNeOyxx7jvJjWrZ8+eqKysRGlpKaKjo+WOQ0RuJkheehqn3SkiO6cAi7fmwylKUCkF6FRKKBT/LTBRlGBxOOFwSlAqBIwf1B1jjF1RdPYXTJkyBffeey/GjRsn46ug9uKZZ57BkCFDMGLECLmjEJGbeWXxlVZbMH3Ffpwqr4Feo4JKefkRWYdThNnmQHSAgMrvPsDjk8bh7rvvdn9Y8gkrV67E/v378fLLL8sdhYjczOvu8ZVWWzB5yS7kl5sRolO3qvQAQKVUQCXase/kWUg3TsKg4be5OSn5kvoJLl74cyARuZhXFZ/dKWL6iv2oMNsQEqBu0325mpoanD17Fl1io+BQBWDGiv1wOEU3piVf0qVLFwiCgNOnT8sdhYjczKuKLzunAKfKzQjWXTrnRnTYseuTF2GrqWzy66qqqlBYWIguXbogQKvB8c9n4lhBEbJzCjyQmnyBIAjcvozIT3jNrE6TxY7FW/Oh1ygbXekV7/sRIZ17QhMUCgA4u3MDCndtgr2uBqKggDq2F/rf+SB0v67Ri+33O1Tu3YDMoA6457pOl8z2JGqOwWDA//3f/+G+++6TOwoRuZHXXPFtOFzy6+zNxpGK9v2AmL6DGt4Ov6Y/rhv/Inr9eSaibn4IWqcZ5Qe3NHw8KvF6lB3eBrvNhg2HSzySn9o/o9GIXbt2QRQ5RE7ky7zmii97/c849sVc2E3lCEtIggAButBoxKYMgaWyDMEd4xs+NyAsCiUlpTCba9C1axecOKiC5Xxpw8e1wWFQ6fSwluVj7YEwjEztLMdLonYmMjISkZGROHr0KBITE+WOQ0Ru4hXFZ7HasDnrbXRJuwWdBvwPKk7sxdGv56Oz8feoLTsLXWgUBIUSwIVjhYqKinDu2A6Y9q7HWbsV6oAgxN907yWPGRgRB3tFIfLKesEpXljnR3Q59bM7WXxEvssrhjp/+HknJNGJzoZboFCqENk7DcFxF67wHJZaKNXahs8tLS2FyVSF2KRBSHrwNST9+QVE9RsMdeClO+srNTo4rbWQIKHExBO2qXU4wYXI93lF8ZWWlUMTFHrJpBZtcDgAQBUQCKfd2vD+sLAwREdHQxRF1NRUo9IqweRQYMfyD3DixAmcPn0ahYWFqKk8D5sTsFmtOH3mF9hsNo+/Lmp/UlNTceDAAf59IfJhXjHUGR0VCVtNJSRJaig/a3UFdKHR0Ed1gaWyDJLohKBQQqPRIDw84pKvL6ktxtkz+9C1a1fY7XbY7XZYK0sgJA5CZVUVXpn5N1QWnkJwcDDi4uIQFxeH2NjYS34fGxuL4OBg7unp54KDgxEfH4/9+/cjLS1N7jhE5AZeUXxDb0iDIChwdudGdEy9CRUn9qG66BQ6dOkDbXAYAsKiUV10CiGdegC4sLwhvEcKNPoQmMsL8cv2bxHWPREajQYajQbW6vNQiA50TzLCIirwzeufQ4CE8vJyFBcXo7i4GEVFRTh16hS2bduGoqIiFBUVAUBDGV5cjvW/RkREQKHwiotkcqP64U4WH5Fv8ori02k1GDr2KWxdMR8FP61GWEISInqlNnw8tv9QlB7a1lB8prMnkb/lX3DaLFAHBiOydxq6D7674fPLDm9HTNJA2KBAQpT+14ktAqKjoxEdHY3k5ORGGSRJQnV19SXFWFRUhMOHDze8XV1djejo6GaLMSYmBhqNxt1/XORmBoMB//znP+WOQURu4hXFBwAP3HYjzqmjERJwYbH5sXWfNnwsNvl32LP4FdhqKqEJCkWv2yc0+ziiw47iA1uQfP8MWJwSRvSLa9XzC4KAkJAQhISEoFevXk1+js1ma1SMu3btani7rKwMISEhja4aLx5WDQoKasOfCsmhf//+OHHiBMxmM/R6vdxxiMjFvOZ0hmqLHXd99BO0KgVUSgWOrfsU2qAwdB9yzxU9nsMpwuoQsXrKjR7buUUURZSXl6OoqOiScqx/u7i4GIIgtFiM4eHhHE71Ao899hj+9Kc/YciQIXJHISIX85orvmCdGuMHdccnW/IQcpVFJUkSzDYHJg1O8Oh2ZQqFomE4tX///k3mqq6uvqQMi4qKcPDgwYZirKmpaTScenExxsTE8CR5D6hfz8fiI/I9XnPFB1w4nWFS1s4LRxIFXPk3d1OdHfGReiwcl9bqY428hdVqbSjEi68a639fXl6O0NDQRvcXLy5HDs9dvUOHDuGVV17B8uXL5Y5CRC7mVcUH/Pc8vgqzDcE6VZuWF0iShGqLA+F6DeaNHYDoYJ0bk8pDFEWUlZU1KsSLS1KtVjdZiPW/Dw8P57KNyxBFEcOHD8fKlSsRHh4udxwiciGvKz7g6k5gT4gMwpujkn2y9FpDkiSYTKYm7y/Wv11bW9swhNpUMUZHR3M4FcDTTz+N9PR03HrrrXJHISIX8sriAy4Mey7NKUDm1vxfT20QoFMpobhoz01RlGBxOOFwSlAqBWQM7I4xxq7tbnjT0ywWS6Nh1IvfLi8vR3h4eKNivLgcAwMD5X4Zbrd8+XIcO3YML730ktxRiMiFvLb46lVb7NhwuARrDxQhr8wMCRIUggBRkiBAQEKUHiP6xeGWxBieu+ciTqez0XDqb2eparXaFosxLCys3Q+n5uXlYdq0aVizZk27fy1E9F9eX3wXc4oXNpy2O0WolQrEhOh46oIMJElCZWVlo/uLF5djXV1dk/cZ69+Ojo6GSuU1k4qbJEkSfv/732PhwoXo3JlHWxH5inZVfNR+1NbWoqSkpNnZqefOnUN4eHizxRgbG+sVw6kvvfQSUlJSMHLkSLmjEJGLsPhIFg6HA6WlpU0Oo9a/T6vVtripuCeGU7/++mts3boVs2fPduvzEJHnsPjIK0mShPPnzzdbjEVFRbBarU3OSr14dqpSqbyqHCUlJXjggQewfv167qhD5CO8+yYL+S1BEBAeHo7w8PBmT0Ovra1tdH/xp59+ani7oqICERERzRZjbGwsAgICWswRExODkJAQnDhxotk9XImofeEVH/ms+uHUlvZODQgIaHHv1A4dOuD1119H165d8cADD8j9kojIBVh85Lfqh1N/O4R6cTHabDYoFApUVVVh9OjRjYoxKirqqodTicizWHxELTCbzcjNzUVGRgaeeeaZRqdvnD9/HpGRkc1eMcbGxkKn889dhIi8Fe/xEbVAr9cjJSUFffv2xbXXXouUlJRLPm632xuGU+v/O3jwIDZu3IiioiKUlJQgMDCw2fuMcXFxCAkJ4QJ5Ig/iFR9RK7z//vvQ6XR4+OGH2/R1oiheMpza1E44TqezxdM2oqKiOKOUyIVYfEStsG3bNnz66adYsGCByx+7pqamxb1Tq6qqEBUV1eym4rGxsdBqtS7PReSrWHxErVBXV4f09HSsX7/+sksgXM1ms10ynPrbJRylpaUICgpqce/U4OBgDqcS/Yr3+IhaISAgANdeey327NmDQYMGefS5NRoNOnfu3Ox+oaIo4ty5c5cU4unTp/Hzzz83vE+SpBaLMTIyksOp5Dd4xUfUSgsWLEBtbS2mTZsmd5Q2q66ubnIYtf73JpOpYTj14mK8eKaqRqOR+2UQuQSLj6iV9u3bhzfffBOff/653FFczmazNZRiU4v9S0tLERwc3OKm4hxOpfaCxUfUSg6HA8OHD8fq1asRGhoqdxyPEkUR5eXlTRZjfTkCaHFTcQ6nkrdg8RG1wbRp0/CHP/wBN998s9xRvIokSaiurm7ybMb6t6urqxEdHd1kMcbFxSEmJobDqeQRLD6iNsjOzkZ+fj6ee+45uaO0O1arteGMxqa2iCstLUWHDh1a3FQ8ODhY7pdBPoDFR9QGubm5mDFjBr766iu5o/ic+uHUpo6gqv9VoVC0uKl4eHg4h1Ppslh8RG0giiJuvfVWLFmyBLGxsXLH8Sv1w6ktFaPZbG4YTm2qGGNiYqBWq+V+KT7DKUooMVlgd4pQKxWICdFBqfD+CU4sPqI2eu6553DDDTfgzjvvlDsK/YbFYrlkOPW3SzjKysoQGhrabDHGxcVBr9fL/TK8mslix4bDJVh7oAinysyAIEGAAAkSJAlIiArCiH5xSE+MQbDOO3/IYPERtdFXX32F3bt3Y9asWXJHoTZyOp1NDqdefL9RpVK1WIxhYWF+OZxqd4rIzinA4q35cIoSVEoBOpUSiouu8ERRgsXhhMMpQakQMH5Qd4wxdoVa6V1/Xiw+ojY6e/YsHnzwQXz33Xdct+ZjJElCVVVVk4v863+tra1FTExMs5uKR0dH+9xwamm1BdNX7Mep8hroNSqoWlFkDqcIs82B+MggzBmVjOhg7zmei8VHdAXuvPNOvPvuu0hISJA7CnlYXV3dJVeJv/21vLwcYWFhzS7biIuLQ2BgoNwvo9VKqy2YvGQXKsw2BOtUbfphT5IkVFscCNdrMG/sAK8pPxYf0RWYNWsWevbsiT/96U9yRyEv43Q6GzYVb26LOK1W2+xpG/XDqd4wmmB3ipiUtRP55WaEBFz5Vaypzo74SD0Wjktr1dWiu3GTaqIrYDQa8e9//5vFR40olcqGAmuKJEmorKxsVIx79+5teF9dXV2Lm4pHR0dDpXL/t+/snAKcKjcjRHfpc4kOO/YsfgX9Rj8NTVBoi49hM5twfOmbcI7+K7JzCjBuYHf3BW4lXvERXYGKigqMHDkSmzZtglKplDsO+Zja2tpm904tKipCRUUFwsPDW9xU/GqHU00WO+7+6CdoVYpGV2mFuzbBXH4WPW8dB+BCmef/sBLF+/8DAIhN/h26D/1jw1XrifWfQRsWi/DkYVg95UbZZ3vyio/oCtR/0zly5AiSkpLkjkM+JjAwEAkJCc3eQ3Y4HCgtLb3k/uKRI0fw/fffN7yt0+la3FT8csOpGw6X/Dp7s/HQZNG+H9AzfVzD28X7fsS53D1InTATAHDwi3egC41EXMowAEB04g3IXZ+FDklDseFwCUamNn3Elqew+IiukNFoRE5ODouPPE6lUqFjx47o2LFjkx+XJAnnz59vdLW4e/fuhmK0Wq1NzkqtL8bsf2/DsS/nwW4qR1hCEgQI0IVGIzZlCCyVZQjuGN/wfKWHtqKTIR3a4DAAQCdDOor3/dhQfMEd42GpLIPTXIG1B4pYfETtlcFgQHZ2Nh588EG5oxBdQhAEhIeHIzw8HImJiU1+Tv1w6sUTb3766ScUFRXhbGERthwrQkjv6xE3bCysRbko+vELxKQOR3lBLjTB4biwbP0Cc/lZ6KO7NDy2Pqozas8V/TePQomAsGg4KwqRFxQBpyjJusMLi4/oCqWmpuL555+H1WqFVquVOw5Rm7Q0nPrvH7Zix8NPIjn9PjgcDtjCI1B1dBvsdhsqy0phsTtx/PhxSJIErVYLp80GlTag4etV2kA4bRZIktQwnKrU6OC01UHChW3OOoYGNHpeT5F/XilRO6XX69GjRw/s27dP7ihELlVaVg5NcCg0Gi0CA/UIDQ1Fh6iO6BASiriu3aFRCtBqtVCpVOjQoQOUGg2cNkvD1ztsdVBqdJfcQ3TaLFDpAqEQBNidohwvqwGLj+gqGAwG5OTkyB2DyKWioyJhq6nExZP+LaYK1FnqUF7rRE1FMcLDQtGjRw9ERERAH9kJNaVnGj7XXPYLAiP+u5xDEp2oO18KfVRniJIk+xZmLD6iq1A/wYXIlwy9IQ2CoMDZnRvhsNtwaudmlOcfQV1tLTon9EJYbFcI5nMNV3TRfQfi7M71sFafh7X6PM7uWI+Yfjc2PF510SnoOkRCExwBAQJiQuTdwYX3+IiuQr9+/ZCfn4/q6moekko+Q6fVYPCYqfjP8rk4tnEpgrtci5hEI4LDI6DX6xHbfyhKD21DSKceAIDY/kNhqSzH7kUzL7yd/DvE9h/a8Hilh7cjNmUoLA4nEqL0sh9dxOIjugoajQbJycnYtWsXhg0bJnccoqt27tw5fP755yjcfxyd/jANcZGh0Gq1OLbu04bPiU3+HfYsfgW2mkpogkIhCALih41C/LBRjR7PZjahsuAIEm66DzV2CSP6Nb2jjSdxqJPoKnG4k3zBL7/8gtdeew333nsvrFYrls55FlGREVCqGu+yolCpMWDirMtuVwYAGn0I0ia+ClFQQqkQcEtijBvStw2Lj+gqGQwG7NixQ+4YRFckNzcXzz//PMaPH4/Q0FCsXLkS06dPR4/unTF+UHeYbQ5c7c6WkiTBbHMgY1B32bcrA7hXJ9FVE0URN998M5YvX46oqCi54xC1yt69e5GZmYmjR49izJgx+OMf/9jo9HlfPZ2BxUfkAjNmzMCwYcNw++23yx2FqFmSJGHr1q1YtGgRysrKMH78eNxxxx3QaDTNfg3P4yOiJn355Zc4dOgQZs6cKXcUokacTic2btyIzMxMCIKAjIwMDB8+vNUni1zNCewJkUF4kyewE/me06dP49FHH8XatWu94gBRIgCw2Wz4+uuvkZWVhejoaEyYMAEDBw68or+jdqeIpTkFyNya/+upDQJ0KiUUFy1NEEUJFocTDqcEpVJAxsDuGGPs6hXDmxdj8RG5gCRJGDFiBObOnYuuXbvKHYf8nNlsxsqVK5GdnY0+ffpgwoQJ6N+/v0seu9pix4bDJVh7oAh5ZWZIkKAQBIjShW2rE6L0GNEvDrckxnjFRJamsPiIXGTmzJlISkrCqFGN1zIReUJFRQWWLVuGlStXYuDAgRg/fjx69uzptudzihc2nLY7RaiVCsSE6GRfnN4aXMBO5CJGoxGbN29m8ZHHFRUVYcmSJfjuu+9wyy23ICsrC506dXL78yoVgqynLFwpFh+RixgMBrz11lsQRREKhXfd0yDflJeXh8zMTGzZsgX33HMPvvzyS0RERMgdy+ux+IhcJCoqCuHh4Th+/Dj69OkjdxzyYQcOHEBmZiYOHDiA+++/H9OnT+desW3A4iNyofrty1h85GqSJGH79u1YtGgRCgsLMXbsWLz22ms8BPkKsPiIXMhgMGDVqlUYN26c3FHIR4iiiO+//x6ZmZmw2WzIyMhAeno6VCp++75SnNVJ5EImkwl33HEHNm7c2OJuGESXY7fbsW7dOixevBgdOnTAhAkTMHjwYN4/dgH+yEDkQiEhIejevTsOHjyI1NRUueNQO1RbW4uvvvoKn3/+Oa655hq88MILuO6667gxggux+IhcrP4+H4uP2qKqqgrLly/Hl19+iQEDBuCdd97hvWI34TUzkYvxfD5qi9LSUrzzzju45557UFpaik8++QSvv/46S8+NeMVH5GL9+/fHiRMnUFtbi8DAQLnjkJc6ffo0Fi9ejM2bN+MPf/gDli1bhujoaLlj+QUWH5GLabVaJCYmYvfu3Rg8eLDcccjLHDlyBIsWLcKePXtw33334auvvkKHDh3kjuVXWHxEblA/3MniI+DCGrxdu3Zh0aJFOHXqFP785z/j5ZdfRkBA+9vuyxew+IjcwGAw4LXXXpM7BslMFEX8+OOPWLRoEWpqajB+/Hj8/ve/h1rtnacW+AsWH5EbJCYmoqioCBUVFQgPD5c7DnmYw+HAd999h8WLF0On02HChAkYNmwY1+B5CRYfkRsolUqkpqZi586dSE9PlzsOeYjFYsHq1auxZMkSdOnSBdOnT4fBYOAaPC/D4iNyk/r7fCw+32cymfDll19i+fLl6N+/P9544w307dtX7ljUDBYfkZsYjUZkZ2fLHYPcqLy8HNnZ2fjXv/6FIUOGYN68eYiPj5c7Fl0Gi4/ITeLj42G1WlFYWIiOHTvKHYdc6JdffkFWVhY2btyI22+/HdnZ2YiNjZU7FrUSi4/ITQRBgMFgQE5ODu6++26545ALHD9+HJmZmdi+fTtGjRqFlStXIiwsTO5Y1EYsPiI3MhqN+Pnnn1l87dyePXuwaNEi5ObmYsyYMXj++eeh1+vljkVXiMcSEblRUVERxo8fj3//+9+c2dfOSJKELVu2IDMzE+fOncO4ceNwxx138LgpH8ArPiI3iouLQ2BgIE6ePIkePXrIHYdawel0YsOGDcjMzIRCoUBGRgZuvvlmrsHzISw+IjerX9bA4vNuNpsNa9asQVZWFmJjYzF16lQMHDiQV+o+iMVH5GZGoxHffPMNxowZI3cUaoLZbMaKFSuQnZ2NxMREvPrqq0hOTpY7FrkR7/ERuVllZSXuuusubNq0CSoVf9b0FhUVFVi6dClWrVqFQYMGYfz48bwq9xP8V0jkZqGhoejUqRMOHz7MKwkvUFhYiM8++wzfffcd0tPTkZWVhU6dOskdizyIxUfkAfX3+Vh88jl58iQWL16MLVu2YOTIkVixYgU3EPdTnKZE5AH1xUeet3//fvzlL3/Bo48+ivj4eKxZswaPP/44S8+P8YqPyANSUlJw5MgRWCwW6HQ6ueP4PEmS8PPPP2PRokUoLi7G2LFjMXv2bGi1WrmjkRdg8RF5QGBgIHr37o29e/fihhtukDuOzxJFEZs2bUJmZibsdjsyMjKQnp7OSUV0Cf5tIPKQ+uFOFp/r2Ww2rFu3DllZWQgNDcXkyZMxePBgLjqnJrH4iDzEaDTirbfekjuGT6mtrcWqVauQnZ2NHj164IUXXsB1113HRefUIhYfkYf07dsXBQUFMJlMCAkJkTtOu1ZZWYlly5ZhxYoVMBgM+Mc//oHevXvLHYvaCRYfkYeo1WqkpKRg586d+J//+R+547RLJSUl+Oyzz7B27VoMHz4cn376Kbp27Sp3LGpnWHxEHlR/n4/F1zb5+fnIysrC5s2bceedd2LZsmWIjo6WOxa1Uyw+Ig8yGo1YtWqV3DHajcOHDyMzMxN79uzB6NGj8a9//YvDxHTVWHxEHtSjRw+YTCaUlpbyiqUZkiRhx44dyMzMxOnTp/HnP/8ZL7/8MgICAuSORj6CxUfkQQqFAmlpacjJycEdd9whdxyvIooifvjhByxatAi1tbUYP348brvtNqjVarmjkY9h8RF5WP19PhbfBQ6HA99++y0WL16MwMBAZGRkYNiwYVyDR27D4iPyMKPRiPnz50OSJL9eb1ZXV4fVq1djyZIl6NatG2bMmAGDweDXfybkGSw+Ig/r1KkT1Go18vPzER8fL3ccjzOZTPjiiy/wxRdfICUlBXPmzEFiYqLcsciPsPiIPEwQBBgMBuzYscOviq+srAzZ2dlYvXo1hg4dinnz5vnV6yfvweIjkoHRaMTGjRtx3333yR3F7c6cOYOsrCxs2rQJI0aMwNKlSxETEyN3LPJjLD4iGRgMBrz55psQRdFnJ3EcO3YMmZmZ2LFjB0aNGoVVq1YhNDRU7lhELD4iOURERCAyMhJHjx71qftbkiRhz549yMzMRG5uLh544AG8+OKLCAwMlDsaUQMWH5FM6pc1+ELxiaKILVu2IDMzE+fPn8e4cePw1ltvQaPRyB2NqBEWH5FMjEYjli1bhoyMDLmjXDGn04n169cjMzMTSqUSEyZMwPDhw312+JZ8gyBJkiR3CCJ/VFNTg9tvvx0bN25sd1dGVqsVa9aswZIlSxAbG4uMjAwMHDiQa/CoXeAVH5FMgoKCcM0112Dfvn0wGAxyx2mVmpoafPnll1i6dCmSkpLw6quvIjk5We5YRG3C4iOSUf16Pm8vvoqKCmRnZ2PVqlUYPHgwPv74Y/To0UPuWERXhAPxRDKqn+DirQoLC/H666/jj3/8I8xmMz777DO88sorLD1q13jFRySj5ORk5OXloaamBkFBQXLHaXDy5ElkZmZi69atGDlyJFauXInw8HC5YxG5BIuPSEYajQZJSUnYvXs3hgwZIncc7N+/H4sWLcLhw4dx//3345lnnvGqQiZyBRYfkczqhzvlKj5JkrBt2zZkZmaiuLgY48aNw+uvvw6tVitLHiJ3Y/ERycxoNGLmzJkef15RFLFx40ZkZmbC6XQiIyMD6enpUCqVHs9C5EksPiKZ9enTB+Xl5Th37hwiIiLc/nw2mw3ffPMNsrKyEB4ejkcffRQ33ngjF52T32DxEclMoVBgwIAByMnJQfqtt6HEZIHdKUKtVCAmRAelwjWLwmtra7Fy5UpkZ2ejZ8+eeOmll3Dddddx0Tn5He7cQiQzk8WOvy9ag62/WKAK6wQIEgQIkCBBkoCEqCCM6BeH9MQYBOvUbX788+fPY/ny5VixYgWMRiMyMjLQq1cvN7wSovaBxUckE7tTRHZOARZvzYfVZkNZaQl6xne7ZMhRFCVYHE44nBKUCgHjB3XHGGNXqJWXH5YsLi7GZ599hnXr1mH48OEYN24cunTp4s6XRNQusPiIZFBabcH0FftxqrwGeo0KKqWA3NwT6NatW7P7djqcIsw2B+IjgzBnVDKig3VNft6pU6ewePFi/Pjjj7jrrrswZswYREVFufPlELUrLD4iDyuttmDykl2oMNsQrFM13GMrLDyLgIBAhIWFNfu1kiSh2uJAuF6DeWMHXFJ+hw4dQmZmJvbt24fRo0fj3nvvRUhIiNtfD1F7w+Ij8iC7U8SkrJ3ILzcjJODS+3WVlZWoqalB586dL/s4pjo74iP1WDB2APbs3oVFixahoKAAY8eOxV133YWAgAB3vQSido+zOok8KDunAKfKzQjRXfpPT3TYkbtiDoIMIyF16tTiTEvRYcexz2bCdPtj+P3j3yK0/AAyMjJw2223QaXiP2miy+G/EiIPMVnsWLw1H3qNslGxFe/7EaFdekMR1AFWqxWW0nwUbP0aNSWnodLpYZz8BoALQ52mGjMUsb1RuG0NtMMfwMp3nkWHQO6yQtRaXLFK5CEbDpfAKUpQNTEjs2jfD4jpOwh6fRDMZjOUai1i+g1G/NB7AVzYZaWi4hxOnDiBqqoqJBiHw1l0FGqVCpuOlnn6pRC1a7ziI/KQ7PU/49gXc2E3lSMsIQkCBOhCoxGbMgSWyjIEd4wHasyorKxERNd4BMfF41zeQdisNpw4cQKBgYHo3Llzw/07lU4Pa1k+1h4Iw8jUy98XJKILeMVH5AEWqw2bs95GbNIg3PDEu4jqnYby47sAALVlZ6ELjYKgUEKv16OurhY2mw0lJSX45ZczECUR3bp1u6T0ACAwIg72ikLklZnhFDlHjai1WHxEHvDDzzshiU50NtwChVKFyN5pCI6LBwA4LLVQqi/co1MqldBotMjLywMgoWPHTtDpdE2elKDU6OC01kKChBKTxZMvh6hd41AnkQeUlpVDExR6yaQWbfCFg11VAYFw2q0N7+/evTtE0QmlUoXzdRXNPqbTZoFKFwiFIMDuFN0XnsjH8IqPyAOioyJhq6nExctmrdUXSk0f1QWWyjJIohMAIAgClMrL/0xae64IQdFdIEpSq7YwI6IL+K+FyAOG3pAGQVDg7M6NEJ0OlB/bheqiUwAAbXAYAsKiG94GAEkUITrsF8pQkiA67BCdjoaPW6vPw1Fnhj4mHgIExIQ0vX0ZETXGoU4iD9BpNRg69ilsXTEfBT+tRlhCEiJ6pTZ8PLb/UJQe2oaQTj0AAFW/5OLAsjkNH//pnUfRoUsvJN8/AwBQdng7YpIGwgYFEqL0Lju6iMgfsPiIPOSB227EOXV0w1Zlx9Z92vCx2OTfYc/iV2CrqYQmKBShXXvjdzMWNvk4osOO4gNbkHz/DFicEkb0i/NIfiJfwaFOIg9JT4yBUiHA0cREFIVKjQETZ0ETFHrZx1Go1Eib9CoUuiAoFQJuSYxxQ1oi38XiI/KQYJ0a4wd1h9nmwNXuDS9JEsw2BzIGdb+iw2mJ/BlPZyDyoJZOZ2iL+tMZFo5La3ILNCJqHv/FEHmQWqnAnFHJCNdrYKqzt/nKT5IkmOrsCNdr8OaoZJYe0RXgFR+RDBqfwH75Aqs/gT0hMghvtnACOxG1jMVHJBO7U8TSnAJkbs3/9dQGATqVEoqLliaIogSLwwmHU4JSKSBjYHeMMXbllR7RVWDxEcms2mLHhsMlWHugCHllZkiQoBAEiJIEAQISovQY0S8OtyTGcCILkQuw+Ii8iFO8sOG03SlCrVQgJkTHxelELsbiIyIiv8IbBURE5FdYfERE5FdYfERE5FdYfERE5FdYfERE5FdYfERE5FdYfERE5FdYfERE5FdYfERE5FdYfERE5FdYfERE5FdYfERE5FdYfERE5FdYfERE5FdYfERE5FdYfERE5FdYfERE5FdYfERE5Ff+H/RpCL3BQLnXAAAAAElFTkSuQmCC\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import networkx as nx\n",
    "from networkx import Graph\n",
    "from cirq import LineQubit\n",
    "\n",
    "g = Graph()\n",
    "nodes = [LineQubit(i) for i in range(4)]\n",
    "edges = [(nodes[i], nodes[(i+1) % 4]) for i in range(4)]\n",
    "g.add_nodes_from(nodes)\n",
    "g.add_edges_from(edges)\n",
    "nx.draw(g, with_labels=True, alpha=0.8, node_size=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "from cirq import Rx\n",
    "from sympy import Symbol\n",
    "\n",
    "def mixer(g: Graph, beta: Symbol):\n",
    "    return [ Rx(rads=2*beta)(q) for q in list(g.nodes())]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "from openfermion import Rzz\n",
    "\n",
    "def problem(g: Graph, gamma: Symbol):\n",
    "    return [ Rzz(rads=2*gamma)(i, j) for i, j in g.edges()]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [
    "from cirq import Circuit, H, measure, Moment\n",
    "\n",
    "beta = Symbol(\"beta\")\n",
    "gamma = Symbol(\"gamma\")\n",
    "\n",
    "qaoa_circuit = Circuit(\n",
    "    H.on_each(*nodes),\n",
    "    problem(g, gamma),\n",
    "    Moment(mixer(g, beta)),\n",
    "    problem(g, gamma),\n",
    "    Moment(mixer(g, beta)),\n",
    "    (measure(q) for q in nodes)\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            ┌──────────────────────────────┐                                                    ┌──────────────────────────────┐\n",
      "0: ───H───ZZ─────────────────ZZ──────────────────────────────────────────────────Rx(2*beta)───ZZ─────────────────ZZ──────────────────────────────────────────────────Rx(2*beta)───M───\n",
      "          │                  │                                                                │                  │\n",
      "1: ───H───ZZ^(4*gamma/pi)────┼──────────────ZZ───────────────────────────────────Rx(2*beta)───ZZ^(4*gamma/pi)────┼──────────────ZZ───────────────────────────────────Rx(2*beta)───M───\n",
      "                             │              │                                                                    │              │\n",
      "2: ───H──────────────────────┼──────────────ZZ^(4*gamma/pi)────ZZ────────────────Rx(2*beta)──────────────────────┼──────────────ZZ^(4*gamma/pi)────ZZ────────────────Rx(2*beta)───M───\n",
      "                             │                                 │                                                 │                                 │\n",
      "3: ───H──────────────────────ZZ^(4*gamma/pi)───────────────────ZZ^(4*gamma/pi)───Rx(2*beta)──────────────────────ZZ^(4*gamma/pi)───────────────────ZZ^(4*gamma/pi)───Rx(2*beta)───M───\n",
      "                            └──────────────────────────────┘                                                    └──────────────────────────────┘\n"
     ]
    }
   ],
   "source": [
    "from cirq.contrib.svg import SVGCircuit\n",
    "\n",
    "# SVGCircuit(qaoa_circuit)\n",
    "print(qaoa_circuit)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "def estimate_cost(graph: Graph, samples: pd.DataFrame) -> float:\n",
    "    \"\"\"Estimate the cost function of the QAOA on the given graph using the\n",
    "    provided computational basis bitstrings.\"\"\"\n",
    "    cost_value = 0.0\n",
    "\n",
    "    # Loop over edge pairs and compute contribution.\n",
    "    for u, v in graph.edges():\n",
    "        u_samples = samples[str(u)]\n",
    "        v_samples = samples[str(v)]\n",
    "\n",
    "        # Determine if it was a +1 or -1 eigenvalue.\n",
    "        u_signs = (-1) ** u_samples\n",
    "        v_signs = (-1) ** v_samples\n",
    "        term_signs = u_signs * v_signs\n",
    "\n",
    "        # Add scaled term to total cost.\n",
    "        term_val = np.mean(term_signs)\n",
    "        cost_value += term_val\n",
    "\n",
    "    return -cost_value"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0.0, 0.0), (0.0, 0.3490658503988659), (0.0, 0.6981317007977318), (0.0, 1.0471975511965976), (0.0, 1.3962634015954636), (0.0, 1.7453292519943295), (0.0, 2.0943951023931953), (0.0, 2.443460952792061), (0.0, 2.792526803190927), (0.0, 3.141592653589793)]\n",
      "[[[0.         0.        ]\n",
      "  [0.         0.34906585]\n",
      "  [0.         0.6981317 ]\n",
      "  [0.         1.04719755]\n",
      "  [0.         1.3962634 ]\n",
      "  [0.         1.74532925]\n",
      "  [0.         2.0943951 ]\n",
      "  [0.         2.44346095]\n",
      "  [0.         2.7925268 ]\n",
      "  [0.         3.14159265]]\n",
      "\n",
      " [[0.34906585 0.        ]\n",
      "  [0.34906585 0.34906585]\n",
      "  [0.34906585 0.6981317 ]\n",
      "  [0.34906585 1.04719755]\n",
      "  [0.34906585 1.3962634 ]\n",
      "  [0.34906585 1.74532925]\n",
      "  [0.34906585 2.0943951 ]\n",
      "  [0.34906585 2.44346095]\n",
      "  [0.34906585 2.7925268 ]\n",
      "  [0.34906585 3.14159265]]\n",
      "\n",
      " [[0.6981317  0.        ]\n",
      "  [0.6981317  0.34906585]\n",
      "  [0.6981317  0.6981317 ]\n",
      "  [0.6981317  1.04719755]\n",
      "  [0.6981317  1.3962634 ]\n",
      "  [0.6981317  1.74532925]\n",
      "  [0.6981317  2.0943951 ]\n",
      "  [0.6981317  2.44346095]\n",
      "  [0.6981317  2.7925268 ]\n",
      "  [0.6981317  3.14159265]]\n",
      "\n",
      " [[1.04719755 0.        ]\n",
      "  [1.04719755 0.34906585]\n",
      "  [1.04719755 0.6981317 ]\n",
      "  [1.04719755 1.04719755]\n",
      "  [1.04719755 1.3962634 ]\n",
      "  [1.04719755 1.74532925]\n",
      "  [1.04719755 2.0943951 ]\n",
      "  [1.04719755 2.44346095]\n",
      "  [1.04719755 2.7925268 ]\n",
      "  [1.04719755 3.14159265]]\n",
      "\n",
      " [[1.3962634  0.        ]\n",
      "  [1.3962634  0.34906585]\n",
      "  [1.3962634  0.6981317 ]\n",
      "  [1.3962634  1.04719755]\n",
      "  [1.3962634  1.3962634 ]\n",
      "  [1.3962634  1.74532925]\n",
      "  [1.3962634  2.0943951 ]\n",
      "  [1.3962634  2.44346095]\n",
      "  [1.3962634  2.7925268 ]\n",
      "  [1.3962634  3.14159265]]\n",
      "\n",
      " [[1.74532925 0.        ]\n",
      "  [1.74532925 0.34906585]\n",
      "  [1.74532925 0.6981317 ]\n",
      "  [1.74532925 1.04719755]\n",
      "  [1.74532925 1.3962634 ]\n",
      "  [1.74532925 1.74532925]\n",
      "  [1.74532925 2.0943951 ]\n",
      "  [1.74532925 2.44346095]\n",
      "  [1.74532925 2.7925268 ]\n",
      "  [1.74532925 3.14159265]]\n",
      "\n",
      " [[2.0943951  0.        ]\n",
      "  [2.0943951  0.34906585]\n",
      "  [2.0943951  0.6981317 ]\n",
      "  [2.0943951  1.04719755]\n",
      "  [2.0943951  1.3962634 ]\n",
      "  [2.0943951  1.74532925]\n",
      "  [2.0943951  2.0943951 ]\n",
      "  [2.0943951  2.44346095]\n",
      "  [2.0943951  2.7925268 ]\n",
      "  [2.0943951  3.14159265]]\n",
      "\n",
      " [[2.44346095 0.        ]\n",
      "  [2.44346095 0.34906585]\n",
      "  [2.44346095 0.6981317 ]\n",
      "  [2.44346095 1.04719755]\n",
      "  [2.44346095 1.3962634 ]\n",
      "  [2.44346095 1.74532925]\n",
      "  [2.44346095 2.0943951 ]\n",
      "  [2.44346095 2.44346095]\n",
      "  [2.44346095 2.7925268 ]\n",
      "  [2.44346095 3.14159265]]\n",
      "\n",
      " [[2.7925268  0.        ]\n",
      "  [2.7925268  0.34906585]\n",
      "  [2.7925268  0.6981317 ]\n",
      "  [2.7925268  1.04719755]\n",
      "  [2.7925268  1.3962634 ]\n",
      "  [2.7925268  1.74532925]\n",
      "  [2.7925268  2.0943951 ]\n",
      "  [2.7925268  2.44346095]\n",
      "  [2.7925268  2.7925268 ]\n",
      "  [2.7925268  3.14159265]]\n",
      "\n",
      " [[3.14159265 0.        ]\n",
      "  [3.14159265 0.34906585]\n",
      "  [3.14159265 0.6981317 ]\n",
      "  [3.14159265 1.04719755]\n",
      "  [3.14159265 1.3962634 ]\n",
      "  [3.14159265 1.74532925]\n",
      "  [3.14159265 2.0943951 ]\n",
      "  [3.14159265 2.44346095]\n",
      "  [3.14159265 2.7925268 ]\n",
      "  [3.14159265 3.14159265]]]\n",
      "test\n"
     ]
    }
   ],
   "source": [
    "from cirq import Linspace, Simulator\n",
    "\n",
    "grid_size = 10\n",
    "\n",
    "gamma_sweep = Linspace(gamma, 0, np.pi, grid_size)\n",
    "beta_sweep = Linspace(beta, 0, np.pi, grid_size)\n",
    "# cartesian product of two sweeps\n",
    "combinations = gamma_sweep * beta_sweep\n",
    "sim = Simulator()\n",
    "samples = sim.run_sweep(\n",
    "    qaoa_circuit, params=gamma_sweep * beta_sweep, repetitions=20_000\n",
    ")\n",
    "\n",
    "exp_values = np.reshape(samples, (-1, grid_size)).tolist()\n",
    "estimate = np.vectorize(lambda s: estimate_cost(g, s.data))\n",
    "exp_values = estimate(exp_values)\n",
    "par_tuples = [tuple(y[1] for y in x) for x in (gamma_sweep * beta_sweep).param_tuples()]\n",
    "print(par_tuples[:10])\n",
    "par_values = np.reshape(par_tuples, (-1, grid_size, 2))\n",
    "print(par_values[:10])\n",
    "print(\"test\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(('gamma', 0.0), ('beta', 0.0))\n",
      "(('gamma', 0.0), ('beta', 0.3490658503988659))\n",
      "(('gamma', 0.0), ('beta', 0.6981317007977318))\n",
      "(('gamma', 0.0), ('beta', 1.0471975511965976))\n",
      "(('gamma', 0.0), ('beta', 1.3962634015954636))\n",
      "(('gamma', 0.0), ('beta', 1.7453292519943295))\n",
      "(('gamma', 0.0), ('beta', 2.0943951023931953))\n",
      "(('gamma', 0.0), ('beta', 2.443460952792061))\n",
      "(('gamma', 0.0), ('beta', 2.792526803190927))\n",
      "(('gamma', 0.0), ('beta', 3.141592653589793))\n",
      "(('gamma', 0.3490658503988659), ('beta', 0.0))\n",
      "(('gamma', 0.3490658503988659), ('beta', 0.3490658503988659))\n",
      "(('gamma', 0.3490658503988659), ('beta', 0.6981317007977318))\n",
      "(('gamma', 0.3490658503988659), ('beta', 1.0471975511965976))\n",
      "(('gamma', 0.3490658503988659), ('beta', 1.3962634015954636))\n",
      "(('gamma', 0.3490658503988659), ('beta', 1.7453292519943295))\n",
      "(('gamma', 0.3490658503988659), ('beta', 2.0943951023931953))\n",
      "(('gamma', 0.3490658503988659), ('beta', 2.443460952792061))\n",
      "(('gamma', 0.3490658503988659), ('beta', 2.792526803190927))\n",
      "(('gamma', 0.3490658503988659), ('beta', 3.141592653589793))\n"
     ]
    }
   ],
   "source": [
    "temp = (gamma_sweep * beta_sweep).param_tuples()\n",
    "for i, x in enumerate(temp):\n",
    "    print(x)\n",
    "    if i == 19:\n",
    "        break"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.title(\"Heatmap of QAOA Cost Function Value\")\n",
    "plt.xlabel(r\"$\\gamma$\")\n",
    "plt.ylabel(r\"$\\beta$\")\n",
    "plt.imshow(exp_values)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gamma = 1.257 Beta = 2.199\n",
      "Estimated cost: 2.7012\n"
     ]
    }
   ],
   "source": [
    "gamma_value = 2 * np.pi / 5\n",
    "beta_value = 7 * np.pi / 10\n",
    "\n",
    "sample_results = sim.sample(\n",
    "    qaoa_circuit, params={gamma: gamma_value, beta: beta_value}, repetitions=20_000\n",
    ")\n",
    "print(f\"Gamma = {round(gamma_value, 3)} Beta = {round(beta_value, 3)}\")\n",
    "print(f\"Estimated cost: {estimate_cost(g, sample_results)}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}