520 lines (520 with data), 195.5 kB
{
"cells": [
{
"cell_type": "code",
"execution_count": 124,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "eI_d8C3_Tnyu",
"outputId": "9796450c-61c7-45de-936c-a1132172f3a9"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since beginning of run: 1696830133.24334\n",
"Mon Oct 9 05:42:13 2023\n"
]
}
],
"source": [
"# This cell is added by sphinx-gallery\n",
"# It can be customized to whatever you like\n",
"%matplotlib inline\n",
"# !pip install pennylane\n",
"\n",
"import time\n",
"seconds = time.time()\n",
"print(\"Time in seconds since beginning of run:\", seconds)\n",
"local_time = time.ctime(seconds)\n",
"print(local_time)"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 388
},
"id": "lScQSFsjTnyw",
"outputId": "957e1bed-15ad-40d9-fff2-f141fba21334"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 400x400 with 1 Axes>"
],
"image/png": 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2jK2mnCaS8vJyvOeee1J/d3Z2Yr9+/XD27NlEv//973+PvXr1ws8++yx1rLa2Fm+88UbiNpw6dQo7OjpSn9bWVldEYjb4jSbU888/j3V1dcTpwJkQRQtzArtap5WAoO0nz7xfl9D9S1Lo/kW4GIkdqP1lRI6KEqT2bH5PYyaFXtuboSv2+MQTT+guKm5vb2e2TipnieT06dOYn5+Pq1atSjt+++2344QJE4iuccUVV+CUKVPSjtXW1mIwGMTi4mIcOnQo/uxnP8NPP/3U8BqzZs3KcoU5JRLSwa9OqL///e9ZK45LiorwwIEDtu+tva7IkywTtNN6Wae/tre3Z7mBRMrasgu1v4zcdQAPeCK4ab5H3tUStG1vA+NsSO18Zb1OKmeJ5NChQwgA+Oabb6Ydnz59OpaXl1v+vrGxEQEgK6ayYsUKXL16Ne7atQtXrVqFl112GY4cORLPnDmjex2aFondwV9SVJSVDhxMksm5ABZaJy9N1o+krQcriwTgVSoE7LRdbt6jV7XStG3XS/XluYeLCkkkBpg6dSpeeeWVlue1tLQgAOArr7xC1C6mtbY0g3/dunWm5zp1c/kJrKwH0TJ3zCBCbTE9dx1AHwSoQVbxH5LndvseeS9izbx3MJmcYSUPeCg/OUskblxbn332GRYWFuITTzxBdK++ffviwoULic5lUmtLZ/DW1dWZCtG6ujpH9/cTWE0gP8SM9DKlVBcZb3LRc9cBVCHAQurxHztWgpv36HWMpb29HUcQbCqHyKcaQc4SCWIi2D5t2rTU352dndi/f3/LYPuSJUswEAiYxj5UtLa2Yl5eHq5evZqoTSxqbekNfloWiQgarRuwtB5Edj/pZUopSgiLikp0yYUHmpqaMBwekXX/bdu2UetHJ1aCk/coQqkYUjJT3fTSInGIlStXYiAQwKVLl+KePXtw6tSpGAqF8MiRI4iIOHnyZJwxY0bW7yKRCP7oRz/KOn7ixAl84IEHcMuWLXjw4EF85ZVXMBwO45AhQ/DUqVNEbaJda8tsEKgxEq0QDQJZjMSv2+Nmwg/WA20YxyXKMJF26+2qeXXsbtu2jWpiAU8rwWuLRAWJolRT3bWzauZaGxkjIcT8+fNxwIABWFBQgOXl5bh169bUd9FoFGtra9POf//99w019i+++AKvv/56LC4uxm7duuHAgQNxypQpKWIiAauV7Xo4cOCA46wtL/2/LCCy9UAb+plS5kFv0n6haaHqWU1uiI23lSBCrMxKUVIJ72nIzu5SgN4OpTlPJKKBJ5GoWL9+va11JKJoWxLOoG+RmKfhWglZs5gLvTa6W3zJe9yKZO0aKUoque4yIJJYLEZFOZBEwhleEAkJtIPJjmbn9xhKriI7U+pxV4KblfVAuxyMF1aCyNauSq5lkJ0m3AsA+4ZCVIhQEglniEYkerGQSk0FXyPNLldiKLkKvUypoqISR6vmWVoPXddsTlpNc11ZDyJZCaLAaD6Xgf1tJ4wgiYQzRCMSo1hISVGRqWaXazGUXEVDQ0PKrel01Twr66G6ugYVJYSJJIB0wnMr+EW2EngjFotleRisys3b7TdJJJwhApGo7iiryr5m9XpkDEVsmMU07ApZFhYJYsJ6SKQje59J5hc4cSXrzVercvN2lQNJJJzhJZHouaMUSATijAaTntARIYcekW98xm+xINoxDRbFJFkRVC7CrSs5M3ZktGGdtEh8AqdEQkOQ6bmjgklfqZ3BRGqRsBK+POMzbrOVvCAgFgKaRTFJVi6zXIRbV7Je7MjKfW0Hkkg4wy6R0BKaVsJ/rs3BZJYdw1rQ047PmAl7p5o97XRZO2ApoGnGHqRFQgaarmTt+6OZmCCJhDPsEgktoWnljrI7mMwGIctAPM1JpSfsI5FKjMViGI/HHQk6lZQqKqJUXUt24CcB7ef9V3hZm1Zzd9GiRUTtMGovDeVAEgln2OlwmkLT6lpGOwdaIXMQsg7E04zPpFsbuzAze6irLpS1Zq9HSl4Kcr8IaK/2X3FDArxT363mlFU7eLRXEgln2Olw2kFtVou1nC5mdHovGkSVrbXXYKK0ubbIYZCYENJJaRkxAbGC3zbIYp2uq45RGvW9vEh915u7obw8DID3e5EgSiLhDtYWiZmmRXuxltPFjG5BgxDT4whmriDFUrPXX1wnhmvpXF9PkW0pKpiXF0KnLkfehSHN4hkKJGpombWDV3slkXCG0xiJldC0Y77SEi411dXYMy8PfwCAzwL5Yka3oEGI6cLfPDgdDo801WD1g9uqhSO2a8nvsHJRpVuKm1wTPI/Ud7O5rM7d+vp6onbwStWXRMIZdomEVGjyNrfXr1+P+Zo2AQCWAOD/B+aLGWnCLSF2CRnrOlRm99IPbrdjZsxFZNeS30CSFZf9Xpxns5Eu4uW1p4qdFHxpkeQgnK4jIRFkPFea9y4s1N0Pvlij6YjuVkmPIyiYWGHtzIIwCm5HIlGh+8CvIEnLzrYU7bsc9ayDkqIiDCkKE4vbzlwm9VbwKGQpiYQzWKxs573S3Gr3Rd5xALeIx+MYi8WwoiLq2IJgHdymkWrqt9X5RiBNb9Y/rwYBehMrDGa16FhY3HbmMqm3gkchS0kknMGCSHhbJFb7wQ+55BKq9+MJt1YUbSuMxsJGLxdHsoCdBZfZluJCBAgQ9QWrlHkzOJnLpGOOpYdAEglnsKq1xXMfBiuL5H/+53+o31N0sNL2adTMol13y2vYWXBpZCk2NTVZvi/eln5bWxtWRiKoQPa2uL0hkaUlqjUpiYQzWBEJ730Y3OwHn0tgqe3TWKHup1XudmB3waUTbZy3pV9TXY09FQUBAKs040n7N411WCwUHkkknMG6+i+vALeb/eD9BHvppXS1fRo1s6yuEQ6P8KWLi9eCS16Wvkpac5PPshwA45Ao9x6nQF6sV7dLIuEMEfYjoQm7+8H7Bc7SS+lq+zwsEkUJ+tbFhcheceJl6WvdaDWQ2BY3zbWlKK7Ii/XyAEkknJFrRIKYO9lAWjhLL7VvMZC3w/nCRr1rJBZL1gjv4hJlbPEo4aJaIu1JMtGSV7SiwjF58XDRSSLhjFwiklzdt53EEmhubsZFixYxjz/QcOG0t7dnrc5PkEg7VdKjiVzLNCNBphttLgD2VBSMRiKurssjaUASCWfkEpHk6r7tJHGFLgGn1m4itxicaNluNeIucpyOAHEmpEcTrDPNRLF0tGDlRpMWSQ4iV4gkl/dtJ4krdAm4p5F0XYLXWrZfSsun938zJkqbxKmQntfvgAQs3GhmSQM0SDXnieTJJ5/EgQMHYiAQwPLycmxsbDQ8d8mSJRnmP2AgEEg75+zZszhz5kwsLS3F7t2745gxY2y9AK+JhJYmJsq+7axgJHQTpVT0Caa+vt60X2lo2W7en19Ky3dZhFUZ87HK9diiZemIaNGYQc/aGXvddTiuKr2P5Q6JOli5ciUWFBTgM888g7t378YpU6ZgKBTCo0eP6p6/ZMkSLCwsxMOHD6c+R44cSTtnzpw5GAwG8cUXX8R33nkHJ0yYgBdffDGePHmSqE1eEQnteIYIFgmrydzc3KxbMqUrzuCs6J+beApNTZqWxsuy/xOE3TtN4Cf+Vii495zHtPxg0ZhB++5puqZzmkjKy8vxnnvuSf3d2dmJ/fr1w9mzZ+uev2TJEgwGg4bXO3v2LJaWluLcuXNTx44dO4aBQABXrFhB1CaviIRFPIPnanotWE1m/a13o6623lXhNsNLpNXprIUpq7RqGll2rN4DbwuHtiKYs0Ry+vRpzM/Px1WrVqUdv/3223HChAm6v1myZAnm5+fjgAED8MILL8QJEybge++9l/q+paUFAQB37tyZ9rvKykq89957da956tQp7OjoSH1aW1u5Ewkr64EkOMhigrCazCTXdRpnsBKOZm4x0VansyY1VmnVbvuRxXtgnfloNP9ou6ZzlkgOHTqEAIBvvvlm2vHp06djeXm57m/efPNNXLZsGe7cuRM3bdqE3/3ud7GwsBBbW1sREfGNN95AAMCPP/447Xc333wz3nLLLbrXnDVrVtogUT88iYR1PEPPVcJqgrASqqTXdRNnyCahPyFJoJ7HehVS8CA1lvdwk3DA4j2wyny0mn/SIiGEEyLJxJdffomXXHIJ/uY3v0FEZ0QigkViVWTRzqp0UguD1QRhJVTtXtdJnCGbhMi2fRXJIuFFanYFPum4dKMI0H4PLOOMJPOPpms6Z4nEiWtLDz/84Q/x1ltvRURnrq1MeBEjWbNmDSqQXXahDyQqipJMfjsWBssJopKiVxYJDcTjcduLGUVJ3eXVT6QC32m8xmnCAc33wMpToK3bpdbq0pt/NNet5CyRICaC7dOmTUv93dnZif379zcMtmfizJkzOGzYMPzP//xPROwKts+bNy91TkdHh/DBdnVglWkGjPZvkslkx8KwM0FINcnGxkbNQkB3uxkagaewtqvZi5S6y7OfrAQ+7yQEmu+BlcIVi8VQyZjrNQC4y4CgaGTx5TSRrFy5EgOBAC5duhT37NmDU6dOxVAolErpnTx5Ms6YMSN1fl1dHTY0NGBLSwtu374db731VuzevTvu3r07dc6cOXMwFArh6tWrcdeuXXjjjTf6Iv1XJYK5ALgsqa2QmrF2BzzJ+aSapN55ABUIcAV1ocpTWDvV7EXYvlgUUvPS5UfrPbDIfIxGIlnbYPexqTjaRU4TCSLi/PnzccCAAVhQUIDl5eW4devW1HfRaBRra2tTf993332pc0tKSrCmpgZ37NiRdj11QWJJSQkGAgEcM2YMNjc3E7fHKyJxY8Y6McGtJgipJql3XlfBwbloN8ZDAl7CWhR3lVN4TWoiJSE4Be2yKFZKnNu6XUbIeSIRDV6vbOe1wY/ZBHG353bXeQCv+kZg6EEUzT4Tflm1LVISglvQImUrpS8Wi1FqcTokkXCG10TiBG1tbbo7IpKY4HoThFSTtDoP4AHfCQw9eK3Zq6Cx0JA3CfndqqMNrypOSCLhDD8SSU11NYYUJStYX1JU5CrI6NYi8fumTKLBTeDaq9Iholp1XsKLihOSSDhDRCIx0yIzNRx1+091S1CnGg6pJqm/KVMQAZRzXmA4gdG7dusm8qKEi/ZZRLHqrMDDYuO1q6MWkkg4QyQiIVkbwirXnVST1DsvHB6JTU1Nrp79XIOVxeAmcM07VuHHwolebALHk1wlkXCGSERCsjaEdHGTU5AOdr9onKLCymLgVZCShkbuZQFL0vZnnsdjEzgvkyQkkXCGKERCGpRTA+1aTaoMAEOK4vudEM8F2NkSmFVBSjvrhkiex4tMLTfrniKRKNFcc9M2r7e8lkTCGaIQCanLSk+TCoLzQLsEH+gv5KzCxD7t+hZDS0sLFhWVpP2mqKgEDxw4YHk/KxKiZUV4tXbEzbonRUnso8KqaKoIW15LIuEMUYjEyiJZv369EJtXSTiD/kLO3phYyGllkcxDgGUIMI9Y2JvFvGhaEV5YJLSyDOcxmEeizFFJJJwhCpEg6qcJBgFSdXpGhMNEVovX8MsCOl4gW8j5F2oxEi3crBsiBe+1I7TWPfVUFOopuaJseS2JhDNEIhK9NMEySBR3Ww6AQUURQtsxgh+zd3jAeiEn/awtMyKnbUXwXjtCyyKJRiJpbaa5P4/Xc1QSCWeIRCQqGhoaDE1vJak58d5OlwQibT8rEpzsxuhE2NshchZWBM9MPjfrnrTnsWizV1teayGJhDNEJBIr8/jKb37TlSZFw/W0bt06rKurSxVozKU6SyzgRHDb/Y0dIvf7CnQ3655YP6cXCxAzIYmEM0QkEiPz+E8AWfsaRCsqiAcoDdfT/v37dTOJFi9e7NgVcy7AiUCz8xt9Im9Gq/pnfl8PJPK6Jy/7VhIJZ7glElaBZT3zOACQva+BDZOZhuspQSJBTM8+CmIo1FdaJARwIlxIfpMeU2nDRDZYevUBv1gbEu4hiYQznBIJ60VHeuaxmyAeDdeT1ba6I0aM1K3DVVRUIoUYY6S/3xpM7BHjfaxKZvB5A0kknOGUSHgtOopGIthTUfDHScJwmlZII+Wzrq7O9BozZszAPn2KM8hPQYACrKoaR6tLJAxQXV2DihIUwjKUGXzewo5cU0DCE8TjcVjT0AB/7OyE2wDgIgC4DQD+0NkJaxoaYN++fdTu8+rmzbDw7Fn4VfLYaxnnvJr8d/DgwabXuuSSS0yvYPV7AIBRo0aZXmPo0KHQ3t4GAIUAMD15/M8A8DX4xz82UOsXCX2sWLEcysqGJP+qzPg2CgAA+/fv59KWSZMmwyuvbAWA5QDwIQAsh1de2QoTJ/47l/tL2AAHYst5OLFIeC06yrxPDST2enaaVkgj5bMrRpLtvrKqIVVfX++0KyQIIUL2HM82SNeZPqRF4gOo2r1T68DpfZYDwNUAMBkABiT/PdbZCV999RX861//srzeihXLYezYa9KuMHbsNbBixXLiNjU1bYGiou5p1ygq6g5NTVs0Z+lrwxLsMXToUKiuroH8/HshMWJaAWA55Of/Aqqra2DIkCEWV3CPlpaW5P/YWUXt7e0wfvwNMGzYMKipqYGhQ4fC+PE3EM0DiQxwILach9sYCY1FR2ZaVeZ9ygCwJwBOB8BXdWIzJBoajbTE9evXp60jUe8NAvjnjXCuaK9erxHhMQ7k4ldzyGA7ZzglEhqLjkgyv/TuY5S5VVER9Ty4WVU1DvPyQmmur7y8kKfBdj8GfmmQnpfrGIx20gyF+mIsFqNSGFFUhUUESCLhDLfrSIwmK4kgsJP5FY/HU1lT+rEZJVke21sNzWttWA9+0l79SHp6aG9vz1q4ClCGAIWYyORz/lxela5X4QfLVhIJZ9Be2U66vsRJcTej3zyeupc4GpqX2rB2ovtNe3VKeqIJt65+n4cAaxAgntbvdkriG1+bb+n6WCzGpNAjC0gi4QzaREJqZTjN/NKLzfRMVgU+18uT6Gnz4fAIJn3DQnCzLtTIE9YVj9e4Evy8StfrKYZl0FWRW5SCqZnIeSJ58sknceDAgRgIBLC8vBwbGxsNz120aBFGIhEMhUIYCoVwzJgxWefX1tZmmM+A1TZeLE0isWNlOC03rRczqUxpSf7QulkhW5ufi3l551PtG5aC24nLRlS3nfUeLHFXZE7bhWqkGOgqhgBYCYBrAHCuxXz1CjlNJCtXrsSCggJ85plncPfu3ThlyhQMhUJ49OhR3fMnTZqECxYswJ07d+LevXvxjjvuwGAwiB999FHqnNraWhw/fjwePnw49bEzmGgSiV0rw03mV6briPfmQqIhXXBl1ppSMH3ty+OoKD0xEonavk9XP89Fu7sW2nsGa9IT3W2nH3Dvg127QmbvH8KiDpkZzBQDPWWvLWmRaM9XADAWizm6PyvkNJGUl5fjPffck/q7s7MT+/Xrh7Nnzyb6/ZkzZ7BXr164bNmy1LHa2lq88cYbHbfJK4sEkW65aRGD3DyRrs1n1pp6GgECGlLp6qOKiihxH3UJ7rK0a6h/001rtVYIvA46W0FvTCb6alfac3npnjOz6PQUQ3VRsNZCCUKiCrdIyFkiOX36NObn5+OqVavSjt9+++04YcIEomscP34cu3fvjv/7v/+bOlZbW4vBYBCLi4tx6NCh+LOf/Qw//fRTw2ucOnUKOzo6Up/W1lZqRILozMqgGZj2e1lwp+gS8nNNtPTszDY7RSUTgkXBzIKIib8VKoLbSCHYtm1b1nsV3SJREY/HMRaLYSSin57ulXvOqv/UDeZUxbAZ3BVO5YmcJZJDhw4hAOCbb76Zdnz69OlYXl5OdI27774bBw0ahCdPnkwdW7FiBa5evRp37dqFq1atwssuuwxHjhyJZ86c0b3GrFmzMjQkoEokPDa1ES1DRxQkihb2NNDSN5kKjUgkanl9q+rH2sWZRiB9d6pCsG3bNlNt3W8uzUxFx0syJLHotIrhsmT/e70fOwkkkRhg9uzZ2Lt3b3znnXdMz2tpaUEAwFdeeUX3e9YWiQoWlgHr0vV+R3t7O0YilQaC6QFToUEitNy4kpy6b6y09W3btmE4PNL2dUWBl+45EhKzsyCYZK7zUgJzlkjcuLbmzp2LwWAQm5qaiO7Vt29fXLhwIdG5ou2QSFIuhXXper+joiKadGF1aelW5dVJhJYb7dmJ+8bqfpmVDMLhEcRzRBR47Z4jtehUxTBaUeEoQYa3EpizRIKYCLZPmzYt9XdnZyf279/fNNj+29/+FgsLC3HLli1E92htbcW8vDxcvXo10fmiEInVQHOaLnwuwijOcM01/wezKxf3QTvBcieuJKfC0lxbF6OSgRNkKkteuufsJqk4dV3zVgJzmkhWrlyJgUAAly5dinv27MGpU6diKBTCI0eOICLi5MmTccaMGanz58yZgwUFBfjXv/41Lb33xIkTiIh44sQJfOCBB3DLli148OBBfOWVVzAcDuOQIUPw1KlTRG0ShUisBhqv0vW5hEz3olHZDkUJEQstJ9lxXYTwWwSox65V3ubuG2MCetxTLd4pjNx7Bw4c0F1IytO60o4V2oVPvVACc5pIEBHnz5+PAwYMwIKCAiwvL8etW7emvotGo1hbW5v6e+DAgRmTPvGZNWsWIiJ+8cUXeP3112NxcTF269YNBw4ciFOmTEkREwlYEwnJoCQZaLlmkXiVMJCIo+hnD9kBqSBpa2tLWkLaMawgwDgEWGj57vS0deOEAjHSfo0geryHVRqyF0pgzhOJaGBFJHZ8oqQDjWbpet5QicMqC8npde0SEq806erqmmQ1ZG26cG9MrGsJWFpCehaQcUKBuBYJiXvP61X6rO4vLZJzALSJRBVs0UiE2CdKOtCc+Ge9ThXW0/ISQvRpV5NV1BpTWliXCQFi943fKxlYZWf9/Oc/95QcWQf9eSuBkkg4gxaRGBV3ayfUQOwMNBJtmlTQsiYaPS0voZHXIEAzJor3zbU9Wb3WXklgXbjQuVvDb5UM9AV1G2ZXCfDGXcc6DZnH+jItJJFwBi0iMSruVmPiqtLC7kCzIgArQctDoyfRyLVxA9J6RV6njJKC5PndttVPlQyyragyTGTRLUerBaN+t0hU8Hpfkkg4gwaRWLqmCCwSFVYDjYQARPFHW2vk07HLSgliRUWUynVFCjZ3xUi0KceJGIlI1hMP6Nfe0o7RmmTfeOOuY+EuzNwbhxfpSyLhDBpEohcsb4aukgrLLFxVdkBCAFaCdtGiRVy0L7JS4vbv7ReLBDEhPKuqxmUITwWrqsYJ6YbiIey0u32mj9F2BKgyVZJYgqa7MNPVrWRY4KwrUkgi4QzaFkkbJNxZ2kGjUBo8pALU6rz6+npuGr3R3t0JgeH83n4LNsfjcayvr8f6+nqhiE6FkaWrVyySBkjGqFf9RMP9pHV1VwFgbwCuFSkkkXAG7RhJGWSXme6tKFTKTNtx6ZgJWp4avb47Q8FE1pbze/st2Cw69CzdhEtOYda/flMGSKHOr+kA2JDsO97rvySRcAYtImlvb0/tVMhq0NghACtBy3sSa7U8mvd2oz16nRptByzbau2CfBVZxNDslMz3C9ra2nBkOJz2TAoktubVdi7rihSSSDjDyx0SncCuENYTtM3NzRiLxbKK/vHS6L22JvywBkUFj7aS7a/OLgZFUjLfDpF6qSDUVFdjb0XJ2viqzKFy6fRZJJFwhpc7JGb+lmTAuBHCekIpEoliLBbjMukyn9Gr1FU/rEFRwaOt5EkRbLPi9J5VUUJZ9dGMxrvXCkJjY6Pp/J8L5IsR3VYLlkTCGbRXtpMsLNQKVKcDxokQ9kqAej3BtfBTxhfPtuonRagLR9n3kfGzateamI9ZrxWEEUmXlpFHws4cd1stWBIJZ9AmErOFhXqkUVJUxKW8tJcC1OsJrgXNNSisXSg818voJ0UEMFFYkn0MTf9Zyces1wpC1/2NLZL169cTjRcatbkkkXAGq6KNehZDppYx12Lg0Rz8Xi3i83qCa9uxZs2a1D7cbtqjZ2GFwyOplz33ou/UcdvU1GTLinRLqvrPSj5mvV6kqt6/ChJZm1qPRBAAR4bDtq/lJtYqiYQzeO1HoqdlrEkOjMwBsyl5vL6+nvr9eQqltrY2DIdHmE7wcHgEUxeXntAvKipxlTWmXz8siABKzqXI0qi0QIrsZyXfd4X1+LYiSvX+T4P+OjI7Soa0SHwIXkRitPpdO2D0FjPSXAHLWyhVV9dYbnGrKEGmQtFtADcTVgKL9vPwyHBzY03QdFvqPasd0mcxvu0QpTY++ioAPgCAQUVx5KZ2Wy1YEglneGmRICTSAoPJgcJ6BSzPtNt0gVuDiS1tM7e4rWFqEVkJfVKftRbWqbIPED2PXeFNM8ON1t4wVv3r1NWnfVY7Y5bF+LZDlDQr/Lq9liQSzuBBJGqQXdGQhqplhBQFS4qKUoOFR7yER9ptusBtT5KG1toamTzOzofNwm9OtnjP+NpeZrDR3hvGqn/D4RHU2m5nzNIa307dZTTnl9NrSSLhDB5EopqpTyetDj0tQ61/tQkSsRO1YrBf92TXn4TxlMbetTaha1LSzoJi5TfXT5Uls7C8zGAz3xvGvG/03g2PMvlegkcAP7Nfac0BSSScwWPP9kxLIw4J/6l2ojU2NmZXCAXAhQQTUtRSH8YFG8tQ68OuqhqnkwU1gkoWFAu/uX6qbBUCLDS9tpcZbE4XHVpZUIk91oM6pFrlSwVIC5bvy2gpAK34qCQSzmBNJHb2Yw/l5aUXewTAQHJA6YF3xVa7MAqeZra3qmpcUtgvRNplxFnGhZqamjRZadbX9jJF1WkZFCsLatu2bagt7Jj41CTfpf8skkyljFWCSuZSgDJIuL1pxUclkXCGFxYJQnrsw+ocI82cVcVW2hZOpp9X+7d+UJ6+24dlXIj02mJbJK9mCUnS9nZl5z2gex0/wEgpO3DgAHVFJHO+Z2Zv6skIu5BEwhluiIRU4Fql8jlZgMSiYqsXgeAuTXmTZ0KWJ7xcF6J3bzPFg9SC8roIJw1YWV40FZHM+W60nsxNfJQ5kXzxxRf40UcfZR1/7733nFzO93BCJHbrY1ml8jlZgMSiYqsXgeAuQpye8TzNyWcwz4LyG7wUukb3bmpq0hWSdi0or4pwugVvS9H3Fsnzzz+P/fv3x+HDh+OVV16JW7duTX131VVX2b1cTsAJkTgtqGY20ewuQKJdsdXrWlxdCxcXYnaqsEK9/IjX4C10tdaznXvzsKCculJpuWC9iF1lznc1RuJ0AWImmBLJ8OHD8ciRI4iI+NZbb+Hll1+Ozz77LCIilpWV2b2cIzz55JM4cOBADAQCWF5ejo2NjabnP/fcczhs2DAMBAJ4xRVX4N/+9re078+ePYszZ87E0tJS7N69O44ZM8bWwLJLJDTKF+jByQIkmhVbvQwEd2nKCibWNfTGzLiPn/ztJOCVaefWXcnSgnLatsbGRltJDlbwQonSm+++ydr65je/mfZ3W1sbVlZWYl1dHReLZOXKlVhQUIDPPPMM7t69G6dMmYKhUAiPHj2qe/4bb7yB+fn5+Pjjj+OePXvwN7/5DXbr1g3ffffd1Dlz5szBYDCIL774Ir7zzjs4YcIEvPjii/HkyZNEbbJLJKw3r7KjLW7bti2Zfpm5wMx+xVYRiiv+9a9/9bwNrME7DkXLXUlzkV/2TplkbdNfUFmFAE+7tpK8il2ZJaK4AVMiufbaa/Gdd95JO3b69Gm89dZbMT8/3+7lbKO8vBzvueee1N+dnZ3Yr18/nD17tu75t9xyC95www1px0aNGoU//elPETFhjZSWluLcuXNT3x87dgwDgQCuWLGCqE2iWCRG9zLa3TASiaZNqHB4BG7YsMGVkPK6QKDXFVx5gEUcysi6EUE5UKFPAgomVtUbt82KeGiV2smFhAEtmBDJ8ePHERGxtbUVDx8+rHvO5s2bSS/nCKdPn8b8/HxctWpV2vHbb78dJ0yYoPubiy66CH//+9+nHXv44YfxW9/6FiIitrS0IADgzp07086prKzEe++9V/eap06dwo6OjtSntbXVFpEgui+oZgW9YP7Y667DqqpxGROxDAF2ZQmjeDyOixYtwvr6elsTy+vJJJLgYwHaz2dl3YhEzMYVk6t02xaLxXSIxypL0f0zNTQ0YF1dHa5fv57Sk3sDJkQyfPhwQwLhhUOHDiEA4Jtvvpl2fPr06VheXq77m27duuF///d/px1bsGABfv3rX0fEhOsLAPDjjz9OO+fmm2/GW265Rfeas2bN0hmc9oiEZnE2PegF88+D/GSqpp421jWh3BbiQ/Q2+4aWVSTian/agt3KuhGFmMkTQ7qORSLRjGfLzOrLzFIkK5ZpBJF28aQBJkRyxx134IABA3Dv3r1px3fu3Inf+c537LfSAUQhEhoWiQoWAlfPddYMVtpYPDWhwuGROmXTgxgOjxBKqBrBrVUkskCgKdjtLBb00l2JSFoxuattkUhU59nYlu/3sgYaCzCLkTz88MNYVFSEr7/+OjY3N+PNN9+MiqLgd7/7XceNtQNRXFuZ4FVGnhR6wXx1wZL5mpGuQnldk60NM1NpRRGqVnBK0qILBFqC3U+LBUmKO2rbFovFDJ6tCrPregXR7YZiolhuNME02P7YY49h9+7dsVu3bjh+/HjL1FvaKC8vx2nTpqX+7uzsxP79+5sG2zOJbvTo0VnB9nnz5qW+7+joYBpsZw1nFslczM/vo7MbIbuSIyJCRIGQ6WKjJdjXrVuX/P08omf1erGgGYFmts34PS7EzLpeNLY4tuNyFNFlqgcmRHLkyBG89957sUePHhgOh/H888/HlStXumqoE6xcuRIDgQAuXboU9+zZg1OnTsVQKJRa2zJ58mScMWNG6vw33ngDzzvvPJw3bx7u3bsXZ82apZv+GwqFcPXq1bhr1y688cYbmab/2oHTQacXzO+KkWRrY6owShTQUyegeEKVNUQKLlu52JwKdv3sJzXpQtwaV3YJ1A7xuIWVAtLQ0ICxWAwrKqLE7fcaTIikR48eWFZWhi+//DIiIq5duxYLCwvx8ccfd95Sh5g/fz4OGDAACwoKsLy8PG11fTQaxdra2rTzn3vuORw6dCgWFBTg5ZdfbrggsaSkBAOBAI4ZMwabm5uJ2+OWSPTIwm4JlUzoBfPHVVVlZW1VVEQxFoul3btrAqr7fngvVHlBJIuElYvNOPupS6EQVbghkhMoC5ecmWJnRFxd1aqVZD/7w7pnQiR6bp7t27fjBRdcgP/xH/9hr4U5BqdEYkYWTkuoZEJv0llNxOwJ6L1Q5QkRgsusCI1k6+BcAw3rgyQBw2jLA0UJIcBc380lrtV/Dx48iJdeeqnby/gaTonEiCwqIxHMjHEg8FuwqCIej2syuLwTqrwhQnCZlYtNJNedn2DHOlSJq6GhQUMe/ut37mXkRTaDecAJkVitbs/MukKgu2UuaYqrCELVK3gZXPbKIhFRM/YaTvssnbT91+9yPxLOcEIkVvW2WFskdv3vLIKTfshc8RKsXGwiuO5owM4YcjPenFpx2QSkZkD6o98lkXAGC4skWlHBrISKl1qpyIv9RAMra5D2dXkrBXbGEI3x5ma+pJP2Lkxkx/lj7Esi4Qy3MRI9smBVQqW5uRnr6uocaVg0IPpiPxHBysXm9rpeKQV2xhCt8ebUitMj7UgkO1OSFDxJWxIJZzglEhKyoCVEjNcOtHOzSKR/PrfghVJgZwzRHG9urTgapM2yNp8eJJFwhtt1JDyCusZrB8q4+WtlxhBb8NRWaQlpu222M4ZYjDevEjBoLQewA0kknCFaiZRMkNYpYu2WyBWLRLREAVIXE812uxXSem2ORKKW488ri8RL8Ny/SAtJJJwhOpFYTfq6ujpu/lo/ZwyJmihg5WJi0W63Qrq6uia5UC89+FxUVGLZLjtjyM/jTQXrHVWNIImEM0QnEt6bIZnBz+tSREwUIHm37Eut2BPSXW0uw8yCoABBrKiImv7ezhjiNd5YWqnSIjlHIDqRINLVzNwIJq+0ercTXVQ3iXG59IS1uWjRImbtdiqkuyxkd+2yE69gFdvgNZ5Z76iqB0kknOEHIqGlmdFwafDU6mlNdFETBSKRStP3UV9fz7zddoV01xgSrz/tgtd4Zr2jqh4kkXCGH4hEhVvNzI1A9UKrpzXRRbBIMq2qbBdR+vYAFRVR6u2m5cKxIkA/BMK9GBM8s8YkkXCGn4jELoyFl/3Jw1urpz3RvQrcGllVXW6tXZi5iyWAgrFYjFq7abtw2tvbk+XVg9z7kxZEtVJpQRIJZ+QikZgJDvdBVj4aHO2JziNwq6fxG1lVkUg0oz/jmKgyOzetP2m0W68NihLEcHiE4/fW3t7uq42eMiGClWrVPjfWiyQSzshFIjFzCbkRTDy1elYTnYV7wYi403eszH6GSCRK3J9O253dj21ZFpD2/dsVYF5v4esGIqYX01oFL4mEM3KNSEgFsBMBwDv9V8SJrgcj4g6HR5haVbFYjHl/Zlt2ahXb9LZWVY3zbWq3U5iNZ68WrtJaBS+JhDNyjUh4+H55aaF+WLdCVnmAPqkbtUXdlEm9Xnr7zNuqKEFdKzbXoe1/Lxeu0lxzIomEM/xIJHpBdH3BIZ7v1wm0u9aJ5kaxIm4eu1SmCz8lSwhWVY1LtuEB07YCTM+ZMaOFHevCy4WrNFfBSyLhDD8RiZ7/tG/R100Eh9guIVKIWt4E0doiaWpqYt72LuGXvdpc321lZD29ysyK9QJ2x41XSpjWkpQWiU/hJyLJ9J9eCQomUjC993ez9CmLWN5EC5JYDit3YJfwm2spBOPxuKGFlLBkcseKRbQ/bninBOsphiVFRVRWwUsi4Qy/EEmm/7TZQrtUBQdrV5Bba8GKgPzgqvMyltMl/JYRCUGjtrqxYkWrqIzobNywGGtmfaMXWA8pCpYUFaW9H5m15QP4hUgy/adrUgPN2wVVTq0FUgLyYiGkU6HoRSqsHYvErK1OyFBkl6PTcUMrU9AqjdcqsL5+/Xq5jsRP8AuROLFIeLXJrFaUmgBAulAvc8LyskhEFopWyI6RpJdbISnvrsIOGYrscnQ6bmhZl1ZpvKzLy+cskbS1teGkSZOwV69eGAwG8a677sITJ06Ynj9t2jQcOnQodu/eHS+66CL8+c9/jseOHUs7T/vC1c+KFSuI2+UXIkHMriLaFSPxJqhupfV1fZS0SWm1UC9zkvNYT2IlFEV036hIF35KRt+XoaKEqI8JP7gc3YwbN9YlSRrvunXrEABwnsk5bpCzRDJ+/HgcPnw4bt26FV9//XUcPHgwTpw40fD8d999F//t3/4NX3rpJdy/fz9u2LABhwwZgj/4wQ/SzgMAXLJkCR4+fDj1OXnyJHG7/EQkelVE9bK2eGnR1msoXk1+1xsBqlLCORweaUpAmdqYUy2RVPhbPUckEvWFpaJm/QA8iIlyK3Fmwt0Ptaq82s/EytoYGQ6ntakMAHcB3fLyOUkke/bsQYBEKqSKtWvXYl5eHh46dIj4Os899xwWFBTgV199lToGALhq1SrHbROZSIwEYaa25GWZCj2tL0EcNTrEEtf83742S/qcdt1UVkJRUXoaWioigUS407Ks/GCRqGC5n4leHES1uPUsEiVJFlqXVzB53GlgXQ85SSSLFy/GUCiUduyrr77C/Px8fOGFF4ivU19fj3379k07BgDYr18/LCoqwpEjR+LixYvx7Nmzhtc4deoUdnR0pD6tra3CEQmtejtuQSJ09LS+hPXRniXIElpy4v+XXDIkuZKavrvKru/e2rKaJ7ywRLR+DtpFFv1SwoYVzOIgeptZBRXFkGDUADst5CSRPPbYYzh06NCs48XFxfjUU08RXeP//b//hwMGDMBf//rXaccfeeQR3Lx5M+7YsQPnzJmDgUAA//CHPxheZ9asWRlCD4QjElr1dpzCSeA5Ho9b7ugHsA0z9/nW+vSLikrwwIEDrtruVFPWE4qK0jvZPnHdN5kwEu5FRSXUA+Mil7BhHdPSxkGaIZFFGdeQQlNTU5YyOCLp0uKxf7uviOTBBx/UFcraz969e10TSUdHB5aXl+P48ePxyy+/ND135syZeOGFFxp+L7pF4tUez1q4ycYxdnVVJUkkmHbdxHfDEWAeFW3Wqe9eTyhGIqoGL777RoX+c7DdiEqkCsB6SlA4PDLNrU4D6jirypB36t/qONP2Dc+57Ssi+eSTT3Dv3r2mn9OnT7tybR0/fhxHjx6NY8aMIQqiv/zyywgAeOrUKaJnEC1Gwjot0Apufd/6ri5tJpH2us3YVf8pTnwPlu3PFIpW7htRs7m0z+GHwDgt6ClBCeVFoWotNTc3owKAvZPEoHoOekMi3mE0Hnjt3+4rIiGFGmx/6623UscaGhosg+0dHR14zTXXYDQaxc8//5zoXo8++ij27t2buG2iEYnXFgktoaMVZPF4HOvq6jTXzd4TA2AkJnYLtL6HlfCm6bs3ct+0tLQI69bJhJ8C425g9ZyKEqQWv3E6T3nt356TRIKYSP+96qqrsLGxETdv3oxDhgxJS//96KOPcNiwYdjY2IiIiY4YNWoUXnnllbh///609N4zZ84gIuJLL72E9fX1+O677+K+ffvwqaeewvPPPx8ffvhh4naJRiSI/LQWRLrb8epdT3u867rZe2Ik/i4zvYe+22JEltuChe/e2FIRP5sr0fcKJtyImS5HJWeIxHpd0wPUiFONBzr1HLB2B+YskbS1teHEiROxZ8+eWFhYiHfeeWfagsSDBw8iAODGjRsREXHjxo0ZGmvX5+DBg4iYSCEuKyvDnj174te+9jUcPnw4Lly4EDs7O4nbJSKR8NBaaG/HSxKgr66uSWZqma/ZMEJXuxZiIuZiThTsCyX6Q8PvErDpfab+nSuurfRyMdnraBLrmtw9b2ZGpZexTDPkLJGIChGJRAVLrcVMo3ai0ZNo6O3t7US7BurByqIRaUW/aII5ve/UveHpxKREQltbGxYVlWSQZRkChJJjxv3zajMqqyARE+HhObALSSScwZtIaAZnnV6L9na8djR0p9p8l/De5Lk14DeLBNEovTmI4fAIIdvrBMaB9mIEWOhK2Whubk65s1QrpB0AazK8JV6s99KDJBLO4EUkNBcZur0WbY3a7vWcuM66hPd0IawBvy3Gy7Yy02tyiZooQAqSLY+dPKPeXMuMi7yaPF5fX8/o6exDEgln8CISmosM3V7LrkZNe88QPddZRUXUcpKTxFh4add23H8ipQinb24lfqIAKayUGadCXjvXNoHYcREtJJFwBg8ioZnSS+taJBq1nRXudjX0trY2w2KIRoK3S3h7W/VYCzP3n4il6f3oliMBq02pMudaDYgbF9FCEgln0CISI+HX1tZGtTQCrQWLJBq1nRRXuwF6o2tnBkv1rtHU1KSpICyGgNaDiKXp/ZYoYAe03Y16c60dslezW7mVvXjPkkg4wy2RWMUraqqrLYu1NTQ0EA802gsWjTRqpxoeSYCerEiitctFpNIcmRC1NH2uWiSI9pUZUpet0VxTN24zgpfFVyWRcIZbIjGLV2gHYg0A9sk0iR3uz8xjwSJLzdV64dga3ws4kUvT+y1RwC6sFAw7Lkc3c83L4quSSDjDDZGQaCyqaayXKti7sBBDimJ7oNlZsMg6RdgJrC2SuOaYP10uIpemF7lqLw/Yddk6sSq8LnUkiYQz3BCJVbwiM+8cIVFq+gHNoHQz0FgHellqrvpVgoOYWECWGy4X0UvTi+waZAWWLlstvC6+KomEM1haJPF43NA0Zr03AY1aUCw1V71rFxWVoKKEmBCXF8iV0vS5BF7JBtIiOcdAK0Zi5EM1Mo3NtuNk7TqiFZCnAe21/ehyIXEdqpt+qcHZXI9RiAyeyQY8i69mQhIJZ7glElIfqp4wZjXQ/J7i6QeXC6nrUO+8qqpxWFU1zleE6QYiLchE5JdswKtkvB4kkXAGrXUkToQfq4GWyymeZuApsEhdh2bn+YEw3UDEBZmI/JMNvHjPkkg4Q4TqvywG2rnkPmElsMj2VjEm6nOV0FWIvmdLLhO5JBLOEIFIWID24iyRQVtgWRETqevQ7y5GNzjXSdRrSCLhjFwlEhU0F2eJCBYCi6S0ibRIzOEVifpZIaIJSSScwYtI1P0MrMoq8IB2sonufrACbYFFKvxJXYcsXYw8hKaIC1r14HeFiDYkkXAGayJpa2vDcVXpW5wqAPjta67BWCzGlVT0JpvfNWbaAouUmEhdhywCuzyEpugLWo3v5U+FiDYkkXAGayKpqa7GUF5eqgzKQgAMZAhyXimB2ZNNjE2i3MJIYEUilWnaNEmRPrUaASkxkQZsaQZ2eQhN0Re0Ina9z4aGBt8rRLQhiYQzWBKJ2X4GvAu56WvuueHDN1olb/a3VqDpW2oBBFjIXJO2CysLzE4laaf38HpBq7Fl7W+FiCYkkXAGSyLJrLfTDO7ra7ltS/Zkq0JRNolyC1VgVVREDfbuLtPVrvW077y8EGq3oxXF3278Hnchre1zRc82y35fc3NCIaIJSSScwYpItG4SlTjWJCe4F4Xc1q1bZzDZFlITQCKAvLJwlwZvdr4IyRFaGD9fWZIs3bu7RM42s35+/ytENGBHrp0HEsKhvb0dJk+aBGsaGlLHfgwAnwHApcm/XwOA2zS/eTX57+DBg5m16+zZswCgQD7cA52AABAFgFchHx6ETgCor6+H/v37w+DBg2HIkCHM2sEaLS0tyf9VZnwTTf67HwCGpP7eunWr6fn9+/d33R/xeBxaWlqo9O3QoUOhuroGXnnlXujsVN/jSgB4GwCWQ9fIug06OxEaGibDvn37TO+b2T79e7wK+fm/gLFjazwdH8bv988AUAYAk1NHRo+OwooVy/k0zM/gQGw5D9oWid5mNiFIZGpB8t8g8N/zWdXkrsywPtS/RdK63YC2ReKmX1hlV+nFhMCBK8qsfaIW0LR6vyNGlAvXZi+Qs66ttrY2nDRpEvbq1QuDwSDeddddeOLECdPfRKPRrMny05/+NO2cDz74AGtqarBHjx5YXFyMDzzwAH711VfE7aJJJFalox999FF84oknMFpRkfZMrLO21OyWaEUF9snPx7kAuAwA53IiMd4w3+sk2+3BKk2VdXaVGhNySoYk7ROxjIjR+yoqKuGaAuxmHQ/rNUA5SyTjx4/H4cOH49atW/H111/HwYMH48SJE01/E41GccqUKXj48OHUR9sxZ86cwSuuuALHjh2LO3fuxDVr1mDfvn3xoYceIm4XTSKx2swmFoulzuUxQfX2jHayta/fQJLFpdVU9c6vqIi66hfecQa7ZChyHMQK+vu8VNp6HjeC3M1e7Lz2cc9JItmzZw8CADY1NaWOrV27FvPy8vDQoUOGv4tGo/iLX/zC8Ps1a9agoih45MiR1LE//elPWFhYiKdPnyZqG0+LJBqJOL6uk0FvtGd0NBLhpmV6WbIik6ytdpSMRNItYDduEavMp7q6Ot12OO0vu64o0TOzSKB9n6TPQ0OQu9mLndc+7jlJJIsXL8ZQKJR27KuvvsL8/Hx84YUXDH8XjUaxb9++WFRUhJdffjnOmDEDP//889T3M2fOxOHDh6f95sCBAwgAuGPHDt1rnjp1Cjs6OlKf1tZWakSCiBiNRLJjIABYBvZTfN0Meq93aPNbyQrabih9jb8NE+617D6hRWSklq6fLRI9kD6PW0GunVfNkMjEjBPOK9I5SUP5ykkieeyxx3Do0KFZx4uLi/Gpp54y/N3TTz+N69atw127duHy5cuxf//++P3vfz/1/ZQpU/D6669P+83nn39uqlHNmjUrbbKqH1pEEovFUoH1lPAHwF1gP8XXzaD3es9oP5WsYCVUs91N+im6VVXjkq43Oum7es+nJ5iqqsYl18t0ucPy8kJYVTWOijDjbY1aufdoKFfqvKrKmONVBPOKxPVNy+3lKyJ58MEHdYWy9rN3717HRJKJDRs2IADg/v37EdEZkbC2SNTBOk+jrTixBNwOei8tEr9pu6zcPPrZVUbZZPT7y8oqTOzSGMhoXwB79y42/A2N+7KClXuPhnLV3NyMCmRXp+gNiYxMNxaJmgxDw+3lKyL55JNPcO/evaaf06dPO3ZtZeKzzz5DAMB169YhojPXViZYLEiksYUujUHv1Z7RfvO/sya+eDyOdXV1pn3Cor/MrML0Z44jwJrkv/pWkx3LyGtr1Mi9R0O5cnsNozlZGYlQVfx8RSSkUIPtb731VupYQ0ODZbA9E5s3b0YAwHfeeQcRu4LtR48eTZ3z9NNPY2FhIZ46dYromiyIhMYWujQGvVd7RvvNIkFkX6nWen0L3f6yul99fb0Oebl/b6K/e7fKlVsFz2hOxmIxV9fNRE4SCWIi/feqq67CxsZG3Lx5Mw4ZMiQt/fejjz7CYcOGYWNjIyIi7t+/Hx955BF866238ODBg7h69WocNGgQVlZWpn6jpv9ef/31+Pbbb+O6deuwuLjYs/RfxHS/sNsUX1oWhRdrAfy21W97ezvVrC09GK9vUZKWQPZ3FRVRR/eysgr1qxy7tyRFt0bdKle0XMaZc5K2KzpniaStrQ0nTpyIPXv2xMLCQrzzzjvTFiQePHgQAQA3btyIiIgffvghVlZWYp8+fTAQCODgwYNx+vTpWR3zz3/+E7/zne9gjx49sG/fvvjLX/7SkwWJLPLDvbIoaEDUldF60PPpRyLu1pHoob29PWs9S4JAChCgEDMzuoqKShy3gcQyyCa2x3PeIlHhRrli5TKmed2cJRJRQYtIWOaHi7i6mBR+aDsvn36XkJ2HXTEJRL3CmZGIeyKzsgr1yL5btx6YWfwQIIhFRSXU7msnm0vErXNZKXg0ryuJhDNoEInXazYknIOnBm3l9qmvr6cqNEmtwuxSK+mWkfo3abu2bduG4fCIrPu2tLQQW6k0M79YkRErJYnGdSWRcAYNIvF6zYZbiKj18QJPn75Xbh9SwZTeF9pMLrK+0BP+4fDIVEULO5YfDSuRVzkSESGJhDPOZYvkXJ5oKkSvicUTbndfJE83Nu9nWu+EVzkSESGJhDNox0h4r9lwg3N5omnBU7iLnoSg1xeKEjIteonoNN1Y3/KjYSX6VbmjBUkknEGLSGgH4Fi7m871iaaFF8Jd1CQEo8rJVm4mZ+nG7CwSv7ub3UISCWfQXkfiVkDwcjed6xNND6IKdy9gd68TZ+nGxpafWR0wEpzripIkEs5gtWe7U/ByN5FMtHM5CC+RgB03k5N0YyPLb1xVFZ4H+Wnnngf5OK6qirjtfnQ304IkEs4QiUh4a1FGE23sddcxs4rskpMkM29hx81kN93Y6J2q95wLgPXJj1mpdqMx4ucFvW4hiYQzRCISVu4ms4kWTRaL0060cVVV1K0iu+sC/LafCSuIQKR2kxHcughJt2IgdQOfiy5LSSScIRKR0LZIzCaa3nfRSAS3bdvGxCqyuy7A6wqyXkMkIuWdjKBuDpemyED25nA11dUYVBScDoCvMnQDO4WXSoAkEs4QiUgQ6fh11QFstr+BUSxmRDhM3Sqym4Xjl3pNLOEVkZoJPx6aPel21Y2NjbpWy0KXCg8NiLA+SxIJZ4hGJG78unoD2GhCOv3OyQS1CtjGYjFb59POKBPBfaSFF0RqZQHx6iOSXQQREUeGw7pWC8lOhawhwvosSSScIRqRqHCi/WkH8LLkhDKakGbfjQyHqWa7WAnGSCRq63xawkwk95EWtImUhAS60m27LKC8vBBWVFzLtY9IswmtFCGvlAJR0o4lkXCGqERiF5kDuNmF1dHU1ETdNI9EophdVbYPGhUE5LHaXNQ4DC0iJSVK8/sp3PvIyr1rZbWMCIeZtc0KoqzPkkTCGU6JRDR3iN4AroGEua83Ia0ma3NzM9bX12N9fT2VVN3EDnDppdIBahBgl+4EYx3gFT0OQ4NISYmya9V5pgW0ybSPrGpvOYWVe9dK61eLRHoBaZGco7BLJCIE0vSgN4DboSvTJbOtRpO1paXF9vOR9InxXhxd7i29e7AK8PphJz83RGqHKPXLl7QhwCDTPmLt7jJ79yIvNhShbZJIOMMukVgF0ry0VIwG8Mirr8a6ujpcv3591m8yJ6uTQCHpb/S3mk24t3i7lES3SFQ4JVI7RJnoCwUBemveTRkC9DTto4RS4I1LUOTFhiK0TRIJZ9jpcMvUxIoKZoPHjKDU7/RiGyVFRcRtcmKW2/mN3r7oCfdWuycCXOSS7m5hlyirqsYhQCDj3SxPvh+9febLTK/LQ6Fy4n7lCadKAI2+k0TCGXY63CyQpgBgb0WhnvJnd1FhTXU1NjU1JdaRRCK2rAsngUK7v+nSlJdhl3vLG5eS6CXd3cIOUer1ReI37Uky0R5XMBHbyrZ0YrEYc21cVPeyW9B8LkkknEHDInkc6K+9UGHmNjL7jrV14fQ3IrqUcrWEhhOijMfjBjGTOAI8kGGtZL8/u8qLE4iwToMFaD6XJBLOcBoj0cYheiqKbU2eBCT58kbfqRsJ2W2Tk0Ch3d/ksktJRGhLwpMSptk7MvouEolSU6iM3DuiZEXRBu3nkkTCGXaJRC+QVpksfEh7cFu5jcy+U7VKu21yEii0+5tcdymJBicLL83ekdF3iRRvdwqVlXtHlHUaWpiRHilx034uSSSc4XQdiVG2E82UPzcWSTwed9UmJ+4eu79xcg/R1u/4AW4WXpq9o8zvaGjVJFmRolgkRqTnJIVeWiQ+hyhb7RoJSDMysCIKEdIQaUGkAKufyIx3TMqN8kIqTEVYp6FtRybplRQVOYp10HwuSSSc4fVWu1YC0owMSIkiF4LJXgRYMwlD1NpcZuC98NKN8kLq3hFBQbIivXkOLAuaz5WzRNLW1oaTJk3CXr16YTAYxLvuugtPnDhheP7BgwfTOlT7ee6551Ln6X2/YsUK4nZ5XWuLVEDacTHkGni7M4wIo6pqnJC1uczgVZacU7elnffs5bi3Ir1lFmRoBhrPlbNEMn78eBw+fDhu3boVX3/9dRw8eDBOnDjR8PwzZ87g4cOH0z51dXXYs2fPNAICAFyyZEnaeSdPniRulxdEomq6DQ0NXAWkX8E7wGoUU0isnxAnbZkUfsqSE8VtZQUWFglN5CSR7NmzBwHSi6mtXbsW8/Ly8NChQ8TXKSsrw7vuuivtGADgqlWriK9x6tQp7OjoSH1aW1tdEwmpz1zPjSVaBoqIsJq06spmGrELKw0e4NWM42LU5jKDn7LkRHBbkcKI9NQYiay1RRmLFy/GUCiUduyrr77C/Px8fOGFF4iu8dZbbyEA4BtvvJF2HACwX79+WFRUhCNHjsTFixfj2bNnDa8za9YsXXeYEyKxGwDOdGPNBXYLGXMNepM2CJC2S55C+B7MYBVTAJiOAM3YVXhSfItEhZ9coH5oqxHpHThwwHMyzEkieeyxx3Do0KFZx4uLi/Gpp54iusbdd9+Nl112WdbxRx55BDdv3ow7duzAOXPmYCAQwD/84Q+G16FpkRjFN0aGw8QLqcqSAlF0U94LaC0MvUkbAMCnNX3fGxI75LkJxFtbJJn1qAJYVTWOwdNL+AVGpOclGfqKSB588EFd7V772bt3r2si+eKLLzAYDOK8efMsz505cyZeeOGFxM/gZj8SLTE0A+AajZWRqYUY+fl3ZWjSIpvyvGBm6UUjETw/L8/cknNp1RnFFPr0+bruLoKSSCREg6+I5JNPPsG9e/eafk6fPu3atfXnP/8Zu3Xrhp988onluS+//DICAJ46dYroGZwSiUoMuyCxgZRW6CkA+HOwt5Bq/fr1wpvyvGBk6akVBKaDRWwJ3MWZ9GIKkUilqaXil/fmpzUwEs7hKyIhhRpsf+utt1LHGhoaiIPt0WgUf/CDHxDd69FHH8XevXsTt82tRVIGiV0ItUIvCIBRHa3YLxkpXqG5udmytAsA4CawiC25tEhUaF0TJOsxeAtpO/cTaUGnBHvkJJEgJtJ/r7rqKmxsbMTNmzfjkCFD0tJ/P/roIxw2bBg2Njam/W7fvn2Yl5eHa9euzbrmSy+9hPX19fjuu+/ivn378KmnnsLzzz8fH374YeJ2uUn/taqx9WqGVuynjBSe0BNyZvXFloP+NsJqjEQl6MpIhJpgt4qdRJNjgcd7dUIKuVoxV0IfOUskbW1tOHHiROzZsycWFhbinXfembYeRF2AuHHjxrTfPfTQQ3jRRRdhZ2dn1jXXrl2LZWVl2LNnT/za176Gw4cPx4ULF+qeawQ3RGJVpO4BA63YDxkpPKEVclbWRrSiAvvk5+PCJGlohWme5v92NvQihVHspG/R17kKabukIFJ9qlyB6C7CnCUSUeGGSKwmaFBRhNb4RJgMen1Yk7Qu9FyAelZd2be+hf/nmmvSjmVmdNEQ7PqxkyhXIe2EFESsmOtX+MVFKImEM9yubK+MRLCnouBcyF7fIOIAQxRrMugJuXYdayOzfVqrTk9D750kpMxsOhqCXS92wktIO7kfKfmIoFiIDr+4CCWRcIZTItETxmoa78hwOG0Vv2gQaTKQrlp3+vvM9xOLxbi2XwSLBNFgQzZIuAO/fc01OK6qippikauE5CcXoSQSznBKJLpasKJgtKKCUUvpTFBak4GmsHCTzWaloU+HjGw6B+/H6ll5Z+MZ3c8suSDTJZi5duk8cO8KFMnSZQE/uQglkXCGEyLxohotrQnqdjKwEBZustks34WL90P6rLyz8fTuR5JcoPbVJWCcsu5mLLOwdEWybqRFImEIJ0TCWzOhOUGtJoPVvt4s3WJOs9mM6nBVuXw/dp+Vdzaeej81k82qnV1rYayJ125f0Rayolo3flkLJomEM0S3SFjcy6xqqdnEFVUj09PQFUi4apy2U9RnVdumEpaddnathTGvCuDkOWkrVyLF8bTwy1owSSSc4TZGwlozYWH9GLlGQopiOnG99hFbuTn0Mrmcvh+vn1UPelr6yHDYVjtJFtE6Gcs0iVdkElch+lowSSSc4ZRIeGkmLCeVOhlIN9jyaoI7cXO4fT8iCjMjLV2x0c729nYsKSrKqjgdAvdl+GkpVyKSuN8giYQz3K4j4aGZsLZ+7ExcL3zEbtwcbt6PSP5wksWvpO1sb2/HaEVFFsk2NTW5Gsu0lCsRSdxvkETCGV7v2U4C1taPnYnL20fspVDxwh9u5L6zIvsRSReX+qmMRDAWi5n2DysliMZ1RSJxP0ISCWf4gUhUsLR+7E5cXj5iEdwcLJ9VJY5t27aZkhYJocbjcYzFYrrWhmjBYCv4JagtKiSRcIafiIQlRJ24uerm0Iv7WNUHIyF7J25AkdZqZEL0oLaokETCGbyIROTJqqK5uRnr6+sty5LwRi66OczqgxmRpRXZNzY22iJdUddqSLiHJBLOYEEkWtLww2T1oo12iFVUa8kpSFfjG7nvjLT0ETZTgUVdqyHhHpJIOIMmkegJ5JKiIuEnK0+B4oa0csXNYRn3sem+0+4sSWqR5KrLUCIBSSScQZNIMgXyXBsT2yvwFihSC7buc9JFgXqkXAzZO0cGIbFwUQsRkhgk2EESCWfQIhI94bAGuspRqPtixBlOVidxGCuBsmjRIurb1YpMrLygF/cJ5eXZWhSoR8pBACzRXAMgsdAwc1sD+S5yG5JIOIMWkegJ5ObkpCzLmNhllCerG3eRkUD5E2SXGncbl1D7aJOGVEXVglknRxjFfUgXBVoRwbOQ2OrZbJfOXExiIIEfEl/cQhIJZ7C0SBASpSeCkF2yu6SoyNa1WVbk1RMogaSGTNMF1djYmE1OALhQIC2Yd+KB07iPlSVJ0vZcS2Kwgh8SX2hBEglnsIiRqAL5cXAXIyEZ+DRcFHoChYXbo6a6OouceidJSxQt2C8xHLc7S2phl8z8qtH75d3SgCQSzqBJJEYC2WlAk2TgazVTt3EYVaDU19e7arcerASfCFsT+y1uwNs15WeN3m/v1i0kkXAGi3Ukdqvq6oF04Kvn0YzDqNecC+mxDDeTToQsIStNWoQ22gGpa4qWBeFnjd5v79YtJJFwBuuV7U61RjsDXy0L7iYOo0VbW1vWJldlABgyCdxawUuNkFST9qvWauSaomlBiNw3JEQpcvtZQBIJZ7AmEqcBTbsWCYsdFPWIyY0bw6ssITuadC5lMtG0IETU6O0SZS69WyvkJJE8+uijOHr0aOzRowcGg0Gi35w9exZnzpyJpaWl2L17dxwzZoyuxjVp0iTs1asXBoNBvOuuu/DEiRO22sar1paT7BySgU97grPU3Lwqy27neXIlk4n2exRRo7dLlLnybkmQk0Ty8MMP4+9+9zu8//77iYlkzpw5GAwG8cUXX8R33nkHJ0yYgBdffDGePHkydc748eNx+PDhuHXrVnz99ddx8ODBOHHiRFttE7n6L8nApz3BeWiePEudOH0ev5djYfEeRdLo3Yx7v79bEuQkkahYsmQJEZGcPXsWS0tLce7cualjx44dw0AggCtWrEBExD179mBmts/atWsxLy8PDx06RNwmkYlEhdXApznBRdQ83SDXnocULJ5bJI1eRFebSJBEgogtLS0IALhz586045WVlXjvvfciIuLixYsxFAqlff/VV19hfn4+vvDCC4bXPnXqFHZ0dKQ+ra2twhOJFWhPcJE0TxrItechBavnFkGjP1cVBFJIIkHEN954AwEAP/7447TjN998M95yyy2IiPjYY4/h0KFDs35bXFyMTz31lOG1Z82alSZw1Y+fiUQFrQkukuZJA7n2PKTI9ec+VxUEEtghkvPAQ8yYMQN++9vfmp6zd+9euPTSSzm1iAwPPfQQ3H///am/jx8/DhdddJGHLaKHIUOGwJAhQ1xfp3fv3vC3detg3759sH//fhg8eDCV63qFXHseUuT6cy9fsQL+feJEmNzQkDpWM3YsLF+xwsNW+Q+eEskvf/lLuOOOO0zPGTRokKNrl5aWAgDA0aNH4YILLkgdP3r0KJSVlaXO+eSTT9J+d+bMGWhvb0/9Xg+BQAACgYCjdp1roEVMoiDXnocUufrcuU6UvOApkRQXF0NxcTGTa1988cVQWloKGzZsSBHH8ePHobGxEe6++24AABg9ejQcO3YMtm/fDldffTUAAPzjH/+As2fPwqhRo5i0S0JCQjzkKlHyguJ1A0jx4Ycfwttvvw0ffvghdHZ2wttvvw1vv/02fPbZZ6lzLr30Uli1ahUAAOTl5cF9990Hjz76KLz00kvw7rvvwu233w79+vWDm266CQAALrvsMhg/fjxMmTIFtm3bBm+88QZMmzYNbr31VujXr58XjykhISHhO3hqkdjBww8/DMuWLUv9fdVVVwEAwMaNG+Haa68FAIDm5mbo6OhInfOrX/0KPv/8c5g6dSocO3YMIpEIrFu3Drp3754659lnn4Vp06bBmDFjQFEU+MEPfgB//OMf+TyUhISERA4gDxHR60b4HcePH4dgMAgdHR1QWFjodXMkJCQkXMOOXPONa0tCQkJCQkxIIpGQkJCQcAVJJBISEhISriCJREJCQkLCFXyTtSUy1HyF48ePe9wSCQkJCTpQ5RlJPpYkEgo4ceIEAEDOlEmRkJCQUHHixAkIBoOm58j0Xwo4e/YsfPzxx9CrVy/Iy8sj+o1an6u1tVWmDGdA9o0xZN8YQ/aNMZz0DSLCiRMnoF+/fqAo5lEQaZFQgKIocOGFFzr6bWFhoRz0BpB9YwzZN8aQfWMMu31jZYmokMF2CQkJCQlXkEQiISEhIeEKkkg8QiAQgFmzZsly9DqQfWMM2TfGkH1jDNZ9I4PtEhISEhKuIC0SCQkJCQlXkEQiISEhIeEKkkgkJCQkJFxBEomEhISEhCtIIpGQkJCQcAVJJJzw2GOPwbe//W04//zzIRQKEf0GEeHhhx+GCy64AHr06AFjx46Fffv2sW2oB2hvb4fbbrsNCgsLIRQKwY9//GP47LPPTH9z7bXXQl5eXtrnZz/7GacWs8WCBQvgG9/4BnTv3h1GjRoF27ZtMz3/+eefh0svvRS6d+8OV155JaxZs4ZTS/nDTt8sXbo0a4xot9nOFbz22mvwve99D/r16wd5eXnw4osvWv5m06ZNEA6HIRAIwODBg2Hp0qWu2iCJhBO+/PJLuPnmm+Huu+8m/s3jjz8Of/zjH2HhwoXQ2NgIX/va16C6uhpOnTrFsKX8cdttt8Hu3bvh73//O7z88svw2muvwdSpUy1/N2XKFDh8+HDq8/jjj3NoLVvEYjG4//77YdasWbBjxw4YPnw4VFdXwyeffKJ7/ptvvgkTJ06EH//4x7Bz50646aab4KabboL33nuPc8vZw27fACRKgmjHyAcffMCxxXzw+eefw/Dhw2HBggVE5x88eBBuuOEGuO666+Dtt9+G++67D37yk59AQ0OD80agBFcsWbIEg8Gg5Xlnz57F0tJSnDt3burYsWPHMBAI4IoVKxi2kC/27NmDAIBNTU2pY2vXrsW8vDw8dOiQ4e+i0Sj+4he/4NBCvigvL8d77rkn9XdnZyf269cPZ8+erXv+LbfcgjfccEPasVGjRuFPf/pTpu30Anb7hnSu5RIAAFetWmV6zq9+9Su8/PLL04796Ec/wurqasf3lRaJoDh48CAcOXIExo4dmzoWDAZh1KhRsGXLFg9bRhdbtmyBUCgEI0aMSB0bO3YsKIoCjY2Npr999tlnoW/fvnDFFVfAQw89BF988QXr5jLFl19+Cdu3b09754qiwNixYw3f+ZYtW9LOBwCorq7OqTEC4KxvAAA+++wzGDhwIFx00UVw4403wu7du3k0V2iwGDOy+q+gOHLkCAAAlJSUpB0vKSlJfZcLOHLkCHz9619PO3beeedBnz59TJ9z0qRJMHDgQOjXrx/s2rULHnzwQWhuboYXXniBdZOZ4dNPP4XOzk7dd/7+++/r/ubIkSM5P0YAnPXNsGHD4JlnnoFvfetb0NHRAfPmzYNvf/vbsHv3bsfVunMBRmPm+PHjcPLkSejRo4fta0qLxAVmzJiRFczL/BgN8lwH676ZOnUqVFdXw5VXXgm33XYb/PnPf4ZVq1ZBS0sLxaeQ8DNGjx4Nt99+O5SVlUE0GoUXXngBiouL4emnn/a6aTkHaZG4wC9/+Uu44447TM8ZNGiQo2uXlpYCAMDRo0fhggsuSB0/evQolJWVObomT5D2TWlpaVaw9MyZM9De3p7qAxKMGjUKAAD2798Pl1xyie32ioC+fftCfn4+HD16NO340aNHDfuitLTU1vl+hZO+yUS3bt3gqquugv3797Noom9gNGYKCwsdWSMAkkhcobi4GIqLi5lc++KLL4bS0lLYsGFDijiOHz8OjY2NtjK/vAJp34wePRqOHTsG27dvh6uvvhoAAP7xj3/A2bNnU+RAgrfffhsAII10/YaCggK4+uqrYcOGDXDTTTcBQGL3zQ0bNsC0adN0fzN69GjYsGED3Hfffaljf//732H06NEcWswPTvomE52dnfDuu+9CTU0Nw5aKj9GjR2eliLseM47D9BK28MEHH+DOnTuxrq4Oe/bsiTt37sSdO3fiiRMnUucMGzYMX3jhhdTfc+bMwVAohKtXr8Zdu3bhjTfeiBdffDGePHnSi0dghvHjx+NVV12FjY2NuHnzZhwyZAhOnDgx9f1HH32Ew4YNw8bGRkRE3L9/Pz7yyCP41ltv4cGDB3H16tU4aNAgrKys9OoRqGHlypUYCARw6dKluGfPHpw6dSqGQiE8cuQIIiJOnjwZZ8yYkTr/jTfewPPOOw/nzZuHe/fuxVmzZmG3bt3w3Xff9eoRmMFu39TV1WFDQwO2tLTg9u3b8dZbb8Xu3bvj7t27vXoEJjhx4kRKngAA/u53v8OdO3fiBx98gIiIM2bMwMmTJ6fOP3DgAJ5//vk4ffp03Lt3Ly5YsADz8/Nx3bp1jtsgiYQTamtrEQCyPhs3bkydAwC4ZMmS1N9nz57FmTNnYklJCQYCARwzZgw2NzfzbzxjtLW14cSJE7Fnz55YWFiId955ZxrBHjx4MK2vPvzwQ6ysrMQ+ffpgIBDAwYMH4/Tp07Gjo8OjJ6CL+fPn44ABA7CgoADLy8tx69atqe+i0SjW1tamnf/cc8/h0KFDsaCgAC+//HL829/+xrnF/GCnb+67777UuSUlJVhTU4M7duzwoNVssXHjRl3ZovZFbW0tRqPRrN+UlZVhQUEBDho0KE3uOIHcj0RCQkJCwhVk1paEhISEhCtIIpGQkJCQcAVJJBISEhISriCJREJCQkLCFSSRSEhISEi4giQSCQkJCQlXkEQiISEhIeEKkkgkJCQkJFxBEomEhISEhCtIIpGQEBQrVqyAHj16wOHDh1PH7rzzztT+GhISokCWSJGQEBSICGVlZVBZWQnz58+HWbNmwTPPPANbt26F/v37e908CYkUZBl5CQlBkZeXB4899hj88Ic/hNLSUpg/fz68/vrrKRL5/ve/D5s2bYIxY8bAX//6V49bK3EuQ1okEhKCIxwOw+7du2H9+vUQjUZTxzdt2gQnTpyAZcuWSSKR8BQyRiIhITDWrVsH77//vu5+5ddeey306tXLo5ZJSHRBEomEhKDYsWMH3HLLLbB48WIYM2YMzJw50+smSUjoQsZIJCQExD//+U+44YYb4Ne//jVMnDgRBg0aBKNHj4YdO3ZAOBz2unkSEmmQFomEhGBob2+H8ePHw4033ggzZswAAIBRo0bBd77zHfj1r3/tceskJLIhLRIJCcHQp08feP/997OO/+1vf/OgNRIS1pBZWxISPsXYsWPhnXfegc8//xz69OkDzz//PIwePdrrZkmcg5BEIiEhISHhCjJGIiEhISHhCpJIJCQkJCRcQRKJhISEhIQrSCKRkJCQkHAFSSQSEhISEq4giURCQkJCwhUkkUhISEhIuIIkEgkJCQkJV5BEIiEhISHhCpJIJCQkJCRcQRKJhISEhIQr/P+Hbb38tIDSMAAAAABJRU5ErkJggg==\n"
},
"metadata": {}
}
],
"source": [
"import pennylane as qml\n",
"from pennylane import numpy as np\n",
"from pennylane.optimize import AdamOptimizer, GradientDescentOptimizer\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"\n",
"# Set a random seed\n",
"np.random.seed(42)\n",
"\n",
"\n",
"# Make a dataset of points inside and outside of a circle\n",
"def circle(samples, center=[0.0, 0.0], radius=np.sqrt(2 / np.pi)):\n",
" \"\"\"\n",
" Generates a dataset of points with 1/0 labels inside a given radius.\n",
"\n",
" Args:\n",
" samples (int): number of samples to generate\n",
" center (tuple): center of the circle\n",
" radius (float: radius of the circle\n",
"\n",
" Returns:\n",
" Xvals (array[tuple]): coordinates of points\n",
" yvals (array[int]): classification labels\n",
" \"\"\"\n",
" Xvals, yvals = [], []\n",
"\n",
" for i in range(samples):\n",
" x = 2 * (np.random.rand(2)) - 1\n",
" y = 0\n",
" if np.linalg.norm(x - center) < radius:\n",
" y = 1\n",
" Xvals.append(x)\n",
" yvals.append(y)\n",
" return np.array(Xvals, requires_grad=False), np.array(yvals, requires_grad=False)\n",
"\n",
"\n",
"def plot_data(x, y, fig=None, ax=None):\n",
" \"\"\"\n",
" Plot data with red/blue values for a binary classification.\n",
"\n",
" Args:\n",
" x (array[tuple]): array of data points as tuples\n",
" y (array[int]): array of data points as tuples\n",
" \"\"\"\n",
" if fig == None:\n",
" fig, ax = plt.subplots(1, 1, figsize=(5, 5))\n",
" reds = y == 0\n",
" blues = y == 1\n",
" ax.scatter(x[reds, 0], x[reds, 1], c=\"red\", s=20, edgecolor=\"k\")\n",
" ax.scatter(x[blues, 0], x[blues, 1], c=\"blue\", s=20, edgecolor=\"k\")\n",
" ax.set_xlabel(\"$x_1$\")\n",
" ax.set_ylabel(\"$x_2$\")\n",
"\n",
"\n",
"Xdata, ydata = circle(500)\n",
"fig, ax = plt.subplots(1, 1, figsize=(4, 4))\n",
"plot_data(Xdata, ydata, fig=fig, ax=ax)\n",
"plt.show()\n",
"\n",
"\n",
"# Define output labels as quantum state vectors\n",
"def density_matrix(state):\n",
" \"\"\"Calculates the density matrix representation of a state.\n",
"\n",
" Args:\n",
" state (array[complex]): array representing a quantum state vector\n",
"\n",
" Returns:\n",
" dm: (array[complex]): array representing the density matrix\n",
" \"\"\"\n",
" return state * np.conj(state).T\n",
"\n",
"\n",
"label_0 = [[1], [0]]\n",
"label_1 = [[0], [1]]\n",
"state_labels = np.array([label_0, label_1], requires_grad=False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NqAg65FbTnyx"
},
"source": [
"Simple classifier with data reloading and fidelity loss\n",
"=======================================================\n"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {
"id": "ZyKIWD9bTnyx"
},
"outputs": [],
"source": [
"dev = qml.device(\"lightning.qubit\", wires=1)\n",
"# Install any pennylane-plugin to run on some particular backend\n",
"\n",
"\n",
"@qml.qnode(dev, interface=\"autograd\")\n",
"def qcircuit(params, x, y):\n",
" \"\"\"A variational quantum circuit representing the Universal classifier.\n",
"\n",
" Args:\n",
" params (array[float]): array of parameters\n",
" x (array[float]): single input vector\n",
" y (array[float]): single output state density matrix\n",
"\n",
" Returns:\n",
" float: fidelity between output state and input\n",
" \"\"\"\n",
" for p in params:\n",
" qml.Rot(*x, wires=0)\n",
" qml.Rot(*p, wires=0)\n",
" return qml.expval(qml.Hermitian(y, wires=[0]))\n",
"\n",
"\n",
"def cost(params, x, y, state_labels=None):\n",
" \"\"\"Cost function to be minimized.\n",
"\n",
" Args:\n",
" params (array[float]): array of parameters\n",
" x (array[float]): 2-d array of input vectors\n",
" y (array[float]): 1-d array of targets\n",
" state_labels (array[float]): array of state representations for labels\n",
"\n",
" Returns:\n",
" float: loss value to be minimized\n",
" \"\"\"\n",
" # Compute prediction for each input in data batch\n",
" loss = 0.0\n",
" dm_labels = [density_matrix(s) for s in state_labels]\n",
" for i in range(len(x)):\n",
" f = qcircuit(params, x[i], dm_labels[y[i]])\n",
" loss = loss + (1 - f) ** 2\n",
" return loss / len(x)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3Bz83H0xTnyx"
},
"source": [
"Utility functions for testing and creating batches\n",
"==================================================\n"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {
"id": "i1-TLseuTnyx"
},
"outputs": [],
"source": [
"def test(params, x, y, state_labels=None):\n",
" \"\"\"\n",
" Tests on a given set of data.\n",
"\n",
" Args:\n",
" params (array[float]): array of parameters\n",
" x (array[float]): 2-d array of input vectors\n",
" y (array[float]): 1-d array of targets\n",
" state_labels (array[float]): 1-d array of state representations for labels\n",
"\n",
" Returns:\n",
" predicted (array([int]): predicted labels for test data\n",
" output_states (array[float]): output quantum states from the circuit\n",
" \"\"\"\n",
" fidelity_values = []\n",
" dm_labels = [density_matrix(s) for s in state_labels]\n",
" predicted = []\n",
"\n",
" for i in range(len(x)):\n",
" fidel_function = lambda y: qcircuit(params, x[i], y)\n",
" fidelities = [fidel_function(dm) for dm in dm_labels]\n",
" best_fidel = np.argmax(fidelities)\n",
"\n",
" predicted.append(best_fidel)\n",
" fidelity_values.append(fidelities)\n",
"\n",
" return np.array(predicted), np.array(fidelity_values)\n",
"\n",
"\n",
"def accuracy_score(y_true, y_pred):\n",
" \"\"\"Accuracy score.\n",
"\n",
" Args:\n",
" y_true (array[float]): 1-d array of targets\n",
" y_predicted (array[float]): 1-d array of predictions\n",
" state_labels (array[float]): 1-d array of state representations for labels\n",
"\n",
" Returns:\n",
" score (float): the fraction of correctly classified samples\n",
" \"\"\"\n",
" score = y_true == y_pred\n",
" return score.sum() / len(y_true)\n",
"\n",
"\n",
"def iterate_minibatches(inputs, targets, batch_size):\n",
" \"\"\"\n",
" A generator for batches of the input data\n",
"\n",
" Args:\n",
" inputs (array[float]): input data\n",
" targets (array[float]): targets\n",
"\n",
" Returns:\n",
" inputs (array[float]): one batch of input data of length `batch_size`\n",
" targets (array[float]): one batch of targets of length `batch_size`\n",
" \"\"\"\n",
" for start_idx in range(0, inputs.shape[0] - batch_size + 1, batch_size):\n",
" idxs = slice(start_idx, start_idx + batch_size)\n",
" yield inputs[idxs], targets[idxs]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "B5s1nRL2Tnyy"
},
"source": [
"Train a quantum classifier on the circle dataset\n",
"================================================\n"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "NqJGMjbpTnyy",
"outputId": "22f961fd-34fb-4487-be49-e155eeae2c54"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch: 0 | Cost: 0.415535 | Train accuracy: 0.460000 | Test Accuracy: 0.448500\n",
"Epoch: 1 | Loss: 0.129240 | Train accuracy: 0.825000 | Test accuracy: 0.784000\n",
"Epoch: 2 | Loss: 0.145561 | Train accuracy: 0.810000 | Test accuracy: 0.791000\n",
"Epoch: 3 | Loss: 0.120865 | Train accuracy: 0.830000 | Test accuracy: 0.805500\n",
"Epoch: 4 | Loss: 0.112476 | Train accuracy: 0.850000 | Test accuracy: 0.820500\n",
"Epoch: 5 | Loss: 0.134883 | Train accuracy: 0.795000 | Test accuracy: 0.796000\n",
"Epoch: 6 | Loss: 0.112353 | Train accuracy: 0.870000 | Test accuracy: 0.828500\n",
"Epoch: 7 | Loss: 0.104956 | Train accuracy: 0.875000 | Test accuracy: 0.845500\n",
"Epoch: 8 | Loss: 0.112949 | Train accuracy: 0.840000 | Test accuracy: 0.815000\n",
"Epoch: 9 | Loss: 0.114769 | Train accuracy: 0.840000 | Test accuracy: 0.825000\n",
"Epoch: 10 | Loss: 0.100041 | Train accuracy: 0.915000 | Test accuracy: 0.896000\n"
]
}
],
"source": [
"# Generate training and test data\n",
"num_training = 200\n",
"num_test = 2000\n",
"\n",
"Xdata, y_train = circle(num_training)\n",
"X_train = np.hstack((Xdata, np.zeros((Xdata.shape[0], 1), requires_grad=False)))\n",
"\n",
"Xtest, y_test = circle(num_test)\n",
"X_test = np.hstack((Xtest, np.zeros((Xtest.shape[0], 1), requires_grad=False)))\n",
"\n",
"\n",
"# Train using Adam optimizer and evaluate the classifier\n",
"num_layers = 3\n",
"learning_rate = 0.50\n",
"epochs = 10\n",
"batch_size = 32\n",
"\n",
"opt = AdamOptimizer(learning_rate, beta1=0.9, beta2=0.999)\n",
"\n",
"# initialize random weights\n",
"params = np.random.uniform(size=(num_layers, 3), requires_grad=True)\n",
"\n",
"predicted_train, fidel_train = test(params, X_train, y_train, state_labels)\n",
"accuracy_train = accuracy_score(y_train, predicted_train)\n",
"\n",
"predicted_test, fidel_test = test(params, X_test, y_test, state_labels)\n",
"accuracy_test = accuracy_score(y_test, predicted_test)\n",
"\n",
"# save predictions with random weights for comparison\n",
"initial_predictions = predicted_test\n",
"\n",
"loss = cost(params, X_test, y_test, state_labels)\n",
"\n",
"print(\n",
" \"Epoch: {:2d} | Cost: {:3f} | Train accuracy: {:3f} | Test Accuracy: {:3f}\".format(\n",
" 0, loss, accuracy_train, accuracy_test\n",
" )\n",
")\n",
"\n",
"for it in range(epochs):\n",
" for Xbatch, ybatch in iterate_minibatches(X_train, y_train, batch_size=batch_size):\n",
" params, _, _, _ = opt.step(cost, params, Xbatch, ybatch, state_labels)\n",
"\n",
" predicted_train, fidel_train = test(params, X_train, y_train, state_labels)\n",
" accuracy_train = accuracy_score(y_train, predicted_train)\n",
" loss = cost(params, X_train, y_train, state_labels)\n",
"\n",
" predicted_test, fidel_test = test(params, X_test, y_test, state_labels)\n",
" accuracy_test = accuracy_score(y_test, predicted_test)\n",
" res = [it + 1, loss, accuracy_train, accuracy_test]\n",
" print(\n",
" \"Epoch: {:2d} | Loss: {:3f} | Train accuracy: {:3f} | Test accuracy: {:3f}\".format(\n",
" *res\n",
" )\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YWspC-9BTnyy"
},
"source": [
"Results\n",
"=======\n"
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 399
},
"id": "ZPszGYA3Tnyy",
"outputId": "fb9423f2-55c7-409e-b881-50cfd2aec062"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Cost: 0.100041 | Train accuracy 0.915000 | Test Accuracy : 0.896000\n",
"Learned weights\n",
"Layer 0: [-0.71048492 1.53235686 -0.02640328]\n",
"Layer 1: [1.02473326 0.27613871 0.08390747]\n",
"Layer 2: [ 2.1703531 -1.37015818 0.32099704]\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1000x300 with 3 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"print(\n",
" \"Cost: {:3f} | Train accuracy {:3f} | Test Accuracy : {:3f}\".format(\n",
" loss, accuracy_train, accuracy_test\n",
" )\n",
")\n",
"\n",
"print(\"Learned weights\")\n",
"for i in range(num_layers):\n",
" print(\"Layer {}: {}\".format(i, params[i]))\n",
"\n",
"\n",
"fig, axes = plt.subplots(1, 3, figsize=(10, 3))\n",
"plot_data(X_test, initial_predictions, fig, axes[0])\n",
"plot_data(X_test, predicted_test, fig, axes[1])\n",
"plot_data(X_test, y_test, fig, axes[2])\n",
"axes[0].set_title(\"Predictions with random weights\")\n",
"axes[1].set_title(\"Predictions after training\")\n",
"axes[2].set_title(\"True test data\")\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sYQWz6IdTnyy"
},
"source": [
"References\n",
"==========\n",
"\n",
"\\[1\\] Pérez-Salinas, Adrián, et al. \\\"Data re-uploading for a universal\n",
"quantum classifier.\\\" arXiv preprint arXiv:1907.02085 (2019).\n",
"\n",
"\\[2\\] Kingma, Diederik P., and Ba, J. \\\"Adam: A method for stochastic\n",
"optimization.\\\" arXiv preprint arXiv:1412.6980 (2014).\n",
"\n",
"\\[3\\] Liu, Dong C., and Nocedal, J. \\\"On the limited memory BFGS method\n",
"for large scale optimization.\\\" Mathematical programming 45.1-3 (1989):\n",
"503-528.\n",
"\n",
"About the author\n",
"================\n"
]
},
{
"cell_type": "code",
"source": [
"seconds = time.time()\n",
"print(\"Time in seconds since end of run:\", seconds)\n",
"local_time = time.ctime(seconds)\n",
"print(local_time)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "8FGVwF_QUYza",
"outputId": "c088fc5f-298b-4993-8a13-da7fefb2c27b"
},
"execution_count": 130,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since end of run: 1696830411.5724926\n",
"Mon Oct 9 05:46:51 2023\n"
]
}
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.18"
},
"colab": {
"provenance": []
}
},
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"nbformat_minor": 0
}