520 lines (520 with data), 196.2 kB
{
"cells": [
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "eI_d8C3_Tnyu",
"outputId": "da6b9800-64d2-4f24-83f7-dc6db5bc467b"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since beginning of run: 1696868737.6670253\n",
"Mon Oct 9 16:25:37 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": 24,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 388
},
"id": "lScQSFsjTnyw",
"outputId": "213f220a-ab3e-4196-93e0-f0238cd361f4"
},
"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": 25,
"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": 26,
"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": 27,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "NqJGMjbpTnyy",
"outputId": "693199c2-40b4-45cf-e141-c16a7d9864bb"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch: 0 | Cost: 0.415535 | Train accuracy: 0.460000 | Test Accuracy: 0.448500\n",
"Epoch: 1 | Loss: 0.208744 | Train accuracy: 0.740000 | Test accuracy: 0.660500\n",
"Epoch: 2 | Loss: 0.220366 | Train accuracy: 0.660000 | Test accuracy: 0.639000\n",
"Epoch: 3 | Loss: 0.146549 | Train accuracy: 0.785000 | Test accuracy: 0.770500\n",
"Epoch: 4 | Loss: 0.141008 | Train accuracy: 0.770000 | Test accuracy: 0.778000\n",
"Epoch: 5 | Loss: 0.113828 | Train accuracy: 0.850000 | Test accuracy: 0.791000\n",
"Epoch: 6 | Loss: 0.139311 | Train accuracy: 0.795000 | Test accuracy: 0.775500\n",
"Epoch: 7 | Loss: 0.107568 | Train accuracy: 0.900000 | Test accuracy: 0.855500\n",
"Epoch: 8 | Loss: 0.103657 | Train accuracy: 0.915000 | Test accuracy: 0.858000\n",
"Epoch: 9 | Loss: 0.109948 | Train accuracy: 0.890000 | Test accuracy: 0.839000\n",
"Epoch: 10 | Loss: 0.119184 | Train accuracy: 0.825000 | Test accuracy: 0.818500\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 = 0.6\n",
"epochs = 10\n",
"batch_size = 30\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": 28,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 399
},
"id": "ZPszGYA3Tnyy",
"outputId": "55e4665b-efaa-4c2c-f204-8ba58574382d"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Cost: 0.119184 | Train accuracy 0.825000 | Test Accuracy : 0.818500\n",
"Learned weights\n",
"Layer 0: [0.54852309 1.62954215 0.47628345]\n",
"Layer 1: [ 0.33939906 -0.17799442 -0.69545662]\n",
"Layer 2: [-0.4907848 1.56504027 0.32099705]\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": "0f9b1c26-306b-442d-9050-98622416a27a"
},
"execution_count": 29,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since end of run: 1696869011.4815438\n",
"Mon Oct 9 16:30:11 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": []
}
},
"nbformat": 4,
"nbformat_minor": 0
}