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quantum
Commit 4a069c83 authored by Nicolas Ollinger's avatar Nicolas Ollinger
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fonts

parent 90bd9ac2
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Showing with 156 additions and 199 deletions
gojupyter 0 → 100755
View file @ 4a069c83
#!/bin/sh
source qisvenv/bin/activate
jupyter-notebook notebooks/
notebooks/qistest.css 0 → 100644
View file @ 4a069c83
@font-face {
font-family: "FiraSans";
src: url("fonts/FiraSans-Light.woff2") format('woff2');
font-weight: normal;
font-style: normal;
}
@font-face {
font-family: "FiraSans";
src: url("fonts/FiraSans-SemiBold.woff2") format('woff2');
font-weight: bold;
font-style: normal;
}
@font-face {
font-family: "FiraSans";
src: url("fonts/FiraSans-SemiBoldItalic.woff2") format('woff2');
font-weight: bold;
font-style: italic;
}
@font-face {
font-family: "FiraSans";
src: url("fonts/FiraSans-LightItalic.woff2") format('woff2');
font-weight: normal;
font-style: italic;
}
.reveal {
font-family: FiraSans;
font-weight: normal;
font-style: normal;
}
.reveal h1 {
font-family: FiraSans;
font-weight: bold;
color: #e07;
}
.reveal h2, h3, h4, h5, h6 {
font-family: FiraSans;
}
.reveal p {
font-weight: normal;
}
notebooks/qistest.ipynb
View file @ 4a069c83
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Informatique quantique : \n",
"## programmation 101\n",
"\n",
"### Nicolas Ollinger, LIFO, U. Orléans"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Mesdames et messieurs 🦊\n",
"\n",
"Tout ce que vous avez *toujours* voulu **savoir** sur les **_circuits_** quantiques 💾 sans _jamais_ oser le demander.\n",
"\n",
"Et aussi $x=y+\\sqrt{z}$ !"
]
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 1,
"metadata": {
"slideshow": {
"slide_type": "slide"
......@@ -14,7 +43,7 @@
"output_type": "stream",
"text": [
"\n",
"Total count are: {'00': 505, '11': 495}\n"
"Total count are: {'00': 4902, '11': 5098}\n"
]
},
{
......@@ -24,7 +53,7 @@
"<Figure size 911.6x292.4 with 1 Axes>"
]
},
"execution_count": 2,
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
......@@ -54,7 +83,7 @@
"circuit.measure([0,1], [0,1])\n",
"\n",
"# Execute the circuit on the qasm simulator\n",
"job = execute(circuit, simulator, shots=1000)\n",
"job = execute(circuit, simulator, shots=10000)\n",
"\n",
"# Grab results from the job\n",
"result = job.result()\n",
......@@ -70,7 +99,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 2,
"metadata": {
"slideshow": {
"slide_type": "slide"
......@@ -79,24 +108,36 @@
"outputs": [
{
"data": {
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3ltvPxNOzkqRBrltHmpm5Hjirg/ZP9FhFkiQNUN2+TzMiXh0R50XEpyPi0HLbkRFR2fPlSZI0cHTrSDMiplIa/PMa4FDgXuBZ4CPlzx/s6QIlSRoounukeQPwADAR2Nmm/UfAKT1VlCRJA1F3R88eD/xhZrZGRNv2dUBNj1UlSdIAtD9zzw7voG0SpXs1JUkatLobmg8AV7T5nBExFvgS8NMeq0qSpAGou6dnrwAWRUQj8GrgHmAqsAl4dw/XJknSgNLd+zQ3RsRbgPcCx1A6Ur0FuCszd3a5siRJB7nuHmlSDsd/Kr8kSRoy9hmaEXEO8OPM3F1+36nMvK/HKpMkaYApcqT5PeAw4Pfl951JoKInipIkaSDaZ2hm5qs6ei9J0lDTrRCMiLkR8YqgjYiKiJjbc2VJkjTwdPfIcRHQ0cTsh5aXSZI0aHU3NIPStcv2JgDbD7wcSZIGrkK3nETEj8pvE/hWROxqs7gCOBp4uIdrkyRpQCl6n+bm8s8AtvDyJ5y0AA8Bt/ZgXZIkDTiFQjMz/xwgItYAX81MT8VKkoac7k6j96XeKkSSpIGuyIxA/wmclJlbIuJxOh4IBEBmvrkni5MkaSApcqT5fWDPwJ+uZgSSJGlQKzIj0Jc6ei9J0lDjtHiSJBVU5Jpml9cx2/KapiRpMCv6lBNJkoa8bl3TlCRpKPOapiRJBXmfpiRJBXmfpiRJBXmfpiRJBe3XNc2IODIi/rj8OrKb614aEU9GxAsRsTQiTuyi70kR8XBEbI6InRHxm4i4soN+50bEiojYVf559v7slyRJXelWaEbEhIj4AbAS+EH59duI+GFETCiw/vnAjcC1QC2lZ3AuiIhJnazyPPB3wFzgKOCvgS9FxKVttvk24B7gLuAt5Z/3RsRbu7NvkiTtS3ePNP8BmAqcCLy6/JoLvIFiz9O8ArgtM2/NzIbMvBxoAj7SUefMXJqZ38nMJzLzycz8FnB/+ffv8QlgUWZ+pbzNrwD/Xm6XJKnHdDc0/zvwocxckpkvll9LgP9ZXtapiBgBHAs80G7RA8DxRX55RNSW+/68TfPbOtjm/UW3KUlSUd16nibQDHT0AOodwOZ9rPtaoALY1K59E/COrlaMiPVAFaV6v5SZN7dZfFgn2zysk21dAlwCUF1dzbJlywCoqalh9OjRrFq1CoBx48YxZcoU6urqAKioqGD27Nk0NjayfXvpP8GMGTN45plngPFdla+DVENDAzt37gTgqKOOorm5mebmZgCOOOIIIoI1a9YAMGHCBKqrq1m+fDkAI0eOZObMmTzxxBPs2lUafH700UfT1NTE5s2lr8rkyZPJTNauXQtAVVUVVVVVrFixAoBRo0YxY8YMHn/8cXbv3g3A7NmzWbduHVu2bAFgypQptLS0sH79egAmTpxIZWUlDQ0NABxyyCFMnz6d+vp6WltbAaitrWX16tVs3boVgKlTp7Jjxw42btwIlL4XY8eOpbGxEYAxY8Ywbdo06urqyEwigtraWlauXMm2bdsAmD59Os899xxNTU3AgX2fNm0qfZ0PP/xwRowYwerVqwEYP348kyZNor6+HoDhw4cza9as/fo7aXDavHlzj3yfuhKZhaaVLXWO+AvgAuB9mbmh3PY64HbgO5n5D12sWwNsoHTP5+I27dcAF2Tm9C7WfQPwGuAPgb8FPp6Zd5aXtQAfzMw72vS/CLg1M0d2tT+1tbW5cOHCfez1vl11u6E5GF138Zb+LkG9xO/s4NRT39nKysqlmTmno2X7M2H7G4A1EbGh/Pl1wAvAH1C65tmZp4FWYGK79onAU13VkJlPlt8+HhETgS8Cd5bbntqfbUqS1F19NmF7ZrZExFJgHnBvm0XzKE2gUNSrgLZHkI+Ut3F9u20+vJ+lSpLUob6esP3rwJ0R8SiwBPgwUAPcDBARd5R/50Xlz5cDTwKN5fXnAlcC32yzzRuBxRHxGUq3wJwNnAKc0IN1S5LU7YFAByQz7ynfz3k1UA0sB96ZmWvLXdrfr1lB6RrmZOBF4HfAZyiHbHmbD0fEeyjdw/nlcp/zM/OXvbgrkqQhqFuhWb5t5K+A91IKuOFtl2dmxb62kZnf5OVHim2Xndzu8w3ADQW2+T2cF1eS1Mu6e5/m/wIuBr4GvAR8CvgGpdtNLu1iPUmSDnrdDc13Ax/OzPmURsL+MDM/BnyB0uAbSZIGre6G5kRgRfn988Ch5ff/BpzeU0VJkjQQdTc011Ea7Qqwiv+aOu9twM6eKkqSpIGou6H5L8Bp5fc3UnriyJPAbXQ9sYEkSQe9bo2ezczPtnn/vfKcsMcDv83Mn/R0cZIkDSQHdJ9mZv4C+EUP1SJJ0oDW3dOzRMQxEXFHRPy6/LozIo7pjeIkSRpIuhWaEXEB8CtKs/n8a/k1EXg0Ii7s+fIkSRo4unt69ivA5zPz2raNEfFZStPYfaunCpMkaaDp7unZKuC7HbTfS+nRYJIkDVrdDc1FwMkdtJ8M/PxAi5EkaSAr8hDqc9p8XAD8TUTM4b9Gzf4hcA6lB0NLkjRo7e9DqC8pv9q6iU6eXiJJ0mBQ5CHU3b4tRZKkwchAlCSpoP2Z3OCPImJxRDwdEc0R8fOIeGdvFCdJ0kDS3ckNPkhp0vbfAZ8GPgM8CfxLRHyg58uTJGng6O7kBp8GrsjM/9um7R8jYimlAP2nHqtMkqQBprunZydReuB0ewuAIw68HEmSBq79eQj1vA7aTwfWHng5kiQNXN09PftV4KbyU00eLre9HXgfcHlPFiZJ0kDT3YdQz4+I3wN/SWkWIIAG4N2Z+cOeLk6SpIGkcGhGxDBKp2EXZ+a/9F5JkiQNTIWvaWbmi8B9wJjeK0eSpIGruwOB6oGpvVGIJEkDXXdD84vA1yLiXRHx+oiobPvqhfokSRowujt69qfln/cB2aY9yp8reqIoSZIGou6G5im9UoUkSQeBQqEZEaOB64F3AcOBB4GPZebTvVibJEkDStFrml8C3k/p9OzdlGYF+vteqkmSpAGp6OnZc4C/yMzvAETEXcCSiKjIzNZeq06SpAGk6JHm64H/2PMhMx8FXgRqeqMoSZIGoqKhWQG0tGt7ke4PJJIk6aBVNPQC+FZE7GrT9mrg1ojYsachM/+kJ4uTJGkgKRqat3fQ9q2eLESSpIGuUGhm5p/3diGSJA103Z1GT5KkIcvQlCSpIENTkqSCDE1JkgoyNCVJKsjQlCSpIENTkqSCDE1JkgoyNCVJKsjQlCSpIENTkqSCDE1JkgoyNCVJKsjQlCSpIENTkqSCDE1JkgoyNCVJKsjQlCSpIENTkqSC+jw0I+LSiHgyIl6IiKURcWIXfasj4tsR8ZuIaI2I2zro8/6IyA5er+7VHZEkDTl9GpoRcT5wI3AtUAs8DCyIiEmdrDISeBr438Avu9j0DqC67SszX+ipuiVJgr4/0rwCuC0zb83Mhsy8HGgCPtJR58xck5kfy8zbgGe62G5m5lNtXz1fuiRpqOuz0IyIEcCxwAPtFj0AHH+Amx8VEWsjYn1E/CQiag9we5IkvcKwPvxdrwUqgE3t2jcB7ziA7TYCHwDqgTHAx4ElETE7M1e27xwRlwCXAFRXV7Ns2TIAampqGD16NKtWrQJg3LhxTJkyhbq6OgAqKiqYPXs2jY2NbN++HYAZM2bwzDPPAOMPoHwNVA0NDezcuROAo446iubmZpqbmwE44ogjiAjWrFkDwIQJE6iurmb58uUAjBw5kpkzZ/LEE0+wa9cuAI4++miamprYvHkzAJMnTyYzWbt2LQBVVVVUVVWxYsUKAEaNGsWMGTN4/PHH2b17NwCzZ89m3bp1bNmyBYApU6bQ0tLC+vXrAZg4cSKVlZU0NDQAcMghhzB9+nTq6+tpbW0FoLa2ltWrV7N161YApk6dyo4dO9i4cSNQ+l6MHTuWxsZGAMaMGcO0adOoq6sjM4kIamtrWblyJdu2bQNg+vTpPPfcczQ1NQEH9n3atKn0v4jDDz+cESNGsHr1agDGjx/PpEmTqK+vB2D48OHMmjVrv/5OGpw2b97cI9+nrkRm9uIutPlFETXABuCkzFzcpv0a4ILMnL6P9X8CPJ2Z799HvwrgMWBRZn6sq761tbW5cOHCgnvQuatuNzQHo+su3tLfJaiX+J0dnHrqO1tZWbk0M+d0tKwvr2k+DbQCE9u1TwR67BpkZrYCvwam9dQ2JUmCPgzNzGwBlgLz2i2aR2kUbY+IiADeTGmAkSRJPaYvr2kCfB24MyIeBZYAHwZqgJsBIuIOgMy8aM8KEfGW8tuxwEvlzy2ZuaK8/AvAL4CV5T4foxSaHY7IlSRpf/VpaGbmPRExAbia0v2Uy4F3ZubacpeO7tesa/f5LGAtMLn8+VDgFuAwYGu5/9zMfLRnq5ckDXV9faRJZn4T+GYny07uoC32sb1PAp/skeIkSeqCc89KklSQoSlJUkGGpiRJBRmakiQVZGhKklSQoSlJUkGGpiRJBRmakiQVZGhKklSQoSlJUkGGpiRJBRmakiQVZGhKklSQoSlJUkGGpiRJBRmakiQVZGhKklSQoSlJUkGGpiRJBRmakiQVZGhKklSQoSlJUkGGpiRJBRmakiQVZGhKklSQoSlJUkGGpiRJBRmakiQVZGhKklSQoSlJUkGGpiRJBRmakiQVZGhKklSQoSlJUkGGpiRJBRmakiQVZGhKklSQoSlJUkGGpiRJBRmakiQVZGhKklSQoSlJUkGGpiRJBRmakiQVZGhKklSQoSlJUkGGpiRJBRmakiQVZGhKklSQoSlJUkGGpiRJBRmakiQVZGhKklSQoSlJUkGGpiRJBRmakiQV1OehGRGXRsSTEfFCRCyNiBP30f+kcr8XImJ1RHz4QLcpSdL+6NPQjIjzgRuBa4Fa4GFgQURM6qT/G4B/LferBf4GuCkizt3fbUqStL/6+kjzCuC2zLw1Mxsy83KgCfhIJ/0/DGzMzMvL/W8FbgeuPIBtSpK0X/osNCNiBHAs8EC7RQ8Ax3ey2ts66H8/MCcihu/nNiVJ2i/D+vB3vRaoADa1a98EvKOTdQ4DHuyg/7Dy9qK724yIS4BLyh+fr6ysbCxSvPZ6LfB0fxfRF/7hk/1dgdQj/M523xGdLejL0BwQMvMW4Jb+ruNgFRG/zsw5/V2HpGL8zvasvgzNp4FWYGK79onAU52s81Qn/V8sby/2Y5uSJO2XPrummZktwFJgXrtF8yiNeO3II530/3Vm7t7PbUqStF/6+vTs14E7I+JRYAml0bE1wM0AEXEHQGZeVO5/M3BZRNwAzAfeDrwfeG/RbarHeWpbOrj4ne1BkZl9+wsjLgWuAqqB5cAnM3Nxedm/A2TmyW36nwT8H2AmsBH428y8ueg2JUnqKX0empIkHayce1aSpIIMTUmSCjI0JUkqyNCUJKmgITcjkLovIg4HplKaTOIloDEznTxC0pDj6Fl1KSI+AnwAmA1sB1YB64FfAD/IzMaIeFVmvtSPZUpSn/D0rDoVERMoPaf0h5TugX0bpUeztQIXAX8XEUdl5ksREf1XqSSA8tOf3hgRI/u7lsHKI011KiIuBy7MzLd2sOwESg8Ffx1wXGYOiacoSANZRHwC+ArwXeA+4FdAc2a2tukzltLsag9m5u5+KfQg5pGmutICjImIowEiYmT5GaZk5kPABcALwOn9V6KkNs4HHqU0BuEHlObvvj4iToiIceU+/wP4goG5fwxNdeV7lAb+fCIixmTmrsxsiYhXAWTmOuBZ4PD+LFISREQVsBu4NTNPpPRMyH8E/hhYDCyMiE8DnwB+2W+FHuQ8PasOtblG+afAjUAlpVM+3wTqKAXlXODvgVmZuaYfypRUFhHVwHuAFZl5f7tltcAHy8vHA6/PzA19X+XBz9BUlyLiUGAScDxwNqVrIVB6XmkAd2bmF/unOkltRcQoIDPzhbaD87L8P/qI+Arwzsys7a8aD3bep6lXiIg/AN4H/CWlh33vpHQa9iHgq8BwStdM/i0zf9tfdUp6uczcuScss90RUUSMBs4F/rk/ahssPNLUK0TEbZQexfZj4BlKp2ZnAW8Efg9cnZleE5EGiPKI2G3tg7Jdn1dTGih0d2a29Flxg4yhqZcp/yt1G6VTOAaB07MAAAGbSURBVIvbtE0C3krpusgU4N2ZuazfCpW0V0TMpzRq9lFgbWY+10GfQzPz2T4vbpBx9KzaOwp4ktLtJkDpNE9mrs3M7wJnUTpV+2f9VJ+kNiLivcCHgK9Rmojk+og4OyKOLF/j3HOt8/Y9t49p/3mkqZcpf7l+AoymNOvP79pPkVee9OAvMvMt/VCipDYi4lZKs3RdB5wDXAwcCTQC/wr8DJgO3JiZI/qrzsHCI029TGbuBP4KGAXcAVwUEa+PiNfA3sEEJwHL+69KSQARMYzSmaFnM3N1Zn41M2cB/w34OaUA/S5wE3Bn/1U6eHikqQ6VT+N8HvgTShO1PwI0A+8AmoAPZubj/VehJICIGA9MzMzflGfs2t12QFBEnA/cDRyTmY/1V52DhaGpLpVvP/kj4F2UpsxbDtybmb/p18Ikdao8a1dkZmtEfIjSqdnR/V3XYGBoqjAfASYdfCLiCqAiM6/v71oGA0NTkgaxiBgOtPoP3p5haEqSVJCjZyVJKsjQlCSpIENTkqSCDE1JkgoyNCVJKsjQlCSpoP8P3xnuwro7BbIAAAAASUVORK5CYII=\n",
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\n",
"text/plain": [
"<Figure size 504x360 with 1 Axes>"
]
},
"execution_count": 3,
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Plot a histogram\n",
"\n",
"plot_histogram(counts)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Algorithme de Bernstein-Vazirani "
]
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 3,
"metadata": {
"slideshow": {
"slide_type": "slide"
......@@ -107,201 +148,62 @@
"name": "stdout",
"output_type": "stream",
"text": [
"Help on method draw in module qiskit.circuit.quantumcircuit:\n",
"\n",
"draw(scale=0.7, filename=None, style=None, output=None, interactive=False, line_length=None, plot_barriers=True, reverse_bits=False, justify=None, vertical_compression='medium', idle_wires=True, with_layout=True, fold=None, ax=None) method of qiskit.circuit.quantumcircuit.QuantumCircuit instance\n",
" Draw the quantum circuit\n",
" \n",
" **text**: ASCII art TextDrawing that can be printed in the console.\n",
" \n",
" **latex**: high-quality images compiled via latex.\n",
" \n",
" **latex_source**: raw uncompiled latex output.\n",
" \n",
" **matplotlib**: images with color rendered purely in Python.\n",
" \n",
" Args:\n",
" scale (float): scale of image to draw (shrink if < 1)\n",
" filename (str): file path to save image to\n",
" style (dict or str): dictionary of style or file name of style\n",
" file. This option is only used by the ``mpl`` output type. If a\n",
" str is passed in that is the path to a json file which contains\n",
" that will be open, parsed, and then used just as the input\n",
" dict. See: :ref:`Style Dict Doc <style-dict-circ-doc>` for more\n",
" information on the contents.\n",
" output (str): Select the output method to use for drawing the\n",
" circuit. Valid choices are ``text``, ``latex``,\n",
" ``latex_source``, or ``mpl``. By default the `'text`' drawer is\n",
" used unless a user config file has an alternative backend set\n",
" as the default. If the output kwarg is set, that backend\n",
" will always be used over the default in a user config file.\n",
" interactive (bool): when set true show the circuit in a new window\n",
" (for `mpl` this depends on the matplotlib backend being used\n",
" supporting this). Note when used with either the `text` or the\n",
" `latex_source` output type this has no effect and will be\n",
" silently ignored.\n",
" line_length (int): Deprecated, see `fold` which supersedes this\n",
" option. Sets the length of the lines generated by `text` output\n",
" type. This useful when the drawing does not fit in the console.\n",
" If None (default), it will try to guess the console width using\n",
" ``shutil.get_terminal_size()``. However, if you're running in\n",
" jupyter the default line length is set to 80 characters. If you\n",
" don't want pagination at all, set ``line_length=-1``.\n",
" reverse_bits (bool): When set to True reverse the bit order inside\n",
" registers for the output visualization.\n",
" plot_barriers (bool): Enable/disable drawing barriers in the output\n",
" circuit. Defaults to True.\n",
" justify (string): Options are ``left``, ``right`` or\n",
" ``none``, if anything else is supplied it defaults to left\n",
" justified. It refers to where gates should be placed in the\n",
" output circuit if there is an option. ``none`` results in\n",
" each gate being placed in its own column.\n",
" vertical_compression (string): ``high``, ``medium`` or ``low``. It\n",
" merges the lines generated by the ``text`` output so the\n",
" drawing will take less vertical room. Default is ``medium``.\n",
" Only used by the ``text`` output, will be silently ignored\n",
" otherwise.\n",
" idle_wires (bool): Include idle wires (wires with no circuit\n",
" elements) in output visualization. Default is True.\n",
" with_layout (bool): Include layout information, with labels on the\n",
" physical layout. Default is True.\n",
" fold (int): Sets pagination. It can be disabled using -1.\n",
" In `text`, sets the length of the lines. This useful when the\n",
" drawing does not fit in the console. If None (default), it will\n",
" try to guess the console width using ``shutil.\n",
" get_terminal_size()``. However, if running in jupyter, the\n",
" default line length is set to 80 characters. In ``mpl`` is the\n",
" number of (visual) layers before folding. Default is 25.\n",
" ax (matplotlib.axes.Axes): An optional Axes object to be used for\n",
" the visualization output. If none is specified a new matplotlib\n",
" Figure will be created and used. Additionally, if specified\n",
" there will be no returned Figure since it is redundant. This is\n",
" only used when the ``output`` kwarg is set to use the ``mpl``\n",
" backend. It will be silently ignored with all other outputs.\n",
" \n",
" Returns:\n",
" :class:`PIL.Image` or :class:`matplotlib.figure` or :class:`str` or\n",
" :class:`TextDrawing`:\n",
" \n",
" * `PIL.Image` (output='latex')\n",
" an in-memory representation of the image of the circuit\n",
" diagram.\n",
" * `matplotlib.figure.Figure` (output='mpl')\n",
" a matplotlib figure object for the circuit diagram.\n",
" * `str` (output='latex_source')\n",
" The LaTeX source code for visualizing the circuit diagram.\n",
" * `TextDrawing` (output='text')\n",
" A drawing that can be printed as ascii art\n",
" \n",
" Raises:\n",
" VisualizationError: when an invalid output method is selected\n",
" ImportError: when the output methods requires non-installed\n",
" libraries.\n",
" \n",
" .. _style-dict-circ-doc:\n",
" \n",
" **Style Dict Details**\n",
" \n",
" The style dict kwarg contains numerous options that define the style of\n",
" the output circuit visualization. The style dict is only used by the\n",
" ``mpl`` output. The options available in the style dict are defined\n",
" below:\n",
" \n",
" Args:\n",
" textcolor (str): The color code to use for text. Defaults to\n",
" `'#000000'`\n",
" subtextcolor (str): The color code to use for subtext. Defaults to\n",
" `'#000000'`\n",
" linecolor (str): The color code to use for lines. Defaults to\n",
" `'#000000'`\n",
" creglinecolor (str): The color code to use for classical register\n",
" lines. Defaults to `'#778899'`\n",
" gatetextcolor (str): The color code to use for gate text. Defaults\n",
" to `'#000000'`\n",
" gatefacecolor (str): The color code to use for gates. Defaults to\n",
" `'#ffffff'`\n",
" barrierfacecolor (str): The color code to use for barriers.\n",
" Defaults to `'#bdbdbd'`\n",
" backgroundcolor (str): The color code to use for the background.\n",
" Defaults to `'#ffffff'`\n",
" fontsize (int): The font size to use for text. Defaults to 13\n",
" subfontsize (int): The font size to use for subtext. Defaults to 8\n",
" displaytext (dict): A dictionary of the text to use for each\n",
" element type in the output visualization. The default values\n",
" are::\n",
" \n",
" {\n",
" 'id': 'id',\n",
" 'u0': 'U_0',\n",
" 'u1': 'U_1',\n",
" 'u2': 'U_2',\n",
" 'u3': 'U_3',\n",
" 'x': 'X',\n",
" 'y': 'Y',\n",
" 'z': 'Z',\n",
" 'h': 'H',\n",
" 's': 'S',\n",
" 'sdg': 'S^\\dagger',\n",
" 't': 'T',\n",
" 'tdg': 'T^\\dagger',\n",
" 'rx': 'R_x',\n",
" 'ry': 'R_y',\n",
" 'rz': 'R_z',\n",
" 'reset': '\\left|0\\right\\rangle'\n",
" }\n",
" \n",
" You must specify all the necessary values if using this. There\n",
" is no provision for passing an incomplete dict in.\n",
" displaycolor (dict): The color codes to use for each circuit\n",
" element. The default values are::\n",
" \n",
" {\n",
" 'id': '#F0E442',\n",
" 'u0': '#E7AB3B',\n",
" 'u1': '#E7AB3B',\n",
" 'u2': '#E7AB3B',\n",
" 'u3': '#E7AB3B',\n",
" 'x': '#58C698',\n",
" 'y': '#58C698',\n",
" 'z': '#58C698',\n",
" 'h': '#70B7EB',\n",
" 's': '#E0722D',\n",
" 'sdg': '#E0722D',\n",
" 't': '#E0722D',\n",
" 'tdg': '#E0722D',\n",
" 'rx': '#ffffff',\n",
" 'ry': '#ffffff',\n",
" 'rz': '#ffffff',\n",
" 'reset': '#D188B4',\n",
" 'target': '#70B7EB',\n",
" 'meas': '#D188B4'\n",
" }\n",
" \n",
" Also, just like `displaytext` there is no provision for an\n",
" incomplete dict passed in.\n",
" \n",
" latexdrawerstyle (bool): When set to True enable latex mode which\n",
" will draw gates like the `latex` output modes.\n",
" usepiformat (bool): When set to True use radians for output\n",
" fold (int): The number of circuit elements to fold the circuit at.\n",
" Defaults to 20\n",
" cregbundle (bool): If set True bundle classical registers\n",
" showindex (bool): If set True draw an index.\n",
" compress (bool): If set True draw a compressed circuit\n",
" figwidth (int): The maximum width (in inches) for the output figure.\n",
" dpi (int): The DPI to use for the output image. Defaults to 150\n",
" margin (list): A list of margin values to adjust spacing around\n",
" output image. Takes a list of 4 ints:\n",
" [x left, x right, y bottom, y top].\n",
" creglinestyle (str): The style of line to use for classical\n",
" registers. Choices are `'solid'`, `'doublet'`, or any valid\n",
" matplotlib `linestyle` kwarg value. Defaults to `doublet`\n",
"\n"
"Total count are: {'1101': 1}\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 1599.6x894.4 with 1 Axes>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"help(circuit.draw)"
"s = '1011'\n",
"k = len(s)\n",
"\n",
"circuit = QuantumCircuit(2*k+1,k)\n",
"\n",
"circuit.x(k)\n",
"for i in range(k+1):\n",
" circuit.h(i)\n",
"circuit.barrier()\n",
"\n",
"for i in range(k):\n",
" if s[i] == '1':\n",
" circuit.x(k+1+i)\n",
"#circuit.barrier()\n",
"for i in range(k):\n",
" circuit.ccx(i,k+1+i,k)\n",
"#circuit.barrier()\n",
"for i in range(k):\n",
" if s[i] == '1':\n",
" circuit.x(k+1+i)\n",
"circuit.barrier()\n",
"\n",
"for i in range(k):\n",
" circuit.h(i)\n",
" \n",
"circuit.measure(list(range(k)), list(range(k)))\n",
"\n",
"# Execute the circuit on the qasm simulator\n",
"job = execute(circuit, simulator, shots=1)\n",
"\n",
"# Grab results from the job\n",
"result = job.result()\n",
"\n",
"# Returns counts\n",
"counts = result.get_counts(circuit)\n",
"print(\"\\nTotal count are:\",counts)\n",
"\n",
"# Draw the circuit\n",
"circuit.draw(output='mpl',scale=1)"
]
},
{
......@@ -329,7 +231,12 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
"version": "3.6.9"
},
"rise": {
"enable_chalkboard": true,
"scroll": true,
"theme": "white"
}
},
"nbformat": 4,
......
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