research/old/random_circuit.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random_circuit\n",
    "\n",
    "Utilities include the function 'random_circuit', which creates a random circuit of a given depth, number of qubits and a set of gates. The function is a modification of qiskits own implementation, and therefore use the same set of operators. \n",
    "\n",
    "The complete set of standard gates can be found at https://qiskit.org/documentation/apidoc/circuit_library.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qreservoirpy import utilities\n",
    "from qiskit import QuantumCircuit\n",
    "from qiskit.circuit.library.standard_gates import IGate, XGate, ZGate, CXGate, YGate, HGate\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a random circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1123.61x367.889 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circ = utilities.random_circuit(num_qubits=4, gates=[\n",
    "    IGate, XGate, YGate, ZGate, CXGate, HGate\n",
    "], depth=10)\n",
    "\n",
    "circ.draw('mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a random gate/instruction\n",
    "\n",
    "One can, of course, having obtained the random circuit, make a gate/instruction using to_gate/to_instruction. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/4buL47j5CV++/Ytwlq9f4FGPAXjAryIWFRWRmpqK3W4nICCA+Ph4KisrycrKorS0lOrqagCSkpKsHbQL3i14hXV/fYFJ8TO4ccwsevXqza7SLbz67k/4y64/kfXIx1zW28/qMXvc79c/xp8/zOLaUd/izikL+PyLYv78YRalFZ+y6MH38fLyiGOUe0fscDiYPn06drudBQsWsHDhQgIDAwFYvHgxjz/+ON7e3thsNhISEiyetvOuH30nM5OfIMDv8pZl0yd9nyuDh7H6g+d455OXue3aNAsn7Hll9j28lf87rht1OwvvX9OyPLR/FC+8NY+tu/7I1DHftnBC13Hrf6rmzZtHeXk5aWlpLFmypCVggIyMDBITE2lqaiIyMpKgIHM+RGx4+PhWAZ91Q+I9AJTZ3f9vQm8pysbpdHL79Y+2Wn7z1XPx7e3P+4WvWzOYBdw24uLiYnJycggODiYzM7PdbcaNGwdAYmJiy7I333yTO+64g4iICPz9/YmLi+Opp56ipqbGJXNfiCNflQPQr89AiyfpefsOF+Bl82L4kAmtlvv09mXo4CT2Hy6waDLXc9uIs7OzaW5uZtasWfTp06fdbfz8zlw3/mvES5YsoVevXvzyl7/knXfe4eGHH+b3v/89N910E83NzS6ZvTtON5/mjfd/Ti8vb484jTx6opKggGB8vC9rsy748iv5qtZBY1ODBZO5ntteE+fm5gKQnJzc4Tbl5WeOXP8a8fr16wkJCWn5esqUKYSEhDBr1iw+/PBDJk+e3EMTX5jfr3uUvYc+4rupvyT8iuFWj9Pjvm44Re92Agbw8T7zUa1fN56it7f7/0V7t4340KFDAERERLS7vqmpifz8fKB1xP8a8Fnjx48HoKKioluzjB8/Hru97YeTnYuPtx8r0w50attX332at/KXccvVDzJz6hPdGfGCDYsdRkNT3UW7vfPd/8t8/Kmr+bLddQ1N9We26e1/0ebpjAt5DEJDQ9mxY0e39nXbiGtrz7xWWFfX/oOak5ODw+EgMDCQqKioc97Wli1bABgxYkS3ZrHb7V3+B8C3kz+AqzY/wxsf/IJvXPUA8+9Y3p3xLoqqykrqG09dtNs73/0fEDSYz7/YS0PT121OqR1fVXB5QLDLj8IX+zHoLLeNODQ0lGPHjlFYWMikSZNarauqqiI9PR2AhIQEbOf4Y8EVFRU8/fTT3HTTTd1+LTk0NLTL+/h4n/913lWbn+G1955l2rj7+eGdL53zfvS0QYMHX/Qj8bkMD7+Knfs3s+/zTxg99PqW5Q2N9fy9sojRQ11/2XMhj0F3fkbOctuIU1JSKC4uZtGiRUybNo3Y2FgACgoKmD17Ng6HAzj3mzxqamr45je/iY+PD6+88kq3Z+nOadL5PlDttfd+xmvvPUvK2Nn86O5XLH9jw4H9By7q31w+3/2/IfEesnN/ydq8pa0ifnv7i9Q3nmLqmFkXb5hOutiPQWe5bcQZGRmsXr2aw4cPM3LkSOLi4qivr6ekpITU1FQiIyPZtGlTq+vhf1VXV8f06dM5ePAgeXl5DBo0yMX3oGNv5b/Aqs0LuaLvEMYOSyH309Wt1vcLHMi42GkWTecaUYNGM+OaH/BW/jKe+cPtTIi7mc+/PPOOrYShUzziGfqz3DbisLAw8vLySE9PZ9u2bZSVlREfH8+KFSuYO3cu0dHRAO1G3NjYyJ133smOHTv44IMPiI+/tD6Ld98/XgP98vjnLM65v836hKFT3D5igIdnLGVgv0je3r6ST4o3EhQQzG3XPsL93/iZ5WcmruSRn09cU1NDUFAQNpuNkydP4u//zydRmpubuffee1m3bh1vv/02U6dOtWRGT/98YtPuP1j3MS5ueyQ+lz179uB0OomNjW0VMMAPfvAD/vd//5cf//jH+Pv78/HHH7esi46ObvclKBErec45x7/YvXs30P6p9DvvvAPAr371KyZNmtTqv40bN7p0TpHO8Mgj8bkiLisrc/E0IhdGR2IRw3nkkfjs+6pF3IFHHolF3IkiFjGcIhYxnCIWMZwiFjGcIhYxnCIWMZwiFjGcIhYxnCIWMZwiFjGcIhYxnCIWMZwiFjGcIhYxnCIWMZwiFjGcIhYxnCIWMZwiFjGcIhYxnCIWMZwiFjGcIhYxnCIWMZwiFjGcIhYxnCIWMZwiFjGcIhYxnCIWMZwiFjGcIhYxnCIWMZwiFjGcIhYxnCIWMZwiFjGcIhYxnCIWMZwiFjGcIhYxnCIWMZwiFjGcIhYxnEdE7HA4yMjIICYmBl9fX8LDw5k/fz61tbXMmTMHm83GsmXLrB5TpFu8rR6gpxUVFZGamordbicgIID4+HgqKyvJysqitLSU6upqAJKSkqwdtAsOf7mP19//GQcqCjl6opLTpxu5ou8QJsTdzF03pDMgaJDVI/a47NxMDlQUcqB8J/bqgwzsF8HrT5ZZPZYl3Dpih8PB9OnTsdvtLFiwgIULFxIYGAjA4sWLefzxx/H29sZms5GQkGDxtJ135Ktyqk9Uce2obxFyeRi9vLw5aN/Nxu0r2bLrjyx/rIh+fa6weswe9co7TxLo359hV46ltu641eNYyq0jnjdvHuXl5aSlpbFkyZJW6zIyMli9ejW7du0iKiqKoKAgi6bsurHDbmTssBvbLB8dNZlfvH43mwte5Z7kDAsmc51VPy5l0IChAMxdMoq6hhqLJ7KO214TFxcXk5OTQ3BwMJmZme1uM27cOAASExNbluXl5ZGSksKgQYO47LLLCAsL45577qG4uNglc1+Igf0iAKipO2bxJD3vbMDixkfi7OxsmpubmTVrFn369Gl3Gz8/P6B1xMeOHWP06NE89NBDXHHFFZSXl5OZmcmkSZP47LPPCAsLc8n8ndHQWE9dQw0NjfUc+mIvL739OAAT4m62eDJxJbeNODc3F4Dk5OQOtykvLwdaRzxjxgxmzJjRarurrrqK4cOHs2bNGubPn98D03bP25+8xAt/fqTl69B+kfx45uuMHnq9hVOJq7ltxIcOHQIgIiKi3fVNTU3k5+cDrSNuz4ABAwDw9r60Hq5rR97GkJA46hpqKKn4lI/2ruOrWofVY4mLXVo/lRdRbW0tAHV1de2uz8nJweFwEBgYSFRUVJv1p0+fprm5mUOHDvHEE08QGhrK3Xff3a1Zxo8fj91u79I+Pt5+rEw7cM5tQvqGEdL3zOn9taNu4/rRd5CWdRVfN55i5tQnujVrdw2LHUZDU/uPdXd05v5fai7kMQgNDWXHjh3d2tdtIw4NDeXYsWMUFhYyadKkVuuqqqpIT08HICEhAZvN1mb/KVOmtBypY2JiyM3NJSQkpFuz2O12KioqurSPb2//Ln+foYMTiL5yDOv/+j8uj7iqspL6xlMX7fa6c/+tdrEfg85y24hTUlIoLi5m0aJFTJs2jdjYWAAKCgqYPXs2DseZ086O3uTx8ssvc/z4cQ4ePMjzzz/Pf/zHf5Cfn8+QIUO6PEtoaGiX9/Hx9uvyPgANjXWcPFXdrX0vxKDBgy/6kdg0F/IYdOdn5Cy3jfjs68CHDx9m5MiRxMXFUV9fT0lJCampqURGRrJp06YOr4eHDx8OwNVXX81NN91EZGQkixcv7tbbM7tzmnS6AbZktb+u+oSd/kFt/6cXlWyhzP4ZCdE3dPn7XagD+w/Qy+fi3d657v+l6mI/Bp3lthGHhYWRl5dHeno627Zto6ysjPj4eFasWMHcuXOJjo4Gzv+kFkDfvn2JiYmhpKSkp8fulKy1D3P0ZBVJMVMZ2DeChqZ6DpTvZOuuP+J3WSAP3fprq0fsce/tfI0vj5158vJ47RGaTjfwxvu/AOCKfhFMGzfbyvFcym0jBhgxYgQbNmxos7ympoaysjK8vLwYNWrUeW/nyy+/ZN++fVx99dU9MWaXJY+ZyXs7V/HBztc4XnsEGzYG9ovglokPcfeUdK7o1/VTftO8+8nL/N/ft7Va9uqmpwFIGDpFEbu7PXv24HQ6iY2Nxd+/9RMo9913HzExMSQlJdG3b18OHDjAb37zG7y9vXnssccsmri1KYl3MyWxe8+Uu4tfP7zV6hEuGR4Z8e7du4H2T6UnTpzIqlWr+O1vf0t9fT3h4eEkJyfz5JNPdvias4iVFPG/SUtLIy0tzdUjiXSb2/4CxLmcK2IR03jkkfjs+6pF3IFHHolF3IkiFjGcIhYxnCIWMZwiFjGcIhYxnCIWMZwiFjGcIhYxnCIWMZwiFjGcIhYxnEf+AoQJvHpD8jyrp+g8r95WT+C5FPElymbDkj+6JuZRxOIRdpVu5UfLW3+kj69PAGEhsaSMnc1t1z5Cr16tcyg/sp+1eUspKsnlyPHDNDubCekbTmL0Ddx89VyGh1/lyrvQIUUsHiU5aSYT4m7GiZNjJ+28t3MVy9f/kM+/LOaxO1e2bPfOJy+TtfZhfLx9SU6aSfTgJHr18qb8yH7ydq/h7e0v8tKP9hAxMN7Ce3OGIhaPMuzKsaSMu6/l6+nX/BdzFsfxzicv8cBNz9G3TwiF+99n6ZsPMmRgPJnf20Tw5YNb3cac1Ez+nP87V4/eIT07LR7NzyeAuIiJOJ1OKo+WAvDS24/jxMlP7stpEzBAr17e3DH5sUviKAyKWISqf8Qb5N+fquqDHKgoZFTkdZdMpOej02nxKPWNp/iq1oHTeeaaeP1Hyymp+JS48AmEhcTy0d71AEQPTrJ20C5QxOJRVm1eyKrNC1stu27U7TzyrRcAOFV/AgB/3yCXz9Zdilg8yi1XP8jkhLtoam7kYNVucrYuwvFVOT69fYF/xnvq65NWjtkluiYWj3Jl8DDGxqYwIS6Ve5Iz+PkD69lXXsBv13wfgMjQM5/NVVrxqZVjdokiFo82MvIaUsbOZuuuHPaU/ZVB/aOIuXIMe8ry+fzLv1k9XqcoYvF4s1KexsurF3/Y9FMAvnfzIgCee/1eqk/Y22x/uvk0a/OWcuiLvS6dsyO6JhaPd2VwDMmJ9/LBp2+w++95jIudxqN3riRr7cM88Pzwf75jy8ubyqMl5O1eQ9XRUl5c8JnVowOKWASAmTc+xZaibP6w+acs+f4WUifMYVTkdazNW8qnJR/w/s5VNDubGdgvgjHRU3n6vj9dMq8j25xOp9PqIUT+3ekG2JJl9RRdkzzPmt880zWxiOEUsYjhFLGI4RSxiOEUsYjhFLGI4RSxiOEUsYjhFLGI4RSxiOEUsYjhFLGI4RSxiOEUsYjhFLGI4RSxiOEUsYjhFLGI4dw+YofDQUZGBjExMfj6+hIeHs78+fOpra1lzpw52Gw2li1bZvWY0kXlR/bz6qaf8sjvJnLnMyHM+EkgD/13Em988Bx1DbVWj+dSbv2H8oqKikhNTcVutxMQEEB8fDyVlZVkZWVRWlpKdXU1AElJSdYOKl32bsErrPvrC0yKn8GNY2bRq1dvdpVu4dV3f8Jfdv2JrEc+5rLeflaP6RJuG7HD4WD69OnY7XYWLFjAwoULCQwMBGDx4sU8/vjjeHt7Y7PZSEhIsHha6arrR9/JzOQnCPC7vGXZ9Enf58rgYaz+4Dne+eRlbrs2zcIJXcdtT6fnzZtHeXk5aWlpLFmypCVggIyMDBITE2lqaiIyMpKgIHM+PEvOGB4+vlXAZ92QeA8AZfZL429Cu4JbRlxcXExOTg7BwcFkZma2u824ceMASExM7PB2UlNTsdlsPPPMMz0xpvSAI1+VA9Cvz0CLJ3Edt4w4Ozub5uZmZs2aRZ8+fdrdxs/vzPVSRxH/6U9/oqioqKdGlB5wuvk0b7z/c3p5eTN1zLetHsdl3DLi3NxcAJKTkzvcprz8zL/Y7UV84sQJHn30UZYsWdIzA0qP+P26R9l76CPu/8bPCL9iuNXjuIxbPrF16NAhACIiItpd39TURH5+PtB+xE899RSxsbHMmjWL++6774LnGT9+PHZ72w/mko75ePuxMu1Ap7d/9d2neSt/Gbdc/SAzpz7Rg5N1bFjsMBqa6rq1b2hoKDt27OjWvm4ZcW3tmdcJ6+raf0BzcnJwOBwEBgYSFRXVat2OHTt48cUX2blz50Wbx263U1FRcdFuzxP49vbv9LarNj/DGx/8gm9c9QDz71jeg1OdW1VlJfWNp1z+fd0y4tDQUI4dO0ZhYSGTJk1qta6qqor09HQAEhISsNlsLetOnz7NQw89RFpaGiNHjryo80jX+Hh37jXeVZuf4bX3nmXauPv54Z0vtfr/6WqDBg++oCNxd7llxCkpKRQXF7No0SKmTZtGbGwsAAUFBcyePRuHwwG0fZPHsmXL+OKLLy76s9HdPU3yZJ35QLXX3vsZr733LCljZ/Oju1/By8vap3gO7D9gyQequWXEGRkZrF69msOHDzNy5Eji4uKor6+npKSE1NRUIiMj2bRpU6vrYYfDwdNPP82SJUtoamri+PHjLevq6+s5fvw4QUFBlv+gyBlv5b/Aqs0LuaLvEMYOSyH309Wt1vcLHMi42GkWTedabvvRpsXFxaSnp7Nt2zYA4uPjmTNnDnPnziU6OpqDBw+yfft2JkyYAJx5i+aYMWPOeZsHDx4kMjKyp0cXzn8kXvzH7/Dezj90uD5h6BR+/fDWiz/YOVj10aZuG3FHampqCAoKwmazcfLkSfz9/VuWt3fam5yczP333893vvMdJk6ciK+vr6tH9kj6fOLOc8vT6XPZs2cPTqeT2NjYloAB+vTpww033NDuPpGRkR2uE7Gax13g7d69Gzj32y1FTOJxR+KuRuxhVxtiIB2JRQzncUfis++rFnEXHnckFnE3iljEcIpYxHCKWMRwiljEcIpYxHCKWMRwiljEcIpYxHCKWMRwiljEcIpYxHCKWMRwiljEcIpYxHCKWMRwiljEcIpYxHCKWMRwiljEcIpYxHCKWMRwiljEcIpYxHCKWMRwiljEcIpYxHCKWMRwiljEcIpYxHCKWMRwiljEcIpYxHCKWMRwiljEcIpYxHCKWMRwiljEcIpYxHCKWMRwiljEcIpYxHCKWMRwiljEcIpYxHAeEbHD4SAjI4OYmBh8fX0JDw9n/vz51NbWMmfOHGw2G8uWLbN6TOmCw1/uI3P1LL77/Ai++fTl3PqkP99dHMfydT/k6Ikqq8dzKW+rB+hpRUVFpKamYrfbCQgIID4+nsrKSrKysigtLaW6uhqApKQkaweVLjnyVTnVJ6q4dtS3CLk8jF5e3hy072bj9pVs2fVHlj9WRL8+V1g9pku4dcQOh4Pp06djt9tZsGABCxcuJDAwEIDFixfz+OOP4+3tjc1mIyEhweJppSvGDruRscNubLN8dNRkfvH63WwueJV7kjMsmMz13Pp0et68eZSXl5OWlsaSJUtaAgbIyMggMTGRpqYmIiMjCQoKsnBSuVgG9osAoKbumMWTuI7bRlxcXExOTg7BwcFkZma2u824ceMASExMbFm2detWbDZbm/90un1pamis56taB0eOl7Nj32aWrnkIgAlxN1s8meu47el0dnY2zc3NzJo1iz59+rS7jZ+fH9A64rNeeOEFxo4d2/J1QEBAzwwqF+TtT17ihT8/0vJ1aL9IfjzzdUYPvd7CqVzLbSPOzc0FIDk5ucNtysvLgfYjjo+PZ+LEiT0znFw01468jSEhcdQ11FBS8Skf7V3HV7UOq8dyKbeN+NChQwBERES0u76pqYn8/Hyg/YgvpvHjx2O323v0e7gbH28/VqYdOO92IX3DCOkbBsC1o27j+tF3kJZ1FV83nmLm1Cd6esxWhsUOo6Gprlv7hoaGsmPHjm7t67YR19bWAlBX1/6DmpOTg8PhIDAwkKioqDbr77nnHhwOBwMGDGDGjBn86le/Ijg4uFuz2O12KioqurWvp/Lt7d+t/YYOTiD6yjGs/+v/uDziqspK6htPufR7ghtHHBoayrFjxygsLGTSpEmt1lVVVZGeng5AQkICNputZd3ll19Oeno6kydPpk+fPnz00UdkZmby8ccfs2PHDnx9fbs1i3SNj7dft/dtaKzj5KnqizhN5wwaPPiCjsTd5bYRp6SkUFxczKJFi5g2bRqxsbEAFBQUMHv2bByOM9dN//6s85gxYxgzZkzL1zfccAOjRo1ixowZZGdn88ADD3R5lu6eJnmy0w2wJavj9dUn7PQPavuDX1SyhTL7ZyRE39Bzw3XgwP4D9PJx+bd134gzMjJYvXo1hw8fZuTIkcTFxVFfX09JSQmpqalERkayadOmTl0P33rrrQQEBLBjx45uRSwXX9bahzl6soqkmKkM7BtBQ1M9B8p3snXXH/G7LJCHbv211SO6jNtGHBYWRl5eHunp6Wzbto2ysjLi4+NZsWIFc+fOJTo6Gujak1r/etot1koeM5P3dq7ig52vcbz2CDZsDOwXwS0TH+LuKelc0W+I1SO6jNtGDDBixAg2bNjQZnlNTQ1lZWV4eXkxatSo897OunXrqK2tZcKECT0xpnTDlMS7mZJ4t9VjXBLcOuKO7NmzB6fTSWxsLP7+rZ8Fve+++xg6dChjx45teWJr8eLFJCUlce+991o0sUjHPDLi3bt3A+2fSo8cOZLVq1ezdOlS6urqCAsLY+7cuSxcuBAfHwuetRA5D0X8b5544gmeeMK1ry+KXAi3/QWIczlXxCKm8cgj8dn3VYu4A488Eou4E0UsYjhFLGI4RSxiOEUsYjhFLGI4RSxiOEUsYjhFLGI4RSxiOEUsYjib0+l0Wj2EyL9zOqG50eopusarN1jxx18UsYjhdDotYjhFLGI4RSxiOEUsYjhFLGI4RSxiOEUsYjhFLGI4RSxiOEUsYjhFLGI4RSxiOEUsYjhFLGI4RSxiOEUsYjhFLGI4RSxiOEUsYjhFLGI4RSxiOEUsYjhFLGI4RSxiOEUsYrj/D0Og3P0uk8C4AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 287.496x535.111 with 1 Axes>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gate = circ.to_gate(label=\"RC\")\n",
    "\n",
    "qc = QuantumCircuit(6)\n",
    "\n",
    "qc.append(gate, range(4))\n",
    "qc.append(gate, range(2, 6))\n",
    "qc.draw('mpl')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "quantum",
   "language": "python",
   "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": "3.9.13"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}