1  {
   2   "cells": [
   3    {
   4     "cell_type": "code",
   5     "execution_count": 1,
   6     "id": "1e9b21a1",
   7     "metadata": {},
   8     "outputs": [],
   9     "source": [
  10      "from qiskit import*\n"
  11     ]
  12    },
  13    {
  14     "cell_type": "code",
  15     "execution_count": 2,
  16     "id": "dd43b36b",
  17     "metadata": {},
  18     "outputs": [],
  19     "source": [
  20      "%matplotlib inline\n",
  21      "from qiskit.tools.visualization import plot_histogram"
  22     ]
  23    },
  24    {
  25     "cell_type": "code",
  26     "execution_count": 3,
  27     "id": "f141d4b5",
  28     "metadata": {},
  29     "outputs": [],
  30     "source": [
  31      "secret_num= 73\n",
  32      "secret_num= '101'"
  33     ]
  34    },
  35    {
  36     "cell_type": "code",
  37     "execution_count": 4,
  38     "id": "f721c9f3",
  39     "metadata": {},
  40     "outputs": [
  41      {
  42       "data": {
  43        "text/plain": [
  44         "<qiskit.circuit.instructionset.InstructionSet at 0x2bc886108b0>"
  45        ]
  46       },
  47       "execution_count": 4,
  48       "metadata": {},
  49       "output_type": "execute_result"
  50      }
  51     ],
  52     "source": [
  53      "length= len(secret_num)\n",
  54      "circuit= QuantumCircuit(length+1,length)\n",
  55      "\n",
  56      "circuit.h(range(length))\n",
  57      "\n",
  58      "\n",
  59      "circuit.x(length)\n",
  60      "circuit.h(length)\n",
  61      "\n",
  62      "circuit.barrier() \n",
  63      "#####################\n",
  64      "for i, y in enumerate(reversed(secret_num)):\n",
  65      "    if y == '1':\n",
  66      "        circuit.cx(i, length)\n",
  67      "#circuit.cx(5,6)\n",
  68      "#circuit.cx(3,6)\n",
  69      "#circuit.cx(0,6)\n",
  70      "\n",
  71      "circuit.barrier()\n",
  72      "#####################\n",
  73      "\n",
  74      "circuit.h(range(length))\n",
  75      "circuit.barrier()\n",
  76      "\n",
  77      "#####################\n",
  78      "\n",
  79      "circuit.measure(range(length),range(length))"
  80     ]
  81    },
  82    {
  83     "cell_type": "code",
  84     "execution_count": 5,
  85     "id": "0614fc5c",
  86     "metadata": {
  87      "scrolled": false
  88     },
  89     "outputs": [
  90      {
  91       "data": {
  92        "image/png": 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iTcvVXyxdf43QO++8g6NHj2LatGlYt24dDh8+bDZ/RkaG8M/qaLYeEZNtGAKALl26mE0vLy9HRkYGRo0aJaIsmx3a9RaOpJon8eqKMvj2GyGoIiJSEu5DtO2ZZ55B27ZtsW/fPuzatcumZW4PQvXXCK1evRqvvPIKEhIS8Mwzzzi2cAWTZRjy9vYGAOTk5JgFn+XLl6OgoAADBgy4623YmhZX7wfOXW3euiOfnNvoxY/NFR1tQPJinucFgNOnT1udJzEx0eq3DFeuXGmvku7azK2m//Nc/t2Tc3+obR8i57FuiTlvrwNg+j289bVUDAbDHddYxcbGAgDef/99m9fRWBACgLVr1+KVV17BxIkTERcXh6qqqobloqOjkZ6ebp8PonCyDEN+fn4IDQ3FkiVL4OXlBR8fHyQnJyM1NRUAmryxFGnbokWLRJdAMsb+kA7HuuXc3NwQEhKC2tpa7N+/3+r8loIQAFy4cAHZ2dkIDAxESEgIjhw54sjyFUuWF1Dr9XokJSUhODgY8fHxmDJlCry9vZGQkABnZ2eEhoaKLpFkaMKECaJLIBljf0iHY91ygYGBcHZ2xunTp1FeXm5xXmtBqN6xY8cAoOGLR3QnWR4ZAoDevXsjLS3NbFpsbCwCAwPRunXrhmnV1dWora2F0WhEdXU1Kioq4OrqqvqLwuhOgYGByM7OFl0GyRT7Qzoc65YrKSnBhx9+iNzcXIvzdevWDZ9++qnVIAQAe/fuxY0bN3D+/HlHlKwKsg1DjcnMzERUVJTZtD/96U/YtGkTAODrr78GYDos2KtXL6nLAwDEzEtv1nQioltxH6Jt586dw3PPPWd1vl9++QX//d//jZCQEMTFxVm8zmnz5s3YvHmzPctUHVmeJmtMWVkZcnJy7rh4euPGjairqzP7T1QQIiIiksp7773HO0vbiWKODLm7u6O2tlZ0GSRjBoNBdAkkY+wP6XCsSWkUc2SIyJq1a9eKLoFkjP0hHY41KQ3DEKlGfHy86BJIxtgf0uFYk9IwDJFq8OZhZAn7Qzoca1IahiEiIiLSNIYhIiIi0jSGIVIN3uSNLGF/SIdjTUrDMESqsX37dtElkIyxP6TDsSalUcx9hkTx8dTmtpVo4cKFfCYSNUlUf2hxH8LfRduFh4e3aLnzuQUAAD/frmavpdi2GjEMWTF2oOgKiEjJuA8hS1atWtWi5ea8vQ4AsOy1OLPX1DI8TUZERESaxjBEqrFmzRrRJZCMsT+kw7EmpWEYItUIDg4WXQLJGPtDOhxrUhqGIVKN6Oho0SWQjLE/pMOxJqVhGCIiIiJNYxgiIiIiTWMYItUYNGiQ6BJIxtgf0uFYk9IwDJFqHD58WHQJJGPsD+lwrElpGIaIiIhI0xiGiIiISNMYhkg1kpOTRZdAMsb+kA7HmpSGYYiIiIg0jWGIVCMmJkZ0CSRj7A/pcKxJafjUeit2ZAL5xWK27ePJJ14TKR33IaRGM2fORFZWluTbDQ8Px6pVq+y+XoYhK/KLgXNXRVdBRErFfQipUVZWFjIyMkSXYTc8TUaqkZCQILoEkjH2h3Q41qQ0DEOkGjNmzBBdAskY+0M6HGtSGoYhUo2hQ4eKLoFkjP0hHY41KQ3DEKlGYWGh6BJIxtgf0uFYk9IwDBEREZGmMQyRagQFBYkugWSM/SEdjjUpDb9aT6qRkpIiugSSMfaHdDjW1BhPT08EBQWhTZs2qKioQHZ2Nq5du9bk/GPGjEFOTg5OnTrl8Np4ZIhUY8GCBaJLIBljf0iHY031fHx8sGjRIpw9exbXr1/HN998g3379uGrr75CYWEhfv75Zyxfvhx+fn5my8XExCApKQlpaWno1KmTw+uUdRgyGo1YsWIFAgIC4ObmhrCwMGRkZKBPnz6Ii4sTXR7JTFJSkugSVK26Fsi8AHxyDEj9ATh/FairE12V7dgf0uFYk4uLC9544w1cuHABCxYsgL+/P8rLy3Ho0CHs27cPBw8eRFlZGXx9ffHKK6/g7NmzSExMROvWrRETE4N//vOfcHZ2xrp163D1quPvWirr02RTp07Fzp07MX/+fERERODAgQOYNGkSCgsL8dJLL4kur1HJiw3w7TcCkWPm2TSdSAmyfgY++h4or/5t2r4Tpsc9THkQ8PYQV5vacB9CStepUyfs2bMHAweangWzfft2rFmzBt988w1qa2sb5tPr9Rg0aBCee+45TJ48GTNnzsT48ePRuXNnODs7Y/HixZg/f74kNcv2yNC2bduwadMm7N69Gy+//DKGDRuGuXPnYvDgwaipqUFERAQqKyvxzDPPwMfHBx06dMBDDz2E7Oxs0aUTqcrJPGDjN+ZBqN4v/wFW7wdKKyQvi4hkyMvLC2lpaRg4cCDOnz+PoUOHYuLEicjIyDALQoDp7M/333+PKVOmIDIyErm5ufDx8YGzszPee+89yYIQIOMwtHTpUowcORLR0dFm0/39/eHi4oKQkBDU1NTA398fhw4dQlFREUaMGIGJEycKqphEU9NzcuSirg7YdQzQWXj/13LgmxxJy2oR9od0ONbatW7dOgQFBeHEiROIiorC119/bdNy9913H7p169bwc1hYGHS6pvY89ifLMJSXl4cTJ05g/Pjxd7yXm5uL4OBguLq6om3btpg3bx58fHzg5OSEF154AcePH0dFBf+ZqkUnT54UXYLq/FwEXC0BrF0adOCsJOXcFfaHdDjW2jRu3DiMGzcOJSUlGD16tM0337z1GqF33nkHly9fRnR0NOLj4x1c8W9kec1QXl4eAKBLly5m08vLy5GRkYFRo0Y1utyBAwfQq1cvuLm5Wd2GrYlz3Nw0dA802DRvvUO73sKR1BVm06oryuDbb0Sz1pORkY4/PzKsWcuo1axZs6zOk5iYaHW+xMREe5V0117cYooYUv7rp7n8B43D6BeTrc5XWgHo9HphV1TLuT/Utg+R81i3xGvLPgRg+j289bXcybHuuXPnAgDmzJmD3Nxcm5a5NQjVXyN08OBBJCcn47XXXsMHH3wAo9HYMH9GRkazPmedjfskWYYhb29vAEBOTo5Z8Fm+fDkKCgowYMCAO5YpLi5GQkIC3nrrLcnqbErkk3MbvfiRSGmqyn+1ab7qyhvK+mqZzHEfQkoTGRmJ/v37o7CwEBs2bLBpmcaCEADs2LEDZ8+eRUBAAEaNGoVPP/3UkaUDkGkY8vPzQ2hoKJYsWQIvLy/4+PggOTkZqampAICIiAiz+cvLy/HEE09g4sSJeOqpp2zahq1pcfV+4Jzjv9XXqOhoA5IX8w8MAJw+fdrqPImJiVZvubBy5Up7lXTXZm41/d/WXhShphZYuBO4Udn0PDoADwa1xfsCP4ec+0Nt+xA5j3VLzHl7HQDT7+Gtr+VOdN0Gg8Hs2rDhw4cDAD766CNUVVVZXb6pIASYPsfmzZvxP//zPxg+fLhZGIqOjkZ6err9Psj/keU1Q3q9HklJSQgODkZ8fDymTJkCb29vJCQkwNnZGaGhoQ3z1tTUYMKECQgICJDFUSESZ9GiRaJLUB1nJ2BYYNPv6wDo9UB0X8lKajH2h3Q41tpTf5Di+++/tzqvpSBUr349tx/8cBRZHhkCgN69eyMtLc1sWmxsLAIDA9G6deuGadOmTYPRaMS6deukLpFkZsKECaJLUKWHgoD/3DR9Y0wH84upnfSm+wx17SCouGZgf0iHY609Pj4+AIBz585ZnM+WIAQAP/30EwCYfcPMkWQbhhqTmZmJqKiohp9//vlnbNq0CW5ubujQoUPD9FOnTsHX11dAhUDMvPRmTSf7CQwM5H2mHECvA2IGAZF+pkB06Lxp+qhQIMofaNfa8vJyoZT+UMM+RCljTfZjMBjQpk0blJaWWpyva9euNt1Q8eLFi+jYsSPKy8vtXWqjFBOGysrKkJOTg+eff75hWs+ePRVxbpdIDXw7Ak8N/i0MPRIith4iko/KykpUVlq4uPD/rF69GpmZmTh48KDF+YxGI65fv26v8qxSTBhyd3e/4+6VREREpCzWgpAIsryAmqglDAaD6BJIxtgf0uFYk9IwDJFqrF27VnQJJGPsD+lwrElpGIZINaS8dTspD/tDOhxrUhqGIVINR9yIi9SD/SEdjjUpDcMQERERaRrDEBEREWkawxCpBm/yRpawP6TDsSalUcx9hkTx8dTmtpVo+/btfAwANUlUf2hxH8LfRfULDw9v9jLncwsAAH6+Xc1eO3q7tmAYsmLsQNEVkK0WLlzIHTA1SVR/aHEfwt9F9Vu1alWzl5nztukZosteizN7LQc8TUZERESaxjBEREREmsYwRKqxZs0a0SWQjLE/pMOxJqVhGCLVCA4OFl0CyRj7Qzoca1IahiFSjejoaNElkIyxP6TDsSalYRgiIiIiTWMYItUYNGiQ6BJIxtgf0uFYk9IwDJFqHD58WHQJJGPsD+lwrElpGIaIiIhI0xiGiIiISNMYhkg1kpOTRZdAMsb+kA7HmpSGYYiIiIg0jWGIVCMmJkZ0CSRj7A/pcKxJafjUeitmnzmFH0pLhWw7zMMD7/QJatGyOzKB/GI7F2QDH09tPqWbiIiUi2HIih9KS/FV8XXRZTRbfjFw7qroKoiIiOSPp8lINRISEkSXQDLG/pAOx5qUhmGIVGPGjBmiSyAZY39Ih2NNSsMwRKoxdOhQ0SXYrKzit9e5RUBljbhabFVrBH655Tq0ojKgrk5cPc2lpP5QOo41KQ2vGSLVKCwsFF2CRVdLgG/PAj/mAsU3f5u+ci+gA9ClPTDwXuD++wB3N2FlmqmuBY79DHz3kym01Rh/e+/NXUBbV6BPF+CB3sC99wA6nbharZF7f6gJx5qUhmGIyMEqqoHdx4ADZ5uepw5Awa/AJ1nA3uPA6DBgaB9AL/DY7al84KPvgV/Lm57nRiVw9GfTf727AH+4H/Byl65GIiJ74GkyUo2goJbdhsCRrpQAf0m1HIRuV10LfHwUWPulKUhJzVhnujXDunTLQeh2OZeBt/cAJ/McVtpdkWN/qBXHmpSGYYhUIyUlRXQJZgpLgb/uN11b0xJnr5gCUaWEgaiuDkg+BHx1pmXLV9YAG74CTubbty57kFt/qBnHmpSGYYhUY8GCBaJLaFBrBDZ9DZRWND3PqqdN/1ny8zVg1zH71mbJ4QvAgZ8sz2OtbmMdsPlb4D83m55HBDn1h9pxrElpGIZINZKSkkSX0ODLU0Cene4AfuAscPayfdZlSUk5sDPTPuuqqAa2f2+fddmLnPpD7TjWpDSyDkNGoxErVqxAQEAA3NzcEBYWhoyMDPTp0wdxcXGiy2tUXXU1qp+bgdoP15tNr935Maon/xF1N24Iqsyy5MUGHPp4sc3TqWlVNUBatn3X+e+T9l1fY749C5Tb8ZTcqV/EPBKGiKi5ZB2Gpk6dijfffBPTp0/HZ599hgkTJmDSpEk4f/48IiIiRJfXKJ2LC5znvALjp6kwHssCANRduAjj3zbB6dWXoWvbVmyB5HBZucDNKvuu88xloLDEvuu8Va0RONiMi7xt9a0D1klEZG+yDUPbtm3Dpk2bsHv3brz88ssYNmwY5s6di8GDB6OmpqYhDD399NPo3Lkz2rdvj8jISBw8eFBw5YCuV0/op/4RtX9Zibrr11GzbDn0Tz4OfWiI6NJULSMjQ3QJAEzfqnLIeq84Zr0AcOVXoMTC9U0tJcXpPVvJpT+0gGNNSiPbMLR06VKMHDkS0dHRZtP9/f3h4uKCkBBTsJg7dy4uXbqEX3/9FQsXLsS4ceNElHsH/Zgnoevpi5rpCYBeD/0fY0WXpHonT0pwLskGeQ56ru+lIsesFwAuOajmwlKg3M5HyVpKLv2hBRxrUhpZ3nQxLy8PJ06cwKxZs+54Lzc3F8HBwXB1dQXw2/0s6urq4OLigsuXL6OiogJubpZv4auz8Va5Tn9ZBn1YaDM/gWn9utAQ1B05Cv0fJkDn4tLsdaSnp0M3KKrZywHAuLlp6B5oaNYyh3a9hSOpK8ymVVeUwbffCJvXkZGRjj8/MqxZ27VFY71wu8TERKvzJSYm2qukJj33YTFc23Zo+NnaN8aaen/mVvOf/5myB5MGP3Z3xTUhcsw8DI5506a6rL1/e90+9wai+JfTd1GddUrqD6VT21i/tuxDAKZ99q2v5U6JdYuouc7GZwbJNgwBQJcuXcyml5eXIyMjA6NGjTKb/vTTTyMlJQWVlZVISEiwGoSkUHfhAozb/gX9xPEwbtkG/YNDoOvUSXRZFkU+OReRY+aZTUtebBBTjJI56Jdbp3PkgVzH7ZB0Dlw3EZE9yDIMeXt7AwBycnLMgs/y5ctRUFCAAQMGmM2/detWbNy4EampqSgtLbVpG7amxRGZ3+Or4uadQ6irqkbNsr9AP3YMnKb8EXXFxahd/g6cli+FrhnPVzAYDPh3C5+EuXo/cO5qixa9K9HRBiQvtv/TO0+ftn5kITEx0eq3DFeuXGmvkpq0eDdw7ZY2vP1ISb36IytNvX+7mDG/w8fLHfNk1G9zgKTD5tPsVffP5045/FlrSuoPpVPbWM95ex0A09+EW1/LnRLrlnPNsgxDfn5+CA0NxZIlS+Dl5QUfHx8kJycjNTUVABr9JpmLiwuefPJJhIWFITIyEr1795a67AbGv/0dOmdn6GNNfzWcnn8ONc/NgDFlJ5zGy+OaJjVatGiR6BIAAN09zcOQvfTwsv8663V30Lo7tJHPQ2fl0h9awLEmpZHlBdR6vR5JSUkIDg5GfHw8pkyZAm9vbyQkJMDZ2RmhoU1fw1NVVYWLFy9KV+xtjMeyYEzdC6c5r0LnbMqaujZt4PTqbBj/sQV1Fy4Iq03tJkyYILoEAMB9Djob6ufAs6zdPAG35l/WZpWjxqIl5NIfWsCxJqWR5ZEhAOjduzfS0tLMpsXGxiIwMBCtW7cGABQVFeHLL7/E6NGj4ezsjPXr1+OXX3654zSalPT9w6HfvePO6f2Cof9kp4CKbBMzL71Z0+UoMDAQ2dl2vtthC0T0Mj2lvrrWfuvs4eXYI0MuTsAgP+DrFj6TrCmD/e27vrshl/7QAo41KY0sjww1JTMz845TZKtXr0bXrl3RuXNn/Otf/8KePXsarjkiEqGNKxBl5xAwLNC+62vM0N6Akx33CD285HVkiIioKbI9MnS7srIy5OTk4Pnnn2+Y1rFjR3z11VcCqyJq3Ogw4EQeUGyHp68E+wD9e979eqy5px0wMgTY88Pdr8tJD0yKctgX64iI7EoxYcjd3R21tXY870CqYzAYRJfQwM0F+H9DgDVfNH26zJZvY3V0BybeL12oeCjI9C3E0wVNz2NL3b+PMF2HJCdy6g+141iT0ijqNBmRJWvXrhVdgpl77wHiDIBrC//JcY8HkDAcaNfarmVZ5KQHpg4Fgrq1bHkdTEHoAXFf5myS3PpDzTjWpDQMQ6Qa8fHxoku4Q0AX4NXRQEDn5i33XwHA7N8BXu6OqcuSVs7AtGhgzADA2cn25bw9gBceBqL7Oq62uyHH/lArjjUpjWJOkxFZk56eLrqERnV0B+KHA9m/mG5umF0ANHafMRcnYEAv01EVR35zzBZ6PWAIBMJ8gQNngYPngLImHuTa3Qt4IMBUeysZ71Hk2h9qxLEmpZHxrotIPfQ604XQwT5AZTWQXwxcLQFqjKYA0a0D0LWDfb/NZQ+ebYHR4cDvwoCiUiCvGLhZabqGybOtKbTJ5aaKREQtxTBEJDFXF9MNFB15E0V70+tM3za7p53oSoiI7E9m/w4lajne5I0sYX9Ih2NNSsMwRKqxfft20SWQjLE/pMOxJqXhaTIrwjw8FLltH0H3eBG1XQBYuHAhn4lETWJ/SIdjTUrDMGTFO32CRJfQImMHiq6AiIhIGXiajIiIiDSNYYhUY82aNaJLIBljf0iHY01KwzBEqhEcHCy6BJIx9od0ONakNAxDpBrR0dGiSyAZY39Ih2NNSsMwRERERJrGMERERESaxq/WkyL07Wv9UegLFy60aT5SH/aHdDjWpEY8MkSq8cYbb4gugWSM/SEdjjUpDcMQERERaRrDEBEREWkawxARERFpGsMQERERaRrDEBEREWkawxARERFpGsMQERERaRrDUBMefvhhhIWFITQ0FDExMSgpKRFdEhERyUx6ejqCg4Ph7++PadOmoba2VnRJVr3wwgvo3r07nJ2Vc9/lS5cuYfjw4QgMDES/fv3w+uuv23X9DENNSE5Oxg8//IAff/wRvr6+WLlypeiSiIhIRoxGI6ZNm4akpCT89NNPKCkpwZYtW0SXZdXEiRNx5MgR0WU0i7OzM95++21kZ2fj6NGjOHDgAHbt2mW39TMMNaF9+/YATM1+48YN6HQ6wRUREZGcHD58GN26dUNQUBAA4Nlnn0VKSorgqqx74IEH0LlzZ9FlNEvXrl0xcOBAAECrVq0QGhqK3Nxcu61fV1dXV2e3tanME088ge+++w5BQUH49NNP4e7uLrokIiK6CxWVVdiU8jkqKqsAAAVXiwAAXTt1NHtd7/ePPABfn8aDQ0pKCnbs2IGtW7cCALKzs/HUU0/h2LFjdq/7m8PHceRETsPPluru6dMZTz48xOo/4p2dnVFTU2P3WuuVlN3E5h2fo6bWaLVmnQ6Y9MRw3OPVwep6i4qKEB4ejv3799vtGXg8MmTB7t27cfnyZQwePBgffvih6HKIiOguubm2QmRYXxRcLWr4gwzgjtcFV4vQ2duzySAEAHV1dWaBw5HHFgaF9UVlVZXVuouKf8UDg0JkcTajnXsb9OvjZ9NY+/f0sSkIVVZWIiYmBjNnzrTrw4AZhqzQ6/V45plnsHHjRtGlEBGRHYQH+SOkz70W52nv0RZPPjzE4jw9evQwO1Vz6dIldO/e3S413s61lQsmjB5mNeSMfmgwvD3bO6SGlnhwUAh6de9icZ4u93jhkQcHWV1XbW0tnn76afTv3x+zZ8+2V4kAGIYaVVJSgoKCgoafU1JSEBwcLLAiIiKyF51OhzGPPggP9zZNzjN+lAGt3VwtrmfgwIHIz8/HqVOnAAAbNmzA2LFj7VrrrXp174Lo+8OafL/vfb6IDLPf0RJ70Ov1mPDYMLi2cmn0faf/e9/Z2cnquuLi4uDh4YF33nnH3mUyDDXm119/xRNPPIGQkBCEhoYiKysL7777LgDHHgYlIiJptG3thpiRQxt9b8jAfvDv5WN1HU5OTli/fj1iYmJw3333wd3dHbGxsfYu1cyIByLMrmmq16a1K8aNHGrT6bHp06eje/fuqK2tRffu3ZGQkOCIUht4tffA48P/q9H3Hhk6EN0a+Ty3+/bbb/G3v/0NmZmZ6N+/P8LDw/Hee+/ZrUZeQN1M6d9lIf9yISY+/hCcnawnWSIikq+P932D746davi5U8cOeOGPY+HiIt978FwpvI7Vm3ai5pZ7Gk0e8zD6WTn1J1JdXR0279yPU2cvNkzr1b0L4iY9Br1e/HEZ8RUoSGVlFb76/gdUVdcwCBERqcAow/0N19jo9TpMfOwhWQchAOh8jxcejf7tGpsB/XrLOggBplOTY0c+CPc2rQEArVq5YMJogyyCEKCCMHT8+HGMGzcO3t7ecHNzQ0BAAObOneuQbR04ehI3KyoxYkiEQ9ZPRETSqv+jrNPpMGJIBHy6eIsuySZDBobAz7cbOrRzxxMjGj8FJTfubVpj7O9MpyYfHz4YXh3aCa7oN4o+TXbkyBEMHToUPXv2xKuvvgpfX19cuHABBw4cwIYNGywuO+ftdRJVSURERCIsey3OpvnkfSzQitmzZ8PDwwPfffcd2rX7LWE+++yzAqsiIiIiJVHskaGbN2/Cw8MDL7zwAlatWuXQbVVWVuHtD/6JHt06Ycr43zl0W0RERCQtxR4ZKi4uhtFohI+P9a8/NqYlp8nOnL/E02tEREQKYetpMsVeQO3p6Qm9Xo/8/HzRpRAREZGCKfY0GQAMGzYM2dnZyMnJMbtmyJ7SDh7D518dRkLsGPTo1skh2yAiIiJxFHtkCABWrFiB0tJSREVFYePGjUhLS8OmTZswbdo0u6y/srIKXx/6EX38ejAIERERqZRirxkCgIiICBw8eBDz58/HrFmzUFFRgR49euAPf/iDXdZ//ddStG7tyvsKERERqZiiT5NJwWg0yuYOmURERGR/DENERESkaTzkQURERJrGMERERESaxjBEREREmsYwRERERJrGMERERESaxjBEREREmsYwRERERJrGMERERESaxjBEREREmsYwRERERJrGMERERESaxjBEREREmsYwRERERJrGMERERESaxjBEREREmsYwRERERJrGMERERESaxjBEREREmsYwRERERJrGMERERESaxjBEREREmsYwRERERJrGMERERESaxjBEREREmsYwRERERJrGMERERESaxjBEREREmvb/AUg9rJSBFpPjAAAAAElFTkSuQmCC\n",
  93        "text/plain": [
  94         "<Figure size 748.797x325.08 with 1 Axes>"
  95        ]
  96       },
  97       "execution_count": 5,
  98       "metadata": {},
  99       "output_type": "execute_result"
 100      }
 101     ],
 102     "source": [
 103      "circuit.draw(output='mpl')"
 104     ]
 105    },
 106    {
 107     "cell_type": "code",
 108     "execution_count": 6,
 109     "id": "4082037d",
 110     "metadata": {},
 111     "outputs": [
 112      {
 113       "name": "stdout",
 114       "output_type": "stream",
 115       "text": [
 116        "{'101': 3}\n"
 117       ]
 118      }
 119     ],
 120     "source": [
 121      "simulator = Aer.get_backend('qasm_simulator')\n",
 122      "result = execute(circuit, backend= simulator, shots= 3).result()\n",
 123      "counts= result.get_counts(circuit)\n",
 124      "\n",
 125      "print(counts)"
 126     ]
 127    },
 128    {
 129     "cell_type": "code",
 130     "execution_count": 7,
 131     "id": "14591c0d",
 132     "metadata": {},
 133     "outputs": [
 134      {
 135       "data": {
 136        "image/png": "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\n",
 137        "text/plain": [
 138         "<Figure size 504x360 with 1 Axes>"
 139        ]
 140       },
 141       "execution_count": 7,
 142       "metadata": {},
 143       "output_type": "execute_result"
 144      }
 145     ],
 146     "source": [
 147      "plot_histogram(counts)"
 148     ]
 149    },
 150    {
 151     "cell_type": "code",
 152     "execution_count": null,
 153     "id": "5a59b37b",
 154     "metadata": {},
 155     "outputs": [],
 156     "source": []
 157    },
 158    {
 159     "cell_type": "code",
 160     "execution_count": null,
 161     "id": "0b970552",
 162     "metadata": {},
 163     "outputs": [],
 164     "source": []
 165    },
 166    {
 167     "cell_type": "code",
 168     "execution_count": 8,
 169     "id": "60a80693",
 170     "metadata": {},
 171     "outputs": [
 172      {
 173       "data": {
 174        "text/plain": [
 175         "<AccountProvider for IBMQ(hub='ibm-q', group='open', project='main')>"
 176        ]
 177       },
 178       "execution_count": 8,
 179       "metadata": {},
 180       "output_type": "execute_result"
 181      }
 182     ],
 183     "source": [
 184      "IBMQ.load_account()"
 185     ]
 186    },
 187    {
 188     "cell_type": "code",
 189     "execution_count": 9,
 190     "id": "4d3d857c",
 191     "metadata": {},
 192     "outputs": [],
 193     "source": [
 194      "provider= IBMQ.get_provider('ibm-q')"
 195     ]
 196    },
 197    {
 198     "cell_type": "code",
 199     "execution_count": 10,
 200     "id": "96b71fcc",
 201     "metadata": {},
 202     "outputs": [],
 203     "source": [
 204      "qcomp=  provider.get_backend('ibmq_16_melbourne')\n",
 205      "job= execute(circuit, qcomp,shots=8000)"
 206     ]
 207    },
 208    {
 209     "cell_type": "code",
 210     "execution_count": 11,
 211     "id": "7dad728b",
 212     "metadata": {},
 213     "outputs": [
 214      {
 215       "name": "stdout",
 216       "output_type": "stream",
 217       "text": [
 218        "Job Status: job has successfully run\n"
 219       ]
 220      }
 221     ],
 222     "source": [
 223      "from qiskit.tools.monitor import job_monitor\n",
 224      "job_monitor(job)"
 225     ]
 226    },
 227    {
 228     "cell_type": "code",
 229     "execution_count": 12,
 230     "id": "64c84fa2",
 231     "metadata": {},
 232     "outputs": [
 233      {
 234       "data": {
 235        "image/png": 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\n",
 236        "text/plain": [
 237         "<Figure size 504x360 with 1 Axes>"
 238        ]
 239       },
 240       "execution_count": 12,
 241       "metadata": {},
 242       "output_type": "execute_result"
 243      }
 244     ],
 245     "source": [
 246      "result= job.result()\n",
 247      "plot_histogram(result.get_counts(circuit))"
 248     ]
 249    },
 250    {
 251     "cell_type": "code",
 252     "execution_count": 13,
 253     "id": "fd2a489c",
 254     "metadata": {},
 255     "outputs": [
 256      {
 257       "name": "stdout",
 258       "output_type": "stream",
 259       "text": [
 260        "{'000': 788, '001': 2242, '010': 243, '011': 584, '100': 697, '101': 2568, '110': 237, '111': 641}\n"
 261       ]
 262      }
 263     ],
 264     "source": [
 265      "print(result.get_counts(circuit))"
 266     ]
 267    },
 268    {
 269     "cell_type": "code",
 270     "execution_count": 14,
 271     "id": "53e35844",
 272     "metadata": {},
 273     "outputs": [],
 274     "source": [
 275      "res= result.get_counts()"
 276     ]
 277    },
 278    {
 279     "cell_type": "code",
 280     "execution_count": 15,
 281     "id": "b4bd85e2",
 282     "metadata": {},
 283     "outputs": [],
 284     "source": [
 285      "x=res"
 286     ]
 287    },
 288    {
 289     "cell_type": "code",
 290     "execution_count": 16,
 291     "id": "b2b8badb",
 292     "metadata": {},
 293     "outputs": [
 294      {
 295       "data": {
 296        "text/plain": [
 297         "{'110': 237,\n",
 298         " '010': 243,\n",
 299         " '011': 584,\n",
 300         " '111': 641,\n",
 301         " '100': 697,\n",
 302         " '000': 788,\n",
 303         " '001': 2242,\n",
 304         " '101': 2568}"
 305        ]
 306       },
 307       "execution_count": 16,
 308       "metadata": {},
 309       "output_type": "execute_result"
 310      }
 311     ],
 312     "source": [
 313      "{k: v for k, v in sorted(x.items(), key=lambda item: item[1])}"
 314     ]
 315    },
 316    {
 317     "cell_type": "code",
 318     "execution_count": 17,
 319     "id": "d1b84b98",
 320     "metadata": {},
 321     "outputs": [
 322      {
 323       "data": {
 324        "text/plain": [
 325         "'101'"
 326        ]
 327       },
 328       "execution_count": 17,
 329       "metadata": {},
 330       "output_type": "execute_result"
 331      }
 332     ],
 333     "source": [
 334      "res.most_frequent()"
 335     ]
 336    },
 337    {
 338     "cell_type": "code",
 339     "execution_count": 18,
 340     "id": "3080be04",
 341     "metadata": {},
 342     "outputs": [
 343      {
 344       "data": {
 345        "text/plain": [
 346         "'101'"
 347        ]
 348       },
 349       "execution_count": 18,
 350       "metadata": {},
 351       "output_type": "execute_result"
 352      }
 353     ],
 354     "source": [
 355      "secret_num"
 356     ]
 357    },
 358    {
 359     "cell_type": "code",
 360     "execution_count": 25,
 361     "id": "35e1c4a5",
 362     "metadata": {},
 363     "outputs": [
 364      {
 365       "data": {
 366        "text/plain": [
 367         "int"
 368        ]
 369       },
 370       "execution_count": 25,
 371       "metadata": {},
 372       "output_type": "execute_result"
 373      }
 374     ],
 375     "source": [
 376      "type(res['101'])"
 377     ]
 378    },
 379    {
 380     "cell_type": "code",
 381     "execution_count": 19,
 382     "id": "5d36b994",
 383     "metadata": {},
 384     "outputs": [],
 385     "source": [
 386      "perce=[0.276,0.400,0.191,0.187,0.292,0.415,0.40,0.179,0.265,0.317,0.431,0.321]"
 387     ]
 388    },
 389    {
 390     "cell_type": "code",
 391     "execution_count": 27,
 392     "id": "230452e1",
 393     "metadata": {},
 394     "outputs": [],
 395     "source": [
 396      "final= []"
 397     ]
 398    },
 399    {
 400     "cell_type": "code",
 401     "execution_count": null,
 402     "id": "d7f504d6",
 403     "metadata": {},
 404     "outputs": [],
 405     "source": []
 406    },
 407    {
 408     "cell_type": "code",
 409     "execution_count": 33,
 410     "id": "9d41b6df",
 411     "metadata": {},
 412     "outputs": [
 413      {
 414       "data": {
 415        "text/plain": [
 416         "[2442,\n",
 417         " 2510,\n",
 418         " 1589,\n",
 419         " 2730,\n",
 420         " 2476,\n",
 421         " 2498,\n",
 422         " 3347,\n",
 423         " 2301,\n",
 424         " 1163,\n",
 425         " 1591,\n",
 426         " 2506,\n",
 427         " 1410,\n",
 428         " 3405,\n",
 429         " 1725,\n",
 430         " 1697,\n",
 431         " 2539,\n",
 432         " 3399,\n",
 433         " 2470,\n",
 434         " 3316,\n",
 435         " 1560,\n",
 436         " 2348,\n",
 437         " 3395,\n",
 438         " 1512,\n",
 439         " 1869,\n",
 440         " 3528,\n",
 441         " 2533,\n",
 442         " 2536,\n",
 443         " 2442,\n",
 444         " 1249,\n",
 445         " 2443,\n",
 446         " 1708,\n",
 447         " 3511,\n",
 448         " 1181,\n",
 449         " 1135,\n",
 450         " 2475,\n",
 451         " 3215,\n",
 452         " 2350,\n",
 453         " 2520,\n",
 454         " 2484]"
 455        ]
 456       },
 457       "execution_count": 33,
 458       "metadata": {},
 459       "output_type": "execute_result"
 460      }
 461     ],
 462     "source": [
 463      "final"
 464     ]
 465    },
 466    {
 467     "cell_type": "code",
 468     "execution_count": 36,
 469     "id": "141d77a6",
 470     "metadata": {},
 471     "outputs": [
 472      {
 473       "data": {
 474        "text/plain": [
 475         "[<matplotlib.lines.Line2D at 0x2bca5afcfd0>]"
 476        ]
 477       },
 478       "execution_count": 36,
 479       "metadata": {},
 480       "output_type": "execute_result"
 481      },
 482      {
 483       "data": {
 484        "image/png": 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\n",
 485        "text/plain": [
 486         "<Figure size 432x288 with 1 Axes>"
 487        ]
 488       },
 489       "metadata": {
 490        "needs_background": "light"
 491       },
 492       "output_type": "display_data"
 493      }
 494     ],
 495     "source": [
 496      "import matplotlib.pyplot as plt\n",
 497      "plt.plot(final)\n"
 498     ]
 499    },
 500    {
 501     "cell_type": "code",
 502     "execution_count": null,
 503     "id": "1855547f",
 504     "metadata": {},
 505     "outputs": [],
 506     "source": []
 507    }
 508   ],
 509   "metadata": {
 510    "kernelspec": {
 511     "display_name": "Python 3",
 512     "language": "python",
 513     "name": "python3"
 514    },
 515    "language_info": {
 516     "codemirror_mode": {
 517      "name": "ipython",
 518      "version": 3
 519     },
 520     "file_extension": ".py",
 521     "mimetype": "text/x-python",
 522     "name": "python",
 523     "nbconvert_exporter": "python",
 524     "pygments_lexer": "ipython3",
 525     "version": "3.8.8"
 526    }
 527   },
 528   "nbformat": 4,
 529   "nbformat_minor": 5
 530  }
 531