jorge_eliecer_Gaitan_Heredia.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyP/CIRI4UoQvBHFrZtycrFX",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/JorgeGaitanDev/curso_computacion_cuantica/blob/patch-6/jorge_eliecer_Gaitan_Heredia.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "__authors__    = \"Jorge Eliecer Gaitan Heredia\"\n",
        "__credits__   = [\"Jorge Eliecer Gaitan Heredia\"]\n",
        "__copyright_amp__ = \"(c) Jorge Eliecer Gaitan Heredia\"\n",
        "__email__   = \"jegaitanh@correo.udistrital.edu.co\"\n",
        "\n",
        "# uutilizamos unittest en python para este proyecto"
      ],
      "metadata": {
        "id": "_h-V_tpJw31X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Primero que todo debemos saber que es el \"Algoritmo de Grover\".\n",
        "- El algoritmo de Grover es uno de los algoritmos cuánticos más famosos introducido por Lov Grover en 1996. Inicialmente, se propuso para problemas de búsqueda no estructurados, es decir, para encontrar un elemento marcado en una base de datos no estructurada. Sin embargo, el algoritmo de Grover es ahora una subrutina de varios otros algoritmos, como Grover Adaptive Search [2]. Para obtener más información sobre el algoritmo de Grover, consulta Grover’s Algorithm en el libro de texto de Qiskit.\n",
        "\n",
        "Qiskit implementa el algoritmo de Grover en la clase Grover. Esta clase también incluye la versión generalizada, Amplificación de Amplitud [3], y permite configurar iteraciones individuales y otros meta-ajustes para el algoritmo de Grover."
      ],
      "metadata": {
        "id": "JbIjf2Wv4b2g"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Para ejecutar el algoritmo de Grover.\n",
        "- Cuando ejecutamos el algoritmo de Grover con la clase \"Grover\", en primer lugar, necesitamos especificar un oráculo para el circuito del algoritmo de Grover yuamos QuantumCircuit como el oráculo del algoritmo de Grover. Sin embargo, hay varias otras clases que podemos usar como oráculo del algoritmo de Grover.\n",
        "\n",
        "Para este algoritmo hay que tener en cuenta que el oráculo para Grover debe ser un oráculo de cambio de fase. Es decir, multiplica las amplitudes de los “estados correctos” por un factor de (punto)."
      ],
      "metadata": {
        "id": "iF8XKjy4FB24"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Inicialmente configuramos una cuenta en ***IBM Quantum Experience*** para poder desarrollar esta aplicación."
      ],
      "metadata": {
        "id": "26E3ixX7xvg9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install git+https://github.com/qiskit-community/qiskit-textbook.git#subdirectory=qiskit-textbook-src"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WkjHbl6f0yL4",
        "outputId": "d0e8ec9b-d91c-48fd-8c11-9544bfb20084"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting git+https://github.com/qiskit-community/qiskit-textbook.git#subdirectory=qiskit-textbook-src\n",
            "  Cloning https://github.com/qiskit-community/qiskit-textbook.git to /tmp/pip-req-build-qu7_p6s_\n",
            "  Running command git clone -q https://github.com/qiskit-community/qiskit-textbook.git /tmp/pip-req-build-qu7_p6s_\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Importamos en este notebook la implemenatación del algoritmo de busqueda de Grover y utilizaremos Qiskit con diferentes oraculos para su desarrollo.\n",
        "- Iniciamos importando pylab y numpy y a su vez qiskit para poder utilizar \"aer_simulator\" y otros factores para su visible implementación"
      ],
      "metadata": {
        "id": "EF4RmzYwx9UG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pylab\n",
        "import numpy as np\n",
        "from qiskit import Aer\n",
        "from qiskit.utils import QuantumInstance\n",
        "from qiskit.tools.visualization import plot_histogram\n",
        "from qiskit.algorithms import Grover, AmplificationProblem\n",
        "from qiskit.circuit.library.phase_oracle import PhaseOracle"
      ],
      "metadata": {
        "id": "ylu-NPp00zKY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Primero veremos como ncontrar soluciones a los problemas 3-SAT.\n",
        "\n",
        "1. Vemos una solucion del problema de 3-Satisfabilidad (3-SAT) y vamos cómo podemos usar la búsqueda cuántica para encontrar sus soluciones satisfactorias. Los problemas de 3-SAT buscando la informacion necesaria, generalmente se expresan en Formas normales conjuntivas (Conjunctive Normal Forms, CNF) y se escriben en el formato DIMACS-CNF (segun lo investigado).\n",
        "\n",
        "2. Traere unos valores de prueba como se muestra a continuación"
      ],
      "metadata": {
        "id": "IZgJRjayy53d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "input_3sat_instance = '''\n",
        "c example DIMACS-CNF 3-SAT\n",
        "p cnf 3 5\n",
        "-1 -2 -3 0\n",
        "1 -2 3 0\n",
        "1 2 -3 0\n",
        "1 -2 -3 0\n",
        "-1 2 3 0\n",
        "'''"
      ],
      "metadata": {
        "id": "hWslJttM07zo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "En el CNF de esta instancia 3-SAT se observa que contiene 3 variables y 5 cláusulas:\n",
        "\n",
        "![image.png](data:image/png;base64,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)\n",
        "\n",
        "Podemos verificar que esta instancia de problema 3-SAT tiene tres soluciones satisfactorias:\n",
        "\n",
        " Donde V1, V2, V3 son iguales a (T, F, T) a (F, F, F) y (T, T, F)\n",
        "\n",
        "O expresado usando la notación DIMACS donde los numeros negativos son \"FALSE\" y los positivos son \"TRUE\":\n",
        "$$\n",
        "\\begin{array}{l}\n",
        "(1, -2, 3) \\\\\n",
        "(-1, -2, -3) \\\\\n",
        "(1, 2, -3)\n",
        "\\end{array}\n",
        "$$\n",
        "\n",
        "Con esta entrada a este problema, creamos el oracle correspondiente para nuestra búsqueda de Grover. En particular, utilizamos el componente PhaseOracle, que admite el análisis de cadenas de formato DIMACS-CNF y la construcción del correspondiente circuito de oráculo necesariamente."
      ],
      "metadata": {
        "id": "RO9Ot_XizyRx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import tempfile\n",
        "from qiskit.exceptions import MissingOptionalLibraryError\n",
        "\n",
        "fp = tempfile.NamedTemporaryFile(mode='w+t', delete=False)\n",
        "fp.write(input_3sat_instance)\n",
        "file_name = fp.name\n",
        "fp.close()\n",
        "oracle = None\n",
        "try:\n",
        "    oracle = PhaseOracle.from_dimacs_file(file_name)\n",
        "except MissingOptionalLibraryError as ex:\n",
        "    print(ex)\n",
        "finally:\n",
        "    os.remove(file_name)"
      ],
      "metadata": {
        "id": "KNqEHqaP0-RP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Digitado lo anterior podemos afirmar que \"oracle\" se puede utilizar para la instacia de Grover."
      ],
      "metadata": {
        "id": "cmPoBtOC1dav"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "problem = None\n",
        "if oracle is not None:\n",
        "    problem = AmplificationProblem(oracle, is_good_state=oracle.evaluate_bitstring)"
      ],
      "metadata": {
        "id": "4uRTqz071BDG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Después podemos configurar el backend y ejecutar la instancia de Grover para obtener el resultado:\n",
        "\n",
        "- Usaremos \"Aer\" para simular todo el proceso."
      ],
      "metadata": {
        "id": "7cih_8lG1xk6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "backend = Aer.get_backend('aer_simulator')\n",
        "quantum_instance = QuantumInstance(backend, shots=1024)\n",
        "grover = Grover(quantum_instance=quantum_instance)\n",
        "result = None\n",
        "if problem is not None:\n",
        "    result = grover.amplify(problem)\n",
        "    print(result.assignment)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xYAXPli21DPe",
        "outputId": "ff78d8b8-e124-4ab7-9823-9aa0653cea1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "011\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Como se vio anteriormente, se obtiene una solución satisfactoria al problema 3-SAT especificado. Y de hecho es una de las tres soluciones satisfactorias.\n",
        "\n",
        "- Dado que utilizamos el 'aer_simulator', también es devuelto el resultado de la medición completa, como se muestra en la gráfica a continuación, donde se puede ver que las cadenas binarias \n",
        "$$\n",
        "\\begin{array}{l}\n",
        "000 \\\\\n",
        "011 \\\\\n",
        "101\n",
        "\\end{array}\n",
        "$$\n",
        "\n",
        "Observamos el orden de los bits en cada cadena, correspondientes a las tres soluciones satisfactorias, tienen todas altas probabilidades asociadas."
      ],
      "metadata": {
        "id": "PnOrkMVp1992"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "if result is not None:\n",
        "    display(plot_histogram(result.circuit_results[0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "D-VrldtG1FK_",
        "outputId": "9101f512-7915-474b-8f89-6766af74301c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Luego vamos a realizar unas expresiones lógicas booleanas para ver su comportamiento.\n",
        "\n",
        "- Observamos que el Grover de Qiskit también se puede utilizar para realizar búsquedas cuánticas en un Oracle construido a partir de otros medios, además de DIMACS. Con esto quiero decir que, el PhaseOracle se puede configurar utilizando expresiones lógicas booleanas arbitrarias, como se muestra en el apartado de siguiente."
      ],
      "metadata": {
        "id": "23UR1Nao29O7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "expression = '(w ^ x) & ~(y ^ z) & (x & y & z)'\n",
        "try:\n",
        "    oracle = PhaseOracle(expression)\n",
        "    problem = AmplificationProblem(oracle, is_good_state=oracle.evaluate_bitstring)\n",
        "    grover = Grover(quantum_instance=QuantumInstance(Aer.get_backend('aer_simulator'), shots=1024))\n",
        "    result = grover.amplify(problem)\n",
        "    display(plot_histogram(result.circuit_results[0]))\n",
        "except MissingOptionalLibraryError as ex:\n",
        "    print(ex)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        },
        "id": "cHbZvAy11I3X",
        "outputId": "dce94abc-13ce-4908-913a-8b3715ae2852"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ],
            "image/png": 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fAnkQYWDyujhwQHbSERERKQz1KZCVhLPIuvQHVmYnHRERkcJQnwL5HPATM2uZvMDM9gV+HD1GRESkyajPbR4/A74OLDKze4Ha337sRejAY8DPc5OeiIhIfuyxQLr7WjM7FvgNoRBa7SLgb8C33X1N7lIUERFpePUaKMDd3wdONrN2QA9CkVzs7htzmZyIiEi+pPSDyVFBfCNHuYiIiBSMVH/NQ0REZK+gAikiIhJDBVJERCSGCqSIiEgMFUgREZEYKpAiIiIxVCBFRERiqECKiIjEUIEUERGJoQIpIiISQwVSREQkhgqkiIhIDBVIERGRGCqQIiIiMVQgRUREYqhAioiIxFCBFBERiaECKSIiEkMFUkREJIYKpIiISAwVSBERkRgqkCIiIjFUIEVERGKoQIqIiMRQgRQREYmhAikiIhJDBVJERCSGCqSIiEgMFUgREZEYKpAiIiIxVCBFRERiqECKiIjEUIEUERGJ0aAF0syGmdmzZrbSzNzMvpG0/OFofuL0WtJjSszsHjNbZ2YfR/G6NuTrEBGRpq+hzyBbAfOBa4BtdTxmEnBwwnRy0vJxwFnA+cBxQBtgopkV5SJhERHZOxU35Mrc/XngeQhni3U8rMrdV8ctMLP9gUuBS9z979G8fwfeB04E/pbtnEVEZO9UiNcgh5rZWjNbZGb3m1mHhGWDgebAi7Uz3H0F8B5wbAPnKSIiTViDnkHWwwvA08AyoBT4KTDZzAa7exXQCagB1iU9b020bBdmdgVwBUDnzp2ZMmUKAN27d6d169bMmzcPgAMOOIA+ffrw8ssvA1BcXMzQoUOZM2cOmzdvBqC8vJw1a9awYsUKAHr27ElJSQnz588HoEOHDhxxxBFMmzYNgJKSEioqKpg1axZbt24FYMiQIVRWVrJy5UoAjjzySIqKinj33XcB6NSpE4cddhgzZswAoGXLlgwZMoSZM2eybVtola6oqGDZsmWsXh1OtHv37k1NTQ0LFy4EoEuXLnTt2pWZM2cC0KpVK8rLy5kxYwZVVVUADB06lEWLFrF27VoA+vbtS1VVFYsXLwagW7dudOzYkVmzZgHQpk0bysrKmDZtGtXV1QAMGzaMd955h/Xr1wMwYMAAtmzZwtKlSwEoLS2lffv2zJkzB4B27doxYMAApk6dirtjZgwfPpx58+axceNGAMrKytiwYQPLly/XftJ+0n4qwP0EI0jFlClTCno/7Y65e0ovNlvMbCvwHXd/eDeP6UxoPj3X3Z82swuAPwDNPSFxM5sMLHb3b+5uneXl5V77BhURkdRdPi61x98/Jjd5ZIuZzXb38rhlhdjEupO7fwBUAj2jWauBIuDApId2jJaJiIhkRUEXSDM7EOgCrIpmzQa2AyMTHtMV6AW82uAJiohIk9Wg1yDNrBXQI/qzGXCImQ0ENkTTrcBThIJYCtwGrAX+AuDuH5nZA8BYM1sLrAd+CbxFuD1EREQkKxr6DLIceDOaWgL/Gf3/x4TON/2AvwKLgEeAhUCFu29JiDGGUDAfA6YDW4HT3L2mgV6DiIjsBRr6PsgpgO3mISfVI0YVcHU0iYiI5ERBX4MUERHJFxVIERGRGCqQIiIiMVQgRUREYqhAioiIxFCBFBERiaECKSIiEkMFUkREJIYKpIiISAwVSBERkRgqkCIiIjFUIEVERGKoQIqIiMRQgRQREYmhAikiIhJDBVJERCSGCqSIiEgMFUgREZEYKpAiIiIxVCBFRERiqECKiIjEUIEUERGJoQIpIiISQwVSREQkhgqkiIhIDBVIERGRGCqQIiIiMVQgRUREYqhAioiIxFCBFBERiaECKSIiEkMFUkREJIYKpIiISAwVSBERkRjF+U5ARPYeNTU1lJeX06VLFyZOnMhxxx3Hli1bAFi7di3HHHMMzzzzTJ6zFAl0BinSSNXU1DBo0CBOPfVUAL7xjW9w2GGHMXDgQAYOHMjcuXPznOGu7rrrLnr16rXz71deeYW5c+cyd+5cKioqOPPMM/OYncjnqUCKNFLJxQbgjjvu2FlwBg4cmKfM4lVWVvLcc89x2WWX7bJs8+bNTJ48ma997Wt5yCy/kg90Lr30UgYMGED//v35+te/ztatW/OcYcNL3ia1Ro8eTatWrRosDxVIaRSSPzD33nsvPXr0wMxYt25dnrNreLsrNoVqzJgxjB07lmbNdv3aeeaZZ/jSl75EmzZt8pBZfiUf6PzqV79i3rx5vPXWWxxyyCHce++9ecwuP+IO/mbNmsXGjRsbNA8VSGkUkj8wX/ziF5k0aRKHHnpoHrPKn7qKzU033UT//v259tprqaqqylN2u5o4cSIdOnRg8ODBscv//Oc/c/755zdwVvkXd6BTe5Dg7mzbtg0zy1d6eRG3TWpqavje977H2LFjGzQXFcgC88knn3DMMccwYMAA+vTpwy233ALAcccdt/PaUufOnfeqpqi4D8ygQYMoLS3NX1J5VFexue2221iwYAFvvPEGGzZs4Pbbb89ThruaPn06zz77LKWlpZx33nlMnjyZiy66CIB169bx+uuvc8opp+Q5y3i5/EzWdaBzySWX0KlTJxYsWMDVV1+dldfRWMRtk3vvvZfTTz+dgw8+uEFzUYEsMCUlJUyePJl58+Yxd+5cXnjhBV577bW9ujPD7prm9kZ1FZuDDz4YM6OkpIRLLrmE119/Pd+p7nTbbbdRWVnJ8uXLmTBhAieccALjx48H4Mknn+TUU0+lRYsWec4yXq4+k7s7q37ooYf44IMP6NWrF4899li2XkrBi9smH3zwAU888UReDhT0jVNgzGznRejt27ezffv2zzWx7G2dGfbUNLc3qqvYrFq1CghNc8888wx9+/bNc6b1M2HChIJuXs3VZ3J3Z9UARUVFnHfeeTz11FPZeSGNQNw26dOnD0uWLKFHjx6Ulpbyr3/9ix49ejRIPiqQBaimpoaBAwfSoUMHRo4cyZAhQ3YuK8TODHU1QWWjN96evkTkMxdeeCH9+vWjX79+rFu3jptvvjnfKcUaMWIEEydO3Pn3lClT+PKXv5zHjPYsF5/JuAOdRx99lCVLlgDhQOfZZ5/lqKOOyuprKWRx22Tjxo2sXr2a5cuXs3z5cvbdd9+d2yjXVCDTUFdBWLZsGUOGDKFHjx6ce+65fPrpp2nFLyoqYu7cuVRWVvL6668zf/78ncsKsTNDXU1Q2eiNt7umuUKW6/dIrcRiM3nyZN5++23mz5/P+PHj0+oOX1fee3uv4Yb6TLo7o0aN2nmgs2rVKn70ox+lHEf7MTtUINNQV0G48cYbufbaa1myZAnt2rXjgQceyGg9bdu25fjjj+eFF14AMuvMsGLFCo4//nh69+5Nnz59uOuuuwCYO3cuX/jCFxg4cCDl5eVpXbeqqwkql73x7r77brp27UplZSX9+/dP63aHurbJvHnzqKiooF+/fpx22mls3rw55dgN9R7Jtrry3tt7DdfK5mcyUe2BTrNmzZg+ffrOA50//vGPabUWNYX9mNzSUKsh7wvVUHNpqKsgTJ48mT/96U8AjBo1iltvvZWrrroqpdgffvghzZs3p23btmzbto2///3v3HjjjUBmnRmKi4v5xS9+QVlZGVu2bGHw4MGMHDmSG264gVtuuYWvfOUrPP/889xwww1MmTIl5fg1NTUMHjyYJUuW8O1vf3tnE9Qll1zC888/T+/evfnFL36RctxEI0aMYMSIEUC4YXj06NEZxatrm1x22WXceeedDB8+nAcffJA77riDn/zkJynFzuV7JJfqynvQoEEZx758XP0fe/+Y1GKvWLGCiy++mDVr1mBmXHHFFVxzzTWce+65LFy4EIBNmzbRtm3blEcYytVnMpdyuR/3JiqQaUouCIcffjht27aluDhs0q5du7Jy5cqU465atYpRo0ZRU1PDjh07OOecc3beHD9hwgS+//3vp5XvwQcfvLOLdOvWrenVqxcrV67EzHaeIX300Ud07tw5rfi1TVCbNm3ijDPOYP78+fTt25eHHnqImpoarr76ah577DEuueSStOLnQl3bZNGiRQwbNgyAkSNHctJJJ6VcICF375FcFhqo+2CnkNV1sJPYA/S73/0u+++/f8qxc/WZzLXGuB8LjQpkmpILwoIFC7ISt3///rz55puxy9I5s4uzfPly3nzzTYYMGcK4ceM46aSTuP7669mxYwevvvpqRrETm6Bqe1HW9sYbO3ZsQRXIRInbpE+fPvz1r3/la1/7Gk888QQrVqxIK2au3iO5VtfBTiGr62Cnd+/eQGjmf/zxx5k8eXLKsXP1mUzlQAdSP9hpjPsx19skVSqQGaotCDNmzGDTpk1UV1dTXFxMZWUlXbp0yXd6u9i6dStnnXUW48aNo02bNtx888386le/4qyzzuLxxx/n0ksvZdKkSSnFjGuCuuGGG3Z2zc6kN15DfGCSt8mDDz7I6NGj+clPfsLpp5/OPvvsk3rQBI3tPVIr7mCnMUg82Kn1yiuv0LFjR3r27JnHzPKjse7HQqBOOmn48MMP2bRpE8DOgtCrVy+OP/54nnzySQAeeeQRvvrVr+YzzV1s376ds846iwsvvHDnTc2PPPLIzv+fffbZaXXSWbVqFccffzz9+/fn6KOPZuTIkZxyyilZ6Y2Xa3Hb5KijjuLFF19k9uzZnH/++Rx++OEpx22s75G4vBvTbQbJBzu1CrH3dy419v1YKHQGmYa6rkn07t2b8847j5tvvplBgwZx6aWXphQ3l2dL7s6ll15Kr169uO6663bO79y5M1OnTmXEiBFMnjw5rSPsupqgpk+fnnKshlTXNlm7di0dOnRgx44d/PSnP+XKK69MOXau3iO5Vlfed999N2PHjmX16tX079+fk08+md///vf5Tvdz4g52AKqrq3n66aeZPXt2yjELrcmvvhrzfiwkKpBpqKsgdO/evaCG90o0ffp0Hn30Ufr167fzZ5B+/vOfc//993PNNddQXV1NixYtuO+++/KcacOpa5ssXryYX//61wCceeaZaV03bYzvEag772z0Gs6lug52ACZNmsRRRx1F165d85Rdw2us+7HQqEDuJYYOHYq7xy5L58g6UWM9yt7dNrnmmmsaOBvJRF0HOyeffHLBD2UnhUsFUkR2yvUtJLmyu4Odhx9+uGGTKQCN9aC10KhApkhvvKYjl8WgsRYaEfmMCuReQoVdmjIdkEgu6DYPERGRGCqQIiIiMVQgRUREYqhAioiIxFCBFBERiaECKSIiEkMFUkREJIYKpIiISIxGWyDN7FtmtszMPjGz2WZ2XL5zEhGRpqNRFkgzOxe4C/g5MAh4FfhfMzskr4mJiEiT0SgLJHAd8LC73+/u77n71cAq4Ko85yUiIk1EoyuQZrYPMBh4MWnRi8CxDZ+RiIg0RY2uQAIHAkXAmqT5a4BODZ+OiIg0RVbXb6gVKjPrDKwEhrv7ywnzfwRc6O5HJj3+CuCK6M8jgYU5Su1AYJ1iK3YTjN0Yc1Zsxa6vQ939oLgFjfHnrtYBNUDHpPkdgdXJD3b3+4D7cp2Umc1y93LFVuymFrsx5qzYip0Nja6J1d0/BWYDI5MWjST0ZhUREclYYzyDBPgl8KiZvQ5MB64EOgO/zWtWIiLSZDTKAunuj5nZAcDNwMHAfOBkd38/j2nlshlXsRU7n7EbY86KrdgZa3SddERERBpCo7sGKSIi0hBUIEVERGKoQIqIiMRQgcwSM7OE/zea7ZqUt+3usYWiMeYMjet9ISIqkFnj7h6NE4u776idn40v8Fx+sUZ5t6n9fzZjm1lRNuPVaow5Q3hfmFnHaD0tzCxrvcjNrCiXBwu1sRvTAYlIptSLNQvMrBPwdaCMMJzda8CT7j4jy+sxwj7bEbcs1WJhZj2A84HjgVJgBvA/wEvuvibduDHraQafP3BIN+/GmHP0nIHAxcDJhDGDZwF/B/4BvOnuNVnM29y9JpM4MXGLgf3c/aNsxo1iF2U734TYrYH9gLXAvsC/4vZpGnEbXc5R7FzmfTDQGthGGC97tbt/kqXYxe5enY1YKa1XBTJzZvYc0AN4D/iA8KsifYElwH8Bj6b7pjSzbwPvADPdfVvC/GaEE6i0d6CZTSW8oacRhuk7ARgKrCf83jnSOisAABOsSURBVOYv0v3iNrOfAXOAF919S8L8ImBHunk3xpyjGLOBLYRivgY4JZpqgIeBm9394zTzfohwUPa4u29MmF8c5Z3Rl6uZfRm4BBgA7EMo6n8lHJR8nEnspPXEHpTUnrWmsV0uIORdFs2aAfwvMMndF9bGznC/FkWpZauA5TznKEa28/4W8B+E773thNHOXgMmA1PdvSqLeZOrIr8Ld9eUwUT4gv4Q6Br93QLYn1Ak7weWAtelGXsosAN4GRgPjAYGJD2mBPh/wMEpxj4+yrtd0vzOwC2EAeH/GyjKIO83gVeAOwmDyyc+piVwN3BIU845et6IKO8WMcsuBd4nfAm2ziDvRcA/gSeAr8bk/SDQO834Cwk/J/et6L02k1DY5wNnp/PejmL3i/bXiUBx0rIiogP4NGMPA5YDfwC+AoyKtnEVsAL4dppxy4GJhBaj5knLigsx5wbIe0T02bsd6BXlfn/0nlwJ/Cx5/6YQ+4vAPOAyYJ+YvJsBBrTP5DXUuf5sB9zbJuBWYHIdy9oANwFbgbI0Yv+KML7sj6M39xvAS8DvCM11pcCQ6AuyVYqxrycc4bWM/i4CmiUsvwj4CDghjbzHRnleCdwLTAHmAn8DfkA4yjwmyrveRaEx5hzFvjKK1TH6uyTxww4MJ5xVnpFG3j+NcvxqtH0mEr5kFwG/Br4AHJ1O3lH8J4H7Y+YfRRjdZCVwcZqfnUcIzXFvAJOiz1JZ0mMqovd7Sl9+wOPAfTHz943Wsx74f2nm/Gm0ff8P+D27HkgdC0wgxQO1XOXcAHn/CfhdzPzm0Xt/LfBABnnXAKuAauAF4NSkx3wxmp9WEd7d1CiHmiswk4Bvm9mX3f2FxAXuvtnMbid8AQ4nNN+l4kBglrv/KOoAdAJhUPZBhCaYcwhfVP9w960pxn4euBE4E/ijR00Wtc0g7j7ezM6O8p6cRt5L3f23UbNZGeGL7mjgdOAM4DDgBU9oymyiOUMoWjcBFwK/dPeqKO/aptupUdPxccBfUoy9H+HL4zl3rzazpwjFvIJw9jcB6AL8bxp5Qzgyf6/2j4Rm2wVmNprwJTjGzF5w97Upxu5HOAisJJzhjAS+amb/JJyx/g/hQPA4j74JU7AP4Yu1Nu+SKO9/AbdG+/g8M3vU3ZenEPcIwhnRLMIB0zBgvJltBJ4FHiXs576eejNgrnLOdd6fAu3NrIW7f2JmLYBqd98O/NbMtgPfM7M+7v5OirFLCUOKTozyPht4Ior5BKFF5zygs+fiGmW2K+7eNhGar/5AuN74fWAwoTND7fK2hC+AM9OI3Rf4Ssz8DoSOKg8TzgxOTiN2EfALwlHpfYTOIwckLO9EaNb5ehqxOwEjYubvT2gm/XE6eSfkvKER5Vx7nf9GQkvCJMK1ms4Jj+ke5Z1ycyXQCvhCHe/L3oSmqbTeI1Gc0YRrvUfU8bq6Re/9lFpIgJ7AVOA/or9bEw4QbiScSb1O+DLfQVKTcT3jXxi9t49Nfg9F/7YHlgEVKcQsJXxRXxX93YLQKe8cwvXvN6L9uAM4vRBybqC8TyL8DOE5SfOLo3/3IzT/D08xbmfCme43a7cD0I5w0Pr9KO+qKO/T0nl/72lSJ50sMLNuhGa4E4GNhB23mnA0WAEc6Uk/5JxC7GIPZwbNCO3tOztdmNlpwHh33z/N2CXA1YQzpJaEQr6B0Ew5hNCUWVZ3hHqv53MdiqK8/+zurdKItS9wFeGMriXhg5dRzsmdBxK2dU02ck6IewbhS7A74b3xEeHsbxCwwd2HpRs7YR3Jr+V0QuedFmnGO4jQhNadcDb6N0Kv2y3R8jOBR9y9dRqxexCayBclze9IOIP/JuGgpW2KcY3wpXw/8GXCdbynCC0tm6L9ey6hOTOlvKPc9nP3pUnzWxO20beAczPM+SuE1pKs5NwAeZcAPyd8l8wiNIs+4e7rzexAwvfLOHdvk0beBwD7uvuKpPnFhGJ5NTA61bzrvX4VyOwxs/6E62DHEi4gtyNcQ/ylu7+dxfUY4cL0k0Abdz8xw3hHAacCAwk5H0w40/mNuy/LMN3kdTUjdMxo7+7nZBDnEOA0wjW2Awk/mJ31nBO2dcY5R/G6Ea6Z9AG6RtMLhJ7OqTZR7mldBvwn4drnNzOI04PwBTqccB2oEvgX4Qv9KGCiu9+QYa61nXKqE+Y9A3zs7hemGbM18A1Cb+GOhDONjwhnIh2BCe5+a4Z5Jx+QPANUufu5acbbj9CL9auElqLqHORc17ZOO+8oxqnABYQDvoMI19WrCQX09+5+ZyZ517HOZwgHsmdlOzaoQKYtOvvqT7getgV4i3C9cHW0/EhCZ4lPPcWNnBD7DMKH4x1CM9YKD7cCNPNw03kxsL+7r08j/9oOLtuT5h/k7h+mGi8mtnsdXcij5a3dfVM947UiXDM5H9hE6FU5m7C9t2eSc1LsDcBiQkeGt919Vbo5J62jGMCTrpGYWYlH1yPTtadbOaIDkv08veuPybF6Ew5KehOa+1oSriG+5OE6WVZEObcjNLNe7O7TM4x3JKEl5zDCAUkLQkes2R5+gD0rzKwt8DRwo7u/kWaMEg+3RHQivC/7EJqxSzLN2cxaJ78Pom3dJpO8Ew8SLNwL2Rs4hLC9WxB6UC/27N+b2wq4B7jL3edmM/bOdahApsfM7iYUx1WEL4tSQrPqX4HbPYPfpoyJfSjh/spnCE0VS3fz9D3FHuzus5Pm7UMoaNvreFomsbNxD+EjhC+LxYTt0RXYTOgZ+mt3n5rl2Buj2Pe5+0sZxB7q7tOS5n1uW1uaN0DXEftzxbL2QCrN3LsRrpUeQ7ju9Q7wqrvPi/ZpS0+9Y1hy7KMJPSoXRvHfcveNtV+4ZtYqlXUkfVHvclCS7vaIibu7A5J9Uz1YMLNewHWEM6+lhO09HXg5nQOy3cReQrgkMRd4JbHZ0sxaesJ91mmsJ+N7HNNcb8YHmbvlObiw2dQnwhHSZuDfiDqJEJoUvk94c28jXD9J+b6cesa+gtDsl2rX956EZqb5wC+BQUnLjdCT7hiS7jnKUuzmqcaOtscWQlNqbWeF/YHL+ex+vFtJ4765esa+haTbSeoZ+6hoe2whXLv7YtLyZoSzgtOIbv/IYuza/Zhy7Oj5hxF6XL9HuJ40h3A7xzuEWy5KM/jsxMWuJLTA/DdwePJrSSH2Qex660Ltdq7tMGKkfhtDXFwj6f7BhPdQKjkfTjhAeBm4jdCL+TXCvX9PASdmsK3jYs+ItvnjwL9lsK07Av9OuOyQvL1rT7yKgJI08o6NHfO4FqnmnXIuuQrclCfgh4Sju9q/k29y/jmwgISeigUS+0eEM6Xa+ytXRh/GG4Bu0WO6RF++XQshNjAGmJbwd/LNwlcSeuAdkUq+DRD7h4RBB35AGHigmtDCcCdRESBcY9pRu30KIXb03N8SbrHolDDvEEIP0/cJPRZT7llaz9gfZhD719FrXh39v3fS8qJoXWeTQpGsR9xmCXFTuhcP+E20PVonzOtIuHb6MuFa76Vpbo89xf44g9j3RNtkA+F2i5NJKobRNrk+eX4+Y6f8OnMZvKlOhGuDi4EeCfOK+eyIpvYoeXSBxf4joVt3J8K1jVGEbtRvEY7i/4fQHfydQolNuMViBfClpO1RO1jAAYRh525JI+dcxv414ZpR+2gaRji4eS/68L9FuFcznW2ds9hR/FeA66P/N2fXg7Q/E3qG7jxbKJDYrxPOQm8hnIHtIDQr/pDobIQwEtDSQogbPe954D+j/xeRVLgJtzW9RujJWUixZxDOSi8hdI6rIlwGuofolh/gJ8CSQoqdci65XkFTnKIvzvcIZ3LnEHMUE32QvlkosQlf/BcAP0ia357QgeE7hGaXHaR4VJnj2C0Ig3qvIpzRtYx5zFzSGIYrV7GjL6OTgW8lzd+HcOR7KqE7/w7gkkKJnRDr1uh1J555NOezA4ehhGuHu9x/ma/YhOv0fyOcHTUjtFacRDiY+Ge0PaYTzkquzXfchPjXRHGOStqX+0T/7024tHJ8ocQm3J/4BHBF9Hcxodn/xmjf1gBvE85QrymU2Gl9FnK9gqY6RTvyMeBdwqgfPyWMdHME4UzqQxIGDCiU2AnraB4z78zoA5/yEWUuYxN6S/4y+rAvJFy7OpNwnfbPhLPAtHLOZeyEdexy/TL6ks3Gts56bMJgFx8QWip2uXE8+sL6JM19mZPYhJ6Yo4gfP/dwwsHmFEJz9C4HQg0dNyHOYYTm8mVEgyYkLe9LGKkmnW2dk9iE23tOA4bELNuX0M/g8XS2SS5jpzOpF2sGoptYTyUMkdWdMDpFO8IIIb9z9wmFEruuHnxRr7wad3czuxMod/cRBRS7yMOvc7QinF0cR+hUU0Y4m5pEGCv0f1OJm8vYtfdOxm2ThMfcShgR5aRCiV0bP9pfPQjj036BMLrLK4Smz96EAa+XeYr3hOYydvJ6CM2JybfV/BHokup7sAHitiY0KV5IOJt+kfDe60t4X85194sLLXbCOnbpwWpmDxOuhx9XqLHrtX4VyNSYWVfCT1tBOM1/l9CztDth2K9/AevcfUOBxjbC2cVCj+7ZjJYb4ebklZ7ivVC5jB2zrn0IvQq3EZpIP/Is/eRSLmPHrGsE8KGnPjZlg8W2MKbmiYSDtGMI15Y3EJpwx3tmtzLlLHbCOoxwoFNDOON7GbjN3Z8qhLhRnGbRQVoLwti0wwitRWWEM7/xwNOJn6cCiL3bn9ozs5aE291+4+4pjS2cy9jpUIFMgZldRbh/awChWC0ldEB5ifADySt28/RCif0xoYNBJeEC/TMe/c5cIcVOvi9rTx+cQo2dTbmMHcVvRjiQOYjwxf9/hPvlPoq+ZJ1w7XBdAcfel9CDeqonjExkYfCNE939uXzH3cM6d943bGb7exZ/oDqXsZPW05zQWpTVH4zPdezY9alA1k/U5LmE0PPrN4QPzYmE30LrTbiuMtrd3031ptk8xu5FKGbXRrFT+rXxXMU2s3aEjkjPEY5yX619zYnFLLoJutJTGCUmj7ETbzbvBazyFG4Cz2Xs6HmtgQcIPXt3EIqBEQ7WJhHO6hZHj03phvsGjl1JKLafEC5HPOruC+obL9dxo9jNCdcH3/eYm9wzuek+n7EzkcvYGfEcX+RsKhNhUNyZdSwbSriOshQ4ULEzix3FrSJ0r68hnG38mDDoe+1juhE6IHRX7MxiR8+9iVCAj47+PoowrvBvCMP6/Q9wUKpx8xz7DcJPOaUcO1dxo1hjCK0tDxE6pHRi11sw2hAGLd+lw1sjiH0KqQ80krPYmUwNspKmMBFGxnmX8HtpsOuP3h4SLb9AsTOLTbgO9VvCze79CZ0MFhMKw2uEkYR+AGxNI2fFjo//CnBdzPwiPhuK74W9JXaOc55BuHTySrT/lhF6Ug8ljK0M4baj1xQ789iZTA22osY+Ee5PnE+4+T3xHq7EX7R/FbhBsdOPTSiy1wI3Jc3fn9B8+yChyXkHKf66umLXGb+YcGY0neisiKTh9YAvRft6QFOPneOcDyJ06rkg+rsr4QeBF0X7bzbhnr8FhEG4FTuD2JlODbaixjwRjXsKfI0wHNYWwvWJwXw2zNRF0fxSxc44dgnRUGTJX0zRvBGkMRyeYu82/hcIzba3EzN+K6H5divhdoYmHzuHcQ8mHOycFLNsEGEAgvXRvlTsDGNnOjXYiprCBLQlNG9dSRhdY2s0LYk+TLcqdmax+azjWPfkL6aEZT8ivWG9FDs+fjPCWdPlhEEoNhJaBUYS7r89mzCAwht7Q+xc5hzFb0nCQNu1U8LynxF+lFqxsxA7k0m9WPfAzDoQRpb/LmGQ5m2E3yScRrj205xwD+ALnvTL6IqdWuyEuNcBawmjZawiDD31tIffwjTCF9cH7j5RsdOPXcf62hKGVbuA8APaWwg9N98g3Os3c2+KncO4sb1JzWxfwihDD7n77YqdndjpUoHcg2jUhj6EnnYbCOOL9iMM+7YWuDmDD4li7znuIEIPwkrgDnd/MYs579Wxo/htgC2JX0zR7S4tCINT9AU+TnN/NrrYDZ1zzGNaAOcCf/YUfhhZsXOkoU9ZG9NEOM3fCgxLmncoYQzGFwlNiWWKnVns3cTtRmjSepEwVmo2c95rYyfE+x1wKeEAp00dj2lXu+6mHrsAcm6bw/24V8XOxpSXlTaWiXDk/jZ1/LIAoePELEJTi2JnELsecffJYc57Xezo+ecTOj5sItyv+jvCQO09+OzXNVoBzwD9mnrsPOR8BmHA89rYtcOo9VXszGJna2rwFTamKdo5/yDco9OT+F9OuJow4K9iZxC7MebcmGNHz629t7I74cdn3yaMbvMmoVPECcBVwKd7Q+zGmLNi53bKy0ob00To7j03+pL6BqF5q1W0bF/gScJQWYqdYezGmHNjjU3opflD4L+S5vcBxhGub64jdAp6oKnHbow5K3bup7ystLFNhAvzjxF6a64jdJh4kPAbgjNJsblFsZtWzo01NuHn046K/r8PSdfUCB0jdgAD94bYjTFnxc7tpF6sKYi63J9CuDn+E8JIGk94moMWK3bDx1XsPa6jGeGLqsbMLieMXLLv3hq7Meas2NmjApmmVH91QLELL65i73Ed1xEGjL5DsRtnzoqdYQ4qkCISJ/oJopocHfQ0utiNMWfFzjAHFUgREZFdNct3AiIiIoVIBVJERCSGCqSIiEgMFUgREZEYKpAiIiIxVCBFRERi/H8mPDY31ofeOAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "La estructura anterior, la expresión lógica booleana de entrada '(w ^ x) & ~(y ^ z) & (x & y & z)' es bastante comprendida, debido a que ^, ~, y & representan los operadores lógicos booleanos XOR, NOT y AND, como se aprendio a lo largo del curso de cuántica. \n",
        "- En base a esto es bastante fácil descubrir la solución satisfactoria examinando sus partes: w ^ x que requiere que w y x tomen valores diferentes; ~(y ^ z) requiere que y y z sean iguales; x & y & z dicta que los tres sean True. Al juntarlos, obtenemos la solución satisfactoria (w, x, y, z) = (False, True, True, True), con lo que el resultado de nuestro Grover concuerda."
      ],
      "metadata": {
        "id": "suy_0_LJ3aeA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ahora vamos a implementar la \"Amplificación de Amplitud\" en el proyecto."
      ],
      "metadata": {
        "id": "-00y0A13LAZG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from qiskit import QuantumCircuit\n",
        "from qiskit.algorithms import AmplificationProblem\n",
        "\n"
      ],
      "metadata": {
        "id": "31RPTny7ND-D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pip install pylatexenc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2JtOB966OXw8",
        "outputId": "bab207aa-99fb-462b-aab7-9965398dcb24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pylatexenc in /usr/local/lib/python3.8/dist-packages (2.10)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#El estado que deseamos encontrar en este caso es '11'.\n",
        "- Enotnces especificamos el oráculo que marca el estado \"11\" como una buena solución.\n",
        "- Definimos el algoritmo de Grover \n",
        "- Ahora podemos echar un vistazo al operador Grover que se utiliza en la ejecución del algoritmo"
      ],
      "metadata": {
        "id": "96NTFqcyPUa6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "good_state = ['11']\n",
        "\n",
        "oracle = QuantumCircuit(2)\n",
        "oracle.cz(0, 1)\n",
        "\n",
        "problem = AmplificationProblem(oracle, is_good_state=good_state)\n",
        "\n",
        "# Los circuitos del algoritmo se envuelven en una puerta para aparecer en la composición como un bloque\n",
        "# así que tenemos que descomponer() el op para verlo expandido en sus compuertas componentes.\n",
        "problem.grover_operator.decompose().draw(output='mpl')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 150
        },
        "id": "1d7kgFoONg4X",
        "outputId": "5e4ab04f-fc61-4b23-b710-826ef45937ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 607.568x144.48 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from qiskit import Aer\n",
        "from qiskit.utils import QuantumInstance\n",
        "from qiskit.algorithms import Grover\n",
        "\n",
        "aer_simulator = Aer.get_backend('aer_simulator')\n",
        "grover = Grover(quantum_instance=aer_simulator)\n",
        "result = grover.amplify(problem)\n",
        "print('Result type:', type(result))\n",
        "print()\n",
        "print('Success!' if result.oracle_evaluation else 'Failure!')\n",
        "print('Top measurement:', result.top_measurement)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b9VPwQbINdCM",
        "outputId": "150d8e7c-c0e4-406c-9ed7-61e198989f2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Result type: <class 'qiskit.algorithms.amplitude_amplifiers.grover.GroverResult'>\n",
            "\n",
            "Success!\n",
            "Top measurement: 11\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "En el proyecto, el resultado de top_measurement es 11 que es un “estado correcto”. Por lo tanto, logramos encontrar la respuesta usando Grover.\n",
        "\n",
        "- En el apartado anterior, usamos QuantumCircuit como el oráculo de Grover. Sin embargo, también podemos usar qiskit.quantum_info.Statevector como oráculo. De esa manera todos los siguientes procesos |11> son el “estado correcto”."
      ],
      "metadata": {
        "id": "Edt7haP4Qdli"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from qiskit.quantum_info import Statevector\n",
        "oracle = Statevector.from_label('11')\n",
        "problem = AmplificationProblem(oracle, is_good_state=['11'])\n",
        "\n",
        "grover = Grover(quantum_instance=aer_simulator)\n",
        "result = grover.amplify(problem)\n",
        "print('Result type:', type(result))\n",
        "print()\n",
        "print('Success!' if result.oracle_evaluation else 'Failure!')\n",
        "print('Top measurement:', result.top_measurement)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o_d8CylBQ65Y",
        "outputId": "b7defb52-7488-4fb1-d24d-07b0b776f341"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Result type: <class 'qiskit.algorithms.amplitude_amplifiers.grover.GroverResult'>\n",
            "\n",
            "Success!\n",
            "Top measurement: 11\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Ahora internamente asignamos a un circuito cuántico para ver si comportamiento."
      ],
      "metadata": {
        "id": "9Ma0vyYTQrzG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "problem.grover_operator.oracle.decompose().draw(output='mpl')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 150
        },
        "id": "SG0P3iH6QdMm",
        "outputId": "180ad7f2-ea52-4d96-8bc8-96bdd2e2ff4f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 206.852x144.48 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKoAAACFCAYAAADLjGNhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAPy0lEQVR4nO3deVxV5b7H8Q/sQEBKMXLAnFAhBhkF0TIcjoRDXSo1KUwMFadu1rkN517t1rWjWVaaOeS5vdKrN+1ETqfwmBqghHoPYo54MAERwQEVFTbj5rl/eNxHBASFPSz9vV+v/XrJs9dez2/B1zXsvfbz2CilFEJYOVtLFyBEU0hQhSZIUIUmSFCFJkhQhSZIUIUmSFCFJkhQhSY0Kajnzp3jxRdfxN3dneDgYPr378/GjRsBcHZ2vu1rc3Nz8fX1vePCGlqvTqcjICAAX19fxowZg16vv+s+TG3q1Kn88ssvABgMBgIDAxk1apSFq9KmRoOqlCIqKoonn3yS7Oxs9u/fz/r168nPzzdHfXU4Ojry66+/cuTIEezt7VmxYoVF6miKvXv3EhYWBsDixYvx8vKycEXa1WhQf/75Z+zt7Zk6daqxrVu3brz66qt1lv3000/x9fXF19eXRYsWGdurq6t56aWX8PLyYvTo0ej1egCioqIIDg7Gx8eHlStX3nHxAwcO5LfffgOu77EmT56Mj48PERERlJWVNdhHaWkpI0eOxN/fH19fX7799lsA1q5dS2hoKAEBAcTHx2MwGG7b/8mTJ3nkkUfo3r07AQEBtGvXjp49e3L16lUyMzPx8PBAp9ORn5/Pjz/+yKRJk+54G8U/qEYsXrxYzZo1q8HnW7durZRSKj09Xfn6+qqSkhJ17do15e3trTIyMlROTo4CVGpqqlJKqYkTJ6qPP/5YKaXUxYsXlVJK6fV65ePjo4qKiuqst6H+qqqq1DPPPKOWLVumcnJylE6nUwcOHFBKKTVmzBi1Zs2aBvtISEhQkyZNMq6zuLhYHTt2TI0aNUpVVlYqpZSaNm2aWr16tVJKqeHDh6szZ87UW09UVJTatWuXUkqp8PBwdejQIaWUUp988on66quvlFJKPf/88yo9PV0lJSWpkSNHNvi7FA2744upGTNm4O/vT0hISK321NRUnn32WVq3bo2zszPPPfccu3fvBqBLly48/vjjAMTExJCamgrA559/jr+/P2FhYZw+fZoTJ0402n9ZWRkBAQH07duXrl27EhcXB0CPHj0ICAgAIDg4mNzc3Ab76NOnD9u3b+ftt99m9+7dtGnThp07d7J//35CQkIICAhg586dZGdnA5CYmIibm1u99Rw9etR4fpyZmYmnpycA27ZtIzIykh9++IH27dsTHBzc5N+xqOuBxhbw8fHh+++/N/68dOlSioqK6Nu3b5M7sbGxqfNzcnIyO3bsYM+ePTg5OTFo0CDKy8sbXdeNc9RbtWrVyvhvnU5HWVlZg314eHiQkZFBYmIis2fPZujQobi4uDBhwgTmz5/f5O0qKyujvLwcFxcXTp8+jaurK/b29uj1eoqLi3Fzc2PJkiVs2bKFxMREysvLuXr1KjExMaxdu7bJ/YgmnKMOGTKE8vJyli9fbmy7cY55s4EDB7Jp0yb0ej2lpaVs3LiRgQMHApCXl8eePXsA+Oabb3jiiSe4cuUKLi4uODk5cfz4cfbu3dtS22TUUB8FBQU4OTkRExPDm2++SUZGBkOHDiUhIYHz588DcOnSJU6dOnXb9R87dsx4gZSZmWn8d1JSEoMHDwZg/vz55Ofnk5uby/r16xkyZIiE9C40GlQbGxs2bdpESkoKPXr0IDQ0lAkTJrBgwYJaywUFBREbG0toaCj9+vVj0qRJBAYGAuDp6cnSpUvx8vLi8uXLTJs2jcjISKqrq/Hy8uKdd94xXh23pIb6OHz4sPGi6f3332f27Nl4e3vzwQcfEBERgZ+fH8OGDaOwsBCAESNGUFBQUGf9Nx/2HR0dycjI4Pjx42zdupXIyMgW3577mY1ScuN0SwsKCmLfvn3Y2dlZupR7hgRVaIJ8hCo0QYIqNEGCKjRBgio0QYIqNEGCKjRBgio0QYIqNEGCKjRBgio0QYIqNEGCKjRBgio0QYIqNEGCKjRBgio0QYIqNEGCKjSh0a9L30/+/jNcO2/pKm7vwfbgOcTSVZifBPUm185DsWWG1BKNkEO/0AQJqtAECarQBAmq0AQJqtAECarQBAmq0AQJqtAECarQBKsOak1NDQsXLqR37944ODjg7+9PSkoKnp6eTJkyxWx1GGoMrPzhTUa/9wjPzH6Q91c/z5XSIrP1L6w8qHFxccydO5f4+Hi2bt3K2LFjiY6OJjs726xj4q9P+pC0o5tZ8uo+1v3H9c9YF6wbb7b+hRV/1r9u3TpWrVpFcnIy4eHhAAwePJiMjAw2bNhAUFCQ2WpJ3LuSmGHv0ulhdwAmj/yICQt6ce7yKTq4dDNbHfczq92jzps3j8jISGNIb+jVqxd2dnb4+fkB12cGDA8Px8PDgz59+hhnYmkpJWXFnC/Oo3fnf+7B3Vx74uTwECcLDrZoX6JhVhnU/Px8jhw5wpgxY+o8l5eXh4+Pj3EWlPj4eF544QWysrL48ssvGTduHJWVlY32YWNjU+eRkpJcZzl9xTUAWju2qdXu7NAWffnVu9i65klJSa63dq0+mspqgwrQsWPHWu1lZWWkpKQYD/tFRUWkpqYa55oaMGAAbm5uJCUltVgtTq0eBKC07Eqt9pLyYpwcHmqxfsTtWWVQXV1dAcjKyqrV/tFHH1FYWGi8kMrLy6NDhw615pjq0aNHo9PuwPU5Xm99hIcPqrOcs2Nb2rftym9nMoxthRez0Zdfxb2T391sXrOEhw+qt3atPprKKi+m3N3d8fPzY968ebRr147OnTuTkJBAYmIigNlnwRsRNoVvkxfg32swDzk9zJ8S36avx1N0bNfdrHXcz6xyj2pra8t3332Hj48P06ZNY+LEibi6ujJjxgx0Op3xQqpr166cO3eOiooK42tzcnLo1q1lr8THDX6HMK+nmbk4hOgPOlNTY+CdF2VSM3PS1PQ948eP5+DBgxw6dMjYFhERQVRUFNOnTyctLY3Ro0eTm5uLvb39Ha8/fb31fxWl7aPQd5ylqzA/qzz0NyQ9Pb3ODH8rVqwgNjaWRYsWYW9vz7p16+4qpMK6aSaoJSUlZGVlMX369Frt7u7u7Nq1y0JVCXPRTFCdnZ0xGAyWLkNYiFVeTAlxKwmq0AQJqtAECWozxczrzo79dd9Tbahd3B0JqhWpNlRZugSrpZmrfi07eDKFVdtmc+rsUWxsbOnnNYq3xq3i4Mlk3lr5O/5t7Nf8z0//yZXSC7w5dhUffTvhny9WivIqPctnHaBX5wDLbYSFSVBNLLvgEH/476d47bkVDA6MRqkajuftMz5fU2Pg/44nsmLWAXQ6OxzsnRjo97zx+c8SppBTeJiu7R+zRPlWQ4JqYj/sXUGY99M8FRJrbPPvOajWMpNHLKhzvyvA2u1zOXgymcUz07C3czBxpdZNzlGbSaezo7qm7rlltaGKB3R2nL2cy6OuHg2+3tbGlkfadqnT/lP6ajanfcG8uK20ae3aojVrkexRm6mjS3cKin6r1VZWUcLla2fp9LA7HV26c6boRMMrqOdO9/1Z21m2+TU+nPwTbq49TVG25sgetZki+saSuG8lh7N3Y6gxcE1/mWWbX6N7xz70cgtkZFg8e45tYfv+NVRWV1BRVcbBk8kNri+74BB/XPsCb0ev4bGuoebbECsne9RmGhr0EhVVepZsnMG54lM42jvj5x7O3Ff+gk73AD3d/PljXCKr/jqbpZte5QGdHf29n6lznnpD6pENlJZfYd7/Rtdq/3zmHnp06mOGLbJOmrof1dTkflTrJYd+oQkSVKEJElShCRJUoQkSVKEJElShCfI+qoks3/I6Wfnp9OocxIx/WWzpcjRP9qgmcCI/g7KKEj6bvpvq6kr+fvpvli5J82SPagKZeXsJ9hgGQFDv33Hs1B48u4TUWe7gyWTeW/0s7p38OXsph55uAfzXxM3mLlcTJKgmUFJWTKd21wf9be3QhtxzR+tdrk+PJ/HsEsqHk7exYN3LxI2Y3yL9W3qWbFPMgC1BNYHWDm3QV1wfO7W04irOjm3rXa7wUrYx0BeunMa1TecW6f9enCVbzlFNwLtbfw6c2AnAgRM78OoahsFQzeVr52otd+rsUbp19MFQY8DGRv4UtyO/HRPo/WgQdnYOvL5sILa2Oh7rGsrZy7l8/dfZtZbLPXeU7h18qKquoLjkPBevFlqoYusnd0/dxJR3T+0+9D3OTi4E9mreyVtT7p6y9F1gprjDS85RzeTmL+yJOyeH/nvcvTJAhgRVNJklB8iQQ7/QxAAZEtT7nFYGyLDqQ7+1TNp7L7t5gAz7B1rRys6xwQEyHOydarXfGCBj7it/MfkAGVa9R42Li2PDhg3MmTOH4OBg0tLSiI6O5sKFC7zxxhtmqyPp1/VsSVtKdsFByqv0bFtQbba+m6spA2T0cgts8PWNDZCxeEaaWQbIsNqgWtOkvc6OLjzdfzqVVWV89r229uT3ygAZVnvob+qkve+++y4eHh7Y2tqSkJBgklpCPJ9iSGC0cXZpLblXBsiwyj3qjUl7X3/99TrP3Tppb2RkJLGxsbzyyivmLlMT7pUBMqw2qNDwpL3Dhw83tg0YMOCu+qhvZuOFU5Ma/ANZi5SUZEKiB992mVu3Y0S/yYzoN7nB5QN7DSFwZlqddv+eg+qcj78c8R4vR7zX7BpvaOon+FZ56G/qpL3i/mGVe1RzTNpb3/9kS9/M0RTh4YNQy2+/F7L0djSlxjtllXvUpk7aay6GGgOVVeVUVVcCUFlVTmVV+R1N4y2axyr3qAAeHh4kJSXVahs/fjze3t44OjqatZYd+9ew8M8TjT+P/Pfr/a/5Q45MhW4mVhvU+tQ3ae+cOXP4+uuvuXDhAocPH2bWrFmkpKTQs2fLvb/3VEhsraHNhflZ5aG/Pjcm7b31jf65c+eSn59PRUUFFy9eJD8/v0VDej8qunKGZZtnkZm3j9e+GMCspU+wfEvdtwrNSTN7VJm013z2Z20n2GMYHdp24+P4n7G3c2D+Ny+RU3jYYoMJayaowjTqG1vgQad2zIxagmMrZ+NyOls7bG11FqtTM4d+YRo3xhb4ZFoyfu7h/OtzyyivLK0V0uyCQ1wpvUC3Dt4Wq1OCep+7dWyB4pILuLv5G5+/qr/EF5tm8vsxX1mqRECCet+7dWyBjBPbCe59fTgig6GaD9fFMGXUQto91LGRNZmWBPU+d+vYAhknduDxaF8AUg59R9bpv/GnH9/i98sHcSx3j8XqlO/138TSHz02ham/159y8M+E+4+9uxf/gym+1y97VFFLc0NqKvL21E0ebG/pChrXlBotvR2m6F8O/UIT5NAvNEGCKjRBgio0QYIqNEGCKjRBgio0QYIqNEGCKjRBgio0QYIqNEGCKjRBgio0QYIqNEGCKjRBgio0QYIqNEGCKjRBgio04f8BHxzGyXcdbWQAAAAASUVORK5CYII=\n"
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lo bueno de implementar Qiskit, es que permite una fácil construcción de oráculos más complejos: PhaseOracle: para analizar expresiones lógicas como '~a | b'. Esto es especialmente útil para resolver problemas de 3-SAT.\n",
        "\n",
        "- Aquí usaremos el PhaseOracle para el ejemplo simple de encontrar el estado |11>, que corresponde a 'a & b'."
      ],
      "metadata": {
        "id": "YSmR5T_mR2wM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from qiskit.circuit.library.phase_oracle import PhaseOracle\n",
        "from qiskit.exceptions import MissingOptionalLibraryError\n",
        "\n",
        "# `Oracle` (`PhaseOracle`) as the `oracle` argument\n",
        "expression = '(a & b)'\n",
        "try:\n",
        "    oracle = PhaseOracle(expression)\n",
        "    problem = AmplificationProblem(oracle)\n",
        "    display(problem.grover_operator.oracle.decompose().draw(output='mpl'))\n",
        "except MissingOptionalLibraryError as ex:\n",
        "    print(ex)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "id": "A173Poq8STIl",
        "outputId": "b54fa1a7-dd6b-4a20-e31f-06673e45503b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 267.052x144.48 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Preparamos el estado para este caso.\n",
        "Utilizamos un argumento state_preparation para especificar un circuito cuántico que prepara un estado cuántico para el punto de inicio de la amplificación de amplitud. Por defecto, un circuito con  se usa para preparar una superposición uniforme (por lo que será la búsqueda de Grover). El circuito de difusión de la amplificación de amplitud refleja el state_preparation automáticamente."
      ],
      "metadata": {
        "id": "c8Wj3-OjSbsX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Especificando `state_preparation` para preparar una superposición de |01>, |10>, y |11>\n",
        "oracle = QuantumCircuit(3)\n",
        "oracle.h(2)\n",
        "oracle.ccx(0,1,2)\n",
        "oracle.h(2)\n",
        "\n",
        "theta = 2 * np.arccos(1 / np.sqrt(3))\n",
        "state_preparation = QuantumCircuit(3)\n",
        "state_preparation.ry(theta, 0)\n",
        "state_preparation.ch(0,1)\n",
        "state_preparation.x(1)\n",
        "state_preparation.h(2)\n",
        "\n",
        "# Sólo nos importa que los dos primeros bits estén en estado 1, por lo que añadimos ambas posibilidades para el último qubit\n",
        "problem = AmplificationProblem(oracle, state_preparation=state_preparation, is_good_state=['110', '111'])\n",
        "\n",
        "# state_preparation\n",
        "print('state preparation circuit:')\n",
        "problem.grover_operator.state_preparation.draw(output='mpl')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "s-afpNUUSZmx",
        "outputId": "2aa6ff07-76e2-4efe-bac7-c84356e88853"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "state preparation circuit:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 267.197x204.68 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Importamos GroverOperator para:\n",
        "- imaginemos que el estado correcto es un estado de tres qubits  pero usamos 2 qubits adicionales como qubits auxiliares."
      ],
      "metadata": {
        "id": "_70AHYceS_J-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from qiskit.circuit.library import GroverOperator, ZGate\n",
        "\n",
        "oracle = QuantumCircuit(5)\n",
        "oracle.append(ZGate().control(2), [0, 1, 2])\n",
        "oracle.draw(output='mpl')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        },
        "id": "MaDpwRPZS_rK",
        "outputId": "3b0bf1e1-0aa3-40a0-a721-092c891b401b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 146.797x325.08 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Luego, por defecto, el operador Grover implementa la reflexión cero en los cinco qubits."
      ],
      "metadata": {
        "id": "V2TerhoMSY7p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "grover_op = GroverOperator(oracle, insert_barriers=True)\n",
        "grover_op.decompose().draw(output='mpl')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "Exd3xp9GTbKG",
        "outputId": "8825f75d-3a5e-4547-ef99-9e62b93f48ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 788.314x325.08 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pero sabemos que solo debemos considerar los tres primeros:"
      ],
      "metadata": {
        "id": "j3S8CoYCThyX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "grover_op = GroverOperator(oracle, reflection_qubits=[0, 1, 2], insert_barriers=True)\n",
        "grover_op.decompose().draw(output='mpl')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "q6Q_cqJKTguG",
        "outputId": "6de6ede4-a036-4c32-b486-e20169fbc8d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 788.314x325.08 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Luego especificamos el \"good_state\"."
      ],
      "metadata": {
        "id": "uWzJT-YmToKH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# una lista de cadenas binarias buen estado\n",
        "oracle = QuantumCircuit(2)\n",
        "oracle.cz(0, 1)\n",
        "good_state = ['11', '00']\n",
        "problem = AmplificationProblem(oracle, is_good_state=good_state)\n",
        "print(problem.is_good_state('11'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8VMOTAQtTwCO",
        "outputId": "158ce148-0799-49f9-9cb5-d20d9d4b5db8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# una lista de enteros buen estado\n",
        "oracle = QuantumCircuit(2)\n",
        "oracle.cz(0, 1)\n",
        "good_state = [0, 1]\n",
        "problem = AmplificationProblem(oracle, is_good_state=good_state)\n",
        "print(problem.is_good_state('11'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vgmBDLbkT6Wc",
        "outputId": "0521f9d9-8c76-46bd-e60d-33bcc5963980"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from qiskit.quantum_info import Statevector\n",
        "\n",
        "# `Statevector` buen estado\n",
        "oracle = QuantumCircuit(2)\n",
        "oracle.cz(0, 1)\n",
        "good_state = Statevector.from_label('11')\n",
        "problem = AmplificationProblem(oracle, is_good_state=good_state)\n",
        "print(problem.is_good_state('11'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nJXb5_ZQUBj2",
        "outputId": "46974cc6-0274-4f7d-87b1-f717659b91f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Buen estado invocable\n",
        "def callable_good_state(bitstr):\n",
        "    if bitstr == \"11\":\n",
        "        return True\n",
        "    return False\n",
        "\n",
        "oracle = QuantumCircuit(2)\n",
        "oracle.cz(0, 1)\n",
        "problem = AmplificationProblem(oracle, is_good_state=good_state)\n",
        "print(problem.is_good_state('11'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UQ5GDvgPULKO",
        "outputId": "22032df1-d1ba-484a-b630-20935679320e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#El número de \"iterations\"\n",
        "El número de repeticiones de aplicar el operador de Grover es importante para obtener el resultado correcto con el algoritmo de Grover. El número de iteraciones se puede establecer mediante el argumento \"iteration\" de \"Grover\"."
      ],
      "metadata": {
        "id": "5FMS4oAkUcge"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# iteracion entera\n",
        "oracle = QuantumCircuit(2)\n",
        "oracle.cz(0, 1)\n",
        "problem = AmplificationProblem(oracle, is_good_state=['11'])\n",
        "grover = Grover(iterations=1)"
      ],
      "metadata": {
        "id": "wPRJmk4gUokv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# iteración de listas\n",
        "oracle = QuantumCircuit(2)\n",
        "oracle.cz(0, 1)\n",
        "problem = AmplificationProblem(oracle, is_good_state=['11'])\n",
        "grover = Grover(iterations=[1, 2, 3])"
      ],
      "metadata": {
        "id": "zwpxJodGUrwG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# using sample_from_iterations\n",
        "oracle = QuantumCircuit(2)\n",
        "oracle.cz(0, 1)\n",
        "problem = AmplificationProblem(oracle, is_good_state=['11'])\n",
        "grover = Grover(iterations=[1, 2, 3], sample_from_iterations=True)"
      ],
      "metadata": {
        "id": "sD-4QPl6UuCJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Cuando se conoce el número de soluciones, también podemos usar un método estático optimal_num_iterations para encontrar el número óptimo de iteraciones. Pero hay que tener en cuenta que las iteraciones de salida son un valor aproximado. Cuando el número de qubits es pequeño, las iteraciones de salida pueden no ser óptimas.\n"
      ],
      "metadata": {
        "id": "2JVp8ngdUwwv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "iterations = Grover.optimal_num_iterations(num_solutions=1, num_qubits=8)\n",
        "iterations"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zIHidn2OUxH-",
        "outputId": "183ec0e0-1584-473a-8863-bcd72928d9db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##PARA FINALIZAR CON EL PROYECTO\n",
        "Aplicamos post_processing\n",
        "- Podemos aplicar un posprocesamiento opcional a la medición superior para facilitar la lectura. Se puede utilizar, por ejemplo, para convertir de la representación de bits de la medición [1, 0, 1] a un formato DIMACS CNF [1, -2, 3]."
      ],
      "metadata": {
        "id": "hy2rdRhiVahl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def to_DIAMACS_CNF_format(bit_rep):\n",
        "    return [index+1 if val==1 else -1 * (index + 1) for index, val in enumerate(bit_rep)]\n",
        "\n",
        "oracle = QuantumCircuit(2)\n",
        "oracle.cz(0, 1)\n",
        "problem = AmplificationProblem(oracle, is_good_state=['11'], post_processing=to_DIAMACS_CNF_format)\n",
        "problem.post_processing([1, 0, 1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6yTiA-AxUc5t",
        "outputId": "d0a4bace-7a95-4c96-d0af-b4ba76098813"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, -2, 3]"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##PROYECTO FINALIZADO.\n",
        "- GRACIAS POR TOMARSE EL TIEMPO DE REVISAR TODO EL PROYECTO."
      ],
      "metadata": {
        "id": "o2JBbFL3WCwE"
      }
    }
  ]
}