Colab_14_L'algoritmo_di_Simon.ipynb

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      "authorship_tag": "ABX9TyOfBbZiMVw6vM+BfB26r4+X",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/EmmanueleVilla/quantum_computing/blob/main/Colab_14_L'algoritmo_di_Simon.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bjWcuosJlImz",
        "outputId": "1df52de2-0595-4085-8779-ac8a782cf5d9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.0/6.0 MB\u001b[0m \u001b[31m27.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h  Building wheel for qiskit (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m162.6/162.6 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for pylatexenc (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install qiskit --quiet && pip install pylatexenc --quiet"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "from qiskit import Aer, QuantumCircuit, transpile\n",
        "from qiskit.visualization import plot_histogram\n"
      ],
      "metadata": {
        "id": "DHiJg_wPrrIz"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Soluzione classica al problema di Simon"
      ],
      "metadata": {
        "id": "gr1ZTwnYrjoz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def xor(a,b):\n",
        "  output = \"\"\n",
        "  for i in range(len(a)):\n",
        "    output += str(int(a[i]) ^ int(b[i]))\n",
        "  return output"
      ],
      "metadata": {
        "id": "XuewZrTWrqRK"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xor(\"111\", \"001\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "zik7xZjtsNeK",
        "outputId": "7ba7c507-6b9d-4ed4-b3b7-805b4770972d"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'110'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def simon_function(x):\n",
        "  secret = \"110\"\n",
        "  if x==\"000\" or x==\"110\":\n",
        "    return \"101\"\n",
        "  if x == \"001\" or x == \"111\":\n",
        "    return \"010\"\n",
        "  if x == \"010\" or x == \"100\":\n",
        "    return \"111\"\n",
        "  if x == \"011\" or x == \"101\":\n",
        "    return \"000\""
      ],
      "metadata": {
        "id": "ORikv9d8sPp5"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def find_secret():\n",
        "  inputs = [\"000\", \"001\", \"010\", \"011\", \"100\", \"101\", \"110\", \"111\"]\n",
        "  outputs = {}\n",
        "  for i in range(len(inputs)):\n",
        "    print(\"Provo l'input \" + inputs[i])\n",
        "    output = simon_function(inputs[i])\n",
        "    print(\"L'output è \" + output)\n",
        "    if output in outputs:\n",
        "      print(\"L'output era già stato ottenuto!\")\n",
        "      a = outputs[output]\n",
        "      b = inputs[i]\n",
        "      return xor(str(a), str(b))\n",
        "\n",
        "    outputs[inputs[i]] = output\n",
        "    print(\"dizionario degli output aggiornato!\")\n",
        "    print(outputs)"
      ],
      "metadata": {
        "id": "bBQXMZFsszRI"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "find_secret()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "7sr1AtNcthKO",
        "outputId": "d5045761-1ddb-4290-d48f-9a41e8e01edf"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Provo l'input 000\n",
            "L'output è 101\n",
            "dizionario degli output aggiornato!\n",
            "{'000': '101'}\n",
            "Provo l'input 001\n",
            "L'output è 010\n",
            "dizionario degli output aggiornato!\n",
            "{'000': '101', '001': '010'}\n",
            "Provo l'input 010\n",
            "L'output è 111\n",
            "dizionario degli output aggiornato!\n",
            "{'000': '101', '001': '010', '010': '111'}\n",
            "Provo l'input 011\n",
            "L'output è 000\n",
            "L'output era già stato ottenuto!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'110'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JIKTPDgOtumE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Soluzione quantistica al problema di Simon"
      ],
      "metadata": {
        "id": "Bqr6fNQvxhF7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "b = '110'\n",
        "n = len(b)\n",
        "simon_circuit = QuantumCircuit(2*n, n)\n",
        "simon_circuit.h(range(n))\n",
        "simon_circuit.barrier()\n",
        "\n",
        "simon_circuit.cx(0, 3)\n",
        "simon_circuit.cx(1, 4)\n",
        "simon_circuit.cx(2, 5)\n",
        "simon_circuit.cx(1, 4)\n",
        "simon_circuit.cx(1, 5)\n",
        "simon_circuit.barrier()\n",
        "\n",
        "simon_circuit.h(range(n))\n",
        "simon_circuit.barrier()\n",
        "simon_circuit.measure([0,1,2], [0,1,2])\n",
        "\n",
        "simon_circuit.draw(output=\"mpl\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "tpjOPBQFxigi",
        "outputId": "b893171b-557f-46b1-b7a8-24cba50e56a4"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 1207.22x618.722 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aer_sim = Aer.get_backend('aer_simulator')\n",
        "results = aer_sim.run(simon_circuit).result()\n",
        "counts = results.get_counts()\n",
        "plot_histogram(counts)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "0jtZg35Ixt_C",
        "outputId": "867d9094-e3e2-4c8f-eb1f-8052b7e015f6"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def dot(x, y):\n",
        "  accum = 0\n",
        "  for i in range(len(x)):\n",
        "    accum += int(x[i]) * int(y[i])\n",
        "  return accum % 2"
      ],
      "metadata": {
        "id": "aOIbkBqOyv03"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for count in counts:\n",
        "  print(\"110 dot \" + str(count) + \" = \" +  str(dot(\"110\", count)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MlDCwNG-zSOF",
        "outputId": "df3a323b-bfcb-4731-eecf-7f9b8667842f"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "110 dot 110 = 0\n",
            "110 dot 001 = 0\n",
            "110 dot 000 = 0\n",
            "110 dot 111 = 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#s dot 001 = 0\n",
        "#s dot 111 = 0"
      ],
      "metadata": {
        "id": "yYIGHGLXzyRk"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}