Exploring_IBMQ_with_Qiskit.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Exploring IBMQ with Qiskit.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/peepeepoopooooo/2022-update-shecodes/blob/main/Exploring_IBMQ_with_Qiskit.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "TuFxtWpbY1sc",
        "outputId": "fcc91d0e-4323-47d4-f8fc-9c5289a6c209"
      },
      "source": [
        "pip install qiskit numpy jupyterlab matplotlib qiskit-ibmq-provider"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Attempting uninstall: jupyter-server\n",
            "    Found existing installation: jupyter-server 1.24.0\n",
            "    Uninstalling jupyter-server-1.24.0:\n",
            "      Successfully uninstalled jupyter-server-1.24.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "chex 0.1.85 requires numpy>=1.24.1, but you have numpy 1.23.5 which is incompatible.\n",
            "notebook 6.5.5 requires jupyter-client<8,>=5.3.4, but you have jupyter-client 8.6.1 which is incompatible.\n",
            "notebook 6.5.5 requires pyzmq<25,>=17, but you have pyzmq 25.1.2 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed arrow-1.3.0 async-lru-2.0.4 dill-0.3.8 fqdn-1.5.1 h11-0.14.0 httpcore-1.0.4 httpx-0.27.0 isoduration-20.11.0 jedi-0.19.1 json5-0.9.22 jsonpointer-2.4 jupyter-client-8.6.1 jupyter-events-0.9.1 jupyter-lsp-2.2.4 jupyter-server-2.13.0 jupyter-server-terminals-0.5.3 jupyterlab-4.1.5 jupyterlab-server-2.25.4 ntlm-auth-1.5.0 numpy-1.23.5 overrides-7.7.0 pbr-6.0.0 ply-3.11 python-json-logger-2.0.7 pyzmq-25.1.2 qiskit-1.0.2 qiskit-ibmq-provider-0.20.2 qiskit-terra-0.46.1 requests-ntlm-1.1.0 rfc3339-validator-0.1.4 rfc3986-validator-0.1.1 rustworkx-0.14.2 stevedore-5.2.0 symengine-0.11.0 types-python-dateutil-2.9.0.20240315 uri-template-1.3.0 websockets-12.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "numpy",
                  "zmq"
                ]
              },
              "id": "21e0b0f45f4f4d3e99b4d4b4b8d08d1a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->jupyterlab) (2.5)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->jupyterlab) (0.5.1)\n",
            "Requirement already satisfied: arrow>=0.15.0 in /usr/local/lib/python3.10/dist-packages (from isoduration->jsonschema>=4.18.0->jupyterlab-server<3,>=2.19.0->jupyterlab) (1.3.0)\n",
            "Requirement already satisfied: types-python-dateutil>=2.8.10 in /usr/local/lib/python3.10/dist-packages (from arrow>=0.15.0->isoduration->jsonschema>=4.18.0->jupyterlab-server<3,>=2.19.0->jupyterlab) (2.9.0.20240315)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JT1ZbXIOjziI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "outputId": "c168ec4e-32b0-4beb-9996-7e435cdd38e1"
      },
      "source": [
        "import qiskit as qk\n",
        "%matplotlib inline\n",
        "\n",
        "circuit = qk.QuantumCircuit(2,2)  # 2qubits 2 bits\n",
        "circuit.x(0) # change valeur si valeur =0 change 1\n",
        "circuit.cx(0, 1) #cnot, controlled not, change la 2eme valeur si la premiere est 1\n",
        "circuit.measure([0,1], [0,1])\n",
        "circuit.draw()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">     ┌───┐     ┌─┐   \n",
              "q_0: ┤ X ├──■──┤M├───\n",
              "     └───┘┌─┴─┐└╥┘┌─┐\n",
              "q_1: ─────┤ X ├─╫─┤M├\n",
              "          └───┘ ║ └╥┘\n",
              "c: 2/═══════════╩══╩═\n",
              "                0  1 </pre>"
            ],
            "text/plain": [
              "     ┌───┐     ┌─┐   \n",
              "q_0: ┤ X ├──■──┤M├───\n",
              "     └───┘┌─┴─┐└╥┘┌─┐\n",
              "q_1: ─────┤ X ├─╫─┤M├\n",
              "          └───┘ ║ └╥┘\n",
              "c: 2/═══════════╩══╩═\n",
              "                0  1 "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2z86SH9NZJFl"
      },
      "source": [
        "from qiskit import IBMQ\n",
        "\n",
        "token = \"22c54cd3ba9bccfbc02f948ff2d77bc064f3385254b1f8bd3c2f35c7c964f7dbf872d83857f248bd668624950bab0f0170c35ea76f5b212922c73a8333963c25\"\n",
        "IBMQ.save_account(token)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FzBpdTuXdSv3",
        "outputId": "f2231783-f027-4316-a26e-bde6330a2264"
      },
      "source": [
        "IBMQ.load_account()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/qiskit/providers/ibmq/ibmqfactory.py:192: UserWarning: Timestamps in IBMQ backend properties, jobs, and job results are all now in local time instead of UTC.\n",
            "  warnings.warn('Timestamps in IBMQ backend properties, jobs, and job results '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AccountProvider for IBMQ(hub='ibm-q', group='open', project='main')>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5v0zGVo2fUIA",
        "outputId": "d9ace05d-912f-4c3b-c46e-dc345b6fd6df"
      },
      "source": [
        "#show providers\n",
        "IBMQ.providers()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<AccountProvider for IBMQ(hub='ibm-q', group='open', project='main')>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MHaFvtMMe_90"
      },
      "source": [
        "provider = IBMQ.get_provider(\"ibm-q\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yOnGzETofsiJ",
        "outputId": "f928b053-1f73-409b-b0a1-c0c45cda293f"
      },
      "source": [
        "for backend in provider.backends():\n",
        "  try:\n",
        "    qubit_count = len(backend.properties().quibits)\n",
        "  except:\n",
        "    qubit_count = \"simulated\"\n",
        "  print(f\"{backend.name()} has {backend.status().pending_jobs} queued and {qubit_count} qubits\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ibmq_qasm_simulator has 35 queued and simulated qubits\n",
            "ibmqx2 has 7 queued and simulated qubits\n",
            "ibmq_16_melbourne has 1228 queued and simulated qubits\n",
            "ibmq_vigo has 764 queued and simulated qubits\n",
            "ibmq_ourense has 725 queued and simulated qubits\n",
            "ibmq_valencia has 214 queued and simulated qubits\n",
            "ibmq_armonk has 7 queued and simulated qubits\n",
            "ibmq_athens has 13 queued and simulated qubits\n",
            "ibmq_santiago has 7 queued and simulated qubits\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BIodRPOPh4O1",
        "outputId": "2298a114-b8b1-4f04-d8f8-c8af6278cca5"
      },
      "source": [
        "from qiskit.tools.monitor import job_monitor\n",
        "\n",
        "backend = provider.get_backend(\"ibmqx2\")\n",
        "job = qk.execute(circuit, backend=backend, shots=500)\n",
        "job_monitor(job)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Job Status: job has successfully run\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "Z9DZxdbdimmc",
        "outputId": "ffc43393-6c6e-4725-bca2-8f184870391a"
      },
      "source": [
        "#result of the job\n",
        "from qiskit.visualization import plot_histogram\n",
        "\n",
        "result = job.result()\n",
        "counts = result.get_counts(circuit)\n",
        "\n",
        "plot_histogram([counts], legend=['Device'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "id": "qWK06lRpl2NB",
        "outputId": "0ffc168e-e7c9-4533-c6fe-11f454a9eea4"
      },
      "source": [
        "circuit2 = qk.QuantumCircuit(2,2)  # 2qubits 2 bits\n",
        "circuit2.h(0)\n",
        "circuit2.cx(0, 1) #cnot, controlled not, change la 2eme valeur si la premiere est 1\n",
        "circuit2.measure([0,1], [0,1])\n",
        "circuit2.draw()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">     ┌───┐     ┌─┐   \n",
              "q_0: ┤ H ├──■──┤M├───\n",
              "     └───┘┌─┴─┐└╥┘┌─┐\n",
              "q_1: ─────┤ X ├─╫─┤M├\n",
              "          └───┘ ║ └╥┘\n",
              "c: 2/═══════════╩══╩═\n",
              "                0  1 </pre>"
            ],
            "text/plain": [
              "     ┌───┐     ┌─┐   \n",
              "q_0: ┤ H ├──■──┤M├───\n",
              "     └───┘┌─┴─┐└╥┘┌─┐\n",
              "q_1: ─────┤ X ├─╫─┤M├\n",
              "          └───┘ ║ └╥┘\n",
              "c: 2/═══════════╩══╩═\n",
              "                0  1 "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        },
        "id": "bW7wN8sdmegE",
        "outputId": "9f306d41-9f56-4b46-8bad-3a95d2270539"
      },
      "source": [
        "job = qk.execute(circuit2, backend=backend, shots=500)\n",
        "job_monitor(job)\n",
        "\n",
        "result = job.result()\n",
        "counts = result.get_counts(circuit2)\n",
        "\n",
        "plot_histogram([counts], legend=['Device'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Job Status: job has successfully run\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    }
  ]
}