{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "WUFELx-9uWS9",
        "outputId": "5376a986-6e40-4c5f-df1b-d538124a10e2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting qiskit[visualization]\n",
            "  Downloading qiskit-2.5.2-cp310-abi3-manylinux_2_28_x86_64.whl.metadata (14 kB)\n",
            "Requirement already satisfied: numpy<3,>=2.0 in /usr/local/lib/python3.13/dist-packages (from qiskit[visualization]) (2.1.3)\n",
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            "Collecting rustworkx>=0.15.0 (from qiskit[visualization])\n",
            "  Downloading rustworkx-0.18.1-cp310-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (10 kB)\n",
            "Requirement already satisfied: dill>=0.3 in /usr/local/lib/python3.13/dist-packages (from qiskit[visualization]) (0.4.1)\n",
            "Collecting stevedore>=3.0.0 (from qiskit[visualization])\n",
            "  Downloading stevedore-5.9.1-py3-none-any.whl.metadata (2.3 kB)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.13/dist-packages (from qiskit[visualization]) (4.16.0)\n",
            "Requirement already satisfied: matplotlib>=3.3 in /usr/local/lib/python3.13/dist-packages (from qiskit[visualization]) (3.10.0)\n",
            "Requirement already satisfied: pydot in /usr/local/lib/python3.13/dist-packages (from qiskit[visualization]) (4.0.1)\n",
            "Requirement already satisfied: Pillow>=4.2.1 in /usr/local/lib/python3.13/dist-packages (from qiskit[visualization]) (11.3.0)\n",
            "Collecting pylatexenc>=1.4 (from qiskit[visualization])\n",
            "  Downloading pylatexenc-2.11-py2.py3-none-any.whl.metadata (3.9 kB)\n",
            "Requirement already satisfied: seaborn>=0.9.0 in /usr/local/lib/python3.13/dist-packages (from qiskit[visualization]) (0.13.2)\n",
            "Requirement already satisfied: sympy>=1.3 in /usr/local/lib/python3.13/dist-packages (from qiskit[visualization]) (1.14.0)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.13/dist-packages (from matplotlib>=3.3->qiskit[visualization]) (1.3.3)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.13/dist-packages (from matplotlib>=3.3->qiskit[visualization]) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.13/dist-packages (from matplotlib>=3.3->qiskit[visualization]) (4.64.0)\n",
            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.13/dist-packages (from matplotlib>=3.3->qiskit[visualization]) (1.5.1)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.13/dist-packages (from matplotlib>=3.3->qiskit[visualization]) (26.3)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.13/dist-packages (from matplotlib>=3.3->qiskit[visualization]) (3.3.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.13/dist-packages (from matplotlib>=3.3->qiskit[visualization]) (2.9.0.post0)\n",
            "Requirement already satisfied: pandas>=1.2 in /usr/local/lib/python3.13/dist-packages (from seaborn>=0.9.0->qiskit[visualization]) (2.2.3)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.13/dist-packages (from sympy>=1.3->qiskit[visualization]) (1.3.0)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.13/dist-packages (from pandas>=1.2->seaborn>=0.9.0->qiskit[visualization]) (2025.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.13/dist-packages (from pandas>=1.2->seaborn>=0.9.0->qiskit[visualization]) (2026.3)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.13/dist-packages (from python-dateutil>=2.7->matplotlib>=3.3->qiskit[visualization]) (1.17.0)\n",
            "Downloading pylatexenc-2.11-py2.py3-none-any.whl (137 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m137.5/137.5 kB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading rustworkx-0.18.1-cp310-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (2.4 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.4/2.4 MB\u001b[0m \u001b[31m61.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading stevedore-5.9.1-py3-none-any.whl (54 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.9/54.9 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading qiskit-2.5.2-cp310-abi3-manylinux_2_28_x86_64.whl (9.8 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.8/9.8 MB\u001b[0m \u001b[31m58.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: pylatexenc, stevedore, rustworkx, qiskit\n",
            "Successfully installed pylatexenc-2.11 qiskit-2.5.2 rustworkx-0.18.1 stevedore-5.9.1\n",
            "Collecting qiskit-aer\n",
            "  Downloading qiskit_aer-0.17.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (8.3 kB)\n",
            "Requirement already satisfied: qiskit>=1.1.0 in /usr/local/lib/python3.13/dist-packages (from qiskit-aer) (2.5.2)\n",
            "Requirement already satisfied: numpy>=1.16.3 in /usr/local/lib/python3.13/dist-packages (from qiskit-aer) (2.1.3)\n",
            "Requirement already satisfied: scipy>=1.0 in /usr/local/lib/python3.13/dist-packages (from qiskit-aer) (1.16.3)\n",
            "Requirement already satisfied: psutil>=5 in /usr/local/lib/python3.13/dist-packages (from qiskit-aer) (5.9.5)\n",
            "Requirement already satisfied: python-dateutil>=2.8.0 in /usr/local/lib/python3.13/dist-packages (from qiskit-aer) (2.9.0.post0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.13/dist-packages (from python-dateutil>=2.8.0->qiskit-aer) (1.17.0)\n",
            "Requirement already satisfied: rustworkx>=0.15.0 in /usr/local/lib/python3.13/dist-packages (from qiskit>=1.1.0->qiskit-aer) (0.18.1)\n",
            "Requirement already satisfied: dill>=0.3 in /usr/local/lib/python3.13/dist-packages (from qiskit>=1.1.0->qiskit-aer) (0.4.1)\n",
            "Requirement already satisfied: stevedore>=3.0.0 in /usr/local/lib/python3.13/dist-packages (from qiskit>=1.1.0->qiskit-aer) (5.9.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.13/dist-packages (from qiskit>=1.1.0->qiskit-aer) (4.16.0)\n",
            "Downloading qiskit_aer-0.17.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.4 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.4/12.4 MB\u001b[0m \u001b[31m92.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: qiskit-aer\n",
            "Successfully installed qiskit-aer-0.17.2\n"
          ]
        }
      ],
      "source": [
        "!pip install qiskit[visualization]\n",
        "!pip install qiskit-aer"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import qiskit\n",
        "import qiskit_aer\n",
        "#print out Qiskit version and Qiskit_Aer version\n",
        "print(\"Qiskit:\", qiskit.__version__)\n",
        "print(\"Qiskit Aer:\", qiskit_aer.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xiRN_iQCurbr",
        "outputId": "bd92fd30-68d0-4d9e-e2db-cb4ebb4d4682"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Qiskit: 2.5.2\n",
            "Qiskit Aer: 0.17.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from qiskit import QuantumCircuit\n",
        "from qiskit.visualization import plot_histogram\n",
        "# Create a quantum circuit with 2 qubits and 2 bits\n",
        "qc = QuantumCircuit(2, 2)\n",
        "# Create the Bell state\n",
        "qc.h(0)\n",
        "qc.cx(0, 1)\n",
        "# Measurement\n",
        "qc.measure([0, 1], [0, 1])\n",
        "# Draw the quantum circuit\n",
        "qc.draw('mpl')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "Pazd10Zou8SP",
        "outputId": "76104850-8410-4fb7-9ff3-bb66ab0c602a"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 454.517x284.278 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from qiskit import transpile\n",
        "from qiskit_aer import AerSimulator\n",
        "simulator = AerSimulator()\n",
        "qc_transpiled = transpile(qc, simulator)\n",
        "job = simulator.run(qc_transpiled, shots=1000)\n",
        "result = job.result()\n",
        "counts = result.get_counts()\n",
        "print(counts)\n",
        "display(plot_histogram(counts))\n",
        "shots = sum(counts.values())\n",
        "probabilities = {k: v / shots for k, v in counts.items()}\n",
        "print(probabilities)\n",
        "display(plot_histogram(probabilities))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 993
        },
        "id": "xBiNB46S3RDQ",
        "outputId": "dab4a17c-7bba-4539-f79d-9144a305b5e0"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'11': 513, '00': 487}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'11': 0.513, '00': 0.487}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}