lab_3_circuit_knitting.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "014b9a89-b00e-4224-8b46-4791deb255b4",
      "metadata": {
        "id": "014b9a89-b00e-4224-8b46-4791deb255b4"
      },
      "source": [
        "![Notebook_Header_01.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "300c2067-dc8f-4ebf-aca1-0d08879fa764",
      "metadata": {
        "id": "300c2067-dc8f-4ebf-aca1-0d08879fa764"
      },
      "source": [
        "# Lab 3: What's next - Sneak Preview of Circuit Kinitting Toolbox"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fe27252e-a56d-4ea9-9000-39e2cf74b3ca",
      "metadata": {
        "jp-MarkdownHeadingCollapsed": true,
        "id": "fe27252e-a56d-4ea9-9000-39e2cf74b3ca"
      },
      "source": [
        "# Table of Contents\n",
        "\n",
        "* [Part 1: Circuit Knitting Toolbox](#Part-1:-Circuit-Knitting-Toolbox)\n",
        "    * [Exercise 1](#exercise1)\n",
        "    * [Exercise 2](#exercise2)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "02512c85-8370-4d05-b1cf-d0720cc94242",
      "metadata": {
        "id": "02512c85-8370-4d05-b1cf-d0720cc94242"
      },
      "source": [
        "# Setup"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7479f158-7305-43f5-9161-2a274c97860e",
      "metadata": {
        "id": "7479f158-7305-43f5-9161-2a274c97860e"
      },
      "source": [
        "As always we'll start with the optional installs. You only need to use the Qiskit install and API Token cells if you don't have those saved from a previous lab."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "07e8428b-043b-4fc9-8929-284ba6b66eb0",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "07e8428b-043b-4fc9-8929-284ba6b66eb0",
        "outputId": "70200efb-708f-4c09-f6b0-8be76c3775cc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting qiskit[visualization]==1.0.2\n",
            "  Downloading qiskit-1.0.2-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.6/5.6 MB\u001b[0m \u001b[31m22.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting rustworkx>=0.14.0 (from qiskit[visualization]==1.0.2)\n",
            "  Downloading rustworkx-0.14.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m43.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy<2,>=1.17 in /usr/local/lib/python3.10/dist-packages (from qiskit[visualization]==1.0.2) (1.25.2)\n",
            "Requirement already satisfied: scipy>=1.5 in /usr/local/lib/python3.10/dist-packages (from qiskit[visualization]==1.0.2) (1.11.4)\n",
            "Requirement already satisfied: sympy>=1.3 in /usr/local/lib/python3.10/dist-packages (from qiskit[visualization]==1.0.2) (1.12.1)\n",
            "Collecting dill>=0.3 (from qiskit[visualization]==1.0.2)\n",
            "  Downloading dill-0.3.8-py3-none-any.whl (116 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m9.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: python-dateutil>=2.8.0 in /usr/local/lib/python3.10/dist-packages (from qiskit[visualization]==1.0.2) (2.8.2)\n",
            "Collecting stevedore>=3.0.0 (from qiskit[visualization]==1.0.2)\n",
            "  Downloading stevedore-5.2.0-py3-none-any.whl (49 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.7/49.7 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from qiskit[visualization]==1.0.2) (4.12.1)\n",
            "Collecting symengine>=0.11 (from qiskit[visualization]==1.0.2)\n",
            "  Downloading symengine-0.11.0-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (39.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m39.4/39.4 MB\u001b[0m \u001b[31m12.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: matplotlib>=3.3 in /usr/local/lib/python3.10/dist-packages (from qiskit[visualization]==1.0.2) (3.7.1)\n",
            "Requirement already satisfied: pydot in /usr/local/lib/python3.10/dist-packages (from qiskit[visualization]==1.0.2) (1.4.2)\n",
            "Requirement already satisfied: Pillow>=4.2.1 in /usr/local/lib/python3.10/dist-packages (from qiskit[visualization]==1.0.2) (9.4.0)\n",
            "Collecting pylatexenc>=1.4 (from qiskit[visualization]==1.0.2)\n",
            "  Downloading pylatexenc-2.10.tar.gz (162 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m162.6/162.6 kB\u001b[0m \u001b[31m11.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: seaborn>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from qiskit[visualization]==1.0.2) (0.13.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3->qiskit[visualization]==1.0.2) (1.2.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3->qiskit[visualization]==1.0.2) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3->qiskit[visualization]==1.0.2) (4.53.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3->qiskit[visualization]==1.0.2) (1.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3->qiskit[visualization]==1.0.2) (24.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3->qiskit[visualization]==1.0.2) (3.1.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.0->qiskit[visualization]==1.0.2) (1.16.0)\n",
            "Requirement already satisfied: pandas>=1.2 in /usr/local/lib/python3.10/dist-packages (from seaborn>=0.9.0->qiskit[visualization]==1.0.2) (2.0.3)\n",
            "Collecting pbr!=2.1.0,>=2.0.0 (from stevedore>=3.0.0->qiskit[visualization]==1.0.2)\n",
            "  Downloading pbr-6.0.0-py2.py3-none-any.whl (107 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m107.5/107.5 kB\u001b[0m \u001b[31m8.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: mpmath<1.4.0,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy>=1.3->qiskit[visualization]==1.0.2) (1.3.0)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.2->seaborn>=0.9.0->qiskit[visualization]==1.0.2) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.2->seaborn>=0.9.0->qiskit[visualization]==1.0.2) (2024.1)\n",
            "Building wheels for collected packages: pylatexenc\n",
            "  Building wheel for pylatexenc (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pylatexenc: filename=pylatexenc-2.10-py3-none-any.whl size=136816 sha256=522a9ca33029a624aa2a27338684bb4d87df420337c8f9e057d1dff4a7c0fabe\n",
            "  Stored in directory: /root/.cache/pip/wheels/d3/31/8b/e09b0386afd80cfc556c00408c9aeea5c35c4d484a9c762fd5\n",
            "Successfully built pylatexenc\n",
            "Installing collected packages: pylatexenc, symengine, rustworkx, pbr, dill, stevedore, qiskit\n",
            "Successfully installed dill-0.3.8 pbr-6.0.0 pylatexenc-2.10 qiskit-1.0.2 rustworkx-0.14.2 stevedore-5.2.0 symengine-0.11.0\n",
            "Collecting qiskit-ibm-runtime\n",
            "  Downloading qiskit_ibm_runtime-0.23.0-py3-none-any.whl (3.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.0/3.0 MB\u001b[0m \u001b[31m16.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: requests>=2.19 in /usr/local/lib/python3.10/dist-packages (from qiskit-ibm-runtime) (2.31.0)\n",
            "Collecting requests-ntlm>=1.1.0 (from qiskit-ibm-runtime)\n",
            "  Downloading requests_ntlm-1.3.0-py3-none-any.whl (6.6 kB)\n",
            "Requirement already satisfied: numpy>=1.13 in /usr/local/lib/python3.10/dist-packages (from qiskit-ibm-runtime) (1.25.2)\n",
            "Requirement already satisfied: urllib3>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from qiskit-ibm-runtime) (2.0.7)\n",
            "Requirement already satisfied: python-dateutil>=2.8.0 in /usr/local/lib/python3.10/dist-packages (from qiskit-ibm-runtime) (2.8.2)\n",
            "Requirement already satisfied: websocket-client>=1.5.1 in /usr/local/lib/python3.10/dist-packages (from qiskit-ibm-runtime) (1.8.0)\n",
            "Collecting ibm-platform-services>=0.22.6 (from qiskit-ibm-runtime)\n",
            "  Downloading ibm-platform-services-0.53.7.tar.gz (323 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m323.9/323.9 kB\u001b[0m \u001b[31m21.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: pydantic in /usr/local/lib/python3.10/dist-packages (from qiskit-ibm-runtime) (2.7.3)\n",
            "Requirement already satisfied: qiskit>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from qiskit-ibm-runtime) (1.0.2)\n",
            "Collecting ibm-cloud-sdk-core<4.0.0,>=3.19.2 (from ibm-platform-services>=0.22.6->qiskit-ibm-runtime)\n",
            "  Downloading ibm-cloud-sdk-core-3.20.1.tar.gz (62 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.1/62.1 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.0->qiskit-ibm-runtime) (1.16.0)\n",
            "Requirement already satisfied: rustworkx>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from qiskit>=1.0.0->qiskit-ibm-runtime) (0.14.2)\n",
            "Requirement already satisfied: scipy>=1.5 in /usr/local/lib/python3.10/dist-packages (from qiskit>=1.0.0->qiskit-ibm-runtime) (1.11.4)\n",
            "Requirement already satisfied: sympy>=1.3 in /usr/local/lib/python3.10/dist-packages (from qiskit>=1.0.0->qiskit-ibm-runtime) (1.12.1)\n",
            "Requirement already satisfied: dill>=0.3 in /usr/local/lib/python3.10/dist-packages (from qiskit>=1.0.0->qiskit-ibm-runtime) (0.3.8)\n",
            "Requirement already satisfied: stevedore>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from qiskit>=1.0.0->qiskit-ibm-runtime) (5.2.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from qiskit>=1.0.0->qiskit-ibm-runtime) (4.12.1)\n",
            "Requirement already satisfied: symengine>=0.11 in /usr/local/lib/python3.10/dist-packages (from qiskit>=1.0.0->qiskit-ibm-runtime) (0.11.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19->qiskit-ibm-runtime) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19->qiskit-ibm-runtime) (3.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19->qiskit-ibm-runtime) (2024.6.2)\n",
            "Requirement already satisfied: cryptography>=1.3 in /usr/local/lib/python3.10/dist-packages (from requests-ntlm>=1.1.0->qiskit-ibm-runtime) (42.0.7)\n",
            "Collecting pyspnego>=0.4.0 (from requests-ntlm>=1.1.0->qiskit-ibm-runtime)\n",
            "  Downloading pyspnego-0.10.2-py3-none-any.whl (129 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m129.9/129.9 kB\u001b[0m \u001b[31m15.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic->qiskit-ibm-runtime) (0.7.0)\n",
            "Requirement already satisfied: pydantic-core==2.18.4 in /usr/local/lib/python3.10/dist-packages (from pydantic->qiskit-ibm-runtime) (2.18.4)\n",
            "Requirement already satisfied: cffi>=1.12 in /usr/local/lib/python3.10/dist-packages (from cryptography>=1.3->requests-ntlm>=1.1.0->qiskit-ibm-runtime) (1.16.0)\n",
            "Collecting urllib3>=1.21.1 (from qiskit-ibm-runtime)\n",
            "  Downloading urllib3-2.2.1-py3-none-any.whl (121 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m121.1/121.1 kB\u001b[0m \u001b[31m15.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting PyJWT<3.0.0,>=2.8.0 (from ibm-cloud-sdk-core<4.0.0,>=3.19.2->ibm-platform-services>=0.22.6->qiskit-ibm-runtime)\n",
            "  Downloading PyJWT-2.8.0-py3-none-any.whl (22 kB)\n",
            "Requirement already satisfied: pbr!=2.1.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from stevedore>=3.0.0->qiskit>=1.0.0->qiskit-ibm-runtime) (6.0.0)\n",
            "Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy>=1.3->qiskit>=1.0.0->qiskit-ibm-runtime) (1.3.0)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.12->cryptography>=1.3->requests-ntlm>=1.1.0->qiskit-ibm-runtime) (2.22)\n",
            "Building wheels for collected packages: ibm-platform-services, ibm-cloud-sdk-core\n",
            "  Building wheel for ibm-platform-services (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ibm-platform-services: filename=ibm_platform_services-0.53.7-py3-none-any.whl size=340285 sha256=e31a652cff15e0fefe6ab0833204d9cb073ce4e066b136e7c05d8dc962237325\n",
            "  Stored in directory: /root/.cache/pip/wheels/8e/54/ad/63e8fdae0e85463bc04a21acf5364cff6def892b95dc40f3ed\n",
            "  Building wheel for ibm-cloud-sdk-core (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ibm-cloud-sdk-core: filename=ibm_cloud_sdk_core-3.20.1-py3-none-any.whl size=101533 sha256=57e7d5ba7b821503599239dc7c059a7b7d2040ddb3c8eacefc481a1e0b52dc94\n",
            "  Stored in directory: /root/.cache/pip/wheels/b1/80/5b/12af32c69e0bde3322cb9c1809952e8ed9f2a2a39509ee5374\n",
            "Successfully built ibm-platform-services ibm-cloud-sdk-core\n",
            "Installing collected packages: urllib3, PyJWT, pyspnego, ibm-cloud-sdk-core, requests-ntlm, ibm-platform-services, qiskit-ibm-runtime\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 2.0.7\n",
            "    Uninstalling urllib3-2.0.7:\n",
            "      Successfully uninstalled urllib3-2.0.7\n",
            "  Attempting uninstall: PyJWT\n",
            "    Found existing installation: PyJWT 2.3.0\n",
            "    Uninstalling PyJWT-2.3.0:\n",
            "      Successfully uninstalled PyJWT-2.3.0\n",
            "Successfully installed PyJWT-2.8.0 ibm-cloud-sdk-core-3.20.1 ibm-platform-services-0.53.7 pyspnego-0.10.2 qiskit-ibm-runtime-0.23.0 requests-ntlm-1.3.0 urllib3-2.2.1\n",
            "Collecting qiskit-aer\n",
            "  Downloading qiskit_aer-0.14.2-cp310-cp310-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[31m39.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: qiskit>=0.45.2 in /usr/local/lib/python3.10/dist-packages (from qiskit-aer) (1.0.2)\n",
            "Requirement already satisfied: numpy>=1.16.3 in /usr/local/lib/python3.10/dist-packages (from qiskit-aer) (1.25.2)\n",
            "Requirement already satisfied: scipy>=1.0 in /usr/local/lib/python3.10/dist-packages (from qiskit-aer) (1.11.4)\n",
            "Requirement already satisfied: psutil>=5 in /usr/local/lib/python3.10/dist-packages (from qiskit-aer) (5.9.5)\n",
            "Requirement already satisfied: rustworkx>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from qiskit>=0.45.2->qiskit-aer) (0.14.2)\n",
            "Requirement already satisfied: sympy>=1.3 in /usr/local/lib/python3.10/dist-packages (from qiskit>=0.45.2->qiskit-aer) (1.12.1)\n",
            "Requirement already satisfied: dill>=0.3 in /usr/local/lib/python3.10/dist-packages (from qiskit>=0.45.2->qiskit-aer) (0.3.8)\n",
            "Requirement already satisfied: python-dateutil>=2.8.0 in /usr/local/lib/python3.10/dist-packages (from qiskit>=0.45.2->qiskit-aer) (2.8.2)\n",
            "Requirement already satisfied: stevedore>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from qiskit>=0.45.2->qiskit-aer) (5.2.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from qiskit>=0.45.2->qiskit-aer) (4.12.1)\n",
            "Requirement already satisfied: symengine>=0.11 in /usr/local/lib/python3.10/dist-packages (from qiskit>=0.45.2->qiskit-aer) (0.11.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.0->qiskit>=0.45.2->qiskit-aer) (1.16.0)\n",
            "Requirement already satisfied: pbr!=2.1.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from stevedore>=3.0.0->qiskit>=0.45.2->qiskit-aer) (6.0.0)\n",
            "Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy>=1.3->qiskit>=0.45.2->qiskit-aer) (1.3.0)\n",
            "Installing collected packages: qiskit-aer\n",
            "Successfully installed qiskit-aer-0.14.2\n",
            "Collecting qiskit-transpiler-service\n",
            "  Downloading qiskit_transpiler_service-0.4.3-py3-none-any.whl (25 kB)\n",
            "Requirement already satisfied: qiskit~=1.0 in /usr/local/lib/python3.10/dist-packages (from qiskit-transpiler-service) (1.0.2)\n",
            "Collecting backoff~=2.0 (from qiskit-transpiler-service)\n",
            "  Downloading backoff-2.2.1-py3-none-any.whl (15 kB)\n",
            "Collecting qiskit-qasm3-import~=0.4 (from qiskit-transpiler-service)\n",
            "  Downloading qiskit_qasm3_import-0.4.2-py3-none-any.whl (27 kB)\n",
            "Requirement already satisfied: requests~=2.0 in /usr/local/lib/python3.10/dist-packages (from qiskit-transpiler-service) (2.31.0)\n",
            "Requirement already satisfied: rustworkx>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from qiskit~=1.0->qiskit-transpiler-service) (0.14.2)\n",
            "Requirement already satisfied: numpy<2,>=1.17 in /usr/local/lib/python3.10/dist-packages (from qiskit~=1.0->qiskit-transpiler-service) (1.25.2)\n",
            "Requirement already satisfied: scipy>=1.5 in /usr/local/lib/python3.10/dist-packages (from qiskit~=1.0->qiskit-transpiler-service) (1.11.4)\n",
            "Requirement already satisfied: sympy>=1.3 in /usr/local/lib/python3.10/dist-packages (from qiskit~=1.0->qiskit-transpiler-service) (1.12.1)\n",
            "Requirement already satisfied: dill>=0.3 in /usr/local/lib/python3.10/dist-packages (from qiskit~=1.0->qiskit-transpiler-service) (0.3.8)\n",
            "Requirement already satisfied: python-dateutil>=2.8.0 in /usr/local/lib/python3.10/dist-packages (from qiskit~=1.0->qiskit-transpiler-service) (2.8.2)\n",
            "Requirement already satisfied: stevedore>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from qiskit~=1.0->qiskit-transpiler-service) (5.2.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from qiskit~=1.0->qiskit-transpiler-service) (4.12.1)\n",
            "Requirement already satisfied: symengine>=0.11 in /usr/local/lib/python3.10/dist-packages (from qiskit~=1.0->qiskit-transpiler-service) (0.11.0)\n",
            "Collecting openqasm3[parser]<0.6,>=0.4 (from qiskit-qasm3-import~=0.4->qiskit-transpiler-service)\n",
            "  Downloading openqasm3-0.5.0-py3-none-any.whl (524 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m524.0/524.0 kB\u001b[0m \u001b[31m10.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests~=2.0->qiskit-transpiler-service) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests~=2.0->qiskit-transpiler-service) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests~=2.0->qiskit-transpiler-service) (2.2.1)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests~=2.0->qiskit-transpiler-service) (2024.6.2)\n",
            "Collecting antlr4-python3-runtime<4.14,>=4.7 (from openqasm3[parser]<0.6,>=0.4->qiskit-qasm3-import~=0.4->qiskit-transpiler-service)\n",
            "  Downloading antlr4_python3_runtime-4.13.1-py3-none-any.whl (144 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m144.5/144.5 kB\u001b[0m \u001b[31m10.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.0->qiskit~=1.0->qiskit-transpiler-service) (1.16.0)\n",
            "Requirement already satisfied: pbr!=2.1.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from stevedore>=3.0.0->qiskit~=1.0->qiskit-transpiler-service) (6.0.0)\n",
            "Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy>=1.3->qiskit~=1.0->qiskit-transpiler-service) (1.3.0)\n",
            "Installing collected packages: openqasm3, antlr4-python3-runtime, backoff, qiskit-qasm3-import, qiskit-transpiler-service\n",
            "Successfully installed antlr4-python3-runtime-4.13.1 backoff-2.2.1 openqasm3-0.5.0 qiskit-qasm3-import-0.4.2 qiskit-transpiler-service-0.4.3\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.10/dist-packages (0.20.3)\n",
            "Collecting circuit-knitting-toolbox\n",
            "  Downloading circuit_knitting_toolbox-0.7.2-py3-none-any.whl (134 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.7/134.7 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.23.0 in /usr/local/lib/python3.10/dist-packages (from circuit-knitting-toolbox) (1.25.2)\n",
            "Requirement already satisfied: qiskit-aer>=0.14.0.1 in /usr/local/lib/python3.10/dist-packages (from circuit-knitting-toolbox) (0.14.2)\n",
            "Requirement already satisfied: qiskit-ibm-runtime>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from circuit-knitting-toolbox) (0.23.0)\n",
            "Requirement already satisfied: qiskit<2.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from circuit-knitting-toolbox) (1.0.2)\n",
            "Requirement already satisfied: rustworkx>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from circuit-knitting-toolbox) (0.14.2)\n",
            "Requirement already satisfied: scipy>=1.5.2 in /usr/local/lib/python3.10/dist-packages (from circuit-knitting-toolbox) (1.11.4)\n",
            "Requirement already satisfied: sympy>=1.3 in /usr/local/lib/python3.10/dist-packages (from qiskit<2.0,>=1.0.0->circuit-knitting-toolbox) (1.12.1)\n",
            "Requirement already satisfied: dill>=0.3 in /usr/local/lib/python3.10/dist-packages (from qiskit<2.0,>=1.0.0->circuit-knitting-toolbox) (0.3.8)\n",
            "Requirement already satisfied: python-dateutil>=2.8.0 in /usr/local/lib/python3.10/dist-packages (from qiskit<2.0,>=1.0.0->circuit-knitting-toolbox) (2.8.2)\n",
            "Requirement already satisfied: stevedore>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from qiskit<2.0,>=1.0.0->circuit-knitting-toolbox) (5.2.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from qiskit<2.0,>=1.0.0->circuit-knitting-toolbox) (4.12.1)\n",
            "Requirement already satisfied: symengine>=0.11 in /usr/local/lib/python3.10/dist-packages (from qiskit<2.0,>=1.0.0->circuit-knitting-toolbox) (0.11.0)\n",
            "Requirement already satisfied: psutil>=5 in /usr/local/lib/python3.10/dist-packages (from qiskit-aer>=0.14.0.1->circuit-knitting-toolbox) (5.9.5)\n",
            "Requirement already satisfied: requests>=2.19 in /usr/local/lib/python3.10/dist-packages (from qiskit-ibm-runtime>=0.23.0->circuit-knitting-toolbox) (2.31.0)\n",
            "Requirement already satisfied: requests-ntlm>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from qiskit-ibm-runtime>=0.23.0->circuit-knitting-toolbox) (1.3.0)\n",
            "Requirement already satisfied: urllib3>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from qiskit-ibm-runtime>=0.23.0->circuit-knitting-toolbox) (2.2.1)\n",
            "Requirement already satisfied: websocket-client>=1.5.1 in /usr/local/lib/python3.10/dist-packages (from qiskit-ibm-runtime>=0.23.0->circuit-knitting-toolbox) (1.8.0)\n",
            "Requirement already satisfied: ibm-platform-services>=0.22.6 in /usr/local/lib/python3.10/dist-packages (from qiskit-ibm-runtime>=0.23.0->circuit-knitting-toolbox) (0.53.7)\n",
            "Requirement already satisfied: pydantic in /usr/local/lib/python3.10/dist-packages (from qiskit-ibm-runtime>=0.23.0->circuit-knitting-toolbox) (2.7.3)\n",
            "Requirement already satisfied: ibm-cloud-sdk-core<4.0.0,>=3.19.2 in /usr/local/lib/python3.10/dist-packages (from ibm-platform-services>=0.22.6->qiskit-ibm-runtime>=0.23.0->circuit-knitting-toolbox) (3.20.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.0->qiskit<2.0,>=1.0.0->circuit-knitting-toolbox) (1.16.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19->qiskit-ibm-runtime>=0.23.0->circuit-knitting-toolbox) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19->qiskit-ibm-runtime>=0.23.0->circuit-knitting-toolbox) (3.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19->qiskit-ibm-runtime>=0.23.0->circuit-knitting-toolbox) (2024.6.2)\n",
            "Requirement already satisfied: cryptography>=1.3 in /usr/local/lib/python3.10/dist-packages (from requests-ntlm>=1.1.0->qiskit-ibm-runtime>=0.23.0->circuit-knitting-toolbox) (42.0.7)\n",
            "Requirement already satisfied: pyspnego>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from requests-ntlm>=1.1.0->qiskit-ibm-runtime>=0.23.0->circuit-knitting-toolbox) (0.10.2)\n",
            "Requirement already satisfied: pbr!=2.1.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from stevedore>=3.0.0->qiskit<2.0,>=1.0.0->circuit-knitting-toolbox) (6.0.0)\n",
            "Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy>=1.3->qiskit<2.0,>=1.0.0->circuit-knitting-toolbox) (1.3.0)\n",
            "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic->qiskit-ibm-runtime>=0.23.0->circuit-knitting-toolbox) (0.7.0)\n",
            "Requirement already satisfied: pydantic-core==2.18.4 in /usr/local/lib/python3.10/dist-packages (from pydantic->qiskit-ibm-runtime>=0.23.0->circuit-knitting-toolbox) (2.18.4)\n",
            "Requirement already satisfied: cffi>=1.12 in /usr/local/lib/python3.10/dist-packages (from cryptography>=1.3->requests-ntlm>=1.1.0->qiskit-ibm-runtime>=0.23.0->circuit-knitting-toolbox) (1.16.0)\n",
            "Requirement already satisfied: PyJWT<3.0.0,>=2.8.0 in /usr/local/lib/python3.10/dist-packages (from ibm-cloud-sdk-core<4.0.0,>=3.19.2->ibm-platform-services>=0.22.6->qiskit-ibm-runtime>=0.23.0->circuit-knitting-toolbox) (2.8.0)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.12->cryptography>=1.3->requests-ntlm>=1.1.0->qiskit-ibm-runtime>=0.23.0->circuit-knitting-toolbox) (2.22)\n",
            "Installing collected packages: circuit-knitting-toolbox\n",
            "Successfully installed circuit-knitting-toolbox-0.7.2\n",
            "Collecting qiskit-serverless\n",
            "  Downloading qiskit_serverless-0.12.0-py3-none-any.whl (42 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.2/42.2 kB\u001b[0m \u001b[31m1.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting ray[data,default]<3,>=2.9.3 (from qiskit-serverless)\n",
            "  Downloading ray-2.24.0-cp310-cp310-manylinux2014_x86_64.whl (65.9 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m65.9/65.9 MB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting requests>=2.32.2 (from qiskit-serverless)\n",
            "  Downloading requests-2.32.3-py3-none-any.whl (64 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.9/64.9 kB\u001b[0m \u001b[31m7.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: importlib-metadata>=5.2.0 in /usr/local/lib/python3.10/dist-packages (from qiskit-serverless) (7.1.0)\n",
            "Requirement already satisfied: qiskit>=1.0.2 in /usr/local/lib/python3.10/dist-packages (from qiskit-serverless) (1.0.2)\n",
            "Requirement already satisfied: qiskit-ibm-runtime>=0.21.1 in /usr/local/lib/python3.10/dist-packages (from qiskit-serverless) (0.23.0)\n",
            "Collecting qiskit-ibm-provider>=0.10.0 (from qiskit-serverless)\n",
            "  Downloading qiskit_ibm_provider-0.11.0-py3-none-any.whl (249 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m249.9/249.9 kB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: cloudpickle==2.2.1 in /usr/local/lib/python3.10/dist-packages (from qiskit-serverless) (2.2.1)\n",
            "Requirement already satisfied: tqdm>=4.66.3 in /usr/local/lib/python3.10/dist-packages (from qiskit-serverless) (4.66.4)\n",
            "Collecting opentelemetry-api>=1.18.0 (from qiskit-serverless)\n",
            "  Downloading opentelemetry_api-1.25.0-py3-none-any.whl (59 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m59.9/59.9 kB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting opentelemetry-sdk>=1.18.0 (from qiskit-serverless)\n",
            "  Downloading opentelemetry_sdk-1.25.0-py3-none-any.whl (107 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m107.0/107.0 kB\u001b[0m \u001b[31m11.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting opentelemetry-exporter-otlp-proto-grpc>=1.18.0 (from qiskit-serverless)\n",
            "  Downloading opentelemetry_exporter_otlp_proto_grpc-1.25.0-py3-none-any.whl (18 kB)\n",
            "Collecting s3fs>=2023.6.0 (from qiskit-serverless)\n",
            "  Downloading s3fs-2024.6.0-py3-none-any.whl (29 kB)\n",
            "Collecting opentelemetry.instrumentation.requests>=0.40b0 (from qiskit-serverless)\n",
            "  Downloading opentelemetry_instrumentation_requests-0.46b0-py3-none-any.whl (12 kB)\n",
            "Collecting ipywidgets>=8.1.2 (from qiskit-serverless)\n",
            "  Downloading ipywidgets-8.1.3-py3-none-any.whl (139 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.4/139.4 kB\u001b[0m \u001b[31m14.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting ipython>=8.10.0 (from qiskit-serverless)\n",
            "  Downloading ipython-8.25.0-py3-none-any.whl (817 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m817.3/817.3 kB\u001b[0m \u001b[31m23.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.10/dist-packages (from importlib-metadata>=5.2.0->qiskit-serverless) (3.19.1)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.10/dist-packages (from ipython>=8.10.0->qiskit-serverless) (4.4.2)\n",
            "Collecting jedi>=0.16 (from ipython>=8.10.0->qiskit-serverless)\n",
            "  Downloading jedi-0.19.1-py2.py3-none-any.whl (1.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m61.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: matplotlib-inline in /usr/local/lib/python3.10/dist-packages (from ipython>=8.10.0->qiskit-serverless) (0.1.7)\n",
            "Requirement already satisfied: prompt-toolkit<3.1.0,>=3.0.41 in /usr/local/lib/python3.10/dist-packages (from ipython>=8.10.0->qiskit-serverless) (3.0.45)\n",
            "Requirement already satisfied: pygments>=2.4.0 in /usr/local/lib/python3.10/dist-packages (from ipython>=8.10.0->qiskit-serverless) (2.16.1)\n",
            "Collecting stack-data (from ipython>=8.10.0->qiskit-serverless)\n",
            "  Downloading stack_data-0.6.3-py3-none-any.whl (24 kB)\n",
            "Collecting traitlets>=5.13.0 (from ipython>=8.10.0->qiskit-serverless)\n",
            "  Downloading traitlets-5.14.3-py3-none-any.whl (85 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m85.4/85.4 kB\u001b[0m \u001b[31m9.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from ipython>=8.10.0->qiskit-serverless) (1.2.1)\n",
            "Requirement already satisfied: typing-extensions>=4.6 in /usr/local/lib/python3.10/dist-packages (from ipython>=8.10.0->qiskit-serverless) (4.12.1)\n",
            "Requirement already satisfied: pexpect>4.3 in /usr/local/lib/python3.10/dist-packages (from ipython>=8.10.0->qiskit-serverless) (4.9.0)\n",
            "Collecting comm>=0.1.3 (from ipywidgets>=8.1.2->qiskit-serverless)\n",
            "  Downloading comm-0.2.2-py3-none-any.whl (7.2 kB)\n",
            "Collecting widgetsnbextension~=4.0.11 (from ipywidgets>=8.1.2->qiskit-serverless)\n",
            "  Downloading widgetsnbextension-4.0.11-py3-none-any.whl (2.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m32.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: jupyterlab-widgets~=3.0.11 in /usr/local/lib/python3.10/dist-packages (from ipywidgets>=8.1.2->qiskit-serverless) (3.0.11)\n",
            "Collecting deprecated>=1.2.6 (from opentelemetry-api>=1.18.0->qiskit-serverless)\n",
            "  Downloading Deprecated-1.2.14-py2.py3-none-any.whl (9.6 kB)\n",
            "Requirement already satisfied: googleapis-common-protos~=1.52 in /usr/local/lib/python3.10/dist-packages (from opentelemetry-exporter-otlp-proto-grpc>=1.18.0->qiskit-serverless) (1.63.1)\n",
            "Requirement already satisfied: grpcio<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from opentelemetry-exporter-otlp-proto-grpc>=1.18.0->qiskit-serverless) (1.64.1)\n",
            "Collecting opentelemetry-exporter-otlp-proto-common==1.25.0 (from opentelemetry-exporter-otlp-proto-grpc>=1.18.0->qiskit-serverless)\n",
            "  Downloading opentelemetry_exporter_otlp_proto_common-1.25.0-py3-none-any.whl (17 kB)\n",
            "Collecting opentelemetry-proto==1.25.0 (from opentelemetry-exporter-otlp-proto-grpc>=1.18.0->qiskit-serverless)\n",
            "  Downloading opentelemetry_proto-1.25.0-py3-none-any.whl (52 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m52.5/52.5 kB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: protobuf<5.0,>=3.19 in /usr/local/lib/python3.10/dist-packages (from opentelemetry-proto==1.25.0->opentelemetry-exporter-otlp-proto-grpc>=1.18.0->qiskit-serverless) (3.20.3)\n",
            "Collecting opentelemetry-instrumentation==0.46b0 (from opentelemetry.instrumentation.requests>=0.40b0->qiskit-serverless)\n",
            "  Downloading opentelemetry_instrumentation-0.46b0-py3-none-any.whl (29 kB)\n",
            "Collecting opentelemetry-semantic-conventions==0.46b0 (from opentelemetry.instrumentation.requests>=0.40b0->qiskit-serverless)\n",
            "  Downloading opentelemetry_semantic_conventions-0.46b0-py3-none-any.whl (130 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m130.5/130.5 kB\u001b[0m \u001b[31m11.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting opentelemetry-util-http==0.46b0 (from opentelemetry.instrumentation.requests>=0.40b0->qiskit-serverless)\n",
            "  Downloading opentelemetry_util_http-0.46b0-py3-none-any.whl (6.9 kB)\n",
            "Requirement already satisfied: setuptools>=16.0 in /usr/local/lib/python3.10/dist-packages (from opentelemetry-instrumentation==0.46b0->opentelemetry.instrumentation.requests>=0.40b0->qiskit-serverless) (67.7.2)\n",
            "Requirement already satisfied: wrapt<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from opentelemetry-instrumentation==0.46b0->opentelemetry.instrumentation.requests>=0.40b0->qiskit-serverless) (1.14.1)\n",
            "Requirement already satisfied: rustworkx>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from qiskit>=1.0.2->qiskit-serverless) (0.14.2)\n",
            "Requirement already satisfied: numpy<2,>=1.17 in /usr/local/lib/python3.10/dist-packages (from qiskit>=1.0.2->qiskit-serverless) (1.25.2)\n",
            "Requirement already satisfied: scipy>=1.5 in /usr/local/lib/python3.10/dist-packages (from qiskit>=1.0.2->qiskit-serverless) (1.11.4)\n",
            "Requirement already satisfied: sympy>=1.3 in /usr/local/lib/python3.10/dist-packages (from qiskit>=1.0.2->qiskit-serverless) (1.12.1)\n",
            "Requirement already satisfied: dill>=0.3 in /usr/local/lib/python3.10/dist-packages (from qiskit>=1.0.2->qiskit-serverless) (0.3.8)\n",
            "Requirement already satisfied: python-dateutil>=2.8.0 in /usr/local/lib/python3.10/dist-packages (from qiskit>=1.0.2->qiskit-serverless) (2.8.2)\n",
            "Requirement already satisfied: stevedore>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from qiskit>=1.0.2->qiskit-serverless) (5.2.0)\n",
            "Requirement already satisfied: symengine>=0.11 in /usr/local/lib/python3.10/dist-packages (from qiskit>=1.0.2->qiskit-serverless) (0.11.0)\n",
            "Requirement already satisfied: requests-ntlm>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from qiskit-ibm-provider>=0.10.0->qiskit-serverless) (1.3.0)\n",
            "Requirement already satisfied: urllib3>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from qiskit-ibm-provider>=0.10.0->qiskit-serverless) (2.2.1)\n",
            "Requirement already satisfied: websocket-client>=1.5.1 in /usr/local/lib/python3.10/dist-packages (from qiskit-ibm-provider>=0.10.0->qiskit-serverless) (1.8.0)\n",
            "Collecting websockets>=10.0 (from qiskit-ibm-provider>=0.10.0->qiskit-serverless)\n",
            "  Downloading websockets-12.0-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (130 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m130.2/130.2 kB\u001b[0m \u001b[31m16.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: ibm-platform-services>=0.22.6 in /usr/local/lib/python3.10/dist-packages (from qiskit-ibm-runtime>=0.21.1->qiskit-serverless) (0.53.7)\n",
            "Requirement already satisfied: pydantic in /usr/local/lib/python3.10/dist-packages (from qiskit-ibm-runtime>=0.21.1->qiskit-serverless) (2.7.3)\n",
            "Requirement already satisfied: click>=7.0 in /usr/local/lib/python3.10/dist-packages (from ray[data,default]<3,>=2.9.3->qiskit-serverless) (8.1.7)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from ray[data,default]<3,>=2.9.3->qiskit-serverless) (3.14.0)\n",
            "Requirement already satisfied: jsonschema in /usr/local/lib/python3.10/dist-packages (from ray[data,default]<3,>=2.9.3->qiskit-serverless) (4.19.2)\n",
            "Requirement already satisfied: msgpack<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from ray[data,default]<3,>=2.9.3->qiskit-serverless) (1.0.8)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from ray[data,default]<3,>=2.9.3->qiskit-serverless) (24.0)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from ray[data,default]<3,>=2.9.3->qiskit-serverless) (6.0.1)\n",
            "Requirement already satisfied: aiosignal in /usr/local/lib/python3.10/dist-packages (from ray[data,default]<3,>=2.9.3->qiskit-serverless) (1.3.1)\n",
            "Requirement already satisfied: frozenlist in /usr/local/lib/python3.10/dist-packages (from ray[data,default]<3,>=2.9.3->qiskit-serverless) (1.4.1)\n",
            "Requirement already satisfied: aiohttp>=3.7 in /usr/local/lib/python3.10/dist-packages (from ray[data,default]<3,>=2.9.3->qiskit-serverless) (3.9.5)\n",
            "Collecting aiohttp-cors (from ray[data,default]<3,>=2.9.3->qiskit-serverless)\n",
            "  Downloading aiohttp_cors-0.7.0-py3-none-any.whl (27 kB)\n",
            "Collecting colorful (from ray[data,default]<3,>=2.9.3->qiskit-serverless)\n",
            "  Downloading colorful-0.5.6-py2.py3-none-any.whl (201 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m201.4/201.4 kB\u001b[0m \u001b[31m16.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting py-spy>=0.2.0 (from ray[data,default]<3,>=2.9.3->qiskit-serverless)\n",
            "  Downloading py_spy-0.3.14-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl (3.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.0/3.0 MB\u001b[0m \u001b[31m68.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting opencensus (from ray[data,default]<3,>=2.9.3->qiskit-serverless)\n",
            "  Downloading opencensus-0.11.4-py2.py3-none-any.whl (128 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m128.2/128.2 kB\u001b[0m \u001b[31m16.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: prometheus-client>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from ray[data,default]<3,>=2.9.3->qiskit-serverless) (0.20.0)\n",
            "Requirement already satisfied: smart-open in /usr/local/lib/python3.10/dist-packages (from ray[data,default]<3,>=2.9.3->qiskit-serverless) (6.4.0)\n",
            "Collecting virtualenv!=20.21.1,>=20.0.24 (from ray[data,default]<3,>=2.9.3->qiskit-serverless)\n",
            "  Downloading virtualenv-20.26.2-py3-none-any.whl (3.9 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.9/3.9 MB\u001b[0m \u001b[31m81.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting memray (from ray[data,default]<3,>=2.9.3->qiskit-serverless)\n",
            "  Downloading memray-1.12.0-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.5 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.5/3.5 MB\u001b[0m \u001b[31m84.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pandas>=1.3 in /usr/local/lib/python3.10/dist-packages (from ray[data,default]<3,>=2.9.3->qiskit-serverless) (2.0.3)\n",
            "Requirement already satisfied: pyarrow>=6.0.1 in /usr/local/lib/python3.10/dist-packages (from ray[data,default]<3,>=2.9.3->qiskit-serverless) (14.0.2)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from ray[data,default]<3,>=2.9.3->qiskit-serverless) (2023.6.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->qiskit-serverless) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->qiskit-serverless) (3.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->qiskit-serverless) (2024.6.2)\n",
            "Collecting aiobotocore<3.0.0,>=2.5.4 (from s3fs>=2023.6.0->qiskit-serverless)\n",
            "  Downloading aiobotocore-2.13.0-py3-none-any.whl (76 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.6/76.6 kB\u001b[0m \u001b[31m8.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting fsspec (from ray[data,default]<3,>=2.9.3->qiskit-serverless)\n",
            "  Downloading fsspec-2024.6.0-py3-none-any.whl (176 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m176.9/176.9 kB\u001b[0m \u001b[31m20.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting botocore<1.34.107,>=1.34.70 (from aiobotocore<3.0.0,>=2.5.4->s3fs>=2023.6.0->qiskit-serverless)\n",
            "  Downloading botocore-1.34.106-py3-none-any.whl (12.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.2/12.2 MB\u001b[0m \u001b[31m45.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting aioitertools<1.0.0,>=0.5.1 (from aiobotocore<3.0.0,>=2.5.4->s3fs>=2023.6.0->qiskit-serverless)\n",
            "  Downloading aioitertools-0.11.0-py3-none-any.whl (23 kB)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp>=3.7->ray[data,default]<3,>=2.9.3->qiskit-serverless) (23.2.0)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp>=3.7->ray[data,default]<3,>=2.9.3->qiskit-serverless) (6.0.5)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp>=3.7->ray[data,default]<3,>=2.9.3->qiskit-serverless) (1.9.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp>=3.7->ray[data,default]<3,>=2.9.3->qiskit-serverless) (4.0.3)\n",
            "Requirement already satisfied: ibm-cloud-sdk-core<4.0.0,>=3.19.2 in /usr/local/lib/python3.10/dist-packages (from ibm-platform-services>=0.22.6->qiskit-ibm-runtime>=0.21.1->qiskit-serverless) (3.20.1)\n",
            "Requirement already satisfied: parso<0.9.0,>=0.8.3 in /usr/local/lib/python3.10/dist-packages (from jedi>=0.16->ipython>=8.10.0->qiskit-serverless) (0.8.4)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.3->ray[data,default]<3,>=2.9.3->qiskit-serverless) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.3->ray[data,default]<3,>=2.9.3->qiskit-serverless) (2024.1)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.10/dist-packages (from pexpect>4.3->ipython>=8.10.0->qiskit-serverless) (0.7.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.10/dist-packages (from prompt-toolkit<3.1.0,>=3.0.41->ipython>=8.10.0->qiskit-serverless) (0.2.13)\n",
            "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic->qiskit-ibm-runtime>=0.21.1->qiskit-serverless) (0.7.0)\n",
            "Requirement already satisfied: pydantic-core==2.18.4 in /usr/local/lib/python3.10/dist-packages (from pydantic->qiskit-ibm-runtime>=0.21.1->qiskit-serverless) (2.18.4)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.0->qiskit>=1.0.2->qiskit-serverless) (1.16.0)\n",
            "Requirement already satisfied: cryptography>=1.3 in /usr/local/lib/python3.10/dist-packages (from requests-ntlm>=1.1.0->qiskit-ibm-provider>=0.10.0->qiskit-serverless) (42.0.7)\n",
            "Requirement already satisfied: pyspnego>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from requests-ntlm>=1.1.0->qiskit-ibm-provider>=0.10.0->qiskit-serverless) (0.10.2)\n",
            "Requirement already satisfied: pbr!=2.1.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from stevedore>=3.0.0->qiskit>=1.0.2->qiskit-serverless) (6.0.0)\n",
            "Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy>=1.3->qiskit>=1.0.2->qiskit-serverless) (1.3.0)\n",
            "Collecting distlib<1,>=0.3.7 (from virtualenv!=20.21.1,>=20.0.24->ray[data,default]<3,>=2.9.3->qiskit-serverless)\n",
            "  Downloading distlib-0.3.8-py2.py3-none-any.whl (468 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m468.9/468.9 kB\u001b[0m \u001b[31m30.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: platformdirs<5,>=3.9.1 in /usr/local/lib/python3.10/dist-packages (from virtualenv!=20.21.1,>=20.0.24->ray[data,default]<3,>=2.9.3->qiskit-serverless) (4.2.2)\n",
            "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema->ray[data,default]<3,>=2.9.3->qiskit-serverless) (2023.12.1)\n",
            "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema->ray[data,default]<3,>=2.9.3->qiskit-serverless) (0.35.1)\n",
            "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema->ray[data,default]<3,>=2.9.3->qiskit-serverless) (0.18.1)\n",
            "Requirement already satisfied: jinja2>=2.9 in /usr/local/lib/python3.10/dist-packages (from memray->ray[data,default]<3,>=2.9.3->qiskit-serverless) (3.1.4)\n",
            "Requirement already satisfied: rich>=11.2.0 in /usr/local/lib/python3.10/dist-packages (from memray->ray[data,default]<3,>=2.9.3->qiskit-serverless) (13.7.1)\n",
            "Collecting textual>=0.41.0 (from memray->ray[data,default]<3,>=2.9.3->qiskit-serverless)\n",
            "  Downloading textual-0.66.0-py3-none-any.whl (558 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m558.7/558.7 kB\u001b[0m \u001b[31m41.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting opencensus-context>=0.1.3 (from opencensus->ray[data,default]<3,>=2.9.3->qiskit-serverless)\n",
            "  Downloading opencensus_context-0.1.3-py2.py3-none-any.whl (5.1 kB)\n",
            "Requirement already satisfied: google-api-core<3.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from opencensus->ray[data,default]<3,>=2.9.3->qiskit-serverless) (2.11.1)\n",
            "Collecting executing>=1.2.0 (from stack-data->ipython>=8.10.0->qiskit-serverless)\n",
            "  Downloading executing-2.0.1-py2.py3-none-any.whl (24 kB)\n",
            "Collecting asttokens>=2.1.0 (from stack-data->ipython>=8.10.0->qiskit-serverless)\n",
            "  Downloading asttokens-2.4.1-py2.py3-none-any.whl (27 kB)\n",
            "Collecting pure-eval (from stack-data->ipython>=8.10.0->qiskit-serverless)\n",
            "  Downloading pure_eval-0.2.2-py3-none-any.whl (11 kB)\n",
            "Collecting jmespath<2.0.0,>=0.7.1 (from botocore<1.34.107,>=1.34.70->aiobotocore<3.0.0,>=2.5.4->s3fs>=2023.6.0->qiskit-serverless)\n",
            "  Downloading jmespath-1.0.1-py3-none-any.whl (20 kB)\n",
            "Requirement already satisfied: cffi>=1.12 in /usr/local/lib/python3.10/dist-packages (from cryptography>=1.3->requests-ntlm>=1.1.0->qiskit-ibm-provider>=0.10.0->qiskit-serverless) (1.16.0)\n",
            "Requirement already satisfied: google-auth<3.0.dev0,>=2.14.1 in /usr/local/lib/python3.10/dist-packages (from google-api-core<3.0.0,>=1.0.0->opencensus->ray[data,default]<3,>=2.9.3->qiskit-serverless) (2.27.0)\n",
            "Collecting requests>=2.32.2 (from qiskit-serverless)\n",
            "  Downloading requests-2.32.2-py3-none-any.whl (63 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m63.9/63.9 kB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: PyJWT<3.0.0,>=2.8.0 in /usr/local/lib/python3.10/dist-packages (from ibm-cloud-sdk-core<4.0.0,>=3.19.2->ibm-platform-services>=0.22.6->qiskit-ibm-runtime>=0.21.1->qiskit-serverless) (2.8.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2>=2.9->memray->ray[data,default]<3,>=2.9.3->qiskit-serverless) (2.1.5)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich>=11.2.0->memray->ray[data,default]<3,>=2.9.3->qiskit-serverless) (3.0.0)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.12->cryptography>=1.3->requests-ntlm>=1.1.0->qiskit-ibm-provider>=0.10.0->qiskit-serverless) (2.22)\n",
            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from google-auth<3.0.dev0,>=2.14.1->google-api-core<3.0.0,>=1.0.0->opencensus->ray[data,default]<3,>=2.9.3->qiskit-serverless) (5.3.3)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth<3.0.dev0,>=2.14.1->google-api-core<3.0.0,>=1.0.0->opencensus->ray[data,default]<3,>=2.9.3->qiskit-serverless) (0.4.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth<3.0.dev0,>=2.14.1->google-api-core<3.0.0,>=1.0.0->opencensus->ray[data,default]<3,>=2.9.3->qiskit-serverless) (4.9)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=11.2.0->memray->ray[data,default]<3,>=2.9.3->qiskit-serverless) (0.1.2)\n",
            "Requirement already satisfied: linkify-it-py<3,>=1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=11.2.0->memray->ray[data,default]<3,>=2.9.3->qiskit-serverless) (2.0.3)\n",
            "Requirement already satisfied: mdit-py-plugins in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=11.2.0->memray->ray[data,default]<3,>=2.9.3->qiskit-serverless) (0.4.1)\n",
            "Requirement already satisfied: uc-micro-py in /usr/local/lib/python3.10/dist-packages (from linkify-it-py<3,>=1->markdown-it-py>=2.2.0->rich>=11.2.0->memray->ray[data,default]<3,>=2.9.3->qiskit-serverless) (1.0.3)\n",
            "Requirement already satisfied: pyasn1<0.7.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3.0.dev0,>=2.14.1->google-api-core<3.0.0,>=1.0.0->opencensus->ray[data,default]<3,>=2.9.3->qiskit-serverless) (0.6.0)\n",
            "Installing collected packages: py-spy, pure-eval, opencensus-context, distlib, colorful, widgetsnbextension, websockets, virtualenv, traitlets, requests, opentelemetry-util-http, opentelemetry-proto, jmespath, jedi, fsspec, executing, deprecated, asttokens, aioitertools, stack-data, opentelemetry-exporter-otlp-proto-common, opentelemetry-api, comm, botocore, opentelemetry-semantic-conventions, opentelemetry-instrumentation, ipython, aiohttp-cors, aiobotocore, textual, s3fs, ray, opentelemetry-sdk, opentelemetry.instrumentation.requests, opencensus, ipywidgets, qiskit-ibm-provider, opentelemetry-exporter-otlp-proto-grpc, memray, qiskit-serverless\n",
            "  Attempting uninstall: widgetsnbextension\n",
            "    Found existing installation: widgetsnbextension 3.6.6\n",
            "    Uninstalling widgetsnbextension-3.6.6:\n",
            "      Successfully uninstalled widgetsnbextension-3.6.6\n",
            "  Attempting uninstall: traitlets\n",
            "    Found existing installation: traitlets 5.7.1\n",
            "    Uninstalling traitlets-5.7.1:\n",
            "      Successfully uninstalled traitlets-5.7.1\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.31.0\n",
            "    Uninstalling requests-2.31.0:\n",
            "      Successfully uninstalled requests-2.31.0\n",
            "  Attempting uninstall: fsspec\n",
            "    Found existing installation: fsspec 2023.6.0\n",
            "    Uninstalling fsspec-2023.6.0:\n",
            "      Successfully uninstalled fsspec-2023.6.0\n",
            "  Attempting uninstall: ipython\n",
            "    Found existing installation: ipython 7.34.0\n",
            "    Uninstalling ipython-7.34.0:\n",
            "      Successfully uninstalled ipython-7.34.0\n",
            "  Attempting uninstall: ipywidgets\n",
            "    Found existing installation: ipywidgets 7.7.1\n",
            "    Uninstalling ipywidgets-7.7.1:\n",
            "      Successfully uninstalled ipywidgets-7.7.1\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",
            "torch 2.3.0+cu121 requires nvidia-cublas-cu12==12.1.3.1; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.0+cu121 requires nvidia-cuda-cupti-cu12==12.1.105; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.0+cu121 requires nvidia-cuda-nvrtc-cu12==12.1.105; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.0+cu121 requires nvidia-cuda-runtime-cu12==12.1.105; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.0+cu121 requires nvidia-cudnn-cu12==8.9.2.26; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.0+cu121 requires nvidia-cufft-cu12==11.0.2.54; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.0+cu121 requires nvidia-curand-cu12==10.3.2.106; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.0+cu121 requires nvidia-cusolver-cu12==11.4.5.107; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.0+cu121 requires nvidia-cusparse-cu12==12.1.0.106; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.0+cu121 requires nvidia-nccl-cu12==2.20.5; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.0+cu121 requires nvidia-nvtx-cu12==12.1.105; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "gcsfs 2023.6.0 requires fsspec==2023.6.0, but you have fsspec 2024.6.0 which is incompatible.\n",
            "google-colab 1.0.0 requires ipython==7.34.0, but you have ipython 8.25.0 which is incompatible.\n",
            "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.2 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed aiobotocore-2.13.0 aiohttp-cors-0.7.0 aioitertools-0.11.0 asttokens-2.4.1 botocore-1.34.106 colorful-0.5.6 comm-0.2.2 deprecated-1.2.14 distlib-0.3.8 executing-2.0.1 fsspec-2024.6.0 ipython-8.25.0 ipywidgets-8.1.3 jedi-0.19.1 jmespath-1.0.1 memray-1.12.0 opencensus-0.11.4 opencensus-context-0.1.3 opentelemetry-api-1.25.0 opentelemetry-exporter-otlp-proto-common-1.25.0 opentelemetry-exporter-otlp-proto-grpc-1.25.0 opentelemetry-instrumentation-0.46b0 opentelemetry-proto-1.25.0 opentelemetry-sdk-1.25.0 opentelemetry-semantic-conventions-0.46b0 opentelemetry-util-http-0.46b0 opentelemetry.instrumentation.requests-0.46b0 pure-eval-0.2.2 py-spy-0.3.14 qiskit-ibm-provider-0.11.0 qiskit-serverless-0.12.0 ray-2.24.0 requests-2.32.2 s3fs-2024.6.0 stack-data-0.6.3 textual-0.66.0 traitlets-5.14.3 virtualenv-20.26.2 websockets-12.0 widgetsnbextension-4.0.11\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "IPython"
                ]
              },
              "id": "49573d01085a4c86ae0acf52904d5051"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting git+https://github.com/qiskit-community/Quantum-Challenge-Grader.git\n",
            "  Cloning https://github.com/qiskit-community/Quantum-Challenge-Grader.git to /tmp/pip-req-build-74yxis2r\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/qiskit-community/Quantum-Challenge-Grader.git /tmp/pip-req-build-74yxis2r\n",
            "  Resolved https://github.com/qiskit-community/Quantum-Challenge-Grader.git to commit 95854ae37d9e64dd77bf73fdfaf54be95b0b300f\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting typeguard (from qc-grader==0.18.11)\n",
            "  Downloading typeguard-4.3.0-py3-none-any.whl (35 kB)\n",
            "Collecting jsonpickle==3.0.3 (from qc-grader==0.18.11)\n",
            "  Downloading jsonpickle-3.0.3-py3-none-any.whl (40 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.8/40.8 kB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting ipycytoscape (from qc-grader==0.18.11)\n",
            "  Downloading ipycytoscape-1.3.3-py2.py3-none-any.whl (3.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.6/3.6 MB\u001b[0m \u001b[31m20.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from qc-grader==0.18.11) (5.15.0)\n",
            "Collecting networkx==3.2.1 (from qc-grader==0.18.11)\n",
            "  Downloading networkx-3.2.1-py3-none-any.whl (1.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m39.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: graphviz in /usr/local/lib/python3.10/dist-packages (from qc-grader==0.18.11) (0.20.3)\n",
            "Requirement already satisfied: qiskit[visualization]==1.0.2 in /usr/local/lib/python3.10/dist-packages (from qc-grader==0.18.11) (1.0.2)\n",
            "Requirement already satisfied: qiskit-ibm-runtime in /usr/local/lib/python3.10/dist-packages (from qc-grader==0.18.11) (0.23.0)\n",
            "Requirement already satisfied: qiskit-aer in /usr/local/lib/python3.10/dist-packages (from qc-grader==0.18.11) (0.14.2)\n",
            "Requirement already satisfied: qiskit-transpiler-service in /usr/local/lib/python3.10/dist-packages (from qc-grader==0.18.11) (0.4.3)\n",
            "Requirement already satisfied: qiskit-serverless==0.12.0 in /usr/local/lib/python3.10/dist-packages (from qc-grader==0.18.11) (0.12.0)\n",
            "Requirement already satisfied: circuit-knitting-toolbox in /usr/local/lib/python3.10/dist-packages (from qc-grader==0.18.11) (0.7.2)\n",
            "Requirement already satisfied: ray[data,default]<3,>=2.9.3 in /usr/local/lib/python3.10/dist-packages (from qiskit-serverless==0.12.0->qc-grader==0.18.11) (2.24.0)\n",
            "Requirement already satisfied: requests>=2.32.2 in /usr/local/lib/python3.10/dist-packages (from qiskit-serverless==0.12.0->qc-grader==0.18.11) (2.32.2)\n",
            "Requirement already satisfied: importlib-metadata>=5.2.0 in /usr/local/lib/python3.10/dist-packages (from qiskit-serverless==0.12.0->qc-grader==0.18.11) (7.1.0)\n",
            "Requirement already satisfied: qiskit-ibm-provider>=0.10.0 in /usr/local/lib/python3.10/dist-packages (from qiskit-serverless==0.12.0->qc-grader==0.18.11) (0.11.0)\n",
            "Requirement already satisfied: cloudpickle==2.2.1 in /usr/local/lib/python3.10/dist-packages (from qiskit-serverless==0.12.0->qc-grader==0.18.11) (2.2.1)\n",
            "Requirement already satisfied: tqdm>=4.66.3 in /usr/local/lib/python3.10/dist-packages (from qiskit-serverless==0.12.0->qc-grader==0.18.11) (4.66.4)\n",
            "Requirement already satisfied: opentelemetry-api>=1.18.0 in /usr/local/lib/python3.10/dist-packages (from qiskit-serverless==0.12.0->qc-grader==0.18.11) (1.25.0)\n",
            "Requirement already satisfied: opentelemetry-sdk>=1.18.0 in /usr/local/lib/python3.10/dist-packages (from qiskit-serverless==0.12.0->qc-grader==0.18.11) (1.25.0)\n",
            "Requirement already satisfied: opentelemetry-exporter-otlp-proto-grpc>=1.18.0 in /usr/local/lib/python3.10/dist-packages (from qiskit-serverless==0.12.0->qc-grader==0.18.11) (1.25.0)\n",
            "Requirement already satisfied: s3fs>=2023.6.0 in /usr/local/lib/python3.10/dist-packages (from qiskit-serverless==0.12.0->qc-grader==0.18.11) (2024.6.0)\n",
            "Requirement already satisfied: opentelemetry.instrumentation.requests>=0.40b0 in /usr/local/lib/python3.10/dist-packages (from qiskit-serverless==0.12.0->qc-grader==0.18.11) (0.46b0)\n",
            "Requirement already satisfied: ipywidgets>=8.1.2 in /usr/local/lib/python3.10/dist-packages (from qiskit-serverless==0.12.0->qc-grader==0.18.11) (8.1.3)\n",
            "Requirement already satisfied: ipython>=8.10.0 in /usr/local/lib/python3.10/dist-packages (from qiskit-serverless==0.12.0->qc-grader==0.18.11) (8.25.0)\n",
            "Requirement already satisfied: rustworkx>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from qiskit[visualization]==1.0.2->qc-grader==0.18.11) (0.14.2)\n",
            "Requirement already satisfied: numpy<2,>=1.17 in /usr/local/lib/python3.10/dist-packages (from qiskit[visualization]==1.0.2->qc-grader==0.18.11) (1.25.2)\n",
            "Requirement already satisfied: scipy>=1.5 in /usr/local/lib/python3.10/dist-packages (from qiskit[visualization]==1.0.2->qc-grader==0.18.11) (1.11.4)\n",
            "Requirement already satisfied: sympy>=1.3 in /usr/local/lib/python3.10/dist-packages (from qiskit[visualization]==1.0.2->qc-grader==0.18.11) (1.12.1)\n",
            "Requirement already satisfied: dill>=0.3 in /usr/local/lib/python3.10/dist-packages (from qiskit[visualization]==1.0.2->qc-grader==0.18.11) (0.3.8)\n",
            "Requirement already satisfied: python-dateutil>=2.8.0 in /usr/local/lib/python3.10/dist-packages (from qiskit[visualization]==1.0.2->qc-grader==0.18.11) (2.8.2)\n",
            "Requirement already satisfied: stevedore>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from qiskit[visualization]==1.0.2->qc-grader==0.18.11) (5.2.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from qiskit[visualization]==1.0.2->qc-grader==0.18.11) (4.12.1)\n",
            "Requirement already satisfied: symengine>=0.11 in /usr/local/lib/python3.10/dist-packages (from qiskit[visualization]==1.0.2->qc-grader==0.18.11) (0.11.0)\n",
            "Requirement already satisfied: matplotlib>=3.3 in /usr/local/lib/python3.10/dist-packages (from qiskit[visualization]==1.0.2->qc-grader==0.18.11) (3.7.1)\n",
            "Requirement already satisfied: pydot in /usr/local/lib/python3.10/dist-packages (from qiskit[visualization]==1.0.2->qc-grader==0.18.11) (1.4.2)\n",
            "Requirement already satisfied: Pillow>=4.2.1 in /usr/local/lib/python3.10/dist-packages (from qiskit[visualization]==1.0.2->qc-grader==0.18.11) (9.4.0)\n",
            "Requirement already satisfied: pylatexenc>=1.4 in /usr/local/lib/python3.10/dist-packages (from qiskit[visualization]==1.0.2->qc-grader==0.18.11) (2.10)\n",
            "Requirement already satisfied: seaborn>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from qiskit[visualization]==1.0.2->qc-grader==0.18.11) (0.13.1)\n",
            "Requirement already satisfied: requests-ntlm>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from qiskit-ibm-runtime->qc-grader==0.18.11) (1.3.0)\n",
            "Requirement already satisfied: urllib3>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from qiskit-ibm-runtime->qc-grader==0.18.11) (2.2.1)\n",
            "Requirement already satisfied: websocket-client>=1.5.1 in /usr/local/lib/python3.10/dist-packages (from qiskit-ibm-runtime->qc-grader==0.18.11) (1.8.0)\n",
            "Requirement already satisfied: ibm-platform-services>=0.22.6 in /usr/local/lib/python3.10/dist-packages (from qiskit-ibm-runtime->qc-grader==0.18.11) (0.53.7)\n",
            "Requirement already satisfied: pydantic in /usr/local/lib/python3.10/dist-packages (from qiskit-ibm-runtime->qc-grader==0.18.11) (2.7.3)\n",
            "Requirement already satisfied: psutil>=5 in /usr/local/lib/python3.10/dist-packages (from qiskit-aer->qc-grader==0.18.11) (5.9.5)\n",
            "Collecting spectate>=1.0.0 (from ipycytoscape->qc-grader==0.18.11)\n",
            "  Downloading spectate-1.0.1-py2.py3-none-any.whl (11 kB)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly->qc-grader==0.18.11) (8.3.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from plotly->qc-grader==0.18.11) (24.0)\n",
            "Requirement already satisfied: backoff~=2.0 in /usr/local/lib/python3.10/dist-packages (from qiskit-transpiler-service->qc-grader==0.18.11) (2.2.1)\n",
            "Requirement already satisfied: qiskit-qasm3-import~=0.4 in /usr/local/lib/python3.10/dist-packages (from qiskit-transpiler-service->qc-grader==0.18.11) (0.4.2)\n",
            "Requirement already satisfied: ibm-cloud-sdk-core<4.0.0,>=3.19.2 in /usr/local/lib/python3.10/dist-packages (from ibm-platform-services>=0.22.6->qiskit-ibm-runtime->qc-grader==0.18.11) (3.20.1)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.10/dist-packages (from importlib-metadata>=5.2.0->qiskit-serverless==0.12.0->qc-grader==0.18.11) (3.19.1)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.10/dist-packages (from ipython>=8.10.0->qiskit-serverless==0.12.0->qc-grader==0.18.11) (4.4.2)\n",
            "Requirement already satisfied: jedi>=0.16 in /usr/local/lib/python3.10/dist-packages (from ipython>=8.10.0->qiskit-serverless==0.12.0->qc-grader==0.18.11) (0.19.1)\n",
            "Requirement already satisfied: matplotlib-inline in /usr/local/lib/python3.10/dist-packages (from ipython>=8.10.0->qiskit-serverless==0.12.0->qc-grader==0.18.11) (0.1.7)\n",
            "Requirement already satisfied: prompt-toolkit<3.1.0,>=3.0.41 in /usr/local/lib/python3.10/dist-packages (from ipython>=8.10.0->qiskit-serverless==0.12.0->qc-grader==0.18.11) (3.0.45)\n",
            "Requirement already satisfied: pygments>=2.4.0 in /usr/local/lib/python3.10/dist-packages (from ipython>=8.10.0->qiskit-serverless==0.12.0->qc-grader==0.18.11) (2.16.1)\n",
            "Requirement already satisfied: stack-data in /usr/local/lib/python3.10/dist-packages (from ipython>=8.10.0->qiskit-serverless==0.12.0->qc-grader==0.18.11) (0.6.3)\n",
            "Requirement already satisfied: traitlets>=5.13.0 in /usr/local/lib/python3.10/dist-packages (from ipython>=8.10.0->qiskit-serverless==0.12.0->qc-grader==0.18.11) (5.14.3)\n",
            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from ipython>=8.10.0->qiskit-serverless==0.12.0->qc-grader==0.18.11) (1.2.1)\n",
            "Requirement already satisfied: pexpect>4.3 in /usr/local/lib/python3.10/dist-packages (from ipython>=8.10.0->qiskit-serverless==0.12.0->qc-grader==0.18.11) (4.9.0)\n",
            "Requirement already satisfied: comm>=0.1.3 in /usr/local/lib/python3.10/dist-packages (from ipywidgets>=8.1.2->qiskit-serverless==0.12.0->qc-grader==0.18.11) (0.2.2)\n",
            "Requirement already satisfied: widgetsnbextension~=4.0.11 in /usr/local/lib/python3.10/dist-packages (from ipywidgets>=8.1.2->qiskit-serverless==0.12.0->qc-grader==0.18.11) (4.0.11)\n",
            "Requirement already satisfied: jupyterlab-widgets~=3.0.11 in /usr/local/lib/python3.10/dist-packages (from ipywidgets>=8.1.2->qiskit-serverless==0.12.0->qc-grader==0.18.11) (3.0.11)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3->qiskit[visualization]==1.0.2->qc-grader==0.18.11) (1.2.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3->qiskit[visualization]==1.0.2->qc-grader==0.18.11) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3->qiskit[visualization]==1.0.2->qc-grader==0.18.11) (4.53.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3->qiskit[visualization]==1.0.2->qc-grader==0.18.11) (1.4.5)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3->qiskit[visualization]==1.0.2->qc-grader==0.18.11) (3.1.2)\n",
            "Requirement already satisfied: deprecated>=1.2.6 in /usr/local/lib/python3.10/dist-packages (from opentelemetry-api>=1.18.0->qiskit-serverless==0.12.0->qc-grader==0.18.11) (1.2.14)\n",
            "Requirement already satisfied: googleapis-common-protos~=1.52 in /usr/local/lib/python3.10/dist-packages (from opentelemetry-exporter-otlp-proto-grpc>=1.18.0->qiskit-serverless==0.12.0->qc-grader==0.18.11) (1.63.1)\n",
            "Requirement already satisfied: grpcio<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from opentelemetry-exporter-otlp-proto-grpc>=1.18.0->qiskit-serverless==0.12.0->qc-grader==0.18.11) (1.64.1)\n",
            "Requirement already satisfied: opentelemetry-exporter-otlp-proto-common==1.25.0 in /usr/local/lib/python3.10/dist-packages (from opentelemetry-exporter-otlp-proto-grpc>=1.18.0->qiskit-serverless==0.12.0->qc-grader==0.18.11) (1.25.0)\n",
            "Requirement already satisfied: opentelemetry-proto==1.25.0 in /usr/local/lib/python3.10/dist-packages (from opentelemetry-exporter-otlp-proto-grpc>=1.18.0->qiskit-serverless==0.12.0->qc-grader==0.18.11) (1.25.0)\n",
            "Requirement already satisfied: protobuf<5.0,>=3.19 in /usr/local/lib/python3.10/dist-packages (from opentelemetry-proto==1.25.0->opentelemetry-exporter-otlp-proto-grpc>=1.18.0->qiskit-serverless==0.12.0->qc-grader==0.18.11) (3.20.3)\n",
            "Requirement already satisfied: opentelemetry-instrumentation==0.46b0 in /usr/local/lib/python3.10/dist-packages (from opentelemetry.instrumentation.requests>=0.40b0->qiskit-serverless==0.12.0->qc-grader==0.18.11) (0.46b0)\n",
            "Requirement already satisfied: opentelemetry-semantic-conventions==0.46b0 in /usr/local/lib/python3.10/dist-packages (from opentelemetry.instrumentation.requests>=0.40b0->qiskit-serverless==0.12.0->qc-grader==0.18.11) (0.46b0)\n",
            "Requirement already satisfied: opentelemetry-util-http==0.46b0 in /usr/local/lib/python3.10/dist-packages (from opentelemetry.instrumentation.requests>=0.40b0->qiskit-serverless==0.12.0->qc-grader==0.18.11) (0.46b0)\n",
            "Requirement already satisfied: setuptools>=16.0 in /usr/local/lib/python3.10/dist-packages (from opentelemetry-instrumentation==0.46b0->opentelemetry.instrumentation.requests>=0.40b0->qiskit-serverless==0.12.0->qc-grader==0.18.11) (67.7.2)\n",
            "Requirement already satisfied: wrapt<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from opentelemetry-instrumentation==0.46b0->opentelemetry.instrumentation.requests>=0.40b0->qiskit-serverless==0.12.0->qc-grader==0.18.11) (1.14.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.0->qiskit[visualization]==1.0.2->qc-grader==0.18.11) (1.16.0)\n",
            "Requirement already satisfied: websockets>=10.0 in /usr/local/lib/python3.10/dist-packages (from qiskit-ibm-provider>=0.10.0->qiskit-serverless==0.12.0->qc-grader==0.18.11) (12.0)\n",
            "Requirement already satisfied: openqasm3[parser]<0.6,>=0.4 in /usr/local/lib/python3.10/dist-packages (from qiskit-qasm3-import~=0.4->qiskit-transpiler-service->qc-grader==0.18.11) (0.5.0)\n",
            "Requirement already satisfied: click>=7.0 in /usr/local/lib/python3.10/dist-packages (from ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (8.1.7)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (3.14.0)\n",
            "Requirement already satisfied: jsonschema in /usr/local/lib/python3.10/dist-packages (from ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (4.19.2)\n",
            "Requirement already satisfied: msgpack<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (1.0.8)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (6.0.1)\n",
            "Requirement already satisfied: aiosignal in /usr/local/lib/python3.10/dist-packages (from ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (1.3.1)\n",
            "Requirement already satisfied: frozenlist in /usr/local/lib/python3.10/dist-packages (from ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (1.4.1)\n",
            "Requirement already satisfied: aiohttp>=3.7 in /usr/local/lib/python3.10/dist-packages (from ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (3.9.5)\n",
            "Requirement already satisfied: aiohttp-cors in /usr/local/lib/python3.10/dist-packages (from ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (0.7.0)\n",
            "Requirement already satisfied: colorful in /usr/local/lib/python3.10/dist-packages (from ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (0.5.6)\n",
            "Requirement already satisfied: py-spy>=0.2.0 in /usr/local/lib/python3.10/dist-packages (from ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (0.3.14)\n",
            "Requirement already satisfied: opencensus in /usr/local/lib/python3.10/dist-packages (from ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (0.11.4)\n",
            "Requirement already satisfied: prometheus-client>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (0.20.0)\n",
            "Requirement already satisfied: smart-open in /usr/local/lib/python3.10/dist-packages (from ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (6.4.0)\n",
            "Requirement already satisfied: virtualenv!=20.21.1,>=20.0.24 in /usr/local/lib/python3.10/dist-packages (from ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (20.26.2)\n",
            "Requirement already satisfied: memray in /usr/local/lib/python3.10/dist-packages (from ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (1.12.0)\n",
            "Requirement already satisfied: pandas>=1.3 in /usr/local/lib/python3.10/dist-packages (from ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (2.0.3)\n",
            "Requirement already satisfied: pyarrow>=6.0.1 in /usr/local/lib/python3.10/dist-packages (from ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (14.0.2)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (2024.6.0)\n",
            "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic->qiskit-ibm-runtime->qc-grader==0.18.11) (0.7.0)\n",
            "Requirement already satisfied: pydantic-core==2.18.4 in /usr/local/lib/python3.10/dist-packages (from pydantic->qiskit-ibm-runtime->qc-grader==0.18.11) (2.18.4)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->qiskit-serverless==0.12.0->qc-grader==0.18.11) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->qiskit-serverless==0.12.0->qc-grader==0.18.11) (3.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->qiskit-serverless==0.12.0->qc-grader==0.18.11) (2024.6.2)\n",
            "Requirement already satisfied: cryptography>=1.3 in /usr/local/lib/python3.10/dist-packages (from requests-ntlm>=1.1.0->qiskit-ibm-runtime->qc-grader==0.18.11) (42.0.7)\n",
            "Requirement already satisfied: pyspnego>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from requests-ntlm>=1.1.0->qiskit-ibm-runtime->qc-grader==0.18.11) (0.10.2)\n",
            "Requirement already satisfied: aiobotocore<3.0.0,>=2.5.4 in /usr/local/lib/python3.10/dist-packages (from s3fs>=2023.6.0->qiskit-serverless==0.12.0->qc-grader==0.18.11) (2.13.0)\n",
            "Requirement already satisfied: pbr!=2.1.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from stevedore>=3.0.0->qiskit[visualization]==1.0.2->qc-grader==0.18.11) (6.0.0)\n",
            "Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy>=1.3->qiskit[visualization]==1.0.2->qc-grader==0.18.11) (1.3.0)\n",
            "Requirement already satisfied: botocore<1.34.107,>=1.34.70 in /usr/local/lib/python3.10/dist-packages (from aiobotocore<3.0.0,>=2.5.4->s3fs>=2023.6.0->qiskit-serverless==0.12.0->qc-grader==0.18.11) (1.34.106)\n",
            "Requirement already satisfied: aioitertools<1.0.0,>=0.5.1 in /usr/local/lib/python3.10/dist-packages (from aiobotocore<3.0.0,>=2.5.4->s3fs>=2023.6.0->qiskit-serverless==0.12.0->qc-grader==0.18.11) (0.11.0)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp>=3.7->ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (23.2.0)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp>=3.7->ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (6.0.5)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp>=3.7->ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (1.9.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp>=3.7->ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (4.0.3)\n",
            "Requirement already satisfied: cffi>=1.12 in /usr/local/lib/python3.10/dist-packages (from cryptography>=1.3->requests-ntlm>=1.1.0->qiskit-ibm-runtime->qc-grader==0.18.11) (1.16.0)\n",
            "Requirement already satisfied: PyJWT<3.0.0,>=2.8.0 in /usr/local/lib/python3.10/dist-packages (from ibm-cloud-sdk-core<4.0.0,>=3.19.2->ibm-platform-services>=0.22.6->qiskit-ibm-runtime->qc-grader==0.18.11) (2.8.0)\n",
            "Requirement already satisfied: parso<0.9.0,>=0.8.3 in /usr/local/lib/python3.10/dist-packages (from jedi>=0.16->ipython>=8.10.0->qiskit-serverless==0.12.0->qc-grader==0.18.11) (0.8.4)\n",
            "Requirement already satisfied: antlr4-python3-runtime<4.14,>=4.7 in /usr/local/lib/python3.10/dist-packages (from openqasm3[parser]<0.6,>=0.4->qiskit-qasm3-import~=0.4->qiskit-transpiler-service->qc-grader==0.18.11) (4.13.1)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.3->ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.3->ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (2024.1)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.10/dist-packages (from pexpect>4.3->ipython>=8.10.0->qiskit-serverless==0.12.0->qc-grader==0.18.11) (0.7.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.10/dist-packages (from prompt-toolkit<3.1.0,>=3.0.41->ipython>=8.10.0->qiskit-serverless==0.12.0->qc-grader==0.18.11) (0.2.13)\n",
            "Requirement already satisfied: distlib<1,>=0.3.7 in /usr/local/lib/python3.10/dist-packages (from virtualenv!=20.21.1,>=20.0.24->ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (0.3.8)\n",
            "Requirement already satisfied: platformdirs<5,>=3.9.1 in /usr/local/lib/python3.10/dist-packages (from virtualenv!=20.21.1,>=20.0.24->ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (4.2.2)\n",
            "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema->ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (2023.12.1)\n",
            "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema->ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (0.35.1)\n",
            "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema->ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (0.18.1)\n",
            "Requirement already satisfied: jinja2>=2.9 in /usr/local/lib/python3.10/dist-packages (from memray->ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (3.1.4)\n",
            "Requirement already satisfied: rich>=11.2.0 in /usr/local/lib/python3.10/dist-packages (from memray->ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (13.7.1)\n",
            "Requirement already satisfied: textual>=0.41.0 in /usr/local/lib/python3.10/dist-packages (from memray->ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (0.66.0)\n",
            "Requirement already satisfied: opencensus-context>=0.1.3 in /usr/local/lib/python3.10/dist-packages (from opencensus->ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (0.1.3)\n",
            "Requirement already satisfied: google-api-core<3.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from opencensus->ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (2.11.1)\n",
            "Requirement already satisfied: executing>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from stack-data->ipython>=8.10.0->qiskit-serverless==0.12.0->qc-grader==0.18.11) (2.0.1)\n",
            "Requirement already satisfied: asttokens>=2.1.0 in /usr/local/lib/python3.10/dist-packages (from stack-data->ipython>=8.10.0->qiskit-serverless==0.12.0->qc-grader==0.18.11) (2.4.1)\n",
            "Requirement already satisfied: pure-eval in /usr/local/lib/python3.10/dist-packages (from stack-data->ipython>=8.10.0->qiskit-serverless==0.12.0->qc-grader==0.18.11) (0.2.2)\n",
            "Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from botocore<1.34.107,>=1.34.70->aiobotocore<3.0.0,>=2.5.4->s3fs>=2023.6.0->qiskit-serverless==0.12.0->qc-grader==0.18.11) (1.0.1)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.12->cryptography>=1.3->requests-ntlm>=1.1.0->qiskit-ibm-runtime->qc-grader==0.18.11) (2.22)\n",
            "Requirement already satisfied: google-auth<3.0.dev0,>=2.14.1 in /usr/local/lib/python3.10/dist-packages (from google-api-core<3.0.0,>=1.0.0->opencensus->ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (2.27.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2>=2.9->memray->ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (2.1.5)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich>=11.2.0->memray->ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (3.0.0)\n",
            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from google-auth<3.0.dev0,>=2.14.1->google-api-core<3.0.0,>=1.0.0->opencensus->ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (5.3.3)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth<3.0.dev0,>=2.14.1->google-api-core<3.0.0,>=1.0.0->opencensus->ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (0.4.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth<3.0.dev0,>=2.14.1->google-api-core<3.0.0,>=1.0.0->opencensus->ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (4.9)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=11.2.0->memray->ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (0.1.2)\n",
            "Requirement already satisfied: mdit-py-plugins in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=11.2.0->memray->ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (0.4.1)\n",
            "Requirement already satisfied: linkify-it-py<3,>=1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=11.2.0->memray->ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (2.0.3)\n",
            "Requirement already satisfied: uc-micro-py in /usr/local/lib/python3.10/dist-packages (from linkify-it-py<3,>=1->markdown-it-py>=2.2.0->rich>=11.2.0->memray->ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (1.0.3)\n",
            "Requirement already satisfied: pyasn1<0.7.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3.0.dev0,>=2.14.1->google-api-core<3.0.0,>=1.0.0->opencensus->ray[data,default]<3,>=2.9.3->qiskit-serverless==0.12.0->qc-grader==0.18.11) (0.6.0)\n",
            "Building wheels for collected packages: qc-grader\n",
            "  Building wheel for qc-grader (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for qc-grader: filename=qc_grader-0.18.11-py3-none-any.whl size=33706 sha256=77f8eef0229da1de81563584e808b73aa900887b7234fa2408ca76942d1050c0\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-cbgqsyir/wheels/7e/67/91/88d6a5b4dd4beb318ed4e79c807e2be98b7d3c2ea66565a116\n",
            "Successfully built qc-grader\n",
            "Installing collected packages: typeguard, spectate, networkx, jsonpickle, ipycytoscape, qc-grader\n",
            "  Attempting uninstall: networkx\n",
            "    Found existing installation: networkx 3.3\n",
            "    Uninstalling networkx-3.3:\n",
            "      Successfully uninstalled networkx-3.3\n",
            "  Attempting uninstall: jsonpickle\n",
            "    Found existing installation: jsonpickle 3.0.4\n",
            "    Uninstalling jsonpickle-3.0.4:\n",
            "      Successfully uninstalled jsonpickle-3.0.4\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",
            "torch 2.3.0+cu121 requires nvidia-cublas-cu12==12.1.3.1; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.0+cu121 requires nvidia-cuda-cupti-cu12==12.1.105; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.0+cu121 requires nvidia-cuda-nvrtc-cu12==12.1.105; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.0+cu121 requires nvidia-cuda-runtime-cu12==12.1.105; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.0+cu121 requires nvidia-cudnn-cu12==8.9.2.26; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.0+cu121 requires nvidia-cufft-cu12==11.0.2.54; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.0+cu121 requires nvidia-curand-cu12==10.3.2.106; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.0+cu121 requires nvidia-cusolver-cu12==11.4.5.107; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.0+cu121 requires nvidia-cusparse-cu12==12.1.0.106; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.0+cu121 requires nvidia-nccl-cu12==2.20.5; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.0+cu121 requires nvidia-nvtx-cu12==12.1.105; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed ipycytoscape-1.3.3 jsonpickle-3.0.3 networkx-3.2.1 qc-grader-0.18.11 spectate-1.0.1 typeguard-4.3.0\n"
          ]
        }
      ],
      "source": [
        "### Install Qiskit and relevant packages, if needed\n",
        "\n",
        "%pip install qiskit[visualization]==1.0.2\n",
        "%pip install qiskit-ibm-runtime\n",
        "%pip install qiskit-aer\n",
        "%pip install qiskit-transpiler-service\n",
        "%pip install graphviz\n",
        "%pip install circuit-knitting-toolbox\n",
        "%pip install qiskit-serverless -U\n",
        "%pip install git+https://github.com/qiskit-community/Quantum-Challenge-Grader.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4bbd0159-55bd-433c-bc1b-8660967b1cb8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4bbd0159-55bd-433c-bc1b-8660967b1cb8",
        "outputId": "49509ced-1647-4684-ceb0-8de4059b908b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "env: QXToken=c0c4be947cf7e48b3b2d6695c5d6d2eb99d7de3a5a308a11b7299512ff0b65340fc87b0cb7a8a980d3527cc89167e2a49ffbb4c7509126d1ec2ea94abdb521ff\n"
          ]
        }
      ],
      "source": [
        "### Save API Token, if needed\n",
        "\n",
        "%set_env QXToken=c0c4be947cf7e48b3b2d6695c5d6d2eb99d7de3a5a308a11b7299512ff0b65340fc87b0cb7a8a980d3527cc89167e2a49ffbb4c7509126d1ec2ea94abdb521ff\n",
        "\n",
        "# Make sure there is no space between the equal sign\n",
        "# and the beginning of your token"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ed2dd69b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "ed2dd69b",
        "outputId": "e359ad0c-f8bd-42d4-a6db-5039f08a9982"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'0.18.11'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "# qc-grader should be 0.18.11 (or higher)\n",
        "import qc_grader\n",
        "\n",
        "qc_grader.__version__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7a0db348-fcbe-4574-8b54-14e7fc90fed8",
      "metadata": {
        "id": "7a0db348-fcbe-4574-8b54-14e7fc90fed8"
      },
      "outputs": [],
      "source": [
        "# Imports\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from qiskit import QuantumCircuit\n",
        "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n",
        "from qiskit.visualization import plot_gate_map\n",
        "from qiskit.quantum_info import SparsePauliOp\n",
        "\n",
        "from qiskit_aer import AerSimulator\n",
        "from qiskit_ibm_runtime import QiskitRuntimeService, SamplerV2 as Sampler\n",
        "from circuit_knitting.cutting import generate_cutting_experiments, cut_gates"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8f0e274c-ba5e-4b69-bec6-822950833197",
      "metadata": {
        "id": "8f0e274c-ba5e-4b69-bec6-822950833197"
      },
      "outputs": [],
      "source": [
        "# Setup the grader\n",
        "\n",
        "from qc_grader.challenges.iqc_2024 import grade_lab3_ckt_ex1, grade_lab3_ckt_ex2"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "50832269-174f-4ad1-92b2-811d1341ae1f",
      "metadata": {
        "id": "50832269-174f-4ad1-92b2-811d1341ae1f"
      },
      "source": [
        "# Introduction\n",
        "Welcome to Lab 3! Today, we are thrilled to give you an exclusive sneak peek into some of the exciting new features that are being developed for the Qiskit stack. This educational challenge notebook invites you to explore and test some groundbreaking new features. Dive in to explore tools like the Circuit Knitting Toolbox to simplify complex quantum circuits, experience the workflow to setup seamless integration of quantum and classical computing with Qiskit Serverless and have a chance to leverage AI-Powered tools like the Qiskit Code Assistant for code generation and circuit optimization with AI-Powered Transpiler Passes. Dive in and discover how these innovations are set to revolutionize quantum development!\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1481eab1-fc75-44e8-a784-0e6f34ea90fc",
      "metadata": {
        "id": "1481eab1-fc75-44e8-a784-0e6f34ea90fc"
      },
      "source": [
        "<div class=\"alert alert-block alert-danger\">\n",
        "\n",
        "Please note, **these features are in beta and release preview**, so you most likely may encounter some bugs. Have fun experimenting and pushing the boundaries of quantum development!\n",
        "\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f74dcdb8-b883-4e5b-9489-c8ec915671fd",
      "metadata": {
        "id": "f74dcdb8-b883-4e5b-9489-c8ec915671fd"
      },
      "source": [
        "# Introducing the Circuit Knitting Toolbox"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "88fd0bc7-891e-4348-833c-5145ea34f5c9",
      "metadata": {
        "id": "88fd0bc7-891e-4348-833c-5145ea34f5c9"
      },
      "source": [
        "In this notebook we will dive into the **Circuit Knitting Toolbox**. This is a tool you can use to simplify complex quantum circuits.\n",
        "\n",
        "It is true that superconducting processors, like the ones IBM uses in our quantum computers, have limited connectivity. This means if we want to continue to push the boundaries of science, we need to be creative to overcome that limit.\n",
        "\n",
        "The natural question is, how can we overcome this? One solution is circuit knitting.\n",
        "\n",
        "As we learned in lab 2, applying gates between distant qubits would require a large swap overhead. Circuit cutting is a technique that reduces the need for swap gates by cutting the gates and/or wires of a large quantum circuit. This decomposes the circuit into smaller ones that can be executed within the constraints of the available quantum hardware. The results of these smaller circuits are then combined to reconstruct the outcome of the original problem.\n",
        "\n",
        "In short; circuit cutting allows us to run larger circuits on quantum hardware at the cost of additional sampling overhead."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "322194b5-6825-43f8-ac2c-f548e36a9671",
      "metadata": {
        "id": "322194b5-6825-43f8-ac2c-f548e36a9671"
      },
      "source": [
        "## Theory\n",
        "\n",
        "A quantum channel $\\varepsilon$ can be decomposed into a linear combination of several channels $\\varepsilon_i$ with coefficients $a_i$. Therefore, a virtual gate also is implemented by a sum over several circuits:\n",
        "\n",
        "$$\\varepsilon(\\cdot) = \\sum_i a_i \\varepsilon(\\cdot)$$\n",
        "The $a_i$ do not form a valid probability decomposition since some $a_i<0$ cannot sample. We transform to a valid probability distribution via:\n",
        "\n",
        "$$\\gamma = \\sum   |a_i| $$\n",
        "\n",
        "So $\\sum\\frac{|a_i|}{\\gamma}=1$. Therefore, $\\frac{|a_i|}{\\gamma}$ form a valid probability distribution:\n",
        "\n",
        "$$\\varepsilon(\\rho) =\\gamma \\sum_i \\frac{|a_i|}{\\gamma} sign(a_i) \\varepsilon_i(\\cdot)$$\n",
        "\n",
        "This is Quasi-Probability Decomposition and it allows us to cut gates.\n",
        "\n",
        "For more information, please refer to the [explanatory material for the circuit cutting module](https://qiskit-extensions.github.io/circuit-knitting-toolbox/circuit_cutting/explanation/index.html)."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "029fbf0b-d627-47d2-9bc4-908db1c0a2b1",
      "metadata": {
        "id": "029fbf0b-d627-47d2-9bc4-908db1c0a2b1"
      },
      "source": [
        "## Cutting a CNOT gate\n",
        "\n",
        "Now, let's focus on a Bell pair where the qubits are far apart. We will explore how to cut a CNOT gate using the Circuit Knitting Toolbox in this scenario."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "da915332-1b9d-47f8-ab9c-8deefedaeb44",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "id": "da915332-1b9d-47f8-ab9c-8deefedaeb44",
        "outputId": "54fd80fa-65a3-4aeb-e843-c7e45225fb47"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 287.294x200.667 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# create a bell pair\n",
        "bell_state = QuantumCircuit(2)\n",
        "bell_state.h(0)\n",
        "bell_state.cx(0,1)\n",
        "bell_state.draw(\"mpl\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4e4fbe03-588a-4610-bc2c-3ca816f446d5",
      "metadata": {
        "id": "4e4fbe03-588a-4610-bc2c-3ca816f446d5"
      },
      "source": [
        "Next, we will run this on `ibm_kyoto`! We will use the `ibm_kyoto` device for the rest of this lab."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d40dbf06-0cb3-4b5c-9472-2953b8bb8fde",
      "metadata": {
        "id": "d40dbf06-0cb3-4b5c-9472-2953b8bb8fde"
      },
      "outputs": [],
      "source": [
        "## If this is your first time accessing the backend\n",
        "## remove # and fill your API key, and run the code\n",
        "service = QiskitRuntimeService(\n",
        "   channel='ibm_quantum',\n",
        "   instance='ibm-q/open/main',\n",
        "   token='c0c4be947cf7e48b3b2d6695c5d6d2eb99d7de3a5a308a11b7299512ff0b65340fc87b0cb7a8a980d3527cc89167e2a49ffbb4c7509126d1ec2ea94abdb521ff'\n",
        ")\n",
        "\n",
        "# service = QiskitRuntimeService(channel=\"ibm_quantum\")\n",
        "\n",
        "# Specify a system to use for transpilation, DO NOT change\n",
        "backend = service.backend(\"ibm_kyoto\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7e500e17-b1cb-46ae-8607-48a34e9f2ca5",
      "metadata": {
        "id": "7e500e17-b1cb-46ae-8607-48a34e9f2ca5"
      },
      "source": [
        "Now, we choose a layout that requires many swaps to execute the gates between qubits 122 and 126, as well as many swaps to return the qubits to their initial positions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "10649371-ce0f-46f3-b75e-6cc91ba46be6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "10649371-ce0f-46f3-b75e-6cc91ba46be6",
        "outputId": "38e3ed0f-7d32-4633-eccb-31b4c43adcea"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "layout=[122, 126]\n",
        "qubit_color = []\n",
        "for i in range(127):\n",
        "    if i in layout:\n",
        "        qubit_color.append(\"#ff0066\")\n",
        "    else:\n",
        "        qubit_color.append(\"#6600cc\")\n",
        "plot_gate_map(backend, qubit_color=qubit_color, qubit_size=60, font_size=25, figsize=(8,8))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8975822b-b9b8-4724-b0d8-76370d5dfea1",
      "metadata": {
        "id": "8975822b-b9b8-4724-b0d8-76370d5dfea1"
      },
      "source": [
        "We will transpile the Bell pair circuit to get the Instruction Set Architecture (ISA) circuit, which the device can understand and execute."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "553ec2c2-b1e4-41fa-9b8a-8fec3761e322",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        },
        "id": "553ec2c2-b1e4-41fa-9b8a-8fec3761e322",
        "outputId": "5b091454-21d1-4a56-bbf8-fbcaa6d5a38f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Transpiled circuit depth:  27\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 1547.94x270.9 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "# transpile the circuit\n",
        "pm = generate_preset_pass_manager(backend=backend, optimization_level=1, initial_layout=layout, seed_transpiler=0)\n",
        "isa_qc = pm.run(bell_state)\n",
        "\n",
        "# original circuit depth\n",
        "isa_qc_depth = isa_qc.depth()\n",
        "print(f\"Transpiled circuit depth: \", isa_qc_depth)\n",
        "isa_qc.draw(\"mpl\", scale=0.6, idle_wires=False, fold=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9a5496a9-307f-4e6c-817e-d5be4f751b5d",
      "metadata": {
        "id": "9a5496a9-307f-4e6c-817e-d5be4f751b5d"
      },
      "source": [
        "We see that many ancilla qubits are used and the depth of the transpiled circuit is 27! This is the case if we don't use the Qiskit Circuit Knitting Toolbox."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bbcd25d3-5a54-4795-934e-9eff5cfafe37",
      "metadata": {
        "id": "bbcd25d3-5a54-4795-934e-9eff5cfafe37"
      },
      "source": [
        "## Circuit Knitting\n",
        "\n",
        "We will replace the distant gates (gates connecting non-local qubits, 122 and 126) with `TwoQubitQPDGate`s by specifying their indices ([QPD](https://qiskit-extensions.github.io/circuit-knitting-toolbox/circuit_cutting/explanation/index.html#circuit-cutting-as-a-quasiprobability-decomposition-qpd) is Quasi-Probability Decomposition which explained in Theory chapter). `cut_gates` will replace the gates in the specified indices with `TwoQubitQPDGate`s and also return a list of `QPDBasis` instances -- one for each gate decomposition. The ``QPDBasis`` object contains information about how to decompose the cut gates into single-qubit operations. For more detail, please refere [\"Error mitigation for short-depth quantum circuits\"](https://arxiv.org/pdf/1612.02058).    \n",
        "In the next cell, we will apply `cut_gates` to the bell circuit with specified indices to cut then get the circuit which the CNOT gate is cut."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3f682fc1-c3e9-445f-b5b6-e48ac81ce29d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        },
        "id": "3f682fc1-c3e9-445f-b5b6-e48ac81ce29d",
        "outputId": "55eb4ef4-6d95-44db-e06d-36f3fb34a9eb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 222.543x120.4 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "# Find the indices of the distant gates\n",
        "cut_indices = [\n",
        "    i\n",
        "    for i, instruction in enumerate(bell_state.data)\n",
        "    if {bell_state.find_bit(q)[0] for q in instruction.qubits} == {0, 1}\n",
        "]\n",
        "\n",
        "# Decompose distant CNOTs into TwoQubitQPDGate instances\n",
        "qpd_circuit, bases = cut_gates(bell_state, cut_indices)\n",
        "\n",
        "qpd_circuit.draw(\"mpl\", scale=0.6)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c9d68e50-347b-41ee-be8e-8196b0e63224",
      "metadata": {
        "id": "c9d68e50-347b-41ee-be8e-8196b0e63224"
      },
      "source": [
        "In the next cell, [`generate_cutting_experiments`](https://qiskit-extensions.github.io/circuit-knitting-toolbox/stubs/circuit_knitting.cutting.generate_cutting_experiments.html#circuit_knitting.cutting.generate_cutting_experiments) accepts a circuit containing `TwoQubitQPDGate` instances and observables as a `PauliList`.\n",
        "\n",
        "To simulate the expectation value of the full-sized circuit, many sub-experiments are generated from the decomposed gates' joint quasiprobability distribution and then executed on one or more backends. The number of samples taken from the distribution is controlled by `num_samples`, and one combined coefficient is given for each unique sample. For more information on how the coefficients are calculated, refer to the [explanatory material](https://qiskit-extensions.github.io/circuit-knitting-toolbox/circuit_cutting/explanation/index.html)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f68eba1c-b567-4c8d-b075-061964f3ec3b",
      "metadata": {
        "id": "f68eba1c-b567-4c8d-b075-061964f3ec3b"
      },
      "outputs": [],
      "source": [
        "observable = SparsePauliOp([\"ZI\"])\n",
        "\n",
        "# Generate the sub-experiments and sampling coefficients\n",
        "sub_experiments, coefficients = generate_cutting_experiments(\n",
        "    circuits=qpd_circuit, observables=observable.paulis, num_samples=np.inf\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5e4d97ca-165d-4962-a696-1cd57f5e1fb1",
      "metadata": {
        "id": "5e4d97ca-165d-4962-a696-1cd57f5e1fb1"
      },
      "source": [
        "Here is an example of an arbitrarily chosen sub-experiment generated from the QPD circuit. Its depth has been reduced significantly from 27 to 7. Many of these probabilistic sub-experiments must be generated and evaluated in order to reconstruct an expectation value of the deeper circuit. For this circuit, we need six sub-experiments to reconstruct it."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6029574f-30f8-4334-975b-9e0fb7d6a354",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "6029574f-30f8-4334-975b-9e0fb7d6a354",
        "outputId": "d19500e3-7986-4d0c-cb0f-2f8bb6d889cd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original circuit depth after transpile:  27\n",
            "QPD sub-experiment depth after transpile:  7\n",
            "Number of sub-experiments: 6\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 636.542x220.733 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "# Transpile the circuit\n",
        "pm = generate_preset_pass_manager(backend=backend, optimization_level=1, initial_layout=layout, seed_transpiler=0)\n",
        "isa_qpd_circuit = pm.run(sub_experiments[5])\n",
        "\n",
        "# depth using circuit cutting\n",
        "isa_qpd_depth = isa_qpd_circuit.depth()\n",
        "\n",
        "print(f\"Original circuit depth after transpile: \", isa_qc_depth)\n",
        "print(f\"QPD sub-experiment depth after transpile: \", isa_qpd_depth)\n",
        "print(f\"Number of sub-experiments:\", len(sub_experiments))\n",
        "isa_qpd_circuit.draw(\"mpl\", scale=0.6, idle_wires=False, fold=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "88d3e6b9-b6c8-407d-8484-dd893cec3b77",
      "metadata": {
        "id": "88d3e6b9-b6c8-407d-8484-dd893cec3b77"
      },
      "source": [
        "The above circuit is the fifth circuit in six sub-experiments. Let's see the depth of all sub-experiments."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "da8e2747-5888-411b-a587-cf801d7c0b24",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "da8e2747-5888-411b-a587-cf801d7c0b24",
        "outputId": "257e8d72-ec7b-46e5-c026-80eae8dfc4d8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[4 3 3 3 7 7]\n"
          ]
        }
      ],
      "source": [
        "x = np.array([c.depth() for c in pm.run(sub_experiments)])\n",
        "print(x)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4916e45e-8126-4671-8d8c-937173a99518",
      "metadata": {
        "id": "4916e45e-8126-4671-8d8c-937173a99518"
      },
      "source": [
        "In order to know the expectation value of the original circuit, a number of sub-experiments must be executed. In this case, the six circuits need to be run and reconstructed using subobservables and sampling coefficients. Therefore, with circuit cutting, the depth of each circuit is decreased significantly, but a lot of circuits need to be executed.\n",
        "For more information, please refer to the tutorial [Gate Cutting to Reduce Circuit Depth](https://qiskit-extensions.github.io/circuit-knitting-toolbox/circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html)."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "100bcadc-7451-454b-8dcc-186f3da66183",
      "metadata": {
        "id": "100bcadc-7451-454b-8dcc-186f3da66183"
      },
      "source": [
        "### Toffoli circuit with circuit cutting"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "373b78fc-3167-4c9d-be73-220530d51500",
      "metadata": {
        "id": "373b78fc-3167-4c9d-be73-220530d51500"
      },
      "source": [
        "<a id=\"exercise1\"></a>\n",
        "<div class=\"alert alert-block alert-success\">\n",
        "    \n",
        "<b> Exercise 1:</b>\n",
        "\n",
        "Which gate connections should be cut to minimize the circuit depth of the Toffoli gate? Assume that each qubit of the Toffoli gate is located at `[122, 124, 126]` on the `ibm_kyoto` device, and we will cut only one connection. Answer with the gates connecting to cut ({0, 1}, etc.), the depth without using circuit cutting (the original circuit depth), and the average depth with using circuit cutting.    \n",
        "**Note:** Use `optimization_level=1` and `seed_transpiler=0` as the argument of `generate_preset_pass_manager()`.\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "47fbb93c-d808-4f88-b560-99f5d8eb4e0b",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "47fbb93c-d808-4f88-b560-99f5d8eb4e0b",
        "outputId": "53e44fd3-6747-4920-edf6-8f03e5241ec8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 203.885x284.278 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "toffoli_layout = [122, 124, 126]\n",
        "\n",
        "toffoli = QuantumCircuit(3)\n",
        "toffoli.ccx(0, 1, 2)\n",
        "toffoli.draw(\"mpl\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b7a0d37f-1545-4d18-921e-a54c89e3a3b6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        },
        "id": "b7a0d37f-1545-4d18-921e-a54c89e3a3b6",
        "outputId": "3d0ba8d0-8408-4602-83d4-ad67ad48e7c8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Transpiled circuit depth:  62\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 1547.94x270.9 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "# To know the original circuit depth\n",
        "\n",
        "### Write your code below here ###\n",
        "# Transpile the circuit\n",
        "toffoli_pm = generate_preset_pass_manager(backend=backend, optimization_level=1, initial_layout=toffoli_layout, seed_transpiler=0)\n",
        "isa_toffoli = toffoli_pm.run(toffoli)\n",
        "\n",
        "# Calculate original circuit depth\n",
        "isa_toffoli_depth = isa_toffoli.depth()\n",
        "\n",
        "### Don't change any code past this line ###\n",
        "print(f\"Transpiled circuit depth: \", isa_toffoli_depth)\n",
        "isa_qc.draw(\"mpl\", scale=0.6, idle_wires=False, fold=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7283b947-754c-4284-b093-75872c277332",
      "metadata": {
        "id": "7283b947-754c-4284-b093-75872c277332"
      },
      "source": [
        "To cut the circuit, we use the decomposed toffoli circuit not ISA circuit."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3f8208fd-c6bc-4819-9961-293d08a74430",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        },
        "id": "3f8208fd-c6bc-4819-9961-293d08a74430",
        "outputId": "b44bfdfb-508f-46c1-e6ba-49e08dc33fa7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 724.331x170.567 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "# To know the depth using circuit cutting\n",
        "\n",
        "# Decompose the toffoli circuit\n",
        "toffoli_ = toffoli.decompose()\n",
        "\n",
        "### Write your code below here ###\n",
        "# Find the indices of the distant gates\n",
        "gates_connecting_to_cut = {0, 2} # Hint: Expected type: set {int, int}. Docs: https://docs.python.org/3/tutorial/datastructures.html#sets\n",
        "\n",
        "cut_indices = [\n",
        "    i\n",
        "    for i, instruction in enumerate(toffoli_.data)\n",
        "    if {toffoli_.find_bit(q)[0] for q in instruction.qubits} == {0, 2}\n",
        "]\n",
        "\n",
        "# Decompose distant CNOTs into TwoQubitQPDGate instances\n",
        "qpd_circuit, bases = cut_gates(toffoli_, cut_indices)\n",
        "\n",
        "### Don't change any code past this line ###\n",
        "\n",
        "qpd_circuit.draw(\"mpl\", scale=0.6)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "12044b71-58dd-4a70-ab55-e8a91226c709",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "12044b71-58dd-4a70-ab55-e8a91226c709",
        "outputId": "fa3050a3-8b6d-494d-e734-b98a119a29da"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Transpiled circuit depth:  62\n",
            "QPD sub-experiment depth after transpile:  35\n",
            "Number of sub-experiments: 36\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 636.542x220.733 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "# set the observables\n",
        "observable = SparsePauliOp([\"ZZZ\"])\n",
        "\n",
        "### Write your code below here ###\n",
        "# Generate the sub-experiments and sampling coefficients\n",
        "sub_experiments, coefficients = generate_cutting_experiments(\n",
        "    circuits=qpd_circuit, observables=observable.paulis, num_samples=np.inf\n",
        ")\n",
        "\n",
        "# Transpile the circuit\n",
        "# Note: Use optimization_level=1 and seed_transpiler=0\n",
        "toffoli_pm = generate_preset_pass_manager(backend=backend, optimization_level=1, initial_layout=toffoli_layout, seed_transpiler=0)\n",
        "isa_qpd_toffoli = toffoli_pm.run(sub_experiments[5])\n",
        "\n",
        "# Depth using circuit cutting\n",
        "isa_qpd_toffoli_depth = isa_qpd_toffoli.depth()\n",
        "\n",
        "### Don't change any code past this line ###\n",
        "print(f\"Transpiled circuit depth: \", isa_toffoli_depth)\n",
        "print(f\"QPD sub-experiment depth after transpile: \", isa_qpd_toffoli_depth)\n",
        "print(f\"Number of sub-experiments:\", len(sub_experiments))\n",
        "isa_qpd_circuit.draw(\"mpl\", scale=0.6, idle_wires=False, fold=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7823180a-5b2b-4e6e-b4e1-ae484207ceef",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7823180a-5b2b-4e6e-b4e1-ae484207ceef",
        "outputId": "1778f65a-0483-445a-9baa-782d46d77745"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "35.333333333333336\n"
          ]
        }
      ],
      "source": [
        "### Write your code below here ###\n",
        "# mean of the depth of all sub-experiments\n",
        "depth_list = [qc.depth() for qc in toffoli_pm.run(sub_experiments)]\n",
        "isa_qpd_toffoli_depth_mean = sum(depth_list) / len(depth_list)\n",
        "\n",
        "### Don't change any code past this line ###\n",
        "print(isa_qpd_toffoli_depth_mean)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "669cdced-8789-440a-9612-06454e58f83a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "669cdced-8789-440a-9612-06454e58f83a",
        "outputId": "c25e3c47-2494-463a-ad9d-7fcf0e60a959"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Submitting your answer. Please wait...\n",
            "Congratulations! 🎉 Your answer is correct.\n"
          ]
        }
      ],
      "source": [
        "# Submit your answer using following code\n",
        "\n",
        "grade_lab3_ckt_ex1(gates_connecting_to_cut, isa_toffoli_depth, depth_list)\n",
        "# Expected result type: set, int, numpy.ndarray"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6af4d942-d389-4292-8ef4-f7ff57b78679",
      "metadata": {
        "id": "6af4d942-d389-4292-8ef4-f7ff57b78679"
      },
      "source": [
        "<a id=\"exercise2\"></a>\n",
        "<div class=\"alert alert-block alert-success\">\n",
        "    \n",
        "<b> Exercise 2:</b>\n",
        "\n",
        "Let's think about cutting two connections.\n",
        "\n",
        "Which two gate connections should be cut to minimize the circuit depth of the Toffoli gate? Assume that each qubit of the Toffoli gate is located at `[122, 124, 126]` on the `ibm_kyoto` device, and we will cut two connections.\n",
        "\n",
        "**Note:** Use `optimization_level=1` and `seed_transpiler=0` as the argument of `generate_preset_pass_manager()`.\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "719d9a1e-ce74-418c-8ac7-e47555d7fc23",
      "metadata": {
        "id": "719d9a1e-ce74-418c-8ac7-e47555d7fc23"
      },
      "source": [
        "**Note:** It may take more than one minute to run under one condition. So it takes a long time to compare results to find a solution. Because cutting the connection in two places would be expected to reduce the circuit depth, but increase the number of sub-experiments."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "465186e4-c3c6-46d1-ac9a-e4db0af9294e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        },
        "id": "465186e4-c3c6-46d1-ac9a-e4db0af9294e",
        "outputId": "ac50c7c0-07c4-40da-ba0b-e8322df8b3c5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 824.664x170.567 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 84
        }
      ],
      "source": [
        "### Write your code below here ###\n",
        "# Find the indices of the distant gates\n",
        "gates_connecting_to_cut_1 = {0, 2}  # Example: Gate connecting qubits 0 and 2\n",
        "gates_connecting_to_cut_2 = {1, 2}  # Example: Gate connecting qubits 1 and 2\n",
        "\n",
        "gates_connecting_to_cut_sets = [gates_connecting_to_cut_1, gates_connecting_to_cut_2]\n",
        "\n",
        "cut_indices = [\n",
        "    i\n",
        "    for i, instruction in enumerate(toffoli_.data)\n",
        "    if any({toffoli_.find_bit(q)[0] for q in instruction.qubits} == cut_set for cut_set in gates_connecting_to_cut_sets)\n",
        "]\n",
        "\n",
        "# Decompose distant CNOTs into TwoQubitQPDGate instances\n",
        "qpd_circuit_2, bases = cut_gates(toffoli_, cut_indices)\n",
        "\n",
        "### Don't change any code past this line ###\n",
        "qpd_circuit_2.draw(\"mpl\", scale=0.6)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ffd895a7-bc76-41d2-860a-81adccee3c86",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "ffd895a7-bc76-41d2-860a-81adccee3c86",
        "outputId": "ad38e422-8e64-44d1-ed14-c6b9e4d71c08"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "QPD sub-experiment depth after transpile:  21\n",
            "Number of sub-experiments: 1296\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 1389.04x270.9 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 85
        }
      ],
      "source": [
        "# set the observables\n",
        "observable = SparsePauliOp([\"ZZZ\"])\n",
        "\n",
        "### Write your code below here ###\n",
        "# Generate the sub-experiments and sampling coefficients\n",
        "\n",
        "sub_experiments_2, coefficients = generate_cutting_experiments(\n",
        "    circuits=qpd_circuit_2, observables=observable.paulis, num_samples=np.inf\n",
        ")\n",
        "\n",
        "# Transpile the circuit\n",
        "# Note: Use optimization_level=1 and seed_transpiler=0\n",
        "toffoli_pm_2 = generate_preset_pass_manager(backend=backend, optimization_level=1, seed_transpiler=0)\n",
        "isa_qpd_circuit_2 = toffoli_pm_2.run(sub_experiments[5])\n",
        "\n",
        "\n",
        "# Depth using circuit cutting\n",
        "isa_qpd_toffoli_depth_2 = isa_qpd_circuit_2.depth()\n",
        "\n",
        "\n",
        "### Don't change any code past this line ###\n",
        "print(f\"QPD sub-experiment depth after transpile: \", isa_qpd_toffoli_depth_2)\n",
        "print(f\"Number of sub-experiments:\", len(sub_experiments_2))\n",
        "isa_qpd_circuit_2.draw(\"mpl\", scale=0.6, idle_wires=False, fold=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cf406267-096f-4fa6-b308-dcde56a1f560",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cf406267-096f-4fa6-b308-dcde56a1f560",
        "outputId": "22a34a6a-89b5-4328-e551-24b444b4da10"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Submitting your answer. Please wait...\n",
            "Congratulations! 🎉 Your answer is correct.\n"
          ]
        }
      ],
      "source": [
        "# Submit your answer using following code\n",
        "\n",
        "grade_lab3_ckt_ex2(gates_connecting_to_cut_1, gates_connecting_to_cut_2, sub_experiments_2)\n",
        "# Expected result type: set, set, list[QuantumCircuit]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b8b10e64-cb3e-4114-b1e9-8b20d4b088fd",
      "metadata": {
        "id": "b8b10e64-cb3e-4114-b1e9-8b20d4b088fd"
      },
      "source": [
        "You will find that more than 1000 sub-experiments will be required. So the next cell that calculates the average circuit depth will take a little longer to execute."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "16db5e80-94bf-49e6-927e-465cc19b5f40",
      "metadata": {
        "id": "16db5e80-94bf-49e6-927e-465cc19b5f40"
      },
      "source": [
        "<div class=\"alert alert-block alert-warning\">\n",
        "\n",
        "**IMPORTANT:** Please note, this step may take a few minutes to run."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "da0e991c-5565-4a5d-8adb-e97ee05eba6a",
      "metadata": {
        "id": "da0e991c-5565-4a5d-8adb-e97ee05eba6a"
      },
      "outputs": [],
      "source": [
        "### Write your code below here ###\n",
        "# mean of the depth of all sub-experiments\n",
        "depth_list_2 =\n",
        "isa_qpd_toffoli_depth_2_mean =\n",
        "\n",
        "### Don't change any code past this line ###\n",
        "print(isa_qpd_toffoli_depth_2_mean)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cde8d325-98bb-4d8b-9015-832420d6c9cf",
      "metadata": {
        "id": "cde8d325-98bb-4d8b-9015-832420d6c9cf"
      },
      "source": [
        "# Visualize results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a6b85775-f723-442f-9222-3a4ce8cdf485",
      "metadata": {
        "id": "a6b85775-f723-442f-9222-3a4ce8cdf485",
        "outputId": "b978e2bd-14b8-45da-91c0-2ac39e4f20ba"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Number of sub-experiments\n",
        "num_sub_experiments_1_cut = len(sub_experiments)\n",
        "num_sub_experiments_2_cut = len(sub_experiments_2)\n",
        "\n",
        "# Data for plotting\n",
        "categories = ['Before Cutting', 'After 1 Cut', 'After 2 Cuts']\n",
        "depth_values = [isa_toffoli_depth, isa_qpd_toffoli_depth_mean, isa_qpd_toffoli_depth_2_mean]\n",
        "num_sub_experiments = [1, num_sub_experiments_1_cut, num_sub_experiments_2_cut]\n",
        "\n",
        "# Create figure and axis\n",
        "fig, ax1 = plt.subplots()\n",
        "\n",
        "# Plot depth values\n",
        "color = 'tab:blue'\n",
        "ax1.set_xlabel('Number of Cuts')\n",
        "ax1.set_ylabel('Circuit Depth', color=color)\n",
        "bars = ax1.bar(categories, depth_values, color=color, alpha=0.6)\n",
        "ax1.tick_params(axis='y', labelcolor=color)\n",
        "\n",
        "# Add value labels on bars\n",
        "for bar in bars:\n",
        "    yval = bar.get_height()\n",
        "    ax1.text(bar.get_x() + bar.get_width() / 2, yval + 1, round(yval, 2), ha='center', color=color, fontsize=10)\n",
        "\n",
        "# Create a second y-axis to plot the number of subexperiments\n",
        "ax2 = ax1.twinx()\n",
        "color = 'tab:green'\n",
        "ax2.set_ylabel('Number of Sub-experiments', color=color)\n",
        "ax2.plot(categories, num_sub_experiments, color=color, marker='o')\n",
        "ax2.tick_params(axis='y', labelcolor=color)\n",
        "\n",
        "# Add value labels on points\n",
        "for i, num in enumerate(num_sub_experiments):\n",
        "    ax2.text(i, num + 0.1, num, ha='center', color=color, fontsize=10)\n",
        "\n",
        "# Add titles and labels\n",
        "plt.title('Circuit Knitting Toolbox Results')\n",
        "fig.tight_layout()  # Adjust layout to make room for both y-axes\n",
        "\n",
        "# Show plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "edd9f67d-a871-4d70-bab1-b6d0a8b107d7",
      "metadata": {
        "id": "edd9f67d-a871-4d70-bab1-b6d0a8b107d7"
      },
      "source": [
        "Congratulations on completing the Circuit Knitting Toolbox section! Keep experimenting with these techniques to further refine your skills and unlock new possibilities in quantum computing. Great job!"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3fcb3609-c47d-416c-9a74-79c925ad3a74",
      "metadata": {
        "id": "3fcb3609-c47d-416c-9a74-79c925ad3a74"
      },
      "source": [
        "# Additional information\n",
        "\n",
        "**Created by:** Kifumi Numata\n",
        "\n",
        "**Advised by:** Jennifer Glick, Junye Huang, Vishal Sharathchandra Bajpe\n",
        "\n",
        "**Version:** 1.0.0"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.7"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}