QOSF_Internship_Assessment_Task.ipynb

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  "metadata": {
    "colab": {
      "name": "QOSF Internship Assessment  Task.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mansimht/QOSF-Internship/blob/main/QOSF_Internship_Assessment_Task.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xsEli_lC9jS3"
      },
      "source": [
        "### Installation of Packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h6z3A0uf1pl0",
        "outputId": "0b1d99ce-f328-4747-c066-5496293d4cd7"
      },
      "source": [
        "!pip install qiskit"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting qiskit\n",
            "  Downloading https://files.pythonhosted.org/packages/96/6b/1c1a5ed225ac2e42ebc07df49e9c1ce0d3221c6d0744e7d6d5983ff0e69e/qiskit-0.23.6.tar.gz\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/56/e2/c256863ec8fab2162bf17d7e9c8564186a4636b4f24b5ea5d38a014b809e/qiskit_terra-0.16.4-cp36-cp36m-manylinux2010_x86_64.whl (8.5MB)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/32/b9/f99bec4fdc4dec234d8a85a8da378750441a203c614be35353f5e8738316/qiskit_ibmq_provider-0.11.1-py3-none-any.whl (195kB)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b2/1d/d5df8cf48cf20a26deb6e0535f020cd2ad625cb27457abb0ab39c2104e38/qiskit_ignis-0.5.2-py3-none-any.whl (203kB)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/0e/82/528741676960a7fd1c75151639601826118f2729ad53034fa3b39290c056/qiskit_aqua-0.8.2-py3-none-any.whl (2.1MB)\n",
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            "Collecting python-constraint>=1.4\n",
            "  Downloading https://files.pythonhosted.org/packages/37/8b/5f1bc2734ca611943e1d6733ee244238679f6410a10cd45ede55a61a8402/python-constraint-1.4.0.tar.bz2\n",
            "Collecting contextvars>=2.4; python_version < \"3.7\"\n",
            "  Downloading https://files.pythonhosted.org/packages/83/96/55b82d9f13763be9d672622e1b8106c85acb83edd7cc2fa5bc67cd9877e9/contextvars-2.4.tar.gz\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/bb/d9/856af84843912e2853b1b6e898ac8b802989fcf9ecf8e8445a1da263bf3b/websockets-8.1-cp36-cp36m-manylinux2010_x86_64.whl (78kB)\n",
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            "  Downloading https://files.pythonhosted.org/packages/03/4b/8b9a1afde8072c4d5710d9fa91433d504325821b038e00237dc8d6d833dc/requests_ntlm-1.1.0-py2.py3-none-any.whl\n",
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            "Collecting yfinance\n",
            "  Downloading https://files.pythonhosted.org/packages/7a/e8/b9d7104d3a4bf39924799067592d9e59119fcfc900a425a12e80a3123ec8/yfinance-0.1.55.tar.gz\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9b/76/ebf5b75a25c4357c9a4758cae7fea778dd7f282b83c2f22e150dc7e3c852/docplex-2.15.194.tar.gz (582kB)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/4a/52/e64a14a99c509cbdfe0405e9f076aef0331cb9548a3efa1d5bacd524978a/immutables-0.15-cp36-cp36m-manylinux1_x86_64.whl (100kB)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f8/1f/acde6ff69864c5e78b56488e3afd93c1ccc8c2651186e2a5f93d93f64859/cryptography-3.4.6-cp36-abi3-manylinux2014_x86_64.whl (3.2MB)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/bd/78/56a7c88a57d0d14945472535d0df9fb4bbad7d34ede658ec7961635c790e/lxml-4.6.2-cp36-cp36m-manylinux1_x86_64.whl (5.5MB)\n",
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            "  Downloading https://files.pythonhosted.org/packages/59/91/aa6bde563e0085a02a435aa99b49ef75b0a4b062635e606dab23ce18d720/inflection-0.5.1-py2.py3-none-any.whl\n",
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            "Building wheels for collected packages: qiskit, python-constraint, contextvars, yfinance, dlx, docplex\n",
            "  Building wheel for qiskit (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for qiskit: filename=qiskit-0.23.6-cp36-none-any.whl size=2883 sha256=dd9090c0f69a814ebedf101c3157e530864bd24c35ba34cc8dd5bd69371c7328\n",
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            "  Created wheel for python-constraint: filename=python_constraint-1.4.0-py2.py3-none-any.whl size=24079 sha256=c565d75fe7f26447658c26944fcbd958ab10ef7af10767f0d5bac60876344012\n",
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            "  Created wheel for dlx: filename=dlx-1.0.4-cp36-none-any.whl size=5711 sha256=464b2c56da4f7ed14ee756a61bed40354754a59fe7c163b3b222a851f5e1a0b3\n",
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            "  Building wheel for docplex (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for docplex: filename=docplex-2.15.194-cp36-none-any.whl size=645116 sha256=ac5c34368ca40a09bb75c7bfc473582275078c91d8f78bb3c257bd809a6c7088\n",
            "  Stored in directory: /root/.cache/pip/wheels/ce/fa/5d/8f4d5fed1b8edb72e453cb2ac6fb75c6776b49d7174eb70457\n",
            "Successfully built qiskit python-constraint contextvars yfinance dlx docplex\n",
            "Installing collected packages: ply, retworkx, sympy, fastjsonschema, python-constraint, immutables, contextvars, qiskit-terra, pybind11, qiskit-aer, websockets, ntlm-auth, cryptography, requests-ntlm, qiskit-ibmq-provider, qiskit-ignis, lxml, yfinance, dlx, docplex, inflection, quandl, qiskit-aqua, qiskit\n",
            "  Found existing installation: sympy 1.1.1\n",
            "    Uninstalling sympy-1.1.1:\n",
            "      Successfully uninstalled sympy-1.1.1\n",
            "  Found existing installation: lxml 4.2.6\n",
            "    Uninstalling lxml-4.2.6:\n",
            "      Successfully uninstalled lxml-4.2.6\n",
            "Successfully installed contextvars-2.4 cryptography-3.4.6 dlx-1.0.4 docplex-2.15.194 fastjsonschema-2.15.0 immutables-0.15 inflection-0.5.1 lxml-4.6.2 ntlm-auth-1.5.0 ply-3.11 pybind11-2.6.2 python-constraint-1.4.0 qiskit-0.23.6 qiskit-aer-0.7.5 qiskit-aqua-0.8.2 qiskit-ibmq-provider-0.11.1 qiskit-ignis-0.5.2 qiskit-terra-0.16.4 quandl-3.6.0 requests-ntlm-1.1.0 retworkx-0.7.2 sympy-1.7.1 websockets-8.1 yfinance-0.1.55\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1LvLio-LDGUF",
        "outputId": "38e313f4-6ab3-4a52-c8b0-70717361c9b2"
      },
      "source": [
        "!pip install pylatexenc"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pylatexenc\n",
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            "\u001b[?25hBuilding wheels for collected packages: pylatexenc\n",
            "  Building wheel for pylatexenc (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pylatexenc: filename=pylatexenc-2.8-cp36-none-any.whl size=131377 sha256=8df0028b5c7dfd47df61a886576bc2a28baf9a7b2eebe7cfa435f844315e6ac3\n",
            "  Stored in directory: /root/.cache/pip/wheels/88/23/90/4cf49f59c1eb67af40a2b3a580536bb5f91d40bc0d4cc8e035\n",
            "Successfully built pylatexenc\n",
            "Installing collected packages: pylatexenc\n",
            "Successfully installed pylatexenc-2.8\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x-gLFbdz6zAK"
      },
      "source": [
        "## **Task:1- SWAP Test**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tyoDgSvp8POV"
      },
      "source": [
        "from qiskit import AncillaRegister,QuantumRegister, ClassicalRegister, QuantumCircuit, execute, Aer"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "id": "DWzpc_co1htp",
        "outputId": "7d6aafaf-0f5c-4c93-c9a7-3d87db93945c"
      },
      "source": [
        "q= QuantumRegister(2)\r\n",
        "c= ClassicalRegister(2)\r\n",
        "qc= QuantumCircuit(q,c)\r\n",
        "\r\n",
        "#q= [0,1] \r\n",
        "qc.h(q[1])\r\n",
        "qc.cnot(q[0],q[1])\r\n",
        "qc.cnot(q[1],q[0])\r\n",
        "qc.cnot(q[0],q[1])\r\n",
        "job = execute(qc,Aer.get_backend('qasm_simulator'),shots=1024)\r\n",
        "\r\n",
        "counts = job.result().get_counts(qc)\r\n",
        "print(counts)\r\n",
        "qc.measure(q,c)\r\n",
        "\r\n",
        "qc.draw()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'00': 1024}\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">                ┌───┐     ┌─┐   \n",
              "q3_0: ───────■──┤ X ├──■──┤M├───\n",
              "      ┌───┐┌─┴─┐└─┬─┘┌─┴─┐└╥┘┌─┐\n",
              "q3_1: ┤ H ├┤ X ├──■──┤ X ├─╫─┤M├\n",
              "      └───┘└───┘     └───┘ ║ └╥┘\n",
              "c1: 2/═════════════════════╩══╩═\n",
              "                           0  1 </pre>"
            ],
            "text/plain": [
              "                ┌───┐     ┌─┐   \n",
              "q3_0: ───────■──┤ X ├──■──┤M├───\n",
              "      ┌───┐┌─┴─┐└─┬─┘┌─┴─┐└╥┘┌─┐\n",
              "q3_1: ┤ H ├┤ X ├──■──┤ X ├─╫─┤M├\n",
              "      └───┘└───┘     └───┘ ║ └╥┘\n",
              "c1: 2/═════════════════════╩══╩═\n",
              "                           0  1 "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        },
        "id": "aDk64rN56MzS",
        "outputId": "9ed51df1-6934-458c-b416-d54fc90acf88"
      },
      "source": [
        "from qiskit import QuantumRegister, ClassicalRegister, QuantumCircuit\r\n",
        "from numpy import pi\r\n",
        "\r\n",
        "qreg = QuantumRegister(2, 'q')\r\n",
        "creg = ClassicalRegister(2, 'c')\r\n",
        "qc1 = QuantumCircuit(qreg, creg)\r\n",
        "\r\n",
        "qc1.x(qreg[0])\r\n",
        "\r\n",
        "qc1.swap(qreg[0], qreg[1])\r\n",
        "qc1.measure(qreg[0], creg[0])\r\n",
        "qc1.measure(qreg[1], creg[1])\r\n",
        "qc1.draw()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">     ┌───┐   ┌─┐   \n",
              "q_0: ┤ X ├─X─┤M├───\n",
              "     └───┘ │ └╥┘┌─┐\n",
              "q_1: ──────X──╫─┤M├\n",
              "              ║ └╥┘\n",
              "c: 2/═════════╩══╩═\n",
              "              0  1 </pre>"
            ],
            "text/plain": [
              "     ┌───┐   ┌─┐   \n",
              "q_0: ┤ X ├─X─┤M├───\n",
              "     └───┘ │ └╥┘┌─┐\n",
              "q_1: ──────X──╫─┤M├\n",
              "              ║ └╥┘\n",
              "c: 2/═════════╩══╩═\n",
              "              0  1 "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 494
        },
        "id": "jZg_aD8CDboX",
        "outputId": "adafaf24-1aa3-4bbc-c5c6-b0f7632e49b5"
      },
      "source": [
        "import numpy as np\r\n",
        "from qiskit.visualization import plot_state_qsphere\r\n",
        "\r\n",
        "si= QuantumRegister(1,'si')\r\n",
        "phi= QuantumRegister(1,'phi')\r\n",
        "a = AncillaRegister(1,'ancilla')\r\n",
        "c= ClassicalRegister(1)\r\n",
        "qc2= QuantumCircuit(si,phi,a,c)\r\n",
        "\r\n",
        "P= 5  #representing the number of times the algorithm will be executed\r\n",
        "for j in range(1,P):\r\n",
        "  qc2.h(a[0])       #Apply a Hadamard gate to the ancilla qubit\r\n",
        "  #iterating over each pair of qubits in the two registers\r\n",
        "  for i in [si,phi]:\r\n",
        "    qc2.cswap(a[0],si[0],phi[0])\r\n",
        "    qc2.h(a[0])       #Apply a Hadamard gate to the ancilla qubit\r\n",
        "    qc2.z(a[0])\r\n",
        "    M= qc2.measure(a[0],c[0])\r\n",
        "  print(M)\r\n",
        "qc2.measure(si[0],c[0])\r\n",
        "qc2.measure(phi[0],c[0])\r\n",
        "qc2.draw()\r\n",
        "\r\n",
        "\r\n",
        "#s= 1 - (2/P*np.sum())\r\n",
        "\r\n"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<qiskit.circuit.instructionset.InstructionSet object at 0x7f0088f59550>\n",
            "<qiskit.circuit.instructionset.InstructionSet object at 0x7f0088f589e8>\n",
            "<qiskit.circuit.instructionset.InstructionSet object at 0x7f0088f58be0>\n",
            "<qiskit.circuit.instructionset.InstructionSet object at 0x7f0088d917b8>\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">                                                                             »\n",
              "     si_0: ──────X───────────────X────────────────────X───────────────X──────»\n",
              "                 │               │                    │               │      »\n",
              "    phi_0: ──────X───────────────X────────────────────X───────────────X──────»\n",
              "           ┌───┐ │ ┌───┐┌───┐┌─┐ │ ┌───┐┌───┐┌─┐┌───┐ │ ┌───┐┌───┐┌─┐ │ ┌───┐»\n",
              "ancilla_0: ┤ H ├─■─┤ H ├┤ Z ├┤M├─■─┤ H ├┤ Z ├┤M├┤ H ├─■─┤ H ├┤ Z ├┤M├─■─┤ H ├»\n",
              "           └───┘   └───┘└───┘└╥┘   └───┘└───┘└╥┘└───┘   └───┘└───┘└╥┘   └───┘»\n",
              "    c28: 1/═══════════════════╩═══════════════╩════════════════════╩═════════»\n",
              "                              0               0                    0         »\n",
              "«                                                                             »\n",
              "«     si_0: ──────────────X───────────────X────────────────────X──────────────»\n",
              "«                         │               │                    │              »\n",
              "«    phi_0: ──────────────X───────────────X────────────────────X──────────────»\n",
              "«           ┌───┐┌─┐┌───┐ │ ┌───┐┌───┐┌─┐ │ ┌───┐┌───┐┌─┐┌───┐ │ ┌───┐┌───┐┌─┐»\n",
              "«ancilla_0: ┤ Z ├┤M├┤ H ├─■─┤ H ├┤ Z ├┤M├─■─┤ H ├┤ Z ├┤M├┤ H ├─■─┤ H ├┤ Z ├┤M├»\n",
              "«           └───┘└╥┘└───┘   └───┘└───┘└╥┘   └───┘└───┘└╥┘└───┘   └───┘└───┘└╥┘»\n",
              "«    c28: 1/══════╩════════════════════╩═══════════════╩════════════════════╩═»\n",
              "«                 0                    0               0                    0 »\n",
              "«                           ┌─┐   \n",
              "«     si_0: ─X──────────────┤M├───\n",
              "«            │              └╥┘┌─┐\n",
              "«    phi_0: ─X───────────────╫─┤M├\n",
              "«            │ ┌───┐┌───┐┌─┐ ║ └╥┘\n",
              "«ancilla_0: ─■─┤ H ├┤ Z ├┤M├─╫──╫─\n",
              "«              └───┘└───┘└╥┘ ║  ║ \n",
              "«    c28: 1/══════════════╩══╩══╩═\n",
              "«                         0  0  0 </pre>"
            ],
            "text/plain": [
              "                                                                             »\n",
              "     si_0: ──────X───────────────X────────────────────X───────────────X──────»\n",
              "                 │               │                    │               │      »\n",
              "    phi_0: ──────X───────────────X────────────────────X───────────────X──────»\n",
              "           ┌───┐ │ ┌───┐┌───┐┌─┐ │ ┌───┐┌───┐┌─┐┌───┐ │ ┌───┐┌───┐┌─┐ │ ┌───┐»\n",
              "ancilla_0: ┤ H ├─■─┤ H ├┤ Z ├┤M├─■─┤ H ├┤ Z ├┤M├┤ H ├─■─┤ H ├┤ Z ├┤M├─■─┤ H ├»\n",
              "           └───┘   └───┘└───┘└╥┘   └───┘└───┘└╥┘└───┘   └───┘└───┘└╥┘   └───┘»\n",
              "    c28: 1/═══════════════════╩═══════════════╩════════════════════╩═════════»\n",
              "                              0               0                    0         »\n",
              "«                                                                             »\n",
              "«     si_0: ──────────────X───────────────X────────────────────X──────────────»\n",
              "«                         │               │                    │              »\n",
              "«    phi_0: ──────────────X───────────────X────────────────────X──────────────»\n",
              "«           ┌───┐┌─┐┌───┐ │ ┌───┐┌───┐┌─┐ │ ┌───┐┌───┐┌─┐┌───┐ │ ┌───┐┌───┐┌─┐»\n",
              "«ancilla_0: ┤ Z ├┤M├┤ H ├─■─┤ H ├┤ Z ├┤M├─■─┤ H ├┤ Z ├┤M├┤ H ├─■─┤ H ├┤ Z ├┤M├»\n",
              "«           └───┘└╥┘└───┘   └───┘└───┘└╥┘   └───┘└───┘└╥┘└───┘   └───┘└───┘└╥┘»\n",
              "«    c28: 1/══════╩════════════════════╩═══════════════╩════════════════════╩═»\n",
              "«                 0                    0               0                    0 »\n",
              "«                           ┌─┐   \n",
              "«     si_0: ─X──────────────┤M├───\n",
              "«            │              └╥┘┌─┐\n",
              "«    phi_0: ─X───────────────╫─┤M├\n",
              "«            │ ┌───┐┌───┐┌─┐ ║ └╥┘\n",
              "«ancilla_0: ─■─┤ H ├┤ Z ├┤M├─╫──╫─\n",
              "«              └───┘└───┘└╥┘ ║  ║ \n",
              "«    c28: 1/══════════════╩══╩══╩═\n",
              "«                         0  0  0 "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "BU5HiqyF1I90",
        "outputId": "619c2996-8ed7-4579-91aa-d08b73091eb9"
      },
      "source": [
        "#Representing Swap test output on BlockSphere\r\n",
        "job = execute(qc2,Aer.get_backend('statevector_simulator'),shots=1024)\r\n",
        "final_state = job.result().get_statevector(qc2)\r\n",
        "plot_state_qsphere(final_state)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "quXPMuXL2ME9",
        "outputId": "0124998a-a411-450f-a171-d899ea514f9e"
      },
      "source": [
        "#Calculate matrix of swap Test\r\n",
        "job = execute(qc2, Aer.get_backend('statevector_simulator'),shots=1,optimization_level=0)\r\n",
        "current_unitary = job.result().get_statevector(qc2)\r\n",
        "for row in current_unitary:\r\n",
        "  print(row)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1-3.6739403974420584e-16j)\n",
            "0j\n",
            "0j\n",
            "0j\n",
            "-0j\n",
            "(-0+0j)\n",
            "(-0+0j)\n",
            "(-0+0j)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ne7gBOCs66r6"
      },
      "source": [
        "# **Task:2-Bit-flip code and the sign-flip code**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        },
        "id": "N15-qI7065nG",
        "outputId": "464c0339-93b5-42fc-b19c-fb516011f280"
      },
      "source": [
        "from qiskit import QuantumRegister, ClassicalRegister, QuantumCircuit\r\n",
        "from numpy import pi\r\n",
        "\r\n",
        "qreg_q = QuantumRegister(2, 'q')\r\n",
        "creg_c = ClassicalRegister(2, 'c')\r\n",
        "qc2 = QuantumCircuit(qreg_q, creg_c)\r\n",
        "\r\n",
        "qc2.reset(qreg_q[0])\r\n",
        "qc2.reset(qreg_q[1])\r\n",
        "qc2.h(qreg_q[0])\r\n",
        "qc2.cx(qreg_q[0], qreg_q[1])\r\n",
        "qc2.z(qreg_q[0])\r\n",
        "qc2.measure(qreg_q[0], creg_c[0])\r\n",
        "qc2.measure(qreg_q[1], creg_c[1])\r\n",
        "qc2.draw()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">          ┌───┐     ┌───┐┌─┐\n",
              "q_0: ─|0>─┤ H ├──■──┤ Z ├┤M├\n",
              "          └───┘┌─┴─┐└┬─┬┘└╥┘\n",
              "q_1: ─|0>──────┤ X ├─┤M├──╫─\n",
              "               └───┘ └╥┘  ║ \n",
              "c: 2/═════════════════╩═══╩═\n",
              "                      1   0 </pre>"
            ],
            "text/plain": [
              "          ┌───┐     ┌───┐┌─┐\n",
              "q_0: ─|0>─┤ H ├──■──┤ Z ├┤M├\n",
              "          └───┘┌─┴─┐└┬─┬┘└╥┘\n",
              "q_1: ─|0>──────┤ X ├─┤M├──╫─\n",
              "               └───┘ └╥┘  ║ \n",
              "c: 2/═════════════════╩═══╩═\n",
              "                      1   0 "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Ov_4Fye-YjU"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}