Atoms_of_Computation.ipynb

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        "<a href=\"https://colab.research.google.com/github/MonitSharma/Quantum-Computing-with-Qiskit-and-IBMQ/blob/main/Atoms_of_Computation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
      "source": [
        "%pip install qiskit"
      ],
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      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%pip install pylatexenc"
      ],
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      "outputs": []
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      "source": [
        "# Atoms of Computation\n",
        "\n",
        "\n",
        "You can program a quantum computer from the comfort of your home. But to understand quantum computing and how it works, we have to begin with how classical computers work,  Let's see this in brief:\n"
      ],
      "metadata": {
        "id": "q-vNOnI-ef-e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from qiskit import QuantumCircuit, assemble, Aer\n",
        "from qiskit.visualization import plot_histogram"
      ],
      "metadata": {
        "id": "R4x3EhWNecmO"
      },
      "execution_count": 2,
      "outputs": []
    },
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      "source": [
        "## Unit of Information: Bits\n",
        "\n",
        "Welcome to the fascinating world of information! 🌟 In this section, we'll delve into the fundamental unit of information: bits. 🧐\n",
        "\n",
        "🤔 But what exactly is a bit? 🤔\n",
        "\n",
        "Imagine a world where everything is reduced to its simplest form—a world of zeros and ones. That's precisely what a bit represents: the building block of digital information. 🧱\n",
        "\n",
        "✨ The term \"bit\" is a contraction of \"binary digit,\" and it carries an incredible power to store and transmit information. It's like the atom of the digital realm, the tiniest speck that can hold a universe of meaning. 🌌\n",
        "\n",
        "To put it simply, a bit can have one of two values: 0 or 1. It's the ultimate yes-or-no, true-or-false, on-or-off switch. But don't be fooled by its seemingly limited range! 🎯\n",
        "\n",
        "Bits come together like puzzle pieces to form larger units of information. Eight bits make up a byte, which can represent a single character of text or a small number. With every additional bit, the possibilities expand exponentially. 💥\n",
        "\n",
        "In the vast landscape of digital communication and computation, bits are the currency of exchange. They flow through the circuits of our devices, bringing life to our screens and enabling the marvels of modern technology. 🌐💻"
      ],
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      "source": [
        "We are used to represent numbers through a string of $0$ to $9$. In the string of digits, each digit represents how many times the number contains a certain power of ten. For example $9213$ means:\n",
        "\n",
        "$$ 9000 + 200 + 10 + 3 $$\n",
        "\n",
        "or we can write it as\n",
        "\n",
        "$$ 9213 = ( 1 \\times 2^{13}) +  (0 \\times 2^{12}) + (0 \\times 2^{11}) + ( 0 \\times 2^{10}) \\\\ + (1 \\times 2^{9}) + (1 \\times 2^{8}) + ( 1 \\times 2^{7}) +  (1 \\times 2^{6}) \\\\\n",
        "+  ( 1 \\times 2^{5}) +  (1 \\times 2^{4}) + ( 1 \\times 2^{3}) +   (1 \\times 2^{2}) \\\\ +  (0 \\times 2^{1}) +  ( 1 \\times 2^{0}) $$\n",
        "\n",
        "\n",
        "Hence in binary it can be written as $10001111111101$"
      ],
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    },
    {
      "cell_type": "code",
      "source": [
        "def decimal_to_binary(decimal):\n",
        "    binary = \"\"\n",
        "    if decimal == 0:\n",
        "        return \"0\"\n",
        "    while decimal > 0:\n",
        "        binary = str(decimal % 2) + binary\n",
        "        decimal //= 2\n",
        "    return binary\n",
        "\n",
        "# Example usage\n",
        "decimal_number = 42\n",
        "binary_number = decimal_to_binary(decimal_number)\n",
        "print(f\"The binary representation of {decimal_number} is: {binary_number}\")\n"
      ],
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          "base_uri": "https://localhost:8080/"
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      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
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          "text": [
            "The binary representation of 42 is: 101010\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def binary_to_decimal(binary):\n",
        "    decimal = 0\n",
        "    power = len(binary) - 1\n",
        "    for digit in binary:\n",
        "        decimal += int(digit) * (2 ** power)\n",
        "        power -= 1\n",
        "    return decimal\n",
        "\n",
        "# Example usage\n",
        "binary_number = \"101010\"\n",
        "decimal_number = binary_to_decimal(binary_number)\n",
        "print(f\"The decimal representation of {binary_number} is: {decimal_number}\")\n"
      ],
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      "execution_count": 4,
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The decimal representation of 101010 is: 42\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Computation as Diagram\n",
        "\n",
        "Whether we are using bits or qubits, we need to manipulate then in order to turn the inputs we have into outputs we need. We will demonstrate with circuit diagrams, with the inputs on the left and the output on the right. These operations are called **gates**. Here's an example of what a circuit looks like:\n",
        "\n",
        "\n",
        "\n",
        "![](https://learn.qiskit.org/content/v2/ch-states/images/classical_circuit.png)\n",
        "You aren't expected to know what this does.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "For a quantum computer, we use the same idea and make a quantum circuit:\n",
        "\n",
        "\n",
        "\n",
        "![](https://learn.qiskit.org/content/v2/ch-states/images/quantum_circuit.png)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "**Now let's make a quantum circuit of our own**\n",
        "\n",
        "\n",
        "Don't get worried about what these gates are, and what they are doing, we will see that shortly in detail.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
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      "cell_type": "markdown",
      "source": [
        "## First Quantum Circuit\n",
        "\n",
        "In making a quantum circuit, we need to do three jobs.\n",
        "1. Encode the input\n",
        "2. Do some computation\n",
        "3. Extract the result\n",
        "\n",
        "![](https://learn.qiskit.org/content/v2/ch-states/images/half-adder.svg)\n",
        "\n",
        "Let's start by creating a quantum circuit"
      ],
      "metadata": {
        "id": "c4XzQLakjRK5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "qcirc = QuantumCircuit(8)"
      ],
      "metadata": {
        "id": "-Zg1HDA0jPJu"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "So, we created a quantum circuit by the name `qcirc` and using the `QuantumCircuit` function of Python. This function takes in the number of **qubits** we need for the quantum circuit.\n",
        "\n"
      ],
      "metadata": {
        "id": "VvuNtDFojmsf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To extract the output, we need to do a measurement, hence use the `measure_all` function of qiskit. It measures all the qubits and give specific output bit"
      ],
      "metadata": {
        "id": "vP1W_wXkj1xX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "qcirc.measure_all()"
      ],
      "metadata": {
        "id": "TJ2YCo86jl6A"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can also visualize how our quantum circuit looks like by using the `draw` function of qiskit."
      ],
      "metadata": {
        "id": "n8Eom3wekPV1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "qcirc.draw()"
      ],
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              "         ░ ┌─┐                     \n",
              "   q_0: ─░─┤M├─────────────────────\n",
              "         ░ └╥┘┌─┐                  \n",
              "   q_1: ─░──╫─┤M├──────────────────\n",
              "         ░  ║ └╥┘┌─┐               \n",
              "   q_2: ─░──╫──╫─┤M├───────────────\n",
              "         ░  ║  ║ └╥┘┌─┐            \n",
              "   q_3: ─░──╫──╫──╫─┤M├────────────\n",
              "         ░  ║  ║  ║ └╥┘┌─┐         \n",
              "   q_4: ─░──╫──╫──╫──╫─┤M├─────────\n",
              "         ░  ║  ║  ║  ║ └╥┘┌─┐      \n",
              "   q_5: ─░──╫──╫──╫──╫──╫─┤M├──────\n",
              "         ░  ║  ║  ║  ║  ║ └╥┘┌─┐   \n",
              "   q_6: ─░──╫──╫──╫──╫──╫──╫─┤M├───\n",
              "         ░  ║  ║  ║  ║  ║  ║ └╥┘┌─┐\n",
              "   q_7: ─░──╫──╫──╫──╫──╫──╫──╫─┤M├\n",
              "         ░  ║  ║  ║  ║  ║  ║  ║ └╥┘\n",
              "meas: 8/════╩══╩══╩══╩══╩══╩══╩══╩═\n",
              "            0  1  2  3  4  5  6  7 "
            ],
            "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: ─░─┤M├─────────────────────\n",
              "         ░ └╥┘┌─┐                  \n",
              "   q_1: ─░──╫─┤M├──────────────────\n",
              "         ░  ║ └╥┘┌─┐               \n",
              "   q_2: ─░──╫──╫─┤M├───────────────\n",
              "         ░  ║  ║ └╥┘┌─┐            \n",
              "   q_3: ─░──╫──╫──╫─┤M├────────────\n",
              "         ░  ║  ║  ║ └╥┘┌─┐         \n",
              "   q_4: ─░──╫──╫──╫──╫─┤M├─────────\n",
              "         ░  ║  ║  ║  ║ └╥┘┌─┐      \n",
              "   q_5: ─░──╫──╫──╫──╫──╫─┤M├──────\n",
              "         ░  ║  ║  ║  ║  ║ └╥┘┌─┐   \n",
              "   q_6: ─░──╫──╫──╫──╫──╫──╫─┤M├───\n",
              "         ░  ║  ║  ║  ║  ║  ║ └╥┘┌─┐\n",
              "   q_7: ─░──╫──╫──╫──╫──╫──╫──╫─┤M├\n",
              "         ░  ║  ║  ║  ║  ║  ║  ║ └╥┘\n",
              "meas: 8/════╩══╩══╩══╩══╩══╩══╩══╩═\n",
              "            0  1  2  3  4  5  6  7 </pre>"
            ]
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    {
      "cell_type": "markdown",
      "source": [
        "These are bit bland, not that colorful. To make quantum circuits more colorful, you need to install `pylatexenc` library\n",
        "\n",
        "Simply run\n",
        "```python\n",
        "%pip install pylatexenc\n",
        "```\n",
        "\n",
        "and then run the below code:"
      ],
      "metadata": {
        "id": "cikZu4jbksWO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "qcirc.draw('mpl')"
      ],
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            "text/plain": [
              "<Figure size 914.831x785.944 with 1 Axes>"
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            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now the circuit is more colorful"
      ],
      "metadata": {
        "id": "Wk7NwUi-k7kO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We started with $8$ qubits, which are represented here via $q_0, q_1,...q_7$, they all were initialised in $0$ state, and we didn't applied any gate in between and then measured directly. So our output will be $00000000$ , one zero for each qubit.\n",
        "\n",
        "Let's see how to run the circuit.\n",
        "(we'll learn about all the simulators and backends later)"
      ],
      "metadata": {
        "id": "RGlwjKMNk97e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sim = Aer.get_backend('aer_simulator')    # decide the backend on which to run the circuit\n",
        "result = sim.run(qcirc).result()          # run the circuit and save the results\n",
        "\n",
        "result"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mnoM2U-KkY8e",
        "outputId": "6a4323fc-c430-42f5-ebd2-34551b3a80e0"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Result(backend_name='aer_simulator', backend_version='0.12.1', qobj_id='', job_id='459ba7d1-f5c8-4905-815b-d3e651b0b0de', success=True, results=[ExperimentResult(shots=1024, success=True, meas_level=2, data=ExperimentResultData(counts={'0x0': 1024}), header=QobjExperimentHeader(creg_sizes=[['meas', 8]], global_phase=0.0, memory_slots=8, n_qubits=8, name='circuit-114', qreg_sizes=[['q', 8]], metadata={}), status=DONE, circuit=Circuit(qubit=8, num_memory=8, num_registers=0, ops={measure[0],[],measure[1],[],measure[2],[],measure[3],[],measure[4],[],measure[5],[],measure[6],[],measure[7],[]}, shots=1024, seed=1860944768, global_phase_angle=0), seed_simulator=1860944768, metadata={'noise': 'ideal', 'batched_shots_optimization': False, 'measure_sampling': True, 'parallel_shots': 1, 'remapped_qubits': False, 'active_input_qubits': [0, 1, 2, 3, 4, 5, 6, 7], 'num_clbits': 8, 'parallel_state_update': 2, 'sample_measure_time': 0.003148062, 'num_qubits': 8, 'device': 'CPU', 'input_qubit_map': [[7, 7], [6, 6], [5, 5], [4, 4], [3, 3], [2, 2], [1, 1], [0, 0]], 'method': 'stabilizer', 'fusion': {'enabled': False}}, time_taken=0.005109367)], date=2023-07-03T07:11:28.740292, status=COMPLETED, header=None, metadata={'time_taken_execute': 0.005148991, 'mpi_rank': 0, 'num_mpi_processes': 1, 'max_gpu_memory_mb': 0, 'max_memory_mb': 12982, 'parallel_experiments': 1, 'num_processes_per_experiments': 1, 'omp_enabled': True}, time_taken=0.005879640579223633)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You see the result. It's very complicated and detailed, about the `job_id` and every detail the circuit. But we don't need much, we only need to see the measurement. So we use:"
      ],
      "metadata": {
        "id": "ft93lVVhl6kf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "counts = result.get_counts()\n",
        "counts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZoMa_nMxl5nv",
        "outputId": "2564b3b3-fe8b-449a-baa9-a9e32bd36d57"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'00000000': 1024}"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This shows how many times a certain shot was measured, and we can see that we measured $00000000$ 100% of the times. The $1024$ at the end tells the number of shots or number of times the circuit was executed. The reason for running many times and showing the result as a histogram is because quantum computers may have some randomness in their results. In this case, since we aren’t doing anything quantum, we get just the 0 result with certainty.\n",
        "\n",
        "\n",
        "\n",
        " Let's plot this"
      ],
      "metadata": {
        "id": "82IHZqWNmNze"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_histogram(counts)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "GMps3OegmNEO",
        "outputId": "406dc543-2c48-43e9-e872-804927b048bb"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### What is a qubit?\n",
        "\n",
        "Let's look at it from the Linear Algebra point of view. Since we are also working with mathematical representations.\n",
        "\n",
        "\n",
        "We'll again start by creating a quantum circuit."
      ],
      "metadata": {
        "id": "yN20TVC8pGtX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "qcirc = QuantumCircuit(1)  # only a single qubit quantum circuit\n",
        "qcirc.draw('mpl')          # let's visualize the circuit"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 127
        },
        "id": "qDoSxxaLpgHX",
        "outputId": "e137db0e-17e9-410d-a1cd-c7473c76e226"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 143.647x117.056 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The state $|0⟩$ is a vector $\\begin{bmatrix} 1 \\\\ 0 \\end{bmatrix}$ and the state $|1\\rangle$ is also a vector, represented by $\\begin{bmatrix} 0 \\\\ 1 \\end{bmatrix}$\n",
        "\n",
        "\n",
        "Let's initialize this as a state"
      ],
      "metadata": {
        "id": "oLwVdyyhpwdo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "qcirc.initialize([1,0])\n",
        "qcirc.draw('mpl')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 127
        },
        "id": "2DUVsawLqp7O",
        "outputId": "f9794a19-b13f-4711-d653-ad479092b8d5"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 185.453x117.056 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "by this `QuantumCircuit.initialize` function, you can initialize your quantum state in any vector you want.\n",
        "\n",
        "This is just qubit initialized in $|0⟩$ state.\n",
        "\n",
        "\n",
        "Let's measure the state, we would be getting the result `0` 100% of time."
      ],
      "metadata": {
        "id": "wYc9CsFiqxo3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "qcirc.measure_all()         # applying the measurement"
      ],
      "metadata": {
        "id": "ndJCWQMrrb_Y"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "qcirc.draw('mpl')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "id": "IpZ7efspr5TI",
        "outputId": "c1627012-56d7-4c45-a01d-096883049659"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 413.165x200.667 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result = sim.run(qcirc).result()\n",
        "counts = result.get_counts()\n",
        "counts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cUG-QguorSwH",
        "outputId": "b2c5cab4-999f-4cd2-ea27-0c31ea3cae23"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'0': 1024}"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_histogram(counts)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "kAMyMS_Srj24",
        "outputId": "281d0182-81a6-4273-85cc-8a68274a29cd"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can also change the number of shots."
      ],
      "metadata": {
        "id": "9ehdeX3lrnnn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "result = sim.run(qcirc, shots = 2000).result()\n",
        "counts = result.get_counts()\n",
        "counts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kqXl1BaorrHf",
        "outputId": "47f0c3b9-48ef-4189-fc0e-e361f5ec0615"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'0': 2000}"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_histogram(counts)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "2Ace6t-9r2Tg",
        "outputId": "326b2cfd-98e8-4a63-be4a-d2b0b9cf8d1d"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Another way of creating the quantum circuit is presented below. In it, we create quantum registers (qubits), classical registers (bits) and the results from the qubit measurement are stored in the classical register (the bit)."
      ],
      "metadata": {
        "id": "oXHPUc1OsAQZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from qiskit import QuantumRegister, ClassicalRegister"
      ],
      "metadata": {
        "id": "Xdf8xEpesQ6P"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "qr = QuantumRegister(1,'q0')            # you can specifically name the qubit q0\n",
        "cr = ClassicalRegister(1,'c0')\n",
        "qc = QuantumCircuit(qr, cr)             # combine the quantum and classical register\n",
        "qc.initialize([1,0],0)\n",
        "qc.measure(0,0)                         # apply the measurement as operator\n",
        "qc.draw('mpl')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "id": "HgCvf-N_sAtg",
        "outputId": "a2664f6e-23f3-4e85-c273-72496605dd3c"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 287.093x200.667 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Run the cirucit on simulator"
      ],
      "metadata": {
        "id": "cJwbl1WFsy3H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "result = sim.run(qc, shots = 2000).result()\n",
        "counts = result.get_counts()\n",
        "counts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8MiqTm7Ks1_X",
        "outputId": "ad771233-0ec7-4aa9-de98-651aae14e578"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'0': 2000}"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_histogram(counts)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "R9WbomuQs4mH",
        "outputId": "fa654275-8ee6-4cc8-c28e-8e292dfefaf7"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Congratulations** you just ran your first quantum circuit. In the next lecture we will learn about quantum operations or Basic gates."
      ],
      "metadata": {
        "id": "YBFXvskKmc83"
      }
    }
  ]
}