application_colab.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dfwOIuffh95_"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/LuchoGabba/qibo/blob/main/application_colab.ipynb\"> <img src='https://colab.research.google.com/assets/colab-badge.svg' /> </a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eeS37ab7h96C"
      },
      "source": [
        "# Talk from Stefano Carrazza\n",
        "\n",
        "Quantum simulation with a hardware acceleration, [arXiv:2009.01845](https://arxiv.org/abs/2009.01845).\n",
        "\n",
        "Follow this video: https://www.youtube.com/watch?v=Aoc4nLmplTE\n",
        "\n",
        "## Install Qibo"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install qibo"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JlCoXW5Qikkq",
        "outputId": "96e9f4a4-d5ce-46a7-957f-71ad82e5a403"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting qibo\n",
            "  Downloading qibo-0.2.1-py3-none-any.whl (180 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m180.0/180.0 kB\u001b[0m \u001b[31m3.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting cma<4.0.0,>=3.3.0 (from qibo)\n",
            "  Downloading cma-3.3.0-py3-none-any.whl (260 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m260.7/260.7 kB\u001b[0m \u001b[31m16.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: joblib<2.0.0,>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from qibo) (1.3.2)\n",
            "Requirement already satisfied: matplotlib<4.0.0,>=3.7.0 in /usr/local/lib/python3.10/dist-packages (from qibo) (3.7.1)\n",
            "Requirement already satisfied: psutil<6.0.0,>=5.9.4 in /usr/local/lib/python3.10/dist-packages (from qibo) (5.9.5)\n",
            "Requirement already satisfied: scipy<2.0.0,>=1.10.1 in /usr/local/lib/python3.10/dist-packages (from qibo) (1.11.3)\n",
            "Requirement already satisfied: sympy<2.0.0,>=1.11.1 in /usr/local/lib/python3.10/dist-packages (from qibo) (1.12)\n",
            "Requirement already satisfied: tabulate<0.10.0,>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from qibo) (0.9.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from cma<4.0.0,>=3.3.0->qibo) (1.23.5)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.7.0->qibo) (1.1.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.7.0->qibo) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.7.0->qibo) (4.43.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.7.0->qibo) (1.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.7.0->qibo) (23.2)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.7.0->qibo) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.7.0->qibo) (3.1.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.7.0->qibo) (2.8.2)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy<2.0.0,>=1.11.1->qibo) (1.3.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib<4.0.0,>=3.7.0->qibo) (1.16.0)\n",
            "Installing collected packages: cma, qibo\n",
            "Successfully installed cma-3.3.0 qibo-0.2.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7P7OD34ah96D",
        "outputId": "17530a5b-6b4d-4298-b554-682ea66fff48"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sat Oct 28 23:12:23 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   42C    P8    10W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Check version"
      ],
      "metadata": {
        "id": "QN2lnDyWl_Va"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "vvTczdFsh96E",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9273893b-3726-4a77-8508-7e6ebb608d4e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Python version: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0]\n",
            "Tensorflow version 2.14.0\n",
            "Qibo version 0.2.1\n"
          ]
        }
      ],
      "source": [
        "import qibo, sys\n",
        "import tensorflow as tf\n",
        "print('Python version:', sys.version)\n",
        "print('Tensorflow version', tf.__version__)\n",
        "print('Qibo version', qibo.__version__)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Quantum cicuits in Qibo\n",
        "\n",
        "We can change to GPU:\n",
        "\n",
        "* Runtime/Entorno de ejecución\n",
        "* Change runtime type/Cambiar tipo de entorno de ejecución\n",
        "* Select T4 GPU\n",
        "\n",
        "It's pretty faster than in CPU."
      ],
      "metadata": {
        "id": "pb2G-gGloIRp"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v6BFsqLeh96F",
        "outputId": "c25cda3d-8792-48c2-84c7-9bfb49f3ef66"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 3.67 s, sys: 1.39 s, total: 5.07 s\n",
            "Wall time: 34.3 s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "from qibo.models import QFT\n",
        "\n",
        "c = QFT(25)\n",
        "result = c()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To access the final state in Dirac notation execute `c.final_state`."
      ],
      "metadata": {
        "id": "VTucm2zKufwk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "c.final_state"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X0lYSxIiuITb",
        "outputId": "2d7b606c-6a99-4d00-8cd1-da78fcbf9616"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.00017+0j)|0000000000000000000000000> + (0.00017+0j)|0000000000000000000000001> + (0.00017+0j)|0000000000000000000000010> + (0.00017+0j)|0000000000000000000000011> + (0.00017+0j)|0000000000000000000000100> + (0.00017+0j)|0000000000000000000000101> + (0.00017+0j)|0000000000000000000000110> + (0.00017+0j)|0000000000000000000000111> + (0.00017+0j)|0000000000000000000001000> + (0.00017+0j)|0000000000000000000001001> + (0.00017+0j)|0000000000000000000001010> + (0.00017+0j)|0000000000000000000001011> + (0.00017+0j)|0000000000000000000001100> + (0.00017+0j)|0000000000000000000001101> + (0.00017+0j)|0000000000000000000001110> + (0.00017+0j)|0000000000000000000001111> + (0.00017+0j)|0000000000000000000010000> + (0.00017+0j)|0000000000000000000010001> + (0.00017+0j)|0000000000000000000010010> + (0.00017+0j)|0000000000000000000010011> + ..."
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To get the matrix representation, execute `.state()`.\n",
        "\n",
        "We obtain a vector of dimension $2^{25}$ with double precision"
      ],
      "metadata": {
        "id": "GhZNYVf0v95S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(result.state())\n",
        "print('\\nIn numpy:')\n",
        "# Or in numpy\n",
        "print(result.state(numpy=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "za8dL0PnvA4F",
        "outputId": "67b613ec-31d7-463e-87f5-a54d1447ff68"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tf.Tensor(\n",
            "[0.00017263+0.j 0.00017263+0.j 0.00017263+0.j ... 0.00017263+0.j\n",
            " 0.00017263+0.j 0.00017263+0.j], shape=(33554432,), dtype=complex128)\n",
            "\n",
            "In numpy:\n",
            "[0.00017263+0.j 0.00017263+0.j 0.00017263+0.j ... 0.00017263+0.j\n",
            " 0.00017263+0.j 0.00017263+0.j]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(result.state().dtype)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n6kWRdm-LgWv",
        "outputId": "1bada41b-9697-4a33-aa03-2f5d3624cbbd"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<dtype: 'complex128'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Quantum Fourier Transform\n",
        "\n",
        "We can implement a QFT circuit by hand.\n",
        "\n",
        "Let us try with a simpler cicuit, 4 qubits, with less qubits, to shorten the diagram."
      ],
      "metadata": {
        "id": "frc5jQ1rqyFH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from qibo import Circuit, gates\n",
        "import numpy as np\n",
        "nqubits = 4\n",
        "circuit = Circuit(nqubits)\n",
        "\n",
        "# Add gates\n",
        "for i1 in range(nqubits):\n",
        "  circuit.add(gates.H(i1))\n",
        "  for i2 in range(i1+1, nqubits):\n",
        "    theta = np.pi / 2 ** (i2-i1)\n",
        "    circuit.add(gates.CU1(i2,i1, theta))\n",
        "\n",
        "print(circuit.draw())\n",
        "print()\n",
        "print(circuit.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dWtSG5EvkPTG",
        "outputId": "97602e17-dd6c-4edd-8450-1c92cb8718f0"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "q0: ─H─U1─U1─U1────────────────\n",
            "q1: ───o──|──|──H─U1─U1────────\n",
            "q2: ──────o──|────o──|──H─U1───\n",
            "q3: ─────────o───────o────o──H─\n",
            "\n",
            "Circuit depth = 7\n",
            "Total number of gates = 10\n",
            "Number of qubits = 4\n",
            "Most common gates:\n",
            "cu1: 6\n",
            "h: 4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "circuit()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kOegu8VGkRRC",
        "outputId": "8f0e8e24-3058-4eab-852f-e4a897cd793b"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 42.5 ms, sys: 0 ns, total: 42.5 ms\n",
            "Wall time: 41.8 ms\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.25+0j)|0000> + (0.25+0j)|0001> + (0.25+0j)|0010> + (0.25+0j)|0011> + (0.25+0j)|0100> + (0.25+0j)|0101> + (0.25+0j)|0110> + (0.25+0j)|0111> + (0.25+0j)|1000> + (0.25+0j)|1001> + (0.25+0j)|1010> + (0.25+0j)|1011> + (0.25+0j)|1100> + (0.25+0j)|1101> + (0.25+0j)|1110> + (0.25+0j)|1111>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Which is a vector with $2^4=16$ dimensions.\n",
        "\n",
        "## Performing measurements"
      ],
      "metadata": {
        "id": "bNGwYYq1Gsd0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "circuit = Circuit(2)\n",
        "circuit.add(gates.RY(0, theta=np.pi / 3))\n",
        "circuit.add(gates.RY(1, theta=np.pi / 2))\n",
        "circuit.add(gates.M(0))\n",
        "circuit.add(gates.M(1))\n",
        "\n",
        "shots = circuit(nshots=1000)\n",
        "print(shots)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LMJ-tYSTIT7O",
        "outputId": "09b53bf3-9696-4e7d-d77d-fec5f78f3aae"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(0.61237+0j)|00> + (0.61237+0j)|01> + (0.35355+0j)|10> + (0.35355+0j)|11>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shots.samples(binary=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t6RUpRNJIsym",
        "outputId": "222085e7-a0ad-48f4-f3d6-bcc15a1c2164"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 0],\n",
              "       [1, 0],\n",
              "       [1, 1],\n",
              "       ...,\n",
              "       [0, 0],\n",
              "       [0, 0],\n",
              "       [1, 0]], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shots.frequencies()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k2I-BvKoI4pT",
        "outputId": "096d2b09-0f09-421e-ec3c-1344b3762a83"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Counter({'00': 419, '10': 129, '11': 120, '01': 332})"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Plot results in a histogram"
      ],
      "metadata": {
        "id": "iU2Bm0P-JDr7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "# from matplotlib import animation\n",
        "matplotlib.rcParams['mathtext.fontset'] = 'cm'\n",
        "matplotlib.rcParams['font.family'] = 'STIXGeneral'\n",
        "matplotlib.rcParams[\"font.size\"] = 10"
      ],
      "metadata": {
        "id": "y8ZPRjeNKPkI"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labels, values = zip(*shots.frequencies().items())\n",
        "plt.figure(figsize=(3,4))\n",
        "plt.bar(labels, values, width=0.5)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "id": "30Sy1Oo0I6ve",
        "outputId": "8ff3eee3-b074-480c-d2e1-07a9c5530b91"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 300x400 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAARsAAAFfCAYAAAB+5GhbAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAZV0lEQVR4nO3dYWiU9wHH8d8lu11S6j1DDXg5U+ahnowyENKaOTUur5TVuJZVrLONr5xEbiWugnlht4CQ9tWCOqsNCF1NAiUVAnUuKzJJUo7mhWXxxSq2pmmOuKTY8FxgzTXxnr0oPvRq7szFyz93ue8HHvCe/13u+Zvky3PP5Z7H4ziOIwBYZCVLvQEAigOxAWAEsQFgBLEBYASxAWAEsQFgBLEBYASxAWDEj5Z6Ax4lmUxqbGxMK1askMfjWerNAfA9juNoampKlZWVKinJvO+S97EZGxtTVVXVUm8GgAxGR0e1du3ajPfJ+9isWLFC0neT8fv9S7w1AL4vHo+rqqrK/T3NJO9j8+Clk9/vJzZAnprPIQ4OEAMwgtgAMILYADCC2AAwgtgAMILYADCC2AAwgtgAMILYADCC2AAwgtgAMILYADAi7z+ImY2fnriyJM/7xRu/XpLnBQoJezYAjCA2AIwgNgCMIDYAjCA2AIwgNgCMIDYAjFhQbP73v//pZz/7mb744gtJ0tDQkI4eParTp0/r0KFDmpycdO+baQxA8VhQbM6cOaP//Oc/kr67lMPu3bv1xz/+UX/4wx/0y1/+Uo2NjY8cA1Bcso5NT0+PfvWrX7m3Ozo6VF5erlAoJEnas2ePuru7NTY2lnEMQHHJKjZffvml7t69q2effdZdF41GFQ6H3dtr1qxRWVmZBgYGMo4BKC7z/mzU/fv31d7erpaWlpT14+PjWrlyZco6y7IUi8UyjqWTSCSUSCTc2/F4fL6bCCCPzXvP5q9//at+//vfP3TxcK/XK6/Xm7IumUzKcZyMY+m0trbKsix34TrfwPIw79icOXNG69evV1lZmcrKyiRJ4XBYgUBAtm2n3Ne2bQWDwYxj6TQ3N8u2bXcZHR3NZj4A8tS8Y3P79m1NT0+7iyTdunVLtbW1unPnjnu/WCym6elp7dixI+NYOj6fz72uN9f3BpaPx/6jvvr6ek1MTGhkZESS1Nvbq127dqmysjLjGIDi8tgnz/L7/erp6VFLS4s2b96swcFBXbx48ZFjAIrLgmPz/YO8NTU1qqmpmfN+mcYAFA8+GwXACGIDwAhiA8AIYgPACGIDwAhiA8AIYgPACGIDwAhiA8AIYgPACGIDwAhiA8AIYgPACGIDwAhiA8AIYgPACGIDwAhiA8AIYgPACGIDwAhiA8AIYgPACGIDwIhFi00sFlusLw2gAGUdm4GBAVVXV8uyLL3wwgv65ptvJH130bpwOCyPxyOPx6OGhgb3MUNDQzp69KhOnz6tQ4cOaXJyMnczAFAQsroipm3b+vDDD/XRRx9pampKW7Zs0bvvvqvDhw/r6tWrikQi7tUvQ6GQJCkej2v37t3q7+9XKBRSe3u7Ghsb1dXVlfvZAMhbWe3ZTE1N6fXXX5fP59Pq1atVXV2tkpLvvsTZs2dVUlKiiooKVVdXa+XKlZKkjo4OlZeXu/HZs2ePuru7NTY2luOpAMhnWcVm7dq1Ki0tlSR9/fXXKi8v18svv6ypqSklEgmdPHlSoVBIkUjEvRZ4NBpVOBx2v8aaNWtUVlamgYGBHE4DQL5b0AHizs5OPfPMM/r444/12WefacWKFbp27Zr++9//qq2tTW+99ZZOnz4tSRofH5ff7095vGVZaQ8gJxIJxePxlAVA4VtQbF566SVduXJFVVVVOnLkiLve6/UqEomoublZnZ2d7jqv15vy+GQy6e75/FBra6ssy3KXqqqqhWwigDyzoNh4PB5t2rRJb7/9tm7cuPHQ+N69e2XbtiQpEAi4/37Atm0Fg8E5v3Zzc7Ns23aX0dHRhWwigDzzWH9ns3r16jmjMTs76x6nqa2t1Z07d9yxWCym6elp7dixY86v6fP55Pf7UxYAhS+r2Ny9e1e9vb3u7ffff1+vvfaa+vr6dOnSJfel0YULF3T8+HFJUn19vSYmJjQyMiJJ6u3t1a5du1RZWZmrOQAoAFn9nc2tW7d04MABPfPMM3ruuee0atUqNTQ0qLOzU01NTerq6lJNTY0aGhq0bds2SZLf71dPT49aWlq0efNmDQ4O6uLFi4syGQD5y+OkO1KbJ+LxuCzLkm3bj3xJ9dMTVwxtVaov3vj1kjwvsNSy+f3kg5gAjCA2AIwgNgCMIDYAjCA2AIwgNgCMyOrvbACYtVR/ziHl/k862LMBYASxAWAEsQFgBLEBYASxAWAEsQFgBLEBYASxAWAEsQFgBLEBYASxAWAEsQFgBLEBYASxAWAEsQFgBLEBYASxAWBE1rEZGBhQdXW1LMvSCy+8oG+++UaSNDIyoiNHjujcuXM6ePCge7ndR40BKA5Zxca2bX344Yf66KOP9Pnnn+vf//633n33XSWTSdXX12vfvn1qbGxUQ0OD9u/fL0kZxwAUj6xiMzU1pddff10+n0+rV69WdXW1SkpK1Nvbq9u3b2v79u2SpLq6Og0NDWlwcDDjGIDikVVs1q5dq9LSUknS119/rfLycr388suKRqNat26dvF6vJKm0tFShUEjXr1/POAageCzo6gqdnZ06efKkfvzjH+uzzz7T+Pj4QxcVtyxLsVhMiUQi7dhcEomEEomEezsejy9kEwHkmQW9G/XSSy/pypUrqqqq0pEjR+T1et09lweSyaQcx8k4NpfW1lZZluUuVVVVC9lEAHlmQbHxeDzatGmT3n77bd24cUOBQEC2bafcx7ZtBYPBjGNzaW5ulm3b7jI6OrqQTQSQZx7r72xWr16tYDCo2tpaDQ8Pu3srMzMzGh4e1s6dOzOOzcXn88nv96csAApfVrG5e/euent73dvvv/++XnvtNW3dulXBYFD9/f2SpL6+PoVCIW3ZsiXjGIDikdUB4lu3bunAgQN65pln9Nxzz2nVqlVqaGiQJPX09OjUqVO6efOmotGoLl++LI/HI4/Hk3YMQPHwOOmO1OaJeDwuy7Jk2/YjX1It1XWRc31NZOCBfL/Wdza/n3w2CoARxAaAEcQGgBHEBoARxAaAEcQGgBHEBoARxAaAEcQGgBHEBoARxAaAEcQGgBHEBoARxAaAEcQGgBHEBoARxAaAEcQGgBHEBoARxAaAEcQGgBHEBoARxAaAEYsSm1gsthhfFkAByyo2H3zwgTZt2iS/368XX3xRk5OTkiTHcRQOh90rYD64SqYkDQ0N6ejRozp9+rQOHTrkPgZAcZn35XeHh4fV09Oj7u5uffrppzp8+LBOnDihCxcu6OrVq4pEIqqpqZEkhUIhSd9dLW/37t3q7+9XKBRSe3u7Ghsb1dXVtTizAZC35r1n09/fr7Nnz+rpp5/Wb3/7W0UiEQ0MDEiSzp49q5KSElVUVKi6ulorV66UJHV0dKi8vNyNz549e9Td3a2xsbFFmAqAfDbv2Lzyyivy+Xzu7TVr1uipp57S1NSUEomETp48qVAopEgkogeXD49GowqHwymPKSsrcyMFoHjM+2XUD924cUOHDx/WihUrdO3aNc3MzOj8+fNqamrS+vXr9eqrr2p8fNzdy3nAsqyMB5ATiYQSiYR7Ox6PL3QTAeSRBb0bdffuXc3Ozur5559313m9XkUiETU3N6uzs9Nd5/V6Ux6bTCbdPZ+5tLa2yrIsd6mqqlrIJgLIM1nH5v79+2pra9OZM2fmHN+7d69s25YkBQIB998P2LatYDCY9us3NzfLtm13GR0dzXYTAeShrGPT1tamY8eO6cknn5Qkffvttynjs7Oz7nGa2tpa3blzxx2LxWKanp7Wjh070n59n88nv9+fsgAofFnFpq2tTRs2bNDk5KQ+/fRTXblyRW+88YYuXbrkvjS6cOGCjh8/Lkmqr6/XxMSERkZGJEm9vb3atWuXKisrczwNAPlu3geI33vvPR07dizleMsTTzyht956S01NTerq6lJNTY0aGhq0bds2SZLf71dPT49aWlq0efNmDQ4O6uLFi7mfBYC8N+/Y7Nu3T/v27Ztz7JVXXkn7uJqaGveP/QAULz6ICcAIYgPACGIDwAhiA8AIYgPACGIDwAhiA8AIYgPACGIDwAhiA8AIYgPACGIDwAhiA8AIYgPACGIDwAhiA8AIYgPACGIDwAhiA8AIYgPACGIDwAhiA8AIYgPACGIDwIisYvPBBx9o06ZN8vv9evHFFzU5OSlJGhkZ0ZEjR3Tu3DkdPHjQvdzuo8YAFI95x2Z4eFg9PT3q7u7WxYsXde3aNZ04cULJZFL19fXat2+fGhsb1dDQoP3790tSxjEAxWXel9/t7+/X2bNn5fP59PTTT+vmzZvq7u5Wb2+vbt++re3bt0uS6urq9Jvf/EaDg4O6d+9e2rFnn312cWYEIC/Ne8/mlVdekc/nc2+vWbNGTz31lKLRqNatWyev1ytJKi0tVSgU0vXr1zOOASgu896z+aEbN27o8OHD+sc//iG/358yZlmWYrGYEolE2rF0EomEEomEezsejy90EwHkkQW9G3X37l3Nzs7q+eefl9frdfdcHkgmk3IcJ+NYOq2trbIsy12qqqoWsokA8kzWsbl//77a2tp05swZSVIgEJBt2yn3sW1bwWAw41g6zc3Nsm3bXUZHR7PdRAB5KOvYtLW16dixY3ryySclSdu3b9fw8LC7tzIzM6Ph4WHt3LlTtbW1acfS8fl88vv9KQuAwpfVMZu2tjZt2LBBk5OTmpyc1Oeff6779+8rGAyqv79fO3bsUF9fn0KhkLZs2SLHcdKOASgu847Ne++9p2PHjqUcb3niiSc0Pj6unp4enTp1Sjdv3lQ0GtXly5fl8Xjk8XjSjgEoLh4n09HaPBCPx2VZlmzbfuRLqp+euGJoq1J98cavl+R5sfwt1c+0NL+f62x+P/lsFAAjiA0AI4gNACOIDQAjiA0AI4gNACOIDQAjiA0AI4gNACOIDQAjiA0AI4gNACOIDQAjiA0AI4gNACOIDQAjiA0AI4gNACOIDQAjiA0AI4gNACOIDQAjiA0AIxYcm+np6Yeu4/19sVhsoV8awDKUdWySyaTeeecdbdy4UZ988om73nEchcNh90qYDQ0N7tjQ0JCOHj2q06dP69ChQ5qcnMzN1gMoGFld61uS7t27p7q6Oh06dChl/dWrVxWJRFRTUyNJCoVCkr67Yt7u3bvV39+vUCik9vZ2NTY2qqur6/G3HkDByHrPpqKiQlVVVQ+tP3v2rEpKSlRRUaHq6mqtXLlSktTR0aHy8nI3Pnv27FF3d7fGxsYec9MBFJKcHCCemppSIpHQyZMnFQqFFIlE9OAS4tFoVOFw2L3vmjVrVFZWpoGBgVw8NYACkfXLqLmsWLFC165d08zMjM6fP6+mpiatX79er776qsbHx929nAcsy0p7ADmRSCiRSLi34/F4LjYRwBLL6VvfXq9XkUhEzc3N6uzsdNd5vd6U+yWTSXfP54daW1tlWZa7zPWSDUDhWZS/s9m7d6/7tnggEHjoLXLbthUMBud8bHNzs2zbdpfR0dHF2EQAhi1KbGZnZ93jNLW1tbpz5447FovFND09rR07dsz5WJ/PJ7/fn7IAKHwLik0ymUy53dfXp0uXLrkvjS5cuKDjx49Lkurr6zUxMaGRkRFJUm9vr3bt2qXKysrH2W4ABSbrA8RfffWV2tvbJX33tnYgEFAsFlNTU5O6urpUU1OjhoYGbdu2TZLk9/vV09OjlpYWbd68WYODg7p48WJuZwEg73mcdEdq80Q8HpdlWbJt+5EvqX564oqhrUr1xRu/XpLnxfK3VD/T0vx+rrP5/eSDmACMIDYAjCA2AIwgNgCMIDYAjCA2AIwgNgCMIDYAjCA2AIzIyflssHT4q2kUCvZsABhBbAAYQWwAGEFsABhBbAAYwbtRKCi8+1a42LMBYASxAWAEsQFgBLEBYASxAWAEsQFgBLEBYMSCYzM9Pf3QNbwBIJ2sY5NMJvXOO+9o48aN+uSTT9z1IyMjOnLkiM6dO6eDBw+6l9t91BiA4pB1bO7du6e6ujqNjo6665LJpOrr67Vv3z41NjaqoaFB+/fvf+QYgOKRdWwqKipUVVWVsq63t1e3b9/W9u3bJUl1dXUaGhrS4OBgxjEAxSMnB4ij0ajWrVsnr9crSSotLVUoFNL169czjgEoHjn5IOb4+PhDFxW3LEuxWEyJRCLt2FwSiYQSiYR7Ox6P52ITASyxnOzZeL1ed8/lgWQyKcdxMo7NpbW1VZZlucsPX7IBKEw5iU0gEHjobXDbthUMBjOOzaW5uVm2bbvL9w9EAyhcOYlNbW2thoeH3b2VmZkZDQ8Pa+fOnRnH5uLz+eT3+1MWAIVvQbFJJpMpt7du3apgMKj+/n5JUl9fn0KhkLZs2ZJxDEDxyPoA8VdffaX29nZJUkdHhwKBgMLhsHp6enTq1CndvHlT0WhUly9flsfjkcfjSTsGoHh4nHRHavNEPB6XZVmybfuRL6mK8ZSRxTZn5mvOfOacze8nH8QEYASxAWAEsQFgBLEBYASxAWAEsQFgBLEBYASxAWAEsQFgBLEBYASxAWAEsQFgBLEBYASxAWAEsQFgBLEBYASxAWAEsQFgBLEBYASxAWAEsQFgBLEBYASxAWDEosYmFost5pcHUEByGhvHcRQOh90rYTY0NEiShoaGdPToUZ0+fVqHDh3S5ORkLp8WQAHI+vK7mVy9elWRSEQ1NTWSpFAopHg8rt27d6u/v1+hUEjt7e1qbGxUV1dXLp8aQJ7L6Z7N2bNnVVJSooqKClVXV2vlypXq6OhQeXm5QqGQJGnPnj3q7u7W2NhYLp8aQJ7LWWympqaUSCR08uRJhUIhRSIROY6jaDSqcDjs3m/NmjUqKyvTwMBArp4aQAHI2cuoFStW6Nq1a5qZmdH58+fV1NSk9evXa3x8XCtXrky5r2VZaQ8eJxIJJRIJ93Y8Hs/VJgJYQjk9ZiNJXq9XkUhEExMT6uzsVEVFhbxeb8p9ksmkHMeZ8/Gtra1qaWnJ9WYBWGKL9tb33r17Zdu2AoGAbNtOGbNtW8FgcM7HNTc3y7ZtdxkdHV2sTQRg0KLFZnZ2VuFwWLW1tbpz5467PhaLaXp6Wjt27JjzcT6fT36/P2UBUPhyFpu+vj5dunTJfXl04cIFHT9+XPX19ZqYmNDIyIgkqbe3V7t27VJlZWWunhpAAcjZMZtYLKampiZ1dXWppqZGDQ0N2rZtmySpp6dHLS0t2rx5swYHB3Xx4sVcPS2AApGz2Bw4cEAHDhyYc6ympsb9Qz8AxYkPYgIwgtgAMILYADCC2AAwgtgAMILYADCC2AAwgtgAMILYADCC2AAwgtgAMILYADCC2AAwgtgAMILYADCC2AAwgtgAMILYADCC2AAwgtgAMILYADCC2AAwgtgAMILYADDCSGxGRkZ05MgRnTt3TgcPHnQvxQugeOTsipjpJJNJ1dfX6y9/+Yvq6uq0YcMG7d+/X9FodLGfGkAeWfQ9m97eXt2+fVvbt2+XJNXV1WloaEiDg4OL/dQA8siixyYajWrdunXyer2SpNLSUoVCIV2/fn2xnxpAHln0l1Hj4+Py+/0p6yzLUiwWm/P+iURCiUTCvW3btiQpHo8/8rmSif89xpYu3Hy2bbEU25yZrznzmfOD+ziO88j7LnpsvF6vu1fzQDKZTLtxra2tamlpeWh9VVXVomxfLlhtS70F5hXbnIttvlJ2c56ampJlWRnvs+ixCQQC6u/vT1ln27aCweCc929ubtaxY8fc28lkUl9//bVWrVolj8ezKNsYj8dVVVWl0dHRh/bClqtim3OxzVcyM2fHcTQ1NaXKyspH3nfRY1NbW6s333xTjuPI4/FoZmZGw8PD2rlz55z39/l88vl8Ket+8pOfLPZmSpL8fn/R/CA+UGxzLrb5Sos/50ft0Tyw6AeIt27dqmAw6O7d9PX1KRQKacuWLYv91ADyyKLv2ZSUlKinp0enTp3SzZs3FY1Gdfny5UV7SQQgPy16bCRp48aN+tvf/iZJOnr0qImnzIrP59Of/vSnh16+LWfFNudim6+Uf3P2OPN5zwoAHhMfxARgBLEBYASxKSLT09PuX2QXi3RzLrb/i3yYb1HGJt0pL5brqTCSyaTeeecdbdy4UZ988om7frnOV0o/53TrC12672VezdcpMvfv33d+/vOfO9euXXMcx3H++c9/OjU1NWnXLwcTExPOl19+6Uhy/vWvfzmOk/7/YbmYa86Z1heyTN/LfJpv0cXm73//u1NeXu58++23juM4zuzsrPPEE084f/7zn+dc//HHHy/l5ubU93/g0v0/LKf5Oo6T9pcsH375cmU+38t8mG/RvYxKd8qLN998s6hOhcGpP5aPQvleFl1s0p3y4ne/+11Wp8IodNme+gP5q1C+l0UXm3SnvPjRj36U1akwCl22p/5A/iqU72XRxSYQCDz0FqBt21q7du2c69OdCqPQpft/WK7zXc4K5XtZdLGpra3V8PCwW/0Hp7xItz7dqTAKXbHNdzkrlO9l0cUm3SkvfvGLXyzrU2Ekk8mU28Vw6o8fzvlR6wvVo76X+TJfI5/6zifpTnlRWlq6bE+F8dVXX6m9vV2S1NHRoUAgoHA4vGznK6Wfc7r1hSzTaVzyab586huAEUX3MgrA0iA2AIwgNgCMIDYAjCA2AIwgNgCMIDYAjCA2AIwgNgCMIDYAjCA2AIwgNgCM+D98Gzx5oGwMZgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Managing simulation deviced\n",
        "\n",
        "Qibo uses Tensorflow to perform the linear algebra calculations required for the simulation of quantum circuits. It is possible to view the devices that are available to Tensorflow:"
      ],
      "metadata": {
        "id": "RGjs6UyxMXQQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(tf.config.list_physical_devices())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2jT3OIqZJbRz",
        "outputId": "f5dfab58-aecd-4e0b-9435-c913f49694b1"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "qibo.set_device(\"/CPU:0\")\n",
        "\n",
        "circuit = Circuit(25)\n",
        "circuit.add((gates.H(i) for i in range(circuit.nqubits)))\n",
        "\n",
        "start_time = time.time()\n",
        "final_state = circuit()\n",
        "print(\"Time elapsed:\", time.time() - start_time)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bb9q2dCdMR05",
        "outputId": "26364623-0e58-4301-c13f-d6ed8246f4f3"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Qibo 0.2.1|INFO|2023-10-29 16:12:29]: Using tensorflow backend on /CPU:0\n",
            "INFO:qibo.config:Using tensorflow backend on /CPU:0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time elapsed: 247.2311589717865\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "qibo.set_device(\"/GPU:0\")\n",
        "\n",
        "circuit = Circuit(25)\n",
        "circuit.add((gates.H(i) for i in range(circuit.nqubits)))\n",
        "\n",
        "start_time = time.time()\n",
        "final_state = circuit()\n",
        "print(\"Time elapsed:\", time.time() - start_time)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ppgsO1w9NUgc",
        "outputId": "05aec604-99c8-49e1-f288-550ebaca78d7"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Qibo 0.2.1|INFO|2023-10-29 16:16:36]: Using tensorflow backend on /GPU:0\n",
            "INFO:qibo.config:Using tensorflow backend on /GPU:0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time elapsed: 0.03386998176574707\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Executing on multiple-GPUs\n",
        "\n",
        "Execute circuits on multiple GPUs with `accelerators` dictionary specifying which devices to use when creating the circuit:"
      ],
      "metadata": {
        "id": "uwsak7vLN0Bp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "circuit = Circuit(30)\n",
        "print(circuit)\n",
        "\n",
        "circuit = Circuit(30, accelerators={\"/GPU:0\": 1, \"/GPU:1\": 1})\n",
        "print(circuit)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zlw3d993Nx6u",
        "outputId": "e1bc957f-9384-4057-8a42-23c8f4d92282"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<qibo.models.circuit.Circuit object at 0x7b2bf03f9d50>\n",
            "<qibo.models.circuit.Circuit object at 0x7b2bf03fa410>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The implementation of `DistributedCircuit`is based on standard techniques of multi-node quantum simulation (see Sec. 3.4 of [arXiv:1704.01127](https://arxiv.org/abs/1704.01127)).\n",
        "\n",
        "quibojit for $N_{qubits}>20$. See [arXiv:2203.08826](https://arxiv.org/abs/2203.08826)\n",
        "\n",
        "<br>\n",
        "\n",
        "## Extra\n",
        "\n",
        "Here's another interesting [Talk by Matteo Robbiati](https://www.youtube.com/watch?v=vlos9LvM80Q).\n",
        "\n",
        "Results of Probability density functions with adiabatic quantum computing in [arxiv:2303.11346](https://arxiv.org/abs/2303.11346).\n",
        "\n",
        "Some more papers:\n",
        "\n",
        "* [arXiv:2202.07017](https://arxiv.org/abs/2202.07017)\n",
        "\n",
        "* [arXiv:2112.02933](https://arxiv.org/abs/2112.02933)\n",
        "\n",
        "* Quantum analytical Adam descent: [arXiv:2210.10787](https://arxiv.org/abs/2210.10787)\n",
        "\n",
        "* Quilolab: [arXiv:2303.10397](https://arxiv.org/abs/2303.10397)"
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