[404218]: / Code / PennyLane / Data-Reuploading / Batch Studies / 36 Batch 83.3% kkawchak.ipynb

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{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "eI_d8C3_Tnyu",
        "outputId": "64d5724c-5507-41e7-93c4-5e5de7b42f44"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1696868352.2274766\n",
            "Mon Oct  9 16:19:12 2023\n"
          ]
        }
      ],
      "source": [
        "# This cell is added by sphinx-gallery\n",
        "# It can be customized to whatever you like\n",
        "%matplotlib inline\n",
        "# !pip install pennylane\n",
        "\n",
        "import time\n",
        "seconds = time.time()\n",
        "print(\"Time in seconds since beginning of run:\", seconds)\n",
        "local_time = time.ctime(seconds)\n",
        "print(local_time)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "lScQSFsjTnyw",
        "outputId": "5ed8e395-76ca-4e75-d7d2-d9d7daa4e6a4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import pennylane as qml\n",
        "from pennylane import numpy as np\n",
        "from pennylane.optimize import AdamOptimizer, GradientDescentOptimizer\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "# Set a random seed\n",
        "np.random.seed(42)\n",
        "\n",
        "\n",
        "# Make a dataset of points inside and outside of a circle\n",
        "def circle(samples, center=[0.0, 0.0], radius=np.sqrt(2 / np.pi)):\n",
        "    \"\"\"\n",
        "    Generates a dataset of points with 1/0 labels inside a given radius.\n",
        "\n",
        "    Args:\n",
        "        samples (int): number of samples to generate\n",
        "        center (tuple): center of the circle\n",
        "        radius (float: radius of the circle\n",
        "\n",
        "    Returns:\n",
        "        Xvals (array[tuple]): coordinates of points\n",
        "        yvals (array[int]): classification labels\n",
        "    \"\"\"\n",
        "    Xvals, yvals = [], []\n",
        "\n",
        "    for i in range(samples):\n",
        "        x = 2 * (np.random.rand(2)) - 1\n",
        "        y = 0\n",
        "        if np.linalg.norm(x - center) < radius:\n",
        "            y = 1\n",
        "        Xvals.append(x)\n",
        "        yvals.append(y)\n",
        "    return np.array(Xvals, requires_grad=False), np.array(yvals, requires_grad=False)\n",
        "\n",
        "\n",
        "def plot_data(x, y, fig=None, ax=None):\n",
        "    \"\"\"\n",
        "    Plot data with red/blue values for a binary classification.\n",
        "\n",
        "    Args:\n",
        "        x (array[tuple]): array of data points as tuples\n",
        "        y (array[int]): array of data points as tuples\n",
        "    \"\"\"\n",
        "    if fig == None:\n",
        "        fig, ax = plt.subplots(1, 1, figsize=(5, 5))\n",
        "    reds = y == 0\n",
        "    blues = y == 1\n",
        "    ax.scatter(x[reds, 0], x[reds, 1], c=\"red\", s=20, edgecolor=\"k\")\n",
        "    ax.scatter(x[blues, 0], x[blues, 1], c=\"blue\", s=20, edgecolor=\"k\")\n",
        "    ax.set_xlabel(\"$x_1$\")\n",
        "    ax.set_ylabel(\"$x_2$\")\n",
        "\n",
        "\n",
        "Xdata, ydata = circle(500)\n",
        "fig, ax = plt.subplots(1, 1, figsize=(4, 4))\n",
        "plot_data(Xdata, ydata, fig=fig, ax=ax)\n",
        "plt.show()\n",
        "\n",
        "\n",
        "# Define output labels as quantum state vectors\n",
        "def density_matrix(state):\n",
        "    \"\"\"Calculates the density matrix representation of a state.\n",
        "\n",
        "    Args:\n",
        "        state (array[complex]): array representing a quantum state vector\n",
        "\n",
        "    Returns:\n",
        "        dm: (array[complex]): array representing the density matrix\n",
        "    \"\"\"\n",
        "    return state * np.conj(state).T\n",
        "\n",
        "\n",
        "label_0 = [[1], [0]]\n",
        "label_1 = [[0], [1]]\n",
        "state_labels = np.array([label_0, label_1], requires_grad=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NqAg65FbTnyx"
      },
      "source": [
        "Simple classifier with data reloading and fidelity loss\n",
        "=======================================================\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "ZyKIWD9bTnyx"
      },
      "outputs": [],
      "source": [
        "dev = qml.device(\"lightning.qubit\", wires=1)\n",
        "# Install any pennylane-plugin to run on some particular backend\n",
        "\n",
        "\n",
        "@qml.qnode(dev, interface=\"autograd\")\n",
        "def qcircuit(params, x, y):\n",
        "    \"\"\"A variational quantum circuit representing the Universal classifier.\n",
        "\n",
        "    Args:\n",
        "        params (array[float]): array of parameters\n",
        "        x (array[float]): single input vector\n",
        "        y (array[float]): single output state density matrix\n",
        "\n",
        "    Returns:\n",
        "        float: fidelity between output state and input\n",
        "    \"\"\"\n",
        "    for p in params:\n",
        "        qml.Rot(*x, wires=0)\n",
        "        qml.Rot(*p, wires=0)\n",
        "    return qml.expval(qml.Hermitian(y, wires=[0]))\n",
        "\n",
        "\n",
        "def cost(params, x, y, state_labels=None):\n",
        "    \"\"\"Cost function to be minimized.\n",
        "\n",
        "    Args:\n",
        "        params (array[float]): array of parameters\n",
        "        x (array[float]): 2-d array of input vectors\n",
        "        y (array[float]): 1-d array of targets\n",
        "        state_labels (array[float]): array of state representations for labels\n",
        "\n",
        "    Returns:\n",
        "        float: loss value to be minimized\n",
        "    \"\"\"\n",
        "    # Compute prediction for each input in data batch\n",
        "    loss = 0.0\n",
        "    dm_labels = [density_matrix(s) for s in state_labels]\n",
        "    for i in range(len(x)):\n",
        "        f = qcircuit(params, x[i], dm_labels[y[i]])\n",
        "        loss = loss + (1 - f) ** 2\n",
        "    return loss / len(x)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Bz83H0xTnyx"
      },
      "source": [
        "Utility functions for testing and creating batches\n",
        "==================================================\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "i1-TLseuTnyx"
      },
      "outputs": [],
      "source": [
        "def test(params, x, y, state_labels=None):\n",
        "    \"\"\"\n",
        "    Tests on a given set of data.\n",
        "\n",
        "    Args:\n",
        "        params (array[float]): array of parameters\n",
        "        x (array[float]): 2-d array of input vectors\n",
        "        y (array[float]): 1-d array of targets\n",
        "        state_labels (array[float]): 1-d array of state representations for labels\n",
        "\n",
        "    Returns:\n",
        "        predicted (array([int]): predicted labels for test data\n",
        "        output_states (array[float]): output quantum states from the circuit\n",
        "    \"\"\"\n",
        "    fidelity_values = []\n",
        "    dm_labels = [density_matrix(s) for s in state_labels]\n",
        "    predicted = []\n",
        "\n",
        "    for i in range(len(x)):\n",
        "        fidel_function = lambda y: qcircuit(params, x[i], y)\n",
        "        fidelities = [fidel_function(dm) for dm in dm_labels]\n",
        "        best_fidel = np.argmax(fidelities)\n",
        "\n",
        "        predicted.append(best_fidel)\n",
        "        fidelity_values.append(fidelities)\n",
        "\n",
        "    return np.array(predicted), np.array(fidelity_values)\n",
        "\n",
        "\n",
        "def accuracy_score(y_true, y_pred):\n",
        "    \"\"\"Accuracy score.\n",
        "\n",
        "    Args:\n",
        "        y_true (array[float]): 1-d array of targets\n",
        "        y_predicted (array[float]): 1-d array of predictions\n",
        "        state_labels (array[float]): 1-d array of state representations for labels\n",
        "\n",
        "    Returns:\n",
        "        score (float): the fraction of correctly classified samples\n",
        "    \"\"\"\n",
        "    score = y_true == y_pred\n",
        "    return score.sum() / len(y_true)\n",
        "\n",
        "\n",
        "def iterate_minibatches(inputs, targets, batch_size):\n",
        "    \"\"\"\n",
        "    A generator for batches of the input data\n",
        "\n",
        "    Args:\n",
        "        inputs (array[float]): input data\n",
        "        targets (array[float]): targets\n",
        "\n",
        "    Returns:\n",
        "        inputs (array[float]): one batch of input data of length `batch_size`\n",
        "        targets (array[float]): one batch of targets of length `batch_size`\n",
        "    \"\"\"\n",
        "    for start_idx in range(0, inputs.shape[0] - batch_size + 1, batch_size):\n",
        "        idxs = slice(start_idx, start_idx + batch_size)\n",
        "        yield inputs[idxs], targets[idxs]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B5s1nRL2Tnyy"
      },
      "source": [
        "Train a quantum classifier on the circle dataset\n",
        "================================================\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "NqJGMjbpTnyy",
        "outputId": "463f4810-11be-4b69-f90a-154c80574a97"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:  0 | Cost: 0.415535 | Train accuracy: 0.460000 | Test Accuracy: 0.448500\n",
            "Epoch:  1 | Loss: 0.157021 | Train accuracy: 0.770000 | Test accuracy: 0.760500\n",
            "Epoch:  2 | Loss: 0.120079 | Train accuracy: 0.860000 | Test accuracy: 0.826000\n",
            "Epoch:  3 | Loss: 0.134867 | Train accuracy: 0.760000 | Test accuracy: 0.751000\n",
            "Epoch:  4 | Loss: 0.110214 | Train accuracy: 0.860000 | Test accuracy: 0.811500\n",
            "Epoch:  5 | Loss: 0.108972 | Train accuracy: 0.895000 | Test accuracy: 0.846000\n",
            "Epoch:  6 | Loss: 0.106510 | Train accuracy: 0.900000 | Test accuracy: 0.841000\n",
            "Epoch:  7 | Loss: 0.117292 | Train accuracy: 0.840000 | Test accuracy: 0.792500\n",
            "Epoch:  8 | Loss: 0.104100 | Train accuracy: 0.895000 | Test accuracy: 0.836500\n",
            "Epoch:  9 | Loss: 0.103405 | Train accuracy: 0.905000 | Test accuracy: 0.852500\n",
            "Epoch: 10 | Loss: 0.103276 | Train accuracy: 0.875000 | Test accuracy: 0.832500\n"
          ]
        }
      ],
      "source": [
        "# Generate training and test data\n",
        "num_training = 200\n",
        "num_test = 2000\n",
        "\n",
        "Xdata, y_train = circle(num_training)\n",
        "X_train = np.hstack((Xdata, np.zeros((Xdata.shape[0], 1), requires_grad=False)))\n",
        "\n",
        "Xtest, y_test = circle(num_test)\n",
        "X_test = np.hstack((Xtest, np.zeros((Xtest.shape[0], 1), requires_grad=False)))\n",
        "\n",
        "\n",
        "# Train using Adam optimizer and evaluate the classifier\n",
        "num_layers = 3\n",
        "learning_rate = 0.6\n",
        "epochs = 10\n",
        "batch_size = 36\n",
        "\n",
        "opt = AdamOptimizer(learning_rate, beta1=0.9, beta2=0.999)\n",
        "\n",
        "# initialize random weights\n",
        "params = np.random.uniform(size=(num_layers, 3), requires_grad=True)\n",
        "\n",
        "predicted_train, fidel_train = test(params, X_train, y_train, state_labels)\n",
        "accuracy_train = accuracy_score(y_train, predicted_train)\n",
        "\n",
        "predicted_test, fidel_test = test(params, X_test, y_test, state_labels)\n",
        "accuracy_test = accuracy_score(y_test, predicted_test)\n",
        "\n",
        "# save predictions with random weights for comparison\n",
        "initial_predictions = predicted_test\n",
        "\n",
        "loss = cost(params, X_test, y_test, state_labels)\n",
        "\n",
        "print(\n",
        "    \"Epoch: {:2d} | Cost: {:3f} | Train accuracy: {:3f} | Test Accuracy: {:3f}\".format(\n",
        "        0, loss, accuracy_train, accuracy_test\n",
        "    )\n",
        ")\n",
        "\n",
        "for it in range(epochs):\n",
        "    for Xbatch, ybatch in iterate_minibatches(X_train, y_train, batch_size=batch_size):\n",
        "        params, _, _, _ = opt.step(cost, params, Xbatch, ybatch, state_labels)\n",
        "\n",
        "    predicted_train, fidel_train = test(params, X_train, y_train, state_labels)\n",
        "    accuracy_train = accuracy_score(y_train, predicted_train)\n",
        "    loss = cost(params, X_train, y_train, state_labels)\n",
        "\n",
        "    predicted_test, fidel_test = test(params, X_test, y_test, state_labels)\n",
        "    accuracy_test = accuracy_score(y_test, predicted_test)\n",
        "    res = [it + 1, loss, accuracy_train, accuracy_test]\n",
        "    print(\n",
        "        \"Epoch: {:2d} | Loss: {:3f} | Train accuracy: {:3f} | Test accuracy: {:3f}\".format(\n",
        "            *res\n",
        "        )\n",
        "    )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YWspC-9BTnyy"
      },
      "source": [
        "Results\n",
        "=======\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "ZPszGYA3Tnyy",
        "outputId": "1c68693a-6980-4676-d8f8-f9d0544a4ed4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cost: 0.103276 | Train accuracy 0.875000 | Test Accuracy : 0.832500\n",
            "Learned weights\n",
            "Layer 0: [ 0.59823989  1.51858458 -0.12696781]\n",
            "Layer 1: [-0.0698444   0.02605476 -0.46625864]\n",
            "Layer 2: [0.44425193 1.46403443 0.32099705]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x300 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "print(\n",
        "    \"Cost: {:3f} | Train accuracy {:3f} | Test Accuracy : {:3f}\".format(\n",
        "        loss, accuracy_train, accuracy_test\n",
        "    )\n",
        ")\n",
        "\n",
        "print(\"Learned weights\")\n",
        "for i in range(num_layers):\n",
        "    print(\"Layer {}: {}\".format(i, params[i]))\n",
        "\n",
        "\n",
        "fig, axes = plt.subplots(1, 3, figsize=(10, 3))\n",
        "plot_data(X_test, initial_predictions, fig, axes[0])\n",
        "plot_data(X_test, predicted_test, fig, axes[1])\n",
        "plot_data(X_test, y_test, fig, axes[2])\n",
        "axes[0].set_title(\"Predictions with random weights\")\n",
        "axes[1].set_title(\"Predictions after training\")\n",
        "axes[2].set_title(\"True test data\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sYQWz6IdTnyy"
      },
      "source": [
        "References\n",
        "==========\n",
        "\n",
        "\\[1\\] Pérez-Salinas, Adrián, et al. \\\"Data re-uploading for a universal\n",
        "quantum classifier.\\\" arXiv preprint arXiv:1907.02085 (2019).\n",
        "\n",
        "\\[2\\] Kingma, Diederik P., and Ba, J. \\\"Adam: A method for stochastic\n",
        "optimization.\\\" arXiv preprint arXiv:1412.6980 (2014).\n",
        "\n",
        "\\[3\\] Liu, Dong C., and Nocedal, J. \\\"On the limited memory BFGS method\n",
        "for large scale optimization.\\\" Mathematical programming 45.1-3 (1989):\n",
        "503-528.\n",
        "\n",
        "About the author\n",
        "================\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "seconds = time.time()\n",
        "print(\"Time in seconds since end of run:\", seconds)\n",
        "local_time = time.ctime(seconds)\n",
        "print(local_time)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "8FGVwF_QUYza",
        "outputId": "bec76602-8c0c-4919-e9de-7012d3be9ff5"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1696868618.901336\n",
            "Mon Oct  9 16:23:38 2023\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.9.18"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}