[404218]: / Code / PennyLane / Data-Reuploading / Other Algorithms, Tests / ZRR 0.48 LR 75.6% kkawchak.ipynb

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{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "CmMk-ooC49eg"
      },
      "outputs": [],
      "source": [
        "# This cell is added by sphinx-gallery\n",
        "# It can be customized to whatever you like\n",
        "%matplotlib inline\n",
        "# !pip install pennylane"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "Ww0nBkeU49ei",
        "outputId": "73efd837-b2be-4663-a711-8cdd20b286a8"
      },
      "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": "d0CKpkxZ49ei"
      },
      "source": [
        "Simple classifier with data reloading and fidelity loss\n",
        "=======================================================\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "rrQEEArP49ei"
      },
      "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.PauliZ(wires=0)\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": "um4ODVoh49ej"
      },
      "source": [
        "Utility functions for testing and creating batches\n",
        "==================================================\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "KlDrfUFy49ej"
      },
      "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": "H6qMKHQf49ej"
      },
      "source": [
        "Train a quantum classifier on the circle dataset\n",
        "================================================\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "BhO9C04z49ej",
        "outputId": "cbb804f9-5dfe-4ffa-d70d-7ea3b4214718"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:  0 | Cost: 0.443627 | Train accuracy: 0.530000 | Test Accuracy: 0.474000\n",
            "Epoch:  1 | Loss: 0.199091 | Train accuracy: 0.740000 | Test accuracy: 0.693000\n",
            "Epoch:  2 | Loss: 0.220345 | Train accuracy: 0.650000 | Test accuracy: 0.655000\n",
            "Epoch:  3 | Loss: 0.197159 | Train accuracy: 0.675000 | Test accuracy: 0.638500\n",
            "Epoch:  4 | Loss: 0.177293 | Train accuracy: 0.750000 | Test accuracy: 0.701000\n",
            "Epoch:  5 | Loss: 0.181487 | Train accuracy: 0.680000 | Test accuracy: 0.696000\n",
            "Epoch:  6 | Loss: 0.166245 | Train accuracy: 0.845000 | Test accuracy: 0.784000\n",
            "Epoch:  7 | Loss: 0.169436 | Train accuracy: 0.795000 | Test accuracy: 0.761500\n",
            "Epoch:  8 | Loss: 0.170903 | Train accuracy: 0.820000 | Test accuracy: 0.763000\n",
            "Epoch:  9 | Loss: 0.166901 | Train accuracy: 0.855000 | Test accuracy: 0.790000\n",
            "Epoch: 10 | Loss: 0.166927 | Train accuracy: 0.780000 | Test accuracy: 0.756000\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 = 0.48\n",
        "epochs = 10\n",
        "batch_size = 32\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": "Wg8Xwodg49ej"
      },
      "source": [
        "Results\n",
        "=======\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "id": "6H1HK2VO49ek",
        "outputId": "558873d9-ab5f-483a-9d48-7ab57657c3ac"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cost: 0.166927 | Train accuracy 0.780000 | Test Accuracy : 0.756000\n",
            "Learned weights\n",
            "Layer 0: [-1.50015759  1.44313985  1.54258179]\n",
            "Layer 1: [-0.33701848  0.08228545  0.16321289]\n",
            "Layer 2: [ 1.62863103 -0.72007558  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": "o18WuBXP49ek"
      },
      "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"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.18"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}