[404218]: / Code / PennyLane / Data-Reuploading / Other Algorithms, Tests / HRR 0.60 LR 85.6% kkawchak.ipynb

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{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 2,
      "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": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "Ww0nBkeU49ei",
        "outputId": "7229a94a-8de5-469e-c633-dd097a0ac323"
      },
      "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": 4,
      "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.Hadamard(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": 5,
      "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": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "BhO9C04z49ej",
        "outputId": "3bc8c72f-2c70-49b0-e6c8-a171f227c99c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:  0 | Cost: 0.391405 | Train accuracy: 0.555000 | Test Accuracy: 0.477000\n",
            "Epoch:  1 | Loss: 0.218178 | Train accuracy: 0.710000 | Test accuracy: 0.663000\n",
            "Epoch:  2 | Loss: 0.189390 | Train accuracy: 0.670000 | Test accuracy: 0.660000\n",
            "Epoch:  3 | Loss: 0.162788 | Train accuracy: 0.775000 | Test accuracy: 0.755000\n",
            "Epoch:  4 | Loss: 0.206824 | Train accuracy: 0.695000 | Test accuracy: 0.668500\n",
            "Epoch:  5 | Loss: 0.157674 | Train accuracy: 0.820000 | Test accuracy: 0.801500\n",
            "Epoch:  6 | Loss: 0.137334 | Train accuracy: 0.825000 | Test accuracy: 0.832500\n",
            "Epoch:  7 | Loss: 0.137349 | Train accuracy: 0.820000 | Test accuracy: 0.821500\n",
            "Epoch:  8 | Loss: 0.134270 | Train accuracy: 0.835000 | Test accuracy: 0.828000\n",
            "Epoch:  9 | Loss: 0.105956 | Train accuracy: 0.875000 | Test accuracy: 0.840500\n",
            "Epoch: 10 | Loss: 0.110670 | Train accuracy: 0.850000 | Test accuracy: 0.856000\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 = 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": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "id": "6H1HK2VO49ek",
        "outputId": "5c3d1b04-bc18-4099-cba7-69d09d5f9323"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cost: 0.110670 | Train accuracy 0.850000 | Test Accuracy : 0.856000\n",
            "Learned weights\n",
            "Layer 0: [ 0.40587279 -1.33333507  2.42724691]\n",
            "Layer 1: [-2.51684213  1.48093547  1.41038809]\n",
            "Layer 2: [-0.79756748  2.36487086  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
}