[404218]: / Code / PennyLane / Data-Reuploading / Other Algorithms, Tests / RRH 0.60 LR 86.4% kkawchak.ipynb

Download this file

474 lines (474 with data), 192.4 kB

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 14,
      "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": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "Ww0nBkeU49ei",
        "outputId": "0b24b9e3-22ed-4a14-c21b-f0dde42d984f"
      },
      "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": 16,
      "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.Rot(*x, wires=0)\n",
        "        qml.Rot(*p, wires=0)\n",
        "        qml.Hadamard(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": 17,
      "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": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "BhO9C04z49ej",
        "outputId": "3e38cb24-7569-41a5-c8cf-72a9340410c2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:  0 | Cost: 0.384727 | Train accuracy: 0.535000 | Test Accuracy: 0.490000\n",
            "Epoch:  1 | Loss: 0.217921 | Train accuracy: 0.665000 | Test accuracy: 0.688500\n",
            "Epoch:  2 | Loss: 0.214936 | Train accuracy: 0.695000 | Test accuracy: 0.736500\n",
            "Epoch:  3 | Loss: 0.137534 | Train accuracy: 0.785000 | Test accuracy: 0.754000\n",
            "Epoch:  4 | Loss: 0.120692 | Train accuracy: 0.865000 | Test accuracy: 0.844000\n",
            "Epoch:  5 | Loss: 0.126682 | Train accuracy: 0.820000 | Test accuracy: 0.790000\n",
            "Epoch:  6 | Loss: 0.112104 | Train accuracy: 0.850000 | Test accuracy: 0.805000\n",
            "Epoch:  7 | Loss: 0.108072 | Train accuracy: 0.900000 | Test accuracy: 0.846000\n",
            "Epoch:  8 | Loss: 0.104640 | Train accuracy: 0.890000 | Test accuracy: 0.829500\n",
            "Epoch:  9 | Loss: 0.109903 | Train accuracy: 0.860000 | Test accuracy: 0.808000\n",
            "Epoch: 10 | Loss: 0.105761 | Train accuracy: 0.910000 | Test accuracy: 0.863500\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": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "id": "6H1HK2VO49ek",
        "outputId": "87272acf-d774-40d2-d26c-634e8682158e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cost: 0.105761 | Train accuracy 0.910000 | Test Accuracy : 0.863500\n",
            "Learned weights\n",
            "Layer 0: [-0.26670294  3.05145028  3.80978705]\n",
            "Layer 1: [-2.66634405  1.72548596 -0.24553465]\n",
            "Layer 2: [ 5.8677201   0.05041564 -0.1971239 ]\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
}