[404218]: / Code / PennyLane / Data-Reuploading / Other Algorithms, Tests / RRH 0.48 LR 89.5% kkawchak.ipynb

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{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 20,
      "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": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "Ww0nBkeU49ei",
        "outputId": "b30e762a-e63f-4391-f900-eb1cf5c2e0cc"
      },
      "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": 22,
      "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": 23,
      "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": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "BhO9C04z49ej",
        "outputId": "9c923c39-1936-46be-806a-e12cc849fd7d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:  0 | Cost: 0.384727 | Train accuracy: 0.535000 | Test Accuracy: 0.490000\n",
            "Epoch:  1 | Loss: 0.242799 | Train accuracy: 0.630000 | Test accuracy: 0.635000\n",
            "Epoch:  2 | Loss: 0.320166 | Train accuracy: 0.580000 | Test accuracy: 0.604000\n",
            "Epoch:  3 | Loss: 0.253946 | Train accuracy: 0.665000 | Test accuracy: 0.654500\n",
            "Epoch:  4 | Loss: 0.205550 | Train accuracy: 0.775000 | Test accuracy: 0.769000\n",
            "Epoch:  5 | Loss: 0.215247 | Train accuracy: 0.645000 | Test accuracy: 0.640500\n",
            "Epoch:  6 | Loss: 0.187378 | Train accuracy: 0.760000 | Test accuracy: 0.769000\n",
            "Epoch:  7 | Loss: 0.186278 | Train accuracy: 0.815000 | Test accuracy: 0.756500\n",
            "Epoch:  8 | Loss: 0.192074 | Train accuracy: 0.815000 | Test accuracy: 0.785000\n",
            "Epoch:  9 | Loss: 0.166373 | Train accuracy: 0.810000 | Test accuracy: 0.803000\n",
            "Epoch: 10 | Loss: 0.129636 | Train accuracy: 0.900000 | Test accuracy: 0.894500\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": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "id": "6H1HK2VO49ek",
        "outputId": "cbb374de-a648-4971-ae8c-7cb97f4a113f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cost: 0.129636 | Train accuracy 0.900000 | Test Accuracy : 0.894500\n",
            "Learned weights\n",
            "Layer 0: [1.5361857  1.45295031 1.47134452]\n",
            "Layer 1: [-1.17944916  1.25209172 -0.29379921]\n",
            "Layer 2: [1.11685549 0.97722195 1.29408934]\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": {
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        "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"
      ]
    }
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