[404218]: / Code / PennyLane / Data-Reuploading / Other Algorithms, Tests / 0.42 LR 81.7% kkawchak.ipynb

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{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 152,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "eI_d8C3_Tnyu",
        "outputId": "2cd9b475-a8a3-4423-989f-5bb5dec7c43f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1696831529.2833145\n",
            "Mon Oct  9 06:05:29 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": 153,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "lScQSFsjTnyw",
        "outputId": "ea3b4ff1-7e09-4c2d-eb9c-d27de880323c"
      },
      "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": 154,
      "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": 155,
      "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": 156,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "NqJGMjbpTnyy",
        "outputId": "2258950b-646d-497a-822b-00ce9bbf3914"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:  0 | Cost: 0.415535 | Train accuracy: 0.460000 | Test Accuracy: 0.448500\n",
            "Epoch:  1 | Loss: 0.180229 | Train accuracy: 0.710000 | Test accuracy: 0.680500\n",
            "Epoch:  2 | Loss: 0.143932 | Train accuracy: 0.790000 | Test accuracy: 0.774500\n",
            "Epoch:  3 | Loss: 0.119152 | Train accuracy: 0.805000 | Test accuracy: 0.805500\n",
            "Epoch:  4 | Loss: 0.116730 | Train accuracy: 0.840000 | Test accuracy: 0.816500\n",
            "Epoch:  5 | Loss: 0.111951 | Train accuracy: 0.855000 | Test accuracy: 0.798000\n",
            "Epoch:  6 | Loss: 0.110568 | Train accuracy: 0.860000 | Test accuracy: 0.807000\n",
            "Epoch:  7 | Loss: 0.107499 | Train accuracy: 0.870000 | Test accuracy: 0.820000\n",
            "Epoch:  8 | Loss: 0.106345 | Train accuracy: 0.870000 | Test accuracy: 0.824000\n",
            "Epoch:  9 | Loss: 0.106285 | Train accuracy: 0.870000 | Test accuracy: 0.816500\n",
            "Epoch: 10 | Loss: 0.105222 | Train accuracy: 0.870000 | Test accuracy: 0.816500\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 = 0.42\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": "YWspC-9BTnyy"
      },
      "source": [
        "Results\n",
        "=======\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 157,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "ZPszGYA3Tnyy",
        "outputId": "f2213aba-5943-4e9d-c5be-958ac5ab4c1d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cost: 0.105222 | Train accuracy 0.870000 | Test Accuracy : 0.816500\n",
            "Learned weights\n",
            "Layer 0: [ 0.39657963  1.56008371 -0.37587406]\n",
            "Layer 1: [-0.07256014 -0.02923613 -0.17449673]\n",
            "Layer 2: [ 3.51734329 -1.45439938  0.32099704]\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": "26931d55-7694-4529-d7fd-62e1fb6ab5ff"
      },
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1696831804.1468627\n",
            "Mon Oct  9 06:10:04 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
}