[404218]: / Code / PennyLane / Data-Reuploading / Other Algorithms, Tests / 0.40 LR 82.6% kkawchak.ipynb

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{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 159,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "eI_d8C3_Tnyu",
        "outputId": "217b81f3-bda1-4b28-80e5-a2504f901a09"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1696831982.5500782\n",
            "Mon Oct  9 06:13:02 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": 160,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "lScQSFsjTnyw",
        "outputId": "b8c6e746-44f7-48d1-e684-32f3f6f01d42"
      },
      "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": 161,
      "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": 162,
      "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": 163,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "NqJGMjbpTnyy",
        "outputId": "61cfad1f-5625-4f93-df67-3181df5ac551"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:  0 | Cost: 0.415535 | Train accuracy: 0.460000 | Test Accuracy: 0.448500\n",
            "Epoch:  1 | Loss: 0.183293 | Train accuracy: 0.715000 | Test accuracy: 0.692500\n",
            "Epoch:  2 | Loss: 0.152583 | Train accuracy: 0.785000 | Test accuracy: 0.748500\n",
            "Epoch:  3 | Loss: 0.144801 | Train accuracy: 0.795000 | Test accuracy: 0.772500\n",
            "Epoch:  4 | Loss: 0.129529 | Train accuracy: 0.805000 | Test accuracy: 0.794500\n",
            "Epoch:  5 | Loss: 0.106536 | Train accuracy: 0.875000 | Test accuracy: 0.834500\n",
            "Epoch:  6 | Loss: 0.116914 | Train accuracy: 0.830000 | Test accuracy: 0.779500\n",
            "Epoch:  7 | Loss: 0.108535 | Train accuracy: 0.850000 | Test accuracy: 0.804000\n",
            "Epoch:  8 | Loss: 0.097126 | Train accuracy: 0.920000 | Test accuracy: 0.885500\n",
            "Epoch:  9 | Loss: 0.098751 | Train accuracy: 0.895000 | Test accuracy: 0.854000\n",
            "Epoch: 10 | Loss: 0.104227 | Train accuracy: 0.875000 | Test accuracy: 0.826000\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 = 0.40\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": 164,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "ZPszGYA3Tnyy",
        "outputId": "87482dc5-d500-403f-90bc-7e420ef1ff3e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cost: 0.104227 | Train accuracy 0.875000 | Test Accuracy : 0.826000\n",
            "Learned weights\n",
            "Layer 0: [ 0.24143542  1.44931068 -0.15201711]\n",
            "Layer 1: [-0.70980709  0.24985373 -0.39043474]\n",
            "Layer 2: [0.93718358 1.415375   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": "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": "bbcc64fc-750b-4aae-c859-e3c1e3d681ab"
      },
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1696832256.8129475\n",
            "Mon Oct  9 06:17:36 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
}