[404218]: / Code / PennyLane / Data-Reuploading / Learning Rate Studies / 0.44 LR 81.6% kkawchak.ipynb

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{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 145,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "eI_d8C3_Tnyu",
        "outputId": "5fb969c9-7def-44b5-c2ce-e112136f1c90"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1696831190.991285\n",
            "Mon Oct  9 05:59:50 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": 146,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "lScQSFsjTnyw",
        "outputId": "87bfa741-1c76-4a39-ead7-5a875ead1832"
      },
      "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": 147,
      "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": 148,
      "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": 149,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "NqJGMjbpTnyy",
        "outputId": "19c1c920-c5b0-4950-dc34-48ac7e932526"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:  0 | Cost: 0.415535 | Train accuracy: 0.460000 | Test Accuracy: 0.448500\n",
            "Epoch:  1 | Loss: 0.157608 | Train accuracy: 0.810000 | Test accuracy: 0.736000\n",
            "Epoch:  2 | Loss: 0.154661 | Train accuracy: 0.790000 | Test accuracy: 0.803500\n",
            "Epoch:  3 | Loss: 0.127102 | Train accuracy: 0.820000 | Test accuracy: 0.769000\n",
            "Epoch:  4 | Loss: 0.107507 | Train accuracy: 0.900000 | Test accuracy: 0.843500\n",
            "Epoch:  5 | Loss: 0.107565 | Train accuracy: 0.890000 | Test accuracy: 0.835000\n",
            "Epoch:  6 | Loss: 0.117160 | Train accuracy: 0.840000 | Test accuracy: 0.780000\n",
            "Epoch:  7 | Loss: 0.106998 | Train accuracy: 0.880000 | Test accuracy: 0.827000\n",
            "Epoch:  8 | Loss: 0.103275 | Train accuracy: 0.880000 | Test accuracy: 0.836500\n",
            "Epoch:  9 | Loss: 0.108314 | Train accuracy: 0.855000 | Test accuracy: 0.812000\n",
            "Epoch: 10 | Loss: 0.109361 | Train accuracy: 0.870000 | Test accuracy: 0.815500\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 = 0.44\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": 150,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "ZPszGYA3Tnyy",
        "outputId": "9aefee1b-43c2-4709-bc52-59be4acf74e4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cost: 0.109361 | Train accuracy 0.870000 | Test Accuracy : 0.815500\n",
            "Learned weights\n",
            "Layer 0: [ 0.1741135   1.57814549 -0.68842265]\n",
            "Layer 1: [ 0.63080943  0.01321707 -0.44093919]\n",
            "Layer 2: [ 3.3564704  -1.46604734  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": "84ece870-831d-4c18-b441-9cc1a3f0153e"
      },
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1696831462.828273\n",
            "Mon Oct  9 06:04:22 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
}