[404218]: / Code / PennyLane / Data-Reuploading / Learning Rate Studies / 0.54 LR 88.7% kkawchak.ipynb

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{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 110,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "eI_d8C3_Tnyu",
        "outputId": "1613ffe7-bfd5-4f13-a2f0-3323fb37b98f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1696829456.046393\n",
            "Mon Oct  9 05:30:56 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": 111,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "lScQSFsjTnyw",
        "outputId": "4d2dc1fd-2f42-49cd-8f9b-5e4f9a3c5842"
      },
      "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": 112,
      "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": 113,
      "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": 114,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "NqJGMjbpTnyy",
        "outputId": "c28cce4f-d4f9-447f-db6d-796547e83eea"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:  0 | Cost: 0.415535 | Train accuracy: 0.460000 | Test Accuracy: 0.448500\n",
            "Epoch:  1 | Loss: 0.126372 | Train accuracy: 0.835000 | Test accuracy: 0.793000\n",
            "Epoch:  2 | Loss: 0.139818 | Train accuracy: 0.815000 | Test accuracy: 0.791500\n",
            "Epoch:  3 | Loss: 0.131110 | Train accuracy: 0.835000 | Test accuracy: 0.814500\n",
            "Epoch:  4 | Loss: 0.121721 | Train accuracy: 0.850000 | Test accuracy: 0.826500\n",
            "Epoch:  5 | Loss: 0.131981 | Train accuracy: 0.815000 | Test accuracy: 0.799000\n",
            "Epoch:  6 | Loss: 0.116053 | Train accuracy: 0.845000 | Test accuracy: 0.818500\n",
            "Epoch:  7 | Loss: 0.106008 | Train accuracy: 0.855000 | Test accuracy: 0.833000\n",
            "Epoch:  8 | Loss: 0.108148 | Train accuracy: 0.875000 | Test accuracy: 0.841000\n",
            "Epoch:  9 | Loss: 0.131097 | Train accuracy: 0.800000 | Test accuracy: 0.796500\n",
            "Epoch: 10 | Loss: 0.101004 | Train accuracy: 0.920000 | Test accuracy: 0.886500\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 = 0.54\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": 115,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "ZPszGYA3Tnyy",
        "outputId": "0821981a-d7cf-4167-e2ab-953aab50d9e8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cost: 0.101004 | Train accuracy 0.920000 | Test Accuracy : 0.886500\n",
            "Learned weights\n",
            "Layer 0: [-0.78852977  1.51739633 -0.09204805]\n",
            "Layer 1: [0.88712502 0.39062508 0.19223129]\n",
            "Layer 2: [ 2.24179815 -1.31961562  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": "6cee328e-13df-4d0f-adfe-77dc0874d067"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1696829731.5578945\n",
            "Mon Oct  9 05:35:31 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
}