[404218]: / Code / PennyLane / Data-Reuploading / Batch Studies / 34 Batch 87.4% kkawchak.ipynb

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{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "eI_d8C3_Tnyu",
        "outputId": "1be67684-6ae5-4a0d-b88a-ce0494185681"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1696867992.7199202\n",
            "Mon Oct  9 16:13:12 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": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "lScQSFsjTnyw",
        "outputId": "11f8640e-a281-456e-8a51-df5213896fce"
      },
      "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": 11,
      "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": 12,
      "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": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "NqJGMjbpTnyy",
        "outputId": "d6433610-d817-4ee1-8dfd-af4668eefdc1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:  0 | Cost: 0.415535 | Train accuracy: 0.460000 | Test Accuracy: 0.448500\n",
            "Epoch:  1 | Loss: 0.149292 | Train accuracy: 0.790000 | Test accuracy: 0.778500\n",
            "Epoch:  2 | Loss: 0.168993 | Train accuracy: 0.740000 | Test accuracy: 0.745000\n",
            "Epoch:  3 | Loss: 0.117200 | Train accuracy: 0.835000 | Test accuracy: 0.814000\n",
            "Epoch:  4 | Loss: 0.105575 | Train accuracy: 0.915000 | Test accuracy: 0.842500\n",
            "Epoch:  5 | Loss: 0.117809 | Train accuracy: 0.825000 | Test accuracy: 0.780500\n",
            "Epoch:  6 | Loss: 0.118255 | Train accuracy: 0.830000 | Test accuracy: 0.789500\n",
            "Epoch:  7 | Loss: 0.097511 | Train accuracy: 0.915000 | Test accuracy: 0.861500\n",
            "Epoch:  8 | Loss: 0.102897 | Train accuracy: 0.875000 | Test accuracy: 0.834500\n",
            "Epoch:  9 | Loss: 0.118172 | Train accuracy: 0.820000 | Test accuracy: 0.801500\n",
            "Epoch: 10 | Loss: 0.098317 | Train accuracy: 0.900000 | Test accuracy: 0.873500\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 = 0.6\n",
        "epochs = 10\n",
        "batch_size = 34\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": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "ZPszGYA3Tnyy",
        "outputId": "3f0953fe-63be-4be0-83b4-f2cae2f041ac"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cost: 0.098317 | Train accuracy 0.900000 | Test Accuracy : 0.873500\n",
            "Learned weights\n",
            "Layer 0: [0.01674116 1.50302379 0.00762977]\n",
            "Layer 1: [-0.61612792  0.02581965 -0.74459266]\n",
            "Layer 2: [1.12971102 1.54082181 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": "6538f6ee-8115-48fb-c702-61cb438f4169"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1696868253.7240133\n",
            "Mon Oct  9 16:17:33 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
}