[404218]: / Code / PennyLane / Data-Reuploading / Layer Studies / 08 Layer 63.6% kkawchak.ipynb

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{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "eI_d8C3_Tnyu",
        "outputId": "5d5c0893-52db-4ad9-d5c2-365516489534"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1696824372.2357798\n",
            "Mon Oct  9 04:06:12 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": 76,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "lScQSFsjTnyw",
        "outputId": "708a3d32-4d96-4fee-d901-030c0b1992d7"
      },
      "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": 77,
      "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": 78,
      "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": 79,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "NqJGMjbpTnyy",
        "outputId": "b0851d24-55d1-448a-d509-d83ffc1f4e03"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:  0 | Cost: 0.450294 | Train accuracy: 0.380000 | Test Accuracy: 0.335500\n",
            "Epoch:  1 | Loss: 0.352055 | Train accuracy: 0.540000 | Test accuracy: 0.487500\n",
            "Epoch:  2 | Loss: 0.291475 | Train accuracy: 0.555000 | Test accuracy: 0.504500\n",
            "Epoch:  3 | Loss: 0.209983 | Train accuracy: 0.690000 | Test accuracy: 0.649000\n",
            "Epoch:  4 | Loss: 0.162946 | Train accuracy: 0.760000 | Test accuracy: 0.741500\n",
            "Epoch:  5 | Loss: 0.204522 | Train accuracy: 0.650000 | Test accuracy: 0.638500\n",
            "Epoch:  6 | Loss: 0.213012 | Train accuracy: 0.660000 | Test accuracy: 0.617000\n",
            "Epoch:  7 | Loss: 0.222011 | Train accuracy: 0.655000 | Test accuracy: 0.602500\n",
            "Epoch:  8 | Loss: 0.241122 | Train accuracy: 0.680000 | Test accuracy: 0.622000\n",
            "Epoch:  9 | Loss: 0.191822 | Train accuracy: 0.745000 | Test accuracy: 0.721000\n",
            "Epoch: 10 | Loss: 0.213404 | Train accuracy: 0.705000 | Test accuracy: 0.635500\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 = 8\n",
        "learning_rate = 0.6\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": 80,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        },
        "id": "ZPszGYA3Tnyy",
        "outputId": "0b75b56d-7669-4bd2-cdb8-0a98717b2047"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cost: 0.213404 | Train accuracy 0.705000 | Test Accuracy : 0.635500\n",
            "Learned weights\n",
            "Layer 0: [ 5.41290474 -1.21173189  5.48651496]\n",
            "Layer 1: [4.19478227 0.67712303 5.97694403]\n",
            "Layer 2: [4.39158824 3.46820068 0.92025064]\n",
            "Layer 3: [2.61251588 1.601932   4.85791617]\n",
            "Layer 4: [ 4.17555103  2.25469448 -0.60850597]\n",
            "Layer 5: [-0.87834347  1.76209905  2.79763758]\n",
            "Layer 6: [5.15424869 3.52325348 2.26947794]\n",
            "Layer 7: [ 3.20857326 -0.54662349  0.41111342]\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": "8aca6e1c-86e1-473c-b2de-78993cc81597"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1696825019.77567\n",
            "Mon Oct  9 04:16:59 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
}