[404218]: / Code / PennyLane / Data-Reuploading / Layer Studies / 01 Layer 61.3% kkawchak.ipynb

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{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "eI_d8C3_Tnyu",
        "outputId": "0080357e-de3e-49e6-8c2f-2d1ae4da6cc7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1696821000.9822428\n",
            "Mon Oct  9 03:10:00 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": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "lScQSFsjTnyw",
        "outputId": "ae74651d-81ce-4db6-f0e3-ab62818c4991"
      },
      "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": 28,
      "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": 29,
      "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": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "NqJGMjbpTnyy",
        "outputId": "99377848-569e-4d27-e591-cd65899871e5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:  0 | Cost: 0.376715 | Train accuracy: 0.420000 | Test Accuracy: 0.371500\n",
            "Epoch:  1 | Loss: 0.247709 | Train accuracy: 0.450000 | Test accuracy: 0.385000\n",
            "Epoch:  2 | Loss: 0.252594 | Train accuracy: 0.635000 | Test accuracy: 0.603000\n",
            "Epoch:  3 | Loss: 0.250708 | Train accuracy: 0.465000 | Test accuracy: 0.510000\n",
            "Epoch:  4 | Loss: 0.247218 | Train accuracy: 0.575000 | Test accuracy: 0.517500\n",
            "Epoch:  5 | Loss: 0.247761 | Train accuracy: 0.605000 | Test accuracy: 0.640000\n",
            "Epoch:  6 | Loss: 0.245120 | Train accuracy: 0.620000 | Test accuracy: 0.528000\n",
            "Epoch:  7 | Loss: 0.245497 | Train accuracy: 0.645000 | Test accuracy: 0.584500\n",
            "Epoch:  8 | Loss: 0.245687 | Train accuracy: 0.635000 | Test accuracy: 0.604000\n",
            "Epoch:  9 | Loss: 0.245128 | Train accuracy: 0.630000 | Test accuracy: 0.552000\n",
            "Epoch: 10 | Loss: 0.245830 | Train accuracy: 0.630000 | Test accuracy: 0.612500\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 = 1\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": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "ZPszGYA3Tnyy",
        "outputId": "b1100713-cc34-4c8b-edae-93fbe6f0aabd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cost: 0.245830 | Train accuracy 0.630000 | Test Accuracy : 0.612500\n",
            "Learned weights\n",
            "Layer 0: [1.36334641 1.65258301 0.95813812]\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": "2bcd41c3-13be-434e-fa6e-82f91a34f5c4"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1696821168.5551014\n",
            "Mon Oct  9 03:12:48 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
}