[404218]: / Code / PennyLane / Data-Reuploading / Batch Studies / 27 Batch 75.4% kkawchak.ipynb

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{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "eI_d8C3_Tnyu",
        "outputId": "f3ea1a73-702e-476f-8bc6-ca7cc16d09e7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1696871584.472043\n",
            "Mon Oct  9 17:13:04 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": 71,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "lScQSFsjTnyw",
        "outputId": "3c5f1413-a33a-4b07-a4e3-24aa3a3a4b98"
      },
      "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": 72,
      "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": 73,
      "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": 74,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "NqJGMjbpTnyy",
        "outputId": "a0222c87-1362-421c-f368-ebb47a29baef"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:  0 | Cost: 0.415535 | Train accuracy: 0.460000 | Test Accuracy: 0.448500\n",
            "Epoch:  1 | Loss: 0.155066 | Train accuracy: 0.795000 | Test accuracy: 0.736000\n",
            "Epoch:  2 | Loss: 0.176461 | Train accuracy: 0.770000 | Test accuracy: 0.759500\n",
            "Epoch:  3 | Loss: 0.189171 | Train accuracy: 0.675000 | Test accuracy: 0.730500\n",
            "Epoch:  4 | Loss: 0.131952 | Train accuracy: 0.815000 | Test accuracy: 0.781500\n",
            "Epoch:  5 | Loss: 0.149434 | Train accuracy: 0.800000 | Test accuracy: 0.780500\n",
            "Epoch:  6 | Loss: 0.124781 | Train accuracy: 0.820000 | Test accuracy: 0.786000\n",
            "Epoch:  7 | Loss: 0.109715 | Train accuracy: 0.875000 | Test accuracy: 0.830500\n",
            "Epoch:  8 | Loss: 0.112461 | Train accuracy: 0.875000 | Test accuracy: 0.824500\n",
            "Epoch:  9 | Loss: 0.146222 | Train accuracy: 0.755000 | Test accuracy: 0.755500\n",
            "Epoch: 10 | Loss: 0.143856 | Train accuracy: 0.760000 | Test accuracy: 0.753500\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 = 27\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": 75,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "ZPszGYA3Tnyy",
        "outputId": "8f8278f2-2620-45cb-9197-3e0dd35e81b2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cost: 0.143856 | Train accuracy 0.760000 | Test Accuracy : 0.753500\n",
            "Learned weights\n",
            "Layer 0: [-0.16464097  1.27961446 -0.12891223]\n",
            "Layer 1: [0.29910872 0.62799222 0.27975764]\n",
            "Layer 2: [-1.15124364  1.19721129  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": [
        "\n",
        "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": "c7e79f88-4620-46af-9e1e-183a2859be18"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1696871862.9797943\n",
            "Mon Oct  9 17:17:42 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
}