[404218]: / Code / PennyLane / Data-Reuploading / Other Algorithms, Tests / 0.48 LR 34 Batch 89.3% kkawchak.ipynb

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{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "eI_d8C3_Tnyu",
        "outputId": "65d49af0-9a6a-4a27-c575-bd0a59968ec4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1696928696.755623\n",
            "Tue Oct 10 09:04:56 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": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "lScQSFsjTnyw",
        "outputId": "2a7b5bcf-9c09-49d2-faf7-9c56806bc480"
      },
      "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": 25,
      "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": 26,
      "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": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "NqJGMjbpTnyy",
        "outputId": "3c79f586-9eef-47af-ce1a-211e7211b68e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:  0 | Cost: 0.415535 | Train accuracy: 0.460000 | Test Accuracy: 0.448500\n",
            "Epoch:  1 | Loss: 0.142398 | Train accuracy: 0.775000 | Test accuracy: 0.762000\n",
            "Epoch:  2 | Loss: 0.121466 | Train accuracy: 0.860000 | Test accuracy: 0.829000\n",
            "Epoch:  3 | Loss: 0.143984 | Train accuracy: 0.775000 | Test accuracy: 0.742000\n",
            "Epoch:  4 | Loss: 0.107346 | Train accuracy: 0.895000 | Test accuracy: 0.847000\n",
            "Epoch:  5 | Loss: 0.106775 | Train accuracy: 0.855000 | Test accuracy: 0.809000\n",
            "Epoch:  6 | Loss: 0.114565 | Train accuracy: 0.840000 | Test accuracy: 0.797500\n",
            "Epoch:  7 | Loss: 0.097608 | Train accuracy: 0.940000 | Test accuracy: 0.890000\n",
            "Epoch:  8 | Loss: 0.104373 | Train accuracy: 0.870000 | Test accuracy: 0.824500\n",
            "Epoch:  9 | Loss: 0.104234 | Train accuracy: 0.880000 | Test accuracy: 0.832500\n",
            "Epoch: 10 | Loss: 0.094841 | Train accuracy: 0.920000 | Test accuracy: 0.892500\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 = 0.48\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": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "id": "ZPszGYA3Tnyy",
        "outputId": "a340fed4-bf19-4e0f-ba63-353b9b022e0d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cost: 0.094841 | Train accuracy 0.920000 | Test Accuracy : 0.892500\n",
            "Learned weights\n",
            "Layer 0: [ 1.95172891e-01  1.43232967e+00 -3.37893814e-04]\n",
            "Layer 1: [-0.75873129  0.13327311 -0.44278003]\n",
            "Layer 2: [1.100772   1.51254451 0.32099704]\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": "fa719995-868d-4bcd-cdbb-de180a96447f"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1696928942.6030915\n",
            "Tue Oct 10 09:09:02 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
}