[404218]: / Code / PennyLane / Data-Reuploading / Layer Studies / 02 Layer 78.5% kkawchak.ipynb

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{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "eI_d8C3_Tnyu",
        "outputId": "6827d724-e2e4-425a-b849-ad79ed6ef578"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1696821204.9989774\n",
            "Mon Oct  9 03:13:24 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": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "lScQSFsjTnyw",
        "outputId": "79f57db9-0071-4080-ad79-cf0e33447015"
      },
      "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": 35,
      "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": 36,
      "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": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "NqJGMjbpTnyy",
        "outputId": "84128799-be9b-4aba-a23f-954517044ef3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:  0 | Cost: 0.376549 | Train accuracy: 0.525000 | Test Accuracy: 0.481000\n",
            "Epoch:  1 | Loss: 0.221619 | Train accuracy: 0.660000 | Test accuracy: 0.618000\n",
            "Epoch:  2 | Loss: 0.206107 | Train accuracy: 0.685000 | Test accuracy: 0.720000\n",
            "Epoch:  3 | Loss: 0.178695 | Train accuracy: 0.765000 | Test accuracy: 0.743000\n",
            "Epoch:  4 | Loss: 0.178946 | Train accuracy: 0.725000 | Test accuracy: 0.771000\n",
            "Epoch:  5 | Loss: 0.176149 | Train accuracy: 0.720000 | Test accuracy: 0.752000\n",
            "Epoch:  6 | Loss: 0.172263 | Train accuracy: 0.780000 | Test accuracy: 0.782500\n",
            "Epoch:  7 | Loss: 0.179615 | Train accuracy: 0.730000 | Test accuracy: 0.769000\n",
            "Epoch:  8 | Loss: 0.171656 | Train accuracy: 0.765000 | Test accuracy: 0.763000\n",
            "Epoch:  9 | Loss: 0.172822 | Train accuracy: 0.755000 | Test accuracy: 0.778500\n",
            "Epoch: 10 | Loss: 0.174416 | Train accuracy: 0.740000 | Test accuracy: 0.785000\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 = 2\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": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "id": "ZPszGYA3Tnyy",
        "outputId": "f409f7c3-e18f-4033-d9be-1443b10129e3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cost: 0.174416 | Train accuracy 0.740000 | Test Accuracy : 0.785000\n",
            "Learned weights\n",
            "Layer 0: [-0.03564119  1.56184054  0.32804436]\n",
            "Layer 1: [-0.0345987   1.54682381  0.89555574]\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": "8cb3c999-8734-45dc-af19-315f2abd8ce9"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1696821430.313932\n",
            "Mon Oct  9 03:17:10 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
}