[404218]: / Code / PennyLane / Data-Reuploading / Layer Studies / 05 Layer 76.7% kkawchak.ipynb

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{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "eI_d8C3_Tnyu",
        "outputId": "e5224ca6-1706-44cd-a5fb-fd655c7167fe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1696822249.1742103\n",
            "Mon Oct  9 03:30:49 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": 55,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "lScQSFsjTnyw",
        "outputId": "e50103ee-151d-456b-92b2-cfeeddc7ca2d"
      },
      "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": 56,
      "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": 57,
      "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": 58,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "NqJGMjbpTnyy",
        "outputId": "3589ff99-ac8f-4270-e688-54a3ec13c90d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:  0 | Cost: 0.443191 | Train accuracy: 0.435000 | Test Accuracy: 0.412500\n",
            "Epoch:  1 | Loss: 0.209339 | Train accuracy: 0.650000 | Test accuracy: 0.646500\n",
            "Epoch:  2 | Loss: 0.248196 | Train accuracy: 0.620000 | Test accuracy: 0.549000\n",
            "Epoch:  3 | Loss: 0.244301 | Train accuracy: 0.575000 | Test accuracy: 0.589000\n",
            "Epoch:  4 | Loss: 0.205516 | Train accuracy: 0.710000 | Test accuracy: 0.667500\n",
            "Epoch:  5 | Loss: 0.253359 | Train accuracy: 0.615000 | Test accuracy: 0.638000\n",
            "Epoch:  6 | Loss: 0.192446 | Train accuracy: 0.710000 | Test accuracy: 0.680500\n",
            "Epoch:  7 | Loss: 0.233160 | Train accuracy: 0.620000 | Test accuracy: 0.620500\n",
            "Epoch:  8 | Loss: 0.229418 | Train accuracy: 0.670000 | Test accuracy: 0.660000\n",
            "Epoch:  9 | Loss: 0.188793 | Train accuracy: 0.700000 | Test accuracy: 0.681000\n",
            "Epoch: 10 | Loss: 0.154858 | Train accuracy: 0.790000 | Test accuracy: 0.766500\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 = 5\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": 59,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        },
        "id": "ZPszGYA3Tnyy",
        "outputId": "53862975-29f7-4741-f4cc-8951aa3c60ee"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cost: 0.154858 | Train accuracy 0.790000 | Test Accuracy : 0.766500\n",
            "Learned weights\n",
            "Layer 0: [-0.15989916  2.80651828  0.60884076]\n",
            "Layer 1: [-0.32680666  4.91518087 -0.18769105]\n",
            "Layer 2: [2.97730376 3.69496942 2.77223277]\n",
            "Layer 3: [-0.20585708 -1.48864649  3.16986278]\n",
            "Layer 4: [-0.52211447 -0.61843811  0.04929449]\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": "09034450-a8fc-4bf0-e001-14700c661781"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1696822649.239251\n",
            "Mon Oct  9 03:37:29 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
}