[404218]: / Code / PennyLane / Data-Reuploading / Layer Studies / 04 Layer 77.5% kkawchak.ipynb

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{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "eI_d8C3_Tnyu",
        "outputId": "77a03271-c8e8-49cb-839c-d31884e15851"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1696821843.1456008\n",
            "Mon Oct  9 03:24:03 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": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "lScQSFsjTnyw",
        "outputId": "e79c78f8-85f2-413d-8373-2578bdf52b6e"
      },
      "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": 49,
      "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": 50,
      "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": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "NqJGMjbpTnyy",
        "outputId": "b70bf41b-8842-424b-8085-59b77a27001a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:  0 | Cost: 0.438434 | Train accuracy: 0.390000 | Test Accuracy: 0.367000\n",
            "Epoch:  1 | Loss: 0.214835 | Train accuracy: 0.645000 | Test accuracy: 0.611000\n",
            "Epoch:  2 | Loss: 0.223727 | Train accuracy: 0.640000 | Test accuracy: 0.650000\n",
            "Epoch:  3 | Loss: 0.203908 | Train accuracy: 0.740000 | Test accuracy: 0.691000\n",
            "Epoch:  4 | Loss: 0.177586 | Train accuracy: 0.760000 | Test accuracy: 0.702000\n",
            "Epoch:  5 | Loss: 0.153435 | Train accuracy: 0.765000 | Test accuracy: 0.739000\n",
            "Epoch:  6 | Loss: 0.192530 | Train accuracy: 0.685000 | Test accuracy: 0.645500\n",
            "Epoch:  7 | Loss: 0.109892 | Train accuracy: 0.915000 | Test accuracy: 0.888000\n",
            "Epoch:  8 | Loss: 0.172102 | Train accuracy: 0.750000 | Test accuracy: 0.757000\n",
            "Epoch:  9 | Loss: 0.144203 | Train accuracy: 0.770000 | Test accuracy: 0.745500\n",
            "Epoch: 10 | Loss: 0.146319 | Train accuracy: 0.770000 | Test accuracy: 0.774500\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 = 4\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": 52,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "ZPszGYA3Tnyy",
        "outputId": "f4fe043b-b223-46e4-8492-b81657937434"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cost: 0.146319 | Train accuracy 0.770000 | Test Accuracy : 0.774500\n",
            "Learned weights\n",
            "Layer 0: [ 1.2869381  -0.7555653  -3.29530366]\n",
            "Layer 1: [ 0.72679512  1.19915059 -0.66281256]\n",
            "Layer 2: [ 3.96947698 -0.8670189   3.29438041]\n",
            "Layer 3: [ 1.19681469 -1.0370143   0.27347452]\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": "2a0ec910-1f7a-4b95-bbdb-9f0457d3a43d"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1696822184.4647448\n",
            "Mon Oct  9 03:29:44 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
}