[404218]: / Code / PennyLane / Data-Reuploading / Other Algorithms, Tests / ZRR 0.58 LR 73.8% kkawchak.ipynb

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{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 185,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "eI_d8C3_Tnyu",
        "outputId": "2898eb1b-fbc3-4db4-cb8d-8a6ce478984f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1696833500.3534086\n",
            "Mon Oct  9 06:38:20 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": 186,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "lScQSFsjTnyw",
        "outputId": "e0898960-f67c-4b71-e0d4-a1725da030ff"
      },
      "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": 187,
      "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.PauliZ(wires= 0)\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": 188,
      "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": 189,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "NqJGMjbpTnyy",
        "outputId": "99400d97-77fc-4576-f1ce-4d55334097d0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:  0 | Cost: 0.443627 | Train accuracy: 0.530000 | Test Accuracy: 0.474000\n",
            "Epoch:  1 | Loss: 0.267964 | Train accuracy: 0.590000 | Test accuracy: 0.583000\n",
            "Epoch:  2 | Loss: 0.239346 | Train accuracy: 0.650000 | Test accuracy: 0.641000\n",
            "Epoch:  3 | Loss: 0.218652 | Train accuracy: 0.670000 | Test accuracy: 0.686500\n",
            "Epoch:  4 | Loss: 0.216588 | Train accuracy: 0.710000 | Test accuracy: 0.727000\n",
            "Epoch:  5 | Loss: 0.176294 | Train accuracy: 0.735000 | Test accuracy: 0.716000\n",
            "Epoch:  6 | Loss: 0.185646 | Train accuracy: 0.750000 | Test accuracy: 0.740500\n",
            "Epoch:  7 | Loss: 0.195464 | Train accuracy: 0.680000 | Test accuracy: 0.732500\n",
            "Epoch:  8 | Loss: 0.173838 | Train accuracy: 0.770000 | Test accuracy: 0.737000\n",
            "Epoch:  9 | Loss: 0.170867 | Train accuracy: 0.750000 | Test accuracy: 0.722000\n",
            "Epoch: 10 | Loss: 0.178652 | Train accuracy: 0.755000 | Test accuracy: 0.738000\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 = 0.58\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": 190,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "ZPszGYA3Tnyy",
        "outputId": "cfbccdde-a957-4b2e-df52-f295594082a1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cost: 0.178652 | Train accuracy 0.755000 | Test Accuracy : 0.738000\n",
            "Learned weights\n",
            "Layer 0: [-1.57984113  3.85635341  2.52552683]\n",
            "Layer 1: [-2.15602807  1.79477399 -0.96188391]\n",
            "Layer 2: [-1.86382807 -0.42412869  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": [
        "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": "023a1181-f66d-4c39-f07d-5f25faa1b120"
      },
      "execution_count": 191,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1696833787.0871825\n",
            "Mon Oct  9 06:43:07 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
}