--- a
+++ b/Code/All Qiskit, PennyLane QML Nov 23/16a Generalization Acc N greater than 5 kkawchak.ipynb
@@ -0,0 +1,890 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "metadata": {
+        "id": "BYEH7VMOLDID"
+      },
+      "outputs": [],
+      "source": [
+        "# This cell is added by sphinx-gallery\n",
+        "# It can be customized to whatever you like\n",
+        "%matplotlib inline\n",
+        "# !pip install pennylane"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tfR_BtWaLDIE"
+      },
+      "source": [
+        "Generalization in QML from few training data {#learning_few_data}\n",
+        "============================================\n",
+        "\n",
+        "::: {.meta}\n",
+        ":property=\\\"og:description\\\": Generalization of quantum machine learning\n",
+        "models. :property=\\\"og:image\\\":\n",
+        "<https://pennylane.ai/qml/_images/few_data_thumbnail.png>\n",
+        ":::\n",
+        "\n",
+        "::: {.related}\n",
+        "tutorial\\_local\\_cost\\_functions Alleviating barren plateaus with local\n",
+        "cost functions\n",
+        ":::\n",
+        "\n",
+        "*Authors: Korbinian Kottmann, Luis Mantilla Calderon, Maurice Weber ---\n",
+        "Posted: 29 August 2022*\n",
+        "\n",
+        "In this tutorial, we dive into the generalization capabilities of\n",
+        "quantum machine learning models. For the example of a [Quantum\n",
+        "Convolutional Neural Network\n",
+        "(QCNN)](https://pennylane.ai/qml/glossary/qcnn.html), we show how its\n",
+        "generalization error behaves as a function of the number of training\n",
+        "samples. This demo is based on the paper *\\\"Generalization in quantum\n",
+        "machine learning from few training data\\\"*. by Caro et al..\n",
+        "\n",
+        "What is generalization in (Q)ML?\n",
+        "--------------------------------\n",
+        "\n",
+        "When optimizing a machine learning model, be it classical or quantum, we\n",
+        "aim to maximize its performance over the data distribution of interest\n",
+        "(e.g., images of cats and dogs). However, in practice, we are limited to\n",
+        "a finite amount of data, which is why it is necessary to reason about\n",
+        "how our model performs on new, previously unseen data. The difference\n",
+        "between the model\\'s performance on the true data distribution and the\n",
+        "performance estimated from our training data is called the\n",
+        "*generalization error*, and it indicates how well the model has learned\n",
+        "to generalize to unseen data. Generalization can be seen as a\n",
+        "manifestation of the bias-variance trade-off; models that perfectly fit\n",
+        "the training data admit a low bias at the cost of a higher variance, and\n",
+        "hence typically perform poorly on unseen test data. In the classical\n",
+        "machine learning community, this trade-off has been extensively studied\n",
+        "and has led to optimization techniques that favour generalization, for\n",
+        "example, by regularizing models via their variance. Below, we see a\n",
+        "canoncial example of this trade-off, with a model having low bias, but\n",
+        "high variance and therefore high generalization error. The low variance\n",
+        "model, on the other hand, has a higher bias but generalizes better.\n",
+        "\n",
+        "![](/demonstrations/learning_few_data/overfitting.png){.align-center\n",
+        "width=\"65.0%\"}\n",
+        "\n",
+        "Let us now dive deeper into generalization properties of quantum machine\n",
+        "learning (QML) models. We start by describing the typical data\n",
+        "processing pipeline of a QML model. A classical data input $x$ is first\n",
+        "encoded in a quantum state via a mapping $x \\mapsto \\rho(x)$. This\n",
+        "encoded state is then processed through a quantum channel\n",
+        "$\\rho(x) \\mapsto \\mathcal{E}_\\alpha(\\rho(x))$ with learnable parameters\n",
+        "$\\alpha$. Finally, a measurement is performed on the resulting state to\n",
+        "get the final prediction. Now, the goal is to minimize the expected loss\n",
+        "over the data-generating distribution $P$, indicating how well our model\n",
+        "performs on new data. Mathematically, for a loss function $\\ell$, the\n",
+        "expected loss, denoted by $R$, is given by\n",
+        "\n",
+        "$$R(\\alpha) = \\mathbb{E}_{(x,y)\\sim P}[\\ell(\\alpha;\\,x,\\,y)]$$\n",
+        "\n",
+        "where $x$ are the features, $y$ are the labels, and $P$ is their joint\n",
+        "distribution. In practice, as the joint distribution $P$ is generally\n",
+        "unknown, this quantity has to be estimated from a finite amount of data.\n",
+        "Given a training set $S = \\{(x_i,\\,y_i)\\}_{i=1}^N$ with $N$ samples, we\n",
+        "estimate the performance of our QML model by calculating the average\n",
+        "loss over the training set\n",
+        "\n",
+        "$$R_S(\\alpha) = \\frac{1}{N}\\sum_{i=1}^N \\ell(\\alpha;\\,x_i,\\,y_i),$$\n",
+        "\n",
+        "which is referred to as the training loss and is an unbiased estimate of\n",
+        "$R(\\alpha)$. This is only a proxy to the true quantity of interest\n",
+        "$R(\\alpha)$, and their difference is called the generalization error\n",
+        "\n",
+        "$$\\mathrm{gen}(\\alpha) =  R(\\alpha) - \\hat{R}_S(\\alpha),$$\n",
+        "\n",
+        "which is the quantity that we explore in this tutorial. Keeping in mind\n",
+        "the bias-variance trade-off, one would expect that more complex models,\n",
+        "i.e. models with a larger number of parameters, achieve a lower error on\n",
+        "the training data but a higher generalization error. Having more\n",
+        "training data, on the other hand, leads to a better approximation of the\n",
+        "true expected loss and hence a lower generalization error. This\n",
+        "intuition is made precise in Ref., where it is shown that\n",
+        "$\\mathrm{gen}(\\alpha)$ roughly scales as $\\mathcal{O}(\\sqrt{T / N})$,\n",
+        "where $T$ is the number of parametrized gates and $N$ is the number of\n",
+        "training samples.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "76al4ZQNLDIF"
+      },
+      "source": [
+        "Generalization bounds for QML models\n",
+        "====================================\n",
+        "\n",
+        "As hinted at earlier, we expect the generalization error to depend both\n",
+        "on the richness of the model class, as well as on the amount of training\n",
+        "data available. As a first result, the authors of Ref. found that for a\n",
+        "QML model with at most $T$ parametrized local quantum channels, the\n",
+        "generalization error depends on $T$ and $N$ according to\n",
+        "\n",
+        "$$\\mathrm{gen}(\\alpha) \\sim \\mathcal{O}\\left(\\sqrt{\\frac{T\\log T}{N}}\\right).$$\n",
+        "\n",
+        "We see that this scaling is in line with our intuition that the\n",
+        "generalization error scales inversely with the number of training\n",
+        "samples and increases with the number of parametrized gates. However, as\n",
+        "is the case for [quantum convolutional neural networks\n",
+        "(QCNNs)](https://pennylane.ai/qml/glossary/qcnn.html), it is possible to\n",
+        "get a more fine-grained bound by including knowledge on the number of\n",
+        "gates $M$ which have been reused (i.e. whose parameters are shared\n",
+        "across wires). Naively, one could suspect that the generalization error\n",
+        "scales as $\\tilde{\\mathcal{O}}(\\sqrt{MT/N})$ by directly applying the\n",
+        "above result (and where $\\tilde{\\mathcal{O}}$ includes logarithmic\n",
+        "factors). However, the authors of Ref. found that such models actually\n",
+        "adhere to the better scaling\n",
+        "\n",
+        "$$\\mathrm{gen}(\\alpha) \\sim \\mathcal{O}\\left(\\sqrt{\\frac{T\\log MT}{N}}\\right).$$\n",
+        "\n",
+        "With this, we see that for QCNNs to have a generalization error\n",
+        "$\\mathrm{gen}(\\alpha)\\leq\\epsilon$, we need a training set of size\n",
+        "$N \\sim T \\log MT / \\epsilon^2$. For the special case of QCNNs, we can\n",
+        "explicitly connect the number of samples needed for good generalization\n",
+        "to the system size $n$ since these models use $\\mathcal{O}(\\log(n))$\n",
+        "independently parametrized gates, each of which is used at most $n$\n",
+        "times. Putting the pieces together, we find that a training set of size\n",
+        "\n",
+        "$$N \\sim \\mathcal{O}(\\mathrm{poly}(\\log n))$$\n",
+        "\n",
+        "is sufficient for the generalization error to be bounded by\n",
+        "$\\mathrm{gen}(\\alpha) \\leq \\epsilon$. In the next part of this tutorial,\n",
+        "we will illustrate this result by implementing a QCNN to classify\n",
+        "different digits in the classical `digits` dataset. Before that, we set\n",
+        "up our QCNN.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UFCGuJ_CLDIF"
+      },
+      "source": [
+        "Quantum convolutional neural networks\n",
+        "\\-\\-\\-\\-\\-\\-\\-\\-\\-\\-\\-\\-\\-\\-\\-\\-\\-\\-\\-\\-\\-\\-\\-\\-\\-\\-\\-\\-\\-\\-\\-\\-\\-\\-\\--Before\n",
+        "we start building a QCNN, let us briefly review the idea of classical\n",
+        "CNNs, which have shown tremendous success in tasks like image\n",
+        "recognition, recommender systems, and sound classification, to name a\n",
+        "few. For a more in-depth explanation of CNNs, we highly recommend\n",
+        "[chapter 9](https://www.deeplearningbook.org/contents/convnets.html) in.\n",
+        "Classical CNNs are a family of neural networks which make use of\n",
+        "convolutions and pooling operations to insert an inductive bias,\n",
+        "favouring invariances to spatial transformations like translations,\n",
+        "rotations, and scaling. A *convolutional layer* consists of a small\n",
+        "kernel (a window) that sweeps over a 2D array representation of an image\n",
+        "and extracts local information while sharing parameters across the\n",
+        "spatial dimensions. In addition to the convolutional layers, one\n",
+        "typically uses pooling layers to reduce the size of the input and to\n",
+        "provide a mechanism for summarizing information from a neighbourhood of\n",
+        "values in the input. On top of reducing dimensionality, these types of\n",
+        "layers have the advantage of making the model more agnostic to certain\n",
+        "transformations like scaling and rotations. These two types of layers\n",
+        "are applied repeatedly in an alternating manner as shown in the figure\n",
+        "below.\n",
+        "\n",
+        "![A graphical representation of a CNN. Obtained using\n",
+        "Ref..](/demonstrations/learning_few_data/cnn_pic.png){.align-center\n",
+        "width=\"75.0%\"}\n",
+        "\n",
+        "We want to build something similar for a quantum circuit. First, we\n",
+        "import the necessary libraries we will need in this demo and set a\n",
+        "random seed for reproducibility:\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 37,
+      "metadata": {
+        "id": "ftmhRXA9LDIF"
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib as mpl\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from sklearn import datasets\n",
+        "import seaborn as sns\n",
+        "import jax;\n",
+        "\n",
+        "jax.config.update('jax_platform_name', 'cpu')\n",
+        "jax.config.update(\"jax_enable_x64\", True)\n",
+        "import jax.numpy as jnp\n",
+        "\n",
+        "import optax  # optimization using jax\n",
+        "\n",
+        "import pennylane as qml\n",
+        "import pennylane.numpy as pnp\n",
+        "\n",
+        "sns.set()\n",
+        "\n",
+        "seed = 0\n",
+        "rng = np.random.default_rng(seed=seed)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "H0DTTMNeLDIF"
+      },
+      "source": [
+        "To construct a convolutional and pooling layer in a quantum circuit, we\n",
+        "will follow the QCNN construction proposed in. The former layer will\n",
+        "extract local correlations, while the latter allows reducing the\n",
+        "dimensionality of the feature vector. In a quantum circuit, the\n",
+        "convolutional layer, consisting of a kernel swept along the entire\n",
+        "image, is a two-qubit unitary that correlates neighbouring qubits. As\n",
+        "for the pooling layer, we will use a conditioned single-qubit unitary\n",
+        "that depends on the measurement of a neighboring qubit. Finally, we use\n",
+        "a *dense layer* that entangles all qubits of the final state using an\n",
+        "all-to-all unitary gate as shown in the figure below.\n",
+        "\n",
+        "![QCNN architecture. Taken from\n",
+        "Ref..](/demonstrations/learning_few_data/qcnn-architecture.png){.align-center\n",
+        "width=\"75.0%\"}\n",
+        "\n",
+        "Breaking down the layers\n",
+        "========================\n",
+        "\n",
+        "The convolutional layer should have the weights of the two-qubit unitary\n",
+        "as an input, which are updated at every training step. In PennyLane, we\n",
+        "model this arbitrary two-qubit unitary with a particular sequence of\n",
+        "gates: two single-qubit `~.pennylane.U3`{.interpreted-text role=\"class\"}\n",
+        "gates (parametrized by three parameters, each), three Ising interactions\n",
+        "between both qubits (each interaction is parametrized by one parameter),\n",
+        "and two additional `~.pennylane.U3`{.interpreted-text role=\"class\"}\n",
+        "gates on each of the two qubits.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 38,
+      "metadata": {
+        "id": "6eI2w54HLDIG"
+      },
+      "outputs": [],
+      "source": [
+        "def convolutional_layer(weights, wires, skip_first_layer=True):\n",
+        "    \"\"\"Adds a convolutional layer to a circuit.\n",
+        "    Args:\n",
+        "        weights (np.array): 1D array with 15 weights of the parametrized gates.\n",
+        "        wires (list[int]): Wires where the convolutional layer acts on.\n",
+        "        skip_first_layer (bool): Skips the first two U3 gates of a layer.\n",
+        "    \"\"\"\n",
+        "    n_wires = len(wires)\n",
+        "    assert n_wires >= 3, \"this circuit is too small!\"\n",
+        "\n",
+        "    for p in [0, 1]:\n",
+        "        for indx, w in enumerate(wires):\n",
+        "            if indx % 2 == p and indx < n_wires - 1:\n",
+        "                if indx % 2 == 0 and not skip_first_layer:\n",
+        "                    qml.U3(*weights[:3], wires=[w])\n",
+        "                    qml.U3(*weights[3:6], wires=[wires[indx + 1]])\n",
+        "                qml.IsingXX(weights[6], wires=[w, wires[indx + 1]])\n",
+        "                qml.IsingYY(weights[7], wires=[w, wires[indx + 1]])\n",
+        "                qml.IsingZZ(weights[8], wires=[w, wires[indx + 1]])\n",
+        "                qml.U3(*weights[9:12], wires=[w])\n",
+        "                qml.U3(*weights[12:], wires=[wires[indx + 1]])\n",
+        "                qml.IsingXX(weights[6], wires=[w, wires[indx + 1]])\n",
+        "                qml.IsingYY(weights[7], wires=[w, wires[indx + 1]])\n",
+        "                qml.IsingZZ(weights[8], wires=[w, wires[indx + 1]])\n",
+        "                qml.U3(*weights[9:12], wires=[w])\n",
+        "                qml.U3(*weights[12:], wires=[wires[indx + 1]])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vFPqYGMvLDIG"
+      },
+      "source": [
+        "The pooling layer\\'s inputs are the weights of the single-qubit\n",
+        "conditional unitaries, which in this case are\n",
+        "`~.pennylane.U3`{.interpreted-text role=\"class\"} gates. Then, we apply\n",
+        "these conditional measurements to half of the unmeasured wires, reducing\n",
+        "our system size by a factor of 2.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 39,
+      "metadata": {
+        "id": "J1FHoMIaLDIG"
+      },
+      "outputs": [],
+      "source": [
+        "def pooling_layer(weights, wires):\n",
+        "    \"\"\"Adds a pooling layer to a circuit.\n",
+        "    Args:\n",
+        "        weights (np.array): Array with the weights of the conditional U3 gate.\n",
+        "        wires (list[int]): List of wires to apply the pooling layer on.\n",
+        "    \"\"\"\n",
+        "    n_wires = len(wires)\n",
+        "    assert len(wires) >= 2, \"this circuit is too small!\"\n",
+        "\n",
+        "    for indx, w in enumerate(wires):\n",
+        "        if indx % 2 == 1 and indx < n_wires:\n",
+        "            m_outcome = qml.measure(w)\n",
+        "            qml.cond(m_outcome, qml.U3)(*weights, wires=wires[indx - 1])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YEYQ0uyBLDIG"
+      },
+      "source": [
+        "We can construct a QCNN by combining both layers and using an arbitrary\n",
+        "unitary to model a dense layer. It will take a set of features --- the\n",
+        "image --- as input, encode these features using an embedding map, apply\n",
+        "rounds of convolutional and pooling layers, and eventually output the\n",
+        "desired measurement statistics of the circuit.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 40,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 252
+        },
+        "id": "cCdVbZ8hLDIG",
+        "outputId": "e1a03e73-edde-406f-d853-783ba18cc7ad"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 4300x700 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "def conv_and_pooling(kernel_weights, n_wires, skip_first_layer=True):\n",
+        "    \"\"\"Apply both the convolutional and pooling layer.\"\"\"\n",
+        "    convolutional_layer(kernel_weights[:15], n_wires, skip_first_layer=skip_first_layer)\n",
+        "    pooling_layer(kernel_weights[15:], n_wires)\n",
+        "\n",
+        "\n",
+        "def dense_layer(weights, wires):\n",
+        "    \"\"\"Apply an arbitrary unitary gate to a specified set of wires.\"\"\"\n",
+        "    qml.ArbitraryUnitary(weights, wires)\n",
+        "\n",
+        "\n",
+        "num_wires = 6\n",
+        "device = qml.device(\"default.qubit\", wires=num_wires)\n",
+        "\n",
+        "\n",
+        "@qml.qnode(device, interface=\"jax\")\n",
+        "def conv_net(weights, last_layer_weights, features):\n",
+        "    \"\"\"Define the QCNN circuit\n",
+        "    Args:\n",
+        "        weights (np.array): Parameters of the convolution and pool layers.\n",
+        "        last_layer_weights (np.array): Parameters of the last dense layer.\n",
+        "        features (np.array): Input data to be embedded using AmplitudEmbedding.\"\"\"\n",
+        "\n",
+        "    layers = weights.shape[1]\n",
+        "    wires = list(range(num_wires))\n",
+        "\n",
+        "    # inputs the state input_state\n",
+        "    qml.AmplitudeEmbedding(features=features, wires=wires, pad_with=0.5)\n",
+        "    qml.Barrier(wires=wires, only_visual=True)\n",
+        "\n",
+        "    # adds convolutional and pooling layers\n",
+        "    for j in range(layers):\n",
+        "        conv_and_pooling(weights[:, j], wires, skip_first_layer=(not j == 0))\n",
+        "        wires = wires[::2]\n",
+        "        qml.Barrier(wires=wires, only_visual=True)\n",
+        "\n",
+        "    assert last_layer_weights.size == 4 ** (len(wires)) - 1, (\n",
+        "        \"The size of the last layer weights vector is incorrect!\"\n",
+        "        f\" \\n Expected {4 ** (len(wires)) - 1}, Given {last_layer_weights.size}\"\n",
+        "    )\n",
+        "    dense_layer(last_layer_weights, wires)\n",
+        "    return qml.probs(wires=(0))\n",
+        "\n",
+        "\n",
+        "fig, ax = qml.draw_mpl(conv_net)(\n",
+        "    np.random.rand(18, 2), np.random.rand(4 ** 2 - 1), np.random.rand(2 ** num_wires)\n",
+        ")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7v7vT3DMLDIG"
+      },
+      "source": [
+        "In the problem we will address, we need to encode 64 features in our\n",
+        "quantum state. Thus, we require six qubits ($2^6 = 64$) to encode each\n",
+        "feature value in the amplitude of each computational basis state.\n",
+        "\n",
+        "Training the QCNN on the digits dataset\n",
+        "=======================================\n",
+        "\n",
+        "In this demo, we are going to classify the digits `0` and `1` from the\n",
+        "classical `digits` dataset. Each hand-written digit image is represented\n",
+        "as an $8 \\times 8$ array of pixels as shown below:\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 41,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 59
+        },
+        "id": "irGiN1UHLDIG",
+        "outputId": "54d8fcdc-c08b-45b4-ab90-f23bb9255b08"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 300x100 with 12 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "digits = datasets.load_digits()\n",
+        "images, labels = digits.data, digits.target\n",
+        "\n",
+        "images = images[np.where((labels == 0) | (labels == 1))]\n",
+        "labels = labels[np.where((labels == 0) | (labels == 1))]\n",
+        "\n",
+        "fig, axes = plt.subplots(nrows=1, ncols=12, figsize=(3, 1))\n",
+        "\n",
+        "for i, ax in enumerate(axes.flatten()):\n",
+        "    ax.imshow(images[i].reshape((8, 8)), cmap=\"gray\")\n",
+        "    ax.axis(\"off\")\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.subplots_adjust(wspace=0, hspace=0)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ktE3yYFPLDIG"
+      },
+      "source": [
+        "For convenience, we create a `load_digits_data` function that will make\n",
+        "random training and testing sets from the `digits` dataset from\n",
+        "`sklearn.dataset`:\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 42,
+      "metadata": {
+        "id": "dHhMKGlqLDIH"
+      },
+      "outputs": [],
+      "source": [
+        "def load_digits_data(num_train, num_test, rng):\n",
+        "    \"\"\"Return training and testing data of digits dataset.\"\"\"\n",
+        "    digits = datasets.load_digits()\n",
+        "    features, labels = digits.data, digits.target\n",
+        "\n",
+        "    # only use first two classes\n",
+        "    features = features[np.where((labels == 0) | (labels == 1))]\n",
+        "    labels = labels[np.where((labels == 0) | (labels == 1))]\n",
+        "\n",
+        "    # normalize data\n",
+        "    features = features / np.linalg.norm(features, axis=1).reshape((-1, 1))\n",
+        "\n",
+        "    # subsample train and test split\n",
+        "    train_indices = rng.choice(len(labels), num_train, replace=False)\n",
+        "    test_indices = rng.choice(\n",
+        "        np.setdiff1d(range(len(labels)), train_indices), num_test, replace=False\n",
+        "    )\n",
+        "\n",
+        "    x_train, y_train = features[train_indices], labels[train_indices]\n",
+        "    x_test, y_test = features[test_indices], labels[test_indices]\n",
+        "\n",
+        "    return (\n",
+        "        jnp.asarray(x_train),\n",
+        "        jnp.asarray(y_train),\n",
+        "        jnp.asarray(x_test),\n",
+        "        jnp.asarray(y_test),\n",
+        "    )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "E348WVjiLDIH"
+      },
+      "source": [
+        "To optimize the weights of our variational model, we define the cost and\n",
+        "accuracy functions to train and quantify the performance on the\n",
+        "classification task of the previously described QCNN:\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 43,
+      "metadata": {
+        "id": "N-MmpJZbLDIH"
+      },
+      "outputs": [],
+      "source": [
+        "@jax.jit\n",
+        "def compute_out(weights, weights_last, features, labels):\n",
+        "    \"\"\"Computes the output of the corresponding label in the qcnn\"\"\"\n",
+        "    cost = lambda weights, weights_last, feature, label: conv_net(weights, weights_last, feature)[\n",
+        "        label\n",
+        "    ]\n",
+        "    return jax.vmap(cost, in_axes=(None, None, 0, 0), out_axes=0)(\n",
+        "        weights, weights_last, features, labels\n",
+        "    )\n",
+        "\n",
+        "\n",
+        "def compute_accuracy(weights, weights_last, features, labels):\n",
+        "    \"\"\"Computes the accuracy over the provided features and labels\"\"\"\n",
+        "    out = compute_out(weights, weights_last, features, labels)\n",
+        "    return jnp.sum(out > 0.5) / len(out)\n",
+        "\n",
+        "\n",
+        "def compute_cost(weights, weights_last, features, labels):\n",
+        "    \"\"\"Computes the cost over the provided features and labels\"\"\"\n",
+        "    out = compute_out(weights, weights_last, features, labels)\n",
+        "    return 1.0 - jnp.sum(out) / len(labels)\n",
+        "\n",
+        "\n",
+        "def init_weights():\n",
+        "    \"\"\"Initializes random weights for the QCNN model.\"\"\"\n",
+        "    weights = pnp.random.normal(loc=0, scale=1, size=(18, 2), requires_grad=True)\n",
+        "    weights_last = pnp.random.normal(loc=0, scale=1, size=4 ** 2 - 1, requires_grad=True)\n",
+        "    return jnp.array(weights), jnp.array(weights_last)\n",
+        "\n",
+        "\n",
+        "value_and_grad = jax.jit(jax.value_and_grad(compute_cost, argnums=[0, 1]))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xl7p4Hz_LDIH"
+      },
+      "source": [
+        "We are going to perform the classification for training sets with\n",
+        "different values of $N$. Therefore, we define the classification\n",
+        "procedure once and then perform it for different datasets. Finally, we\n",
+        "update the weights using the `pennylane.AdamOptimizer`{.interpreted-text\n",
+        "role=\"class\"} and use these updated weights to calculate the cost and\n",
+        "accuracy on the testing and training set:\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 44,
+      "metadata": {
+        "id": "_08Zw5jALDIH"
+      },
+      "outputs": [],
+      "source": [
+        "def train_qcnn(n_train, n_test, n_epochs):\n",
+        "    \"\"\"\n",
+        "    Args:\n",
+        "        n_train  (int): number of training examples\n",
+        "        n_test   (int): number of test examples\n",
+        "        n_epochs (int): number of training epochs\n",
+        "        desc  (string): displayed string during optimization\n",
+        "\n",
+        "    Returns:\n",
+        "        dict: n_train,\n",
+        "        steps,\n",
+        "        train_cost_epochs,\n",
+        "        train_acc_epochs,\n",
+        "        test_cost_epochs,\n",
+        "        test_acc_epochs\n",
+        "\n",
+        "    \"\"\"\n",
+        "    # load data\n",
+        "    x_train, y_train, x_test, y_test = load_digits_data(n_train, n_test, rng)\n",
+        "\n",
+        "    # init weights and optimizer\n",
+        "    weights, weights_last = init_weights()\n",
+        "\n",
+        "    # learning rate decay\n",
+        "    cosine_decay_scheduler = optax.cosine_decay_schedule(0.1, decay_steps=n_epochs, alpha=0.95)\n",
+        "    optimizer = optax.adam(learning_rate=cosine_decay_scheduler)\n",
+        "    opt_state = optimizer.init((weights, weights_last))\n",
+        "\n",
+        "    # data containers\n",
+        "    train_cost_epochs, test_cost_epochs, train_acc_epochs, test_acc_epochs = [], [], [], []\n",
+        "\n",
+        "    for step in range(n_epochs):\n",
+        "        # Training step with (adam) optimizer\n",
+        "        train_cost, grad_circuit = value_and_grad(weights, weights_last, x_train, y_train)\n",
+        "        updates, opt_state = optimizer.update(grad_circuit, opt_state)\n",
+        "        weights, weights_last = optax.apply_updates((weights, weights_last), updates)\n",
+        "\n",
+        "        train_cost_epochs.append(train_cost)\n",
+        "\n",
+        "        # compute accuracy on training data\n",
+        "        train_acc = compute_accuracy(weights, weights_last, x_train, y_train)\n",
+        "        train_acc_epochs.append(train_acc)\n",
+        "\n",
+        "        # compute accuracy and cost on testing data\n",
+        "        test_out = compute_out(weights, weights_last, x_test, y_test)\n",
+        "        test_acc = jnp.sum(test_out > 0.5) / len(test_out)\n",
+        "        test_acc_epochs.append(test_acc)\n",
+        "        test_cost = 1.0 - jnp.sum(test_out) / len(test_out)\n",
+        "        test_cost_epochs.append(test_cost)\n",
+        "\n",
+        "    return dict(\n",
+        "        n_train=[n_train] * n_epochs,\n",
+        "        step=np.arange(1, n_epochs + 1, dtype=int),\n",
+        "        train_cost=train_cost_epochs,\n",
+        "        train_acc=train_acc_epochs,\n",
+        "        test_cost=test_cost_epochs,\n",
+        "        test_acc=test_acc_epochs,\n",
+        "    )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Jupsq2nDLDIH"
+      },
+      "source": [
+        "::: {.note}\n",
+        "::: {.title}\n",
+        "Note\n",
+        ":::\n",
+        "\n",
+        "There are some small intricacies for speeding up this code that are\n",
+        "worth mentioning. We are using `jax` for our training because it allows\n",
+        "for\n",
+        "[just-in-time](https://jax.readthedocs.io/en/latest/jax-101/02-jitting.html)\n",
+        "(`jit`) compilation. A function decorated with `@jax.jit` will be\n",
+        "compiled upon its first execution and cached for future executions. This\n",
+        "means the first execution will take longer, but all subsequent\n",
+        "executions are substantially faster. Further, we use `jax.vmap` to\n",
+        "vectorize the execution of the QCNN over all input states, as opposed to\n",
+        "looping through the training and test set at every execution.\n",
+        ":::\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "szuIyWDTLDIH"
+      },
+      "source": [
+        "Training for different training set sizes yields different accuracies,\n",
+        "as seen below. As we increase the training data size, the overall test\n",
+        "accuracy, a proxy for the models\\' generalization capabilities,\n",
+        "increases:\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 45,
+      "metadata": {
+        "id": "BqiDPAv0LDIH"
+      },
+      "outputs": [],
+      "source": [
+        "n_test = 100\n",
+        "n_epochs = 100\n",
+        "n_reps = 100\n",
+        "\n",
+        "\n",
+        "def run_iterations(n_train):\n",
+        "    results_df = pd.DataFrame(\n",
+        "        columns=[\"train_acc\", \"train_cost\", \"test_acc\", \"test_cost\", \"step\", \"n_train\"]\n",
+        "    )\n",
+        "\n",
+        "    for _ in range(n_reps):\n",
+        "        results = train_qcnn(n_train=n_train, n_test=n_test, n_epochs=n_epochs)\n",
+        "        results_df = pd.concat(\n",
+        "            [results_df, pd.DataFrame.from_dict(results)], axis=0, ignore_index=True\n",
+        "        )\n",
+        "\n",
+        "    return results_df\n",
+        "\n",
+        "\n",
+        "# run training for multiple sizes\n",
+        "train_sizes = [2, 5, 10, 20, 40, 80]\n",
+        "results_df = run_iterations(n_train=2)\n",
+        "for n_train in train_sizes[1:]:\n",
+        "    results_df = pd.concat([results_df, run_iterations(n_train=n_train)])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sLca_WTyLDIH"
+      },
+      "source": [
+        "Finally, we plot the loss and accuracy for both the training and testing\n",
+        "set for all training epochs, and compare the test and train accuracy of\n",
+        "the model:\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 46,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 505
+        },
+        "id": "Qr1cGcVXLDIH",
+        "outputId": "9780a59e-4a36-452c-e113-dd49aa0233c4"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1650x500 with 3 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# aggregate dataframe\n",
+        "df_agg = results_df.groupby([\"n_train\", \"step\"]).agg([\"mean\", \"std\"])\n",
+        "df_agg = df_agg.reset_index()\n",
+        "\n",
+        "sns.set_style('whitegrid')\n",
+        "colors = sns.color_palette()\n",
+        "fig, axes = plt.subplots(ncols=3, figsize=(16.5, 5))\n",
+        "\n",
+        "generalization_errors = []\n",
+        "\n",
+        "# plot losses and accuracies\n",
+        "for i, n_train in enumerate(train_sizes):\n",
+        "    df = df_agg[df_agg.n_train == n_train]\n",
+        "\n",
+        "    dfs = [df.train_cost[\"mean\"], df.test_cost[\"mean\"], df.train_acc[\"mean\"], df.test_acc[\"mean\"]]\n",
+        "    lines = [\"o-\", \"x--\", \"o-\", \"x--\"]\n",
+        "    labels = [fr\"$N={n_train}$\", None, fr\"$N={n_train}$\", None]\n",
+        "    axs = [0,0,2,2]\n",
+        "\n",
+        "    for k in range(4):\n",
+        "        ax = axes[axs[k]]\n",
+        "        ax.plot(df.step, dfs[k], lines[k], label=labels[k], markevery=10, color=colors[i], alpha=0.8)\n",
+        "\n",
+        "\n",
+        "    # plot final loss difference\n",
+        "    dif = df[df.step == 100].test_cost[\"mean\"] - df[df.step == 100].train_cost[\"mean\"]\n",
+        "    generalization_errors.append(dif)\n",
+        "\n",
+        "# format loss plot\n",
+        "ax = axes[0]\n",
+        "ax.set_title('Train and Test Losses', fontsize=14)\n",
+        "ax.set_xlabel('Epoch')\n",
+        "ax.set_ylabel('Loss')\n",
+        "\n",
+        "# format generalization error plot\n",
+        "ax = axes[1]\n",
+        "ax.plot(train_sizes, generalization_errors, \"o-\", label=r\"$gen(\\alpha)$\")\n",
+        "ax.set_xscale('log')\n",
+        "ax.set_xticks(train_sizes)\n",
+        "ax.set_xticklabels(train_sizes)\n",
+        "ax.set_title(r'Generalization Error $gen(\\alpha) = R(\\alpha) - \\hat{R}_N(\\alpha)$', fontsize=14)\n",
+        "ax.set_xlabel('Training Set Size')\n",
+        "\n",
+        "# format loss plot\n",
+        "ax = axes[2]\n",
+        "ax.set_title('Train and Test Accuracies', fontsize=14)\n",
+        "ax.set_xlabel('Epoch')\n",
+        "ax.set_ylabel('Accuracy')\n",
+        "ax.set_ylim(0.5, 1.05)\n",
+        "\n",
+        "legend_elements = [\n",
+        "    mpl.lines.Line2D([0], [0], label=f'N={n}', color=colors[i]) for i, n in enumerate(train_sizes)\n",
+        "    ] + [\n",
+        "    mpl.lines.Line2D([0], [0], marker='o', ls='-', label='Train', color='Black'),\n",
+        "    mpl.lines.Line2D([0], [0], marker='x', ls='--', label='Test', color='Black')\n",
+        "    ]\n",
+        "\n",
+        "axes[0].legend(handles=legend_elements, ncol=3)\n",
+        "axes[2].legend(handles=legend_elements, ncol=3)\n",
+        "\n",
+        "axes[1].set_yscale('log', base=2)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3Jp181qLLDIH"
+      },
+      "source": [
+        "------------------------------------------------------------------------\n",
+        "\n",
+        "The key takeaway of this work is that some quantum learning models can\n",
+        "achieve high-fidelity predictions using a few training data points. We\n",
+        "implemented a model known as the quantum convolutional neural network\n",
+        "(QCNN) using PennyLane for a binary classification task. Using six\n",
+        "qubits, we have trained the QCNN to distinguish between handwritten\n",
+        "digits of $0$\\'s and $1$\\'s. With $80$ samples, we have achieved a model\n",
+        "with accuracy greater than $97\\%$ in $100$ training epochs. Furthermore,\n",
+        "we have compared the test and train accuracy of this model for a\n",
+        "different number of training samples and found the scaling of the\n",
+        "generalization error agrees with the theoretical bounds obtained in[^1].\n",
+        "\n",
+        "References\n",
+        "==========\n",
+        "\n",
+        "About the authors\n",
+        "=================\n",
+        "\n",
+        "[^1]: Matthias C. Caro, Hsin-Yuan Huang, M. Cerezo, Kunal Sharma, Andrew\n",
+        "    Sornborger, Lukasz Cincio, Patrick J. Coles. \\\"Generalization in\n",
+        "    quantum machine learning from few training data\\\"\n",
+        "    [arxiv:2111.05292](https://arxiv.org/abs/2111.05292), 2021.\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "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": "3.9.17"
+    },
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}