docs/tutorials/11_quantum_convolutional_neural_networks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ed021640",
   "metadata": {},
   "source": [
    "# The Quantum Convolution Neural Network "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16dc439a",
   "metadata": {},
   "source": [
    "## 1. Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb7649f5",
   "metadata": {},
   "source": [
    "Throughout this tutorial, we discuss a Quantum Convolutional Neural Network (QCNN), first proposed by Cong et. al. [1]. We implement such a QCNN on Qiskit by modeling both the convolutional layers and pooling layers using a quantum circuit. After building such a network, we train it to differentiate horizontal and vertical lines from a pixelated image. The following tutorial is thus divided accordingly;\n",
    "\n",
    "1. Differences between a QCNN and CCNN\n",
    "2. Components of a QCNN\n",
    "3. Data Generation\n",
    "4. Building a QCNN\n",
    "5. Training our QCNN\n",
    "6. Testing our QCNN\n",
    "7. References\n",
    "\n",
    "We first begin by importing the libraries and packages we will need for this tutorial. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3ceca583",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from IPython.display import clear_output\n",
    "from qiskit import QuantumCircuit\n",
    "from qiskit.algorithms.optimizers import COBYLA\n",
    "from qiskit.circuit import ParameterVector\n",
    "from qiskit.circuit.library import ZFeatureMap\n",
    "from qiskit.quantum_info import SparsePauliOp\n",
    "from qiskit.utils import algorithm_globals\n",
    "from qiskit_machine_learning.algorithms.classifiers import NeuralNetworkClassifier\n",
    "from qiskit_machine_learning.neural_networks import EstimatorQNN\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "algorithm_globals.random_seed = 12345"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8875c12",
   "metadata": {},
   "source": [
    "## 1. Differences between a QCNN and CCNN"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3a65761",
   "metadata": {},
   "source": [
    "### 1.1 Classical Convolutional Neural Networks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d397710c",
   "metadata": {},
   "source": [
    "Classical Convolutional Neural Networks (CCNNs) are a subclass of artificial neural networks which have the ability to determine particular features and patterns of a given input. Because of this, they are commonly used in image recognition and audio processing.\n",
    "\n",
    "The capability of determining features is a result of the two types of layers used in a CCNN, the convolutional layer and pooling layer. \n",
    "\n",
    "An example of a CCNN can be seen in Figure 1, where a CCNN is trained to determine whether an input image either contains a cat or a dog. To do so, the input image passes through a series of alternating convolutional (C) and pooling layers (P), all of which detect patterns and associate each pattern to a cat or a dog. The fully connected layer (FC) provides us with an output which allows us to determine whether the input image was a cat or dog.\n",
    "\n",
    "The convolutional layer makes  use of a kernel, which can determine features and patterns of a particular input. An example of this is feature detection in an image, where different layers detect particular patterns in the input image. This is demonstrated in Figure 1, where the $l^{th}$ layer recognizes features and patterns along the $ij$ plane. It can then associate such features with a given output in the training process, and can use this process to train the dataset. \n",
    "\n",
    "On the other hand, a pooling layer reduces the dimensionality of the input data, reducing the computational cost and amount of learning parameters in the CCNN. A schematic of a CCNN can be seen below.\n",
    "\n",
    "For further information on CCNN, see [2]."
   ]
  },
  {
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"
    }
   },
   "cell_type": "markdown",
   "id": "1f18d774",
   "metadata": {},
   "source": [
    "![Screenshot%202022-08-09%20at%2017.03.09.png](attachment:Screenshot%202022-08-09%20at%2017.03.09.png)\n",
    "Figure 1. A schematic demonstration of the use of a CCNN to classify between images of a cat and dog. Here, we see the several convolutional and pooling layers being applied, all of which are decreasing in dimensionality due to the use of the pooling layers. The output of the CCNN determines whether the input image was a cat or dog. Image obtained form [1]. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b18ecb7",
   "metadata": {},
   "source": [
    "### 1.2 Quantum Convolutional Neural Networks "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15711fe7",
   "metadata": {},
   "source": [
    "Quantum Convolutional Neural Networks (QCNN) behave in a similar manner to CCNNs. First, we encode our pixelated image into a quantum circuit using a given feature map, such Qiskit's ZFeatureMap or ZZFeatureMap or others available in the circuit library.\n",
    "\n",
    "After encoding our image, we apply alternating convolutional and pooling layers, as defined in the next section. By applying these alternating layers, we reduce the dimensionality of our circuit until we are left with one qubit. We can then classify our input image by measuring the output of this one remaining qubit.\n",
    "\n",
    "The Quantum Convolutional Layer will consist of a series of two qubit unitary operators, which recognize and determine relationships between the qubits in our circuit. This unitary gates are defined below in the next section. \n",
    "\n",
    "For the Quantum Pooling Layer, we cannot do the same as is done classically to reduce the dimension, i.e. the number of qubits in our circuit. Instead, we reduce the number of qubits by performing operations upon each until a specific point and then disregard certain qubits in a specific layer. It is these layers where we stop performing operations on certain qubits that we call our 'pooling layer'. Details of the pooling layer is discussed further in the next section.\n",
    "\n",
    "In the QCNN, each layer contains parametrized circuits, meaning we alter our output result by adjusting the parameters of each layer. When training our QCNN, it is these parameters that are adjusted to reduce the loss function of our QCNN. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "894b249a",
   "metadata": {},
   "source": [
    "A simple example of four qubit QCNN can be seen below. "
   ]
  },
  {
   "attachments": {
    "figure2.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "c2777d65",
   "metadata": {},
   "source": [
    "![figure2.png](attachment:figure2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "159a69bf",
   "metadata": {},
   "source": [
    "\n",
    "Figure 2: Example QCNN containing four qubits. The first Convolutional Layer acts on all the qubits. This is followed by the first pooling layer, which reduces the dimensionality of the QCNN from four qubits to two qubits by disregarding the first two. The second Convolutional layer then detects features between the two qubits still in use in the QCNN, followed by another pooling layer, which reduces the dimensionality from two qubits to one, which will be our output qubit."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdb2541e",
   "metadata": {},
   "source": [
    "## 2. Components of a QCNN"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f5c01c6",
   "metadata": {},
   "source": [
    "As discussed in Section 1 of this tutorial, a CCNN will contain both convolutional and pooling layers. Here, we define these layers for the QCNN in terms of gates applied to a Quantum Circuit and demonstrate an example for each layer for 4 qubits. \n",
    "\n",
    "Each of these layers will contain parameters which are tuned throughout the training process to minimize the loss function and train the QCNN to classify between horizontal and vertical lines. \n",
    "\n",
    "In theory, one could apply any parametrized circuit for both the convolutional and pooling layers of our network. For example in [2], the Gellmann Matrices (which are the three dimensional generalization of the Pauli Matrices) are used as generators for each unitary gate acting on a pair of qubits. \n",
    "\n",
    "Here, we take a different approach and form our parametrized circuit based on the two qubit unitary as proposed in [3]. This states that every unitary matrix in $U(4)$ can be decomposed such that \n",
    "\n",
    "$$U = (A_1 \\otimes A_2) \\cdot N(\\alpha, \\beta, \\gamma) \\cdot (A_3 \\otimes A_4)$$\n",
    "\n",
    "where $A_j \\in \\text{SU}(2)$, $\\otimes$ is the tensor product, and $N(\\alpha, \\beta, \\gamma) = exp(i[\\alpha \\sigma_x\\sigma_x + \\beta \\sigma_y\\sigma_y + \\gamma \\sigma_z\\sigma_z ])$, where $\\alpha, \\beta, \\gamma$ are the parameters that we can adjust. \n",
    "\n",
    "From this, it is evident that each unitary depends on 15 parameters and implies that in order for the QCNN to be able to span the whole Hilbert space, each unitary in our QCNN must contain 15 parameters each. \n",
    "\n",
    "Tuning this large amount of parameters would be difficult and would lead to long training times. To overcome this problem, we restrict our ansatz to a particular subspace of the Hilbert space and define the two qubit unitary gate as $N(\\alpha, \\beta, \\gamma)$. These two qubit unitaries, as seen in [3] can be seen below and are applied to all neighboring qubits each of the layers in the QCNN. \n",
    "\n",
    "Note that by only using $N(\\alpha, \\beta, \\gamma)$ as our two qubit unitary for the parametrized layers, we are restricting our QCNN to a particular subspace, one in which the optimal solution may not be contained in and reducing the accuracy of the QCNN. For the purpose of this tutorial, we will use this parametrized circuit to decrease the training time of our QCNN."
   ]
  },
  {
   "attachments": {
    "circuit2.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "8b474663",
   "metadata": {},
   "source": [
    "![circuit2.png](attachment:circuit2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d16e806",
   "metadata": {},
   "source": [
    "Figure 3:\n",
    "Parametrized two qubit unitary circuit for $N(\\alpha, \\beta, \\gamma) = exp(i[\\alpha \\sigma_x\\sigma_x + \\beta \\sigma_y\\sigma_y + \\gamma \\sigma_z\\sigma_z ])$ as seen in [3], where $\\alpha =  \\frac{\\pi}{2} - 2\\theta$, $\\beta = 2\\phi - \\frac{\\pi}{2}$ and $\\gamma =  \\frac{\\pi}{2} - 2\\lambda$ as seen in the circuit. This two qubit unitary will be applied to all neighboring qubits in our feature map. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4972aa4",
   "metadata": {},
   "source": [
    "### 2.1 Convolutional Layer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7332be0a",
   "metadata": {},
   "source": [
    "The next step in this tutorial is to define the Convolutional Layers of our QCNN. These layers are then applied to the qubits after the data has been encoded through use of the feature map. \n",
    "\n",
    "To do so we first need to determine a parametrized unitary gate, which will be used to create our convolutional and pooling layers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "809524ce",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 507.852x144.48 with 1 Axes>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We now define a two qubit unitary as defined in [3]\n",
    "def conv_circuit(params):\n",
    "    target = QuantumCircuit(2)\n",
    "    target.rz(-np.pi / 2, 1)\n",
    "    target.cx(1, 0)\n",
    "    target.rz(params[0], 0)\n",
    "    target.ry(params[1], 1)\n",
    "    target.cx(0, 1)\n",
    "    target.ry(params[2], 1)\n",
    "    target.cx(1, 0)\n",
    "    target.rz(np.pi / 2, 0)\n",
    "    return target\n",
    "\n",
    "\n",
    "# Let's draw this circuit and see what it looks like\n",
    "params = ParameterVector(\"θ\", length=3)\n",
    "circuit = conv_circuit(params)\n",
    "circuit.draw(\"mpl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c1b7140",
   "metadata": {},
   "source": [
    "Now that we have defined these unitaries, it is time to create a function for the convolutional layer in our QCNN. To do so, we apply the two qubit unitary to neighboring qubits as seen in the ``conv_layer`` function below.\n",
    "\n",
    "Note that we first apply the two qubit unitary to all even pairs of qubits followed by applying to odd pairs of qubits in a circular coupling manner, i.e. the as well as neighboring qubits being coupled, the first and final qubit are also coupled through a unitary gate.\n",
    "\n",
    "Note that we add barriers into our quantum circuits for convenience when plotting, however they are not required for the actual QCNN and can be extracted from the following circuits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "68562ff2",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1591.6x565.88 with 1 Axes>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def conv_layer(num_qubits, param_prefix):\n",
    "    qc = QuantumCircuit(num_qubits, name=\"Convolutional Layer\")\n",
    "    qubits = list(range(num_qubits))\n",
    "    param_index = 0\n",
    "    params = ParameterVector(param_prefix, length=num_qubits * 3)\n",
    "    for q1, q2 in zip(qubits[0::2], qubits[1::2]):\n",
    "        qc = qc.compose(conv_circuit(params[param_index : (param_index + 3)]), [q1, q2])\n",
    "        qc.barrier()\n",
    "        param_index += 3\n",
    "    for q1, q2 in zip(qubits[1::2], qubits[2::2] + [0]):\n",
    "        qc = qc.compose(conv_circuit(params[param_index : (param_index + 3)]), [q1, q2])\n",
    "        qc.barrier()\n",
    "        param_index += 3\n",
    "\n",
    "    qc_inst = qc.to_instruction()\n",
    "\n",
    "    qc = QuantumCircuit(num_qubits)\n",
    "    qc.append(qc_inst, qubits)\n",
    "    return qc\n",
    "\n",
    "\n",
    "circuit = conv_layer(4, \"θ\")\n",
    "circuit.decompose().draw(\"mpl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f59677b5",
   "metadata": {},
   "source": [
    "### 2.2 Pooling Layer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23856709",
   "metadata": {},
   "source": [
    "The purpose of a pooling layer is to reduce the dimensions of our Quantum Circuit, i.e. reduce the number of qubits in our circuit, while retaining as much information as possible from previously learned data. Reducing the amount of qubits also reduces the computational cost of the overall circuit, as the number of parameters that the QCNN needs to learn decreases. \n",
    "\n",
    "However, one cannot simply decrease the amount of qubits in our quantum circuit. Because of this, we must define the pooling layer in a different manner compared with the classical approach. \n",
    "\n",
    "To 'artificially' reduce the number of qubits in our circuit, we first begin by creating pairs of the $N$ qubits in our system. \n",
    "\n",
    "After initially pairing all the qubits, we apply our generalized 2 qubit unitary to each pair, as described previously. After applying this two qubit unitary, we then ignore one qubit from each pair of qubits for the remainder of the neural network. \n",
    "\n",
    "This layer therefore has the overall effect of 'combining' the information of the two qubits into one qubit by first applying the unitary circuit, encoding information from one qubit into another, before disregarding one of qubits for the remainder of the circuit and not performing any operations or measurements on it. \n",
    "\n",
    "We note that one could also apply a dynamic circuit to reduce the dimensionality in the pooling layers. This would involve performing measurements on certain qubits in the circuit and having an intermediate classical feedback loop in our pooling layers. By applying these measurements, one would also be reducing the dimensionality of the circuit. \n",
    "\n",
    "In this tutorial, we apply the former approach, and disregard qubits in each pooling layer. Using this approach, we thus create a QCNN Pooling Layer which transforms the dimensions of our $N$ qubit Quantum Circuit to $N/2$. \n",
    "\n",
    "To do so, we first define a two qubit unitary, which transforms the two qubit system to one. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3c742cc9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 387.452x144.48 with 1 Axes>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def pool_circuit(params):\n",
    "    target = QuantumCircuit(2)\n",
    "    target.rz(-np.pi / 2, 1)\n",
    "    target.cx(1, 0)\n",
    "    target.rz(params[0], 0)\n",
    "    target.ry(params[1], 1)\n",
    "    target.cx(0, 1)\n",
    "    target.ry(params[2], 1)\n",
    "\n",
    "    return target\n",
    "\n",
    "\n",
    "params = ParameterVector(\"θ\", length=3)\n",
    "circuit = pool_circuit(params)\n",
    "circuit.draw(\"mpl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b9ff63b",
   "metadata": {},
   "source": [
    "After applying this two qubit unitary circuit, we neglect the first qubit (q0) in future layers and only use the second qubit (q1) in our QCNN\n",
    "\n",
    "We apply this two qubit pooling layer to different pairs of qubits to create our pooling layer for N qubits. As an example we then plot it for four qubits. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8b37f922",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 808.997x264.88 with 1 Axes>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def pool_layer(sources, sinks, param_prefix):\n",
    "    num_qubits = len(sources) + len(sinks)\n",
    "    qc = QuantumCircuit(num_qubits, name=\"Pooling Layer\")\n",
    "    param_index = 0\n",
    "    params = ParameterVector(param_prefix, length=num_qubits // 2 * 3)\n",
    "    for source, sink in zip(sources, sinks):\n",
    "        qc = qc.compose(pool_circuit(params[param_index : (param_index + 3)]), [source, sink])\n",
    "        qc.barrier()\n",
    "        param_index += 3\n",
    "\n",
    "    qc_inst = qc.to_instruction()\n",
    "\n",
    "    qc = QuantumCircuit(num_qubits)\n",
    "    qc.append(qc_inst, range(num_qubits))\n",
    "    return qc\n",
    "\n",
    "\n",
    "sources = [0, 1]\n",
    "sinks = [2, 3]\n",
    "circuit = pool_layer(sources, sinks, \"θ\")\n",
    "circuit.decompose().draw(\"mpl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bafef540",
   "metadata": {},
   "source": [
    "In this particular example, we reduce the dimensionality of our four qubit circuit to the last two qubits, i.e. the last two qubits in this particular example. These qubits are then used in the next layer, while the first two are neglected for the remainder of the QCNN. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03d0497d",
   "metadata": {},
   "source": [
    "## 3. Data Generation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88c3fbdc",
   "metadata": {},
   "source": [
    "One common use of a CCNN is an image classifier, where a CCNN detects particular features and patterns (such as straight lines or curves) of the pixelated images through the use of the feature maps in the convolutional layer. By learning the relationship between these features, it can then classify and label handwritten digits with ease. \n",
    "\n",
    "Because of a classical CNN's ability to recognize features and patterns easily, we will train our QCNN to also determine patterns and features of a given set of pixelated images, and classify between two different patterns. \n",
    "\n",
    "To simplify the dataset, we only consider 2 x 4 pixelated images. The patterns we will train the QCNN to distinguish will be a horizontal or vertical line, which can be placed anywhere in the image, alongside a noisy background. \n",
    "\n",
    "We first begin by generating this dataset. To create a 'horizontal' or 'vertical' line, we assign pixels value to be $\\frac{\\pi}{2}$ which will represent the line in our pixelated image. We create a noisy background by assigning every other pixel a random value between $0$ and $\\frac{\\pi}{4}$ which will create a noisy background. \n",
    "\n",
    "Note that when we create our dataset, we need to split it into the training set and testing set of images, the datasets we train and test our neural network respectively.\n",
    "\n",
    "We also need to label our datasets such that the QCNN can learn to differentiate between the two patterns. In this example we label images with a horizontal line with -1 and images with a vertical line +1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3a674ebf",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def generate_dataset(num_images):\n",
    "    images = []\n",
    "    labels = []\n",
    "    hor_array = np.zeros((6, 8))\n",
    "    ver_array = np.zeros((4, 8))\n",
    "\n",
    "    j = 0\n",
    "    for i in range(0, 7):\n",
    "        if i != 3:\n",
    "            hor_array[j][i] = np.pi / 2\n",
    "            hor_array[j][i + 1] = np.pi / 2\n",
    "            j += 1\n",
    "\n",
    "    j = 0\n",
    "    for i in range(0, 4):\n",
    "        ver_array[j][i] = np.pi / 2\n",
    "        ver_array[j][i + 4] = np.pi / 2\n",
    "        j += 1\n",
    "\n",
    "    for n in range(num_images):\n",
    "        rng = algorithm_globals.random.integers(0, 2)\n",
    "        if rng == 0:\n",
    "            labels.append(-1)\n",
    "            random_image = algorithm_globals.random.integers(0, 6)\n",
    "            images.append(np.array(hor_array[random_image]))\n",
    "        elif rng == 1:\n",
    "            labels.append(1)\n",
    "            random_image = algorithm_globals.random.integers(0, 4)\n",
    "            images.append(np.array(ver_array[random_image]))\n",
    "\n",
    "        # Create noise\n",
    "        for i in range(8):\n",
    "            if images[-1][i] == 0:\n",
    "                images[-1][i] = algorithm_globals.random.uniform(0, np.pi / 4)\n",
    "    return images, labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5b3ca82",
   "metadata": {},
   "source": [
    "Let's now create our dataset below and split it into our test and training datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ed1828c5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "images, labels = generate_dataset(50)\n",
    "\n",
    "train_images, test_images, train_labels, test_labels = train_test_split(\n",
    "    images, labels, test_size=0.3\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6f6952d",
   "metadata": {},
   "source": [
    "Let's see some examples in our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0afeaa5f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 2, figsize=(10, 6), subplot_kw={\"xticks\": [], \"yticks\": []})\n",
    "for i in range(4):\n",
    "    ax[i // 2, i % 2].imshow(\n",
    "        train_images[i].reshape(2, 4),  # Change back to 2 by 4\n",
    "        aspect=\"equal\",\n",
    "    )\n",
    "plt.subplots_adjust(wspace=0.1, hspace=0.025)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85a67058",
   "metadata": {},
   "source": [
    "As we can see each image contains either a vertical or horizontal line, that the QCNN will learn how to differentiate. Now that we have built our dataset, it is time to discuss the components of the QCNN and build our model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eed5d2d6",
   "metadata": {},
   "source": [
    "## 4. Modeling our QCNN"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64efb8d8",
   "metadata": {},
   "source": [
    "Now that we have defined both the convolutional layers it is now time to build our QCNN, which will consist of alternating pooling and convolutional layers.\n",
    "\n",
    "As the images in our dataset contains 8 pixels, we will use 8 qubits in our QCNN. \n",
    "\n",
    "We encode our dataset into our QCNN by applying a feature map. One can create a feature map using one of Qiskit's built in feature maps, such as ZFeatureMap or ZZFeatureMap. \n",
    "\n",
    "After analyzing several different Feature maps for this dataset, it was found that QCNN obtains the greatest accuracy when the Z feature map is used. Therefore, throughout the remainder of the tutorial we will use the Z feature Map, of which can be seen below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0840db7b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 327.397x505.68 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_map = ZFeatureMap(8)\n",
    "feature_map.decompose().draw(\"mpl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cee9549c",
   "metadata": {},
   "source": [
    "We create a function for our QCNN, which will contain three sets of alternating convolutional and pooling layers, which can be seen in the schematic below. Through the use of the pooling layers, we thus reduce the dimensionality of our QCNN from eight qubits to one. "
   ]
  },
  {
   "attachments": {
    "Screenshot%202022-08-10%20at%2021.42.39.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "83ddcb80",
   "metadata": {},
   "source": [
    "![Screenshot%202022-08-10%20at%2021.42.39.png](attachment:Screenshot%202022-08-10%20at%2021.42.39.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7e31982",
   "metadata": {},
   "source": [
    "To classify our image dataset of horizontal and vertical lines, we measure the expectation value of the Pauli Z operator of the final qubit. Based on the obtained value being +1 or -1, we can conclude that the input image contained either a horizontal or vertical line. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e930ed08",
   "metadata": {},
   "source": [
    "## 5. Training our QCNN"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e04e424",
   "metadata": {},
   "source": [
    "The next step is to build our model using our training data. \n",
    "\n",
    "To classify our system, we perform a measurement from the output circuit. The value we obtain will thus classify whether our input data contains either a vertical line or horizontal line. \n",
    "\n",
    "The measurement we have chosen in this tutorial is $<Z>$, i.e. the expectation value of the Pauli Z qubit for the final qubit. Measuring this expectation value, we obtain +1 or -1, which correspond to a vertical or horizontal line respectively. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cc478975",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "feature_map = ZFeatureMap(8)\n",
    "\n",
    "ansatz = QuantumCircuit(8, name=\"Ansatz\")\n",
    "\n",
    "# First Convolutional Layer\n",
    "ansatz.compose(conv_layer(8, \"с1\"), list(range(8)), inplace=True)\n",
    "\n",
    "# First Pooling Layer\n",
    "ansatz.compose(pool_layer([0, 1, 2, 3], [4, 5, 6, 7], \"p1\"), list(range(8)), inplace=True)\n",
    "\n",
    "# Second Convolutional Layer\n",
    "ansatz.compose(conv_layer(4, \"c2\"), list(range(4, 8)), inplace=True)\n",
    "\n",
    "# Second Pooling Layer\n",
    "ansatz.compose(pool_layer([0, 1], [2, 3], \"p2\"), list(range(4, 8)), inplace=True)\n",
    "\n",
    "# Third Convolutional Layer\n",
    "ansatz.compose(conv_layer(2, \"c3\"), list(range(6, 8)), inplace=True)\n",
    "\n",
    "# Third Pooling Layer\n",
    "ansatz.compose(pool_layer([0], [1], \"p3\"), list(range(6, 8)), inplace=True)\n",
    "\n",
    "# Combining the feature map and ansatz\n",
    "circuit = QuantumCircuit(8)\n",
    "circuit.compose(feature_map, range(8), inplace=True)\n",
    "circuit.compose(ansatz, range(8), inplace=True)\n",
    "\n",
    "observable = SparsePauliOp.from_list([(\"Z\" + \"I\" * 7, 1)])\n",
    "\n",
    "# we decompose the circuit for the QNN to avoid additional data copying\n",
    "qnn = EstimatorQNN(\n",
    "    circuit=circuit.decompose(),\n",
    "    observables=observable,\n",
    "    input_params=feature_map.parameters,\n",
    "    weight_params=ansatz.parameters,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a4f6b6e7",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1591.6x1047.48 with 1 Axes>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.draw(\"mpl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db7ec49c",
   "metadata": {},
   "source": [
    "We will also define a callback function to use when training our model. This allows us to view and plot the loss function per each iteration in our training process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d97cc662",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def callback_graph(weights, obj_func_eval):\n",
    "    clear_output(wait=True)\n",
    "    objective_func_vals.append(obj_func_eval)\n",
    "    plt.title(\"Objective function value against iteration\")\n",
    "    plt.xlabel(\"Iteration\")\n",
    "    plt.ylabel(\"Objective function value\")\n",
    "    plt.plot(range(len(objective_func_vals)), objective_func_vals)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4371b5a7",
   "metadata": {},
   "source": [
    "In this example, we will use the COBYLA optimizer to train our classifier, which is a numerical optimization method commonly used for classification machine learning algorithms.\n",
    "\n",
    "We then place the the callback function, optimizer and operator of our QCNN created above into Qiskit's built in Neural Network Classifier, which we can then use to train our model. \n",
    "\n",
    "Since model training may take a long time we have already pre-trained the model for some iterations and saved the pre-trained weights. We'll continue training from that point by setting `initial_point` to a vector of pre-trained weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f2949fc6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "with open(\"11_qcnn_initial_point.json\", \"r\") as f:\n",
    "    initial_point = json.load(f)\n",
    "\n",
    "classifier = NeuralNetworkClassifier(\n",
    "    qnn,\n",
    "    optimizer=COBYLA(maxiter=200),  # Set max iterations here\n",
    "    callback=callback_graph,\n",
    "    initial_point=initial_point,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9061806",
   "metadata": {},
   "source": [
    "After creating this classifier, we can train our QCNN using our training dataset and each image's corresponding label. Because we previously defined the callback function, we plot the overall loss of our system per iteration.\n",
    "\n",
    "It may take some time to train the QCNN so be patient!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0219ff4a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy from the train data : 88.57%\n"
     ]
    }
   ],
   "source": [
    "x = np.asarray(train_images)\n",
    "y = np.asarray(train_labels)\n",
    "\n",
    "objective_func_vals = []\n",
    "plt.rcParams[\"figure.figsize\"] = (12, 6)\n",
    "classifier.fit(x, y)\n",
    "\n",
    "# score classifier\n",
    "print(f\"Accuracy from the train data : {np.round(100 * classifier.score(x, y), 2)}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e95d1100",
   "metadata": {},
   "source": [
    "As we can see from above, the QCNN converges slowly, hence our `initial_point` was already close to an optimal solution. The next step is to determine whether our QCNN can classify data seen in our test image data set. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72b3cf40",
   "metadata": {},
   "source": [
    "## 6. Testing our QCNN"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "571b1b32",
   "metadata": {},
   "source": [
    "After building and training our dataset we now test whether our QCNN can predict images that are not from our test data set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7f2a34ae",
   "metadata": {
    "scrolled": false,
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy from the test data : 80.0%\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_predict = classifier.predict(test_images)\n",
    "x = np.asarray(test_images)\n",
    "y = np.asarray(test_labels)\n",
    "print(f\"Accuracy from the test data : {np.round(100 * classifier.score(x, y), 2)}%\")\n",
    "\n",
    "# Let's see some examples in our dataset\n",
    "fig, ax = plt.subplots(2, 2, figsize=(10, 6), subplot_kw={\"xticks\": [], \"yticks\": []})\n",
    "for i in range(0, 4):\n",
    "    ax[i // 2, i % 2].imshow(test_images[i].reshape(2, 4), aspect=\"equal\")\n",
    "    if y_predict[i] == -1:\n",
    "        ax[i // 2, i % 2].set_title(\"The QCNN predicts this is a Horizontal Line\")\n",
    "    if y_predict[i] == +1:\n",
    "        ax[i // 2, i % 2].set_title(\"The QCNN predicts this is a Vertical Line\")\n",
    "plt.subplots_adjust(wspace=0.1, hspace=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f296307",
   "metadata": {},
   "source": [
    "From above, we can indeed see that our QCNN can classify horizontal and vertical lines! Congratulations! Through the use of quantum circuits and quantum convolutional and pooling layers, you have built a Quantum Convolutional Neural Network! "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a1d8370",
   "metadata": {},
   "source": [
    "## 7. References"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a16174c0",
   "metadata": {},
   "source": [
    "[1] Cong, I., Choi, S. & Lukin, M.D. Quantum convolutional neural networks. Nat. Phys. 15, 1273–1278 (2019). https://doi.org/10.1038/s41567-019-0648-8\n",
    "\n",
    "[2] IBM Convolutional Neural Networks https://www.ibm.com/cloud/learn/convolutional-neural-networks\n",
    "\n",
    "[3] Vatan, Farrokh, and Colin Williams. \"Optimal quantum circuits for general two-qubit gates.\" Physical Review A 69.3 (2004): 032315."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "220ffdcf",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.22.1</td></tr><tr><td><code>qiskit-aer</code></td><td>0.11.1</td></tr><tr><td><code>qiskit-ignis</code></td><td>0.7.0</td></tr><tr><td><code>qiskit</code></td><td>0.33.0</td></tr><tr><td><code>qiskit-machine-learning</code></td><td>0.5.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.7.9</td></tr><tr><td>Python compiler</td><td>MSC v.1916 64 bit (AMD64)</td></tr><tr><td>Python build</td><td>default, Aug 31 2020 17:10:11</td></tr><tr><td>OS</td><td>Windows</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>31.837730407714844</td></tr><tr><td colspan='2'>Fri Nov 04 00:37:45 2022 GMT Standard Time</td></tr></table>"
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       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>&copy; Copyright IBM 2017, 2022.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
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       "<IPython.core.display.HTML object>"
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   "source": [
    "import qiskit.tools.jupyter\n",
    "\n",
    "%qiskit_version_table\n",
    "%qiskit_copyright"
   ]
  }
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