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{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "id": "omoggHOXqtzF"
      },
      "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": "9Z7Jd2A_qtzG"
      },
      "source": [
        "Tensor-network quantum circuits {#tn_circuits}\n",
        "===============================\n",
        "\n",
        "::: {.meta}\n",
        ":property=\\\"og:description\\\": This demonstration explains how to\n",
        "simulate tensor-network quantum circuits. :property=\\\"og:image\\\":\n",
        "<https://pennylane.ai/qml/_images/thumbnail_tn_circuits.png>\n",
        ":::\n",
        "\n",
        "::: {.related}\n",
        "tutorial\\_variational\\_classifier Variational classifier\n",
        ":::\n",
        "\n",
        "*Authors: Diego Guala*^1^ *, Esther Cruz-Rico*^2^ *, Shaoming Zhang*^2^\n",
        "*, Juan Miguel Arrazola*^1^ *--- Posted: 29 March 2022. Last updated: 27\n",
        "June 2022.*\n",
        "\n",
        "| ^1^ Xanadu, Toronto, ON, M5G 2C8, Canada\n",
        "| ^2^ BMW Group, Munich, Germany\n",
        "\n",
        "This demonstration explains how to use PennyLane templates to design and\n",
        "implement tensor-network quantum circuits as in Ref.. Tensor-network\n",
        "quantum circuits emulate the shape and connectivity of tensor networks\n",
        "such as matrix product states and tree tensor networks.\n",
        "\n",
        "We begin with a short introduction to tensor networks and explain their\n",
        "relationship to quantum circuits. Next, we illustrate how PennyLane\\'s\n",
        "templates make it easy to design, customize, and simulate these\n",
        "circuits. Finally, we show how to use the circuits to learn to classify\n",
        "the bars and stripes data set. This is a toy problem where the template\n",
        "learns to recognize whether an image exhibits horizontal stripes or\n",
        "vertical bars.\n",
        "\n",
        "Tensors and Tensor Networks\n",
        "---------------------------\n",
        "\n",
        "Tensors are multi-dimensional arrays of numbers. Intuitively, they can\n",
        "be interpreted as a generalization of scalars, vectors, and matrices.\n",
        "Tensors can be described by their rank, indices, and the dimension of\n",
        "the indices. The rank is the number of indices in a tensor --- a scalar\n",
        "has rank zero, a vector has rank one, and a matrix has rank two. The\n",
        "dimension of an index is the number of values that index can take. For\n",
        "example, a vector with three elements has one index that can take three\n",
        "values. This is vector is therefore a rank one tensor and its index has\n",
        "dimension three.\n",
        "\n",
        "To define tensor networks, it is important to first understand tensor\n",
        "contraction. Two or more tensors can be contracted by summing over\n",
        "repeated indices. In diagrammatic notation, the repeated indices appear\n",
        "as lines connecting tensors, as in the figure below. We see two tensors\n",
        "of rank two connected by one repeated index, $k$. The dimension of the\n",
        "repeated index is called the bond dimension.\n",
        "\n",
        "![image](../demonstrations/tn_circuits/simple_tn_color.PNG){.align-center\n",
        "width=\"50.0%\"}\n",
        "\n",
        "The contraction of the tensors above is equivalent to the standard\n",
        "matrix multiplication formula and can be expressed as\n",
        "\n",
        "$$C_{ij} = \\sum_{k}A_{ik}B_{kj},$$\n",
        "\n",
        "where $C_{ij}$ denotes the entry for the $i$-th row and $j$-th column of\n",
        "the product $C=AB$.\n",
        "\n",
        "A tensor network is a collection of tensors where a subset of all\n",
        "indices are contracted. As mentioned above, we can use diagrammatic\n",
        "notation to specify which indices and tensors will be contracted\n",
        "together by connecting individual tensors with lines. Tensor networks\n",
        "can represent complicated operations involving several tensors with many\n",
        "indices contracted in sophisticated patterns.\n",
        "\n",
        "Two well-known tensor network architectures are matrix product states\n",
        "(MPS) and tree tensor networks (TTN). These follow specific patterns of\n",
        "connections between tensors and can be extended to have many or few\n",
        "indices. Examples of these architectures with only a few tensors can be\n",
        "seen in the figure below. An MPS is shown on the left and a TTN on the\n",
        "right.\n",
        "\n",
        "![image](../demonstrations/tn_circuits/MPS_TTN_Color.PNG){.align-center\n",
        "width=\"50.0%\"}\n",
        "\n",
        "These tensor networks are commonly used to efficiently represent certain\n",
        "many-body quantum states. Every quantum circuit can be represented as a\n",
        "tensor network, with the bond dimension dependent on the width and\n",
        "connectivity of the circuit. Moreover, one can design quantum circuits\n",
        "that have the same connectivity as well-known tensor networks like MPS\n",
        "and TTN. We call these **tensor-network quantum circuits**. Note that\n",
        "the connectivity of a tensor network is related to how entanglement is\n",
        "distributed and how correlations spread in the resulting tensor-network\n",
        "quantum circuit. We therefore design circuits based on the tensor\n",
        "networks that best capture the information we want to extract.\n",
        "\n",
        "In tensor-network quantum circuits, the tensor network architecture acts\n",
        "as a guideline for the shape of the quantum circuit. More specifically,\n",
        "the tensors in the tensor networks above are replaced with unitary\n",
        "operations to obtain quantum circuits, as illustrated in the figure\n",
        "below.\n",
        "\n",
        "![image](../demonstrations/tn_circuits/MPS_TTN_Circuit_Color.PNG){.align-center\n",
        "width=\"70.0%\"}\n",
        "\n",
        "Since the unitary operations $U_1$ to $U_3$ are in principle completely\n",
        "general, it is not always clear how to implement them with a specific\n",
        "gate set. Instead, we can replace the unitary operations with\n",
        "variational quantum circuits determined by a specific template of\n",
        "choice. The PennyLane tensor network templates allow us to do precisely\n",
        "this: implement tensor-network quantum circuits with user-defined\n",
        "circuit ansatze as the unitary operations. In this sense, just as a\n",
        "template is a strategy for arranging parametrized gates, tensor-network\n",
        "quantum circuits are strategies for structuring circuit templates. They\n",
        "can therefore be interpreted as templates of templates, i.e., as\n",
        "meta-templates.\n",
        "\n",
        "PennyLane Implementation\n",
        "------------------------\n",
        "\n",
        "We now demonstrate how to use PennyLane to build and simulate\n",
        "tensor-network quantum circuits.\n",
        "\n",
        "The first step is to define the circuit that will be broadcast into the\n",
        "tensor network shape. We call this a block. The block defines a\n",
        "variational quantum circuit that takes the position of tensors in the\n",
        "network.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "id": "PTO3jCY1qtzH"
      },
      "outputs": [],
      "source": [
        "import pennylane as qml\n",
        "from pennylane import numpy as np\n",
        "\n",
        "\n",
        "def block(weights, wires):\n",
        "    qml.RX(weights[0], wires=wires[0])\n",
        "    qml.RY(weights[1], wires=wires[1])\n",
        "    qml.CNOT(wires=wires)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qJWWfT73qtzH"
      },
      "source": [
        "With the block defined, we can build the full tensor-network quantum\n",
        "circuit. The following code broadcasts the above block into the shape of\n",
        "an MPS tensor network and computes the expectation value of a Pauli Z\n",
        "operator on the bottom qubit.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
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          "height": 532
        },
        "id": "RB_v1htyqtzH",
        "outputId": "3eeb15ea-ffdd-46f6-b318-41d33e37995d"
      },
      "outputs": [
        {
          "output_type": "display_data",
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          },
          "metadata": {}
        }
      ],
      "source": [
        "dev = qml.device(\"default.qubit\", wires=20)\n",
        "\n",
        "\n",
        "@qml.qnode(dev, interface=\"autograd\")\n",
        "def circuit(template_weights):\n",
        "    qml.MPS(\n",
        "        wires=range(20),\n",
        "        n_block_wires=2,\n",
        "        block=block,\n",
        "        n_params_block=2,\n",
        "        template_weights=template_weights,\n",
        "    )\n",
        "    return qml.expval(qml.PauliZ(wires=19))\n",
        "\n",
        "\n",
        "np.random.seed(1)\n",
        "weights = np.random.random(size=[19, 2])\n",
        "qml.drawer.use_style(\"pennylane\")\n",
        "fig, ax = qml.draw_mpl(circuit, expansion_strategy=\"device\")(weights)\n",
        "fig.set_size_inches((48, 24))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U51UHgieqtzI"
      },
      "source": [
        "Using the `~pennylane.MPS`{.interpreted-text role=\"class\"} template we\n",
        "can easily change the block type, depth, and size. For example, the\n",
        "block can contain a template like\n",
        "`~pennylane.StronglyEntanglingLayers`{.interpreted-text role=\"class\"},\n",
        "yielding a deeper block.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "id": "o5CdoHOVqtzI"
      },
      "outputs": [],
      "source": [
        "def deep_block(weights, wires):\n",
        "    qml.StronglyEntanglingLayers(weights, wires)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZA19lHijqtzI"
      },
      "source": [
        "We can use the `~pennylane.MPS`{.interpreted-text role=\"class\"} template\n",
        "again and simply set `n_params_block = 3` to suit the new block.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "id": "NYC8u_nBqtzI"
      },
      "outputs": [],
      "source": [
        "dev = qml.device(\"default.qubit\", wires=4)\n",
        "\n",
        "\n",
        "@qml.qnode(dev, interface=\"autograd\")\n",
        "def circuit(template_weights):\n",
        "    qml.MPS(\n",
        "        wires=range(4),\n",
        "        n_block_wires=2,\n",
        "        block=deep_block,\n",
        "        n_params_block=3,\n",
        "        template_weights=template_weights,\n",
        "    )\n",
        "    return qml.expval(qml.PauliZ(wires=3))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VLvvNISZqtzI"
      },
      "source": [
        "To ensure that the weights of the block and `template_weights` sent to\n",
        "the `~pennylane.MPS`{.interpreted-text role=\"class\"} template are\n",
        "compatible, we use the\n",
        "`~pennylane.StronglyEntanglingLayers.shape`{.interpreted-text\n",
        "role=\"class\"} function and replicate the elemnts for the number of\n",
        "expected blocks. Since this example will have three blocks, we replicate\n",
        "the elements three times using `[list]*3`. The resulting circuit is\n",
        "illustrated in the figure below the code. Note that this circuit retains\n",
        "the layout of an MPS, but each block is now a deeper circuit with more\n",
        "gates. Both this circuit and the previous circuit can be represented by\n",
        "an MPS with a bond dimension of two.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "WnFzFoclqtzI",
        "outputId": "f9e0bea1-26ea-4f7f-f32a-20f85e6a720b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2100x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "shape = qml.StronglyEntanglingLayers.shape(n_layers=2, n_wires=2)\n",
        "template_weights = [np.random.random(size=shape)] * 3\n",
        "fig, ax = qml.draw_mpl(circuit, expansion_strategy=\"device\")(template_weights)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l8XS5uCuqtzI"
      },
      "source": [
        "In addition to deep blocks, we can easily expand to wider blocks with\n",
        "more input wires. In the next example, we use the\n",
        "`~pennylane.SimplifiedTwoDesign`{.interpreted-text role=\"class\"}\n",
        "template as the block.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "id": "wW-4yYPTqtzI"
      },
      "outputs": [],
      "source": [
        "def wide_block(weights, wires):\n",
        "    qml.SimplifiedTwoDesign(initial_layer_weights=weights[0], weights=weights[1], wires=wires)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jl-yrmJmqtzI"
      },
      "source": [
        "To implement this wider block, we can use the\n",
        "`~pennylane.MPS`{.interpreted-text role=\"class\"} template as before. To\n",
        "account for the extra wires per block, we simply set the `n_block_wires`\n",
        "argument to a higher number. The figure below shows the resulting\n",
        "circuit. Notice that, in the circuit diagram, gates are left-justified.\n",
        "Therefore parts of later blocks appear near the beginning of the\n",
        "circuit. Furthermore, this circuit has a higher bond dimension than the\n",
        "previous ones and would correspond to an MPS with a bond dimension of\n",
        "four.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "eDZXVDOUqtzJ",
        "outputId": "772f850b-4b37-452d-9220-15c1bd8fdbb9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/autoray/autoray.py:80: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  return get_lib_fn(backend, fn)(*args, **kwargs)\n",
            "/usr/local/lib/python3.10/dist-packages/pennylane/operation.py:1054: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  self.data = tuple(np.array(p) if isinstance(p, (list, tuple)) else p for p in params)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x900 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "dev = qml.device(\"default.qubit\", wires=8)\n",
        "\n",
        "\n",
        "@qml.qnode(dev, interface=\"autograd\")\n",
        "def circuit(template_weights):\n",
        "    qml.MPS(\n",
        "        wires=range(8),\n",
        "        n_block_wires=4,\n",
        "        block=wide_block,\n",
        "        n_params_block=2,\n",
        "        template_weights=template_weights,\n",
        "    )\n",
        "    return qml.expval(qml.PauliZ(wires=7))\n",
        "\n",
        "\n",
        "shapes = qml.SimplifiedTwoDesign.shape(n_layers=1, n_wires=4)\n",
        "weights = [np.random.random(size=shape) for shape in shapes]\n",
        "template_weights = [weights] * 3\n",
        "fig, ax = qml.draw_mpl(circuit, expansion_strategy=\"device\")(template_weights)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nu25mRjnqtzJ"
      },
      "source": [
        "We can also broadcast a block to the tree tensor network architecture by\n",
        "using the `~pennylane.TTN`{.interpreted-text role=\"class\"} template.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        },
        "id": "jxr_y7MVqtzJ",
        "outputId": "5042bc8f-2af2-481d-e7c0-03c5749443fa"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "def block(weights, wires):\n",
        "    qml.RX(weights[0], wires=wires[0])\n",
        "    qml.RX(weights[1], wires=wires[1])\n",
        "    qml.CNOT(wires=wires)\n",
        "\n",
        "\n",
        "dev = qml.device(\"default.qubit\", wires=8)\n",
        "\n",
        "\n",
        "@qml.qnode(dev, interface=\"autograd\")\n",
        "def circuit(template_weights):\n",
        "    qml.TTN(\n",
        "        wires=range(8),\n",
        "        n_block_wires=2,\n",
        "        block=block,\n",
        "        n_params_block=2,\n",
        "        template_weights=template_weights,\n",
        "    )\n",
        "    return qml.expval(qml.PauliZ(wires=7))\n",
        "\n",
        "\n",
        "weights = np.random.random(size=[7, 2])\n",
        "fig, ax = qml.draw_mpl(circuit, expansion_strategy=\"device\")(weights)\n",
        "fig.set_size_inches((4, 4))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WbzXoXTOqtzJ"
      },
      "source": [
        "Classifying the bars and stripes data set\n",
        "=========================================\n",
        "\n",
        "Next, we use a tensor-network quantum circuit to tackle a toy machine\n",
        "learning problem. For this, we use the bars and stripes data set and\n",
        "optimize a parametrized circuit to label the images as either bars or\n",
        "stripes. The data set is composed of binary black and white images of\n",
        "size $n \\times n$ pixels. In images that should receive the bars label,\n",
        "all pixels in any given column have the same color. In images with the\n",
        "stripes label, all pixels in any given row have the same color. The full\n",
        "data set for $4\\times 4$ images is shown in the image below:\n",
        "\n",
        "![](../demonstrations/tn_circuits/BAS.png){.align-center height=\"300px\"}\n",
        "\n",
        "A quantum circuit that successfully performs this task accepts any image\n",
        "from the data set as input and outputs the correct label. We will\n",
        "therefore choose a data encoding strategy that can record the input\n",
        "image in a qubit register, a processing circuit that can analyze the\n",
        "data, and a final measurement that can serve as a label of either\n",
        "stripes or bars.\n",
        "\n",
        "The first step is to generate the bars and stripes data set. For\n",
        "$2\\times 2$ images, we can manually define the full data set, giving\n",
        "white pixels a value of 1 and black pixels a value of 0:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "jFqOsGo_qtzJ",
        "outputId": "3874d0db-6d96-47e0-ecd5-43466102463d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 300x300 with 4 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "BAS = [[1, 1, 0, 0], [0, 0, 1, 1], [1, 0, 1, 0], [0, 1, 0, 1]]\n",
        "j = 1\n",
        "plt.figure(figsize=[3, 3])\n",
        "for i in BAS:\n",
        "    plt.subplot(2, 2, j)\n",
        "    j += 1\n",
        "    plt.imshow(np.reshape(i, [2, 2]), cmap=\"gray\")\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ijzq5BxfqtzJ"
      },
      "source": [
        "The next step is to define the parameterized quantum circuit that will\n",
        "be trained to label the images. This involves determining the block and\n",
        "the tensor-network architecture. For the block, a circuit consisting of\n",
        "`~pennylane.RY`{.interpreted-text role=\"class\"} rotations and\n",
        "`~pennylane.CNOT`{.interpreted-text role=\"class\"} gates suffices for\n",
        "this simple data set.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {
        "id": "6gyHkHrBqtzJ"
      },
      "outputs": [],
      "source": [
        "def block(weights, wires):\n",
        "    qml.RY(weights[0], wires=wires[0])\n",
        "    qml.RY(weights[1], wires=wires[1])\n",
        "    qml.RY(weights[0], wires=wires[0])\n",
        "    qml.RY(weights[1], wires=wires[1])\n",
        "    qml.CNOT(wires=wires)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mwn7JsGYqtzJ"
      },
      "source": [
        "As for the tensor-network architecture, we use the tree tensor-network\n",
        "quantum circuit. We use\n",
        "`~pennylane.BasisStatePreparation`{.interpreted-text role=\"class\"} to\n",
        "encode the input images. The following code implements the\n",
        "`~pennylane.BasisStatePreparation`{.interpreted-text role=\"class\"}\n",
        "encoding, followed by a `~pennylane.TTN`{.interpreted-text role=\"class\"}\n",
        "circuit using the above `block`. Finally, we compute the expectation\n",
        "value of a `~pennylane.PauliZ`{.interpreted-text role=\"class\"}\n",
        "measurement as the output. The circuit diagram below shows the full\n",
        "circuit. The `~pennylane.BasisStatePreparation`{.interpreted-text\n",
        "role=\"class\"} encoding appears in the initial\n",
        "`~pennylane.PauliX`{.interpreted-text role=\"class\"} gates.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 87,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        },
        "id": "6y0ZOlGaqtzJ",
        "outputId": "f0e3162e-1ef2-4336-c71e-8b3531f7b3d6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x350 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "dev = qml.device(\"default.qubit\", wires=4)\n",
        "\n",
        "\n",
        "@qml.qnode(dev, interface=\"autograd\")\n",
        "def circuit(image, template_weights):\n",
        "    qml.BasisStatePreparation(image, wires=range(4))\n",
        "    qml.TTN(\n",
        "        wires=range(4),\n",
        "        n_block_wires=2,\n",
        "        block=block,\n",
        "        n_params_block=2,\n",
        "        template_weights=template_weights,\n",
        "    )\n",
        "    return qml.expval(qml.PauliZ(wires=3))\n",
        "\n",
        "\n",
        "weights = np.random.random(size=[3, 2])\n",
        "fig, ax = qml.draw_mpl(circuit, expansion_strategy=\"device\")(BAS[0], weights)\n",
        "fig.set_size_inches((6, 3.5))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B1hZ5gssqtzJ"
      },
      "source": [
        "When the output of the above circuit is less than zero, we label the\n",
        "image \\\"stripes\\\", otherwise we label it \\\"bars\\\". Based on these\n",
        "labels, we define a cost function to train the circuit. The cost\n",
        "function in the following code adds the expectation value result if the\n",
        "label should be negative and subtracts the result if the label should be\n",
        "positive. In other words, the cost will be minimized when the stripes\n",
        "images output negative one and the bars images output positive one.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "metadata": {
        "id": "O1mGhdIxqtzJ"
      },
      "outputs": [],
      "source": [
        "def costfunc(params):\n",
        "    cost = 0\n",
        "    for i in range(len(BAS)):\n",
        "        if i < len(BAS) / 2:\n",
        "            cost += circuit(BAS[i], params)\n",
        "        else:\n",
        "            cost -= circuit(BAS[i], params)\n",
        "    return cost"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OVv9FDaSqtzJ"
      },
      "source": [
        "Finally, we initialize the parameters and use PennyLane's built-in\n",
        "optimizer train the circuit over 100 iterations. This optimizer will\n",
        "attempt to minimize the cost function.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "ecXuIDimqtzJ",
        "outputId": "aa263ae3-be4e-40a5-d2c9-89a388738fa9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Step 0, cost: -0.13330292786325848\n",
            "Step 20, cost: -3.9999999988195585\n",
            "Step 40, cost: -4.0\n",
            "Step 60, cost: -4.0\n",
            "Step 80, cost: -3.9999999999999982\n"
          ]
        }
      ],
      "source": [
        "params = np.random.random(size=[3, 2], requires_grad=True)\n",
        "optimizer = qml.GradientDescentOptimizer(stepsize=0.1)\n",
        "\n",
        "for k in range(100):\n",
        "    if k % 20 == 0:\n",
        "        print(f\"Step {k}, cost: {costfunc(params)}\")\n",
        "    params = optimizer.step(costfunc, params)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XdGSFaDXqtzJ"
      },
      "source": [
        "With the circuit trained and the parameters stored in `params`, we can\n",
        "now show the full circuits and the resulting output for each image.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1898
        },
        "id": "2HJMoXFIqtzJ",
        "outputId": "f4cba555-e72e-4bb2-eb55-d6013adadc16"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 180x180 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 180x180 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 180x180 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 180x180 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKoAAACuCAYAAACm55aUAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAKAUlEQVR4nO3af0xV9R/H8dcRZ6iBSTCMXX60SVe5MK7gIlnXQksNNoTBILAJ626MWavV5rTWiq3WD3Ne19Z/JjdKGRoFf9T8QwSdEyNWsNCBa4zGYsYCghvC9HLe3z/a906+IXC5ly++6fXY7gb38Dmfz7n3yTnnDgwRERDd51Ys9QKI5oOhkgoMlVRgqKQCQyUVGCqpwFBJBYZKKjBUUsGvUBMSEmC1WmG3232Pn3/+OeiLmpycREREBLq6uqY9Pzg4iLVr1+L333+/59jy8nIcP3486GsCgB9++AGZmZlYs2YN8vLy5j2upaUFdrvdr7kWMgYA3G63X2uby9NPP41HH30UdrsdNpsN2dnZs77+i8XvM2pdXR06Ojp8j5SUlKAvKjQ0FPv27UN1dfW052tqarBr1y5ER0cHfc75eOSRR3D8+HG4XK4lmX+puFwudHR04Nq1a4iPj8fRo0f93sfU1FRAawjKpb+npwcWiwW9vb0AgKNHj2LPnj0wTRNutxs7duxAbm4ukpKSsH37dvT19c25T6fTiS+//BJ37tzxPVddXQ2n04mmpiZs27YNW7Zsgc1mw2effRaMw5iTxWLB448/jgceeOAf29rb25Gdne3X/rxeL3bv3o2tW7fCZrOhtLQU4+Pj07bv378fycnJSE9PR0dHh2/bF198gYyMDKSlpWH79u3o7Oxc8HH5s96//voL69evBwDcvHkTWVlZSE9Ph81mw8svvwzTNAH8fWbPyspCQUEBUlJS0NbWhvfeew+bN2/2XY1//fXX+U8ufoiPj5fHHntMUlNTfY9bt26JiMjp06clPT1dmpubJSEhQQYHB0VEpLq6WlatWiXXr18XEZGPPvpInn322XnNl5aWJt98842IiLS2tkpMTIx4vV4ZHh4Wr9crIiJDQ0MSFxcn/f39IiJSVlYmLpdrzn13d3dPO467H+Xl5bOOra6ulr17987rGEREmpubJTU19R/Pm6Ypf/zxh+/ryspK+eCDD3xjAMj58+dFRKSurk6sVquYpimXL1+W5557TiYnJ0VE5NKlS5KUlDTn2o4cOXLPY/76669nHPPUU09JQkKCpKamSmRkpKSkpMiff/4pIiITExPi8XhERMTr9UpOTo7U1tb61rF69Wrp7u4WEZHh4WFZt26dr5fx8XGZmJiY92u40t/fqrq6uhnvnUpKStDc3Izdu3ejqakJUVFRvm2ZmZnYvHkzAKCiogJvvfUWpqamEBISMutcTqcTJ0+eRF5eHk6ePImysjKEhIRgaGgITqcTN27cwMqVKzE0NISuri5YLJZ5H4fVap12hloKIgKXy4Vvv/0WXq8Xo6OjyMzM9G1PSEjAzp07AQBFRUWoqKhAf38/Ghsb0dnZiYyMDN/PDg8PY2JiYtb5Dh48iIMHD/q9TpfLhby8PIgIqqqq8OKLL6K+vh6maeLQoUO4fPkyRASDg4NITk7G888/D+Dv991qtQIAwsPDkZiYiBdeeAG7du1CTk6OX++X36Hei9frRVdXFyIiIvDbb78FZZ+lpaU4fPgwent7cebMGbS3twMAKisrkZ2djfr6ehiGgbS0NExOTvq1756eHhQXF8+4bcuWLf+4P14Mp0+fxoULF3Dx4kWEh4fjk08+wYULF+7584ZhwDAMiAjKysrw/vvv+zXfxx9/jFOnTs247Z133kF+fv6s4w3DQHFxse8e9dixYxgcHMT333+P0NBQvP7669PehwcffND3dUhICK5evYorV66gpaUFTzzxBGpra+FwOOa19qCFevjwYVitVtTU1PjuWzZu3AgAaG1tRXd3NzZt2oQTJ04gKytrzrMpADz00EPIzc1FcXEx7Ha7b38jIyOIj4+HYRi4dOnSgu7Pgn1GbWtrwxtvvIGmpqZ5jxkZGUFkZCTCw8Ph8XjgdrsRFxfn297X14fm5mZkZWXhq6++QnR0NCwWC3Jzc7Fv3z5UVlYiLi4Opmnixx9/xNatW2edb6Fn1Ls1NTX5zpIjIyPYsGEDQkNDcfPmTZw9exYFBQUzjvN4PPB4PHA4HHA4HLh27Rp++umnxQu1uLgYq1ev9n3vcrkwPj6Oc+fOoa2tDWvWrMGxY8dQVFSEK1euAPj7EnDo0CH88ssvePjhh1FTUwMAGBgYQHZ29qzBOJ1O7NixwzcGAD788EMcOHAA7777Lux2+7RL4N3a29vx9ttv47vvvvP3MGfU09ODnTt34tatW5iYmIDFYsGbb76JAwcOoK+vb9rr8r+uX78+7VK3bds2nDhxAo2NjbBarYiKioLD4Zj2AcNms8HtduOVV17BqlWrUFtbC8Mw4HA4cOTIEeTn58Pr9eL27dvIycmZM9SFeu2111BVVQXTNLF+/Xp8/vnnAIBXX30VhYWFsNlsiImJwTPPPHPPfYyOjqKwsBDj4+MwDAOJiYkoKyub9xoMkcX9D3+3242GhgY0NDQs5jRL7qWXXkJJSQmefPLJpV7KshS0S/+/3aeffrrUS1jWFv2MShQM/Fs/qcBQSQWGSioE9GHKNE0MDAwgLCwMhmEEa020zIgIPB4PYmJisGLFws6NAYU6MDCA2NjYQHZB/yL9/f1+/dn0bgGFGhYWFsjwZWt0dHSpl3BfGRsbQ2xsbEC9BBQqL/czCw8PX+ol3JcC6YUfpkgFhkoqMFRSgaGSCgyVVGCopAJDJRUYKqnAUEkFhkoqMFRSgaGSCgyVVGCopAJDJRUYKqnAUEkFhkoqMFRSgaGSCgyVVGCopAJDJRUYKqnAUEkFhkoqMFRSgaGSCgyVVGCopAJDJRUYKqnAUEkFhkoqMFRSgaGSCgyVVGCopAJDJRUYKqnAUEkFhkoqMFRSgaGSCgyVVGCopAJDJRUYKqnAUEkFhkoqMFRSgaGSCgyVVGCopAJDJRUYKqnAUEkFhkoqMFRSgaGSCgyVVGCopAJDJRUYKqnAUEkFhkoqMFRSgaGSCgyVVGCopAJDJRUYKqnAUEkFhkoqMFRSgaGSCgyVVGCopAJDJRUYKqnAUEkFhkoqMFRSgaGSCgyVVGCopAJDJRUYKqnAUEkFhkoqMFRSgaGSCgyVVGCopAJDJRUYKqnAUEkFhkoqMFRSgaGSCgyVVGCopAJDJRUYKqnAUEkFhkoqMFRSgaGSCgyVVGCopAJDJRUYKqnAUEkFhkoqMFRSgaGSCgyVVGCopAJDJRUYKqnAUEkFhkoqMFRSgaGSCgyVVGCopAJDJRUYKqnAUEkFhkoqMFRSgaGSCgyVVGCopAJDJRVWBjJYRIK1jmVlbGxsqZdwX/nv6xFILwGF6vF4Ahm+bK1bt26pl3Bf8ng8C35tDAkgc9M0MTAwgLCwMBiGsdDd0DInIvB4PIiJicGKFQu72wwoVKL/F36YIhUYKqnAUEkFhkoqMFRSgaGSCgyVVPgPfOuL+z0/2KIAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "for image in BAS:\n",
        "    fig, ax = qml.draw_mpl(circuit, expansion_strategy=\"device\")(image, params)\n",
        "    plt.figure(figsize=[1.8, 1.8])\n",
        "    plt.imshow(np.reshape(image, [2, 2]), cmap=\"gray\")\n",
        "    plt.title(\n",
        "        f\"Exp. Val. = {circuit(image,params):.0f};\"\n",
        "        + f\" Label = {'Bars' if circuit(image,params)>0 else 'Stripes'}\",\n",
        "        fontsize=8,\n",
        "    )\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Rl2lHItqtzK"
      },
      "source": [
        "The resulting labels are all correct. For images with stripes, the\n",
        "circuit outputs an expectation value of minus one, corresponding to\n",
        "stripes and for images with bars the circuit outputs an expectation\n",
        "value of positive one, corresponding to bars.\n",
        "\n",
        "References {#tn_circuits_references}\n",
        "==========\n",
        "\n",
        "About the authors\n",
        "=================\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
}