{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(sec-extend)=\n",
    "# Advanced: Extending lambeq\n",
    "\n",
    "In this tutorial you will find examples of how to extend `lambeq` to add more {term}`readers <reader>`, {term}`rewrite rules <rewrite rule>` and {term}`ansätze <ansatz (plural: ansätze)>`, so you can start making your own [contributions](https://github.com/CQCL/lambeq/pulls) to the toolkit.\n",
    "\n",
    "{download}`⬇️ Download code <../_code/extend-lambeq.ipynb>`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating readers\n",
    "\n",
    "The {py:class}`.Reader` class is an abstract base class for converting sentences to diagrams. Each {term}`reader` can be seen as a different {term}`compositional model`, and `lambeq` can accommodate any compositional model that represents sentences in a {term}`string diagram`/{term}`tensor network` form.\n",
    "\n",
    "A concrete subclass of {py:class}`.Reader` should implement the {py:meth}`.Reader.sentence2diagram` method, which converts a single sentence into a rigid diagram.\n",
    "\n",
    "### Reader example: \"Comb\" reader\n",
    "\n",
    "In this example we will create a reader that, given a sentence, it generates the following tensor network:\n",
    "\n",
    "<center>\n",
    "<img src=\"../_static/images/linear.png\" alt=\"drawing\" width=\"320\" style=\"margin: 20px 2px 2px 2px;\"/>\n",
    "</center>\n",
    "\n",
    "Note that the particular compositional model is not appropriate for classical experiments, since the tensor that implements the layer can become very large for long sentences. However, the model can be implemented without problems on a quantum computer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from lambeq import AtomicType, Reader\n",
    "from lambeq.backend.grammar import Box, Id, Word\n",
    "\n",
    "\n",
    "N = AtomicType.NOUN\n",
    "\n",
    "class CombReader(Reader):\n",
    "    def sentence2diagram(self, sentence):\n",
    "        words = Id().tensor(*[Word(w, N) for w in sentence.split()])\n",
    "        layer = Box('LAYER', words.cod, N)\n",
    "        return words >> layer\n",
    "\n",
    "diagram = CombReader().sentence2diagram('John gave Mary a flower')\n",
    "diagram.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that, in the above code, the method {py:meth}`~lambeq.backend.grammar.Diagram.tensor` refers to the monoidal product and not to a physical tensor object. What the specific line does, using the monoidal identity {py:obj}`Id()` as a starting point, is to tensor one-by-one the boxes of the words in the sentence accumulatively, from left to right, into a single diagram, as in a standard fold operation.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Id().tensor(*[Word(w, N) for w in ['John', 'gave', 'Mary', 'a', 'flower']]).draw(figsize=(5,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This diagram is then combined with the `layer` box to create the final output of the {term}`reader`.\n",
    "\n",
    "```{note}\n",
    "In an actual implementation, the `layer` box should be shared among all sentences so it can be trained properly.\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating rewrite rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lambeq import BobcatParser\n",
    "\n",
    "\n",
    "parser = BobcatParser(verbose='text')\n",
    "d = parser.sentence2diagram('The food is fresh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SimpleRewriteRule example: Negation functor\n",
    "\n",
    "The {py:class}`.SimpleRewriteRule` class contains functionality that facilitates the creation of simple {term}`rewrite rules <rewrite rule>`, without the need to define a new {py:class}`.RewriteRule` class from scratch. A {py:class}`.SimpleRewriteRule` finds words with codomain `cod` and name in list `words`, then replaces their boxes with the diagram in `template`.\n",
    "\n",
    "Here is an example of a negation {term}`functor` using {py:class}`.SimpleRewriteRule`. The functor adds a \"NOT\" box to the wire of certain auxiliary verbs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lambeq import AtomicType, SimpleRewriteRule\n",
    "\n",
    "\n",
    "N = AtomicType.NOUN\n",
    "S = AtomicType.SENTENCE\n",
    "adj = N @ N.l\n",
    "\n",
    "NOT = Box('NOT', S, S)\n",
    "\n",
    "negation_rewrite = SimpleRewriteRule(\n",
    "    cod=N.r @ S @ S.l @ N,\n",
    "    template=SimpleRewriteRule.placeholder(N.r @ S @ S.l @ N) >> Id(N.r) @ NOT @ Id(S.l @ N),\n",
    "    words=['is', 'was', 'has', 'have'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{note}\n",
    "The placeholder `SimpleRewriteRule.placeholder(t)` in the template above will be replaced by a box with the same name as the original box and type `t`.\n",
    "```\n",
    "\n",
    "A list of {py:class}`.RewriteRule`s can be passed to {py:class}`.Rewriter` to create a rewriting {term}`functor`. If no list is provided, then the default rewriting rules are used (see [Diagram Rewriting](./rewrite.ipynb))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lambeq import Rewriter\n",
    "from lambeq.backend import draw_equation\n",
    "\n",
    "\n",
    "not_d = Rewriter([negation_rewrite])(d)\n",
    "draw_equation(d, not_d, symbol='->', figsize=(14, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RewriteRule example: \"Past\" functor\n",
    "\n",
    "Sometimes, a {term}`rewrite rule` may become too complicated to be implemented using the {py:class}`.SimpleRewriteRule` class, so the more general {py:class}`.RewriteRule` class should be used instead. A concrete subclass of a {py:class}`.RewriteRule` should implement the methods {py:meth}`~.RewriteRule.matches` and {py:meth}`~.RewriteRule.rewrite`.\n",
    "\n",
    "A rewriter uses the {py:meth}`~.RewriteRule.matches` methods of its {py:class}`.RewriteRule`s to detect if a rule can be applied. If there is a match, then the matching box is replaced with the result of `rewrite(box)`.\n",
    "\n",
    "In the following example, a {term}`functor` that changes the tense of certain auxiliary verbs is implemented by directly subclassing {py:class}`.RewriteRule`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lambeq import RewriteRule\n",
    "\n",
    "\n",
    "class PastRewriteRule(RewriteRule):\n",
    "    mapping = {\n",
    "        'is': 'was',\n",
    "        'are': 'were',\n",
    "        'has': 'had'\n",
    "    }\n",
    "    def matches(self, box):\n",
    "        return box.name in self.mapping\n",
    "    \n",
    "    def rewrite(self, box):\n",
    "        new_name = self.mapping[box.name]\n",
    "        return type(box)(name=new_name, cod=box.cod)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "past_d = Rewriter([PastRewriteRule()])(d)\n",
    "draw_equation(d, past_d, symbol='->', figsize=(9, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating ansätze"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = parser.sentence2diagram('We will go')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "{term}`Ansätze <ansatz (plural: ansätze)>` for the quantum pipeline are implemented by extending the {py:class}`.CircuitAnsatz` class, while ansätze for the classical pipeline need to extend the {py:class}`.TensorAnsatz` class. Both classes extend {py:class}`.BaseAnsatz`, sharing a common interface. Once an {term}`ansatz <ansatz (plural: ansätze)>` is instantiated, it can be used as a {term}`functor` to convert diagrams to either a {term}`circuit <quantum circuit>` or a tensor diagram.\n",
    "\n",
    "An {term}`ansatz <ansatz (plural: ansätze)>` should be initialised with an `ob_map` argument, a dictionary which maps a rigid type to the number of {term}`qubits <qubit>` in the quantum case, or to a dimension size (e.g. `Dim(2, 2)`) for the classical case. Some {term}`ansätze <ansatz (plural: ansätze)>` may require additional arguments (see the [API documentation](../api/lambeq.ansatz.rst) for more details).\n",
    "\n",
    "In `lambeq`, a {term}`functor` is defined by specifying the mappings for objects `ob` and arrows `ar`. The {py:class}`.CircuitAnsatz` and {py:class}`.TensorAnsatz` classes already implement methods which extend `ob_map` to map not just base (atomic) types, but also compound types, into {term}`qubits <qubit>` and dimensions respectively. Therefore, to complete a new {term}`ansatz <ansatz (plural: ansätze)>` class, you only need to provide the mapping from rigid boxes to diagrams. This typically involves the following steps:\n",
    "\n",
    "1. Obtain the label of the box using the `_summarise_box` method. This provides a unique token which can be used to parameterise the box.\n",
    "2. Apply the {term}`functor` to the domain and the codomain of the box.\n",
    "3. Construct and return an {term}`ansatz <ansatz (plural: ansätze)>` with new domain and codomain -- see how to construct diagrams using the low-level `lambeq` backend [here](./discocat.ipynb).\n",
    "\n",
    "### CircuitAnsatz example: \"Real-valued\" ansatz\n",
    "\n",
    "This {term}`ansatz <ansatz (plural: ansätze)>` always returns a tensor with real-valued entries, since the ansatz is constructed using only the CNOT and Y rotation gates, which both implement real-valued unitaries.\n",
    "The {py:class}`.CircuitAnsatz` provides functionality to add postselections or discards to ensure that domains and codomains for the boxes match. To extend the {py:class}`.CircuitAnsatz` to create a new ansatz thus only involves providing a function to generate the circuit within a box."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lambeq import CircuitAnsatz\n",
    "from lambeq.backend.quantum import CX, Id, Ry\n",
    "\n",
    "\n",
    "class RealAnsatz(CircuitAnsatz):\n",
    "    def __init__(self, ob_map, n_layers, n_single_qubit_params = 1, discard = False):\n",
    "\n",
    "        super().__init__(ob_map,\n",
    "                         n_layers,\n",
    "                         n_single_qubit_params,\n",
    "                         discard,\n",
    "                         [Ry, ])\n",
    "\n",
    "    def params_shape(self, n_qubits):\n",
    "        return (self.n_layers + 1, n_qubits)\n",
    "    \n",
    "    def circuit(self, n_qubits, params):\n",
    "        circuit = Id(n_qubits)\n",
    "        n_layers = params.shape[0] - 1\n",
    "\n",
    "        for i in range(n_layers):\n",
    "            syms = params[i]\n",
    "\n",
    "            # adds a layer of Y rotations\n",
    "            circuit >>= Id().tensor(*[Ry(sym) for sym in syms])\n",
    "\n",
    "            # adds a ladder of CNOTs\n",
    "            for j in range(n_qubits - 1):\n",
    "                circuit >>= Id(j) @ CX @ Id(n_qubits - j - 2)\n",
    "\n",
    "        # adds a final layer of Y rotations\n",
    "        circuit >>= Id().tensor(*[Ry(sym) for sym in params[-1]])\n",
    "\n",
    "        return circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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RR2XlypUmiAbXIiAGAAAAAADgYfnz5zezx4aEhEhERIQcPXrUPH7w4EETJCtXrpwJmmnwDK5HQAwAAAAAAMALChcuLFu2bJE8efJIkyZN5KuvvjLBseLFi8umTZukQIEC3m6i32KWSQAAAAdSDl3IqvzlOABv8pf/I385DsDfaIbY1q1bpX79+qZeWPny5U2QTINlcB8CYgAAAEkUKVLEDE3o1q2b+As9Hj0uAOnD+wEATylVqpRERkaamST79u1rgmRwrwCbzWZz8z4AAEASmg6/bds24SPYd/3+++9y7tw5t+5Dp1pXa9asEXfTi1890Qbgm+8HnsT7AQD8/8gQAwAASEEvFt19wRgUFGR+6pTrAKz9fgAA8DyK6gMAAAAAAMBSCIgBAAAAAADAUgiIAQAAAAAAwFIIiAEAAAAAAMBSCIgBAAD4oWPHjklAQIDs378/zWWioqLMMpcuXfJo2wAAALyNgBgAAIBF1a1bV06ePCkFChQw9xcsWCAFCxb0drMAAADcLtD9uwAAAIAvCgoKkpCQEG83AwAAwOPIEAMAAPBBV69elR49ekjevHmlRIkSMmXKFAkPD5chQ4aY53Wo46pVq5Kto9ldmuWV1MGDB00mWK5cuaRKlSqyY8cOh0Mm9feePXvK5cuXzWN6Gzt2rIeOFgAAwLMIiAEAAPig4cOHm+DV6tWrZfPmzSZgtXfv3gxtZ9iwYbJv3z6pU6eOtG7dWs6fP59qOQ2aTZ8+XfLnz2+GUertpZdectHRAAAA+BYCYgAAAD4mNjZW5s2bJ5MnT5YmTZpI1apVZeHChRIfH5/ubQ0cOFDat28vlSpVklmzZpl6YbptR8Mn9TnNDNNhlHrT7DQAAAB/REAMAADAxxw5ckTi4uIkLCws8bFChQpJxYoV070tzQqzCwwMlJo1a0pMTIzL2goAAJAVERADAADIgjSTy2azJXvs1q1bXmsPAABAVkJADAAAwMeUK1dOcuTIIXv27El87OLFi3L48OHE+0WLFjV1vux++eUXuXbtWqpt7d69O/F3HXIZHR1thk86osMmExISXHgkAAAAvinQ2w0AAABAclq7q3fv3qYgfuHChaVYsWIyevRoyZbtf99lNm7cWGbMmGGGRGoQa8SIESaIltLMmTOlQoUKJgg2bdo0E1jr1auXw/2WKVPG1C+LjIyUatWqSXBwsLkBAAD4GzLEAAAAfNCkSZOkfv36ZlbIiIgIqVevnoSGhiY+P2XKFClZsqRZpmvXrmZGSEfBqwkTJpibBrh27twpa9askSJFijjcp8402b9/f+nUqZPJQJs4caJbjxEAAMBbAmwpi08AAAC30lkDt23blqr+E6ylbNmy5ufRo0edXic8PFyqV68u06dPd2PLAAAA/B8ZYgAAAAAAALAUAmIAAAAAAACwFIrqAwAAZBFRUVHebgIAAIBfIEMMAAAAAAAAlkJADAAAAAAAAJZCQAwAAAAAAACWQkAMAAAAAAAAlkJRfQAAgCRu3bolq1atcvt+rl69an5+9tlnbt/Xk08+KYGBnPYBAADYcWYEAACQxNq1a6Vjx44e258n9rVy5Upp166d2/cDAACQVRAQAwAASKJx48YSGhoqv/76q2zevFkeeughyYpiYmKkefPmUqFCBWnUqJG3mwMAAOBTAmw2m83bjQAAwEqaNGki27ZtEz6Cfdf58+elYcOGcunSJfnqq6/kgQcekKzk6NGjUr9+fSlUqJBERUVJ4cKFvd0kAAAAn0JRfQAAgBQ0gLRlyxbJnTu3CWD++eefklVoWyMiIiQ4ONgcA8EwAACA1AiIAQAAOFCiRAnZunWrxMfHmwDT2bNnxdedOXPGtDUhIUEiIyMlJCTE200CAADwSQTEAAAA0lC6dGkTWLp48aI0a9bMDKH0VfY26k8N5JUqVcrbTQIAAPBZBMQAAADuQIvS69DD33//XVq2bCmxsbHia65cuSKtWrWSEydOmGCYthkAAABpIyAGAABwF1WrVpVNmzbJgQMHpE2bNnL9+nXxFdqWtm3bmrZpG6tUqeLtJgEAAPg8AmIAAABOqFmzpqxbt052794tTz/9tMTFxXm7SaYNHTp0MG3StmkbAQAAcHcExAAAAJxUv359WblypRlC2b17d1O8PiNmzpwpZcqUkVy5cklYWJh8++236d6GFvvv1q2bGSK5atUq0zYAAAA4h4AYAABAOjRv3lyWLVsmn3/+ufTp00du376drvV13aFDh8prr70me/fulWrVqplt6gyRztJ99u3bV1asWCGffvqpKaYPAAAA5xEQAwAASKd27drJwoULzW3w4MFis9mcXnfq1KkmmNWzZ0+pXLmyzJ49W4KDg2X+/PlOra/7+uc//2n2/dFHH5n6YQAAAEifwHQuDwAAABF55pln5OrVq/L8889Lvnz55O2333aq5ld0dLS8/PLLiY9ly5ZNIiIiZNeuXU7td9SoUWbI5Zw5c6Rr166ZOgYAAACrIiAGAACQQf369ZPY2FgZNmyY5M2b1wSr7uTcuXOm7ljx4sWTPa73Dx48eNf9adBtwoQJiVlmAAAAyBgCYgAAAJmg9cA0KDZ69GgTFNPhjO7w73//2+zjjTfekBdffNEt+wAAALAKAmIAAACZ9Morr8iVK1dMPTENivXq1cvhckWKFJHs2bPL6dOnkz2u90NCQtLc/rx582TIkCEyfPhwGTNmjMvbDwAAYDUU1QcAAMikgIAAmThxovTv39/MPKkzSToSFBQkoaGhEhkZmWzGSL1fp04dh+ssXbrUDI8cMGCAvPPOO2ZfAAAAyBwyxAAAAFxAA1Va7F4L7Xfr1s3MHNm6dWuHQyyfffZZqVmzptSuXVumT59u1tFZJ1P64osvpHv37mZ7M2bMIBgGAADgIgTEAAAAXERnjJw/f74JcD399NOybt06adKkSbJlOnXqJGfPnpVXX31VTp06JdWrV5eNGzemKrS/detWs422bduabeq2AQAA4BoBNpvN5qJtAQAAJ2iAZNu2bcJHsP+6efOmtGvXTr788kvZsmWL1K1bN13rf/3119KsWTNp2LChrFq1ygy1BAAAgOvwVSMAAICL5cyZUz7//HMzLLJVq1ayd+9ep9fVZXWdWrVqmW0QDAMAAHA9AmIAAABuoDXEtAbYgw8+KM2bN5eff/75ruscOHDAZIY99NBDZt3cuXN7pK0AAABWQ0AMAADATfLnz2/qg5UoUUIiIiLkyJEjaS6rzzVt2lTuu+8+2bBhg+TLl8+jbQUAALASAmIAAABuVKhQIVNHTANcWj/ujz/+SLXMiRMnzHO6zObNm806AAAAcB8CYgAAAG6mM0jqrJFKA1+nT59OfE5/1+wxpcuknG0SAAAArkdADAAAwANKlixpAl5XrlwxdcIuXLhgbjpMUh+LjIw0ywAAAMD9Aj2wDwAAAIhI+fLlTVCsQYMG0rJlS/PYyZMnZceOHVKuXDlvNw8AAMAyCIgBAAB4UOXKlU2dsEaNGklAQIBs27bNPAYAAADPISAGAADgYTVq1JBDhw6Z30NCQrzdHAAAAMshIAYAAOAFBMIAAAC8h6L6AAAAAAAAsBQCYgAAAAAAALAUAmIAAAAAAACwFAJiAAAAAAAAsBQCYgAAAAAAALAUAmIAAAAAAACwFAJiAAAAAAAAsBQCYgAAAAAAALAUAmIAAAAAAACwFAJiAAAAAAAAsBQCYgAAAAAAALAUAmIAAAAAAACwFAJiAAAAAAAAsBQCYgAAAAAAALAUAmIAAAAAAACwFAJiAAAAAAAAsBQCYgAAAAAAALAUAmIAAAAAAACwFAJiAAAAAAAAsBQCYgAAAAAAALAUAmIAAAAAAACwFAJiAAAAAAAAsBQCYgAAAAAAALAUAmIAAAAAAACwFAJiAAAAAAAAsBQCYgAAAAAAALAUAmIAAAAAAACwFAJiAAAAAAAAsBQCYgAAAAAAALAUAmIAAAAAAACwFAJiAAAAAAAAsBQCYgAAAAAAALAUAmIAAAAAAACwFAJiAAAAAAAAsBQCYgAAAAAAALAUAmIAAAAAAACwFAJiAAAAAAAAsBQCYgAAAAAAALCUQG83AAAAq2nQoIEUKlTI280AAAAALCvAZrPZvN0IAAAAAAAAwFMYMgkAAAAAAABLISAGAAAAAAAASyEgBgAAAAAAAEshIAYAAAAAAABLISAGAAAAAAAASyEgBgAAAAAAAEshIAYAAAAAAABLISAGAAAAAAAASyEgBgAAAAAAAEshIAYAAAAAAABLISAGAAAAAAAASyEgBgAAAAAAAEshIAYAAAAAAABLISAGAAAAAAAASyEgBgAAAAAAAEshIAYAAAAAAABLISAGAAAAAAAASyEgBgAAAAAAAEshIAYAAAAAAABLISAGAAAAAAAASyEgBgAAAAAAAEshIAYAAAAAAABLISAGAAAAAAAASyEgBgAAAAAAAEshIAYAAAAAAABLISAGAAAAAAAASyEgBgAAAAAAAEshIAYAAAAAAABLISAGAAAAAAAASyEgBgAAAAAAAEshIAYAAAAAAABLISAGAAAAAAAASyEgBgAAAAAAAEshIAYAAAAAAABLISAGAAAAAAAASyEgBgAAAAAAAEshIAYAAAAAAABLISAGAAAAAAAASyEgBgAAAAAAAEshIAYAAAAAAABLISAGAAAAAAAASyEgBgAAAAAAAEshIAYAAAAAAABLISAGAAAAAAAASyEgBgAAAAAAAEshIAYAAAAAAABLISAGAAAAAAAASyEgBgAAAAAAAEshIAYAAAAAAABLISAGAAAAAAAASyEgBgAAAAAAAEshIAYAAAAAAABLISAGAAAAAAAASyEgBgAAAAAAAEshIAYAAAAAAABLISAGAAAAAAAASyEgBgAAAAAAAEshIAYAAAAAAABLISAGAAAAAAAASyEgBgAAAAAAAEshIAYAAAAAAABLISAGAAAAAAAASyEgBgAAAAAAAEshIAYAAAAAAABLISAGAAAAAAAASyEgBgAAAAAAAEshIAYAAAAAAABLISAGAAAAAAAASyEgBgAAAAAAAEshIAYAAAAAAABLISAGAAAAAAAASyEgBgAAAAAAAEshIAYAAAAAAABLISAGAAAAAAAASyEgBgAAAAAAAEshIAYAAAAAAABLISAGAAAAAAAASyEgBgAAAAAAAEshIAYAAAAAAABLISAGAAAAAAAASyEgBgAAAAAAAEshIAYAAAAAAABLISAGAAAAAAAASyEgBgAAAAAAAEshIAYAAAAAAABLISAGAAAAAAAASyEgBgAAAAAAAEshIAYAAAAAAABLISAGAAAAAAAASyEgBgAAAAAAAEshIAYAAAAAAABLISAGAAAAAAAAsZL/D+199k6FJjbNAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "real_d = RealAnsatz({N: 1, S: 1}, n_layers=2)(d)\n",
    "real_d.draw(figsize=(12, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TensorAnsatz example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lambeq import TensorAnsatz, Symbol\n",
    "from lambeq.backend import tensor\n",
    "from lambeq.backend.tensor import Dim\n",
    "\n",
    "\n",
    "class UnitSpiderAnsatz(TensorAnsatz):\n",
    "\n",
    "    def _ar(self, functor, box):\n",
    "        # step 1: obtain label\n",
    "        name = self._summarise_box(box)\n",
    "\n",
    "        # step 2: map domain and codomain\n",
    "        dom, cod = functor(box.dom), functor(box.cod)\n",
    "\n",
    "        # step 3: construct and return ansatz\n",
    "        syms = Symbol(name, 2, 2)\n",
    "        tnsr = tensor.Box(box.name, Dim(2), Dim(2), syms)\n",
    "\n",
    "        return tensor.Spider(Dim(2), len(box.dom), 1) >> tnsr >> tensor.Spider(Dim(2), 1, len(box.cod))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ansatz = UnitSpiderAnsatz({N: Dim(2), S: Dim(2)})\n",
    "positive_d = ansatz(d)\n",
    "positive_d.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-8., -8.])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "syms = sorted(positive_d.free_symbols)\n",
    "sym_dict = {k: -np.ones(k.size) for k in syms}\n",
    "subbed_diagram = positive_d.lambdify(*syms)(*sym_dict.values())\n",
    "\n",
    "subbed_diagram.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Contributions\n",
    "\n",
    "We encourage you to implement your own {term}`readers <reader>`, {term}`rewrite rules <rewrite rule>` and {term}`ansätze <ansatz (plural: ansätze)>` and [contribute to lambeq](https://github.com/CQCL/lambeq/pulls) -- detailed guidelines are available [here](../CONTRIBUTING.rst). Below you can find some sources of inspiration:\n",
    "\n",
    "- rewrites for relative pronouns: {cite:p}`sadrzadeh_2013`, {cite:p}`sadrzadeh_2014`\n",
    "- rewrites to deal with coordination: {cite:p}`kartsaklis_2016a`\n",
    "- rewrites to reduce the dimension size of verbs: {cite:p}`kartsaklis_2016b`\n",
    "- rewrites to language circuits (DisCoCirc): {cite:p}`coecke_2021a,coecke2021b`\n",
    "- ansätze benchmarked by their expressibility: {cite:p}`sim_2019`\n",
    "- high-level examples of ansätze: [\\[link\\]](https://pennylane.ai/qml/glossary/circuit_ansatz.html)\n"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}