{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How it works\n",
    "\n",
    "To convert Python code into a quantum circuit, qlasskit implements a series of transformations:\n",
    "\n",
    "1. It begins with the Python *AST* (Abstract Syntax Tree), converting it into a more streamlined form using the `ast2ast` module. \n",
    "2. Next, the streamlined AST is translated into *boolean expressions* as an intermediate step by the `ast2logic` module. \n",
    "During this phase, boolean expressions are refined and optimized in preparation for the final transformation.\n",
    "3. Finally, the `compiler` module takes these optimized boolean expressions and compiles them into a \n",
    "*quantum circuit*.\n",
    "\n",
    "Unlike other libraries that translate individual operations into quantum circuits before combining them, \n",
    "qlasskit constructs a single boolean expression for each output qubit of the entire function. \n",
    "This unique approach facilitates advanced optimization leveraging boolean algebraic properties.\n",
    "\n",
    "![Transformations](_images/trasformations/qlasskit_transformations.png)\n",
    "\n",
    "\n",
    "For instance, let assume we have the following function:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qlasskit import qlassf, Qint\n",
    "from qiskit import QuantumCircuit\n",
    "\n",
    "\n",
    "@qlassf\n",
    "def f_comp(b: bool, n: Qint[2]) -> Qint[2]:\n",
    "    for i in range(3):\n",
    "        n += 1 if b else 2\n",
    "    return n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we decompose the algorithm in 3 separate additions and we compile them separately, we obtain the following circuit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Operations: OrderedDict([('cx', 12), ('barrier', 3), ('x', 3), ('ccx', 3)])\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1893.93x953.167 with 1 Axes>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@qlassf\n",
    "def f1(b: bool, n: Qint[2]) -> Qint[2]:\n",
    "    return n + (1 if b else 2)\n",
    "\n",
    "\n",
    "qc = QuantumCircuit(f_comp.num_qubits * 2 - 1)\n",
    "\n",
    "for i in range(3):\n",
    "    qc.barrier(label=f\"it_{i}\")\n",
    "    qc.append(f1.gate(), [0] + list(range(1 + i * 2, 5 + i * 2)))\n",
    "\n",
    "print(\"Operations:\", qc.decompose().count_ops())\n",
    "qc.decompose().draw(\"mpl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While if we compile the whole function to a quantum circuit using qlasskit, we obtain the following quantum circuit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Operations: OrderedDict([('cx', 7), ('x', 1), ('ccx', 1)])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 789.163x535.111 with 1 Axes>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc = QuantumCircuit(f_comp.num_qubits)\n",
    "qc.append(f_comp.gate(), f_comp.qubits)\n",
    "\n",
    "print(\"Operations:\", qc.decompose().count_ops())\n",
    "qc.decompose().draw(\"mpl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see from the circuit drawings, qlasskit approach needs half the number of qubits and half the number of gates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AST Traslator\n",
    "Given a python function, the `qlasskit.ast2logic` module walks its syntax tree translating all the statements / \n",
    "expressions to boolean expressions.\n",
    "\n",
    "\n",
    "For instance, the following function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "@qlassf\n",
    "def f(n: Qint[4]) -> bool:\n",
    "    return n == 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is translated to this boolean expression:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(_ret, n.0 & n.1 & ~n.2 & ~n.3)]\n"
     ]
    }
   ],
   "source": [
    "print(f.expressions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Compiler\n",
    "\n",
    "The boolean expressions are then being fed to the `qlasskit.compiler`` which compiles boolean expressions\n",
    "to invertible circuits, introducing auxiliary qubits. In this step, the compiler will automatically uncompute \n",
    "auxiliary qubits in order to reduce the number of qubits needed and the circuit footprint. \n",
    "\n",
    "For the compilation, two backends are supported:\n",
    "\n",
    "- InternalCompiler\n",
    "- Tweedledum.xag_synth\n",
    "\n",
    "\n",
    "## Result \n",
    "\n",
    "The result of the compiler is a quantum circuit represented with qlasskit `QCircuit`. This circuit\n",
    "can now be exported to one of the supported framework as a gate or as a standalone circuit.\n",
    "\n",
    "\n",
    "The previous example function `f`, is translated to the following quantum circuit: the \n",
    "result is available at qubit `q6`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 705.552x618.722 with 1 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.export().draw(\"mpl\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "qlasskit_310-env",
   "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": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}