14_QuantumAssociativeMemory.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 14: Quantum Associative Memory\n",
    "\n",
    "We store In four binary patterns,\n",
    "\\begin{equation}\n",
    "  |101 \\rangle_4,~~  |011 \\rangle_3,~~ |111 \\rangle_2,~~ |010 \\rangle_1\n",
    "\\end{equation}\n",
    "by entanglement  with the four index qubits $|  index_j    \\rangle$ in superposition \n",
    "\\begin{equation}\n",
    " | index_4    \\rangle= |   11 \\rangle~~~ | index_3 \\rangle= |   10 \\rangle~~~ \n",
    "   | index_2    \\rangle= |   01 \\rangle~~~| index_1   \\rangle= |   00 \\rangle\n",
    "\\end{equation}   \n",
    "The  four patterns with their indexes represented in a uniform distribution of the states\n",
    "\\begin{equation}\n",
    "| \\psi  \\rangle= \\frac{1}{2} \\cdot \\left(   |00010 \\rangle_1 + |01111 \\rangle_2 + |10011 \\rangle_3 +|11101 \\rangle_4 \\right).\n",
    "\\end{equation}\n",
    " We represent the patterns by the qubits $0$, $1$ and $2$ and the index by the qubits $3$ and $4$. the qubit $5$ represents the auxiliary qubit for the Grover's amplification.\n",
    " For simplicity our query vector is represented by the pattern $|111\\rangle$. The quantum  oracle marks the solution by writing a one in the qubit $5$. The solution is the stored pattern $|111\\rangle$  with the index $|index_2 \\rangle= |   01 \\rangle$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from qiskit import QuantumCircuit, Aer, execute\n",
    "from qiskit.quantum_info import Statevector\n",
    "from qiskit.visualization import plot_histogram\n",
    "from qiskit.circuit.library import MCXGate\n",
    "from math import pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">           ░ ┌───┐      ░      ┌───┐           ░      ┌───┐                ░                 ░      \n",
       "q_0: ──────░─┤ X ├──────░──────┤ X ├───────────░──────┤ X ├────────────────░─────────────────░───■──\n",
       "           ░ └─┬─┘      ░      └─┬─┘┌───┐      ░      └─┬─┘┌───┐           ░      ┌───┐      ░   │  \n",
       "q_1: ──────░───┼────────░────────┼──┤ X ├──────░────────┼──┤ X ├───────────░──────┤ X ├──────░───■──\n",
       "           ░   │  ┌───┐ ░        │  └─┬─┘      ░        │  └─┬─┘┌───┐      ░      └─┬─┘      ░   │  \n",
       "q_2: ──────░───┼──┤ X ├─░────────┼────┼────────░────────┼────┼──┤ X ├──────░────────┼────────░───■──\n",
       "     ┌───┐ ░   │  └─┬─┘ ░ ┌───┐  │    │  ┌───┐ ░        │    │  └─┬─┘      ░ ┌───┐  │  ┌───┐ ░   │  \n",
       "q_3: ┤ H ├─░───■────■───░─┤ X ├──■────■──┤ X ├─░────────■────■────■────────░─┤ X ├──■──┤ X ├─░───┼──\n",
       "     ├───┤ ░   │    │   ░ └───┘  │    │  └───┘ ░ ┌───┐  │    │    │  ┌───┐ ░ ├───┤  │  ├───┤ ░   │  \n",
       "q_4: ┤ H ├─░───■────■───░────────■────■────────░─┤ X ├──■────■────■──┤ X ├─░─┤ X ├──■──┤ X ├─░───┼──\n",
       "     └───┘ ░            ░                      ░ └───┘               └───┘ ░ └───┘     └───┘ ░ ┌─┴─┐\n",
       "q_5: ──────░────────────░──────────────────────░───────────────────────────░─────────────────░─┤ X ├\n",
       "           ░            ░                      ░                           ░                 ░ └───┘</pre>"
      ],
      "text/plain": [
       "           ░ ┌───┐      ░      ┌───┐           ░      ┌───┐                ░                 ░      \n",
       "q_0: ──────░─┤ X ├──────░──────┤ X ├───────────░──────┤ X ├────────────────░─────────────────░───■──\n",
       "           ░ └─┬─┘      ░      └─┬─┘┌───┐      ░      └─┬─┘┌───┐           ░      ┌───┐      ░   │  \n",
       "q_1: ──────░───┼────────░────────┼──┤ X ├──────░────────┼──┤ X ├───────────░──────┤ X ├──────░───■──\n",
       "           ░   │  ┌───┐ ░        │  └─┬─┘      ░        │  └─┬─┘┌───┐      ░      └─┬─┘      ░   │  \n",
       "q_2: ──────░───┼──┤ X ├─░────────┼────┼────────░────────┼────┼──┤ X ├──────░────────┼────────░───■──\n",
       "     ┌───┐ ░   │  └─┬─┘ ░ ┌───┐  │    │  ┌───┐ ░        │    │  └─┬─┘      ░ ┌───┐  │  ┌───┐ ░   │  \n",
       "q_3: ┤ H ├─░───■────■───░─┤ X ├──■────■──┤ X ├─░────────■────■────■────────░─┤ X ├──■──┤ X ├─░───┼──\n",
       "     ├───┤ ░   │    │   ░ └───┘  │    │  └───┘ ░ ┌───┐  │    │    │  ┌───┐ ░ ├───┤  │  ├───┤ ░   │  \n",
       "q_4: ┤ H ├─░───■────■───░────────■────■────────░─┤ X ├──■────■────■──┤ X ├─░─┤ X ├──■──┤ X ├─░───┼──\n",
       "     └───┘ ░            ░                      ░ └───┘               └───┘ ░ └───┘     └───┘ ░ ┌─┴─┐\n",
       "q_5: ──────░────────────░──────────────────────░───────────────────────────░─────────────────░─┤ X ├\n",
       "           ░            ░                      ░                           ░                 ░ └───┘"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc = QuantumCircuit(6)\n",
    "\n",
    "#1St magazine\n",
    "#0-2 data\n",
    "#Index\n",
    "#3-4\n",
    "qc.h(3)\n",
    "qc.h(4)\n",
    "\n",
    "#Aux Bit\n",
    "#qc.x(5)\n",
    "#qc.h(5)\n",
    "\n",
    "qc.barrier()\n",
    "\n",
    "#First patern\n",
    "\n",
    "qc.ccx(3,4,0)\n",
    "qc.ccx(3,4,2)\n",
    "\n",
    "qc.barrier()\n",
    "#Second patern\n",
    "\n",
    "qc.x(3)\n",
    "\n",
    "qc.ccx(3,4,0)\n",
    "qc.ccx(3,4,1)\n",
    "\n",
    "qc.x(3)\n",
    "\n",
    "qc.barrier()\n",
    "#Third patern\n",
    "\n",
    "qc.x(4)\n",
    "\n",
    "qc.ccx(3,4,0)\n",
    "qc.ccx(3,4,1)\n",
    "qc.ccx(3,4,2)\n",
    "\n",
    "qc.x(4)\n",
    "\n",
    "qc.barrier()\n",
    "#Fourth patern\n",
    "\n",
    "qc.x(3)\n",
    "qc.x(4)\n",
    "\n",
    "qc.ccx(3,4,1)\n",
    "\n",
    "qc.x(4)\n",
    "qc.x(3)\n",
    "\n",
    "qc.barrier()\n",
    "#Oracle\n",
    "gate = MCXGate(3)\n",
    "qc.append(gate,[0, 1, 2, 5])\n",
    "\n",
    "qc.draw(fold=160)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The patterns are represented  by the qubits $0$, $1$ and $2$ and the index by the qubits $3$ and $4$. he qubit $5$ represents the auxiliary qubit for the Grover's amplification.\n",
    "Our query vector is represented by the pattern $|111\\rangle$. The   ccX gate (controlled controlled not gate)  marks the solution by writing a one in the qubit $5$. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simulator = Aer.get_backend('statevector_simulator')\n",
    "result=execute(qc,simulator).result()\n",
    "counts = result.get_counts()\n",
    "plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The  histogram representing the four patterns and their indexes  produces a uniform superposition. The solution  $|111\\rangle$  with the the index $|index_2 \\rangle= |   01 \\rangle $ and the auxiliary qubit equal one, corresponds to the state $|01111\\rangle$. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grover's amplification to the index qubits $3$ and $4$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">                ░ ┌───┐      ░      ┌───┐           ░      ┌───┐                ░                 ░       ░       ░                   \n",
       "q_0: ───────────░─┤ X ├──────░──────┤ X ├───────────░──────┤ X ├────────────────░─────────────────░───■───░───────░───────────────────\n",
       "                ░ └─┬─┘      ░      └─┬─┘┌───┐      ░      └─┬─┘┌───┐           ░      ┌───┐      ░   │   ░       ░                   \n",
       "q_1: ───────────░───┼────────░────────┼──┤ X ├──────░────────┼──┤ X ├───────────░──────┤ X ├──────░───■───░───────░───────────────────\n",
       "                ░   │  ┌───┐ ░        │  └─┬─┘      ░        │  └─┬─┘┌───┐      ░      └─┬─┘      ░   │   ░       ░                   \n",
       "q_2: ───────────░───┼──┤ X ├─░────────┼────┼────────░────────┼────┼──┤ X ├──────░────────┼────────░───■───░───────░───────────────────\n",
       "     ┌───┐      ░   │  └─┬─┘ ░ ┌───┐  │    │  ┌───┐ ░        │    │  └─┬─┘      ░ ┌───┐  │  ┌───┐ ░   │   ░       ░ ┌───┐┌───┐   ┌───┐\n",
       "q_3: ┤ H ├──────░───■────■───░─┤ X ├──■────■──┤ X ├─░────────■────■────■────────░─┤ X ├──■──┤ X ├─░───┼───░───────░─┤ H ├┤ Z ├─■─┤ H ├\n",
       "     ├───┤      ░   │    │   ░ └───┘  │    │  └───┘ ░ ┌───┐  │    │    │  ┌───┐ ░ ├───┤  │  ├───┤ ░   │   ░       ░ ├───┤├───┤ │ ├───┤\n",
       "q_4: ┤ H ├──────░───■────■───░────────■────■────────░─┤ X ├──■────■────■──┤ X ├─░─┤ X ├──■──┤ X ├─░───┼───░───────░─┤ H ├┤ Z ├─■─┤ H ├\n",
       "     ├───┤┌───┐ ░            ░                      ░ └───┘               └───┘ ░ └───┘     └───┘ ░ ┌─┴─┐ ░ ┌───┐ ░ └───┘└───┘   └───┘\n",
       "q_5: ┤ X ├┤ H ├─░────────────░──────────────────────░───────────────────────────░─────────────────░─┤ X ├─░─┤ H ├─░───────────────────\n",
       "     └───┘└───┘ ░            ░                      ░                           ░                 ░ └───┘ ░ └───┘ ░                   </pre>"
      ],
      "text/plain": [
       "                ░ ┌───┐      ░      ┌───┐           ░      ┌───┐                ░                 ░       ░       ░                   \n",
       "q_0: ───────────░─┤ X ├──────░──────┤ X ├───────────░──────┤ X ├────────────────░─────────────────░───■───░───────░───────────────────\n",
       "                ░ └─┬─┘      ░      └─┬─┘┌───┐      ░      └─┬─┘┌───┐           ░      ┌───┐      ░   │   ░       ░                   \n",
       "q_1: ───────────░───┼────────░────────┼──┤ X ├──────░────────┼──┤ X ├───────────░──────┤ X ├──────░───■───░───────░───────────────────\n",
       "                ░   │  ┌───┐ ░        │  └─┬─┘      ░        │  └─┬─┘┌───┐      ░      └─┬─┘      ░   │   ░       ░                   \n",
       "q_2: ───────────░───┼──┤ X ├─░────────┼────┼────────░────────┼────┼──┤ X ├──────░────────┼────────░───■───░───────░───────────────────\n",
       "     ┌───┐      ░   │  └─┬─┘ ░ ┌───┐  │    │  ┌───┐ ░        │    │  └─┬─┘      ░ ┌───┐  │  ┌───┐ ░   │   ░       ░ ┌───┐┌───┐   ┌───┐\n",
       "q_3: ┤ H ├──────░───■────■───░─┤ X ├──■────■──┤ X ├─░────────■────■────■────────░─┤ X ├──■──┤ X ├─░───┼───░───────░─┤ H ├┤ Z ├─■─┤ H ├\n",
       "     ├───┤      ░   │    │   ░ └───┘  │    │  └───┘ ░ ┌───┐  │    │    │  ┌───┐ ░ ├───┤  │  ├───┤ ░   │   ░       ░ ├───┤├───┤ │ ├───┤\n",
       "q_4: ┤ H ├──────░───■────■───░────────■────■────────░─┤ X ├──■────■────■──┤ X ├─░─┤ X ├──■──┤ X ├─░───┼───░───────░─┤ H ├┤ Z ├─■─┤ H ├\n",
       "     ├───┤┌───┐ ░            ░                      ░ └───┘               └───┘ ░ └───┘     └───┘ ░ ┌─┴─┐ ░ ┌───┐ ░ └───┘└───┘   └───┘\n",
       "q_5: ┤ X ├┤ H ├─░────────────░──────────────────────░───────────────────────────░─────────────────░─┤ X ├─░─┤ H ├─░───────────────────\n",
       "     └───┘└───┘ ░            ░                      ░                           ░                 ░ └───┘ ░ └───┘ ░                   "
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc = QuantumCircuit(6)\n",
    "\n",
    "#1St magazine\n",
    "#0-2 data\n",
    "#Index\n",
    "#3-4\n",
    "qc.h(3)\n",
    "qc.h(4)\n",
    "\n",
    "#Aux Bit\n",
    "qc.x(5)\n",
    "qc.h(5)\n",
    "\n",
    "qc.barrier()\n",
    "\n",
    "#First patern\n",
    "\n",
    "qc.ccx(3,4,0)\n",
    "qc.ccx(3,4,2)\n",
    "\n",
    "qc.barrier()\n",
    "#Second patern\n",
    "\n",
    "qc.x(3)\n",
    "\n",
    "qc.ccx(3,4,0)\n",
    "qc.ccx(3,4,1)\n",
    "\n",
    "qc.x(3)\n",
    "\n",
    "qc.barrier()\n",
    "#Third patern\n",
    "\n",
    "qc.x(4)\n",
    "\n",
    "qc.ccx(3,4,0)\n",
    "qc.ccx(3,4,1)\n",
    "qc.ccx(3,4,2)\n",
    "\n",
    "qc.x(4)\n",
    "\n",
    "qc.barrier()\n",
    "#Fourth patern\n",
    "\n",
    "qc.x(3)\n",
    "qc.x(4)\n",
    "\n",
    "qc.ccx(3,4,1)\n",
    "\n",
    "qc.x(4)\n",
    "qc.x(3)\n",
    "\n",
    "qc.barrier()\n",
    "#Oracle\n",
    "gate = MCXGate(3)\n",
    "qc.append(gate,[0, 1, 2, 5])\n",
    "\n",
    "qc.barrier()\n",
    "#Redo Aux Bit\n",
    "qc.h(5)\n",
    "qc.barrier()\n",
    "#Diffusor\n",
    "qc.h([3,4])\n",
    "qc.z([3,4])\n",
    "qc.cz(3,4)\n",
    "qc.h([3,4])\n",
    "\n",
    "qc.draw(fold=160)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simulator = Aer.get_backend('statevector_simulator')\n",
    "result=execute(qc,simulator).result()\n",
    "\n",
    "counts = result.get_counts()\n",
    "plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the index qubits are entangled with the patterns we do not get the correct results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To apply Grover's amplification to the two index qubits, the  entanglement of the index qubits with the patterns has to be un-computed. For the four state we require just one rotation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "def store():\n",
    "    qc = QuantumCircuit(5)\n",
    "    #First patern\n",
    "\n",
    "    qc.ccx(3,4,0)\n",
    "    qc.ccx(3,4,2)\n",
    "\n",
    "    qc.barrier()\n",
    "    #Second patern\n",
    "\n",
    "    qc.x(3)\n",
    "\n",
    "    qc.ccx(3,4,0)\n",
    "    qc.ccx(3,4,1)\n",
    "\n",
    "    qc.x(3)\n",
    "\n",
    "    qc.barrier()\n",
    "    #Third patern\n",
    "\n",
    "    qc.x(4)\n",
    "\n",
    "    qc.ccx(3,4,0)\n",
    "    qc.ccx(3,4,1)\n",
    "    qc.ccx(3,4,2)\n",
    "\n",
    "    qc.x(4)\n",
    "\n",
    "    qc.barrier()\n",
    "    #Fourth patern\n",
    "\n",
    "    qc.x(3)\n",
    "    qc.x(4)\n",
    "\n",
    "    qc.ccx(3,4,1)\n",
    "\n",
    "    qc.x(4)\n",
    "    qc.x(3)\n",
    "\n",
    "    qc.name=\"Store\"\n",
    "    return qc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "def store_inv():\n",
    "    qc=store()\n",
    "    qc_inv=qc.inverse()\n",
    "    qc_inv.name=\"Store†\"\n",
    "    return qc_inv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">                ░ ┌────────┐ ░       ░ ┌─────────┐ ░       ░                    ░ \n",
       "q_0: ───────────░─┤0       ├─░───■───░─┤0        ├─░───────░────────────────────░─\n",
       "                ░ │        │ ░   │   ░ │         │ ░       ░                    ░ \n",
       "q_1: ───────────░─┤1       ├─░───■───░─┤1        ├─░───────░────────────────────░─\n",
       "                ░ │        │ ░   │   ░ │         │ ░       ░                    ░ \n",
       "q_2: ───────────░─┤2 Store ├─░───■───░─┤2 Store† ├─░───────░────────────────────░─\n",
       "     ┌───┐      ░ │        │ ░   │   ░ │         │ ░       ░ ┌───┐┌───┐   ┌───┐ ░ \n",
       "q_3: ┤ H ├──────░─┤3       ├─░───┼───░─┤3        ├─░───────░─┤ H ├┤ Z ├─■─┤ H ├─░─\n",
       "     ├───┤      ░ │        │ ░   │   ░ │         │ ░       ░ ├───┤├───┤ │ ├───┤ ░ \n",
       "q_4: ┤ H ├──────░─┤4       ├─░───┼───░─┤4        ├─░───────░─┤ H ├┤ Z ├─■─┤ H ├─░─\n",
       "     ├───┤┌───┐ ░ └────────┘ ░ ┌─┴─┐ ░ └─────────┘ ░ ┌───┐ ░ └───┘└───┘   └───┘ ░ \n",
       "q_5: ┤ X ├┤ H ├─░────────────░─┤ X ├─░─────────────░─┤ H ├─░────────────────────░─\n",
       "     └───┘└───┘ ░            ░ └───┘ ░             ░ └───┘ ░                    ░ </pre>"
      ],
      "text/plain": [
       "                ░ ┌────────┐ ░       ░ ┌─────────┐ ░       ░                    ░ \n",
       "q_0: ───────────░─┤0       ├─░───■───░─┤0        ├─░───────░────────────────────░─\n",
       "                ░ │        │ ░   │   ░ │         │ ░       ░                    ░ \n",
       "q_1: ───────────░─┤1       ├─░───■───░─┤1        ├─░───────░────────────────────░─\n",
       "                ░ │        │ ░   │   ░ │         │ ░       ░                    ░ \n",
       "q_2: ───────────░─┤2 Store ├─░───■───░─┤2 Store† ├─░───────░────────────────────░─\n",
       "     ┌───┐      ░ │        │ ░   │   ░ │         │ ░       ░ ┌───┐┌───┐   ┌───┐ ░ \n",
       "q_3: ┤ H ├──────░─┤3       ├─░───┼───░─┤3        ├─░───────░─┤ H ├┤ Z ├─■─┤ H ├─░─\n",
       "     ├───┤      ░ │        │ ░   │   ░ │         │ ░       ░ ├───┤├───┤ │ ├───┤ ░ \n",
       "q_4: ┤ H ├──────░─┤4       ├─░───┼───░─┤4        ├─░───────░─┤ H ├┤ Z ├─■─┤ H ├─░─\n",
       "     ├───┤┌───┐ ░ └────────┘ ░ ┌─┴─┐ ░ └─────────┘ ░ ┌───┐ ░ └───┘└───┘   └───┘ ░ \n",
       "q_5: ┤ X ├┤ H ├─░────────────░─┤ X ├─░─────────────░─┤ H ├─░────────────────────░─\n",
       "     └───┘└───┘ ░            ░ └───┘ ░             ░ └───┘ ░                    ░ "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc = QuantumCircuit(6)\n",
    "\n",
    "#1St magazine\n",
    "#0-2 data\n",
    "#Index\n",
    "#3-4\n",
    "qc.h(3)\n",
    "qc.h(4)\n",
    "\n",
    "#Aux Bit\n",
    "qc.x(5)\n",
    "qc.h(5)\n",
    "\n",
    "qc.barrier()\n",
    "\n",
    "qc.append(store(),range(5))\n",
    "\n",
    "qc.barrier()\n",
    "#Oracle\n",
    "gate = MCXGate(3)\n",
    "qc.append(gate,[0, 1, 2, 5])\n",
    "\n",
    "qc.barrier()\n",
    "\n",
    "qc.append(store_inv(),range(5))\n",
    "\n",
    "qc.barrier()\n",
    "#Redo Aux Bit\n",
    "qc.h(5)\n",
    "qc.barrier()\n",
    "#Diffusor\n",
    "qc.h([3,4])\n",
    "qc.z([3,4])\n",
    "qc.cz(3,4)\n",
    "qc.h([3,4])\n",
    "\n",
    "qc.barrier()\n",
    "\n",
    "qc.draw(fold=160)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simulator = Aer.get_backend('statevector_simulator')\n",
    "result=execute(qc,simulator).result()\n",
    "counts = result.get_counts()\n",
    "plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We measure the correct result represented by one state with the $|index_2 \\rangle= |  01 \\rangle$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of un-computing, we would like to apply Grover’s algorithm to the five qubits representing the patterns and the indexes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">                ░ ┌────────┐ ░       ░       ░ ┌───┐┌───┐┌───┐┌───┐┌───┐ ░ ┌───┐┌───┐\n",
       "q_0: ───────────░─┤0       ├─░───■───░───────░─┤ H ├┤ X ├┤ H ├┤ X ├┤ H ├─░─┤ X ├┤ H ├\n",
       "                ░ │        │ ░   │   ░       ░ ├───┤├───┤└───┘└─┬─┘└───┘ ░ ├───┤├───┤\n",
       "q_1: ───────────░─┤1       ├─░───■───░───────░─┤ H ├┤ X ├───────■────────░─┤ X ├┤ H ├\n",
       "                ░ │        │ ░   │   ░       ░ ├───┤├───┤       │        ░ ├───┤├───┤\n",
       "q_2: ───────────░─┤2 Store ├─░───■───░───────░─┤ H ├┤ X ├───────■────────░─┤ X ├┤ H ├\n",
       "     ┌───┐      ░ │        │ ░   │   ░       ░ ├───┤├───┤       │        ░ ├───┤├───┤\n",
       "q_3: ┤ H ├──────░─┤3       ├─░───┼───░───────░─┤ H ├┤ X ├───────■────────░─┤ X ├┤ H ├\n",
       "     ├───┤      ░ │        │ ░   │   ░       ░ ├───┤├───┤       │        ░ ├───┤├───┤\n",
       "q_4: ┤ H ├──────░─┤4       ├─░───┼───░───────░─┤ H ├┤ X ├───────■────────░─┤ X ├┤ H ├\n",
       "     ├───┤┌───┐ ░ └────────┘ ░ ┌─┴─┐ ░ ┌───┐ ░ └───┘└───┘                ░ └───┘└───┘\n",
       "q_5: ┤ X ├┤ H ├─░────────────░─┤ X ├─░─┤ H ├─░───────────────────────────░───────────\n",
       "     └───┘└───┘ ░            ░ └───┘ ░ └───┘ ░                           ░           </pre>"
      ],
      "text/plain": [
       "                ░ ┌────────┐ ░       ░       ░ ┌───┐┌───┐┌───┐┌───┐┌───┐ ░ ┌───┐┌───┐\n",
       "q_0: ───────────░─┤0       ├─░───■───░───────░─┤ H ├┤ X ├┤ H ├┤ X ├┤ H ├─░─┤ X ├┤ H ├\n",
       "                ░ │        │ ░   │   ░       ░ ├───┤├───┤└───┘└─┬─┘└───┘ ░ ├───┤├───┤\n",
       "q_1: ───────────░─┤1       ├─░───■───░───────░─┤ H ├┤ X ├───────■────────░─┤ X ├┤ H ├\n",
       "                ░ │        │ ░   │   ░       ░ ├───┤├───┤       │        ░ ├───┤├───┤\n",
       "q_2: ───────────░─┤2 Store ├─░───■───░───────░─┤ H ├┤ X ├───────■────────░─┤ X ├┤ H ├\n",
       "     ┌───┐      ░ │        │ ░   │   ░       ░ ├───┤├───┤       │        ░ ├───┤├───┤\n",
       "q_3: ┤ H ├──────░─┤3       ├─░───┼───░───────░─┤ H ├┤ X ├───────■────────░─┤ X ├┤ H ├\n",
       "     ├───┤      ░ │        │ ░   │   ░       ░ ├───┤├───┤       │        ░ ├───┤├───┤\n",
       "q_4: ┤ H ├──────░─┤4       ├─░───┼───░───────░─┤ H ├┤ X ├───────■────────░─┤ X ├┤ H ├\n",
       "     ├───┤┌───┐ ░ └────────┘ ░ ┌─┴─┐ ░ ┌───┐ ░ └───┘└───┘                ░ └───┘└───┘\n",
       "q_5: ┤ X ├┤ H ├─░────────────░─┤ X ├─░─┤ H ├─░───────────────────────────░───────────\n",
       "     └───┘└───┘ ░            ░ └───┘ ░ └───┘ ░                           ░           "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc = QuantumCircuit(6)\n",
    "\n",
    "#1St magazine\n",
    "#0-2 data\n",
    "#Index\n",
    "#3-4\n",
    "qc.h(3)\n",
    "qc.h(4)\n",
    "\n",
    "#Aux Bit\n",
    "qc.x(5)\n",
    "qc.h(5)\n",
    "\n",
    "qc.barrier()\n",
    "\n",
    "qc.append(store(),range(5))\n",
    "\n",
    "qc.barrier()\n",
    "#Oracle\n",
    "gate = MCXGate(3)\n",
    "qc.append(gate,[0, 1, 2, 5])\n",
    "\n",
    "qc.barrier()\n",
    "#Redo Aux Bit\n",
    "qc.h(5)\n",
    "qc.barrier()\n",
    "#Diffusor\n",
    "gate = MCXGate(4)\n",
    "\n",
    "qc.h([0,1,2,3,4])\n",
    "qc.x([0,1,2,3,4])\n",
    "qc.h([0])\n",
    "qc.append(gate, [1,2,3,4,0])\n",
    "qc.h([0])\n",
    "qc.barrier()\n",
    "qc.x([0,1,2,3,4])\n",
    "qc.h([0,1,2,3,4])\n",
    "\n",
    "\n",
    "qc.draw(fold=160)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simulator = Aer.get_backend('statevector_simulator')\n",
    "result=execute(qc,simulator).result()\n",
    "counts = result.get_counts()\n",
    "plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Measuring the states after one rotation of Grover's amplification indicates us that something wired is happening.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quantum Associative Memory (QuAM)\n",
    "\n",
    "In the quantum associative memory  (QuAM)  as proposed by Venture and Martinez, a modified version of Grover’s search algorithm is applied to determine the answer vector to a query vector. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from qiskit import QuantumCircuit, Aer, execute\n",
    "from qiskit.quantum_info import Statevector\n",
    "from qiskit.visualization import plot_histogram\n",
    "from qiskit.circuit.library import MCXGate\n",
    "from math import pi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the initial distribution we mark the target state $|0110 \\rangle$ by a negative phase and perform a Grover's rotation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">                ░ ┌───┐      ░      ┌───┐      ░                 ░      ┌───┐           ░ ┌───┐     ┌───┐ ░       ░ ┌───┐┌───┐┌───┐┌───┐┌───┐┌───┐┌───┐\n",
       "q_0: ───────────░─┤ X ├──────░──────┤ X ├──────░─────────────────░──────┤ X ├───────────░─┤ X ├──■──┤ X ├─░───────░─┤ H ├┤ X ├┤ H ├┤ X ├┤ H ├┤ X ├┤ H ├\n",
       "                ░ └─┬─┘┌───┐ ░      └─┬─┘      ░      ┌───┐      ░      └─┬─┘┌───┐      ░ └───┘  │  └───┘ ░       ░ ├───┤├───┤└───┘└─┬─┘├───┤├───┤└───┘\n",
       "q_1: ───────────░───┼──┤ X ├─░────────┼────────░──────┤ X ├──────░────────┼──┤ X ├──────░────────■────────░───────░─┤ H ├┤ X ├───────■──┤ X ├┤ H ├─────\n",
       "     ┌───┐      ░   │  └─┬─┘ ░ ┌───┐  │  ┌───┐ ░      └─┬─┘      ░ ┌───┐  │  └─┬─┘┌───┐ ░        │        ░       ░ ├───┤├───┤       │  ├───┤├───┤     \n",
       "q_2: ┤ H ├──────░───■────■───░─┤ X ├──■──┤ X ├─░────────■────────░─┤ X ├──■────■──┤ X ├─░────────■────────░───────░─┤ H ├┤ X ├───────■──┤ X ├┤ H ├─────\n",
       "     ├───┤      ░   │    │   ░ └───┘  │  └───┘ ░ ┌───┐  │  ┌───┐ ░ ├───┤  │    │  ├───┤ ░ ┌───┐  │  ┌───┐ ░       ░ ├───┤├───┤       │  ├───┤├───┤     \n",
       "q_3: ┤ H ├──────░───■────■───░────────■────────░─┤ X ├──■──┤ X ├─░─┤ X ├──■────■──┤ X ├─░─┤ X ├──■──┤ X ├─░───────░─┤ H ├┤ X ├───────■──┤ X ├┤ H ├─────\n",
       "     ├───┤┌───┐ ░            ░                 ░ └───┘     └───┘ ░ └───┘          └───┘ ░ └───┘┌─┴─┐└───┘ ░ ┌───┐ ░ └───┘└───┘          └───┘└───┘     \n",
       "q_4: ┤ X ├┤ H ├─░────────────░─────────────────░─────────────────░──────────────────────░──────┤ X ├──────░─┤ H ├─░────────────────────────────────────\n",
       "     └───┘└───┘ ░            ░                 ░                 ░                      ░      └───┘      ░ └───┘ ░                                    </pre>"
      ],
      "text/plain": [
       "                ░ ┌───┐      ░      ┌───┐      ░                 ░      ┌───┐           ░ ┌───┐     ┌───┐ ░       ░ ┌───┐┌───┐┌───┐┌───┐┌───┐┌───┐┌───┐\n",
       "q_0: ───────────░─┤ X ├──────░──────┤ X ├──────░─────────────────░──────┤ X ├───────────░─┤ X ├──■──┤ X ├─░───────░─┤ H ├┤ X ├┤ H ├┤ X ├┤ H ├┤ X ├┤ H ├\n",
       "                ░ └─┬─┘┌───┐ ░      └─┬─┘      ░      ┌───┐      ░      └─┬─┘┌───┐      ░ └───┘  │  └───┘ ░       ░ ├───┤├───┤└───┘└─┬─┘├───┤├───┤└───┘\n",
       "q_1: ───────────░───┼──┤ X ├─░────────┼────────░──────┤ X ├──────░────────┼──┤ X ├──────░────────■────────░───────░─┤ H ├┤ X ├───────■──┤ X ├┤ H ├─────\n",
       "     ┌───┐      ░   │  └─┬─┘ ░ ┌───┐  │  ┌───┐ ░      └─┬─┘      ░ ┌───┐  │  └─┬─┘┌───┐ ░        │        ░       ░ ├───┤├───┤       │  ├───┤├───┤     \n",
       "q_2: ┤ H ├──────░───■────■───░─┤ X ├──■──┤ X ├─░────────■────────░─┤ X ├──■────■──┤ X ├─░────────■────────░───────░─┤ H ├┤ X ├───────■──┤ X ├┤ H ├─────\n",
       "     ├───┤      ░   │    │   ░ └───┘  │  └───┘ ░ ┌───┐  │  ┌───┐ ░ ├───┤  │    │  ├───┤ ░ ┌───┐  │  ┌───┐ ░       ░ ├───┤├───┤       │  ├───┤├───┤     \n",
       "q_3: ┤ H ├──────░───■────■───░────────■────────░─┤ X ├──■──┤ X ├─░─┤ X ├──■────■──┤ X ├─░─┤ X ├──■──┤ X ├─░───────░─┤ H ├┤ X ├───────■──┤ X ├┤ H ├─────\n",
       "     ├───┤┌───┐ ░            ░                 ░ └───┘     └───┘ ░ └───┘          └───┘ ░ └───┘┌─┴─┐└───┘ ░ ┌───┐ ░ └───┘└───┘          └───┘└───┘     \n",
       "q_4: ┤ X ├┤ H ├─░────────────░─────────────────░─────────────────░──────────────────────░──────┤ X ├──────░─┤ H ├─░────────────────────────────────────\n",
       "     └───┘└───┘ ░            ░                 ░                 ░                      ░      └───┘      ░ └───┘ ░                                    "
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc = QuantumCircuit(5)\n",
    "\n",
    "#1St magazine\n",
    "#0-1 data\n",
    "#Index\n",
    "#2-3\n",
    "qc.h(2)\n",
    "qc.h(3)\n",
    "\n",
    "\n",
    "#Aux Bit\n",
    "qc.x(4)\n",
    "qc.h(4)\n",
    "\n",
    "qc.barrier()\n",
    "#First patern\n",
    "qc.ccx(2,3,0)\n",
    "qc.ccx(2,3,1)\n",
    "\n",
    "qc.barrier()\n",
    "#Second patern\n",
    "qc.x(2)\n",
    "qc.ccx(2,3,0)\n",
    "qc.x(2)\n",
    "\n",
    "qc.barrier()\n",
    "#Third patern\n",
    "qc.x(3)\n",
    "qc.ccx(2,3,1)\n",
    "qc.x(3)\n",
    "\n",
    "qc.barrier()\n",
    "#Fourth patern\n",
    "qc.x(2)\n",
    "qc.x(3)\n",
    "qc.ccx(2,3,0)\n",
    "qc.ccx(2,3,1)\n",
    "qc.x(3)\n",
    "qc.x(2)\n",
    "\n",
    "#Oracle\n",
    "qc.barrier()\n",
    "qc.x(0)\n",
    "qc.x(3)\n",
    "gate = MCXGate(4)\n",
    "qc.append(gate, [0,1,2,3,4])\n",
    "qc.x(3)\n",
    "qc.x(0)\n",
    "qc.barrier()\n",
    "qc.h(4)\n",
    "#Diffusor 0, 1, 2, 3 \n",
    "qc.barrier()\n",
    "\n",
    "qc.h([0,1,2,3])\n",
    "qc.x([0,1,2,3])\n",
    "qc.h(0)\n",
    "gate = MCXGate(3)\n",
    "qc.append(gate, [1,2,3,0])\n",
    "qc.h(0)\n",
    "qc.x([0,1,2,3])\n",
    "qc.h([0,1,2,3])\n",
    "\n",
    "#qc.rz(2*pi,1)\n",
    "\n",
    "\n",
    "qc.draw(fold=160)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simulator = Aer.get_backend('statevector_simulator')\n",
    "\n",
    "# Run and get counts\n",
    "result=execute(qc,simulator).result()\n",
    "counts = result.get_counts()\n",
    "plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "with the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$\n",
       "\\text{Statevector} = \n",
       "\\begin{bmatrix}\n",
       "0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & - \\frac{1}{8} & - \\frac{1}{8} & - \\frac{1}{8} & \\frac{3}{8} & - \\frac{1}{8} & - \\frac{1}{8} & - \\frac{5}{8} & - \\frac{1}{8} & - \\frac{1}{8} & \\frac{3}{8} & - \\frac{1}{8} & - \\frac{1}{8} & - \\frac{1}{8} & - \\frac{1}{8} & - \\frac{1}{8} & \\frac{3}{8}  \\\\\n",
       " \\end{bmatrix}\n",
       "$$"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simulator = Aer.get_backend('statevector_simulator')\n",
    "final_state = simulator.run(qc).result().get_statevector()\n",
    "\n",
    "from qiskit.visualization import array_to_latex\n",
    "array_to_latex(final_state,max_size=256,precision=4,prefix=\"\\\\text{Statevector} = \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, now me mark all  four states that represent our distribution by a negative phase and perform a Grover's rotation  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">                ░ ┌───┐      ░      ┌───┐      ░                 ░      ┌───┐           ░ ┌───┐     ┌───┐ ░       ░ ┌───┐┌───┐┌───┐┌───┐┌───┐┌───┐┌───┐ ░       ░ ┌───┐     ┌───┐                                    ░       ░ ┌───┐┌───┐┌───┐┌───┐┌───┐┌───┐┌───┐\n",
       "q_0: ───────────░─┤ X ├──────░──────┤ X ├──────░─────────────────░──────┤ X ├───────────░─┤ X ├──■──┤ X ├─░───────░─┤ H ├┤ X ├┤ H ├┤ X ├┤ H ├┤ X ├┤ H ├─░───────░─┤ X ├──■──┤ X ├───────■──────────────■─────────■───░───────░─┤ H ├┤ X ├┤ H ├┤ X ├┤ H ├┤ X ├┤ H ├\n",
       "                ░ └─┬─┘┌───┐ ░      └─┬─┘      ░      ┌───┐      ░      └─┬─┘┌───┐      ░ └───┘  │  └───┘ ░       ░ ├───┤├───┤└───┘└─┬─┘├───┤├───┤└───┘ ░       ░ └───┘  │  └───┘       │  ┌───┐       │  ┌───┐  │   ░       ░ ├───┤├───┤└───┘└─┬─┘├───┤├───┤└───┘\n",
       "q_1: ───────────░───┼──┤ X ├─░────────┼────────░──────┤ X ├──────░────────┼──┤ X ├──────░────────■────────░───────░─┤ H ├┤ X ├───────■──┤ X ├┤ H ├──────░───────░────────■──────────────■──┤ X ├───────■──┤ X ├──■───░───────░─┤ H ├┤ X ├───────■──┤ X ├┤ H ├─────\n",
       "     ┌───┐      ░   │  └─┬─┘ ░ ┌───┐  │  ┌───┐ ░      └─┬─┘      ░ ┌───┐  │  └─┬─┘┌───┐ ░        │        ░       ░ ├───┤├───┤       │  ├───┤├───┤      ░       ░        │  ┌───┐       │  ├───┤┌───┐  │  ├───┤  │   ░       ░ ├───┤├───┤       │  ├───┤├───┤     \n",
       "q_2: ┤ H ├──────░───■────■───░─┤ X ├──■──┤ X ├─░────────■────────░─┤ X ├──■────■──┤ X ├─░────────■────────░───────░─┤ H ├┤ X ├───────■──┤ X ├┤ H ├──────░───────░────────■──┤ X ├───────■──┤ X ├┤ X ├──■──┤ X ├──■───░───────░─┤ H ├┤ X ├───────■──┤ X ├┤ H ├─────\n",
       "     ├───┤      ░   │    │   ░ └───┘  │  └───┘ ░ ┌───┐  │  ┌───┐ ░ ├───┤  │    │  ├───┤ ░ ┌───┐  │  ┌───┐ ░       ░ ├───┤├───┤       │  ├───┤├───┤      ░       ░ ┌───┐  │  ├───┤┌───┐  │  ├───┤└───┘  │  └───┘  │   ░       ░ ├───┤├───┤       │  ├───┤├───┤     \n",
       "q_3: ┤ H ├──────░───■────■───░────────■────────░─┤ X ├──■──┤ X ├─░─┤ X ├──■────■──┤ X ├─░─┤ X ├──■──┤ X ├─░───────░─┤ H ├┤ X ├───────■──┤ X ├┤ H ├──────░───────░─┤ X ├──■──┤ X ├┤ X ├──■──┤ X ├───────■─────────■───░───────░─┤ H ├┤ X ├───────■──┤ X ├┤ H ├─────\n",
       "     ├───┤┌───┐ ░            ░                 ░ └───┘     └───┘ ░ └───┘          └───┘ ░ └───┘┌─┴─┐└───┘ ░ ┌───┐ ░ └───┘└───┘          └───┘└───┘      ░ ┌───┐ ░ └───┘┌─┴─┐└───┘└───┘┌─┴─┐└───┘     ┌─┴─┐     ┌─┴─┐ ░ ┌───┐ ░ └───┘└───┘          └───┘└───┘     \n",
       "q_4: ┤ X ├┤ H ├─░────────────░─────────────────░─────────────────░──────────────────────░──────┤ X ├──────░─┤ H ├─░─────────────────────────────────────░─┤ H ├─░──────┤ X ├──────────┤ X ├──────────┤ X ├─────┤ X ├─░─┤ H ├─░────────────────────────────────────\n",
       "     └───┘└───┘ ░            ░                 ░                 ░                      ░      └───┘      ░ └───┘ ░                                     ░ └───┘ ░      └───┘          └───┘          └───┘     └───┘ ░ └───┘ ░                                    </pre>"
      ],
      "text/plain": [
       "                ░ ┌───┐      ░      ┌───┐      ░                 ░      ┌───┐           ░ ┌───┐     ┌───┐ ░       ░ ┌───┐┌───┐┌───┐┌───┐┌───┐┌───┐┌───┐ ░       ░ ┌───┐     ┌───┐                                    ░       ░ ┌───┐┌───┐┌───┐┌───┐┌───┐┌───┐┌───┐\n",
       "q_0: ───────────░─┤ X ├──────░──────┤ X ├──────░─────────────────░──────┤ X ├───────────░─┤ X ├──■──┤ X ├─░───────░─┤ H ├┤ X ├┤ H ├┤ X ├┤ H ├┤ X ├┤ H ├─░───────░─┤ X ├──■──┤ X ├───────■──────────────■─────────■───░───────░─┤ H ├┤ X ├┤ H ├┤ X ├┤ H ├┤ X ├┤ H ├\n",
       "                ░ └─┬─┘┌───┐ ░      └─┬─┘      ░      ┌───┐      ░      └─┬─┘┌───┐      ░ └───┘  │  └───┘ ░       ░ ├───┤├───┤└───┘└─┬─┘├───┤├───┤└───┘ ░       ░ └───┘  │  └───┘       │  ┌───┐       │  ┌───┐  │   ░       ░ ├───┤├───┤└───┘└─┬─┘├───┤├───┤└───┘\n",
       "q_1: ───────────░───┼──┤ X ├─░────────┼────────░──────┤ X ├──────░────────┼──┤ X ├──────░────────■────────░───────░─┤ H ├┤ X ├───────■──┤ X ├┤ H ├──────░───────░────────■──────────────■──┤ X ├───────■──┤ X ├──■───░───────░─┤ H ├┤ X ├───────■──┤ X ├┤ H ├─────\n",
       "     ┌───┐      ░   │  └─┬─┘ ░ ┌───┐  │  ┌───┐ ░      └─┬─┘      ░ ┌───┐  │  └─┬─┘┌───┐ ░        │        ░       ░ ├───┤├───┤       │  ├───┤├───┤      ░       ░        │  ┌───┐       │  ├───┤┌───┐  │  ├───┤  │   ░       ░ ├───┤├───┤       │  ├───┤├───┤     \n",
       "q_2: ┤ H ├──────░───■────■───░─┤ X ├──■──┤ X ├─░────────■────────░─┤ X ├──■────■──┤ X ├─░────────■────────░───────░─┤ H ├┤ X ├───────■──┤ X ├┤ H ├──────░───────░────────■──┤ X ├───────■──┤ X ├┤ X ├──■──┤ X ├──■───░───────░─┤ H ├┤ X ├───────■──┤ X ├┤ H ├─────\n",
       "     ├───┤      ░   │    │   ░ └───┘  │  └───┘ ░ ┌───┐  │  ┌───┐ ░ ├───┤  │    │  ├───┤ ░ ┌───┐  │  ┌───┐ ░       ░ ├───┤├───┤       │  ├───┤├───┤      ░       ░ ┌───┐  │  ├───┤┌───┐  │  ├───┤└───┘  │  └───┘  │   ░       ░ ├───┤├───┤       │  ├───┤├───┤     \n",
       "q_3: ┤ H ├──────░───■────■───░────────■────────░─┤ X ├──■──┤ X ├─░─┤ X ├──■────■──┤ X ├─░─┤ X ├──■──┤ X ├─░───────░─┤ H ├┤ X ├───────■──┤ X ├┤ H ├──────░───────░─┤ X ├──■──┤ X ├┤ X ├──■──┤ X ├───────■─────────■───░───────░─┤ H ├┤ X ├───────■──┤ X ├┤ H ├─────\n",
       "     ├───┤┌───┐ ░            ░                 ░ └───┘     └───┘ ░ └───┘          └───┘ ░ └───┘┌─┴─┐└───┘ ░ ┌───┐ ░ └───┘└───┘          └───┘└───┘      ░ ┌───┐ ░ └───┘┌─┴─┐└───┘└───┘┌─┴─┐└───┘     ┌─┴─┐     ┌─┴─┐ ░ ┌───┐ ░ └───┘└───┘          └───┘└───┘     \n",
       "q_4: ┤ X ├┤ H ├─░────────────░─────────────────░─────────────────░──────────────────────░──────┤ X ├──────░─┤ H ├─░─────────────────────────────────────░─┤ H ├─░──────┤ X ├──────────┤ X ├──────────┤ X ├─────┤ X ├─░─┤ H ├─░────────────────────────────────────\n",
       "     └───┘└───┘ ░            ░                 ░                 ░                      ░      └───┘      ░ └───┘ ░                                     ░ └───┘ ░      └───┘          └───┘          └───┘     └───┘ ░ └───┘ ░                                    "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc = QuantumCircuit(5)\n",
    "\n",
    "#1St magazine\n",
    "#0-1 data\n",
    "#Index\n",
    "#2-3\n",
    "qc.h(2)\n",
    "qc.h(3)\n",
    "\n",
    "#Aux Bit\n",
    "qc.x(4)\n",
    "qc.h(4)\n",
    "\n",
    "qc.barrier()\n",
    "#First patern\n",
    "qc.ccx(2,3,0)\n",
    "qc.ccx(2,3,1)\n",
    "\n",
    "qc.barrier()\n",
    "#Second patern\n",
    "qc.x(2)\n",
    "qc.ccx(2,3,0)\n",
    "qc.x(2)\n",
    "\n",
    "qc.barrier()\n",
    "#Third patern\n",
    "qc.x(3)\n",
    "qc.ccx(2,3,1)\n",
    "qc.x(3)\n",
    "\n",
    "qc.barrier()\n",
    "#Fourth patern\n",
    "qc.x(2)\n",
    "qc.x(3)\n",
    "qc.ccx(2,3,0)\n",
    "qc.ccx(2,3,1)\n",
    "qc.x(3)\n",
    "qc.x(2)\n",
    "\n",
    "#Oracle\n",
    "qc.barrier()\n",
    "qc.x(0)\n",
    "qc.x(3)\n",
    "gate = MCXGate(4)\n",
    "qc.append(gate, [0,1,2,3,4])\n",
    "qc.x(3)\n",
    "qc.x(0)\n",
    "qc.barrier()\n",
    "qc.h(4)\n",
    "#Diffusor 0, 1, 2, 3 \n",
    "qc.barrier()\n",
    "\n",
    "qc.h([0,1,2,3])\n",
    "qc.x([0,1,2,3])\n",
    "qc.h(0)\n",
    "gate = MCXGate(3)\n",
    "qc.append(gate, [1,2,3,0])\n",
    "qc.h(0)\n",
    "qc.x([0,1,2,3])\n",
    "qc.h([0,1,2,3])\n",
    "\n",
    "#Secend rotation\n",
    "\n",
    "qc.barrier()\n",
    "qc.h(4)\n",
    "qc.barrier()\n",
    "\n",
    "#Oracle Trick\n",
    "qc.x(0)\n",
    "qc.x(3)\n",
    "gate = MCXGate(4)\n",
    "qc.append(gate, [0,1,2,3,4])\n",
    "qc.x(3)\n",
    "qc.x(0)\n",
    "\n",
    "qc.x(2)\n",
    "qc.x(3)\n",
    "gate = MCXGate(4)\n",
    "qc.append(gate, [0,1,2,3,4])\n",
    "qc.x(3)\n",
    "qc.x(2)\n",
    "\n",
    "qc.x(1)\n",
    "qc.x(2)\n",
    "gate = MCXGate(4)\n",
    "qc.append(gate, [0,1,2,3,4])\n",
    "qc.x(2)\n",
    "qc.x(1)\n",
    "\n",
    "gate = MCXGate(4)\n",
    "qc.append(gate, [0,1,2,3,4])\n",
    "\n",
    "qc.barrier()\n",
    "qc.h(4)\n",
    "#Diffusor 0, 1, 2, 3 \n",
    "qc.barrier()\n",
    "\n",
    "qc.h([0,1,2,3])\n",
    "qc.x([0,1,2,3])\n",
    "qc.h(0)\n",
    "gate = MCXGate(3)\n",
    "qc.append(gate, [1,2,3,0])\n",
    "qc.h(0)\n",
    "qc.x([0,1,2,3])\n",
    "qc.h([0,1,2,3])\n",
    "\n",
    "qc.draw(fold=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simulator = Aer.get_backend('statevector_simulator')\n",
    "\n",
    "# Run and get counts\n",
    "result=execute(qc,simulator).result()\n",
    "counts = result.get_counts()\n",
    "plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "with the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$\n",
       "\\text{Statevector} = \n",
       "\\begin{bmatrix}\n",
       "0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & \\frac{1}{8} & \\frac{1}{8} & \\frac{1}{8} & - \\frac{1}{8} & \\frac{1}{8} & \\frac{1}{8} & \\frac{7}{8} & \\frac{1}{8} & \\frac{1}{8} & - \\frac{1}{8} & \\frac{1}{8} & \\frac{1}{8} & \\frac{1}{8} & \\frac{1}{8} & \\frac{1}{8} & - \\frac{1}{8}  \\\\\n",
       " \\end{bmatrix}\n",
       "$$"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simulator = Aer.get_backend('statevector_simulator')\n",
    "final_state = simulator.run(qc).result().get_statevector()\n",
    "\n",
    "from qiskit.visualization import array_to_latex\n",
    "array_to_latex(final_state,max_size=256,precision=4,prefix=\"\\\\text{Statevector} = \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We mark again all  four states that represent our distribution by a negative phase and perform a Grover's rotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">                ░ ┌───┐      ░      ┌───┐      ░                 ░      ┌───┐           ░ ┌───┐»\n",
       "q_0: ───────────░─┤ X ├──────░──────┤ X ├──────░─────────────────░──────┤ X ├───────────░─┤ X ├»\n",
       "                ░ └─┬─┘┌───┐ ░      └─┬─┘      ░      ┌───┐      ░      └─┬─┘┌───┐      ░ └───┘»\n",
       "q_1: ───────────░───┼──┤ X ├─░────────┼────────░──────┤ X ├──────░────────┼──┤ X ├──────░──────»\n",
       "     ┌───┐      ░   │  └─┬─┘ ░ ┌───┐  │  ┌───┐ ░      └─┬─┘      ░ ┌───┐  │  └─┬─┘┌───┐ ░      »\n",
       "q_2: ┤ H ├──────░───■────■───░─┤ X ├──■──┤ X ├─░────────■────────░─┤ X ├──■────■──┤ X ├─░──────»\n",
       "     ├───┤      ░   │    │   ░ └───┘  │  └───┘ ░ ┌───┐  │  ┌───┐ ░ ├───┤  │    │  ├───┤ ░ ┌───┐»\n",
       "q_3: ┤ H ├──────░───■────■───░────────■────────░─┤ X ├──■──┤ X ├─░─┤ X ├──■────■──┤ X ├─░─┤ X ├»\n",
       "     ├───┤┌───┐ ░            ░                 ░ └───┘     └───┘ ░ └───┘          └───┘ ░ └───┘»\n",
       "q_4: ┤ X ├┤ H ├─░────────────░─────────────────░─────────────────░──────────────────────░──────»\n",
       "     └───┘└───┘ ░            ░                 ░                 ░                      ░      »\n",
       "«          ┌───┐ ░       ░ ┌───┐┌───┐┌───┐┌───┐┌───┐┌───┐┌───┐ ░       ░ ┌───┐     ┌───┐          »\n",
       "«q_0: ──■──┤ X ├─░───────░─┤ H ├┤ X ├┤ H ├┤ X ├┤ H ├┤ X ├┤ H ├─░───────░─┤ X ├──■──┤ X ├───────■──»\n",
       "«       │  └───┘ ░       ░ ├───┤├───┤└───┘└─┬─┘├───┤├───┤└───┘ ░       ░ └───┘  │  └───┘       │  »\n",
       "«q_1: ──■────────░───────░─┤ H ├┤ X ├───────■──┤ X ├┤ H ├──────░───────░────────■──────────────■──»\n",
       "«       │        ░       ░ ├───┤├───┤       │  ├───┤├───┤      ░       ░        │  ┌───┐       │  »\n",
       "«q_2: ──■────────░───────░─┤ H ├┤ X ├───────■──┤ X ├┤ H ├──────░───────░────────■──┤ X ├───────■──»\n",
       "«       │  ┌───┐ ░       ░ ├───┤├───┤       │  ├───┤├───┤      ░       ░ ┌───┐  │  ├───┤┌───┐  │  »\n",
       "«q_3: ──■──┤ X ├─░───────░─┤ H ├┤ X ├───────■──┤ X ├┤ H ├──────░───────░─┤ X ├──■──┤ X ├┤ X ├──■──»\n",
       "«     ┌─┴─┐└───┘ ░ ┌───┐ ░ └───┘└───┘          └───┘└───┘      ░ ┌───┐ ░ └───┘┌─┴─┐└───┘└───┘┌─┴─┐»\n",
       "«q_4: ┤ X ├──────░─┤ H ├─░─────────────────────────────────────░─┤ H ├─░──────┤ X ├──────────┤ X ├»\n",
       "«     └───┘      ░ └───┘ ░                                     ░ └───┘ ░      └───┘          └───┘»\n",
       "«                               ░       ░ ┌───┐┌───┐┌───┐┌───┐┌───┐┌───┐┌───┐ ░       ░ ┌───┐     »\n",
       "«q_0: ────────────■─────────■───░───────░─┤ H ├┤ X ├┤ H ├┤ X ├┤ H ├┤ X ├┤ H ├─░───────░─┤ X ├──■──»\n",
       "«     ┌───┐       │  ┌───┐  │   ░       ░ ├───┤├───┤└───┘└─┬─┘├───┤├───┤└───┘ ░       ░ └───┘  │  »\n",
       "«q_1: ┤ X ├───────■──┤ X ├──■───░───────░─┤ H ├┤ X ├───────■──┤ X ├┤ H ├──────░───────░────────■──»\n",
       "«     ├───┤┌───┐  │  ├───┤  │   ░       ░ ├───┤├───┤       │  ├───┤├───┤      ░       ░        │  »\n",
       "«q_2: ┤ X ├┤ X ├──■──┤ X ├──■───░───────░─┤ H ├┤ X ├───────■──┤ X ├┤ H ├──────░───────░────────■──»\n",
       "«     ├───┤└───┘  │  └───┘  │   ░       ░ ├───┤├───┤       │  ├───┤├───┤      ░       ░ ┌───┐  │  »\n",
       "«q_3: ┤ X ├───────■─────────■───░───────░─┤ H ├┤ X ├───────■──┤ X ├┤ H ├──────░───────░─┤ X ├──■──»\n",
       "«     └───┘     ┌─┴─┐     ┌─┴─┐ ░ ┌───┐ ░ └───┘└───┘          └───┘└───┘      ░ ┌───┐ ░ └───┘┌─┴─┐»\n",
       "«q_4: ──────────┤ X ├─────┤ X ├─░─┤ H ├─░─────────────────────────────────────░─┤ H ├─░──────┤ X ├»\n",
       "«               └───┘     └───┘ ░ └───┘ ░                                     ░ └───┘ ░      └───┘»\n",
       "«     ┌───┐                                    ░       ░ ┌───┐┌───┐┌───┐┌───┐┌───┐┌───┐┌───┐\n",
       "«q_0: ┤ X ├───────■──────────────■─────────■───░───────░─┤ H ├┤ X ├┤ H ├┤ X ├┤ H ├┤ X ├┤ H ├\n",
       "«     └───┘       │  ┌───┐       │  ┌───┐  │   ░       ░ ├───┤├───┤└───┘└─┬─┘├───┤├───┤└───┘\n",
       "«q_1: ────────────■──┤ X ├───────■──┤ X ├──■───░───────░─┤ H ├┤ X ├───────■──┤ X ├┤ H ├─────\n",
       "«     ┌───┐       │  ├───┤┌───┐  │  ├───┤  │   ░       ░ ├───┤├───┤       │  ├───┤├───┤     \n",
       "«q_2: ┤ X ├───────■──┤ X ├┤ X ├──■──┤ X ├──■───░───────░─┤ H ├┤ X ├───────■──┤ X ├┤ H ├─────\n",
       "«     ├───┤┌───┐  │  ├───┤└───┘  │  └───┘  │   ░       ░ ├───┤├───┤       │  ├───┤├───┤     \n",
       "«q_3: ┤ X ├┤ X ├──■──┤ X ├───────■─────────■───░───────░─┤ H ├┤ X ├───────■──┤ X ├┤ H ├─────\n",
       "«     └───┘└───┘┌─┴─┐└───┘     ┌─┴─┐     ┌─┴─┐ ░ ┌───┐ ░ └───┘└───┘          └───┘└───┘     \n",
       "«q_4: ──────────┤ X ├──────────┤ X ├─────┤ X ├─░─┤ H ├─░────────────────────────────────────\n",
       "«               └───┘          └───┘     └───┘ ░ └───┘ ░                                    </pre>"
      ],
      "text/plain": [
       "                ░ ┌───┐      ░      ┌───┐      ░                 ░      ┌───┐           ░ ┌───┐»\n",
       "q_0: ───────────░─┤ X ├──────░──────┤ X ├──────░─────────────────░──────┤ X ├───────────░─┤ X ├»\n",
       "                ░ └─┬─┘┌───┐ ░      └─┬─┘      ░      ┌───┐      ░      └─┬─┘┌───┐      ░ └───┘»\n",
       "q_1: ───────────░───┼──┤ X ├─░────────┼────────░──────┤ X ├──────░────────┼──┤ X ├──────░──────»\n",
       "     ┌───┐      ░   │  └─┬─┘ ░ ┌───┐  │  ┌───┐ ░      └─┬─┘      ░ ┌───┐  │  └─┬─┘┌───┐ ░      »\n",
       "q_2: ┤ H ├──────░───■────■───░─┤ X ├──■──┤ X ├─░────────■────────░─┤ X ├──■────■──┤ X ├─░──────»\n",
       "     ├───┤      ░   │    │   ░ └───┘  │  └───┘ ░ ┌───┐  │  ┌───┐ ░ ├───┤  │    │  ├───┤ ░ ┌───┐»\n",
       "q_3: ┤ H ├──────░───■────■───░────────■────────░─┤ X ├──■──┤ X ├─░─┤ X ├──■────■──┤ X ├─░─┤ X ├»\n",
       "     ├───┤┌───┐ ░            ░                 ░ └───┘     └───┘ ░ └───┘          └───┘ ░ └───┘»\n",
       "q_4: ┤ X ├┤ H ├─░────────────░─────────────────░─────────────────░──────────────────────░──────»\n",
       "     └───┘└───┘ ░            ░                 ░                 ░                      ░      »\n",
       "«          ┌───┐ ░       ░ ┌───┐┌───┐┌───┐┌───┐┌───┐┌───┐┌───┐ ░       ░ ┌───┐     ┌───┐          »\n",
       "«q_0: ──■──┤ X ├─░───────░─┤ H ├┤ X ├┤ H ├┤ X ├┤ H ├┤ X ├┤ H ├─░───────░─┤ X ├──■──┤ X ├───────■──»\n",
       "«       │  └───┘ ░       ░ ├───┤├───┤└───┘└─┬─┘├───┤├───┤└───┘ ░       ░ └───┘  │  └───┘       │  »\n",
       "«q_1: ──■────────░───────░─┤ H ├┤ X ├───────■──┤ X ├┤ H ├──────░───────░────────■──────────────■──»\n",
       "«       │        ░       ░ ├───┤├───┤       │  ├───┤├───┤      ░       ░        │  ┌───┐       │  »\n",
       "«q_2: ──■────────░───────░─┤ H ├┤ X ├───────■──┤ X ├┤ H ├──────░───────░────────■──┤ X ├───────■──»\n",
       "«       │  ┌───┐ ░       ░ ├───┤├───┤       │  ├───┤├───┤      ░       ░ ┌───┐  │  ├───┤┌───┐  │  »\n",
       "«q_3: ──■──┤ X ├─░───────░─┤ H ├┤ X ├───────■──┤ X ├┤ H ├──────░───────░─┤ X ├──■──┤ X ├┤ X ├──■──»\n",
       "«     ┌─┴─┐└───┘ ░ ┌───┐ ░ └───┘└───┘          └───┘└───┘      ░ ┌───┐ ░ └───┘┌─┴─┐└───┘└───┘┌─┴─┐»\n",
       "«q_4: ┤ X ├──────░─┤ H ├─░─────────────────────────────────────░─┤ H ├─░──────┤ X ├──────────┤ X ├»\n",
       "«     └───┘      ░ └───┘ ░                                     ░ └───┘ ░      └───┘          └───┘»\n",
       "«                               ░       ░ ┌───┐┌───┐┌───┐┌───┐┌───┐┌───┐┌───┐ ░       ░ ┌───┐     »\n",
       "«q_0: ────────────■─────────■───░───────░─┤ H ├┤ X ├┤ H ├┤ X ├┤ H ├┤ X ├┤ H ├─░───────░─┤ X ├──■──»\n",
       "«     ┌───┐       │  ┌───┐  │   ░       ░ ├───┤├───┤└───┘└─┬─┘├───┤├───┤└───┘ ░       ░ └───┘  │  »\n",
       "«q_1: ┤ X ├───────■──┤ X ├──■───░───────░─┤ H ├┤ X ├───────■──┤ X ├┤ H ├──────░───────░────────■──»\n",
       "«     ├───┤┌───┐  │  ├───┤  │   ░       ░ ├───┤├───┤       │  ├───┤├───┤      ░       ░        │  »\n",
       "«q_2: ┤ X ├┤ X ├──■──┤ X ├──■───░───────░─┤ H ├┤ X ├───────■──┤ X ├┤ H ├──────░───────░────────■──»\n",
       "«     ├───┤└───┘  │  └───┘  │   ░       ░ ├───┤├───┤       │  ├───┤├───┤      ░       ░ ┌───┐  │  »\n",
       "«q_3: ┤ X ├───────■─────────■───░───────░─┤ H ├┤ X ├───────■──┤ X ├┤ H ├──────░───────░─┤ X ├──■──»\n",
       "«     └───┘     ┌─┴─┐     ┌─┴─┐ ░ ┌───┐ ░ └───┘└───┘          └───┘└───┘      ░ ┌───┐ ░ └───┘┌─┴─┐»\n",
       "«q_4: ──────────┤ X ├─────┤ X ├─░─┤ H ├─░─────────────────────────────────────░─┤ H ├─░──────┤ X ├»\n",
       "«               └───┘     └───┘ ░ └───┘ ░                                     ░ └───┘ ░      └───┘»\n",
       "«     ┌───┐                                    ░       ░ ┌───┐┌───┐┌───┐┌───┐┌───┐┌───┐┌───┐\n",
       "«q_0: ┤ X ├───────■──────────────■─────────■───░───────░─┤ H ├┤ X ├┤ H ├┤ X ├┤ H ├┤ X ├┤ H ├\n",
       "«     └───┘       │  ┌───┐       │  ┌───┐  │   ░       ░ ├───┤├───┤└───┘└─┬─┘├───┤├───┤└───┘\n",
       "«q_1: ────────────■──┤ X ├───────■──┤ X ├──■───░───────░─┤ H ├┤ X ├───────■──┤ X ├┤ H ├─────\n",
       "«     ┌───┐       │  ├───┤┌───┐  │  ├───┤  │   ░       ░ ├───┤├───┤       │  ├───┤├───┤     \n",
       "«q_2: ┤ X ├───────■──┤ X ├┤ X ├──■──┤ X ├──■───░───────░─┤ H ├┤ X ├───────■──┤ X ├┤ H ├─────\n",
       "«     ├───┤┌───┐  │  ├───┤└───┘  │  └───┘  │   ░       ░ ├───┤├───┤       │  ├───┤├───┤     \n",
       "«q_3: ┤ X ├┤ X ├──■──┤ X ├───────■─────────■───░───────░─┤ H ├┤ X ├───────■──┤ X ├┤ H ├─────\n",
       "«     └───┘└───┘┌─┴─┐└───┘     ┌─┴─┐     ┌─┴─┐ ░ ┌───┐ ░ └───┘└───┘          └───┘└───┘     \n",
       "«q_4: ──────────┤ X ├──────────┤ X ├─────┤ X ├─░─┤ H ├─░────────────────────────────────────\n",
       "«               └───┘          └───┘     └───┘ ░ └───┘ ░                                    "
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc = QuantumCircuit(5)\n",
    "\n",
    "#1St magazine\n",
    "#0-1 data\n",
    "#Index\n",
    "#2-3\n",
    "qc.h(2)\n",
    "qc.h(3)\n",
    "\n",
    "#Aux Bit\n",
    "qc.x(4)\n",
    "qc.h(4)\n",
    "\n",
    "qc.barrier()\n",
    "#First patern\n",
    "qc.ccx(2,3,0)\n",
    "qc.ccx(2,3,1)\n",
    "\n",
    "qc.barrier()\n",
    "#Second patern\n",
    "qc.x(2)\n",
    "qc.ccx(2,3,0)\n",
    "qc.x(2)\n",
    "\n",
    "qc.barrier()\n",
    "#Third patern\n",
    "qc.x(3)\n",
    "qc.ccx(2,3,1)\n",
    "qc.x(3)\n",
    "\n",
    "qc.barrier()\n",
    "#Fourth patern\n",
    "qc.x(2)\n",
    "qc.x(3)\n",
    "qc.ccx(2,3,0)\n",
    "qc.ccx(2,3,1)\n",
    "qc.x(3)\n",
    "qc.x(2)\n",
    "\n",
    "#Oracle\n",
    "qc.barrier()\n",
    "qc.x(0)\n",
    "qc.x(3)\n",
    "gate = MCXGate(4)\n",
    "qc.append(gate, [0,1,2,3,4])\n",
    "qc.x(3)\n",
    "qc.x(0)\n",
    "qc.barrier()\n",
    "qc.h(4)\n",
    "#Diffusor 0, 1, 2, 3 \n",
    "qc.barrier()\n",
    "\n",
    "qc.h([0,1,2,3])\n",
    "qc.x([0,1,2,3])\n",
    "qc.h(0)\n",
    "gate = MCXGate(3)\n",
    "qc.append(gate, [1,2,3,0])\n",
    "qc.h(0)\n",
    "qc.x([0,1,2,3])\n",
    "qc.h([0,1,2,3])\n",
    "\n",
    "#Secend rotation\n",
    "qc.barrier()\n",
    "qc.h(4)\n",
    "qc.barrier()\n",
    "\n",
    "#Oracle Trick\n",
    "qc.x(0)\n",
    "qc.x(3)\n",
    "gate = MCXGate(4)\n",
    "qc.append(gate, [0,1,2,3,4])\n",
    "qc.x(3)\n",
    "qc.x(0)\n",
    "\n",
    "qc.x(2)\n",
    "qc.x(3)\n",
    "gate = MCXGate(4)\n",
    "qc.append(gate, [0,1,2,3,4])\n",
    "qc.x(3)\n",
    "qc.x(2)\n",
    "\n",
    "qc.x(1)\n",
    "qc.x(2)\n",
    "gate = MCXGate(4)\n",
    "qc.append(gate, [0,1,2,3,4])\n",
    "qc.x(2)\n",
    "qc.x(1)\n",
    "\n",
    "gate = MCXGate(4)\n",
    "qc.append(gate, [0,1,2,3,4])\n",
    "\n",
    "qc.barrier()\n",
    "qc.h(4)\n",
    "#Diffusor 0, 1, 2, 3 \n",
    "qc.barrier()\n",
    "\n",
    "qc.h([0,1,2,3])\n",
    "qc.x([0,1,2,3])\n",
    "qc.h(0)\n",
    "gate = MCXGate(3)\n",
    "qc.append(gate, [1,2,3,0])\n",
    "qc.h(0)\n",
    "qc.x([0,1,2,3])\n",
    "qc.h([0,1,2,3])\n",
    "\n",
    "#Third rotation\n",
    "qc.barrier()\n",
    "qc.h(4)\n",
    "qc.barrier()\n",
    "\n",
    "#Oracle Trick\n",
    "qc.x(0)\n",
    "qc.x(3)\n",
    "gate = MCXGate(4)\n",
    "qc.append(gate, [0,1,2,3,4])\n",
    "qc.x(3)\n",
    "qc.x(0)\n",
    "\n",
    "qc.x(2)\n",
    "qc.x(3)\n",
    "gate = MCXGate(4)\n",
    "qc.append(gate, [0,1,2,3,4])\n",
    "qc.x(3)\n",
    "qc.x(2)\n",
    "\n",
    "qc.x(1)\n",
    "qc.x(2)\n",
    "gate = MCXGate(4)\n",
    "qc.append(gate, [0,1,2,3,4])\n",
    "qc.x(2)\n",
    "qc.x(1)\n",
    "\n",
    "gate = MCXGate(4)\n",
    "qc.append(gate, [0,1,2,3,4])\n",
    "qc.barrier()\n",
    "qc.h(4)\n",
    "\n",
    "#Diffusor 0, 1, 2, 3 \n",
    "qc.barrier()\n",
    "\n",
    "qc.h([0,1,2,3])\n",
    "qc.x([0,1,2,3])\n",
    "qc.h(0)\n",
    "gate = MCXGate(3)\n",
    "qc.append(gate, [1,2,3,0])\n",
    "qc.h(0)\n",
    "qc.x([0,1,2,3])\n",
    "qc.h([0,1,2,3])\n",
    "\n",
    "qc.draw(fold=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simulator = Aer.get_backend('statevector_simulator')\n",
    "\n",
    "# Run and get counts\n",
    "result=execute(qc,simulator).result()\n",
    "counts = result.get_counts()\n",
    "plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "with the amplitude one indicating the target state. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$\n",
       "\\text{Statevector} = \n",
       "\\begin{bmatrix}\n",
       "0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & -1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0  \\\\\n",
       " \\end{bmatrix}\n",
       "$$"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simulator = Aer.get_backend('statevector_simulator')\n",
    "final_state = simulator.run(qc).result().get_statevector()\n",
    "\n",
    "from qiskit.visualization import array_to_latex\n",
    "array_to_latex(final_state,max_size=256,precision=4,prefix=\"\\\\text{Statevector} = \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Depending on the distribution and the relation between $m$ (the number of patterns) and  $n$ (the dimension of patterns), we  have to correct by marking all present state till  all states without the target state reach an  uniform distribution.\n"
   ]
  }
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