ICC - Algoritmo de Groover.ipynb

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    "## Algoritmo de Busqueda de Grover\n",
    "\n",
    "### Introducción\n",
    "\n",
    "Suponga que tiene una lista de N items. Entre estos items hay uno con una propiedad unica que se desea encontrar. Piense en cada elemento de la lista como una caja de un color particular, digamos que todos los items en la lista tienen color gris mientras que el elemento que estamos buscando tiene color rosa.\n",
    "\n",
    "Clasicamente buscar un elemento en una lista desordenada tiene una complejidad O(N) donde N es el numero de elementos en la lista. El algoritmo de busqueda de Grover que trabaja en un computador cuantico puede encontrar el elemento que estamos buscando en una complejidad $O(\\sqrt{N})$\n",
    "\n",
    "### El Oraculo\n",
    "\n",
    "Como se provee la lista de items al computador cuantico? una manera comun es codificar cada elemento en terminos de una función $f(x)=0$ para todos los items no marcados x y $f(w)=1$ para el item que estamos buscando. Para usar un computador cuantico para este problema, debemos proveer los items en superposición a esta función. \n",
    "\n",
    "De esta forma convertimos la función en una matriz unitaria llamada oraculo. Primero escogemos una codificación binaria de los items $x,y \\in {0,1}^n$ de tal forma que N=2^n y de esta forma se puedan representar en terminos de qubits en un computador cuantico. Entonces definimos la matriz oraculo $U_f$ que actue sobre cualquiera de los estados bases $|x\\rangle$ haciendo:\n",
    "\n",
    "$U_f|x\\rangle=(-1)^{f(x)}|x\\rangle$\n",
    "\n",
    "Vemos que si x es un item que no buscamos el oraculo no hace nada al estad. Sin embargo, cuando aplicamos el oraculo al estado base $|w\\rangle$ lo que hace es cambiar la fase de este, siendo $U_f|w\\rangle= -|w\\rangle$.\n",
    "\n",
    "### Amplificación de Amplitudes\n",
    "\n",
    "Antes de buscar en la lista de items no se tiene idea donde se encuentra el item marcado. Por lo tanto cualquier escogencia de su ubicacion es tan buena como cualquier otra. lo cual puede ser expresado en terminos de un estado cuantico llamado superposición uniforme\n",
    "\n",
    "$|s\\rangle= \\dfrac{1}{\\sqrt{N}} \\sum_{x=0}^{n-1} |x\\rangle$\n",
    "\n",
    "Si se mide en este punto el estado, este colapsará, y las probabilidades de encontrar el valor esperado es $\\dfrac{1}{2^n}$ y en promedio nos tomaría intentar $2^n$ veces para encontrar el valor que buscamos.\n",
    "\n",
    "El procedimiento llamado amplificación de aplitud, el cual es como un computador cuantico mejora la probabilidad. Este procedimiento amplifica la aplitud del item marcado, y disminuye la aplitud de los otros items, de tal forma que midiendo el estado final retorna el item buscado con buena certeza.\n",
    "\n",
    "Este algoritmo tiene una buena interpretación geometrica. Se genera una rotación en un espacio bidimentional. Los 2 estados especiales que necesitamos considerar son el que tiene el elemento de busqueda $\\lvert w\\rangle$ y el de la superposición $\\lvert s\\rangle$. Estos 2 vectores componen un estado bidimensional. No son necesariamente perpendiculares debido a que $\\lvert w\\rangle$ tiene una amplitud correspondiente en $\\lvert s\\rangle$. Sin embargo, se introduce un estado adicional $\\lvert s'\\rangle$ que es un espacio entre estos dos vectores y es perpendicular. Se consigue desde |$s\\rangle$ removiendo $\\lvert w\\rangle$ y reescalando.\n",
    "\n",
    "- **Paso 0:** La amplificación empieza con la superposición |$s\\rangle$.\n",
    "- **Paso 1:** Se aplica la reflexión $U_f\\lvert x\\rangle$\n",
    "- **Paso 2:** Se aplica una reflexión aditiva $U_s$ sobre $\\lvert s\\rangle$. en notación bra-ket $U_s=2\\lvert s\\rangle\\langle s\\lvert -1$  \n",
    "  Esto lo que hace es aumentar la amplitud del elemento que buscamos y disminuye la de los demás\n",
    "- Se repite desde el **Paso 1** un numero $\\sqrt{N}$ de veces,\n",
    "\n",
    "\n",
    "### Video explicativo\n",
    "\n",
    "#### **Grover's Algorithm Outline**\n",
    "[![IMAGE ALT TEXT](http://img.youtube.com/vi/7tc3DCAJC7E/0.jpg)](http://www.youtube.com/watch?v=7tc3DCAJC7E \"Grover's Algorithm Outline\")"
   ]
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   "source": [
    "### Codigo en QISKit para encotrar 10 con 2 Qubit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=1015x223 at 0x1897A3A9400>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit import ClassicalRegister, QuantumRegister\n",
    "from qiskit import QuantumCircuit, execute\n",
    "from qiskit.tools.visualization import plot_histogram, circuit_drawer\n",
    "\n",
    "q = QuantumRegister(3)\n",
    "c = ClassicalRegister(2)\n",
    "qc = QuantumCircuit(q, c)\n",
    "#qc.x(q[0])\n",
    "#qc.x(q[1])\n",
    "qc.h(q[1])\n",
    "qc.h(q[2])\n",
    "#Oracle:\n",
    "#qc.h(q[0]); qc.cx(q[1],q[0]); qc.tdg(q[0]); \n",
    "#qc.cx(q[2],q[0]); qc.t(q[0]); qc.cx(q[1],q[0]); qc.tdg(q[0])\n",
    "#qc.cx(q[2],q[0]); qc.t(q[0]); qc.tdg(q[1]); qc.h(q[0]); qc.cx(q[2],q[1]);\n",
    "#qc.tdg(q[1]);qc.cx(q[2],q[1]); qc.s(q[1]); qc.t(q[2])\n",
    "\n",
    "qc.s(q[2])\n",
    "qc.h(q[1])\n",
    "qc.cx(q[2],q[1])\n",
    "qc.h(q[1])\n",
    "qc.s(q[2])\n",
    "\n",
    "#Operador de Grover\n",
    "qc.h(q[1]); \n",
    "#qc.barrier(q[1])\n",
    "qc.h(q[2]); qc.x(q[1]); qc.x(q[2]); qc.h(q[1]);qc.cx(q[2],q[1])\n",
    "qc.h(q[1]); qc.x(q[1]); qc.x(q[2]); qc.h(q[1]); qc.h(q[2]);\n",
    "\n",
    "#qc.measure(q,c)\n",
    "qc.measure(q[1],c[0])\n",
    "qc.measure(q[2],c[1])\n",
    "job_exp = execute(qc, backend=\"local_qasm_simulator\", shots=1024, max_credits=3)\n",
    "plot_histogram(job_exp.result().get_counts(qc))\n",
    "print(job_exp.result().get_counts(qc))\n",
    "circuit_drawer(qc)"
   ]
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}