{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9fb07d3a-5975-4222-95bc-44bdf9d16e9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import QuantumCircuit, QuantumRegister, transpile\n",
    "from qiskit_ibm_runtime import QiskitRuntimeService\n",
    "service = QiskitRuntimeService.save_account(channel=\"ibm_quantum\", token=\"HIER IHR TOKEN EINSETZEN\", set_as_default=True, overwrite=True)\n",
    "\n",
    "from qiskit.providers.basic_provider import BasicSimulator\n",
    "backend = BasicSimulator()\n",
    "\n",
    "from qiskit.visualization import plot_histogram\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4024bad1-00b6-4590-8e73-d98bbe45805f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def oracle(qubits,secret):\n",
    "    # Bits so setzen, dass beim gesuchten Element 'secret' als Binärzahl qubits[2]qubits[1]qubits[0] nachher 111 rauskommt\n",
    "    if (secret==0 or secret==2 or secret==4 or secret==6):   \n",
    "        qubits.x(0)  # NOT auf qubits[0]\n",
    "    if (secret==0 or secret==1 or secret==4 or secret==5):   \n",
    "        qubits.x(1)  # NOT auf qubits[1]\n",
    "    if (secret==0 or secret==1 or secret==2 or secret==3):   \n",
    "        qubits.x(2)  # NOT auf qubits[2]\n",
    "    # nun ein von qubit[0], qubit[1] und qubit[2] kontrolliertes CNOT auf's Hilfqubit qubit[3]\n",
    "    controls = list(range(3))\n",
    "    qubits.mcx(controls,3)\n",
    "    # x-Qubits entsprechend oben zurücksetzen.\n",
    "    if (secret==0 or secret==2 or secret==4 or secret==6):   \n",
    "        qubits.x(0)\n",
    "    if (secret==0 or secret==1 or secret==4 or secret==5):   \n",
    "        qubits.x(1)\n",
    "    if (secret==0 or secret==1 or secret==2 or secret==3):   \n",
    "        qubits.x(2)\n",
    "    return qubits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2be6a348-657a-42ac-9bd9-64c65c36f306",
   "metadata": {},
   "outputs": [],
   "source": [
    "def grover_iteration(circuit,secret):\n",
    "    # Aufruf des Quanten-Orakels\n",
    "    oracle(circuit,secret)\n",
    "    circuit.barrier()  # nur für die Schaltkreis-Darstellung\n",
    "    # H-Gatter für alle x-Qubits\n",
    "    for i in range(3):\n",
    "        circuit.h(i)\n",
    "    circuit.barrier()  # nur für die Schaltkreis-Darstellung\n",
    "    # Beginn der Realisierung von D mit Schaltkreis von Übungsblatt 7, Aufgabe 3b);\n",
    "    # auf die Multiplikation mit (-1) wird verzichtet, da das alle Amplituden betrifft und \n",
    "    # daher insgesamt für den weiteren Verlauf und die abschließenden Messungen irrelevant ist.\n",
    "    for i in range(3):\n",
    "        circuit.x(i)   # P_x-Gatter für die drei Qubits\n",
    "    circuit.barrier()  # nur für die Schaltkreis-Darstellung\n",
    "    circuit.h(2)       # H-Gatter unten\n",
    "    circuit.ccx(0,1,2) # Toffoli-Gatter\n",
    "    circuit.h(2)       # H-Gatter unten\n",
    "    circuit.barrier()  # nur für die Schaltkreis-Darstellung\n",
    "    for i in range(3):\n",
    "        circuit.x(i)   # P_x-Gatter für die drei Qubits\n",
    "    # Ende der Realisierung von -D\n",
    "    circuit.barrier()  # nur für die Schaltkreis-Darstellung\n",
    "    # H-Gatter für alle x-Qubits\n",
    "    for i in range(3):\n",
    "        circuit.h(i)\n",
    "    circuit.barrier()  # nur für die Schaltkreis-Darstellung\n",
    "    return circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d17615bc-d969-46bf-9eba-8b36082895af",
   "metadata": {},
   "outputs": [],
   "source": [
    "secret=random.randint(0,7) # Auswahl des gesuchten Elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1b13d50a-12a0-4ed9-ba77-9bd727028f48",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3 x-Qubits, ein y-Qubit und ein Hilfqubit für's Orakel; Standard-Initialisierung mit 0\n",
    "circuit = QuantumCircuit(4,3) \n",
    "circuit.x(3)       # unterstes Qubit auf 1 setzen\n",
    "circuit.barrier()  # nur für die Schaltkreis-Darstellung\n",
    "# H-Gatter für alle Qubits\n",
    "for i in range(4):\n",
    "    circuit.h(i)    \n",
    "circuit.barrier()    # nur für die Schaltkreis-Darstellung\n",
    "# Grover-Iteration(en)\n",
    "grover_iteration(circuit,secret)\n",
    "#grover_iteration(circuit,secret)\n",
    "#grover_iteration(circuit,secret)\n",
    "#grover_iteration(circuit,secret)\n",
    "#grover_iteration(circuit,secret)\n",
    "# Messungen\n",
    "for i in range(3):\n",
    "    circuit.measure(i, i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "84bdf329-ccd0-4616-ba4c-5d34ed8001fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circ = transpile(circuit, backend)   # Transformation auf Hardware-nahen Schaltkreis\n",
    "erg = backend.run(circ, shots=1024).result()\n",
    "plot_histogram(erg.get_counts(circuit))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0fd9cf30-51cb-4db5-90f3-599c4fe1448d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "print(secret)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b6015988-37b0-4a34-8789-4c5bb5f2b30a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2043.33x451.5 with 1 Axes>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.draw('mpl')  # Ausgabe des Schaltkreis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65981017-1240-4ff0-89be-add013507687",
   "metadata": {},
   "outputs": [],
   "source": [
    "circ.draw('mpl')  # Ausgabe des Hardware-nahen Schaltkreis (Toffoli-Gatter wird aufgelöst entspr. Übblatt 6, Aufg. 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d7f7570-d4aa-4369-8be9-5359be74372f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}