Source/qiskit/save_quantum_credential.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2026-06-10T07:57:50.775574545Z",
     "start_time": "2026-06-10T07:57:47.638484557Z"
    }
   },
   "source": [
    "from qiskit.providers.fake_provider import GenericBackendV2\n",
    "from qiskit_ibm_runtime import QiskitRuntimeService\n",
    "from qiskit_ibm_runtime.options.simulator_options import NoiseModel\n",
    "\n",
    "QiskitRuntimeService.save_account(\n",
    "    token = \"w0M6nwo-lPFp3rNXZ_beYC14hqurYjT349-jvjqRL7NR\",\n",
    "    instance=\"crn:v1:bluemix:public:quantum-computing:us-east:a/c7f19863638942cda35191f3f08bd45e:7ec8b2ce-30b0-48d0-87e9-5821d9ee8e68::\",\n",
    "    overwrite=True,\n",
    ")\n",
    "\n",
    "service = QiskitRuntimeService(channel='ibm_quantum_platform')"
   ],
   "outputs": [],
   "execution_count": 17
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "|#### 양자 반가산기 ( 최적화 )",
   "id": "b5af58b836566916"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-06-10T07:57:50.824069767Z",
     "start_time": "2026-06-10T07:57:50.777548104Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# !pip install matplotlib\n",
    "# !pip install pylatexenc"
   ],
   "id": "eefe74b65e501d8f",
   "outputs": [],
   "execution_count": 18
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-06-10T07:57:50.986295002Z",
     "start_time": "2026-06-10T07:57:50.824979415Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\n",
    "from numpy import pi\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# 큐비트 : a, b, 합(sum), 자리올림(carry)\n",
    "qc = QuantumCircuit(4)\n",
    "\n",
    "# A와 B에 넣을 값을 선택합니다.\n",
    "A = 0\n",
    "B = 0\n",
    "\n",
    "# 선택한 값에 따라 A, B 큐비트를 준비합니다.\n",
    "if A :\n",
    "    qc.x(0)\n",
    "if B :\n",
    "    qc.x(1)\n",
    "\n",
    "# XOR(합)를 큐비트 2에 계산\n",
    "qc.cx(0, 2)\n",
    "qc.cx(1, 2)\n",
    "\n",
    "# AND(자리올림)를 큐비트 3에 계산\n",
    "qc.ccx(0, 1, 3) # A AND B\n",
    "\n",
    "# 측정\n",
    "qc.measure_all()\n",
    "\n",
    "qc.draw(\"mpl\")\n",
    "\n"
   ],
   "id": "c7144ef2302d3d35",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 831.22x451.5 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 19
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-06-10T08:00:42.503543386Z",
     "start_time": "2026-06-10T08:00:35.327501864Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Qiskit Runtime 서비스를 불러옵니다.\n",
    "from qiskit_ibm_runtime import QiskitRuntimeService\n",
    "\n",
    "# 저장된 인증 정보를 불러옵니다.\n",
    "service = QiskitRuntimeService()\n",
    "\n",
    "# 가장 한가한 백엔드를 사용하거나, \"ibm_brisbane\"처럼 특정 백엔드를 지정하는 줄의 주석을 해제하세요.\n",
    "backend = service.least_busy(operational=True, simulator=False, min_num_qubits=127)\n",
    "# backend = service.backend(\"ibm_fez\")\n",
    "# backend = service.get_backend(\"ibm_brisbane\")\n",
    "print(backend.name)\n"
   ],
   "id": "890bf1bc9e4876db",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ibm_marrakesh\n"
     ]
    }
   ],
   "execution_count": 26
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-06-10T08:10:53.279179151Z",
     "start_time": "2026-06-10T08:10:52.791030368Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 선택한 양자 컴퓨터에서 실행되도록 회로를 트랜스파일하고 최적화합니다.\n",
    "# 2단계: 트랜스파일\n",
    "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n",
    "\n",
    "target = backend.target\n",
    "pm = generate_preset_pass_manager(target=target, optimization_level=3)\n",
    "qc_isa = pm.run(qc)\n",
    "qc_isa.draw(\"mpl\")"
   ],
   "id": "f41d5e1fb052cad3",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2281.06x953.167 with 1 Axes>"
      ],
      "image/png": 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     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 33
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-06-10T08:12:48.400263361Z",
     "start_time": "2026-06-10T08:12:48.352789089Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Sampler 사용\n",
    "# Runtime 프리미티브와 세션을 불러옵니다.\n",
    "from qiskit_ibm_runtime import SamplerV2 as Sampler\n",
    "\n",
    "sampler = Sampler(mode=backend)"
   ],
   "id": "880e9471abc9f2db",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<qiskit_ibm_runtime.sampler.SamplerV2 object at 0x799c044b8620>\n"
     ]
    }
   ],
   "execution_count": 39
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-06-10T08:33:33.304325098Z",
     "start_time": "2026-06-10T08:18:02.686090356Z"
    }
   },
   "cell_type": "code",
   "source": [
    "job = sampler.run([qc_isa], shots = 100)\n",
    "# job = sampler_sim.run([qc_isa]) # 시뮬레이터에서 실행하려면 주석을 해제하세요.\n",
    "res = job.result()\n",
    "counts = res[0].data.meas.get_counts()"
   ],
   "id": "5edb73b75783f920",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cooney/Source/qiskit/.venv/lib/python3.12/site-packages/qiskit_ibm_runtime/qiskit_runtime_service.py:1013: UserWarning: The backend ibm_marrakesh currently has a status of maintenance.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "execution_count": 40
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-06-10T08:35:44.124603048Z",
     "start_time": "2026-06-10T08:35:38.925381717Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 백엔드 기반 Sampler를 불러옵니다.\n",
    "from qiskit.primitives import BackendSamplerV2\n",
    "\n",
    "# Aer 시뮬레이터를 불러오고, 현재 선택된 백엔드를 바탕으로 노이즈 모델을 생성합니다.\n",
    "from qiskit_aer import AerSimulator\n",
    "from qiskit_aer.noise import noise_model\n",
    "\n",
    "noise_model = NoiseModel.from_backend(backend)\n",
    "\n",
    "# Aer로 시뮬레이터를 정의하고, 이를 Sampler에 사용합니다.\n",
    "backend_sim = AerSimulator(noise_model=noise_model)\n",
    "sampler_sim = BackendSamplerV2(backend=backend_sim)\n",
    "\n",
    "# 또는, 일반적인 특성을 가진 가짜(fake) 백엔드를 불러와 시뮬레이터를 정의할 수도 있습니다.\n",
    "# backend_gen = GenericBackendV2(num_qubits=18)\n",
    "# sampler_gen = BackendSamplerV2(backend=backend_gen)"
   ],
   "id": "df864b4066125ce3",
   "outputs": [],
   "execution_count": 47
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-06-10T08:36:55.835708315Z",
     "start_time": "2026-06-10T08:36:53.809688521Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# job = sampler.run([qc_isa], shots = 100)\n",
    "job = sampler_sim.run([qc_isa]) # 시뮬레이터에서 실행하려면 주석을 해제하세요.\n",
    "res = job.result()\n",
    "counts = res[0].data.meas.get_counts()"
   ],
   "id": "ab5068a86ab345bc",
   "outputs": [],
   "execution_count": 51
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-06-10T08:37:06.218642464Z",
     "start_time": "2026-06-10T08:37:06.112763266Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from qiskit.visualization import plot_histogram\n",
    "\n",
    "print(\"counts = \", counts)\n",
    "plot_histogram(counts)"
   ],
   "id": "b8614d9ce333ef23",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "counts =  {'0000': 976, '1100': 2, '0001': 8, '1000': 16, '0010': 10, '0100': 11, '1001': 1}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 52
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "7ece9e76e18c118d"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}