content/lab_3/ko/lab-3-ai-transpiler-ko.ipynb

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   ]
  },
  {
   "cell_type": "markdown",
   "id": "f67ec71c-206b-45fc-a6f2-ec58b4891d4c",
   "metadata": {},
   "source": [
    "# Lab 3: What's Next - Sneak Preview of AI-Powered Transpilation with Qiskit Transpiler Service"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8979f3ca-d980-49c5-bda4-d7f59ed033f6",
   "metadata": {},
   "source": [
    "# 목차\n",
    "\n",
    "* [Introduction to AI-Powered Transpiler Passes](Introduction-to-AI-Powered-Transpiler-Passes)\n",
    "    * [문제 1](#exercise1)\n",
    "    * [문제 2](#exercise2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf00347e-9ffd-4f4d-9270-21eb512f8f96",
   "metadata": {},
   "source": [
    "# 셋업"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c4bb49c-26f3-4b0a-b8fa-e25f2882a688",
   "metadata": {
    "id": "5a76cfa8-9f5d-4716-a007-c13bcb54b82b"
   },
   "source": [
    "언제나처럼 필요한 패키지를 설치하는 것으로 시작하도록 하겠습니다. 아래의 커맨드를 실행하여 이번 lab에서 새롭게 다루게 될 qiskit-transpiler-service를 포함한 패키지들을 설치해주세요. Qiskit 설치와 API 토큰 등록은 이전에 진행하지 않은 경우에만 하시면 됩니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abb25eb9-4327-4ae0-9db4-745b87194a3d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "### 필요한 경우, 이 코드를 실행해 Qiskit과 추가 패키지들을 설치해주세요\n",
    "### 중요: 필수적인 패키지들의 호환성을 위해 파이썬 3.10 또는 3.11 버전을 사용해주세요\n",
    "%pip install qiskit[visualization]==1.0.2\n",
    "%pip install qiskit-ibm-runtime\n",
    "%pip install qiskit-aer\n",
    "%pip install graphviz\n",
    "%pip install qiskit-transpiler-service\n",
    "%pip install git+https://github.com/qiskit-community/Quantum-Challenge-Grader.git -U"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef1f5086",
   "metadata": {},
   "outputs": [],
   "source": [
    "### 아직 API 토큰을 등록하지 않으신 경우, 이 코드를 실행해 토큰을 등록해주세요\n",
    "\n",
    "%set_env QXToken=deleteThisAndPasteYourTokenHere\n",
    "\n",
    "# 등호 앞뒤에 공백이 없도록 주의해주세요"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37500cae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# qc-grader는 0.18.10 이후의 버전이 설치되어 있어야 합니다\n",
    "import qc_grader\n",
    "\n",
    "qc_grader.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1aff5103-c92a-45b0-93bb-f5b034de45c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 사용할 함수를 불러옵니다\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from qiskit.circuit.library import EfficientSU2\n",
    "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n",
    "from qiskit_ibm_runtime import QiskitRuntimeService\n",
    "from qiskit_transpiler_service.transpiler_service import TranspilerService"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08927292-6f1a-4490-901a-fdcdc3b411f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 답안을 채점할 grader 함수를 불러옵니다\n",
    "from qc_grader.challenges.iqc_2024 import grade_lab3_ait_ex1, grade_lab3_ait_ex2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "545418e9-a530-4e11-a323-d01177dd1da0",
   "metadata": {},
   "source": [
    "## 개요\n",
    "\n",
    "Lab 3에 오신 여러분을 환영합니다! 이번 챌린지에서 여러분께 Qiskit 스택을 위해 개발되고 있는 새롭고 재미있는 기능들을 독점 선공개할 수 있게 되어 정말 기쁩니다. 이번 lab은 각 기능별로 나눠진 노트북들을 통해 새로운 놀랍고 혁신적인 기능들을 소개하고, 여러분이 이것들을 체험하고 테스트해볼 수 있도록 구성되어 있습니다. 회로를 최적화하는 AI 트랜스파일러와 Qiskit 코드를 생성하는 Qiskit Code Assistant 등의 새로운 AI 기능들을 사용해보고, Qiskit Serverless를 통해 양자컴퓨터와 고전컴퓨터의 리소스를 간편하고 매끄럽게 결합하는 워크플로우를 체험해보고, Circuit Knitting Toolbox를 사용해 복잡한 양자 회로를 자르고 이어붙여 더 좋은 성능을 이끌어내보세요! 이 모든 기능들이 앞으로의 양자컴퓨팅을 어떻게 혁신해나갈 수 있는지 함께 탐험해보세요!\n",
    "\n",
    "<div class=\"alert alert-block alert-danger\">\n",
    "\n",
    "**이번 lab에서 다루는 기능들은 모두 베타 또는 시연용 버전이며**, 여러분이 기능을 사용하시는 중에 버그를 발견하시게 될 수도 있습니다. 최첨단의 양자컴퓨팅 개발 현장에서 함께 실험하고 기능을 개선해나갈 수 있는 기회를 즐겨주세요!\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "430cc7b6-46f2-4077-9746-edbb079ff0db",
   "metadata": {},
   "source": [
    "# Introducing AI-powered transpiler passes using Qiskit Transpiler Service\n",
    "\n",
    "이번 hands-on 연습문제에서는 Qiskit의 AI 트랜스파일러 패스가 제공하는 최첨단의 트랜스파일 성능을 체험해볼 것입니다. 최근에 출시된 Qiskit Transpiler Service 베타 버전에서는 여러분의 양자 회로를 AI와 IBM Cloud&reg; 리소스를 활용해 더 효율적으로 최적화할 수 있습니다.\n",
    "\n",
    "추상적으로만 존재하는 양자 회로를 실제적인 양자 디바이스의 조건과 특성에 맞게 트랜스파일하는 것은 양자컴퓨팅 워크플로우에서 매우 중요한 단계입니다. Lab 2에서 공부했듯, 이 과정은 전통적으로 휴리스틱 알고리즘에 의존해왔습니다만, 이 새로운 AI 트랜스파일러 패스는 실제 응용분야에서 활용할 수 있을 정도로 빠르고 효율적이면서도 높은 성능의 트랜스파일을 구현할 수 있는 가능성을 보여줍니다. 이번 챌린지에서는 Qiskit Transpiler Service 베타 액세스를 통해 이것을 실험해보도록 하겠습니다.\n",
    "\n",
    "**이 도구에 대해 더 알아보고 싶으시다면 [블로그](https://www.ibm.com/quantum/blog/ai-transpiler-passes)와 [arXiv 논문](https://arxiv.org/abs/2405.13196)을 확인해보세요.** [IBM Quantum Platform docs](https://docs.quantum.ibm.com/transpile/qiskit-transpiler-service)에서 이 도구에 대한 설명도 제공하고 있습니다.\n",
    "\n",
    "이번 연습문제에서는 이 AI 트랜스파일러 패스 베타 버전을 이용해보고 기존의 트랜스파일러와 비교하며, 이것이 어떻게 여러분의 양자컴퓨팅 워크플로우를 향상시킬 수 있을지 이해하는 시간을 가져보도록 하겠습니다. 미래의 양자 회로 최적화 기술 속으로 한 번 떠나볼까요?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75b7f2b4-31e5-4ebc-82fd-330f0bcdd807",
   "metadata": {},
   "source": [
    "## 1단계: 양자 회로 만들기\n",
    "\n",
    "우선 Qiskit 라이브러리를 이용해 양자 회로를 만들어봅시다. 이 단계는 양자 회로의 구조와 게이트 동작을 정의하는 단계입니다. 여기서 정의되는 양자 회로가 이후의 transpilation과 최적화를 수행할 대상이 됩니다. 여기서는 이 도구의 [원본 논문](https://arxiv.org/abs/2405.13196)에서 사용된 회로와 아주 비슷한 회로를 사용해보도록 하겠습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da050482-4412-41e2-9548-9e84a45749c3",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "\n",
    "**중요:** 여러분의 환경에 `qiskit-transpiler-service` 패키지가 잘 설치되어 있는지, IBM Quantum Platform의 토큰을 저장해뒀는지 꼭 확인하세요. IBM Quantum Platform의 토큰을 저장하는 방법은 [IBM Quantum Documentation 페이지](https://docs.quantum.ibm.com/start/setup-channel#set-up-to-use-ibm-quantum-platform)를 참고하세요.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "392639ca-7190-4c32-b531-b41960e7ea91",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "NUM_QUBITS = 61"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8502685d-24fd-4a0b-b993-45b185b72a07",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "circuit = EfficientSU2(NUM_QUBITS, entanglement=\"circular\", reps=1).decompose()\n",
    "\n",
    "print(f\"Original circuit -> Depth: {circuit.depth()}, CNOTs: {circuit.num_nonlocal_gates()}\")\n",
    "circuit.draw(fold=-1, output=\"mpl\", style=\"iqp\", scale=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46eec6f3-d4d5-40fd-97fa-d7fca0fa8344",
   "metadata": {},
   "source": [
    "## 2단계: 회로를 트랜스파일하고 최적화하기\n",
    "\n",
    "다음 단계는 Qiskit Transpiler Service를 이용해 회로를 트랜스파일하고 최적화하는 것입니다. 이것을 두 번으로 나눠서 진행해보도록 하겠습니다: 한 번은 기존의 방법으로, 다른 한 번은 AI 트랜스파일러 패스를 이용해 수행합니다. 이 섹션에서 바로 Qiskit Transpiler Service를 이용해보도록 하겠습니다. Qiskit Transpiler Service는 기존의 휴리스틱 트랜스파일 알고리즘과 AI 트랜스파일 모두를 클라우드에서 실행할 수 있도록 지원하고 있습니다. 이 패키지를 통해 여러분은 IBM Quantum™의 클라우드 리소스와 고성능의 AI 트랜스파일러 패스를 활용해 트랜스파일을 수행할 수 있습니다. **Qiskit Transpiler Service에 대해 더 알아보고 싶으시다면 [이 문서](https://docs.quantum.ibm.com/transpile/qiskit-transpiler-service)를 확인해보세요.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd951713-03e5-4f81-8ff3-c3e60c3d3772",
   "metadata": {},
   "source": [
    "#### Qiskit Transpiler Service - without AI\n",
    "Without AI: Qiskit Transpiler Service에서 제공하는 기존의 휴리스틱 알고리즘을 활용해 회로를 트랜스파일 해보세요."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c665e734-ebb8-4f66-95d9-6e752d9c7370",
   "metadata": {},
   "source": [
    "<a id=\"exercise1\"></a>\n",
    "<div class=\"alert alert-block alert-success\">\n",
    "    \n",
    "<b> 문제 1:</b> \n",
    "\n",
    "Qiskit Transpiler Service를 설정해 회로를 클라우드에서 트랜스파일 해봅시다. 최신 버전의 Qiskit Transpiler Service를 사용하고 있는게 맞는지 꼭 확인해보세요. 이번 문제에서는 `ibm_brisbane` 백엔드를 사용하도록 하겠습니다. 백엔드를 설정하고 트랜스파일러를 optimization level `3`, **AI 플래그를 켜지 않은 상태로** 설정해보세요.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c00dae26-e073-45d0-ba88-4d24db222cc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "transpiler_ai_false = TranspilerService( \n",
    "    # 여기에 코드를 작성해주세요\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47949823-1053-43d7-8139-57784c763034",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 아래의 grader 함수를 사용해 답안을 제출해주세요\n",
    "\n",
    "grade_lab3_ait_ex1(transpiler_ai_false) # 답안의 형식은 TranspilerService 클래스 입니다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b63cdca-a872-406f-bab0-08965eaacbd0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "circuit_ai_false = transpiler_ai_false.run(circuit)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8698617",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "\n",
    "`FileNotFoundError: [Errno 2] No such file or directory: ‘/root/.qiskit/qiskit-ibm.json’` 또는 `TranspilerError: 'Service error: 403 Client Error: Forbidden for url` 에러가 생겼다면 다음의 코드를 실행해 IBM Quantum 토큰을 저장하세요.\n",
    "\n",
    "<pre><code>\n",
    "QiskitRuntimeService.save_account(\n",
    "    channel=\"ibm_quantum\",\n",
    "    token=\"<MY_IBM_QUANTUM_TOKEN>\",\n",
    "    set_as_default=True,\n",
    "    # 토큰을 수정할 때는 `overwrite=True`로 설정하는 것을 잊지마세요\n",
    "    overwrite=True,\n",
    ")\n",
    " \n",
    "# 저장된 계정을 불러옵니다\n",
    "service = QiskitRuntimeService()\n",
    "\n",
    "</code></pre>\n",
    "\n",
    "IBM Quantum 토큰을 설정하는 방법을 더 알아보고 싶으시다면 [이 문서](https://docs.quantum.ibm.com/start/setup-channel#set-up-to-use-ibm-quantum-platform)를 확인해보세요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "358aa7f2-3b15-47eb-a869-185751aa44de",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "print(f\"Transpiled without AI -> Depth: {circuit_ai_false.depth()}, CNOTs: {circuit_ai_false.num_nonlocal_gates()}\")\n",
    "circuit_ai_false.draw(fold=-1, output=\"mpl\", scale=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fda47e18-4953-4d48-a726-260925dffa97",
   "metadata": {},
   "source": [
    "#### Qiskit Transpiler Service - with AI\n",
    "AI 트랜스파일러 패스를 활성화해 이 기능이 회로의 최적화 성능을 어떻게 향상시킬 수 있는지 확인해보세요."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41e2ff99-2cd5-4079-8c0c-9fd973f29d99",
   "metadata": {},
   "source": [
    "<a id=\"exercise2\"></a>\n",
    "<div class=\"alert alert-block alert-success\">\n",
    "    \n",
    "<b> 문제 2:</b> \n",
    "\n",
    "이제 Qiskit Transpiler Service의 AI 플래그를 `on`으로 설정해 사용해봅시다. 최신 버전의 Qiskit Transpiler Service를 사용하고 있는게 맞는지 꼭 확인해보세요. 백엔드는 계속해서 `ibm_brisbane`을 사용하도록 하겠습니다. 트랜스파일러의 optimization level을 `3`으로 설정하고, AI 플래그를 활성화해 고성능의 AI 최적화 작업을 실행해보세요. 이 설정으로 서비스를 실행하면 주어진 양자 회로의 게이트 개수와 회로 깊이를 최적화하는 작업에 AI를 활용해 트랜스파일 시도할 것입니다.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6cedf51d-4391-4ef7-8c8a-8f14fdb72e45",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "transpiler_ai_true = TranspilerService(\n",
    "    # 여기에 코드를 작성해주세요\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a9b1623-b617-415d-b112-6d209eae4574",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 아래의 grader 함수를 사용해 답안을 제출해주세요\n",
    "\n",
    "grade_lab3_ait_ex2(transpiler_ai_true) # 답안의 형식은 TranspilerService 클래스 입니다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c14843e-e056-4580-820e-1dbd75ea3152",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "circuit_ai_true = transpiler_ai_true.run(circuit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e04470d2-d901-4757-bbdc-bce4703b1406",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "print(f\"Transpiled with AI -> Depth: {circuit_ai_true.depth()}, CNOTs: {circuit_ai_true.num_nonlocal_gates()}\")\n",
    "circuit_ai_true.draw(fold=-1, output=\"mpl\", scale=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91e2aa6f-fa83-4496-b709-b5a9b9893b49",
   "metadata": {},
   "source": [
    "## 3단계: 그래프 그리기\n",
    "여러분이 수행한 트랜스파일과 최적화의 결과를 시각화해봅시다. 그래프를 통해 기존의 방법과 AI를 사용한 방법의 성능을 비교할 수 있는 척도(회로 깊이, 게이트 개수 등)를 확인해보도록 하겠습니다. 이것은 여러분의 회로를 최적화하는 데에 AI 트랜스파일러 패스를 활용하는 것이 어떤 영향을 줄 수 있는지 이해하는 데에 도움이 될 것입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03164f5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Qiskit SDK를 사용해 로컬에서 트랜스파일을 수행합니다\n",
    "service = QiskitRuntimeService()\n",
    "backend = service.backend(\"ibm_sherbrooke\")\n",
    "pm = generate_preset_pass_manager(backend=backend, optimization_level=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42d031ed-ba31-4b6e-8bc4-bdb2257898fd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 트랜스파일을 수행하고 결과를 산출합니다\n",
    "num_qubits = [11, 21, 41, 61, 81]\n",
    "num_cnots_local = []\n",
    "num_cnots_with_ai = []\n",
    "num_cnots_without_ai = []\n",
    "\n",
    "for nq in num_qubits:\n",
    "    circuit = EfficientSU2(nq, entanglement=\"circular\", reps=1).decompose()\n",
    "\n",
    "    # Qiskit의 로컬 트랜스파일러를 사용\n",
    "    circuit_local = pm.run(circuit)\n",
    "    # Transpiler service를 AI 없이 사용\n",
    "    circuit_without_ai = transpiler_ai_false.run(circuit)\n",
    "    # Transpiler service를 AI와 함께 사용\n",
    "    circuit_with_ai = transpiler_ai_true.run(circuit)\n",
    "\n",
    "    num_cnots_local.append(circuit_local.num_nonlocal_gates())\n",
    "    num_cnots_without_ai.append(circuit_without_ai.num_nonlocal_gates())\n",
    "    num_cnots_with_ai.append(circuit_with_ai.num_nonlocal_gates())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20505da5-f19d-490b-a3ae-2d00450b2a07",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "plt.plot(num_qubits, num_cnots_with_ai, '.-')\n",
    "plt.plot(num_qubits, num_cnots_without_ai, '.-')\n",
    "plt.plot(num_qubits, num_cnots_local, '--')\n",
    "plt.xlabel(\"Number of qubits\")\n",
    "plt.ylabel(\"CNOT count\")\n",
    "plt.legend([\"Qiskit Transpiler Service with AI\", \"Qiskit Transpiler Service without AI\", \"Qiskit SDK\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cce599f-0073-498d-82b4-a27fc2078ab1",
   "metadata": {},
   "source": [
    "<a id=\"exercise5\"></a>\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "    \n",
    "<b> 보너스 문제! </b> \n",
    "\n",
    "AI 트랜스파일러가 무엇을 할 수 있는지 경험해보셨나요? 이제 다른 high-depth, high-width 회로들로 Qiskit Transpiler Service를 실험해보세요!\n",
    "\n",
    "AI 트랜스파일러 패스는 Lab 2에서 배운 것과 같이 \"기존의\" Qiskit 트랜스파일러 패스 자리에 갈아끼우는 방식으로도 사용할 수 있습니다. 이 방법에 대해 더 알아보고 싶으시다면 [이 문서](https://docs.quantum.ibm.com/transpile/ai-transpiler-passes)를 확인해보세요.\n",
    "\n",
    "디스코드에 여러분이 얻은 결과를 올리고, 게이트 깊이와 CNOT 개수가 어떻게 나아졌는지 함께 공유해보세요!\n",
    "\n",
    "우리 커뮤니티에 여러분이 발견한 것을 공유하고 어떤 성취를 얻었는지 논의해보세요!\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b05aaa22-2e3b-433d-81cd-a583ba5598c4",
   "metadata": {},
   "source": [
    "AI 트랜스파일러 섹션을 완료한 것을 축하드립니다! 여러분은 Qiskit AI 트랜스파일러 패스의 최첨단의 성능을 체험해보았습니다. 이 혁신적인 기능은 양자 회로를 최적화하는 데에 AI를 활용하여 효율적이고 효과적인 계산을 수행하는 법을 제시합니다. 계속해서 이 AI 최적화 기능의 실험에 참여해주신다면, 여러분이 바로 양자컴퓨팅 기술의 최전선을 더 전진시키는 당사자가 될 수도 있을 것입니다. 새로운 기능들에 계속해서 관심 가져주시고, 양자컴퓨팅 개발에 AI를 활용할 수 있는 가능성도 지켜봐주세요. 수고 많으셨습니다. 계속해서 멋진 모습 기대하겠습니다!"
   ]
  },
  {
   "cell_type": "markdown",
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    "# Additional information\n",
    "\n",
    "**Created by:** Vishal Sharathchandra Bajpe, David Kremer\n",
    "\n",
    "**Advised by:** Junye Huang, Juan Cruz Benito\n",
    "\n",
    "**Translated by:** Boseong Kim\n",
    "\n",
    "**Version:** 1.4.2"
   ]
  }
 ],
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