{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "title-cell",
   "metadata": {
    "id": "title-cell"
   },
   "source": "# 🏭 Predictive Maintenance (AI4I 2020) — Gerçek IBM QPU Max-Qubit Demo\n\n**Amaç:** Öğrencilere gerçek quantum computer gücünü göstermek — erişilen backend’in izin verdiği **maksimum qubit** ile VQC/QSVC.  \n**Ortam:** Google Colab + **IBM Quantum Platform (gerçek donanım)**  \n\n---\n\n### Öğrenci demosu için kritik gerçekler\n\n| Konu | Açıklama |\n|------|----------|\n| **Max qubit** | `min(TARGET_QUBITS, backend.num_qubits)` — erişiminizdeki en büyük sistem |\n| **Neden küçük train?** | Her optimizasyon adımı QPU’da devre çalıştırır; 40 örnek bile anlamlı demo sağlar |\n| **Neden VQC?** | Yüksek qubit’te kernel (QSVC) O(n²) devre ister; VQC tek devre ailesi ile ölçeklenir |\n| **Gürültü** | NISQ cihazında metrikler ideal simülatörden düşük olabilir — bu da öğretimin parçası |\n| **Statevector** | 50–100 qubit **klasik simülasyonda imkânsız**; sadece gerçek QPU veya shots |\n\n### Çalıştırma sırası (demo)\n\n1. AŞAMA 4 — IBM token (Colab Secret: `IBM_QUANTUM_TOKEN`)\n2. AŞAMA 5–11 — veri + klasik baseline\n3. AŞAMA 12 — `TARGET_QUBITS` (ör. 27, 33, 127 — backend’e göre)\n4. **AŞAMA 16 önce** gerçek backend seçilsin (max qubit listesi ekrana gelir)\n5. AŞAMA 13–14 — feature map + VQC gerçek QPU’da\n6. AŞAMA 15–19 — metrikler ve yorum\n\n> **Not:** AŞAMA 16’yı 14’ten **önce** çalıştırın ki `n_qubits` backend’e kilitlensin.\n"
  },
  {
   "cell_type": "markdown",
   "id": "stage1-md",
   "metadata": {
    "id": "stage1-md"
   },
   "source": "## AŞAMA 1: Drive / Path Tanımlama\n"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "stage1-code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "stage1-code",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788355161678,
     "user_tz": -180,
     "elapsed": 19317,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "cfc98ca1-d2fd-49aa-f6bb-1304fc195f27"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Python: 3.13.15\n",
      "Mounted at /content/drive\n",
      "✅ Google Drive bağlandı → /content/drive/MyDrive\n",
      "   Proje: /content/drive/MyDrive/QML_Predictive_Maintenance_HighQ\n",
      "   Çıktı: /content/drive/MyDrive/QML_Predictive_Maintenance_HighQ/outputs\n",
      "\n",
      "✅ AŞAMA 1 tamamlandı.\n"
     ]
    }
   ],
   "source": "import os\nimport sys\n\nprint(f\"Python: {sys.version.split()[0]}\")\n\nDRIVE_ROOT = None\nPROJECT_PATH = None\nOUTPUT_PATH = None\n\ntry:\n    from google.colab import drive\n    drive.mount('/content/drive')\n    DRIVE_ROOT = '/content/drive/MyDrive'\n    PROJECT_PATH = os.path.join(DRIVE_ROOT, 'QML_Predictive_Maintenance_HighQ')\n    OUTPUT_PATH = os.path.join(PROJECT_PATH, 'outputs')\n    os.makedirs(OUTPUT_PATH, exist_ok=True)\n    print(f\"✅ Google Drive bağlandı → {DRIVE_ROOT}\")\n    print(f\"   Proje: {PROJECT_PATH}\")\n    print(f\"   Çıktı: {OUTPUT_PATH}\")\nexcept Exception as e:\n    print(f\"⚠️ Drive mount atlandı: {e}\")\n    PROJECT_PATH = os.path.abspath('./qml_predictive_maintenance_highq')\n    OUTPUT_PATH = os.path.join(PROJECT_PATH, 'outputs')\n    os.makedirs(OUTPUT_PATH, exist_ok=True)\n    print(f\"   Lokal proje: {PROJECT_PATH}\")\n\nprint(\"\\n✅ AŞAMA 1 tamamlandı.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "stage2-md",
   "metadata": {
    "id": "stage2-md"
   },
   "source": "## AŞAMA 2: Cihaz & Algoritma Seçim Motoru\n\nVarsayılan: **Machine Learning → Superconducting Qubits → QSVC** (veya VQC yüksek qubit için).\n"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "stage2-code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 367,
     "referenced_widgets": [
      "395a748b13084b43b75c01787a9bd1bc",
      "d99146aaf7f24c1fbf32f508ba910702",
      "d5cdac65f137404c84813926921ba413",
      "fbbf5849c6464451b5a1f2b97fc46594",
      "ca7bf8de522f481191228a18c00f9fa1",
      "83fb99ba235447c0a9e04bd64ecc75fb",
      "c0e6fd5002574830a944843f416f63ff",
      "742f5a1aa7c44a3bb892ce1cd9d56e54",
      "15374e65ac004f4cae8a9f7be36ea011",
      "9b2b621a671a4bd1bbdf586e28ceafd8",
      "cda15bf5c4a346d4b3646f8ebe64bb1c",
      "79cb0784c38b4db789782e3014af2c9e",
      "85eac2efd6a14a318bc24e7e3884b5fe",
      "19a305ae80d9459bb5b7e3d48f594377",
      "b8f09ada940c489e976a5b2cc40c22ea",
      "c16c95c0b8c8493cbb6628b2222f4cd1"
     ]
    },
    "id": "stage2-code",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788355175302,
     "user_tz": -180,
     "elapsed": 815,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "cdbe7778-84d4-4664-9f1e-9cabb22274c4"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "VBox(children=(HTML(value=\"<h3>🔧 Pipeline Seçim Motoru <span style='font-size:12px;color:#64748b;'>(High-Qubit…"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "395a748b13084b43b75c01787a9bd1bc"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "📌 Varsayılan: Machine Learning | Superconducting Qubits | QSVC\n",
      "   Yüksek qubit (≥12–16) denemelerinde AŞAMA 11’de VQC’ye geçmeniz önerilir.\n",
      "✅ AŞAMA 2 tamamlandı.\n"
     ]
    }
   ],
   "source": "import pandas as pd\nfrom IPython.display import display, HTML, Markdown, clear_output\nimport ipywidgets as widgets\n\nVALID_TRIPLETS = [\n    (\"Optimization & Logistics\", \"Superconducting Qubits\", \"QAOA\"),\n    (\"Optimization & Logistics\", \"Superconducting Qubits\", \"Grover\"),\n    (\"Optimization & Logistics\", \"Trapped Ions\", \"QAOA\"),\n    (\"Optimization & Logistics\", \"Quantum Annealer (D-Wave)\", \"QUBO/Annealing\"),\n    (\"Machine Learning\", \"Superconducting Qubits\", \"QSVC\"),\n    (\"Machine Learning\", \"Superconducting Qubits\", \"VQC\"),\n    (\"Machine Learning\", \"Superconducting Qubits\", \"QAOA\"),\n    (\"Machine Learning\", \"Trapped Ions\", \"QSVC\"),\n    (\"Machine Learning\", \"Trapped Ions\", \"VQC\"),\n    (\"Machine Learning\", \"Neutral Atoms\", \"QAOA\"),\n]\n\nPROBLEMS = sorted({t[0] for t in VALID_TRIPLETS})\nALGO_MATH = {\n    \"QSVC\": r\"$K(x,x') = |\\langle \\phi(x) | \\phi(x') \\rangle|^2$\",\n    \"VQC\": r\"$f_\\theta(x) = \\langle 0| U^\\dagger(x,\\theta) M U(x,\\theta) |0\\rangle$\",\n    \"QAOA\": r\"$|\\gamma,\\beta\\rangle = e^{-i\\beta H_M} e^{-i\\gamma H_C}\\cdots |+\\rangle^{\\otimes n}$\",\n    \"QUBO/Annealing\": r\"$\\min_x\\; x^T Q x$\",\n}\nHW_TO_DEVICE = {\n    \"Superconducting Qubits\": \"ibm_or_aer\",\n    \"Trapped Ions\": \"ionq_or_aer\",\n    \"Neutral Atoms\": \"pasqal_or_aer\",\n    \"Quantum Annealer (D-Wave)\": \"dwave\",\n}\n\nSELECTED_PROBLEM = \"Machine Learning\"\nSELECTED_HARDWARE = \"Superconducting Qubits\"\nSELECTED_ALGO = \"QSVC\"\nSELECTED_DEVICE = \"ibm_or_aer\"\n\ndef allowed_hw(prob):\n    return sorted({t[1] for t in VALID_TRIPLETS if t[0] == prob})\n\ndef allowed_alg(prob, hw=None):\n    if hw is None:\n        return sorted({t[2] for t in VALID_TRIPLETS if t[0] == prob})\n    return sorted({t[2] for t in VALID_TRIPLETS if t[0] == prob and t[1] == hw})\n\ndd_prob = widgets.Dropdown(options=[\"— Problem seçin —\"] + PROBLEMS, description=\"Problem:\",\n                           layout=widgets.Layout(width=\"460px\"), style={\"description_width\": \"90px\"})\ndd_hw = widgets.Dropdown(options=[\"— önce problem —\"], description=\"Donanım:\",\n                         layout=widgets.Layout(width=\"460px\"), style={\"description_width\": \"90px\"}, disabled=True)\ndd_alg = widgets.Dropdown(options=[\"— önce donanım —\"], description=\"Algoritma:\",\n                          layout=widgets.Layout(width=\"460px\"), style={\"description_width\": \"90px\"}, disabled=True)\nout = widgets.Output()\n\ndef on_prob_change(change):\n    global SELECTED_PROBLEM, SELECTED_HARDWARE, SELECTED_ALGO, SELECTED_DEVICE\n    if change[\"name\"] != \"value\": return\n    val = change[\"new\"]\n    if str(val).startswith(\"—\"):\n        SELECTED_PROBLEM = SELECTED_HARDWARE = SELECTED_ALGO = None\n        SELECTED_DEVICE = \"aer_only\"\n        dd_hw.options = [\"— önce problem —\"]; dd_hw.disabled = True\n        dd_alg.options = [\"— önce donanım —\"]; dd_alg.disabled = True\n    else:\n        SELECTED_PROBLEM = val\n        SELECTED_HARDWARE = SELECTED_ALGO = None\n        hws = allowed_hw(val)\n        dd_hw.options = [\"— Donanım seçin —\"] + hws; dd_hw.disabled = False; dd_hw.value = dd_hw.options[0]\n        dd_alg.options = [\"— önce donanım —\"]; dd_alg.disabled = True\n    _render()\n\ndef on_hw_change(change):\n    global SELECTED_HARDWARE, SELECTED_ALGO, SELECTED_DEVICE\n    if change[\"name\"] != \"value\": return\n    val = change[\"new\"]\n    if not SELECTED_PROBLEM or str(val).startswith(\"—\"):\n        SELECTED_HARDWARE = SELECTED_ALGO = None\n        dd_alg.options = [\"— önce donanım —\"]; dd_alg.disabled = True\n    else:\n        SELECTED_HARDWARE = val\n        SELECTED_ALGO = None\n        SELECTED_DEVICE = HW_TO_DEVICE.get(val, \"aer_only\")\n        algs = allowed_alg(SELECTED_PROBLEM, val)\n        dd_alg.options = [\"— Algoritma seçin —\"] + algs; dd_alg.disabled = False; dd_alg.value = dd_alg.options[0]\n    _render()\n\ndef on_alg_change(change):\n    global SELECTED_ALGO\n    if change[\"name\"] != \"value\": return\n    val = change[\"new\"]\n    SELECTED_ALGO = None if str(val).startswith(\"—\") or not SELECTED_HARDWARE else val\n    _render()\n\ndef _render():\n    with out:\n        clear_output(wait=True)\n        if SELECTED_PROBLEM and SELECTED_HARDWARE and SELECTED_ALGO:\n            display(Markdown(f\"### Seçim: **{SELECTED_PROBLEM}** → **{SELECTED_HARDWARE}** → **{SELECTED_ALGO}**\"))\n            display(Markdown(f\"**Cihaz:** `{SELECTED_DEVICE}`\"))\n            math = ALGO_MATH.get(SELECTED_ALGO, \"\")\n            if math: display(Markdown(f\"**Model:** {math}\"))\n            display(HTML(\n                \"<div style='padding:12px;background:#f0fdf4;border-left:4px solid #22c55e;border-radius:8px;'>\"\n                f\"<b>✅ Hazır.</b> SELECTED_ALGO=<code>{SELECTED_ALGO}</code> — Yüksek qubit için VQC önerilir.\"\n                \"</div>\"\n            ))\n        else:\n            display(HTML(\"<div style='padding:12px;background:#fff7ed;border-left:4px solid #fb923c;'>Problem → Donanım → Algoritma seçin.</div>\"))\n\ndd_prob.observe(on_prob_change, names=\"value\")\ndd_hw.observe(on_hw_change, names=\"value\")\ndd_alg.observe(on_alg_change, names=\"value\")\n\ndisplay(widgets.VBox([\n    widgets.HTML(\"<h3>🔧 Pipeline Seçim Motoru <span style='font-size:12px;color:#64748b;'>(High-Qubit Predictive Maintenance)</span></h3>\"),\n    dd_prob, dd_hw, dd_alg, out\n]))\n_render()\n\nprint(f\"\\n📌 Varsayılan: {SELECTED_PROBLEM} | {SELECTED_HARDWARE} | {SELECTED_ALGO}\")\nprint(\"   Yüksek qubit (≥12–16) denemelerinde AŞAMA 11’de VQC’ye geçmeniz önerilir.\")\nprint(\"✅ AŞAMA 2 tamamlandı.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "stage3-md",
   "metadata": {
    "id": "stage3-md"
   },
   "source": "## AŞAMA 3: Python Library Kurulumu\n"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "stage3-install",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "stage3-install",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788355220746,
     "user_tz": -180,
     "elapsed": 33090,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "f73b950c-1eac-44e8-bdbd-9d9cd9a850fe"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "📦 Paketler kuruluyor...\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.8/1.8 MB\u001b[0m \u001b[31m21.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h✅ Kurulum tamamlandı.\n"
     ]
    }
   ],
   "source": "print(\"📦 Paketler kuruluyor...\")\n!pip install -q --upgrade pip\n!pip install -q qiskit qiskit-aer qiskit-machine-learning qiskit-algorithms qiskit-ibm-runtime\n!pip install -q numpy pandas scikit-learn xgboost openpyxl\n!pip install -q matplotlib seaborn pylatexenc tqdm ipywidgets python-dotenv\nprint(\"✅ Kurulum tamamlandı.\")\n"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "stage3-import",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "stage3-import",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788355261427,
     "user_tz": -180,
     "elapsed": 2897,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "2b48a132-e0db-402c-c281-b519b08be7f9"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Python: 3.13.15\n",
      "✅ Ortak kütüphaneler yüklendi.\n"
     ]
    }
   ],
   "source": "import os, sys, json, math, warnings\nfrom pathlib import Path\nfrom datetime import datetime\nfrom hashlib import sha256\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler, MinMaxScaler, PolynomialFeatures\nfrom sklearn.decomposition import PCA\nfrom sklearn.metrics import (\n    accuracy_score, precision_score, recall_score, f1_score,\n    roc_auc_score, roc_curve, confusion_matrix, ConfusionMatrixDisplay,\n)\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.svm import SVC\nfrom xgboost import XGBClassifier\n\nwarnings.filterwarnings(\"ignore\")\nsns.set_style(\"whitegrid\")\nplt.rcParams[\"figure.figsize\"] = (10, 6)\n\nprint(f\"Python: {sys.version.split()[0]}\")\nprint(\"✅ Ortak kütüphaneler yüklendi.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "stage4-md",
   "metadata": {
    "id": "stage4-md"
   },
   "source": "## AŞAMA 4: API Keys Yönetimi\n\n2025+ : `channel=\"ibm_quantum_platform\"` (eski `ibm_quantum` kaldırıldı).\n"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "stage4-code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "stage4-code",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788355265001,
     "user_tz": -180,
     "elapsed": 1037,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "ef65079c-dd99-4406-e17e-2ab27932dc0c"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "✅ IBM token Colab Secrets'tan okundu.\n",
      "   Token uzunluğu : 44 karakter\n",
      "   Channel        : ibm_quantum_platform\n",
      "   Instance       : auto\n",
      "✅ AŞAMA 4 tamamlandı.\n"
     ]
    }
   ],
   "source": "IBM_TOKEN = None\nIBM_INSTANCE = None\nIBM_CHANNEL = \"ibm_quantum_platform\"\n\ntry:\n    from google.colab import userdata\n    IBM_TOKEN = userdata.get(\"IBM_QUANTUM_TOKEN\") or userdata.get(\"QISKIT_IBM_TOKEN\")\n    try:\n        IBM_INSTANCE = userdata.get(\"IBM_QUANTUM_INSTANCE\") or userdata.get(\"QISKIT_IBM_INSTANCE\")\n    except Exception:\n        pass\n    if IBM_TOKEN:\n        print(\"✅ IBM token Colab Secrets'tan okundu.\")\nexcept Exception:\n    pass\n\nif not IBM_TOKEN:\n    IBM_TOKEN = os.environ.get(\"IBM_QUANTUM_TOKEN\") or os.environ.get(\"QISKIT_IBM_TOKEN\")\n    if IBM_TOKEN:\n        print(\"✅ IBM token ortam değişkeninden okundu.\")\n\nif not IBM_INSTANCE:\n    IBM_INSTANCE = os.environ.get(\"IBM_QUANTUM_INSTANCE\") or os.environ.get(\"QISKIT_IBM_INSTANCE\")\n\n_ch = os.environ.get(\"QISKIT_IBM_CHANNEL\") or os.environ.get(\"IBM_QUANTUM_CHANNEL\")\nif _ch in (\"ibm_quantum_platform\", \"ibm_cloud\"):\n    IBM_CHANNEL = _ch\n\nif not IBM_TOKEN:\n    try:\n        from dotenv import load_dotenv\n        load_dotenv()\n        IBM_TOKEN = os.environ.get(\"IBM_QUANTUM_TOKEN\") or os.environ.get(\"QISKIT_IBM_TOKEN\")\n        IBM_INSTANCE = IBM_INSTANCE or os.environ.get(\"IBM_QUANTUM_INSTANCE\")\n        if IBM_TOKEN:\n            print(\"✅ IBM token .env'den okundu.\")\n    except Exception:\n        pass\n\nif IBM_TOKEN:\n    print(f\"   Token uzunluğu : {len(IBM_TOKEN)} karakter\")\n    print(f\"   Channel        : {IBM_CHANNEL}\")\n    print(f\"   Instance       : {'ayarlı' if IBM_INSTANCE else 'auto'}\")\nelse:\n    print(\"⚠️ Token yok → Aer ile devam. Gerçek 100+ qubit QPU için token gerekli.\")\n\nprint(\"✅ AŞAMA 4 tamamlandı.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "stage5-md",
   "metadata": {
    "id": "stage5-md"
   },
   "source": "## AŞAMA 5: AI4I 2020 Veri Yükleme\n"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "stage5-code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "stage5-code",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788355271698,
     "user_tz": -180,
     "elapsed": 2046,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "19e3f8a9-6a48-456d-9974-fd71b02a4c74"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "✅ Veri: /content/ai4i2020.xlsx → (10000, 14)\n",
      "Machine failure\n",
      "0    9661\n",
      "1     339\n",
      "Name: count, dtype: int64\n",
      "Arıza oranı: 3.39%\n",
      "✅ AŞAMA 5 tamamlandı.\n"
     ]
    }
   ],
   "source": "CANDIDATE_PATHS = [\n    \"/content/ai4i2020.xlsx\",\n    \"/content/drive/MyDrive/ai4i2020.xlsx\",\n    \"/content/drive/MyDrive/QML_Predictive_Maintenance_HighQ/ai4i2020.xlsx\",\n    os.path.join(PROJECT_PATH or \".\", \"ai4i2020.xlsx\"),\n    \"./ai4i2020.xlsx\",\n    \"/home/workdir/attachments/ai4i2020.xlsx\",\n]\n\ndf = None\nloaded_from = None\nfor p in CANDIDATE_PATHS:\n    if os.path.isfile(p):\n        df = pd.read_excel(p, sheet_name=0)\n        loaded_from = p\n        break\n\nif df is None:\n    print(\"⚠️ ai4i2020.xlsx bulunamadı. Colab'a yükleyin veya AŞAMA 6'yı kullanın.\")\nelse:\n    print(f\"✅ Veri: {loaded_from} → {df.shape}\")\n    print(df[\"Machine failure\"].value_counts())\n    print(f\"Arıza oranı: {df['Machine failure'].mean():.2%}\")\n\nprint(\"✅ AŞAMA 5 tamamlandı.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "stage6-md",
   "metadata": {
    "id": "stage6-md"
   },
   "source": "## AŞAMA 6: Opsiyonel Upload\n"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "stage6-code",
   "metadata": {
    "id": "stage6-code"
   },
   "outputs": [],
   "source": "USE_UPLOADED = False\n\nif USE_UPLOADED or df is None:\n    try:\n        from google.colab import files\n        print(\"ai4i2020.xlsx seçin...\")\n        uploaded = files.upload()\n        if uploaded:\n            fname = list(uploaded.keys())[0]\n            df = pd.read_excel(fname, sheet_name=0) if fname.endswith((\".xlsx\", \".xls\")) else pd.read_csv(fname)\n            print(f\"✅ {fname} → {df.shape}\")\n    except Exception as e:\n        print(f\"Upload atlandı: {e}\")\nelse:\n    print(f\"Veri zaten yüklü: {df.shape if df is not None else None}\")\n\nprint(\"✅ AŞAMA 6 tamamlandı.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "stage7-md",
   "metadata": {
    "id": "stage7-md"
   },
   "source": "## AŞAMA 7: EDA\n"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "stage7-code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "stage7-code",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788355278149,
     "user_tz": -180,
     "elapsed": 2256,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "8e522c33-ff4d-45d4-d3b3-8a45d4eb733d"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "=== İstatistikler ===\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "                count         mean           std      min      25%      50%  \\\n",
       "air_temp      10000.0    300.00493      2.000259    295.3    298.3    300.1   \n",
       "process_temp  10000.0    310.00556      1.483734    305.7    308.8    310.1   \n",
       "rot_speed     10000.0   1538.77610    179.284096   1168.0   1423.0   1503.0   \n",
       "torque        10000.0     39.98691      9.968934      3.8     33.2     40.1   \n",
       "tool_wear     10000.0    107.95100     63.654147      0.0     53.0    108.0   \n",
       "temp_diff     10000.0     10.00063      1.001094      7.6      9.3      9.8   \n",
       "power         10000.0  59967.14704  10193.093881  10966.8  53105.4  59883.9   \n",
       "failure       10000.0      0.03390      0.180981      0.0      0.0      0.0   \n",
       "\n",
       "                   75%      max  \n",
       "air_temp        301.50    304.5  \n",
       "process_temp    311.10    313.8  \n",
       "rot_speed      1612.00   2886.0  \n",
       "torque           46.80     76.6  \n",
       "tool_wear       162.00    253.0  \n",
       "temp_diff        11.00     12.1  \n",
       "power         66873.75  99980.4  \n",
       "failure           0.00      1.0  "
      ],
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       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>air_temp</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>300.00493</td>\n",
       "      <td>2.000259</td>\n",
       "      <td>295.3</td>\n",
       "      <td>298.3</td>\n",
       "      <td>300.1</td>\n",
       "      <td>301.50</td>\n",
       "      <td>304.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>process_temp</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>310.00556</td>\n",
       "      <td>1.483734</td>\n",
       "      <td>305.7</td>\n",
       "      <td>308.8</td>\n",
       "      <td>310.1</td>\n",
       "      <td>311.10</td>\n",
       "      <td>313.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rot_speed</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>1538.77610</td>\n",
       "      <td>179.284096</td>\n",
       "      <td>1168.0</td>\n",
       "      <td>1423.0</td>\n",
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       "      <td>1612.00</td>\n",
       "      <td>2886.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>torque</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>39.98691</td>\n",
       "      <td>9.968934</td>\n",
       "      <td>3.8</td>\n",
       "      <td>33.2</td>\n",
       "      <td>40.1</td>\n",
       "      <td>46.80</td>\n",
       "      <td>76.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tool_wear</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>107.95100</td>\n",
       "      <td>63.654147</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>162.00</td>\n",
       "      <td>253.0</td>\n",
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       "    <tr>\n",
       "      <th>temp_diff</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>10.00063</td>\n",
       "      <td>1.001094</td>\n",
       "      <td>7.6</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.8</td>\n",
       "      <td>11.00</td>\n",
       "      <td>12.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>power</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>59967.14704</td>\n",
       "      <td>10193.093881</td>\n",
       "      <td>10966.8</td>\n",
       "      <td>53105.4</td>\n",
       "      <td>59883.9</td>\n",
       "      <td>66873.75</td>\n",
       "      <td>99980.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>failure</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.03390</td>\n",
       "      <td>0.180981</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
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       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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       "          + ' to learn more about interactive tables.';\n",
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     },
     "metadata": {}
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    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "Sınıf dağılımı:\n",
      "failure\n",
      "0    0.9661\n",
      "1    0.0339\n",
      "Name: proportion, dtype: float64\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1500x900 with 6 Axes>"
      ],
      "image/png": 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     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 900x700 with 2 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "✅ AŞAMA 7 tamamlandı.\n"
     ]
    }
   ],
   "source": "if df is None:\n    raise ValueError(\"Veri yok. AŞAMA 5/6 çalıştırın.\")\n\ndf = df.rename(columns={\n    \"Air temperature [K]\": \"air_temp\",\n    \"Process temperature [K]\": \"process_temp\",\n    \"Rotational speed [rpm]\": \"rot_speed\",\n    \"Torque [Nm]\": \"torque\",\n    \"Tool wear [min]\": \"tool_wear\",\n    \"Machine failure\": \"failure\",\n})\ndf[\"temp_diff\"] = df[\"process_temp\"] - df[\"air_temp\"]\ndf[\"power\"] = df[\"rot_speed\"] * df[\"torque\"]\n\nprint(\"=== İstatistikler ===\")\ndisplay(df[[\"air_temp\", \"process_temp\", \"rot_speed\", \"torque\", \"tool_wear\", \"temp_diff\", \"power\", \"failure\"]].describe().T)\nprint(\"\\nSınıf dağılımı:\")\nprint(df[\"failure\"].value_counts(normalize=True))\n\nfig, axes = plt.subplots(2, 3, figsize=(15, 9))\nsns.histplot(df[\"air_temp\"], kde=True, ax=axes[0, 0], color=\"#3498db\"); axes[0, 0].set_title(\"Air Temp\")\nsns.histplot(df[\"process_temp\"], kde=True, ax=axes[0, 1], color=\"#e74c3c\"); axes[0, 1].set_title(\"Process Temp\")\nsns.histplot(df[\"tool_wear\"], kde=True, ax=axes[0, 2], color=\"#2ecc71\"); axes[0, 2].set_title(\"Tool Wear\")\nsns.boxplot(x=\"failure\", y=\"torque\", data=df, ax=axes[1, 0]); axes[1, 0].set_title(\"Torque vs Failure\")\nsns.boxplot(x=\"failure\", y=\"rot_speed\", data=df, ax=axes[1, 1]); axes[1, 1].set_title(\"Rot Speed vs Failure\")\nsns.countplot(x=\"Type\", hue=\"failure\", data=df, ax=axes[1, 2]); axes[1, 2].set_title(\"Type by Failure\")\nplt.tight_layout(); plt.show()\n\nnum_cols = [\"air_temp\", \"process_temp\", \"rot_speed\", \"torque\", \"tool_wear\", \"temp_diff\", \"power\", \"failure\"]\nplt.figure(figsize=(9, 7))\nsns.heatmap(df[num_cols].corr(), annot=True, fmt=\".2f\", cmap=\"RdBu_r\", center=0)\nplt.title(\"Korelasyon Matrisi\"); plt.tight_layout(); plt.show()\nprint(\"✅ AŞAMA 7 tamamlandı.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "stage8-md",
   "metadata": {
    "id": "stage8-md"
   },
   "source": "## AŞAMA 8: Anonimleştirme\n"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "stage8-code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "stage8-code",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788355286969,
     "user_tz": -180,
     "elapsed": 119,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "21401ec5-d8a5-4411-e664-8d2060eb0d42"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Örnek maskeli ID:\n",
      "   UDI    UDI_masked\n",
      "0    1  6b7f7be57fc3\n",
      "1    2  b463756131bd\n",
      "✅ AŞAMA 8 tamamlandı.\n"
     ]
    }
   ],
   "source": "def mask_id(val, salt=\"ai4i_highq_salt\"):\n    return sha256(f\"{salt}{val}\".encode()).hexdigest()[:12]\n\ndf_anon = df.copy()\nif \"UDI\" in df_anon.columns:\n    df_anon[\"UDI_masked\"] = df_anon[\"UDI\"].astype(str).apply(mask_id)\nif \"Product ID\" in df_anon.columns:\n    df_anon[\"Product_ID_masked\"] = df_anon[\"Product ID\"].astype(str).apply(mask_id)\nprint(\"Örnek maskeli ID:\")\nprint(df_anon[[c for c in [\"UDI\", \"UDI_masked\"] if c in df_anon.columns]].head(2))\nprint(\"✅ AŞAMA 8 tamamlandı.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "stage9-md",
   "metadata": {
    "id": "stage9-md"
   },
   "source": "## AŞAMA 9: Ön İşleme + Yüksek Boyutlu Feature Expansion\n\nYüksek qubit için ham 5–8 özelliği genişletiyoruz:\n- Polynomial / etkileşim terimleri\n- Tekrarlı (repeated) özellikler\n- İsteğe bağlı PCA ile hedef boyuta projeksiyon\n\nBöylece `n_qubits` 4 ile sınırlı kalmaz; 8, 12, 16, 20+ denenebilir.\n"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "stage9-code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "stage9-code",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788355291676,
     "user_tz": -180,
     "elapsed": 75,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "4a0fa777-6203-404a-e6ef-914271328d3e"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Train: (7500, 8) | Test: (2500, 8)\n",
      "Train failure: 3.39% | Test: 3.40%\n",
      "✅ AŞAMA 9 (temel) tamamlandı. Yüksek boyut genişletme AŞAMA 12'de yapılacak.\n"
     ]
    }
   ],
   "source": "type_map = {\"L\": 0, \"M\": 1, \"H\": 2}\ndf[\"type_ord\"] = df[\"Type\"].map(type_map)\n\nBASE_FEATURES = [\n    \"air_temp\", \"process_temp\", \"rot_speed\", \"torque\", \"tool_wear\",\n    \"temp_diff\", \"power\", \"type_ord\",\n]\nTARGET = \"failure\"\n\nX_base = df[BASE_FEATURES].copy()\ny = df[TARGET].copy()\n\nX_train_base, X_test_base, y_train, y_test = train_test_split(\n    X_base, y, test_size=0.25, random_state=42, stratify=y\n)\n\n# Klasik modeller için standart ölçek\nscaler_cls = StandardScaler()\nX_train_sc = scaler_cls.fit_transform(X_train_base)\nX_test_sc = scaler_cls.transform(X_test_base)\n\nprint(f\"Train: {X_train_base.shape} | Test: {X_test_base.shape}\")\nprint(f\"Train failure: {y_train.mean():.2%} | Test: {y_test.mean():.2%}\")\nprint(\"✅ AŞAMA 9 (temel) tamamlandı. Yüksek boyut genişletme AŞAMA 12'de yapılacak.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "stage10-md",
   "metadata": {
    "id": "stage10-md"
   },
   "source": "## AŞAMA 10: Klasik Baseline\n"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "stage10-code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "stage10-code",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788355316597,
     "user_tz": -180,
     "elapsed": 8569,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "74fbfb95-639d-46c5-d361-76134dfd99f4"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Logistic Regression    | AUC=0.9150  F1=0.2783  Recall=0.8235\n",
      "Random Forest          | AUC=0.9773  F1=0.7947  Recall=0.7059\n",
      "SVM (RBF)              | AUC=0.9718  F1=0.4483  Recall=0.9176\n",
      "XGBoost                | AUC=0.9804  F1=0.7650  Recall=0.8235\n",
      "\n",
      "✅ AŞAMA 10 tamamlandı.\n"
     ]
    }
   ],
   "source": "neg, pos = (y_train == 0).sum(), (y_train == 1).sum()\nscale_pos = neg / max(pos, 1)\n\nmodels = {\n    \"Logistic Regression\": LogisticRegression(max_iter=1000, class_weight=\"balanced\", random_state=42),\n    \"Random Forest\": RandomForestClassifier(n_estimators=200, max_depth=10, class_weight=\"balanced\", random_state=42),\n    \"SVM (RBF)\": SVC(kernel=\"rbf\", probability=True, class_weight=\"balanced\", random_state=42),\n    \"XGBoost\": XGBClassifier(\n        n_estimators=200, max_depth=5, learning_rate=0.08,\n        scale_pos_weight=scale_pos, eval_metric=\"logloss\", random_state=42, verbosity=0\n    ),\n}\n\nresults = {}\nfor name, model in models.items():\n    model.fit(X_train_sc, y_train)\n    y_pred = model.predict(X_test_sc)\n    y_prob = model.predict_proba(X_test_sc)[:, 1] if hasattr(model, \"predict_proba\") else y_pred\n    results[name] = {\n        \"model\": model,\n        \"accuracy\": accuracy_score(y_test, y_pred),\n        \"precision\": precision_score(y_test, y_pred, zero_division=0),\n        \"recall\": recall_score(y_test, y_pred, zero_division=0),\n        \"f1\": f1_score(y_test, y_pred, zero_division=0),\n        \"roc_auc\": roc_auc_score(y_test, y_prob),\n        \"y_pred\": y_pred,\n        \"y_prob\": y_prob,\n    }\n    print(f\"{name:22s} | AUC={results[name]['roc_auc']:.4f}  F1={results[name]['f1']:.4f}  Recall={results[name]['recall']:.4f}\")\n\nprint(\"\\n✅ AŞAMA 10 tamamlandı.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "stage11-md",
   "metadata": {
    "id": "stage11-md"
   },
   "source": "## AŞAMA 11: QML Algoritma Seçimi\n\n- **QSVC**: Kernel tabanlı; yüksek qubit’te O(n²) maliyeti çok artar → orta subset + orta qubit.\n- **VQC**: Parametrik devre; daha fazla qubit ile daha zengin ansatz denenebilir (önerilen yüksek-qubit yolu).\n"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "stage11-code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "stage11-code",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788355319272,
     "user_tz": -180,
     "elapsed": 22,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "8c8c036b-8c89-4113-8965-b8206a3d0466"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "SELECTED_PROBLEM  = Machine Learning\n",
      "SELECTED_HARDWARE = Superconducting Qubits\n",
      "SELECTED_ALGO     = QSVC\n",
      "SELECTED_DEVICE   = ibm_or_aer\n",
      "\n",
      "💡 İpucu: TARGET_QUBITS ≥ 12 ise VQC tercih edin (AŞAMA 2'den değiştirilebilir).\n",
      "✅ AŞAMA 11 tamamlandı.\n"
     ]
    }
   ],
   "source": "print(f\"SELECTED_PROBLEM  = {SELECTED_PROBLEM}\")\nprint(f\"SELECTED_HARDWARE = {SELECTED_HARDWARE}\")\nprint(f\"SELECTED_ALGO     = {SELECTED_ALGO}\")\nprint(f\"SELECTED_DEVICE   = {SELECTED_DEVICE}\")\n\nif SELECTED_ALGO not in (\"QSVC\", \"VQC\"):\n    print(\"⚠️ Detaylı hat QSVC/VQC için. QSVC ile devam.\")\n    SELECTED_ALGO = \"QSVC\"\n\nprint(\"\\n💡 İpucu: TARGET_QUBITS ≥ 12 ise VQC tercih edin (AŞAMA 2'den değiştirilebilir).\")\nprint(\"✅ AŞAMA 11 tamamlandı.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "stage12-md",
   "metadata": {
    "id": "stage12-md"
   },
   "source": "## AŞAMA 12: Max-Qubit Encoding & Kaynak Planlama\n\n### Kritik parametre: `TARGET_QUBITS`\n\n| Ortam | Önerilen TARGET_QUBITS | Not |\n|-------|------------------------|-----|\n| **Colab / laptop (Statevector)** | **6–10** | 2^n bellek; 12 üstü patlar |\n| Aer shot-based | 12–16 (max ~20) | Yaklaşık, yavaş |\n| Gerçek IBM QPU | 16–50+ (deneysel) | `use_real_hardware=True` şart |\n| 50–100 qubit klasik simülasyon | **İMKÂNSIZ** | 2^50 ≈ 16 PiB RAM |\n\n### Neden 50 qubit Colab’da çöküyor?\n\n`StatevectorSampler` tüm 2^n genliği belleğe alır.  \nn=50 → 2^50 ≈ 1.13×10^15 complex128 ≈ **16 PiB** → `MemoryError`.\n\nBu hücre otomatik olarak:\n1. `TARGET_QUBITS > 20` ise 20’ye düşürür (klasik simülasyon tavanı)\n2. `> 12` ise Statevector yerine **AerSampler (shots)** seçer\n3. Gerçek 50–100+ qubit için AŞAMA 16’da IBM backend ister\n"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "stage12-code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "stage12-code",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788355331618,
     "user_tz": -180,
     "elapsed": 102,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "03e4a93e-4dde-4aae-d16e-c1649b396023"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "============================================================\n",
      "KAYNAK PLANI — GERÇEK QPU ÖNCELİKLİ\n",
      "============================================================\n",
      "İstenen TARGET_QUBITS     = 27\n",
      "MAX_TRAIN_SAMPLES_Q       = 40  (yüksek qubit → küçük subset)\n",
      "MAX_TEST_SAMPLES_Q        = 20\n",
      "\n",
      "Not: Gerçek backend bağlanınca (AŞAMA 16) n_qubits backend kapasitesine göre\n",
      "     min(TARGET_QUBITS, backend.num_qubits) olarak kilitlenecek.\n",
      "Şimdilik planlanan n_qubits = 27\n",
      "USE_STATEVECTOR (yalnızca Aer için) = False\n",
      "Polynomial sonrası boyut: 44\n",
      "Feature resize (PCA): → 27\n",
      "Planlanan n_qubits = 27\n",
      "\n",
      "Quantum train subset: (40, 27) | failure: 50.00%\n",
      "Quantum test  subset: (20, 27) | failure: 30.00%\n",
      "\n",
      "💡 AŞAMA 16'da gerçek backend seçilince n_qubits backend'e göre güncellenebilir.\n",
      "✅ AŞAMA 12 tamamlandı.\n"
     ]
    }
   ],
   "source": "# ============================================================\n# AŞAMA 12: MAX-QUBIT ENCODING (GERÇEK IBM QPU ODAKLI)\n# ============================================================\n#\n# Öğrenci demosu stratejisi:\n#   - Gerçek IBM backend varsa → backend.num_qubits kadar (veya TARGET) kullan\n#   - Yoksa → güvenli simülasyon tavanı (≤12 statevector / ≤20 shots)\n#   - Yüksek qubit = daha zengin Hilbert uzayı; örnek sayısı yine küçük tutulur\n#     (QPU kuyruk + gürültü + süre). Bu \"max qubit gücü\"nü göstermek için doğru yoldur.\n#\n\n# ========== KULLANICI AYARI ==========\nTARGET_QUBITS = 27          # İstek: mümkün olan en yüksek. Backend daha azsa otomatik düşer.\nUSE_POLY_FEATURES = True\nPOLY_DEGREE = 2\nMAX_TRAIN_SAMPLES_Q = 40    # Yüksek qubit demosunda KÜÇÜK tutun (QPU süresi)\nMAX_TEST_SAMPLES_Q = 20\n# =====================================\n\n# Simülasyon güvenlik tavanları (sadece Aer yolu için)\nSTATEVECTOR_SAFE_MAX = 12\nSHOT_BASED_SAFE_MAX = 20\n\nprint(\"=\" * 60)\nprint(\"KAYNAK PLANI — GERÇEK QPU ÖNCELİKLİ\")\nprint(\"=\" * 60)\nprint(f\"İstenen TARGET_QUBITS     = {TARGET_QUBITS}\")\nprint(f\"MAX_TRAIN_SAMPLES_Q       = {MAX_TRAIN_SAMPLES_Q}  (yüksek qubit → küçük subset)\")\nprint(f\"MAX_TEST_SAMPLES_Q        = {MAX_TEST_SAMPLES_Q}\")\n\n# AŞAMA 16 henüz çalışmamış olabilir; bayrakları hazırla\n# (AŞAMA 16 sonrası n_qubits gerekirse yeniden ayarlanır — bu hücre önce çalışır)\nUSE_STATEVECTOR = TARGET_QUBITS <= STATEVECTOR_SAFE_MAX\nprint(f\"\\nNot: Gerçek backend bağlanınca (AŞAMA 16) n_qubits backend kapasitesine göre\")\nprint(\"     min(TARGET_QUBITS, backend.num_qubits) olarak kilitlenecek.\")\nprint(f\"Şimdilik planlanan n_qubits = {TARGET_QUBITS}\")\nprint(f\"USE_STATEVECTOR (yalnızca Aer için) = {USE_STATEVECTOR}\")\n\n# --- Feature expansion ---\nX_tr = X_train_base.values.copy()\nX_te = X_test_base.values.copy()\n\nif USE_POLY_FEATURES:\n    poly = PolynomialFeatures(degree=POLY_DEGREE, include_bias=False, interaction_only=False)\n    X_tr = poly.fit_transform(X_tr)\n    X_te = poly.transform(X_te)\n    print(f\"Polynomial sonrası boyut: {X_tr.shape[1]}\")\n\ndef _resize_features(Xtr, Xte, n_target):\n    n_feat = Xtr.shape[1]\n    if n_feat > n_target:\n        pca = PCA(n_components=n_target, random_state=42)\n        return pca.fit_transform(Xtr), pca.transform(Xte), \"PCA\"\n    if n_feat < n_target:\n        reps = int(np.ceil(n_target / n_feat))\n        return (\n            np.tile(Xtr, (1, reps))[:, :n_target],\n            np.tile(Xte, (1, reps))[:, :n_target],\n            \"tile\",\n        )\n    return Xtr, Xte, \"exact\"\n\nX_tr, X_te, how = _resize_features(X_tr, X_te, TARGET_QUBITS)\nprint(f\"Feature resize ({how}): → {TARGET_QUBITS}\")\n\nn_qubits = TARGET_QUBITS\nprint(f\"Planlanan n_qubits = {n_qubits}\")\n\nscaler_q = MinMaxScaler(feature_range=(0, np.pi))\nX_train_q = scaler_q.fit_transform(X_tr)\nX_test_q = scaler_q.transform(X_te)\n\n# Dengeli küçük subset — QPU demosu için şart\nnp.random.seed(42)\nidx_pos = np.where(y_train.values == 1)[0]\nidx_neg = np.where(y_train.values == 0)[0]\nn_pos = min(MAX_TRAIN_SAMPLES_Q // 2, len(idx_pos))\nn_neg = min(MAX_TRAIN_SAMPLES_Q - n_pos, len(idx_neg))\nsel = np.concatenate([\n    np.random.choice(idx_pos, n_pos, replace=False),\n    np.random.choice(idx_neg, n_neg, replace=False),\n])\nnp.random.shuffle(sel)\nX_train_q_sub = X_train_q[sel]\ny_train_q = y_train.values[sel]\n\nidx_t_pos = np.where(y_test.values == 1)[0]\nidx_t_neg = np.where(y_test.values == 0)[0]\nn_t_pos = min(max(MAX_TEST_SAMPLES_Q // 3, 1), len(idx_t_pos))\nn_t_neg = min(MAX_TEST_SAMPLES_Q - n_t_pos, len(idx_t_neg))\nsel_t = np.concatenate([\n    np.random.choice(idx_t_pos, n_t_pos, replace=False),\n    np.random.choice(idx_t_neg, n_t_neg, replace=False),\n])\nnp.random.shuffle(sel_t)\nX_test_q_sub = X_test_q[sel_t]\ny_test_q = y_test.values[sel_t]\n\nprint(f\"\\nQuantum train subset: {X_train_q_sub.shape} | failure: {y_train_q.mean():.2%}\")\nprint(f\"Quantum test  subset: {X_test_q_sub.shape} | failure: {y_test_q.mean():.2%}\")\nprint(\"\\n💡 AŞAMA 16'da gerçek backend seçilince n_qubits backend'e göre güncellenebilir.\")\nprint(\"✅ AŞAMA 12 tamamlandı.\")\n\n"
  },
  {
   "cell_type": "markdown",
   "id": "stage13-md",
   "metadata": {
    "id": "stage13-md"
   },
   "source": "## AŞAMA 13: Yüksek Boyutlu Feature Map + Kernel / Ansatz\n\n- **ZZFeatureMap** veya **PauliFeatureMap** (yüksek boyut için)\n- QSVC → FidelityQuantumKernel\n- VQC → RealAmplitudes / EfficientSU2 ansatz (qubit sayısına göre)\n\nBloch küresi sadece `n_qubits ≤ 5` iken çizilir (görselleştirme sınırı).\n"
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "stage13-code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 378
    },
    "id": "stage13-code",
    "executionInfo": {
     "status": "error",
     "timestamp": 1788405176422,
     "user_tz": -180,
     "elapsed": 43,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "0069e26c-3aec-41d5-c430-cecc087153df"
   },
   "outputs": [
    {
     "output_type": "error",
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'qiskit'",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_1040/3623069476.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mqiskit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcircuit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlibrary\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mZZFeatureMap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPauliFeatureMap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRealAmplitudes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mEfficientSU2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mqiskit_machine_learning\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernels\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mFidelityQuantumKernel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mqiskit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquantum_info\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mStatevector\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mqiskit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvisualization\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mplot_bloch_multivector\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mIPython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdisplay\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'qiskit'",
      "",
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
     ],
     "errorDetails": {
      "actions": [
       {
        "action": "open_url",
        "actionText": "Open Examples",
        "url": "/notebooks/snippets/importing_libraries.ipynb"
       }
      ]
     }
    }
   ],
   "source": "from qiskit.circuit.library import ZZFeatureMap, PauliFeatureMap, RealAmplitudes, EfficientSU2\nfrom qiskit_machine_learning.kernels import FidelityQuantumKernel\nfrom qiskit.quantum_info import Statevector\nfrom qiskit.visualization import plot_bloch_multivector\nfrom IPython.display import display\n\n# Feature map seçimi: yüksek qubit'te PauliFeatureMap bazen daha esnek\nif n_qubits <= 8:\n    feature_map = ZZFeatureMap(feature_dimension=n_qubits, reps=2, entanglement=\"linear\")\n    print(\"Feature map: ZZFeatureMap (reps=2, linear)\")\nelse:\n    # Yüksek boyutta daha sığ tutmak için reps=1\n    feature_map = PauliFeatureMap(feature_dimension=n_qubits, reps=1, paulis=[\"Z\", \"ZZ\"], entanglement=\"linear\")\n    print(\"Feature map: PauliFeatureMap (reps=1, Z+ZZ) — yüksek qubit için sığ tutularak depth kontrolü\")\n\nprint(f\"n_qubits = {n_qubits} | feature_map.num_qubits = {feature_map.num_qubits}\")\n\n# Devre çizimi sadece küçükse\nif n_qubits <= 6:\n    display(feature_map.decompose().draw(output=\"mpl\", style=\"clifford\", fold=80))\nelse:\n    print(\"(Devre çizimi n_qubits>6 için atlandı — çok geniş)\")\n\nquantum_kernel = None\nif SELECTED_ALGO == \"QSVC\":\n    quantum_kernel = FidelityQuantumKernel(feature_map=feature_map)\n    print(\"✅ FidelityQuantumKernel oluşturuldu.\")\n\n# VQC için ansatz (yüksek qubit)\nansatz = None\nif SELECTED_ALGO == \"VQC\" or n_qubits >= 12:\n    ansatz = EfficientSU2(num_qubits=n_qubits, reps=1, entanglement=\"linear\")\n    print(f\"✅ EfficientSU2 ansatz hazır (VQC için). parametre sayısı ≈ {ansatz.num_parameters}\")\n\n# Bloch: sadece çok küçük sistemlerde\nif n_qubits <= 4 and len(X_train_q_sub) > 0:\n    print(\"\\nBloch örneği (n_qubits≤4):\")\n    x_vec = X_train_q_sub[0]\n    qc = feature_map.assign_parameters(x_vec)\n    sv = Statevector.from_instruction(qc)\n    fig = plot_bloch_multivector(sv, title=f\"Örnek state | n_qubits={n_qubits}\")\n    display(fig)\n    plt.close(fig)\nelse:\n    print(f\"\\nBloch atlandı (n_qubits={n_qubits} > 4). Yüksek boyutta statevector görselleştirmesi pratik değil.\")\n\nprint(\"✅ AŞAMA 13 tamamlandı.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "stage14-md",
   "metadata": {
    "id": "stage14-md"
   },
   "source": "## AŞAMA 14: QSVC / VQC Eğitimi (ISA Transpile + Gerçek QPU)\n\n> **Zorunlu sıra:** Önce **AŞAMA 16** (`REAL_QPU`, `backend`, `sampler`).\n\n### Neden ISA transpile?\nMart 2024 sonrası IBM Runtime, soyut kapılı devreleri (`PauliFeatureMap` vb.) **reddeder**.  \nDevre, seçilen backend’in basis gate’leri ve bağlantı haritasına (`target`) uydurulmalıdır.\n\nBu hücre `generate_preset_pass_manager` ile feature map + ansatz’ı **ISA** hale getirir, sonra VQC’yi gerçek QPU sampler ile eğitir.\n"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "stage14-code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "stage14-code",
    "outputId": "bc8c5755-0d00-40c0-96ed-29f938e201dc"
   },
   "outputs": [
    {
     "metadata": {
      "tags": null
     },
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Soyut feature map qubits=27 | ansatz params≈108\n",
      "ISA transpile → ibm_fez ...\n",
      "   feature_map: ISA transpile OK | qubits 27→156 | depth≈319\n",
      "   ansatz: ISA transpile OK | qubits 27→156 | depth≈116\n",
      "Sampler: IBM Runtime SamplerV2 @ ibm_fez (GERÇEK QPU)\n",
      "VQC | n_qubits=27 | train=40 | REAL_QPU=True\n",
      "   COBYLA maxiter=8\n",
      "   fit başlıyor (gerçek QPU'da her iterasyon kuyruğa girer; sabır)...\n"
     ]
    },
    {
     "metadata": {
      "tags": null
     },
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:urllib3.connectionpool:Retrying (PostForcelistRetry(total=4, connect=3, read=None, redirect=None, status=None)) after connection broken by 'ConnectionResetError(104, 'Connection reset by peer')': /api/v1/jobs/dac2955e36ac739g4nbg?exclude_params=true\n",
      "WARNING:urllib3.connectionpool:Retrying (PostForcelistRetry(total=4, connect=3, read=None, redirect=None, status=None)) after connection broken by 'ConnectionResetError(104, 'Connection reset by peer')': /api/v1/jobs/dac2a816e67c73a23dd0?exclude_params=true\n",
      "WARNING:urllib3.connectionpool:Retrying (PostForcelistRetry(total=4, connect=3, read=None, redirect=None, status=None)) after connection broken by 'ConnectionResetError(104, 'Connection reset by peer')': /api/v1/jobs/dac2n7h6e67c73a24050?exclude_params=true\n",
      "WARNING:urllib3.connectionpool:Retrying (PostForcelistRetry(total=4, connect=3, read=None, redirect=None, status=None)) after connection broken by 'ConnectionResetError(104, 'Connection reset by peer')': /api/v1/jobs/dac2n7h6e67c73a24050?exclude_params=true\n",
      "WARNING:urllib3.connectionpool:Retrying (PostForcelistRetry(total=4, connect=3, read=None, redirect=None, status=None)) after connection broken by 'ConnectionResetError(104, 'Connection reset by peer')': /api/v1/jobs/dac2n7h6e67c73a24050?exclude_params=true\n",
      "WARNING:urllib3.connectionpool:Retrying (PostForcelistRetry(total=4, connect=3, read=None, redirect=None, status=None)) after connection broken by 'ConnectionResetError(104, 'Connection reset by peer')': /api/v1/jobs/dac2n7h6e67c73a24050?exclude_params=true\n",
      "WARNING:urllib3.connectionpool:Retrying (PostForcelistRetry(total=4, connect=3, read=None, redirect=None, status=None)) after connection broken by 'ConnectionResetError(104, 'Connection reset by peer')': /api/v1/jobs/dac2n7h6e67c73a24050?exclude_params=true\n",
      "WARNING:urllib3.connectionpool:Retrying (PostForcelistRetry(total=4, connect=3, read=None, redirect=None, status=None)) after connection broken by 'ConnectionResetError(104, 'Connection reset by peer')': /api/v1/jobs/dac2n7h6e67c73a24050?exclude_params=true\n",
      "WARNING:urllib3.connectionpool:Retrying (PostForcelistRetry(total=4, connect=3, read=None, redirect=None, status=None)) after connection broken by 'ConnectionResetError(104, 'Connection reset by peer')': /api/v1/jobs/dac2n7h6e67c73a24050?exclude_params=true\n",
      "WARNING:urllib3.connectionpool:Retrying (PostForcelistRetry(total=4, connect=3, read=None, redirect=None, status=None)) after connection broken by 'ConnectionResetError(104, 'Connection reset by peer')': /api/v1/jobs/dac2n7h6e67c73a24050?exclude_params=true\n",
      "WARNING:urllib3.connectionpool:Retrying (PostForcelistRetry(total=4, connect=3, read=None, redirect=None, status=None)) after connection broken by 'RemoteDisconnected('Remote end closed connection without response')': /api/v1/jobs/dac2n7h6e67c73a24050?exclude_params=true\n"
     ]
    }
   ],
   "source": "# ============================================================\n# AŞAMA 14: QSVC / VQC — ISA transpile + gerçek IBM QPU\n# ============================================================\n#\n# 2024+ kuralı: Runtime, backend target'ına uymayan (non-ISA) devreleri reddeder.\n# PauliFeatureMap / ZZFeatureMap soyut kapılar içerir → mutlaka transpile.\n#\n\nfrom qiskit_machine_learning.algorithms import QSVC, VQC\nfrom qiskit_machine_learning.optimizers import COBYLA\nfrom qiskit.circuit.library import EfficientSU2, ZZFeatureMap, PauliFeatureMap\nfrom qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n\n# --- 1) Soyut feature map + ansatz ---\nif n_qubits <= 8:\n    feature_map = ZZFeatureMap(feature_dimension=n_qubits, reps=1, entanglement=\"linear\")\nelse:\n    # Yüksek qubit: sığ PauliFeatureMap (depth / noise)\n    feature_map = PauliFeatureMap(\n        feature_dimension=n_qubits, reps=1, paulis=[\"Z\", \"ZZ\"], entanglement=\"linear\"\n    )\n\nansatz = EfficientSU2(num_qubits=n_qubits, reps=1, entanglement=\"linear\")\nprint(f\"Soyut feature map qubits={feature_map.num_qubits} | ansatz params≈{ansatz.num_parameters}\")\n\n\ndef to_isa(circuit, backend_obj, label=\"circuit\"):\n    \"\"\"Backend basis gate + coupling map'e transpile (ISA).\"\"\"\n    if backend_obj is None:\n        print(f\"   {label}: transpile atlandı (simülatör)\")\n        return circuit\n    pm = generate_preset_pass_manager(\n        optimization_level=1,  # 1 = hızlı; demo için yeterli. 2-3 daha yavaş ama daha iyi\n        backend=backend_obj,\n    )\n    isa_circ = pm.run(circuit)\n    print(\n        f\"   {label}: ISA transpile OK | \"\n        f\"qubits {circuit.num_qubits}→{isa_circ.num_qubits} | \"\n        f\"depth≈{isa_circ.depth()}\"\n    )\n    return isa_circ\n\n\n# --- 2) Gerçek QPU ise ISA'ya çevir ---\nif REAL_QPU and backend is not None:\n    print(f\"ISA transpile → {backend_name} ...\")\n    feature_map = to_isa(feature_map, backend, \"feature_map\")\n    ansatz = to_isa(ansatz, backend, \"ansatz\")\nelse:\n    print(\"Simülatör yolu: ISA transpile gerekmez.\")\n\n\ndef build_sampler():\n    global sampler\n    if REAL_QPU and sampler is not None:\n        print(f\"Sampler: IBM Runtime SamplerV2 @ {backend_name} (GERÇEK QPU)\")\n        return sampler\n    if n_qubits <= 12:\n        from qiskit.primitives import StatevectorSampler\n        print(f\"Sampler: StatevectorSampler (sim, n_qubits={n_qubits})\")\n        return StatevectorSampler()\n    try:\n        from qiskit_aer.primitives import SamplerV2 as AerSampler\n        print(f\"Sampler: AerSamplerV2 shots (sim, n_qubits={n_qubits})\")\n        return AerSampler()\n    except Exception:\n        from qiskit_aer.primitives import Sampler as AerSamplerLegacy\n        print(f\"Sampler: AerSampler legacy (sim, n_qubits={n_qubits})\")\n        return AerSamplerLegacy()\n\n\nsafe_sampler = build_sampler()\n\nq_metrics = {}\ny_pred_q = None\ny_score_q = None\n\n# Yüksek qubit / gerçek QPU → VQC\nrun_vqc = (SELECTED_ALGO == \"VQC\") or (n_qubits > 12) or REAL_QPU\n\nif (not run_vqc) and SELECTED_ALGO == \"QSVC\":\n    print(f\"QSVC | n_qubits={n_qubits} | train={len(y_train_q)}\")\n    from qiskit_machine_learning.kernels import FidelityQuantumKernel\n    # Kernel sim üzerinde kalabilir; gerçek QPU kernel çok pahalı\n    quantum_kernel = FidelityQuantumKernel(feature_map=feature_map)\n    qsvc = QSVC(quantum_kernel=quantum_kernel)\n    qsvc.fit(X_train_q_sub, y_train_q)\n    y_pred_q = qsvc.predict(X_test_q_sub)\n    try:\n        y_score_q = qsvc.decision_function(X_test_q_sub)\n    except Exception:\n        y_score_q = y_pred_q.astype(float)\n    trained_model_name = \"Quantum QSVC\"\nelse:\n    print(f\"VQC | n_qubits={n_qubits} | train={len(y_train_q)} | REAL_QPU={REAL_QPU}\")\n    if REAL_QPU:\n        maxiter = 8 if n_qubits >= 20 else 12\n    else:\n        maxiter = 40 if n_qubits <= 10 else (25 if n_qubits <= 16 else 15)\n    optimizer = COBYLA(maxiter=maxiter)\n    print(f\"   COBYLA maxiter={maxiter}\")\n\n    vqc = VQC(\n        feature_map=feature_map,\n        ansatz=ansatz,\n        optimizer=optimizer,\n        sampler=safe_sampler,\n    )\n    print(\"   fit başlıyor (gerçek QPU'da her iterasyon kuyruğa girer; sabır)...\")\n    vqc.fit(X_train_q_sub, y_train_q)\n    y_pred_q = vqc.predict(X_test_q_sub)\n    try:\n        y_score_q = (\n            vqc.decision_function(X_test_q_sub)\n            if hasattr(vqc, \"decision_function\")\n            else y_pred_q.astype(float)\n        )\n    except Exception:\n        y_score_q = y_pred_q.astype(float)\n    trained_model_name = f\"Quantum VQC ({'IBM QPU' if REAL_QPU else 'sim'})\"\n\nq_metrics = {\n    \"accuracy\": accuracy_score(y_test_q, y_pred_q),\n    \"precision\": precision_score(y_test_q, y_pred_q, zero_division=0),\n    \"recall\": recall_score(y_test_q, y_pred_q, zero_division=0),\n    \"f1\": f1_score(y_test_q, y_pred_q, zero_division=0),\n    \"roc_auc\": roc_auc_score(y_test_q, y_score_q) if len(np.unique(y_test_q)) > 1 else 0.5,\n}\n\nprint(f\"=== {trained_model_name} | n_qubits={n_qubits} | backend={backend_name} ===\")\nfor k, v in q_metrics.items():\n    print(f\"  {k:12s}: {v:.4f}\")\nprint(\"✅ AŞAMA 14 tamamlandı.\")\n\n"
  },
  {
   "cell_type": "markdown",
   "id": "stage15-md",
   "metadata": {
    "id": "stage15-md"
   },
   "source": "## AŞAMA 15: Confusion Matrix ve ROC\n"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "stage15-code",
   "metadata": {
    "id": "stage15-code"
   },
   "outputs": [],
   "source": "best_classical_name = max(results, key=lambda n: results[n][\"roc_auc\"])\nbest_c = results[best_classical_name]\n\nfig, axes = plt.subplots(1, 2, figsize=(12, 5))\ncm_c = confusion_matrix(y_test, best_c[\"y_pred\"])\ncm_q = confusion_matrix(y_test_q, y_pred_q)\nConfusionMatrixDisplay(cm_c, display_labels=[\"Arıza Yok\", \"Arıza Var\"]).plot(ax=axes[0], colorbar=False)\naxes[0].set_title(f\"Klasik: {best_classical_name}\")\nConfusionMatrixDisplay(cm_q, display_labels=[\"Arıza Yok\", \"Arıza Var\"]).plot(ax=axes[1], colorbar=False)\naxes[1].set_title(f\"{trained_model_name} (n_qubits={n_qubits})\")\nplt.tight_layout(); plt.show()\n\nfig, ax = plt.subplots(figsize=(8, 6))\nfpr_c, tpr_c, _ = roc_curve(y_test, best_c[\"y_prob\"])\nax.plot(fpr_c, tpr_c, label=f\"{best_classical_name} (AUC={best_c['roc_auc']:.3f})\")\nif len(np.unique(y_test_q)) > 1:\n    fpr_q, tpr_q, _ = roc_curve(y_test_q, y_score_q)\n    ax.plot(fpr_q, tpr_q, label=f\"{trained_model_name} (AUC={q_metrics['roc_auc']:.3f})\", linestyle=\"--\")\nax.plot([0, 1], [0, 1], \"k--\", alpha=0.4)\nax.set_xlabel(\"FPR\"); ax.set_ylabel(\"TPR\")\nax.set_title(f\"ROC — AI4I Failure (n_qubits={n_qubits})\")\nax.legend(loc=\"lower right\")\nplt.tight_layout(); plt.show()\nprint(\"✅ AŞAMA 15 tamamlandı.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "stage16-md",
   "metadata": {
    "id": "stage16-md"
   },
   "source": "## AŞAMA 16: 100+ Qubit Backend Seçimi\n\n- `use_real_hardware = True` + token → IBM Quantum Platform\n- `min_qubits` filtresi ile büyük sistemleri tercih et\n- Open Plan’da 100+ qubit her zaman müsait olmayabilir; kod yedek strateji uygular\n"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "stage16-code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "stage16-code",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788355395056,
     "user_tz": -180,
     "elapsed": 23421,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "57fa54e8-7900-4394-8f2b-403f43917bae"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "qiskit_runtime_service._discover_account:WARNING:2026-09-02 13:22:53,848: Loading account with the given token. A saved account will not be used.\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "IBM channel=ibm_quantum_platform | instance=auto\n",
      "Erişilebilir instance sayısı: 4\n",
      "   - {'crn': 'crn:v1:bluemix:public:quantum-computing:us-east:a/94208d6a0cb048fdabcd30c8b76a7a63:26d7f684-ed7b-4cc3-ad18-24406fabeff9::', 'plan': 'open', 'name': 'student-cuneytsargullar', 'tags': [], 'pricing_type': 'free'}\n",
      "   - {'crn': 'crn:v1:bluemix:public:quantum-computing:us-east:a/94208d6a0cb048fdabcd30c8b76a7a63:6be74454-8282-4e15-bf8c-321384df6657::', 'plan': 'open', 'name': 'student-ozlemdundar_sau', 'tags': [], 'pricing_type': 'free'}\n",
      "   - {'crn': 'crn:v1:bluemix:public:quantum-computing:us-east:a/94208d6a0cb048fdabcd30c8b76a7a63:9491e712-84de-4791-aaa4-9413bbe9ed4d::', 'plan': 'open', 'name': 'student-mehmetbozdemir21', 'tags': [], 'pricing_type': 'free'}\n",
      "   - {'crn': 'crn:v1:bluemix:public:quantum-computing:us-east:a/94208d6a0cb048fdabcd30c8b76a7a63:cc2550b4-f46f-4b49-918b-c1335af0fd7e::', 'plan': 'open', 'name': 'open-instance', 'tags': [], 'pricing_type': 'free'}\n",
      "\n",
      "📡 Erişilebilir gerçek backend'ler (qubit sayısına göre):\n",
      "   ibm_fez                         qubits=156    pending=8      operational=True\n",
      "   ibm_marrakesh                   qubits=156    pending=17     operational=True\n",
      "   ibm_kingston                    qubits=156    pending=2      operational=True\n",
      "\n",
      "   → En büyük sistem: ibm_fez (156 qubit)\n",
      "   · least_busy{'operational': True, 'simulator': False, 'min_qubits': 20}: 'No backend matches the criteria.'\n",
      "   · least_busy{'operational': True, 'simulator': False, 'min_qubits': 10}: 'No backend matches the criteria.'\n",
      "\n",
      "✅ GERÇEK QPU SEÇİLDİ: ibm_fez\n",
      "   Qubit sayısı     : 156\n",
      "   Seçim yöntemi    : least_busy({'operational': True, 'simulator': False})\n",
      "   Channel          : ibm_quantum_platform\n",
      "   n_qubits = 27 (backend yeterli)\n",
      "\n",
      "Aktif backend : ibm_fez\n",
      "REAL_QPU      : True\n",
      "n_qubits      : 27\n",
      "✅ AŞAMA 16 tamamlandı.\n"
     ]
    }
   ],
   "source": "# ============================================================\n# AŞAMA 16: GERÇEK IBM DONANIM — MAX QUBIT SEÇİMİ\n# ============================================================\n#\n# Öğrenci demosu: use_real_hardware=True varsayılan.\n# Backend bulunduğunda n_qubits = min(TARGET_QUBITS, backend.num_qubits)\n# ve feature matrisleri gerekirse yeniden boyutlandırılır.\n#\n\nuse_real_hardware = True    # ← Öğrenci demosu: gerçek QPU\nFORCE_INSTANCE = None       # örn. \"open-instance\" veya CRN\n# Büyük sistem tercihi (erişiminiz varsa)\nMIN_QUBITS_PREFERRED = 20   # en az bu kadar qubitli backend ara\n\nbackend_name = \"aer_simulator\"\nsampler = None\nservice = None\nbackend = None\nREAL_QPU = False\n\ndef _pick_backend(service, min_q):\n    \"\"\"Önce büyük sistemleri dene, sonra gevşet.\"\"\"\n    strategies = [\n        dict(operational=True, simulator=False, min_qubits=min_q),\n        dict(operational=True, simulator=False, min_qubits=max(min_q // 2, 5)),\n        dict(operational=True, simulator=False),\n    ]\n    for kwargs in strategies:\n        try:\n            b = service.least_busy(**kwargs)\n            if b is not None:\n                return b, f\"least_busy({kwargs})\"\n        except Exception as e:\n            print(f\"   · least_busy{kwargs}: {e}\")\n    try:\n        candidates = service.backends(simulator=False, operational=True)\n        if not candidates:\n            candidates = service.backends(simulator=False)\n        # En çok qubitli operasyonel cihazı tercih et (demo etkisi)\n        def _nq(b):\n            return getattr(b, \"num_qubits\", 0) or 0\n        def _pend(b):\n            try:\n                return getattr(b.status(), \"pending_jobs\", 9999)\n            except Exception:\n                return 9999\n        # Önce qubit sayısına göre azalan, sonra kuyruk\n        pool = sorted(candidates, key=lambda b: (-_nq(b), _pend(b)))\n        if pool:\n            return pool[0], f\"backends() en büyük ({len(pool)} aday)\"\n    except Exception as e:\n        print(f\"   · backends list: {e}\")\n    return None, \"yok\"\n\n\nif use_real_hardware and IBM_TOKEN:\n    try:\n        from qiskit_ibm_runtime import QiskitRuntimeService\n        from qiskit_ibm_runtime import SamplerV2 as Sampler\n\n        channel = IBM_CHANNEL if IBM_CHANNEL in (\"ibm_quantum_platform\", \"ibm_cloud\") else \"ibm_quantum_platform\"\n        instance = FORCE_INSTANCE or IBM_INSTANCE\n        svc_kwargs = {\"channel\": channel, \"token\": IBM_TOKEN}\n        svc_kwargs[\"instance\"] = instance if instance else \"auto\"\n        print(f\"IBM channel={channel} | instance={svc_kwargs['instance']}\")\n\n        service = QiskitRuntimeService(**svc_kwargs)\n\n        try:\n            insts = service.instances()\n            if insts:\n                print(f\"Erişilebilir instance sayısı: {len(list(insts))}\")\n                for inst in list(insts)[:8]:\n                    print(f\"   - {inst}\")\n        except Exception:\n            pass\n\n        # Tüm gerçek backend'leri qubit sayısına göre listele (öğrenciye göster)\n        try:\n            all_hw = service.backends(simulator=False)\n            print(\"\\n📡 Erişilebilir gerçek backend'ler (qubit sayısına göre):\")\n            rows = []\n            for b in sorted(all_hw, key=lambda x: -(getattr(x, \"num_qubits\", 0) or 0)):\n                nq = getattr(b, \"num_qubits\", \"?\")\n                try:\n                    st = b.status()\n                    pend = getattr(st, \"pending_jobs\", \"?\")\n                    op = getattr(st, \"operational\", \"?\")\n                except Exception:\n                    pend, op = \"?\", \"?\"\n                rows.append((b.name, nq, pend, op))\n                print(f\"   {b.name:30s}  qubits={nq!s:5s}  pending={pend!s:5s}  operational={op}\")\n            if rows:\n                print(f\"\\n   → En büyük sistem: {rows[0][0]} ({rows[0][1]} qubit)\")\n        except Exception as e:\n            print(f\"   Backend listesi alınamadı: {e}\")\n\n        backend, how = _pick_backend(service, MIN_QUBITS_PREFERRED)\n        if backend is None:\n            raise RuntimeError(\n                \"Uygun gerçek backend bulunamadı. \"\n                \"Instance / kota / bakım kontrol edin. IBM_QUANTUM_INSTANCE deneyin.\"\n            )\n\n        backend_name = backend.name\n        backend_nq = int(getattr(backend, \"num_qubits\", 0) or 0)\n        sampler = Sampler(mode=backend)\n        REAL_QPU = True\n\n        print(f\"\\n✅ GERÇEK QPU SEÇİLDİ: {backend_name}\")\n        print(f\"   Qubit sayısı     : {backend_nq}\")\n        print(f\"   Seçim yöntemi    : {how}\")\n        print(f\"   Channel          : {channel}\")\n\n        # ---- n_qubits'i backend kapasitesine kilitle ----\n        planned = int(TARGET_QUBITS)\n        if backend_nq > 0:\n            final_nq = min(planned, backend_nq)\n        else:\n            final_nq = planned\n\n        if final_nq != n_qubits:\n            print(f\"\\n🔧 n_qubits ayarı: planlanan {n_qubits} → backend limitli {final_nq}\")\n            # Feature matrislerini yeniden boyutlandır\n            X_tr2, X_te2, how2 = _resize_features(\n                scaler_q.inverse_transform(X_train_q) if False else X_train_base.values,\n                X_test_base.values,\n                final_nq,\n            )\n            # Daha güvenli: mevcut angle-scaled matrislerden pad/PCA\n            def _fit_to_nq(X, n_target):\n                if X.shape[1] == n_target:\n                    return X\n                if X.shape[1] > n_target:\n                    # ilk n_target sütun (zaten ölçekli)\n                    return X[:, :n_target]\n                reps = int(np.ceil(n_target / X.shape[1]))\n                return np.tile(X, (1, reps))[:, :n_target]\n\n            X_train_q = _fit_to_nq(X_train_q, final_nq)\n            X_test_q = _fit_to_nq(X_test_q, final_nq)\n            # subset indeksleri aynı kalsın\n            X_train_q_sub = X_train_q[sel] if \"sel\" in dir() else X_train_q[:len(y_train_q)]\n            X_test_q_sub = X_test_q[sel_t] if \"sel_t\" in dir() else X_test_q[:len(y_test_q)]\n            # sel global olabilir; güvenli yol:\n            X_train_q_sub = _fit_to_nq(X_train_q_sub, final_nq)\n            X_test_q_sub = _fit_to_nq(X_test_q_sub, final_nq)\n            n_qubits = final_nq\n            print(f\"   Güncel n_qubits = {n_qubits}\")\n            print(f\"   train subset shape = {X_train_q_sub.shape}\")\n        else:\n            print(f\"   n_qubits = {n_qubits} (backend yeterli)\")\n\n        if backend_nq < TARGET_QUBITS:\n            print(f\"⚠️ Backend ({backend_nq}q) istenen TARGET ({TARGET_QUBITS}) altında.\")\n            print(\"   Daha büyük sistem için Premium/Pay-as-you-go instance gerekebilir.\")\n\n    except Exception as e:\n        print(f\"⚠️ Gerçek donanım bağlantısı başarısız: {e}\")\n        print(\"   Aer simülatöre düşülüyor (öğrenci demosu için token/instance kontrol edin).\")\n        use_real_hardware = False\n        REAL_QPU = False\n        backend_name = \"aer_simulator\"\n        sampler = None\n        backend = None\n        # Simülasyon tavanı\n        if n_qubits > SHOT_BASED_SAFE_MAX:\n            print(f\"   Simülasyon için n_qubits {n_qubits} → {SHOT_BASED_SAFE_MAX}\")\n            n_qubits = SHOT_BASED_SAFE_MAX\nelse:\n    print(f\"use_real_hardware={use_real_hardware}, token={'var' if IBM_TOKEN else 'YOK'}\")\n    if not IBM_TOKEN:\n        print(\"⚠️ Token yok — gerçek QPU için AŞAMA 4'te IBM_QUANTUM_TOKEN gerekli.\")\n    if n_qubits > SHOT_BASED_SAFE_MAX:\n        print(f\"Simülasyon tavanı: n_qubits {n_qubits} → {SHOT_BASED_SAFE_MAX}\")\n        n_qubits = SHOT_BASED_SAFE_MAX\n    REAL_QPU = False\n\nprint(f\"\\nAktif backend : {backend_name}\")\nprint(f\"REAL_QPU      : {REAL_QPU}\")\nprint(f\"n_qubits      : {n_qubits}\")\nprint(\"✅ AŞAMA 16 tamamlandı.\")\n\n"
  },
  {
   "cell_type": "markdown",
   "id": "stage17-md",
   "metadata": {
    "id": "stage17-md"
   },
   "source": "## AŞAMA 17: Sonuç Kaydı\n"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "stage17-code",
   "metadata": {
    "id": "stage17-code"
   },
   "outputs": [],
   "source": "run_id = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\nout_dir = OUTPUT_PATH\nos.makedirs(out_dir, exist_ok=True)\n\nsummary = {\n    \"run_id\": run_id,\n    \"problem\": \"AI4I2020_Machine_Failure_HighQubit\",\n    \"n_samples\": len(df),\n    \"n_qubits\": n_qubits,\n    \"TARGET_QUBITS\": TARGET_QUBITS,\n    \"MAX_TRAIN_SAMPLES_Q\": MAX_TRAIN_SAMPLES_Q,\n    \"MAX_TEST_SAMPLES_Q\": MAX_TEST_SAMPLES_Q,\n    \"backend\": backend_name,\n    \"use_real_hardware\": use_real_hardware,\n    \"quantum_model\": trained_model_name,\n    \"classical_best\": best_classical_name,\n    \"classical_auc\": best_c[\"roc_auc\"],\n    \"classical_f1\": best_c[\"f1\"],\n    \"quantum_auc\": q_metrics[\"roc_auc\"],\n    \"quantum_f1\": q_metrics[\"f1\"],\n    \"quantum_accuracy\": q_metrics[\"accuracy\"],\n}\n\nsummary_path = os.path.join(out_dir, f\"ai4i_highq_summary_{run_id}.json\")\nwith open(summary_path, \"w\") as f:\n    json.dump(summary, f, indent=2, default=str)\nprint(f\"✅ Özet: {summary_path}\")\n\nmetric_rows = []\nfor name, r in results.items():\n    metric_rows.append({\n        \"model\": name, \"type\": \"classical\",\n        \"roc_auc\": r[\"roc_auc\"], \"f1\": r[\"f1\"],\n        \"precision\": r[\"precision\"], \"recall\": r[\"recall\"], \"accuracy\": r[\"accuracy\"],\n    })\nmetric_rows.append({\n    \"model\": trained_model_name, \"type\": \"quantum\",\n    \"roc_auc\": q_metrics[\"roc_auc\"], \"f1\": q_metrics[\"f1\"],\n    \"precision\": q_metrics[\"precision\"], \"recall\": q_metrics[\"recall\"],\n    \"accuracy\": q_metrics[\"accuracy\"],\n})\nmetrics_df = pd.DataFrame(metric_rows)\nmetrics_path = os.path.join(out_dir, f\"ai4i_highq_metrics_{run_id}.csv\")\nmetrics_df.to_csv(metrics_path, index=False)\nprint(f\"✅ Metrikler: {metrics_path}\")\nprint(\"✅ AŞAMA 17 tamamlandı.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "stage18-md",
   "metadata": {
    "id": "stage18-md"
   },
   "source": "## AŞAMA 18: Model Karşılaştırma\n"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "stage18-code",
   "metadata": {
    "id": "stage18-code"
   },
   "outputs": [],
   "source": "comparison = pd.DataFrame({\n    name: {\"ROC-AUC\": r[\"roc_auc\"], \"F1\": r[\"f1\"], \"Precision\": r[\"precision\"],\n           \"Recall\": r[\"recall\"], \"Accuracy\": r[\"accuracy\"]}\n    for name, r in results.items()\n})\ncomparison[trained_model_name] = {\n    \"ROC-AUC\": q_metrics[\"roc_auc\"], \"F1\": q_metrics[\"f1\"],\n    \"Precision\": q_metrics[\"precision\"], \"Recall\": q_metrics[\"recall\"],\n    \"Accuracy\": q_metrics[\"accuracy\"],\n}\ncomparison = comparison.T.round(4)\n\nprint(f\"=== Karşılaştırma (n_qubits={n_qubits}) ===\")\ndisplay(comparison.style.highlight_max(axis=0, color=\"lightgreen\"))\n\nfig, ax = plt.subplots(figsize=(11, 5))\ncomparison[[\"ROC-AUC\", \"F1\"]].plot(kind=\"bar\", ax=ax, color=[\"#3498db\", \"#e74c3c\"])\nax.set_title(f\"Model Karşılaştırması – n_qubits={n_qubits}\\n(Quantum subset üzerinde)\")\nax.set_ylabel(\"Skor\"); ax.set_ylim(0, 1.05)\nplt.xticks(rotation=20); plt.tight_layout(); plt.show()\n\ncomp_path = os.path.join(out_dir, f\"comparison_highq_{run_id}.csv\")\ncomparison.to_csv(comp_path)\nprint(f\"✅ Tablo: {comp_path}\")\nprint(\"✅ AŞAMA 18 tamamlandı.\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "stage19-md",
   "metadata": {
    "id": "stage19-md"
   },
   "source": "## AŞAMA 19: Yorumlama & 100+ Qubit Ölçekleme Rehberi\n\n### Neden hâlâ subset var?\n\nQSVC/Fidelity Kernel **örnek sayısı n** ile **O(n²)** devre ister.  \n100 qubit × 10.000 örnek → pratik değil (süre, kuyruk, klasik bellek).  \nBu yüzden **qubit sayısını artırırken örnek sayısını bilinçli düşük tutuyoruz**.\n\n### 100+ qubit cihazlarda ne kazanırsınız?\n\n1. Daha fazla özellik / daha zengin feature map (daha yüksek kapasiteli Hilbert uzayı)\n2. Daha geniş VQC ansatz (daha fazla parametre, daha karmaşık karar sınırı)\n3. Gelecekteki hata-düzeltmeli algoritmalar için yol\n4. Bugün pratik kazanç sınırlıdır: gürültü + derinlik + connectivity hâlâ darboğaz\n\n### Önerilen deney sırası\n\n| Adım | TARGET_QUBITS | Algoritma | Subset | Ortam |\n|------|---------------|-----------|--------|-------|\n| 1 | 8 | QSVC | 80–120 | Aer |\n| 2 | 12–16 | VQC | 60–100 | Aer / küçük QPU |\n| 3 | 20–40 | VQC | 40–80 | Büyük IBM backend |\n| 4 | 50–100+ | VQC / özel ansatz | çok küçük / batch | 100+ qubit sistem + error mitigation |\n\n### Operasyonel metrikler\n\n- Predictive maintenance’te **Recall** (kaçırılan arıza) genelde kritik.\n- Threshold’u 0.5’te bırakmayın; maliyet matrisi ile ayarlayın.\n- Klasik modeller bu veri setinde hâlâ çok güçlü baseline’dır; quantum şu an POC / araştırma katmanıdır.\n\n### Kontrol listesi\n\n- [ ] TARGET_QUBITS sistem belleği / QPU boyutuna uygun mu?\n- [ ] Kernel değerlendirme sayısı kabul edilebilir mi?\n- [ ] n_qubits > 16 ise sampling / gerçek backend kullanıldı mı?\n- [ ] Backend num_qubits ≥ n_qubits mi?\n- [ ] Recall / Precision trade-off operasyona uygun mu?\n\n---\n\n**Özet:** Bu notebook 100+ qubit **sınıfı sistemlere hazır** bir iskelet sunar: qubit sayısı ölçeklenebilir, feature expansion var, VQC yolu açık, büyük backend seçimi desteklenir.  \nTam veri + 100 qubit ile “tek tuş eğitim” bugünün NISQ gerçekliğinde mümkün değildir; doğru yaklaşım **yüksek qubit + kontrollü örnek boyutu + doğru algoritma (VQC)** kombinasyonudur.\n"
  },
  {
   "cell_type": "markdown",
   "id": "closing-md",
   "metadata": {
    "id": "closing-md"
   },
   "source": "## Kapanış\n\n- **Dosya:** High-Qubit versiyon — AI4I 2020 Predictive Maintenance  \n- **Ana kontrol:** AŞAMA 12 → `TARGET_QUBITS`  \n- **Gerçek 100+ qubit:** AŞAMA 4 token + AŞAMA 16 `use_real_hardware=True`  \n- **Öneri:** Önce 8–12 qubit Aer, sonra 16–20 VQC, ardından erişiminiz olan en büyük IBM backend  \n\n**Tarih:** 2026 · Qiskit + IBM Quantum Runtime · Eğitim / POC amaçlı\n"
  }
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          "text/markdown": "### Seçim: **Machine Learning** → **Superconducting Qubits** → **QSVC**"
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          "text/markdown": "**Cihaz:** `ibm_or_aer`"
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          "text/markdown": "**Model:** $K(x,x') = |\\langle \\phi(x) | \\phi(x') \\rangle|^2$"
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