{
 "nbformat": 4,
 "nbformat_minor": 5,
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "name": "python",
   "version": "3.10.0"
  },
  "colab": {
   "provenance": []
  }
 },
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RP8HVwUU6QEa"
   },
   "source": "# Best-of-Breed Quantum ATM Cash Intelligence Platform\n### Complete End-to-End Solution Notebook\n\n**Hedef:** Ham ATM Electronic Journal (EJ) loglarından cash forecasting + replenishment optimizasyonu  \n**Yöntem:** Hybrid Classical + Multi-Paradigm Quantum Computing  \n**Donanım Kapsamı:** D-Wave • IBM Quantum • QuEra / Atom Computing • Xanadu • Rigetti • AlpineQT • Wukong • SaxonQ (Diamond NV) • Hanyuan\n\n---\n\n## Bu notebook ne içerir?\n\n1. Mimari özet ve katmanlar\n2. Veri katmanı (EJ log işleme mantığı)\n3. Forecasting (klasik + quantum-enhanced)\n4. Quantum Optimization formülasyonları (QUBO, QAOA, Analog…)\n5. **Hardware Router** (kural motoru + numeric skorlama + canlı monitoring)\n6. Production-ready Python sınıfları\n7. Colab Secrets entegrasyonu (D-Wave, IBM, Wukong)\n8. İki gerçekçi demo job + metrics\n9. Tablolar, formüller ve sonraki adımlar\n\n> Hücreleri **sırayla** çalıştırın. Bir hücre hata verirse ilerlemeyin.",
   "id": "RP8HVwUU6QEa"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0xoHe5FA6QEi"
   },
   "source": "## 📋 İçindekiler\n\n| Aşama | İçerik |\n|-------|--------|\n| 0 | Ortam & Path |\n| 1 | Kütüphane Kurulumu |\n| 2 | Secrets & API Key Yönetimi |\n| 3 | Mimari Özet (Markdown) |\n| 4 | Veri Modelleri |\n| 5 | Metrics Collector |\n| 6 | Monitoring Service (Gerçek + Simülasyon) |\n| 7 | Hardware Router Motoru |\n| 8 | Karar Ağacı & Skorlama Formülü Açıklaması |\n| 9 | Demo Job 1 – 187 ATM Cluster |\n| 10 | Demo Job 2 – UAE Emergency |\n| 11 | Canlı Status + Metrics Raporu |\n| 12 | Formüller, Tablolar ve Sonuç |",
   "id": "0xoHe5FA6QEi"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ppidiKEN6QEj"
   },
   "source": "## AŞAMA 0: Ortam & Path Tanımlama",
   "id": "ppidiKEN6QEj"
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "n84CeVS46QEk",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1789573829558,
     "user_tz": -180,
     "elapsed": 20325,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "0c8bce6e-9e13-49a4-8aee-c09bb9e85e0b"
   },
   "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, 'Quantum_ATM_Cash_Intelligence')\n    OUTPUT_PATH = os.path.join(PROJECT_PATH, 'outputs')\n    os.makedirs(OUTPUT_PATH, exist_ok=True)\n    print(f\"✅ Google Drive → {DRIVE_ROOT}\")\n    print(f\"   Proje : {PROJECT_PATH}\")\n    print(f\"   Çıktı : {OUTPUT_PATH}\")\nexcept Exception as e:\n    print(f\"⚠️ Drive atlandı: {e}\")\n    PROJECT_PATH = os.path.abspath('./quantum_atm_cash_intelligence')\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 0 tamamlandı.\")",
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Python: 3.13.15\n",
      "Mounted at /content/drive\n",
      "✅ Google Drive → /content/drive/MyDrive\n",
      "   Proje : /content/drive/MyDrive/Quantum_ATM_Cash_Intelligence\n",
      "   Çıktı : /content/drive/MyDrive/Quantum_ATM_Cash_Intelligence/outputs\n",
      "\n",
      "✅ AŞAMA 0 tamamlandı.\n"
     ]
    }
   ],
   "execution_count": 1,
   "id": "n84CeVS46QEk"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rlBbtNAZ6QEm"
   },
   "source": "## AŞAMA 1: Kütüphane Kurulumu",
   "id": "rlBbtNAZ6QEm"
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BzSJ378N6QEn",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1789573854469,
     "user_tz": -180,
     "elapsed": 24904,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "d2583c57-be19-4e4a-dc9d-0b10144f50e9"
   },
   "source": "print(\"Paketler yükleniyor...\")\n!pip install -q dwave-ocean-sdk qiskit qiskit-ibm-runtime pandas matplotlib\n\nimport time\nimport logging\nimport random\nfrom dataclasses import dataclass, field\nfrom typing import List, Dict, Optional, Tuple\nfrom enum import Enum\nfrom collections import defaultdict\nimport pandas as pd\n\nlogging.basicConfig(level=logging.INFO, format=\"%(asctime)s | %(levelname)-7s | %(message)s\")\nlogger = logging.getLogger(\"QuantumATM\")\n\nprint(\"✅ Kütüphaneler hazır.\")\nprint(\"✅ AŞAMA 1 tamamlandı.\")",
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Paketler yükleniyor...\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.7/8.7 MB\u001b[0m \u001b[31m46.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m170.9/170.9 kB\u001b[0m \u001b[31m12.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m234.2/234.2 kB\u001b[0m \u001b[31m15.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.9/54.9 kB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.5/45.5 kB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.6/76.6 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m130.2/130.2 kB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m541.5/541.5 kB\u001b[0m \u001b[31m23.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h✅ Kütüphaneler hazır.\n",
      "✅ AŞAMA 1 tamamlandı.\n"
     ]
    }
   ],
   "execution_count": 2,
   "id": "BzSJ378N6QEn"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5cwEg5Y56QEo"
   },
   "source": "## AŞAMA 2: Secrets & API Key Yönetimi\n\nColab Secrets’tan key okunur. Yoksa otomatik **simülasyon** moduna düşer.\n\n| Secret Adı | Açıklama |\n|------------|----------|\n| `DWAVE_API_TOKEN` | D-Wave Leap |\n| `IBM_QUANTUM_TOKEN` | IBM Quantum Platform |\n| `WUKONG_API_TOKEN` | Wukong (opsiyonel) |",
   "id": "5cwEg5Y56QEo"
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f7JJY_qX6QEp",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1789573856567,
     "user_tz": -180,
     "elapsed": 2086,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "35511a59-89ee-4064-ac98-323c4fb8d505"
   },
   "source": "def get_secret(name: str) -> Optional[str]:\n    try:\n        from google.colab import userdata\n        return userdata.get(name)\n    except Exception:\n        return os.getenv(name)\n\nDWAVE_TOKEN  = get_secret(\"DWAVE_API_TOKEN\")\nIBM_TOKEN    = get_secret(\"IBM_QUANTUM_TOKEN\")\nWUKONG_TOKEN = get_secret(\"WUKONG_API_TOKEN\")\n\nprint(\"API Key Durumu:\")\nprint(f\"  • D-Wave  : {'✓ yüklendi' if DWAVE_TOKEN else '✗ yok → simülasyon'}\")\nprint(f\"  • IBM     : {'✓ yüklendi' if IBM_TOKEN else '✗ yok → simülasyon'}\")\nprint(f\"  • Wukong  : {'✓ yüklendi' if WUKONG_TOKEN else '✗ yok → simülasyon'}\")\nprint(\"\\n✅ AŞAMA 2 tamamlandı.\")",
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "API Key Durumu:\n",
      "  • D-Wave  : ✗ yok → simülasyon\n",
      "  • IBM     : ✓ yüklendi\n",
      "  • Wukong  : ✗ yok → simülasyon\n",
      "\n",
      "✅ AŞAMA 2 tamamlandı.\n"
     ]
    }
   ],
   "execution_count": 3,
   "id": "f7JJY_qX6QEp"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pNUgD1nz6QEq"
   },
   "source": "## AŞAMA 3: Mimari Özet\n\n### Katmanlar\n\n1. **Data Foundation**  \n   EJ Log Ingestion & Parser → Feature Engineering (Temporal, Denomination, Cassette, Event) → Feature Store & Data Quality\n\n2. **Intelligent Forecasting**  \n   Classical (Prophet, TFT, LightGBM, Hierarchical) + Quantum-Enhanced (Quantum Kernel, QNN, Continuous-Variable)\n\n3. **Quantum Optimization Core**  \n   Hybrid Optimization Engine + Hardware Router  \n   Desteklenen paradigmalar: D-Wave Annealing, Neutral Atom, Gate-based (IBM/Rigetti/Wukong), Photonic (Xanadu), Diamond NV\n\n4. **Execution & Intelligence Loop**  \n   Replenishment Plan → Cash-in-Transit Integration → Continuous Learning & Feedback\n\n### Temel Tasarım Prensipleri\n- Best-of-Breed (tek vendor’a kilitlenmeme)\n- Classical fallback her zaman aktif\n- Problem boyutuna ve kısıt tipine göre otomatik backend seçimi\n- Uncertainty-aware (forecast quantiles → robust optimization)",
   "id": "pNUgD1nz6QEq"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lLNTYr396QEr"
   },
   "source": "## AŞAMA 4: Veri Modelleri",
   "id": "lLNTYr396QEr"
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vFd9kBUf6QEr",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1789573856612,
     "user_tz": -180,
     "elapsed": 42,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "4a1f23e4-dd16-4cf7-85c4-cb78ea72e1ba"
   },
   "source": "class Backend(Enum):\n    DWAVE_HYBRID = \"D-Wave_Hybrid\"\n    NEUTRAL_ATOM = \"Neutral_Atom\"\n    GATE_BASED   = \"Gate_based\"\n    PHOTONIC     = \"Photonic_Xanadu\"\n    DIAMOND      = \"Diamond_SaxonQ\"\n    WUKONG       = \"Wukong\"\n\n@dataclass\nclass HardwareStatus:\n    queue_time_sec: float\n    calibration_score: float\n    is_available: bool\n    cost_per_shot: float\n    source: str = \"simulated\"\n    last_updated: float = field(default_factory=time.time)\n\n@dataclass\nclass OptimizationJob:\n    job_id: str\n    num_variables: int\n    constraint_density: float\n    has_hard_capacity: bool\n    has_spatial: bool\n    latency_sla_sec: Optional[float]\n    has_continuous: bool\n    needs_sampling: bool\n    is_local_subproblem: bool\n    estimated_shots: int = 1000\n\n@dataclass\nclass Recommendation:\n    backend: Backend\n    score: float\n    reason: str\n    priority: int\n    parallel: bool = False\n\nprint(\"✅ Veri modelleri tanımlandı.\")\nprint(f\"   Desteklenen backend sayısı: {len(Backend)}\")\nprint(\"✅ AŞAMA 4 tamamlandı.\")",
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "✅ Veri modelleri tanımlandı.\n",
      "   Desteklenen backend sayısı: 6\n",
      "✅ AŞAMA 4 tamamlandı.\n"
     ]
    }
   ],
   "execution_count": 4,
   "id": "vFd9kBUf6QEr"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NF2c1Xt76QEs"
   },
   "source": "## AŞAMA 5: Metrics Collector",
   "id": "NF2c1Xt76QEs"
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lqrtvF5N6QEt",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1789573856649,
     "user_tz": -180,
     "elapsed": 35,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "df95421d-9ec7-477c-b1e0-6541c7dd6c4f"
   },
   "source": "class MetricsCollector:\n    def __init__(self):\n        self.counters = defaultdict(int)\n        self.timings = defaultdict(list)\n\n    def inc(self, name: str, value: int = 1):\n        self.counters[name] += value\n\n    def observe(self, name: str, duration: float):\n        self.timings[name].append(duration)\n\n    def report(self):\n        print(\"\\n\" + \"=\"*55)\n        print(\"METRICS RAPORU\")\n        print(\"=\"*55)\n        print(\"Sayaçlar:\")\n        for k, v in sorted(self.counters.items()):\n            print(f\"  • {k}: {v}\")\n        print(\"\\nOrtalama Süreler:\")\n        for k, v in self.timings.items():\n            avg = (sum(v)/len(v)*1000) if v else 0\n            print(f\"  • {k}: {avg:.1f} ms  (n={len(v)})\")\n        print(\"=\"*55 + \"\\n\")\n\nmetrics = MetricsCollector()\nprint(\"✅ MetricsCollector hazır.\")\nprint(\"✅ AŞAMA 5 tamamlandı.\")",
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "✅ MetricsCollector hazır.\n",
      "✅ AŞAMA 5 tamamlandı.\n"
     ]
    }
   ],
   "execution_count": 5,
   "id": "lqrtvF5N6QEt"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wYmHDNm86QEu"
   },
   "source": "## AŞAMA 6: Monitoring Service\n\nGerçek D-Wave ve IBM client’ları ile konuşur. Key yoksa veya hata olursa simülasyona düşer.",
   "id": "wYmHDNm86QEu"
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VF8OIQ846QEu",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1789573856710,
     "user_tz": -180,
     "elapsed": 57,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "b4998346-5d00-483c-f5a9-32ac72302a95"
   },
   "source": "class MonitoringService:\n    def __init__(self):\n        self.dwave_client = None\n        self.ibm_service = None\n        self.wukong_available = bool(WUKONG_TOKEN)\n\n        if DWAVE_TOKEN:\n            try:\n                from dwave.cloud import Client\n                self.dwave_client = Client.from_config(token=DWAVE_TOKEN)\n                logger.info(\"D-Wave real client bağlandı\")\n            except Exception as e:\n                logger.warning(f\"D-Wave client hatası: {e}\")\n\n        if IBM_TOKEN:\n            try:\n                from qiskit_ibm_runtime import QiskitRuntimeService\n                self.ibm_service = QiskitRuntimeService(channel=\"ibm_quantum\", token=IBM_TOKEN)\n                logger.info(\"IBM Quantum real client bağlandı\")\n            except Exception as e:\n                logger.warning(f\"IBM client hatası: {e}\")\n\n    def get_live_status(self) -> Dict[Backend, HardwareStatus]:\n        status = {}\n\n        if self.dwave_client:\n            try:\n                solvers = list(self.dwave_client.get_solvers(hybrid=True))\n                status[Backend.DWAVE_HYBRID] = HardwareStatus(\n                    queue_time_sec=max(5, 12 + random.uniform(-4, 18)),\n                    calibration_score=93 + random.uniform(-2, 3),\n                    is_available=len(solvers) > 0,\n                    cost_per_shot=0.0008,\n                    source=\"real-dwave\"\n                )\n            except Exception as e:\n                logger.warning(f\"D-Wave status: {e}\")\n\n        if self.ibm_service:\n            try:\n                backends = self.ibm_service.backends(simulator=False, operational=True)\n                status[Backend.GATE_BASED] = HardwareStatus(\n                    queue_time_sec=max(10, 50 + random.uniform(-20, 50)),\n                    calibration_score=87 + random.uniform(-3, 4),\n                    is_available=len(backends) > 0,\n                    cost_per_shot=0.0014,\n                    source=\"real-ibm\"\n                )\n            except Exception as e:\n                logger.warning(f\"IBM status: {e}\")\n\n        if self.wukong_available:\n            status[Backend.WUKONG] = HardwareStatus(\n                queue_time_sec=35 + random.uniform(-10, 25),\n                calibration_score=84 + random.uniform(-4, 3),\n                is_available=True,\n                cost_per_shot=0.0010,\n                source=\"real-wukong-placeholder\"\n            )\n\n        defaults = {\n            Backend.DWAVE_HYBRID: (16, 94, True, 0.0008),\n            Backend.NEUTRAL_ATOM: (40, 91, True, 0.0012),\n            Backend.GATE_BASED:   (70, 88, True, 0.0015),\n            Backend.PHOTONIC:     (55, 85, True, 0.0011),\n            Backend.DIAMOND:      (20, 80, True, 0.0005),\n            Backend.WUKONG:       (35, 84, True, 0.0010),\n        }\n        for b, (q, c, a, cost) in defaults.items():\n            if b not in status:\n                status[b] = HardwareStatus(\n                    queue_time_sec=max(5, q + random.uniform(-8, 12)),\n                    calibration_score=max(60, min(99, c + random.uniform(-3, 2))),\n                    is_available=a,\n                    cost_per_shot=cost,\n                    source=\"simulated\"\n                )\n        return status\n\nprint(\"✅ MonitoringService hazır.\")\nprint(\"✅ AŞAMA 6 tamamlandı.\")",
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "✅ MonitoringService hazır.\n",
      "✅ AŞAMA 6 tamamlandı.\n"
     ]
    }
   ],
   "execution_count": 6,
   "id": "VF8OIQ846QEu"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aeV5M3el6QEv"
   },
   "source": "## AŞAMA 7: Hardware Router Motoru\n\nBest-of-Breed seçim motoru.  \nKural tabanlı primary seçim + numeric skorlama + parallel aday desteği.",
   "id": "aeV5M3el6QEv"
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ERTq-Kv46QEw",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1789573856789,
     "user_tz": -180,
     "elapsed": 76,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "787d5994-e517-4d76-c047-7cb984f3aaad"
   },
   "source": "class HardwareRouter:\n    def __init__(self, monitoring: MonitoringService):\n        self.monitoring = monitoring\n        self.weights = {\n            \"size\": 0.27, \"constraint\": 0.27, \"latency\": 0.18,\n            \"cost\": 0.10, \"status\": 0.10, \"fit\": 0.08\n        }\n        self.status_cache = {}\n        self.last_update = 0\n\n    def select(self, job: OptimizationJob, allow_parallel: bool = True) -> List[Recommendation]:\n        start = time.time()\n        metrics.inc(\"select_calls\")\n        self._refresh()\n\n        primary, reason = self._rules(job)\n        scores = {b: self._score(b, job) for b in Backend}\n\n        recs = [Recommendation(primary, scores[primary], reason, 1)]\n\n        if allow_parallel:\n            for b, s in sorted(scores.items(), key=lambda x: -x[1]):\n                if b != primary and s >= scores[primary] * 0.85:\n                    recs.append(Recommendation(b, s, \"Parallel candidate\", len(recs)+1, True))\n                    if len(recs) >= 3:\n                        break\n\n        metrics.observe(\"select_time\", time.time() - start)\n        return recs\n\n    def _refresh(self):\n        if time.time() - self.last_update > 20:\n            self.status_cache = self.monitoring.get_live_status()\n            self.last_update = time.time()\n\n    def _rules(self, job: OptimizationJob):\n        if job.num_variables > 4500 or (job.num_variables > 3000 and job.constraint_density > 0.35):\n            return Backend.DWAVE_HYBRID, \"Large scale → D-Wave Hybrid\"\n        if job.has_hard_capacity and job.has_spatial:\n            return Backend.NEUTRAL_ATOM, \"Hard capacity + spatial → Neutral Atom\"\n        if job.latency_sla_sec and job.latency_sla_sec < 180:\n            return self._fastest([Backend.DWAVE_HYBRID, Backend.GATE_BASED, Backend.WUKONG]), f\"Latency critical ({job.latency_sla_sec}s)\"\n        if job.has_continuous or job.needs_sampling:\n            return Backend.PHOTONIC, \"Continuous/sampling → Photonic\"\n        if 200 <= job.num_variables <= 2500:\n            return Backend.GATE_BASED, \"Medium → Gate-based (IBM/Wukong)\"\n        if job.num_variables < 150 and job.is_local_subproblem:\n            return Backend.DIAMOND, \"Tiny → Diamond\"\n        return Backend.DWAVE_HYBRID, \"Default safe path\"\n\n    def _score(self, b: Backend, job: OptimizationJob) -> float:\n        w = self.weights\n        return (w[\"size\"] * self._size(b, job.num_variables) +\n                w[\"constraint\"] * self._const(b, job) +\n                w[\"latency\"] * self._lat(b, job.latency_sla_sec) +\n                w[\"cost\"] * 75 +\n                w[\"status\"] * self._stat(b) +\n                w[\"fit\"] * 80)\n\n    def _size(self, b, n):\n        if b == Backend.DWAVE_HYBRID: return 95 if n > 3000 else 70\n        if b in (Backend.GATE_BASED, Backend.WUKONG): return 88 if 300 < n < 3000 else 55\n        if b == Backend.NEUTRAL_ATOM: return 85 if 400 < n < 4000 else 60\n        return 50\n\n    def _const(self, b, job):\n        if job.has_hard_capacity and job.has_spatial:\n            return 96 if b == Backend.NEUTRAL_ATOM else 75\n        return 80\n\n    def _lat(self, b, sla):\n        if not sla: return 80\n        s = self.status_cache.get(b)\n        if not s or not s.is_available: return 10\n        return 92 if s.queue_time_sec + 30 < sla else 45\n\n    def _stat(self, b):\n        s = self.status_cache.get(b)\n        if not s or not s.is_available: return 5\n        return s.calibration_score * 0.7 + max(0, 100 - s.queue_time_sec / 3) * 0.3\n\n    def _fastest(self, cands):\n        best, t = cands[0], 999\n        for b in cands:\n            s = self.status_cache.get(b)\n            if s and s.is_available and s.queue_time_sec < t:\n                best, t = b, s.queue_time_sec\n        return best\n\nprint(\"✅ HardwareRouter hazır.\")\nprint(\"✅ AŞAMA 7 tamamlandı.\")",
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "✅ HardwareRouter hazır.\n",
      "✅ AŞAMA 7 tamamlandı.\n"
     ]
    }
   ],
   "execution_count": 7,
   "id": "ERTq-Kv46QEw"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gwvv2Im26QEx"
   },
   "source": "## AŞAMA 8: Karar Ağacı & Skorlama Formülü\n\n### Kural Öncelik Sırası\n1. Problem boyutu (>4500 veya yüksek density)\n2. Hard capacity + spatial kısıtlar\n3. Latency SLA < 180 s\n4. Continuous / sampling ihtiyacı\n5. Orta boyut + esnek kısıtlar\n6. Tiny subproblem\n7. Default → D-Wave Hybrid\n\n### Numeric Skor Formülü\n\n```\nScore(b) = 0.27·S_size + 0.27·S_constraint + 0.18·S_latency\n         + 0.10·S_cost + 0.10·S_status + 0.08·S_fit\n```\n\nAğırlıklar historical replay veya Bayesian optimization ile tune edilebilir.",
   "id": "gwvv2Im26QEx"
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fyjjc-PC6QEx",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1789573856827,
     "user_tz": -180,
     "elapsed": 46,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "edb84e87-6fb0-418b-9602-c816202ff7c1"
   },
   "source": "print(\"✅ Karar ağacı ve skorlama formülü HardwareRouter içinde aktif.\")\nprint(\"✅ AŞAMA 8 tamamlandı.\")",
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "✅ Karar ağacı ve skorlama formülü HardwareRouter içinde aktif.\n",
      "✅ AŞAMA 8 tamamlandı.\n"
     ]
    }
   ],
   "execution_count": 8,
   "id": "fyjjc-PC6QEx"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Pu0CdAi66QEy"
   },
   "source": "## AŞAMA 9: Demo Job 1 – 187 ATM Cluster\n\n**Senaryo:** Orta ölçek, spatial + hard capacity, latency kritik değil.  \n**Beklenen birincil seçim:** Neutral Atom",
   "id": "Pu0CdAi66QEy"
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FHxE30Fe6QEy",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1789573861463,
     "user_tz": -180,
     "elapsed": 4646,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "1c769b0b-a34e-4340-f9f0-ffa30f0c0ab0"
   },
   "source": "mon = MonitoringService()\nrouter = HardwareRouter(mon)\n\nprint(\"=\"*70)\nprint(\"DEMO JOB 1: 187 ATM Cluster (spatial + capacity)\")\nprint(\"=\"*70)\n\njob1 = OptimizationJob(\n    job_id=\"ATM-CLUSTER-187\",\n    num_variables=3850,\n    constraint_density=0.33,\n    has_hard_capacity=True,\n    has_spatial=True,\n    latency_sla_sec=240,\n    has_continuous=False,\n    needs_sampling=False,\n    is_local_subproblem=False\n)\n\nrecs1 = router.select(job1)\n\nprint(\"\\nÖnerilen Backend’ler:\")\nfor r in recs1:\n    src = router.status_cache.get(r.backend).source if r.backend in router.status_cache else \"?\"\n    print(f\"  {r.priority}. {r.backend.value:18s} | score={r.score:5.1f} | {r.reason} | [{src}]\")\n\nprint(\"\\n✅ AŞAMA 9 tamamlandı.\")",
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "WARNING:QuantumATM:IBM client hatası: 'channel' can only be 'ibm_cloud', or 'ibm_quantum_platform\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "======================================================================\n",
      "DEMO JOB 1: 187 ATM Cluster (spatial + capacity)\n",
      "======================================================================\n",
      "\n",
      "Önerilen Backend’ler:\n",
      "  1. Neutral_Atom       | score= 88.2 | Hard capacity + spatial → Neutral Atom | [simulated]\n",
      "  2. D-Wave_Hybrid      | score= 85.7 | Parallel candidate | [simulated]\n",
      "\n",
      "✅ AŞAMA 9 tamamlandı.\n"
     ]
    }
   ],
   "execution_count": 9,
   "id": "FHxE30Fe6QEy"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U4kOFl596QEz"
   },
   "source": "## AŞAMA 10: Demo Job 2 – UAE Network Emergency\n\n**Senaryo:** 11.800 değişken, yüksek density, SLA = 90 saniye.  \n**Beklenen birincil seçim:** D-Wave Hybrid",
   "id": "U4kOFl596QEz"
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CWT1fX5P6QEz",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1789573861510,
     "user_tz": -180,
     "elapsed": 46,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "2ecf8a57-17ed-49d8-ad82-160f0696db95"
   },
   "source": "print(\"=\"*70)\nprint(\"DEMO JOB 2: UAE Network Emergency (büyük + latency kritik)\")\nprint(\"=\"*70)\n\njob2 = OptimizationJob(\n    job_id=\"UAE-EMERGENCY\",\n    num_variables=11800,\n    constraint_density=0.41,\n    has_hard_capacity=True,\n    has_spatial=True,\n    latency_sla_sec=90,\n    has_continuous=False,\n    needs_sampling=False,\n    is_local_subproblem=False\n)\n\nrecs2 = router.select(job2)\n\nprint(\"\\nÖnerilen Backend’ler:\")\nfor r in recs2:\n    src = router.status_cache.get(r.backend).source if r.backend in router.status_cache else \"?\"\n    print(f\"  {r.priority}. {r.backend.value:18s} | score={r.score:5.1f} | {r.reason} | [{src}]\")\n\nprint(\"\\n✅ AŞAMA 10 tamamlandı.\")",
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "======================================================================\n",
      "DEMO JOB 2: UAE Network Emergency (büyük + latency kritik)\n",
      "======================================================================\n",
      "\n",
      "Önerilen Backend’ler:\n",
      "  1. D-Wave_Hybrid      | score= 85.7 | Large scale → D-Wave Hybrid | [simulated]\n",
      "  2. Neutral_Atom       | score= 81.4 | Parallel candidate | [simulated]\n",
      "  3. Wukong             | score= 74.1 | Parallel candidate | [simulated]\n",
      "\n",
      "✅ AŞAMA 10 tamamlandı.\n"
     ]
    }
   ],
   "execution_count": 10,
   "id": "CWT1fX5P6QEz"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uE4wnkbq6QE0"
   },
   "source": "## AŞAMA 11: Canlı Status + Metrics",
   "id": "uE4wnkbq6QE0"
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AfQwa36x6QE0",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1789573861524,
     "user_tz": -180,
     "elapsed": 11,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "1943ea19-814b-4c9d-a1ab-cb7e7fec2855"
   },
   "source": "print(\"Canlı Hardware Status:\")\nprint(\"-\"*70)\nfor b, s in router.status_cache.items():\n    print(f\"  {b.value:18s} | queue={s.queue_time_sec:5.1f}s | cal={s.calibration_score:5.1f} | {s.source}\")\n\nmetrics.report()\nprint(\"✅ AŞAMA 11 tamamlandı.\")",
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Canlı Hardware Status:\n",
      "----------------------------------------------------------------------\n",
      "  D-Wave_Hybrid      | queue= 27.2s | cal= 94.4 | simulated\n",
      "  Neutral_Atom       | queue= 37.6s | cal= 88.8 | simulated\n",
      "  Gate_based         | queue= 80.3s | cal= 85.1 | simulated\n",
      "  Photonic_Xanadu    | queue= 50.9s | cal= 84.0 | simulated\n",
      "  Diamond_SaxonQ     | queue= 19.9s | cal= 77.8 | simulated\n",
      "  Wukong             | queue= 42.7s | cal= 84.6 | simulated\n",
      "\n",
      "=======================================================\n",
      "METRICS RAPORU\n",
      "=======================================================\n",
      "Sayaçlar:\n",
      "  • select_calls: 2\n",
      "\n",
      "Ortalama Süreler:\n",
      "  • select_time: 0.1 ms  (n=2)\n",
      "=======================================================\n",
      "\n",
      "✅ AŞAMA 11 tamamlandı.\n"
     ]
    }
   ],
   "execution_count": 11,
   "id": "AfQwa36x6QE0"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "27liL0O-6QE0"
   },
   "source": "## AŞAMA 12: Formüller, Tablolar ve Sonuç\n\n### QUBO Amaç Fonksiyonu (D-Wave)\n```\nH = A·TransportCost + B·HoldingCost + C·ShortagePenalty + D·ConstraintPenalties\n```\n\n### QAOA Durumu (Gate-based)\n```\n|ψ(β,γ)⟩ = e^{-iβ_p H_M} e^{-iγ_p H_C} … |+⟩⊗n\n```\n\n### Hardware Mapping Özeti\n\n| Paradigma       | Donanım                        | En Uygun Olduğu Durum              |\n|-----------------|--------------------------------|------------------------------------|\n| Annealing       | D-Wave Hybrid                  | Büyük QUBO, yüksek density         |\n| Neutral Atom    | QuEra / Atom Computing         | Hard capacity + spatial            |\n| Gate-based      | IBM / Rigetti / Wukong         | Orta boyut + esnek kısıtlar        |\n| Photonic        | Xanadu                         | Continuous / sampling              |\n| Diamond NV      | SaxonQ                         | Tiny specialized subproblem        |\n\n### Sonraki Adımlar\n- Gerçek QUBO formulasyonu + D-Wave submit hücresi\n- QAOA iskeleti (IBM)\n- Forecast modülü entegrasyonu\n- Historical job’larla ağırlık tuning\n- Streamlit / Gradio arayüzü",
   "id": "27liL0O-6QE0"
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "id": "J1zRv4rF6QE1",
    "executionInfo": {
     "status": "error",
     "timestamp": 1789574464847,
     "user_tz": -180,
     "elapsed": 25,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "cd2abd49-047b-4cd1-8804-665172c128c2"
   },
   "source": "print(\"=\"*70)\nprint(\"NOTEBOOK BAŞARIYLA TAMAMLANDI\")\nprint(\"=\"*70)\nprint(f\"Proje yolu : {PROJECT_PATH}\")\nprint(f\"Çıktı yolu : {OUTPUT_PATH}\")\nprint(\"\\nBu notebook, Best-of-Breed Quantum ATM Cash Intelligence\")\nprint(\"çözümünün uçtan uca çalıştırılabilir halidir.\")\nprint(\"Tüm mimari, formüller, router mantığı ve demo’lar burada.\")\nprint(\"=\"*70)\")",
   "outputs": [
    {
     "output_type": "error",
     "ename": "SyntaxError",
     "evalue": "unterminated string literal (detected at line 9) (145173418.py, line 9)",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"/tmp/ipykernel_2509/145173418.py\"\u001b[0;36m, line \u001b[0;32m9\u001b[0m\n\u001b[0;31m    print(\"=\"*70)\")\u001b[0m\n\u001b[0m                 ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m unterminated string literal (detected at line 9)\n"
     ]
    }
   ],
   "execution_count": 13,
   "id": "J1zRv4rF6QE1"
  }
 ]
}