{
 "nbformat": 4,
 "nbformat_minor": 5,
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "name": "python"
  },
  "colab": {
   "provenance": []
  }
 },
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Bhbr8AUChTkx"
   },
   "source": "# QML 20-Aşamalı İnteraktif Engine\n**Joseph Volkan Özcan QML Şablonu + Gemini Destekli**\n\nBu notebook 20 aşamalı Quantum Machine Learning pipeline’ını adım adım çalıştırır.\n\n- Problemi doğal dilde yaz\n- Gemini analiz etsin ve yönlendirsin\n- Encoding → Cihaz → Circuit → Simülasyon → VQC iskeleti → Rapor\n\nColab’ın sağdaki Gemini paneli ile de uyumludur (kod önerilerini Accept & Run yapabilirsin).\n\n---",
   "id": "Bhbr8AUChTkx"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ol1uMFvGhTk3"
   },
   "source": "## 0. Kurulum",
   "id": "Ol1uMFvGhTk3"
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9F9xu7sNhTk4",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788795802672,
     "user_tz": -180,
     "elapsed": 42977,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "7c430221-5420-4100-a81f-590330d9f7b5"
   },
   "source": "!pip install -U -q qiskit qiskit-aer qiskit-machine-learning pillow opencv-python-headless google-genai matplotlib\n\nimport sys, numpy as np, warnings\nwarnings.filterwarnings(\"ignore\")\nprint(\"Python:\", sys.version.split()[0])\n\nimport qiskit\nfrom qiskit import QuantumCircuit, transpile\nfrom qiskit.circuit.library import ZFeatureMap, ZZFeatureMap, RealAmplitudes, EfficientSU2\nfrom qiskit.quantum_info import Statevector, SparsePauliOp\nfrom qiskit_aer import AerSimulator\nfrom qiskit_machine_learning.neural_networks import SamplerQNN, EstimatorQNN\nfrom qiskit_machine_learning.algorithms import VQC\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.decomposition import PCA\nfrom sklearn.model_selection import train_test_split\nfrom IPython.display import display, Markdown, clear_output\nimport matplotlib.pyplot as plt\n\nprint(f\"Qiskit              : {qiskit.__version__}\")\nprint(\"Kurulum tamam!\")",
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.5/56.5 kB\u001b[0m \u001b[31m1.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m80.3/80.3 kB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.8/9.8 MB\u001b[0m \u001b[31m30.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.4/12.4 MB\u001b[0m \u001b[31m35.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m282.3/282.3 kB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.9/6.9 MB\u001b[0m \u001b[31m24.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m23.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.0/10.0 MB\u001b[0m \u001b[31m20.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m260.0/260.0 kB\u001b[0m \u001b[31m13.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.4/2.4 MB\u001b[0m \u001b[31m15.1 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[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "google-colab 1.0.0 requires google-auth==2.49.0, but you have google-auth 2.57.1 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0mPython: 3.13.15\n",
      "Qiskit              : 2.5.2\n",
      "Kurulum tamam!\n"
     ]
    }
   ],
   "execution_count": 1,
   "id": "9F9xu7sNhTk4"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GtFe1hTMhTk7"
   },
   "source": "## 1. Gemini Bağlantısı\n\nColab **🔑 Secrets** → `GEMINI_API_KEY` ekle.",
   "id": "GtFe1hTMhTk7"
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9GU4Ks2dhTk8",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788795827942,
     "user_tz": -180,
     "elapsed": 2370,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "6a623ecc-1e67-4d7d-d94b-4d9edbd57160"
   },
   "source": "from google.colab import userdata\nfrom google import genai\n\ntry:\n    client = genai.Client(api_key=userdata.get(\"GEMINI_API_KEY\"))\n    print(\"✅ Gemini bağlantısı başarılı\")\nexcept Exception as e:\n    print(\"❌ GEMINI_API_KEY eksik:\", e)\n    client = None\n\ndef ask_gemini(prompt: str, model: str = \"gemini-3.8-flash\") -> str:\n    if client is None:\n        return \"Gemini bağlantısı yok.\"\n    models = [model, \"gemini-3.6-flash\", \"gemini-3.7-flash\", \"gemini-3.5-flash\"]\n    import time\n    for m in models:\n        try:\n            print(f\"Gemini ({m}) düşünüyor...\")\n            r = client.models.generate_content(model=m, contents=prompt)\n            return r.text\n        except Exception as e:\n            print(f\"  {m} → {str(e)[:60]}...\")\n            time.sleep(1)\n    return \"Tüm modeller başarısız oldu. Lütfen daha sonra tekrar dene.\"",
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "✅ Gemini bağlantısı başarılı\n"
     ]
    }
   ],
   "execution_count": 2,
   "id": "9GU4Ks2dhTk8"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_c76xpwNhTk9"
   },
   "source": "## 2. Pipeline Motoru + Cihaz Kataloğu",
   "id": "_c76xpwNhTk9"
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "w6kd6_iKhTk-",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788795833071,
     "user_tz": -180,
     "elapsed": 249,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "a6a63dd8-d948-42e3-d4c4-7b001fa5031c"
   },
   "source": "DEVICE_CATALOG = {\n    \"simulator\":        {\"name\": \"Aer Simulator\",       \"max_qubits\": 32,   \"type\": \"simulator\",      \"paradigm\": \"gate\"},\n    \"ibm_brisbane\":     {\"name\": \"IBM Brisbane\",        \"max_qubits\": 127,  \"type\": \"superconducting\", \"paradigm\": \"gate\"},\n    \"wukong\":           {\"name\": \"Origin Wukong-180\",   \"max_qubits\": 180,  \"type\": \"superconducting\", \"paradigm\": \"gate\"},\n    \"ionq_aria\":        {\"name\": \"IonQ Aria\",           \"max_qubits\": 25,   \"type\": \"trapped-ion\",     \"paradigm\": \"gate\"},\n    \"quera_aquila\":     {\"name\": \"QuEra Aquila\",        \"max_qubits\": 256,  \"type\": \"cold-atom\",       \"paradigm\": \"analog\"},\n    \"quera_gemini\":     {\"name\": \"QuEra Gemini\",        \"max_qubits\": 260,  \"type\": \"cold-atom\",       \"paradigm\": \"gate\"},\n    \"xanadu_aurora\":    {\"name\": \"Xanadu Aurora\",       \"max_qubits\": 35,   \"type\": \"photonic\",        \"paradigm\": \"continuous-variable\"},\n    \"quandela_belenos\": {\"name\": \"Quandela Belenos\",    \"max_qubits\": 12,   \"type\": \"photonic\",        \"paradigm\": \"gate\"},\n    \"dwave_advantage2\": {\"name\": \"D-Wave Advantage2\",   \"max_qubits\": 4400, \"type\": \"adiabatic\",       \"paradigm\": \"annealing\"},\n}\n\nclass QuantumAppPipeline:\n    def __init__(self, seed=42):\n        np.random.seed(seed)\n        self.data_type = self.processed_data = self.device_key = self.device_info = None\n        self.max_qubits = 10\n        self.recommended_qubits = 0\n        self.encoding_type = self.circuit = self.pca = None\n        self.X_train = self.X_test = self.y_train = self.y_test = None\n\n    def select_data_type(self, dt):\n        dt = dt.lower().strip()\n        assert dt in {\"csv\",\"image\",\"video\",\"geospatial\"}, \"csv|image|video|geospatial\"\n        self.data_type = dt\n        print(f\"[1] Veri tipi → {dt}\")\n        return self\n\n    def load_data(self, source, **kw):\n        from pathlib import Path\n        if self.data_type == \"csv\":\n            import pandas as pd\n            df = pd.read_csv(source) if isinstance(source,(str,Path)) else pd.DataFrame(source)\n            self.processed_data = df.select_dtypes(include=[np.number]).values.astype(np.float64)\n        elif self.data_type == \"image\":\n            from PIL import Image\n            img = Image.open(source) if isinstance(source,(str,Path)) else source\n            img = img.convert(\"L\").resize(kw.get(\"resize\",(32,32)))\n            self.processed_data = (np.array(img,np.float64)/255.).flatten()[None,:]\n        elif self.data_type == \"video\":\n            import cv2\n            cap = cv2.VideoCapture(str(source))\n            frames=[]\n            for _ in range(kw.get(\"max_frames\",6)):\n                ret,f = cap.read()\n                if not ret: break\n                f = cv2.cvtColor(f,cv2.COLOR_BGR2GRAY)\n                f = cv2.resize(f, kw.get(\"resize\",(24,24)))\n                frames.append(f.astype(np.float64)/255.)\n            cap.release()\n            self.processed_data = np.array(frames).flatten()[None,:]\n        else:\n            import pandas as pd\n            self.processed_data = pd.read_csv(source).select_dtypes(include=[np.number]).values.astype(np.float64)\n        print(f\"[2] Veri yüklendi → shape {self.processed_data.shape}\")\n        return self\n\n    def select_device(self, key):\n        assert key in DEVICE_CATALOG, list(DEVICE_CATALOG)\n        self.device_key = key\n        self.device_info = DEVICE_CATALOG[key]\n        self.max_qubits = self.device_info[\"max_qubits\"]\n        print(f\"[3] Cihaz → {self.device_info['name']} ({self.max_qubits}q, {self.device_info['paradigm']})\")\n        if self.device_info[\"paradigm\"] != \"gate\":\n            print(\"   ⚠ Gate-model değil → circuit referans amaçlı\")\n        return self\n\n    def analyze_and_recommend(self, force_qubits=None):\n        n = self.processed_data.shape[1]\n        amp = int(np.ceil(np.log2(max(n,2))))\n        mq = self.max_qubits\n        if force_qubits:\n            rq = min(force_qubits, mq)\n            enc = \"angle\" if rq>=n else (\"amplitude\" if rq>=amp else \"dense\")\n        else:\n            if n <= mq: enc, rq = \"angle\", n\n            elif amp <= mq: enc, rq = \"amplitude\", amp\n            else: enc, rq = \"dense\", mq\n        self.recommended_qubits, self.encoding_type = rq, enc\n        return {\"n_features\":n, \"device\":self.device_info[\"name\"], \"paradigm\":self.device_info[\"paradigm\"],\n                \"recommended_qubits\":rq, \"recommended_encoding\":enc, \"amplitude_qubits\":amp}\n\n    def encode(self, encoding=None, n_qubits=None, reps=2):\n        encoding = encoding or self.encoding_type\n        n_qubits = min(n_qubits or self.recommended_qubits, self.max_qubits)\n        data = self.processed_data.copy()\n        if data.shape[1] > n_qubits and encoding != \"amplitude\":\n            self.pca = PCA(n_components=n_qubits, random_state=42)\n            data = self.pca.fit_transform(data)\n            print(f\"   PCA → {data.shape[1]} boyut\")\n        if encoding in (\"angle\",\"dense\",\"basis\"):\n            if data.shape[0]==1:\n                data = data - data.min()\n                rng = data.max()-data.min()\n                data = data/rng*np.pi if rng>1e-12 else np.full_like(data, np.pi/2)\n            else:\n                data = MinMaxScaler((0,np.pi)).fit_transform(data)\n        if encoding==\"angle\":\n            n=min(data.shape[1],n_qubits)\n            qc = ZFeatureMap(n,reps=reps).assign_parameters(data[0,:n])\n        elif encoding==\"amplitude\":\n            sample = np.maximum(data[0].astype(np.float64),0.)\n            if np.allclose(sample, sample.flat[0]):\n                sample = np.maximum(self.processed_data[0].astype(np.float64),0.)\n            dim=2**n_qubits\n            padded=np.zeros(dim); padded[:min(len(sample),dim)]=sample[:dim]\n            norm=np.linalg.norm(padded)\n            padded = padded/norm if norm>1e-12 else np.ones(dim)/np.sqrt(dim)\n            qc=QuantumCircuit(n_qubits,name=\"Amplitude\"); qc.initialize(padded,range(n_qubits))\n        elif encoding==\"dense\":\n            n=min(data.shape[1],n_qubits)\n            qc=ZZFeatureMap(n,reps=reps,entanglement=\"full\").assign_parameters(data[0,:n])\n        else:\n            qc=QuantumCircuit(n_qubits)\n            for i,v in enumerate(data[0,:n_qubits]):\n                if v>0.5: qc.x(i)\n        self.circuit, self.encoding_type = qc, encoding\n        print(f\"[4] Circuit → {qc.num_qubits}q, depth={qc.depth()}, encoding={encoding}\")\n        return qc\n\n    def summary(self):\n        print(\"=\"*55)\n        print(f\"Veri tipi   : {self.data_type}\")\n        print(f\"Shape       : {None if self.processed_data is None else self.processed_data.shape}\")\n        print(f\"Cihaz       : {self.device_info['name'] if self.device_info else '-'}\")\n        print(f\"Paradigm    : {self.device_info['paradigm'] if self.device_info else '-'}\")\n        print(f\"Encoding    : {self.encoding_type}\")\n        print(f\"Qubit       : {self.circuit.num_qubits if self.circuit else '-'}\")\n        print(\"=\"*55)\n\nprint(\"Pipeline motoru hazır.\")",
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Pipeline motoru hazır.\n"
     ]
    }
   ],
   "execution_count": 3,
   "id": "w6kd6_iKhTk-"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Wwi4Sw5ShTk_"
   },
   "source": "---\n# AŞAMA 1 – Problem Tanımı\nProblemini doğal dilde yaz.",
   "id": "Wwi4Sw5ShTk_"
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "l3ls2IGyhTlA",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788795880454,
     "user_tz": -180,
     "elapsed": 22,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "6f767057-2559-42c9-e774-431f932f3319"
   },
   "source": "#@title 📝 Problem tanımı\nproblem_description = \"M\\u00FC\\u015Fteri churn tahmin etmek istiyorum. Elimde yakla\\u015F\\u0131k 40.000 sat\\u0131rl\\u0131k CSV var, 18 say\\u0131sal \\u00F6zellik ve 1 hedef s\\u00FCtun bulunuyor. Quantum ML ile denemek istiyorum.\" #@param {type:\"string\"}\nprint(\"Problem:\\n\", problem_description)",
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Problem:\n",
      " Müşteri churn tahmin etmek istiyorum. Elimde yaklaşık 40.000 satırlık CSV var, 18 sayısal özellik ve 1 hedef sütun bulunuyor. Quantum ML ile denemek istiyorum.\n"
     ]
    }
   ],
   "execution_count": 4,
   "id": "l3ls2IGyhTlA"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KJMRJE4shTlA"
   },
   "source": "# AŞAMA 2 – Gemini Analizi",
   "id": "KJMRJE4shTlA"
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "GpqP9ud4hTlB",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788795953827,
     "user_tz": -180,
     "elapsed": 65645,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "b1b1ec5b-adad-4d9a-85de-8556581879b4"
   },
   "source": "prompt = f\"\"\"Sen QML uzmanısın. Kullanıcının problemini analiz et ve Türkçe cevap ver:\n1. Problem özeti\n2. Önerilen veri tipi (csv/image/video/geospatial)\n3. Önerilen encoding (angle/amplitude/dense/basis)\n4. Uygun cihazlar (gate-model öncelikli: simulator, wukong, ibm_brisbane, ionq_aria, quera_gemini)\n5. Qubit tahmini ve PCA ihtiyacı\n6. VQC için kısa tavsiye\n7. Kritik uyarılar\n\nProblem: {problem_description}\"\"\"\n\nanalysis = ask_gemini(prompt)\ndisplay(Markdown(analysis))",
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "WARNING:google_genai.models:Direct use of automatic function calling (AFC) in Models.generate_content is not recommended. Instead, we recommend to use AFC in Chat.send_message. Similarly, direct use of AFC in Models.generate_content_stream is not recommended. Instead, we recommend to use AFC in Chat.send_message_stream.\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Gemini (gemini-3.8-flash) düşünüyor...\n",
      "  gemini-3.8-flash → 503 UNAVAILABLE. {'error': {'code': 503, 'message': 'This mo...\n",
      "Gemini (gemini-3.6-flash) düşünüyor...\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ],
      "text/markdown": "Bir Quantum Machine Learning (QML) uzmanı olarak müşteri churn (terk) tahmini probleminizi analiz ettim. Günümüz NISQ (Noisy Intermediate-Scale Quantum) teknolojisi koşullarını göz önüne alarak hazırladığım teknik yol haritası aşağıdadır:\n\n---\n\n### 1. Problem Özeti\n* **Problem Türü:** Tablosal (Tabular) veri üzerinde İkili Sınıflandırma (Binary Classification: Churn / Non-Churn).\n* **Veri Boyutu:** 40.000 satır (örnek), 18 özellik (feature) + 1 hedef değişken.\n* **Hedef:** Veriyi kuantum durumlarına haritalandırarak Variational Quantum Classifier (VQC) veya Quantum Support Vector Machine (QSVM) ile sınıflandırma performansını ve kuantum avantajı olasılığını test etmek.\n\n### 2. Önerilen Veri Tipi\n* **`csv` (Tablosal Veri)**\n  * Veriniz tamamen sayısal özelliklerden oluşan klasik bir tablosal veridir.\n\n### 3. Önerilen Encoding (Kodlama)\n* **Öneri:** **`angle` (Açı Kodlaması)** veya **`dense` (Yoğun Açı Kodlaması)**\n* **Nedeni:** \n  * *Amplitude Encoding* teorik olarak $N$ özelliği $\\log_2(N)$ qubit'e sığdırabilse de, NISQ cihazlarında **çok derin ve gürültüye hassas** devreler (state preparation) gerektirir.\n  * *Angle Encoding*, her bir özelliği bir qubit'in dönme açısına ($R_x, R_y, R_z$) atar. Devre derinliği düşüktür ve günümüz donanımlarında gürültüye karşı çok daha dayanıklıdır. *Dense Encoding* kullanırsanız, 1 qubit'e ($R_x$ ve $R_y$ kullanarak) 2 özellik sığdırabilirsiniz.\n\n### 4. Uygun Cihazlar\nGate-model öncelikli sıralama:\n1. **Simulator (Öncelikli ve Zorunlu Başlangıç):** `qiskit_aer` veya PennyLane'in `default.qubit` simülatörü. Tüm mimari, hiperparametre ve PCA boyutlandırması burada doğrulanmalıdır.\n2. **ionq_aria:** Yüksek kapı doğruluğu (fidelity) ve *all-to-all* (tümü-tümüne) qubit bağlantısallığı sayesinde VQC devrelerinde dolaşıklık (entanglement) oluştururken en az gürültüyü üretir.\n3. **ibm_brisbane:** 127-qubitlik kapı tabanlı (gate-model) sistem. Ölçeklenebilir VQC denemeleri ve gürültü bastırma (error mitigation) tekniklerini test etmek için uygundur.\n*(Not: QuEra Aquila/Gemini gibi nötral atom sistemleri daha çok analog/Hamiltonian simülasyonları içindir; bu tür klasik tablosal sınıflandırma problemleri için kapı tabanlı IonQ ve IBM sistemleri çok daha uygundur).*\n\n### 5. Qubit Tahmini ve PCA İhtiyacı\n* **PCA İhtiyacı:** **KESİNLİKLE EVET.**\n  * 18 özelliği doğrudan 18 qubit'e aktarmak, NISQ cihazlarında yüksek gürültüye, devasa parametre uzayına ve gradyan kaybolmasına (Barren Plateaus) yol açar.\n* **Öneri:** Klasik PCA (Principal Component Analysis) veya bir Autoencoder kullanarak 18 özelliği **4 ila 8 ana bileşene** düşürün (Bilgi koruma oranı %85-%90 civarında tutulmalıdır).\n* **Qubit Tahmini:** **4 - 8 Qubit** (Angle Encoding ile) veya **2 - 4 Qubit** (Dense Encoding ile).\n\n### 6. VQC için Kısa Tavsiye\n* **Ansatz Seçimi:** Donanım dostu olan `RealAmplitudes` veya `EfficientSU2` devre mimarilerini tercih edin.\n* **Devre Derinliği (Repetitions):** Katman sayısını ($L$) **2 veya 3** ile sınırlandırın.\n* **Optimizasyon:** Simülatörde `Adam` veya `COBYLA`; gerçek QPU üzerinde gürültüye dayanıklı olan `SPSA` (Simultaneous Perturbation Stochastic Approximation) kullanın.\n* **Eğitim Stratejisi:** 40.000 satırın tamamını kuantum devresine sokmak maliyet ve zaman açısından imkansıza yakındır. Eğitim için stratifiye (dengeli) **500 - 1000 satırlık** bir alt küme (mini-batch/subsample) ile modeli eğitin, testi daha geniş bir kümede yapın.\n\n### 7. Kritik Uyarılar ⚠️\n1. **Klasik Baseline Gerçeği:** XGBoost, LightGBM veya CatBoost gibi klasik algoritmalar 40.000 satırlık bu veride şu anki tüm QML modellerinden **çok daha hızlı ve büyük ihtimalle daha yüksek doğruluk (Accuracy/F1)** verecektir. Bu çalışmayı bir \"PoC (Proof of Concept) ve Ar-Ge denemesi\" olarak konumlandırın.\n2. **Maliyet ve Süre:** Gerçek QPU'larda (IBM/IonQ) 40.000 satır üzerinden adım adım gradyan hesaplamak (parameter shift rule) binlerce dolar tutabilir ve günlerce sürebilir. Alt-örnekleme (Subsampling) yapmadan gerçek cihaza gitmeyin.\n3. **Barren Plateaus (Kısır Platolar):** Özellik sayısı veya VQC katman sayısı arttıkça gradyanlar katlanarak sıfıra yaklaşır ve model öğrenemez hale gelir. Qubit ve katman sayısını minimumda tutun.\n4. **Sınıf Dengesizliği (Class Imbalance):** Churn verilerinde genelde Churn olanların oranı düşüktür (%10-%20). QML modeline veriyi beslemeden önce SMOTE veya Random Under-Sampling ile veri setini %50-%50 dengeli hale getirin; aksi takdirde VQC her şeye \"Churn Değil\" deme eğiliminde olur."
     },
     "metadata": {}
    }
   ],
   "execution_count": 5,
   "id": "GpqP9ud4hTlB"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oO8-XrezhTlB"
   },
   "source": "# AŞAMA 3-4 – Veri Tipi + Veri Yükleme",
   "id": "oO8-XrezhTlB"
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6ZnqaPEJhTlC",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788795978592,
     "user_tz": -180,
     "elapsed": 291,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "f65457ec-dc08-490e-9ef7-b6e706a2f2b6"
   },
   "source": "#@title Veri tipi ve örnek veri\nselected_data_type = \"csv\" #@param [\"csv\",\"image\",\"video\",\"geospatial\"]\nuse_sample = True #@param {type:\"boolean\"}\n\npipe = QuantumAppPipeline()\npipe.select_data_type(selected_data_type)\n\nif use_sample and selected_data_type==\"csv\":\n    import pandas as pd\n    from sklearn.datasets import make_classification\n    X, y = make_classification(n_samples=300, n_features=18, n_informative=12, n_classes=2, random_state=42)\n    df = pd.DataFrame(X, columns=[f\"f{i}\" for i in range(18)])\n    df[\"target\"] = y\n    df.to_csv(\"sample_churn.csv\", index=False)\n    pipe.load_data(\"sample_churn.csv\")\n    # supervised ayırım\n    pipe.X_train, pipe.X_test, pipe.y_train, pipe.y_test = train_test_split(\n        X, y, test_size=0.25, random_state=42)\n    print(\"Örnek supervised veri hazır (train/test split yapıldı)\")\nelif use_sample and selected_data_type==\"image\":\n    from PIL import Image\n    img = Image.fromarray((np.random.rand(64,64)*255).astype(np.uint8))\n    img.save(\"sample.png\")\n    pipe.load_data(\"sample.png\", resize=(16,16))\nelse:\n    print(\"Kendi dosyanı yükle → pipe.load_data('dosya.csv')\")",
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[1] Veri tipi → csv\n",
      "[2] Veri yüklendi → shape (300, 19)\n",
      "Örnek supervised veri hazır (train/test split yapıldı)\n"
     ]
    }
   ],
   "execution_count": 6,
   "id": "6ZnqaPEJhTlC"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EBa8Z6jShTlD"
   },
   "source": "# AŞAMA 5-6 – Ön İşleme + Özellik Azaltma (otomatik)",
   "id": "EBa8Z6jShTlD"
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "S_4TmnEQhTlD",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788795987782,
     "user_tz": -180,
     "elapsed": 22,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "72223bea-99e0-4535-d364-634e54fd215f"
   },
   "source": "print(\"Ön işleme pipeline içinde (MinMax + gerekirse PCA) otomatik yapılıyor.\")\nprint(f\"Ham özellik sayısı: {pipe.processed_data.shape[1]}\")",
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Ön işleme pipeline içinde (MinMax + gerekirse PCA) otomatik yapılıyor.\n",
      "Ham özellik sayısı: 19\n"
     ]
    }
   ],
   "execution_count": 7,
   "id": "S_4TmnEQhTlD"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mgXd48YrhTlE"
   },
   "source": "# AŞAMA 7-9 – Encoding + Cihaz + Qubit Bütçesi",
   "id": "mgXd48YrhTlE"
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KVnbnYIXhTlE",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788796008478,
     "user_tz": -180,
     "elapsed": 75,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "0a479bb2-f8bf-42eb-c64b-223582f35324"
   },
   "source": "#@title Cihaz ve encoding\nselected_device = \"simulator\" #@param [\"simulator\",\"wukong\",\"ibm_brisbane\",\"ionq_aria\",\"quera_gemini\",\"quera_aquila\",\"xanadu_aurora\",\"quandela_belenos\",\"dwave_advantage2\"]\nencoding_choice = \"auto\" #@param [\"auto\",\"angle\",\"amplitude\",\"dense\",\"basis\"]\nmanual_qubits = 0 #@param {type:\"integer\"}\n\npipe.select_device(selected_device)\nreport = pipe.analyze_and_recommend(force_qubits=manual_qubits if manual_qubits>0 else None)\nprint(\"\\nRapor:\")\nfor k,v in report.items():\n    print(f\"  {k:22}: {v}\")\n\nif encoding_choice==\"auto\":\n    qc = pipe.encode()\nelse:\n    qc = pipe.encode(encoding=encoding_choice, n_qubits=manual_qubits if manual_qubits>0 else None)\npipe.summary()",
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[3] Cihaz → Aer Simulator (32q, gate)\n",
      "\n",
      "Rapor:\n",
      "  n_features            : 19\n",
      "  device                : Aer Simulator\n",
      "  paradigm              : gate\n",
      "  recommended_qubits    : 19\n",
      "  recommended_encoding  : angle\n",
      "  amplitude_qubits      : 5\n",
      "[4] Circuit → 19q, depth=1, encoding=angle\n",
      "=======================================================\n",
      "Veri tipi   : csv\n",
      "Shape       : (300, 19)\n",
      "Cihaz       : Aer Simulator\n",
      "Paradigm    : gate\n",
      "Encoding    : angle\n",
      "Qubit       : 19\n",
      "=======================================================\n"
     ]
    }
   ],
   "execution_count": 8,
   "id": "KVnbnYIXhTlE"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dU0GQJrlhTlF"
   },
   "source": "# AŞAMA 10-11 – Circuit + Ölçüm Ekleme",
   "id": "dU0GQJrlhTlF"
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_lj6b7EZhTlG",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788796015005,
     "user_tz": -180,
     "elapsed": 109,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "068c1a02-1388-40ee-a216-c3b339401e4a"
   },
   "source": "qc_meas = pipe.circuit.copy()\nqc_meas.measure_all()\nprint(\"Ölçüm eklenmiş circuit:\")\nprint(qc_meas.draw(output=\"text\", fold=100))",
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Ölçüm eklenmiş circuit:\n",
      "         »\n",
      "    q_0: »\n",
      "         »\n",
      "    q_1: »\n",
      "         »\n",
      "    q_2: »\n",
      "         »\n",
      "    q_3: »\n",
      "         »\n",
      "    q_4: »\n",
      "         »\n",
      "    q_5: »\n",
      "         »\n",
      "    q_6: »\n",
      "         »\n",
      "    q_7: »\n",
      "         »\n",
      "    q_8: »\n",
      "         »\n",
      "    q_9: »\n",
      "         »\n",
      "   q_10: »\n",
      "         »\n",
      "   q_11: »\n",
      "         »\n",
      "   q_12: »\n",
      "         »\n",
      "   q_13: »\n",
      "         »\n",
      "   q_14: »\n",
      "         »\n",
      "   q_15: »\n",
      "         »\n",
      "   q_16: »\n",
      "         »\n",
      "   q_17: »\n",
      "         »\n",
      "   q_18: »\n",
      "         »\n",
      "meas: 19/»\n",
      "         »\n",
      "«         ┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐»\n",
      "«    q_0: ┤0                                                                                                                                                ├»\n",
      "«         │                                                                                                                                                 │»\n",
      "«    q_1: ┤1                                                                                                                                                ├»\n",
      "«         │                                                                                                                                                 │»\n",
      "«    q_2: ┤2                                                                                                                                                ├»\n",
      "«         │                                                                                                                                                 │»\n",
      "«    q_3: ┤3                                                                                                                                                ├»\n",
      "«         │                                                                                                                                                 │»\n",
      "«    q_4: ┤4                                                                                                                                                ├»\n",
      "«         │                                                                                                                                                 │»\n",
      "«    q_5: ┤5                                                                                                                                                ├»\n",
      "«         │                                                                                                                                                 │»\n",
      "«    q_6: ┤6                                                                                                                                                ├»\n",
      "«         │                                                                                                                                                 │»\n",
      "«    q_7: ┤7                                                                                                                                                ├»\n",
      "«         │                                                                                                                                                 │»\n",
      "«    q_8: ┤8                                                                                                                                                ├»\n",
      "«         │                                                                                                                                                 │»\n",
      "«    q_9: ┤9  ZFeatureMap(1.7789,1.4738,1.1562,2.1998,2.0089,1.8585,1.8361,2.302,1.7198,2.4309,2.4873,1.5512,0.66601,1.5535,2.1355,2.0419,0.25839,1.8959,π) ├»\n",
      "«         │                                                                                                                                                 │»\n",
      "«   q_10: ┤10                                                                                                                                               ├»\n",
      "«         │                                                                                                                                                 │»\n",
      "«   q_11: ┤11                                                                                                                                               ├»\n",
      "«         │                                                                                                                                                 │»\n",
      "«   q_12: ┤12                                                                                                                                               ├»\n",
      "«         │                                                                                                                                                 │»\n",
      "«   q_13: ┤13                                                                                                                                               ├»\n",
      "«         │                                                                                                                                                 │»\n",
      "«   q_14: ┤14                                                                                                                                               ├»\n",
      "«         │                                                                                                                                                 │»\n",
      "«   q_15: ┤15                                                                                                                                               ├»\n",
      "«         │                                                                                                                                                 │»\n",
      "«   q_16: ┤16                                                                                                                                               ├»\n",
      "«         │                                                                                                                                                 │»\n",
      "«   q_17: ┤17                                                                                                                                               ├»\n",
      "«         │                                                                                                                                                 │»\n",
      "«   q_18: ┤18                                                                                                                                               ├»\n",
      "«         └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘»\n",
      "«meas: 19/═══════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════»\n",
      "«                                                                                                                                                            »\n",
      "«          ░ ┌─┐                                                      \n",
      "«    q_0: ─░─┤M├──────────────────────────────────────────────────────\n",
      "«          ░ └╥┘┌─┐                                                   \n",
      "«    q_1: ─░──╫─┤M├───────────────────────────────────────────────────\n",
      "«          ░  ║ └╥┘┌─┐                                                \n",
      "«    q_2: ─░──╫──╫─┤M├────────────────────────────────────────────────\n",
      "«          ░  ║  ║ └╥┘┌─┐                                             \n",
      "«    q_3: ─░──╫──╫──╫─┤M├─────────────────────────────────────────────\n",
      "«          ░  ║  ║  ║ └╥┘┌─┐                                          \n",
      "«    q_4: ─░──╫──╫──╫──╫─┤M├──────────────────────────────────────────\n",
      "«          ░  ║  ║  ║  ║ └╥┘┌─┐                                       \n",
      "«    q_5: ─░──╫──╫──╫──╫──╫─┤M├───────────────────────────────────────\n",
      "«          ░  ║  ║  ║  ║  ║ └╥┘┌─┐                                    \n",
      "«    q_6: ─░──╫──╫──╫──╫──╫──╫─┤M├────────────────────────────────────\n",
      "«          ░  ║  ║  ║  ║  ║  ║ └╥┘┌─┐                                 \n",
      "«    q_7: ─░──╫──╫──╫──╫──╫──╫──╫─┤M├─────────────────────────────────\n",
      "«          ░  ║  ║  ║  ║  ║  ║  ║ └╥┘┌─┐                              \n",
      "«    q_8: ─░──╫──╫──╫──╫──╫──╫──╫──╫─┤M├──────────────────────────────\n",
      "«          ░  ║  ║  ║  ║  ║  ║  ║  ║ └╥┘┌─┐                           \n",
      "«    q_9: ─░──╫──╫──╫──╫──╫──╫──╫──╫──╫─┤M├───────────────────────────\n",
      "«          ░  ║  ║  ║  ║  ║  ║  ║  ║  ║ └╥┘┌─┐                        \n",
      "«   q_10: ─░──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫─┤M├────────────────────────\n",
      "«          ░  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║ └╥┘┌─┐                     \n",
      "«   q_11: ─░──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫─┤M├─────────────────────\n",
      "«          ░  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║ └╥┘┌─┐                  \n",
      "«   q_12: ─░──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫─┤M├──────────────────\n",
      "«          ░  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║ └╥┘┌─┐               \n",
      "«   q_13: ─░──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫─┤M├───────────────\n",
      "«          ░  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║ └╥┘┌─┐            \n",
      "«   q_14: ─░──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫─┤M├────────────\n",
      "«          ░  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║ └╥┘┌─┐         \n",
      "«   q_15: ─░──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫─┤M├─────────\n",
      "«          ░  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║ └╥┘┌─┐      \n",
      "«   q_16: ─░──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫─┤M├──────\n",
      "«          ░  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║ └╥┘┌─┐   \n",
      "«   q_17: ─░──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫─┤M├───\n",
      "«          ░  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║ └╥┘┌─┐\n",
      "«   q_18: ─░──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫──╫─┤M├\n",
      "«          ░  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║  ║ └╥┘\n",
      "«meas: 19/════╩══╩══╩══╩══╩══╩══╩══╩══╩══╩══╩══╩══╩══╩══╩══╩══╩══╩══╩═\n",
      "«             0  1  2  3  4  5  6  7  8  9  10 11 12 13 14 15 16 17 18\n"
     ]
    }
   ],
   "execution_count": 9,
   "id": "_lj6b7EZhTlG"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "k7nauKKehTlH"
   },
   "source": "# AŞAMA 12-13 – Transpile + Simülasyon",
   "id": "k7nauKKehTlH"
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 555
    },
    "id": "504OEAsAhTlH",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788796025626,
     "user_tz": -180,
     "elapsed": 5388,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "ebaddb22-de46-4911-d90d-dad53c6a7320"
   },
   "source": "simulator = AerSimulator()\ntranspiled = transpile(qc_meas, simulator, optimization_level=1)\njob = simulator.run(transpiled, shots=1024)\nresult = job.result()\ncounts = result.get_counts()\n\nprint(\"Simülasyon sonuçları (en sık 8 durum):\")\nfor bitstring, cnt in sorted(counts.items(), key=lambda x: -x[1])[:8]:\n    print(f\"  {bitstring}: {cnt}\")\n\nfrom qiskit.visualization import plot_histogram\nplot_histogram(counts, title=\"Ölçüm Histogramı\", figsize=(10,4))",
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Simülasyon sonuçları (en sık 8 durum):\n",
      "  0101111100111111111: 20\n",
      "  0101110100101111111: 20\n",
      "  0101110100111111111: 18\n",
      "  0101110101111111111: 16\n",
      "  0101111100101111111: 15\n",
      "  0101111101111111111: 15\n",
      "  0101110110111111111: 11\n",
      "  0101010100111111111: 11\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "execution_count": 10,
   "id": "504OEAsAhTlH"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7Cg7nD2UhTlI"
   },
   "source": "# AŞAMA 14 – Statevector Kontrolü",
   "id": "7Cg7nD2UhTlI"
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oAU1XlAxhTlI",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788796030078,
     "user_tz": -180,
     "elapsed": 764,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "098b8de8-af3b-4241-ae3d-d270ff3d5f37"
   },
   "source": "try:\n    sv = Statevector.from_instruction(pipe.circuit)\n    print(\"Norm:\", round(np.linalg.norm(sv.data),6))\n    print(\"İlk 6 amplitude:\", np.round(sv.data[:6],4))\nexcept Exception as e:\n    print(\"Statevector alınamadı:\", e)",
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Norm: 1.0\n",
      "İlk 6 amplitude: [0.-0.j 0.-0.j 0.+0.j 0.+0.j 0.+0.j 0.-0.j]\n"
     ]
    }
   ],
   "execution_count": 11,
   "id": "oAU1XlAxhTlI"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mr5Wdvh5hTlJ"
   },
   "source": "# AŞAMA 15 – Ansatz Seçimi",
   "id": "mr5Wdvh5hTlJ"
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zHrD0oFMhTlJ",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788796037774,
     "user_tz": -180,
     "elapsed": 48,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "7c2f3354-639d-40fe-98b5-1b6d1ff73376"
   },
   "source": "#@title Ansatz\nansatz_type = \"RealAmplitudes\" #@param [\"RealAmplitudes\",\"EfficientSU2\"]\nansatz_reps = 2 #@param {type:\"integer\"}\n\nn_q = pipe.circuit.num_qubits\nif ansatz_type == \"RealAmplitudes\":\n    ansatz = RealAmplitudes(n_q, reps=ansatz_reps)\nelse:\n    ansatz = EfficientSU2(n_q, reps=ansatz_reps)\n\nprint(f\"Ansatz: {ansatz_type}, qubits={n_q}, parameters={ansatz.num_parameters}\")\nprint(ansatz.draw(output=\"text\", fold=80))",
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Ansatz: RealAmplitudes, qubits=19, parameters=57\n",
      "      »\n",
      " q_0: »\n",
      "      »\n",
      " q_1: »\n",
      "      »\n",
      " q_2: »\n",
      "      »\n",
      " q_3: »\n",
      "      »\n",
      " q_4: »\n",
      "      »\n",
      " q_5: »\n",
      "      »\n",
      " q_6: »\n",
      "      »\n",
      " q_7: »\n",
      "      »\n",
      " q_8: »\n",
      "      »\n",
      " q_9: »\n",
      "      »\n",
      "q_10: »\n",
      "      »\n",
      "q_11: »\n",
      "      »\n",
      "q_12: »\n",
      "      »\n",
      "q_13: »\n",
      "      »\n",
      "q_14: »\n",
      "      »\n",
      "q_15: »\n",
      "      »\n",
      "q_16: »\n",
      "      »\n",
      "q_17: »\n",
      "      »\n",
      "q_18: »\n",
      "      »\n",
      "«      ┌───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐\n",
      "« q_0: ┤0                                                                                                                                                                                                                                                                                                                                                              ├\n",
      "«      │                                                                                                                                                                                                                                                                                                                                                               │\n",
      "« q_1: ┤1                                                                                                                                                                                                                                                                                                                                                              ├\n",
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      "« q_2: ┤2                                                                                                                                                                                                                                                                                                                                                              ├\n",
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      "« q_3: ┤3                                                                                                                                                                                                                                                                                                                                                              ├\n",
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      "« q_4: ┤4                                                                                                                                                                                                                                                                                                                                                              ├\n",
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      "« q_5: ┤5                                                                                                                                                                                                                                                                                                                                                              ├\n",
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      "« q_6: ┤6                                                                                                                                                                                                                                                                                                                                                              ├\n",
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      "« q_7: ┤7                                                                                                                                                                                                                                                                                                                                                              ├\n",
      "«      │                                                                                                                                                                                                                                                                                                                                                               │\n",
      "« q_8: ┤8                                                                                                                                                                                                                                                                                                                                                              ├\n",
      "«      │                                                                                                                                                                                                                                                                                                                                                               │\n",
      "« q_9: ┤9  RealAmplitudes(θ[0],θ[1],θ[2],θ[3],θ[4],θ[5],θ[6],θ[7],θ[8],θ[9],θ[10],θ[11],θ[12],θ[13],θ[14],θ[15],θ[16],θ[17],θ[18],θ[19],θ[20],θ[21],θ[22],θ[23],θ[24],θ[25],θ[26],θ[27],θ[28],θ[29],θ[30],θ[31],θ[32],θ[33],θ[34],θ[35],θ[36],θ[37],θ[38],θ[39],θ[40],θ[41],θ[42],θ[43],θ[44],θ[45],θ[46],θ[47],θ[48],θ[49],θ[50],θ[51],θ[52],θ[53],θ[54],θ[55],θ[56]) ├\n",
      "«      │                                                                                                                                                                                                                                                                                                                                                               │\n",
      "«q_10: ┤10                                                                                                                                                                                                                                                                                                                                                             ├\n",
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      "«q_11: ┤11                                                                                                                                                                                                                                                                                                                                                             ├\n",
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      "«q_12: ┤12                                                                                                                                                                                                                                                                                                                                                             ├\n",
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      "«q_13: ┤13                                                                                                                                                                                                                                                                                                                                                             ├\n",
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      "«q_14: ┤14                                                                                                                                                                                                                                                                                                                                                             ├\n",
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      "«q_15: ┤15                                                                                                                                                                                                                                                                                                                                                             ├\n",
      "«      │                                                                                                                                                                                                                                                                                                                                                               │\n",
      "«q_16: ┤16                                                                                                                                                                                                                                                                                                                                                             ├\n",
      "«      │                                                                                                                                                                                                                                                                                                                                                               │\n",
      "«q_17: ┤17                                                                                                                                                                                                                                                                                                                                                             ├\n",
      "«      │                                                                                                                                                                                                                                                                                                                                                               │\n",
      "«q_18: ┤18                                                                                                                                                                                                                                                                                                                                                             ├\n",
      "«      └───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘\n"
     ]
    }
   ],
   "execution_count": 12,
   "id": "zHrD0oFMhTlJ"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eOsz9ubPhTlK"
   },
   "source": "# AŞAMA 16 – Cost Function / Observable",
   "id": "eOsz9ubPhTlK"
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "rR_KS17ihTlK"
   },
   "source": "# Basit Z observable (sınıflandırma için örnek)\nobservable = SparsePauliOp.from_list([(\"Z\"*n_q, 1.0)])\nprint(\"Observable:\", observable)\nprint(\"(Gerçek problemde etikete göre farklı Pauli string’ler seçilebilir)\")",
   "outputs": [],
   "execution_count": null,
   "id": "rR_KS17ihTlK"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fjqLlKMihTlL"
   },
   "source": "# AŞAMA 17 – Optimizer Seçimi",
   "id": "fjqLlKMihTlL"
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "vZQjdqWihTlL"
   },
   "source": "#@title Optimizer\noptimizer_name = \"COBYLA\" #@param [\"COBYLA\",\"SPSA\",\"NFT\"]\nmaxiter = 50 #@param {type:\"integer\"}\n\nfrom qiskit_algorithms.optimizers import COBYLA, SPSA, NFT\nopt_map = {\"COBYLA\": COBYLA(maxiter=maxiter), \"SPSA\": SPSA(maxiter=maxiter), \"NFT\": NFT(maxiter=maxiter)}\noptimizer = opt_map[optimizer_name]\nprint(f\"Optimizer: {optimizer_name}, maxiter={maxiter}\")",
   "outputs": [],
   "execution_count": null,
   "id": "vZQjdqWihTlL"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PFi7iDGUhTlM"
   },
   "source": "# AŞAMA 18 – VQC Eğitim İskeleti (Supervised örnek)",
   "id": "PFi7iDGUhTlM"
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "DV_CwuGbhTlM"
   },
   "source": "# Sadece supervised csv örneği için çalışır\nif pipe.X_train is not None and pipe.data_type==\"csv\":\n    print(\"VQC iskeleti hazırlanıyor (küçük örnek, hızlı deneme)...\")\n\n    # Özellikleri qubit sayısına indir\n    n_q = min(4, pipe.circuit.num_qubits)  # hızlı deneme için 4 qubit\n    pca_vqc = PCA(n_components=n_q, random_state=42)\n    Xtr = MinMaxScaler().fit_transform(pca_vqc.fit_transform(pipe.X_train))\n    Xte = MinMaxScaler().fit_transform(pca_vqc.transform(pipe.X_test))\n\n    feature_map = ZFeatureMap(n_q, reps=1)\n    ansatz_vqc = RealAmplitudes(n_q, reps=1)\n\n    from qiskit.primitives import StatevectorSampler\n    sampler = StatevectorSampler()\n\n    vqc = VQC(\n        sampler=sampler,\n        feature_map=feature_map,\n        ansatz=ansatz_vqc,\n        optimizer=COBYLA(maxiter=30),\n    )\n\n    print(\"Eğitim başlıyor (30 iterasyon, örnek)...\")\n    vqc.fit(Xtr[:80], pipe.y_train[:80])  # küçük alt küme\n    score = vqc.score(Xte[:40], pipe.y_test[:40])\n    print(f\"Test accuracy (küçük örnek): {score:.3f}\")\nelse:\n    print(\"Supervised CSV verisi yok → VQC atlandı. Kendi etiketli verinle bu hücreyi uyarlayabilirsin.\")",
   "outputs": [],
   "execution_count": null,
   "id": "DV_CwuGbhTlM"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "twg5TCzMhTlN"
   },
   "source": "# AŞAMA 19 – Değerlendirme Özeti",
   "id": "twg5TCzMhTlN"
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "VxVgR67AhTlO"
   },
   "source": "print(\"=== Değerlendirme Özeti ===\")\npipe.summary()\nprint(f\"Simülasyon shots   : 1024\")\nprint(f\"Ansatz             : {ansatz_type}\")\nprint(f\"Optimizer          : {optimizer_name}\")\nprint(\"Classical baseline  : (kendi modelinle karşılaştır)\")",
   "outputs": [],
   "execution_count": null,
   "id": "VxVgR67AhTlO"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ww-hgK-IhTlP"
   },
   "source": "# AŞAMA 20 – Gemini Final Raporu + Sonraki Adımlar",
   "id": "ww-hgK-IhTlP"
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "MgdZe6rYhTlR"
   },
   "source": "final_prompt = f\"\"\"\nKullanıcı şu problemi çözmek istedi: {problem_description}\n\nYapılanlar:\n- Veri tipi: {pipe.data_type}\n- Cihaz: {pipe.device_info['name'] if pipe.device_info else '-'}\n- Encoding: {pipe.encoding_type}\n- Qubit: {pipe.circuit.num_qubits if pipe.circuit else '-'}\n- Ansatz: {ansatz_type}\n- Optimizer: {optimizer_name}\n\nTürkçe, net ve uygulanabilir şekilde:\n1. Bu pipeline problemin için uygun mu?\n2. Eksik kalan en kritik 2-3 adım nedir?\n3. Gerçek donanıma (Wukong / IBM) geçmek için ne yapılmalı?\n4. Bir sonraki sprint için somut öneriler ver.\n\"\"\"\n\nreport = ask_gemini(final_prompt)\ndisplay(Markdown(report))",
   "outputs": [],
   "execution_count": null,
   "id": "MgdZe6rYhTlR"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ktY2DuaNhTlT"
   },
   "source": "---\n### Kullanım Notları\n\n- Sağdaki **Colab Gemini paneli** ile de kod önerisi alabilirsin (Accept & Run).\n- VQC hücresi örnek amaçlıdır; gerçek problemde daha fazla iterasyon, daha iyi feature map ve hyperparameter araması gerekir.\n- Annealing / Photonic / Analog cihazlar için encoding farklıdır (QUBO, CV, Hamiltonian).\n- Gerçek donanıma geçerken ilgili provider token’ı (IBM Quantum, Origin Quantum vb.) gerekir.\n\n20 aşama tamamlandı. İyi çalışmalar!",
   "id": "ktY2DuaNhTlT"
  }
 ]
}