{ "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": "Ef2wiYKeEEjm" }, "source": "# Quantum Problem Solver – Gemini Destekli İnteraktif Pipeline\n\nBu notebook ile:\n1. Problemini **doğal dilde** yazarsın\n2. **Gemini** problemi analiz eder, veri tipini ve yaklaşımı önerir\n3. Adım adım interaktif olarak quantum encoding + cihaz seçimi yaparsın\n\n---", "id": "Ef2wiYKeEEjm" }, { "cell_type": "markdown", "metadata": { "id": "eZlNra-jEEjv" }, "source": "## 0. Kurulum", "id": "eZlNra-jEEjv" }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "2a-AFKrBEEjx", "executionInfo": { "status": "ok", "timestamp": 1788787998514, "user_tz": -180, "elapsed": 21096, "user": { "displayName": "Joseph Volkan ÖZCAN", "userId": "11389530967736397480" } }, "outputId": "80162318-18a8-4618-fb8b-2ca081246ce8" }, "source": "# Temel paketler\n!pip install -U -q qiskit qiskit-aer pillow opencv-python-headless google-genai\n\nimport sys\nprint(\"Python:\", sys.version.split()[0])\n\nimport numpy as np\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.decomposition import PCA\nimport qiskit\nfrom qiskit import QuantumCircuit\nfrom qiskit.circuit.library import ZFeatureMap, ZZFeatureMap\nfrom qiskit.quantum_info import Statevector\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[31m3.0 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[31m81.1 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[31m74.2 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[31m78.3 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[31m32.6 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[31m10.3 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[31m44.4 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[31m1.8 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": "2a-AFKrBEEjx" }, { "cell_type": "markdown", "metadata": { "id": "5Dxu0aKmEEj0" }, "source": "## 1. Gemini API Ayarları\n\n1. [Google AI Studio](https://aistudio.google.com/apikey) adresinden API key al\n2. Colab sol menüdeki **🔑 Secrets** ikonuna tıkla\n3. `GEMINI_API_KEY` adında bir secret oluştur ve key’ini yapıştır\n4. Aşağıdaki hücreyi çalıştır", "id": "5Dxu0aKmEEj0" }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "TeLtv99SEEj1", "executionInfo": { "status": "ok", "timestamp": 1788788097060, "user_tz": -180, "elapsed": 2395, "user": { "displayName": "Joseph Volkan ÖZCAN", "userId": "11389530967736397480" } }, "outputId": "896a362f-1131-402c-d2ba-8190c164088a" }, "source": "from google.colab import userdata\nfrom google import genai\nfrom IPython.display import display, Markdown\n\n# API Key'i Secrets'tan al\ntry:\n API_KEY = userdata.get(\"GEMINI_API_KEY\")\n client = genai.Client(api_key=API_KEY)\n print(\"✅ Gemini bağlantısı başarılı!\")\nexcept Exception as e:\n print(\"❌ GEMINI_API_KEY bulunamadı.\")\n print(\"Sol menüdeki 🔑 Secrets kısmına GEMINI_API_KEY ekle.\")\n print(\"Hata:\", e)\n client = None", "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "✅ Gemini bağlantısı başarılı!\n" ] } ], "execution_count": 2, "id": "TeLtv99SEEj1" }, { "cell_type": "markdown", "metadata": { "id": "n_tg5Ee_EEj2" }, "source": "## 2. Quantum Pipeline Motoru", "id": "n_tg5Ee_EEj2" }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "BigR4w9hEEj2", "executionInfo": { "status": "ok", "timestamp": 1788788101484, "user_tz": -180, "elapsed": 271, "user": { "displayName": "Joseph Volkan ÖZCAN", "userId": "11389530967736397480" } }, "outputId": "0b8f1a51-f54f-4a96-aae1-5b17891d66d2" }, "source": "from typing import Optional, Dict, Any\nfrom pathlib import Path\n\nDEVICE_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 = None\n self.processed_data = None\n self.device_key = None\n self.device_info = None\n self.max_qubits = 10\n self.recommended_qubits = 0\n self.encoding_type = None\n self.circuit = None\n self.pca = None\n\n def select_data_type(self, data_type: str):\n data_type = data_type.lower().strip()\n if data_type not in {\"csv\", \"image\", \"video\", \"geospatial\"}:\n raise ValueError(\"csv | image | video | geospatial\")\n self.data_type = data_type\n print(f\"[Pipeline] Veri tipi: {data_type}\")\n return self\n\n def load_data(self, source, **kwargs):\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 resize = kwargs.get(\"resize\", (32, 32))\n img = img.convert(\"L\").resize(resize)\n self.processed_data = (np.array(img, dtype=np.float64) / 255.0).flatten().reshape(1, -1)\n elif self.data_type == \"video\":\n import cv2\n cap = cv2.VideoCapture(str(source))\n frames = []\n for _ in range(kwargs.get(\"max_frames\", 6)):\n ret, frame = cap.read()\n if not ret: break\n frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n frame = cv2.resize(frame, kwargs.get(\"resize\", (24, 24)))\n frames.append(frame.astype(np.float64) / 255.0)\n cap.release()\n self.processed_data = np.array(frames).flatten().reshape(1, -1)\n else: # geospatial basit CSV\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\"[Pipeline] Veri yüklendi → {self.processed_data.shape}\")\n return self\n\n def select_device(self, key: str):\n if key not in DEVICE_CATALOG:\n raise ValueError(f\"Cihaz bulunamadı: {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\"[Pipeline] Cihaz: {self.device_info['name']} ({self.max_qubits} qubit, {self.device_info['paradigm']})\")\n if self.device_info[\"paradigm\"] != \"gate\":\n print(\"⚠ Bu cihaz gate-model değil. Circuit referans amaçlıdır.\")\n return self\n\n def analyze_and_recommend(self, force_qubits=None):\n n_feat = self.processed_data.shape[1]\n amp_q = int(np.ceil(np.log2(max(n_feat, 2))))\n max_q = self.max_qubits\n\n if force_qubits:\n rec_q = min(force_qubits, max_q)\n enc = \"angle\" if rec_q >= n_feat else (\"amplitude\" if rec_q >= amp_q else \"dense\")\n else:\n if n_feat <= max_q:\n enc, rec_q = \"angle\", n_feat\n elif amp_q <= max_q:\n enc, rec_q = \"amplitude\", amp_q\n else:\n enc, rec_q = \"dense\", max_q\n\n self.recommended_qubits = rec_q\n self.encoding_type = enc\n return {\n \"n_features\": n_feat,\n \"device\": self.device_info[\"name\"],\n \"paradigm\": self.device_info[\"paradigm\"],\n \"recommended_qubits\": rec_q,\n \"recommended_encoding\": enc,\n \"amplitude_qubits_needed\": amp_q\n }\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\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\"[Pipeline] PCA uygulandı → {data.shape[1]} boyut\")\n\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\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), 0.0)\n if np.allclose(sample, sample.flat[0]):\n sample = np.maximum(self.processed_data[0].astype(np.float64), 0.0)\n dim = 2 ** n_qubits\n padded = np.zeros(dim)\n 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\")\n 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: # basis\n qc = QuantumCircuit(n_qubits)\n for i, v in enumerate(data[0, :n_qubits]):\n if v > 0.5: qc.x(i)\n\n # Add measurements to all qubits\n qc.measure_all()\n\n self.circuit = qc\n self.encoding_type = encoding\n print(f\"[Pipeline] Circuit: {qc.num_qubits} qubit, depth={qc.depth()}\")\n return qc\n\n def summary(self):\n print(\"\\n\" + \"=\"*55)\n print(\"ÖZET\")\n print(\"=\"*55)\n print(f\"Veri tipi : {self.data_type}\")\n print(f\"Veri 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 yüklendi.\")", "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Pipeline motoru yüklendi.\n" ] } ], "execution_count": 3, "id": "BigR4w9hEEj2" }, { "cell_type": "markdown", "metadata": { "id": "O2HyNBrQEEj4" }, "source": "## 3. 🎯 ADIM 1 – Problemini Anlat\n\nAşağıdaki kutuya **ne tür bir problem çözmek istediğini** ve **elindeki verinin ne olduğunu** yaz.\n\nÖrnekler:\n- \"Müşteri churn tahmin etmek istiyorum. Elimde 50 bin satırlık CSV var, 20 özellik içeriyor.\"\n- \"Uydu görüntülerinden tarım alanlarını sınıflandırmak istiyorum.\"\n- \"Kredi risk skorlaması için tabular data kullanacağım.\"\n- \"Kısa videolardan hareket tanıma yapmak istiyorum.\"", "id": "O2HyNBrQEEj4" }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "N8A29IGqEEj5", "executionInfo": { "status": "ok", "timestamp": 1788788149114, "user_tz": -180, "elapsed": 60, "user": { "displayName": "Joseph Volkan ÖZCAN", "userId": "11389530967736397480" } }, "outputId": "b9b410c9-865a-4706-e90f-5388d27cc4ee" }, "source": "#@title 📝 Problemini buraya yaz { run: \"auto\" }\nproblem_description = \"airline crew & pairing rostering için örnek bir case qml ile yapmak istiyorum\" #@param {type:\"string\"}\n\nprint(\"Girilen problem:\")\nprint(problem_description)", "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Girilen problem:\n", "airline crew & pairing rostering için örnek bir case qml ile yapmak istiyorum\n" ] } ], "execution_count": 4, "id": "N8A29IGqEEj5" }, { "cell_type": "markdown", "metadata": { "id": "wW__BvubEEj6" }, "source": "## 4. 🤖 ADIM 2 – Gemini Problemi Analiz Etsin", "id": "wW__BvubEEj6" }, { "cell_type": "code", "metadata": { "id": "LvdPgFcDEEj6", "executionInfo": { "status": "ok", "timestamp": 1788788358714, "user_tz": -180, "elapsed": 32, "user": { "displayName": "Joseph Volkan ÖZCAN", "userId": "11389530967736397480" } } }, "source": "def ask_gemini(prompt: str, model: str = \"gemini-3.6-flash\") -> str:\n \"\"\"Yüksek talep olursa otomatik olarak alternatif modellere düşer.\"\"\"\n if client is None:\n return \"Gemini bağlantısı yok. API key kontrol et.\"\n\n # Denenecek modeller (yeniden eskiye)\n models_to_try = [\n model,\n \"gemini-3.6-flash\",\n \"gemini-3.7-flash\",\n \"gemini-3.5-flash\",\n \"gemini-2.5-flash-lite\" # en hafif yedek\n ]\n\n import time\n last_error = None\n\n for m in models_to_try:\n try:\n print(f\"Deneniyor: {m} ...\")\n response = client.models.generate_content(\n model=m,\n contents=prompt\n )\n print(f\"✅ Başarılı model: {m}\\n\")\n return response.text\n except Exception as e:\n last_error = e\n print(f\" → {m} başarısız: {str(e)[:80]}...\")\n time.sleep(1.5) # kısa bekleme\n\n return f\"Hata: Tüm modeller başarısız oldu.\\nSon hata: {last_error}\"", "outputs": [], "execution_count": 10, "id": "LvdPgFcDEEj6" }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 645 }, "id": "b26efe26", "executionInfo": { "status": "ok", "timestamp": 1788788526949, "user_tz": -180, "elapsed": 23135, "user": { "displayName": "Joseph Volkan ÖZCAN", "userId": "11389530967736397480" } }, "outputId": "a7b806d7-8fac-46fb-9d9c-8d749fd49833" }, "source": "print(\"Gemini analiz ediyor...\\n\")\ngemini_analysis = ask_gemini(analysis_prompt)\ndisplay(Markdown(gemini_analysis))", "id": "b26efe26", "execution_count": 12, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Gemini analiz ediyor...\n", "\n", "Deneniyor: gemini-3.6-flash ...\n", "✅ Başarılı model: gemini-3.6-flash\n", "\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/markdown": "**1. Problemin özeti** \nHavayolu mürettebat eşleştirmesi ve vardiya planlaması (crew pairing & rostering), karmaşık yasal ve operasyonel kısıtlar altında uçuş rotalarını en düşük maliyetle oluşturmayı ve bunları personele adil/verimli şekilde atamayı amaçlayan, NP-zor sınıfında bir kombinatorik optimizasyon problemidir.\n\n**2. Önerilen veri tipi** \ncsv\n\n**3. Neden bu veri tipi?** \nUçuş bilgileri, personel yetkinlikleri, dinlenme süreleri, maliyet matrisleri ve vardiya kısıtları tamamen yapılandırılmış (tablosal) verilerden oluşur. İlişkisel veri yapısını ve matris formundaki girdi özelliklerini (features) QML algoritmalarına aktarmanın en doğrudan ve standart yolu CSV formatıdır.\n\n**4. Önerilen encoding yöntemleri** \n* **Basis Encoding:** Mürettebat-uçuş eşleşmelerini (evet/hayır, 1/0) doğrudan kuantum durumlarına ($|0110\\rangle$ gibi) birebir haritalamak için idealdir.\n* **Amplitude Encoding:** Eğer eşleştirme adaylarının (pairings) çok sayıda özelliğini (maliyet, süre, çakışma skoru vb.) az sayıda kuantum biti (qubit) ile temsil etmek isterseniz, yüksek boyutlu vektörleri kuantum genliklerine sıkıştırmak için kullanılmalıdır.\n\n**5. Uygun cihaz önerileri** \n1. **simulator:** (Algoritmayı doğrulamak, küçük ölçekli veri setlerinde konsept kanıtlama - PoC yapmak için ilk durak).\n2. **ionq_aria:** (İyon tuzağı mimarisi sayesinde tüm qubit'ler arası bağlantısallık - all-to-all connectivity - sunar; bu da karmaşık kısıt grafiklerine sahip mürettebat problemlerinin devrelere dönüştürülmesini kolaylaştırır).\n3. **ibm_brisbane:** (Büyük ölçekli Kapı-Modelli [gate-model] işlemci; QAOA veya VQE tabanlı QML yaklaşımlarını daha fazla qubit üzerinde denemek için uygundur).\n\n**6. Dikkat edilmesi gerekenler** \n* **Boyut Patlaması (Combinatorial Explosion):** Gerçek hayat mürettebat verileri milyonlarca olası eşleşme üretir. QML modeline geçmeden önce klasik yöntemlerle (örneğin *Column Generation*) arama uzayı daraltılmalı ve mutlaka **PCA veya Özellik Seçimi (Feature Selection)** uygulanmalıdır.\n* **Qubit Sınırlaması:** Her uçuş veya personel bir karar değişkeni olabileceğinden, qubit ihtiyacı hızla artar. Problemi küçük modüllere bölerek (decomposition) hibrit (Klasik-Kuantum) bir mimari kurgulanmalıdır.\n* **Donanım Uyumsuzluğu:** Bu bir karar ve optimizasyon problemi olduğundan, sürekli analitik modeller için uygun olan fotonik (`quandela_belenos`) veya analog nötr atom (`quera_gemini`) sistemleri yerine doğrudan Kapı-Modelli (Gate-model) veya QAOA destekleyen sistemler tercih edilmelidir.\n\n**7. Kısa bir sonraki adım önerisi** \n3-5 uçuş ve 2-3 mürettebattan oluşan sentetik bir CSV veri seti hazırlayın; problemi bir QUBO (Quadratic Unconstrained Binary Optimization) formuna getirip `simulator` üzerinde Qiskit veya Pennylane kullanarak küçük bir QAOA veya VQE/QNN modeliyle test edin." }, "metadata": {} } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 837 }, "id": "31836525", "executionInfo": { "status": "ok", "timestamp": 1788788503812, "user_tz": -180, "elapsed": 16909, "user": { "displayName": "Joseph Volkan ÖZCAN", "userId": "11389530967736397480" } }, "outputId": "3e22b05d-096c-4ba9-8bac-b16aae2fd9d4" }, "source": "print(\"Gemini analiz ediyor...\\n\")\ngemini_analysis = ask_gemini(analysis_prompt)\ndisplay(Markdown(gemini_analysis))", "id": "31836525", "execution_count": 11, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Gemini analiz ediyor...\n", "\n", "Deneniyor: gemini-3.6-flash ...\n", "✅ Başarılı model: gemini-3.6-flash\n", "\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/markdown": "Mevcut problemi bir Quantum Machine Learning (QML) uzmanı gözüyle analiz ettim. Detaylar aşağıdadır:\n\n### 1. Problemin Özeti\nHavayolu mürettebat eşleştirme ve vardiya planlaması (Crew Pairing & Rostering), çok sayıda yasal kısıt ve maliyet parametresi altında optimal uçuş-personel eşleşmesini bulmayı hedefleyen karmaşık bir kombinatöriyel optimizasyon ve veri sınıflandırma problemidir. QML bağlamında bu case, uçuş rotalarının \"uygun/uygun değil\" olarak sınıflandırılması veya maliyet/risk puanının kuantum destekli modellerle tahmin edilmesi şeklinde kurgulanabilir.\n\n### 2. Önerilen Veri Tipi\ncsv\n\n### 3. Neden Bu Veri Tipi?\nMürettebat planlama verileri; uçuş süreleri, dinlenme vakitleri, vardiya maliyetleri, mürettebat yetkinlikleri ve havalimanı kısıtları gibi sayısal ve kategorik değişkenlerden oluşur. Bu tür yapılı/tablosal (tabular) verilerin temsil edilmesi ve QML algoritmalarına beslenmesi için en standart ve uygun format CSV'dir.\n\n### 4. Önerilen Encoding Yöntemleri ve Kısa Gerekçe\n* **Angle Encoding:** Düşük boyutlu öznitelikler (features) için en ideal ve gürültüye dayanıklı yöntemdir. Öznitelik değerleri kuantum kapılarının rotasyon açılarına ($R_x, R_y, R_z$) dönüştürülür, sığ devre (shallow circuit) derinliği sağlar.\n* **Amplitude Encoding:** Eğer uçuş kısıt parametre sayısı yüksekse tercih edilmelidir. $N$ adet özniteliği $\\log_2(N)$ sayıda qubit'in genliğine (amplitude) sıkıştırarak ciddi bir qubit tasarrufu sağlar.\n\n### 5. Uygun Cihaz Önerileri\n1. **simulator:** Algoritmanın, feature map'lerin ve kuantum devresinin gürültüsüz ortamda doğrulanması için ilk aşamada kesinlikle kullanılmalıdır.\n2. **ionq_aria:** İyon tuzağı (trapped-ion) mimarisi, yüksek kapı sadakati (gate fidelity) ve tümüyle bağlı (all-to-all connectivity) qubit yapısı sunduğu için karmaşık QML sınıflandırma devrelerinde yüksek performans gösterir.\n3. **ibm_brisbane:** Yüksek qubit kapasitesine (127 qubits) sahip gate-model bir sistemdir; ölçeklenmiş büyük verilerde ve gerçek gürültü ortamındaki performans testlerinde tercih edilebilir.\n\n### 6. Dikkat Edilmesi Gerekenler\n* **PCA İhtiyacı:** Gerçek veri setlerindeki öznitelik sayısı mevcut NISQ (Noisy Intermediate-Scale Quantum) donanımları için çok fazladır. Veriyi kuantum devresine sokmadan önce PCA veya Autoencoder ile mutlaka 4 ila 8 özniteliğe düşürmelisiniz.\n* **Qubit Sayısı ve Kapı Derinliği:** Yüksek kapı derinliği gürültüye (decoherence) yol açar. Bu yüzden Variational Quantum Classifier (VQC) veya QSVC kullanırken sığ devreler tercih edilmelidir.\n* **Ayrım (QML vs. Optimizasyon):** Eğer amaç doğrudan kombinatöriyel rotayı seçmekse bu bir Quantum Annealing/QAOA problemidir (QuEra veya D-Wave uyumludur). Ancak veri üzerinden *öğrenme ve tahminleme* yapılacaksa gate-model QML kullanılmalıdır. Fotonik veya nötr atom cihazları saf ML classification case'i için bu aşamada kurulum karmaşıklığı yaratabilir.\n\n### 7. Kısa Bir Sonraki Adım Önerisi\nUçuş çiftlerini ve kısıtları içeren küçük bir **CSV** veriseti (örneğin 100 satır) hazırlayın; Scikit-Learn ile **PCA** uygulayarak 4 ana bileşene indirin ve Qiskit/PennyLane kütüphaneleri üzerinden bir **Variational Quantum Classifier (VQC)** prototipi oluşturup önce **simulator** üzerinde eğitin." }, "metadata": {} } ] }, { "cell_type": "markdown", "metadata": { "id": "7e5pffj0EEj7" }, "source": "## 5. 📊 ADIM 3 – Veri Tipini Onayla ve Veriyi Yükle\n\nGemini’nin önerdiği veri tipini aşağıya yaz (veya kendin seç). \nSonra kendi dosyanı yükle veya örnek veri kullan.", "id": "7e5pffj0EEj7" }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "u2P34FnqEEj8", "executionInfo": { "status": "ok", "timestamp": 1788788526959, "user_tz": -180, "elapsed": 14, "user": { "displayName": "Joseph Volkan ÖZCAN", "userId": "11389530967736397480" } }, "outputId": "fe5f95db-1236-413e-e504-60cddcdc99b8" }, "source": "#@title Veri tipi ve dosya { run: \"auto\" }\nselected_data_type = \"csv\" #@param [\"csv\", \"image\", \"video\", \"geospatial\"]\nuse_sample_data = True #@param {type:\"boolean\"}\n\npipe = QuantumAppPipeline()\npipe.select_data_type(selected_data_type)\n\nif use_sample_data and selected_data_type == \"csv\":\n import pandas as pd\n # Örnek churn benzeri veri\n sample = pd.DataFrame(np.random.rand(200, 18), columns=[f\"feat_{i}\" for i in range(18)])\n sample.to_csv(\"sample_data.csv\", index=False)\n pipe.load_data(\"sample_data.csv\")\n print(\"Örnek CSV yüklendi (200 satır × 18 özellik)\")\nelif use_sample_data 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_img.png\")\n pipe.load_data(\"sample_img.png\", resize=(16, 16))\n print(\"Örnek resim yüklendi\")\nelse:\n print(\"Kendi dosyanı yüklemek için sol menüden Upload yap, sonra aşağıdaki satırı düzenle:\")\n print('# pipe.load_data(\"senin_dosyan.csv\")')", "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[Pipeline] Veri tipi: csv\n", "[Pipeline] Veri yüklendi → (200, 18)\n", "Örnek CSV yüklendi (200 satır × 18 özellik)\n" ] } ], "execution_count": 13, "id": "u2P34FnqEEj8" }, { "cell_type": "markdown", "metadata": { "id": "OlPZnGBjEEj9" }, "source": "## 6. 🖥️ ADIM 4 – Cihaz Seçimi\n\nGemini’nin önerdiği cihazlardan birini seç.", "id": "OlPZnGBjEEj9" }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "1zo3VqluEEj-", "executionInfo": { "status": "ok", "timestamp": 1788788534964, "user_tz": -180, "elapsed": 39, "user": { "displayName": "Joseph Volkan ÖZCAN", "userId": "11389530967736397480" } }, "outputId": "3904e7b9-1c39-4827-e888-fbb5cfae1f2a" }, "source": "#@title Cihaz seç { run: \"auto\" }\nselected_device = \"wukong\" #@param [\"simulator\", \"ibm_brisbane\", \"wukong\", \"ionq_aria\", \"quera_aquila\", \"quera_gemini\", \"xanadu_aurora\", \"quandela_belenos\", \"dwave_advantage2\"]\n\npipe.select_device(selected_device)\nreport = pipe.analyze_and_recommend()\n\nprint(\"\\n--- Analiz Raporu ---\")\nfor k, v in report.items():\n print(f\"{k:25}: {v}\")", "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[Pipeline] Cihaz: Origin Wukong-180 (180 qubit, gate)\n", "\n", "--- Analiz Raporu ---\n", "n_features : 18\n", "device : Origin Wukong-180\n", "paradigm : gate\n", "recommended_qubits : 18\n", "recommended_encoding : angle\n", "amplitude_qubits_needed : 5\n" ] } ], "execution_count": 14, "id": "1zo3VqluEEj-" }, { "cell_type": "markdown", "metadata": { "id": "w-BgV0rOEEkA" }, "source": "## 7. ⚙️ ADIM 5 – Encoding & Circuit Üret", "id": "w-BgV0rOEEkA" }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "_ySsDGUJEEkB", "executionInfo": { "status": "ok", "timestamp": 1788788543483, "user_tz": -180, "elapsed": 45, "user": { "displayName": "Joseph Volkan ÖZCAN", "userId": "11389530967736397480" } }, "outputId": "ad2c380a-f355-4596-d280-32d1853bdeae" }, "source": "#@title Encoding ayarları { run: \"auto\" }\nencoding_choice = \"auto\" #@param [\"auto\", \"angle\", \"amplitude\", \"dense\", \"basis\"]\nmanual_qubits = 0 #@param {type:\"integer\"}\n\nif encoding_choice == \"auto\":\n qc = pipe.encode()\nelse:\n nq = manual_qubits if manual_qubits > 0 else None\n qc = pipe.encode(encoding=encoding_choice, n_qubits=nq)\n\npipe.summary()\nprint(\"\\nCircuit çizimi:\")\nprint(qc.draw(output=\"text\", fold=100))", "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[Pipeline] Circuit: 18 qubit, depth=1\n", "\n", "=======================================================\n", "ÖZET\n", "=======================================================\n", "Veri tipi : csv\n", "Veri shape : (200, 18)\n", "Cihaz : Origin Wukong-180\n", "Paradigm : gate\n", "Encoding : angle\n", "Qubit : 18\n", "=======================================================\n", "\n", "Circuit çizimi:\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", "« ┌────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐\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", "« │ ZFeatureMap(1.167,2.9956,2.325,1.8766,0.49192,0.48707,0.14858,2.7265,1.8965,2.218,0.049096,3.0584,2.6313,0.67251,0.57109,0.55492,0.95361,1.6357) │\n", "« q_9: ┤9 ├\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" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/tmp/ipykernel_1073/3643440645.py:124: DeprecationWarning: The class ``qiskit.circuit.library.data_preparation._z_feature_map.ZFeatureMap`` is deprecated as of Qiskit 2.1. It will be removed in Qiskit 3.0. Use the z_feature_map function as a replacement. Note that this will no longer return a BlueprintCircuit, but just a plain QuantumCircuit.\n", " qc = ZFeatureMap(n, reps=reps).assign_parameters(data[0, :n])\n" ] } ], "execution_count": 15, "id": "_ySsDGUJEEkB" }, { "cell_type": "markdown", "metadata": { "id": "pUD-XBs4EEkC" }, "source": "## 8. 💬 ADIM 6 – Gemini’den Sonraki Adım Tavsiyesi Al", "id": "pUD-XBs4EEkC" }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 717 }, "id": "_4VVjo9YEEkD", "executionInfo": { "status": "ok", "timestamp": 1788788600876, "user_tz": -180, "elapsed": 48644, "user": { "displayName": "Joseph Volkan ÖZCAN", "userId": "11389530967736397480" } }, "outputId": "7b9882b7-cc03-40e5-8f36-c7b7e08bc6e3" }, "source": "followup_prompt = f\"\"\"\nKullanıcı şu problemi çözmek istiyor:\n{problem_description}\n\nŞu ana kadar yapılanlar:\n- Veri tipi: {pipe.data_type}\n- Cihaz: {pipe.device_info['name'] if pipe.device_info else 'seçilmedi'}\n- Encoding: {pipe.encoding_type}\n- Qubit sayısı: {pipe.circuit.num_qubits if pipe.circuit else 'yok'}\n\nKısa ve net olarak:\n1. Bu seçimlerin problem için uygun olup olmadığını söyle\n2. Bir sonraki mantıklı adımı öner (örneğin VQC eğitimi, feature map iyileştirme, gerçek donanıma gönderme, hibrit model vb.)\n3. Varsa kritik uyarıları belirt\n\nCevabını Türkçe ve uygulanabilir tut.\n\"\"\"\n\nprint(\"Gemini sonraki adımları öneriyor...\\n\")\nnext_steps = ask_gemini(followup_prompt)\ndisplay(Markdown(next_steps))", "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Gemini sonraki adımları öneriyor...\n", "\n", "Deneniyor: gemini-3.6-flash ...\n", "✅ Başarılı model: gemini-3.6-flash\n", "\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/markdown": "Mevcut seçimleriniz ve hedeflerinize göre değerlendirme, sonraki adımlar ve kritik uyarılar aşağıdadır:\n\n---\n\n### 1. Seçimlerin Değerlendirmesi\n* **Veri (CSV) & 18 Qubit:** **Uygun.** 18 qubit, konsept kanıtlama (PoC) ve küçük ölçekli bir havayolu ekibini (örneğin 3-4 uçak, 10-15 mürettebat) modellemek için oldukça yeterli bir boyuttur.\n* **Origin Wukong-180:** **Uygun.** Süperiletken tabanlı güçlü bir gerçek donanımdır.\n* **Angle Encoding:** **Kısmen Uygun / Yetersiz.** Açı kodlaması qubit sayısını verimli kullanır ancak uçuşlar ve mürettebat arasındaki karmaşık çakışmaları/bağımlılıkları tek başına ifade etmekte zayıf kalır.\n* **Problem Tipi Uyumu:** *Ekiplerin uçuşlara atanması (Rostering)* özünde bir **Kombinatuvar Optimizasyon** problemidir. Klasik Makine Öğrenmesi (VQC) yerine **QAOA** (Quantum Approximate Optimization Algorithm) kullanmak problem doğasına daha uygundur. *(Not: Eğer amacınız bir atamanın \"geçerli/geçersiz\" olduğunu sınıflandırmaksa QML/VQC uygundur).*\n\n---\n\n### 2. Bir Sonraki Mantıklı Adım\nSırasıyla şu adımları izlemeniz en mantıklı yoldur:\n\n1. **Veri Ön İşleme ve Kodlama İyileştirmesi:**\n * CSV verilerini $[0, \\pi]$ veya $[-\\pi, \\pi]$ aralığına normalize edin.\n * Basit Angle Encoding yerine, uçuşlar arası ilişkileri temsil edebilmek için DOLANIKLIK (Entanglement) içeren **ZZ-Feature Map** kullanın.\n2. **Hibrit Model (VQC veya QAOA) Oluşturma:**\n * Donanıma gitmeden önce **QPanda** (Origin Quantum'un SDK'sı) veya PyTorch/Qiskit entegrasyonu ile yerel bir simülatörde modeli eğitin.\n3. **Gerçek Donanıma (Wukong-180) Gönderme:**\n * Simülatörde optimizasyon parametrelerini (weights/thetas) bulduktan sonra, nihayi çıkarım (inference) veya son eğitim turları için devreyi Origin Wukong-180'e gönderin.\n\n---\n\n### 3. Kritik Uyarılar\n* ⚠️ **Gürültü (Noise) ve Devre Derinliği:** Wukong-180 üzerinde 18 qubitlik derin bir kuantum devresi çalıştırırsanız, kuantum gürültüsü nedeniyle sonuçlar anlamsızlaşabilir. Devre derinliğini (ansatz depth) olabildiğince sığ tutun ve *Error Mitigation* (Hata Azaltma) tekniklerini aktif edin.\n* ⚠️ **Problem Tanımı:** Eşleştirme problemini bir optimizasyon matrisi (QUBO - Quadratic Unconstrained Binary Optimization) olarak yazmadıysanız, QML modeli ideal çözümü bulmakta zorlanabilir.\n* ⚠️ **Doğrudan Donanımda Eğitmeyin:** Tüm VQC/QAOA eğitimini (tüm epoch'ları) gerçek donanımda yapmak çok fazla kuyruk süresi ve kaynak tüketir. **Eğitimi simülatörde yapıp, doğrulmayı gerçek cihazda yapın (Hibrit yaklaşım).**" }, "metadata": {} } ], "execution_count": 16, "id": "_4VVjo9YEEkD" }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "2318e376", "executionInfo": { "status": "ok", "timestamp": 1788788702713, "user_tz": -180, "elapsed": 10757, "user": { "displayName": "Joseph Volkan ÖZCAN", "userId": "11389530967736397480" } }, "outputId": "41c84437-ccbf-46bc-f78b-aff952f3011d" }, "source": "from qiskit_aer import AerSimulator\nfrom qiskit import transpile\n\n# Mevcut circuit'i al\nqc = pipe.circuit.copy() # veya qc = ... (senin değişkenin)\n\n# Ölçüm ekle (tüm qubit'leri ölç)\nqc.measure_all()\n\nprint(\"Ölçüm eklenmiş circuit:\")\nprint(qc.draw(output=\"text\", fold=100))\n\n# Simülatörde çalıştır\nsimulator = AerSimulator()\ntranspiled = transpile(qc, simulator)\njob = simulator.run(transpiled, shots=1024)\nresult = job.result()\ncounts = result.get_counts()\n\nprint(\"\\nSimülasyon sonuçları (ölçüm frekansları):\")\nprint(counts)\n\n# İsteğe bağlı: en sık çıkan durumlar\nfrom qiskit.visualization import plot_histogram\nplot_histogram(counts)", "id": "2318e376", "execution_count": 20, "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", "meas: 18/»\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", "« │ ZFeatureMap(1.167,2.9956,2.325,1.8766,0.49192,0.48707,0.14858,2.7265,1.8965,2.218,0.049096,3.0584,2.6313,0.67251,0.57109,0.55492,0.95361,1.6357) │»\n", "« q_9: ┤9 ├»\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", "«meas: 18/══════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════»\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", "«meas: 18/════╩══╩══╩══╩══╩══╩══╩══╩══╩══╩══╩══╩══╩══╩══╩══╩══╩══╩═\n", "« 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17\n", "\n", "Simülasyon sonuçları (ölçüm frekansları):\n", "{'111000001101111101': 1, '110111000100101101': 1, '110000000100001011': 1, '110010000100001000': 1, '111101001100001101': 1, '010000001100001101': 1, '110000000110011001': 1, '111111001100001101': 1, '100110011100101101': 1, '110000001110101101': 1, '101101000000100001': 1, '111001000100011001': 1, '111100000100101101': 1, '100010001110001100': 1, '100010000010001100': 1, '111000001110000001': 1, '101000000100101001': 1, '110001000000001100': 1, '101000000110001001': 1, '110000001110001100': 1, '110110000100111100': 1, '110010000100000100': 1, '110000000100101001': 2, 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1, '110000000110001000': 1, '110100001100001111': 1, '111101001100011101': 2, '111000001110101101': 1, '100010001010111101': 1, '110000000100111101': 1, '101000001110101100': 1, '110010001100000101': 1, '101000000100000001': 1, '111001000100001101': 1, '110010000100000001': 1, '111101001100101000': 1, '100010000000001101': 2, '110010001100010111': 1, '101001000100011001': 1, '110111000000001101': 1, '110000000111001101': 1, '110011000100001000': 1, '100000001100001000': 3, '111000000100001100': 1, '110000001110101001': 2, '110100001010001101': 2, '111100000000001001': 1, '110101001100001101': 3, '100000000100011101': 1, '110010000100000000': 1, '100010001100000001': 1, '100100000100011101': 1, '100010000100011100': 1, '110110001110101101': 1, '110110000100000101': 1, '110011001000001101': 2, '110111000100101001': 1, '101001001100011001': 1, '110010010100110101': 1, '100100000100101001': 2, '100110001110001101': 2, '110110001100111101': 1, '101101001100001101': 2, '100111001100001001': 1, '110010001000011101': 1, '111010000100001001': 6, '111000000100111001': 1, '111010000110011001': 1, '101000001100101101': 1, '110100001100101101': 2, '100010001110011100': 1}\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "
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\n" }, "metadata": {}, "execution_count": 20 } ] }, { "cell_type": "markdown", "metadata": { "id": "9bc089aa" }, "source": "Bu, `AerSimulator` kullanarak devrenin yerel bir simülasyonudur. Gerçek donanımda (örn. Wukong) çalıştırmak için ek adımlar ve kimlik doğrulama gereklidir.", "id": "9bc089aa" }, { "cell_type": "markdown", "metadata": { "id": "EZf55iGSEEkE" }, "source": "## 9. İsteğe Bağlı – Statevector Kontrolü", "id": "EZf55iGSEEkE" }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "lBOUgw0oEEkE", "executionInfo": { "status": "ok", "timestamp": 1788788703674, "user_tz": -180, "elapsed": 966, "user": { "displayName": "Joseph Volkan ÖZCAN", "userId": "11389530967736397480" } }, "outputId": "f8defa66-923c-4a63-8df8-ce7482ef5a91" }, "source": "if pipe.circuit is not None:\n try:\n sv = Statevector.from_instruction(pipe.circuit)\n print(\"Statevector norm:\", round(np.linalg.norm(sv.data), 6))\n print(\"İlk 6 amplitude :\", np.round(sv.data[:6], 4))\n except Exception as e:\n print(\"Statevector alınamadı (circuit çok büyük veya initialize içeriyor olabilir):\", e)\nelse:\n print(\"Önce circuit üret.\")", "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Statevector norm: 1.0\n", "İlk 6 amplitude : [-0. -0.0002j 0.0002-0.0004j 0. -0.j 0.0001+0.j\n", " -0.0001-0.0002j 0.0002-0.0004j]\n" ] } ], "execution_count": 21, "id": "lBOUgw0oEEkE" }, { "cell_type": "markdown", "metadata": { "id": "zN8kpZE1EEkF" }, "source": "---\n\n### Nasıl Kullanılır?\n\n1. **Secrets** kısmına `GEMINI_API_KEY` ekle\n2. Problemini **3. hücredeki** kutuya yaz\n3. Gemini analizini oku\n4. Veri tipi + cihaz + encoding seçimlerini formlardan yap\n5. Circuit’i üret ve Gemini’nin sonraki adım önerisini al\n\nBu yapı tamamen interaktif ilerler. İstediğin zaman geri dönüp farklı cihaz veya encoding deneyebilirsin.\n\nBaşarılar! 🚀", "id": "zN8kpZE1EEkF" } ] }