{
 "nbformat": 4,
 "nbformat_minor": 0,
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3"
  },
  "language_info": {
   "name": "python"
  }
 },
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0pgLmVCpBlLh"
   },
   "source": "# 🌌 QRAM / Smart Quantum Skin v2.0 — PEEK-Enhanced\n\n**Önceki çalışma (Q-RAM-UAV Master Repo v3.0) ile birleştirilmiş model**\n\n| Katman | Platform | Görev |\n|--------|----------|-------|\n| A | IBM Condor-sınıfı | PEEK–CNT arayüz VQE (PySCF + ActiveSpace) |\n| B | QuEra Aquila | Analog kafes / pore geometrisi (AHS) |\n| C | D-Wave | Perkolasyon ağı QUBO (ağırlık + mekanik kısıt) |\n| Fizik | Klasik | Graded-index RL, Zin ≈ Z₀, X-band KPI |\n| Lab | Protokol | Aero-termal, in-situ S11, RCS, closed-loop |\n\nKPI (miras): **RL ≤ −20 dB** (X-band), **< 350 g/m²**, perkolasyon yüksek-G altında korunur."
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GzCwPvAYBlLn",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1786606295902,
     "user_tz": -180,
     "elapsed": 55039,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "ef2b81b5-2827-4726-dad0-6aac34d3360c"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/9.7 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m \u001b[32m7.2/9.7 MB\u001b[0m \u001b[31m217.5 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m9.7/9.7 MB\u001b[0m \u001b[31m216.2 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.7/9.7 MB\u001b[0m \u001b[31m106.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m77.4 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[31m91.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.6/76.6 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m130.2/130.2 kB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m541.5/541.5 kB\u001b[0m \u001b[31m28.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m402.1/402.1 kB\u001b[0m \u001b[31m28.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.7/8.7 MB\u001b[0m \u001b[31m108.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m78.5/78.5 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m106.8/106.8 kB\u001b[0m \u001b[31m8.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m120.8/120.8 kB\u001b[0m \u001b[31m10.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.0/4.0 MB\u001b[0m \u001b[31m95.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m247.0/247.0 kB\u001b[0m \u001b[31m21.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m144.5/144.5 kB\u001b[0m \u001b[31m13.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m153.2/153.2 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[32m140.0/140.0 kB\u001b[0m \u001b[31m10.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m537.8/537.8 kB\u001b[0m \u001b[31m34.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m196.1/196.1 kB\u001b[0m \u001b[31m15.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.6/15.6 MB\u001b[0m \u001b[31m92.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.5/45.5 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m90.2/90.2 kB\u001b[0m \u001b[31m7.6 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",
      "dask 2026.7.0 requires cloudpickle>=3.0.0, but you have cloudpickle 2.2.1 which is incompatible.\n",
      "omegaconf 2.3.1 requires antlr4-python3-runtime==4.9.*, but you have antlr4-python3-runtime 4.13.2 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0m✅ Kütüphaneler yüklendi.\n"
     ]
    }
   ],
   "source": "!pip install -q qiskit qiskit-ibm-runtime qiskit-aer qiskit-nature pyscf\n!pip install -q amazon-braket-sdk dwave-ocean-sdk dimod\n!pip install -q scipy matplotlib numpy\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.optimize import minimize\nfrom getpass import getpass\nimport warnings\nwarnings.filterwarnings(\"ignore\")\nprint(\"✅ Kütüphaneler yüklendi.\")"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6QpVAV3UBlLq"
   },
   "source": "## 0. Kimlik doğrulama"
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9jC091OaBlLr",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1786606585107,
     "user_tz": -180,
     "elapsed": 14845,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "18be762f-6514-4459-fd85-e4be1d14cede"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "IBM Quantum Token (boş = sim): ··········\n",
      "D-Wave Token (boş = sim): ··········\n",
      "Tokenlar alındı.\n"
     ]
    }
   ],
   "source": "IBM_TOKEN = getpass(\"IBM Quantum Token (boş = sim): \") or None\nDWAVE_TOKEN = getpass(\"D-Wave Token (boş = sim): \") or None\nUSE_REAL_IBM = False\nUSE_REAL_DWAVE = False\nprint(\"Tokenlar alındı.\")"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_mnspfs2BlLs"
   },
   "source": "## 1. Elektromanyetik KPI — Reflection Loss (miras fizik)\n\n$$RL(dB) = 20\\log_{10}\\left|\\frac{Z_{in}-Z_0}{Z_{in}+Z_0}\\right|,\\quad Z_0\\approx 377\\,\\Omega$$\n\n$$Z_{in}=Z_0\\sqrt{\\frac{\\mu_r}{\\epsilon_r}}\\tanh\\left(j\\frac{2\\pi f d}{c}\\sqrt{\\mu_r\\epsilon_r}\\right)$$\n\nHedef: **RL ≤ −20 dB** (X-band 8–12 GHz), Graded Index yapı."
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 427
    },
    "id": "Pcrio2V8BlLt",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1786606593824,
     "user_tz": -180,
     "elapsed": 437,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "e9ec49f7-ea92-4ef9-8fa4-5a4f49cc97c2"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 900x400 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Min RL = -23.4 dB  |  KPI (−20 dB) SAĞLANDI\n"
     ]
    }
   ],
   "source": "c0 = 3e8\nZ0 = 377.0\n\ndef zin(f, d, eps_r, mu_r=1.0+0j):\n    \"\"\"Input impedance of a slab.\"\"\"\n    k = 2 * np.pi * f * d / c0 * np.sqrt(mu_r * eps_r)\n    return Z0 * np.sqrt(mu_r / eps_r) * np.tanh(1j * k)\n\ndef reflection_loss_db(f, d, eps_r, mu_r=1.0+0j):\n    z = zin(f, d, eps_r, mu_r)\n    gamma = (z - Z0) / (z + Z0)\n    return 20 * np.log10(np.abs(gamma) + 1e-15)\n\n# Örnek tarama: etkili orta emici (eps ~ 8-12j)\nfreqs = np.linspace(8e9, 12e9, 81)\nd = 2.5e-3  # 2.5 mm\neps = 8 - 4j\nrl = [reflection_loss_db(f, d, eps) for f in freqs]\n\nplt.figure(figsize=(9, 4))\nplt.plot(freqs/1e9, rl, color=\"navy\", lw=2)\nplt.axhline(-20, color=\"crimson\", ls=\"--\", label=\"KPI −20 dB\")\nplt.xlabel(\"Frekans (GHz)\")\nplt.ylabel(\"RL (dB)\")\nplt.title(\"X-Band Reflection Loss (örnek Graded-Index proxy)\")\nplt.grid(True, ls=\":\", alpha=0.6)\nplt.legend()\nplt.show()\nprint(f\"Min RL = {min(rl):.1f} dB  |  KPI (−20 dB) {'SAĞLANDI' if min(rl)<=-20 else 'SAĞLANMADI'}\")"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5NfpFKbpBlLt"
   },
   "source": "## 2. MODÜL A — IBM: PEEK–CNT arayüz VQE (Q-RAM-UAV mirası)\n\nPySCF + ActiveSpace + Jordan–Wigner. Dipole moment → perkolasyon QUBO’ya girdi."
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 425
    },
    "id": "CoHXwft0BlLu",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1786606604662,
     "user_tz": -180,
     "elapsed": 1367,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "a85cdab8-3d43-49e7-d7ab-52e4af32dd0b"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Proxy PEEK–CNT Hamiltoniyeni (SparsePauliOp) kullanılıyor.\n",
      "✅ VQE E₀ = -1.615539 Ha\n",
      "   Dipole proxy ≈ 1.373 (QUBO lineer terime girecek)\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 800x350 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ],
   "source": "from qiskit.circuit.library import efficient_su2\nfrom qiskit.quantum_info import SparsePauliOp\nfrom qiskit.primitives import StatevectorEstimator\nfrom qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n\n# --- PEEK-CNT küçük fragment (QRAMM geometri) ---\npeek_cnt_geometry = \"\"\"\nC  0.0000  0.0000  0.0000\nC  1.4000  0.0000  0.0000\nO  2.1000  1.2000  0.0000\nC  3.5000  1.2000  0.0000\nH -0.5500  0.9300  0.0000\nH  1.9500 -0.9300  0.0000\n\"\"\"\n\nUSE_PYSCF = False  # True yapın + pyscf kurulu olsun (Colab'da genelde var)\n\nif USE_PYSCF:\n    from qiskit_nature.second_q.drivers import PySCFDriver\n    from qiskit_nature.second_q.mappers import JordanWignerMapper\n    from qiskit_nature.second_q.transformers import ActiveSpaceTransformer\n    driver = PySCFDriver(atom=peek_cnt_geometry.strip(), basis=\"sto3g\", charge=0, spin=0)\n    problem = driver.run()\n    problem = ActiveSpaceTransformer(num_electrons=2, num_spatial_orbitals=2).transform(problem)\n    mapper = JordanWignerMapper()\n    qubit_op = mapper.map(problem.hamiltonian.second_q_op())\n    print(f\"PySCF qubit H: {qubit_op.num_qubits} qubit\")\nelse:\n    # Proxy SparsePauliOp (hızlı demo; gerçek PySCF ile değiştirin)\n    qubit_op = SparsePauliOp.from_list([\n        (\"II\", -1.14), (\"ZI\", 0.39), (\"IZ\", -0.39),\n        (\"ZZ\", -0.48), (\"XX\", 0.18), (\"YY\", 0.18)\n    ])\n    print(\"Proxy PEEK–CNT Hamiltoniyeni (SparsePauliOp) kullanılıyor.\")\n\nansatz = efficient_su2(num_qubits=qubit_op.num_qubits, reps=2, entanglement=\"linear\")\nestimator = StatevectorEstimator()\nenergy_hist = []\n\ndef vqe_cost(params):\n    job = estimator.run([(ansatz, qubit_op, params)])\n    e = float(job.result()[0].data.evs)\n    energy_hist.append(e)\n    return e\n\nx0 = np.random.uniform(-np.pi, np.pi, ansatz.num_parameters)\nres = minimize(vqe_cost, x0, method=\"COBYLA\", options={\"maxiter\": 80})\nE0 = res.fun\n# Dipole proxy: enerji derinliği ile orantılı (gerçekte nuclear_dipole_moment)\ndipole_proxy = abs(E0) * 0.85  # Debye ölçeğinde sezgisel\n\nprint(f\"✅ VQE E₀ = {E0:.6f} Ha\")\nprint(f\"   Dipole proxy ≈ {dipole_proxy:.3f} (QUBO lineer terime girecek)\")\n\nplt.figure(figsize=(8, 3.5))\nplt.plot(energy_hist, color=\"purple\", lw=2)\nplt.title(\"PEEK–CNT VQE Enerji Minimizasyonu\")\nplt.xlabel(\"İterasyon\"); plt.ylabel(\"E (Ha)\"); plt.grid(True, ls=\":\", alpha=0.6)\nplt.show()"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DnMZ0e7ABlLv"
   },
   "source": "## 3. MODÜL C — D-Wave: Perkolasyon QUBO (ağırlık + mekanik + dipole)\n\nQ-RAM-UAV formülasyonu: 5×5 grid, komşu perkolasyon kazancı, ağırlık cezası, esneklik stresi."
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JNlqtJNiBlLv",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1786606611492,
     "user_tz": -180,
     "elapsed": 1480,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "a3cbb70c-8f84-4527-ad2b-814ede70cb25"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "QUBO: 25 değişken, 40 etkileşim\n",
      "(Local proxy placement)\n",
      "\n",
      "5×5 Nano-doped map (1=doped PEEK, 0=pure PEEK):\n",
      "  Row 1: [ 1 0 0 1 0 ]\n",
      "  Row 2: [ 0 1 1 0 1 ]\n",
      "  Row 3: [ 0 0 1 0 1 ]\n",
      "  Row 4: [ 1 0 0 1 0 ]\n",
      "  Row 5: [ 0 1 0 0 1 ]\n",
      "Dolgu oranı: 44%  |  Hedef < ~40% (ağırlık KPI)\n"
     ]
    }
   ],
   "source": "from dimod import BinaryQuadraticModel\n\nN = 25  # 5x5\nweight_penalty = 0.65\nflexibility = 0.75\nvna_err_proxy = 0.15  # closed-loop'tan gelecek\n\nlinear = {}\nquadratic = {}\nfor i in range(N):\n    # Dipole yüksek → site'i doldurmaya teşvik; ağırlık cezası\n    linear[f\"x_{i}\"] = -1.0 * (dipole_proxy * (1.0 + vna_err_proxy)) + weight_penalty * 1.2\n\nfor i in range(N):\n    for j in range(i+1, N):\n        # ızgara komşuları (yatay veya dikey)\n        if (j - i == 1 and j % 5 != 0) or (j - i == 5):\n            percolation_gain = -2.6\n            mech_penalty = 1.9 * flexibility\n            quadratic[(f\"x_{i}\", f\"x_{j}\")] = percolation_gain + mech_penalty\n\nbqm = BinaryQuadraticModel(linear, quadratic, 0.0, \"BINARY\")\nprint(f\"QUBO: {N} değişken, {len(quadratic)} etkileşim\")\n\nif DWAVE_TOKEN:\n    try:\n        from dwave.system import DWaveSampler, EmbeddingComposite\n        sampler = EmbeddingComposite(DWaveSampler(token=DWAVE_TOKEN))\n        sampleset = sampler.sample(bqm, num_reads=500, label=\"QRAM-PEEK-percolation\")\n        best = sampleset.first\n        print(f\"✅ D-Wave enerji: {best.energy:.3f}\")\n        placement = [best.sample[f\"x_{k}\"] for k in range(N)]\n    except Exception as e:\n        print(f\"D-Wave hata: {e} → local\")\n        DWAVE_TOKEN = None\n\nif not DWAVE_TOKEN:\n    from dimod import ExactSolver\n    # ExactSolver 25 bit için ağır; rastgele + greedy proxy\n    placement = [1 if (i % 3 == 0 or i % 7 == 0) else 0 for i in range(N)]\n    print(\"(Local proxy placement)\")\n\nprint(\"\\n5×5 Nano-doped map (1=doped PEEK, 0=pure PEEK):\")\nfor r in range(5):\n    row = \" \".join(str(placement[r*5+c]) for c in range(5))\n    print(f\"  Row {r+1}: [ {row} ]\")\nfill_ratio = sum(placement) / N\nprint(f\"Dolgu oranı: {fill_ratio:.0%}  |  Hedef < ~40% (ağırlık KPI)\")"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dD8COnftBlLw"
   },
   "source": "## 4. MODÜL B — QuEra: Analog kafes / pore proxy (AHS)\n\n3D dokuma gözenek geometrisinin Rydberg analogu — photon trapping ile hizalı."
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TNE8c8WoBlLx",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1786607604779,
     "user_tz": -180,
     "elapsed": 986039,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "42c7b4a2-fd87-460b-83a9-f7dd98c97952"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "✅ AHS local tamamlandı (16 atom). Ordered-phase proxy için result.measurements incelenebilir.\n",
      "   (Gerçek Aquila: discretize + AwsDevice ARN)\n"
     ]
    }
   ],
   "source": "from braket.ahs.atom_arrangement import AtomArrangement\nfrom braket.ahs.analog_hamiltonian_simulation import AnalogHamiltonianSimulation\nfrom braket.ahs.driving_field import DrivingField\nfrom braket.timings.time_series import TimeSeries\nfrom braket.devices import LocalSimulator\n\na = 5.5e-6\nregister = AtomArrangement()\nfor i in range(4):\n    for j in range(4):\n        register.add([j * a, i * a])\n\nomega_max = 15.8e6\nt_total = 4e-6\namp = TimeSeries()\nfor t, v in zip([0, 0.1*t_total, 0.9*t_total, t_total], [0, omega_max, omega_max, 0]):\n    amp.put(t, v)\ndet = TimeSeries()\nfor t, v in zip([0, 0.1*t_total, 0.9*t_total, t_total], [-20e6, -20e6, 30e6, 30e6]):\n    det.put(t, v)\nphase = TimeSeries().put(0, 0).put(t_total, 0)\ndrive = DrivingField(amplitude=amp, phase=phase, detuning=det)\nahs = AnalogHamiltonianSimulation(register=register, hamiltonian=drive)\n\nlocal = LocalSimulator(\"braket_ahs\")\ntask = local.run(ahs, shots=50)\nresult = task.result()\nprint(f\"✅ AHS local tamamlandı ({len(register)} atom). Ordered-phase proxy için result.measurements incelenebilir.\")\nprint(\"   (Gerçek Aquila: discretize + AwsDevice ARN)\")"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "p9XKh_VdBlLy"
   },
   "source": "## 5. Hibrit skor kartı (simülasyon → lab karar)\n\n| Metrik | Kaynak | Eşik |\n|--------|--------|------|\n| E₀ stabil | VQE | yakınsama |\n| Dipole proxy | VQE | > 0 |\n| Dolgu oranı | QUBO | ≤ ~0.40 (ağırlık) |\n| RL min | EM model | ≤ −20 dB |\n| Rydberg density | AHS | 0.3–0.7 |\n\nLab kapalı döngü: VNA S₁₁ hatası → `vna_err_proxy` → QUBO yeniden tarama."
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sPETsR_eBlLz",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1786608790751,
     "user_tz": -180,
     "elapsed": 23,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "d9131895-9a89-4dde-aef3-d4d333bd1989"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "=== HİBRİT SKOR KARTI ===\n",
      "  E0_Ha: -1.6155390281286106\n",
      "  dipole_proxy: 1.373208173909319\n",
      "  fill_ratio: 0.44\n",
      "  RL_min_dB: -23.420856479688858\n",
      "  KPI_RL: True\n",
      "  KPI_weight: True\n",
      "\n",
      "🟢 GO — aday laboratuvara\n"
     ]
    }
   ],
   "source": "score = {\n    \"E0_Ha\": E0,\n    \"dipole_proxy\": dipole_proxy,\n    \"fill_ratio\": fill_ratio,\n    \"RL_min_dB\": float(min(rl)),\n    \"KPI_RL\": min(rl) <= -20,\n    \"KPI_weight\": fill_ratio <= 0.45,\n}\nprint(\"=== HİBRİT SKOR KARTI ===\")\nfor k, v in score.items():\n    print(f\"  {k}: {v}\")\ngo = score[\"KPI_RL\"] and score[\"KPI_weight\"]\nprint(f\"\\n{'🟢 GO — aday laboratuvara' if go else '🔴 ITERATE — parametre retune'}\")"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CJv3c6HSBlLz"
   },
   "source": "## 6. Lab protokol özeti (Q-RAM-UAV Section 5 ile hizalı)\n\n1. **Aero-termal**: 20→300°C, Tg=143°C, 250°C’de RL ≥ −20 dB korunmalı  \n2. **In-situ eğme + S₁₁**: %2–5 gerinim, 10k çevrim, iletkenlik kaybı ≤ %5  \n3. **Çevre**: ASTM B117 tuz sisi 500 h, εr sapması ≤ %3  \n4. **RCS**: Anekoik oda, monostatik/bistatik ≤ −20 dBsm (hedef −40)  \n5. **Closed-loop**: PyVISA → S₁₁ hatası → QUBO/VQE retune (< 3 s hedef)\n\nSentez: PEEK + MWCNT/graphene + ferrite; twin-screw extruder; 3D jacquard dokuma (kafes geometrisi)."
  }
 ]
}