{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1049253d",
   "metadata": {
    "id": "1049253d"
   },
   "source": "# Veri Kodlama (Data Encoding)\n### Kuantum Makine Öğrenmesi için veri gömme / yükleme yöntemleri\n\nBu notebook, kuantum makine öğrenmesinde **veri kodlama** problemini tanıtır;  \nbasis, amplitude, angle ve arbitrary encoding yöntemlerini açıklar ve Qiskit 2.x ile uygular.\n\n> Kaynak: Qiskit Textbook / QML learning materials  \n> Türkçe çeviri + Qiskit 2.x uyumlu kod · QML Study Companion"
  },
  {
   "cell_type": "markdown",
   "id": "b67f47a2",
   "metadata": {
    "id": "b67f47a2"
   },
   "source": "## 0. Kurulum"
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bdf117c0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bdf117c0",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788945163081,
     "user_tz": -180,
     "elapsed": 37989,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "8e769702-a6df-455b-9c20-57acedce0e9f"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.8/9.8 MB\u001b[0m \u001b[31m24.5 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[31m45.3 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[31m48.7 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[31m1.9 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[31m765.9 kB/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[31m26.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m16.7/16.7 MB\u001b[0m \u001b[31m43.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m137.5/137.5 kB\u001b[0m \u001b[31m4.9 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",
      "numba 0.61.2 requires numpy<2.3,>=1.24, but you have numpy 2.5.3 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0mKurulum tamam.\n"
     ]
    }
   ],
   "source": "!pip install -q -U \"qiskit>=2.0\" \"qiskit-aer>=0.15\"\n!pip install -q -U matplotlib numpy pylatexenc\nprint(\"Kurulum tamam.\")"
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dce8adc1",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dce8adc1",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1788945289914,
     "user_tz": -180,
     "elapsed": 844,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "87733f51-3773-4808-a828-951cd045398d"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Qiskit: 2.5.2  |  functional lib: True\n"
     ]
    }
   ],
   "source": "import math\nimport numpy as np\nimport qiskit\nfrom qiskit import QuantumCircuit, transpile\nfrom qiskit.quantum_info import Statevector\nfrom qiskit.visualization import plot_histogram, plot_bloch_multivector\n\ntry:\n    from qiskit.circuit.library import zz_feature_map, efficient_su2\n    _FUNC = True\nexcept ImportError:\n    from qiskit.circuit.library import ZZFeatureMap, EfficientSU2\n    _FUNC = False\n\nprint(f\"Qiskit: {qiskit.__version__}  |  functional lib: {_FUNC}\")"
  },
  {
   "cell_type": "markdown",
   "id": "c1f4a654",
   "metadata": {
    "id": "c1f4a654"
   },
   "source": "---\n## 1. Giriş\n\nMakine öğrenmesi modellerinin başarısı için **veri temsili** kritiktir.\n\n- **Klasik ML:** Veriyi sayısal olarak nasıl temsil ederiz ki klasik algoritma en iyi işlesin?\n- **Kuantum ML:** Daha temel bir soru — veriyi kuantum sistemine nasıl **temsil edip verimli şekilde yükleriz** ki kuantum ML algoritması işleyebilsin?\n\nBu süreç genellikle **data encoding** (veri kodlama) olarak adlandırılır; bazen *embedding* (gömme) veya *loading* (yükleme) de denir.\n\nEncoding, kuantum makine öğrenmesi algoritmalarının kritik bir parçasıdır ve **hesaplama gücünü doğrudan etkiler**."
  },
  {
   "cell_type": "markdown",
   "id": "5efd3f71",
   "metadata": {
    "id": "5efd3f71"
   },
   "source": "---\n## 2. Yöntemler\n\nKlasik bir veri seti $\\mathscr{X}$ düşünelim: $M$ örnek, her biri $N$ özellikli:\n\n$$\\mathscr{X} = \\{x^{(1)}, \\dots, x^{(m)}, \\dots, x^{(M)}\\}$$\n\nBurada $x^{(m)}$, $N$ boyutlu bir vektördür ($m = 1, \\ldots, M$).\n\nBu veri setini kübit sisteminde temsil etmek için çeşitli gömme teknikleri kullanılır.  \nAşağıda başlıca yöntemler özetlenir ve uygulanır (Kaynaklar [1], [2])."
  },
  {
   "cell_type": "markdown",
   "id": "0875932a",
   "metadata": {
    "id": "0875932a"
   },
   "source": "---\n## 3. Basis Encoding (Temel Durum Kodlama)\n\n**Fikir:** Klasik bir $N$-bit diziyi, $N$-kübit sistemin **hesaplama tabanı (computational basis)** durumuyla eşleştir.\n\nÖrnek: $x = 5$ → 4-bit string $0101$ → 4-kübit durum $|0101\\rangle$.\n\nGenel olarak $x = (b_1, b_2, \\ldots, b_N)$ için:\n\n$$|x\\rangle = |b_1, b_2, \\ldots, b_N\\rangle, \\quad b_n \\in \\{0,1\\}$$\n\nTüm veri seti, taban durumlarının **süperpozisyonu** olarak yazılabilir:\n\n$$|\\mathscr{X}\\rangle = \\frac{1}{\\sqrt{M}} \\sum_{m=1}^{M} |x^{(m)}\\rangle$$\n\n**Örnek:** $\\mathscr{X} = \\{x^{(1)}=101,\\; x^{(2)}=111\\}$  \n→  $|\\mathscr{X}\\rangle = \\frac{1}{\\sqrt{2}}\\big(|101\\rangle + |111\\rangle\\)$\n\nQiskit’te bu durumu `initialize` ile hazırlarız."
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60ade230",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 272
    },
    "id": "60ade230",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1787133335404,
     "user_tz": -180,
     "elapsed": 411,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "40786845-71eb-4eea-abb3-f9f512688eea"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Basis encoding devresi (decompose sonrası):\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<Figure size 621.941x284.278 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": "# |101⟩ ve |111⟩ eşit süperpozisyon (3 kübit)\n# indeksler: 101₂=5, 111₂=7\ndesired_state = [\n    0, 0, 0, 0, 0,\n    1 / math.sqrt(2),  # |101⟩\n    0,\n    1 / math.sqrt(2),  # |111⟩\n]\n\nqc = QuantumCircuit(3)\nqc.initialize(desired_state, [0, 1, 2])\nprint(\"Basis encoding devresi (decompose sonrası):\")\nqc.decompose().decompose().draw(\"mpl\", fold=-1)"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1faf24b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "a1faf24b",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1787133335406,
     "user_tz": -180,
     "elapsed": 73,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "2935744b-77de-4ca6-8857-49d054d9e3ce"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Olasılıklar (sıfır olmayanlar):\n",
      "  |101⟩  →  0.5000\n",
      "  |111⟩  →  0.5000\n"
     ]
    }
   ],
   "source": "# Durumu doğrula\nsv = Statevector.from_instruction(qc)\nprobs = np.abs(sv.data) ** 2\nprint(\"Olasılıklar (sıfır olmayanlar):\")\nfor i, p in enumerate(probs):\n    if p > 1e-10:\n        print(f\"  |{i:03b}⟩  →  {p:.4f}\")"
  },
  {
   "cell_type": "markdown",
   "id": "6a0ffcb0",
   "metadata": {
    "id": "6a0ffcb0"
   },
   "source": "**Dezavantajlar:**  \nAnlaşılması kolay olsa da durum vektörleri **seyrek** kalabilir; verimli uygulama şemaları genellikle yoktur. Büyük veri setlerinde kübit / derinlik maliyeti hızla artar."
  },
  {
   "cell_type": "markdown",
   "id": "4ceb0bfd",
   "metadata": {
    "id": "4ceb0bfd"
   },
   "source": "---\n## 4. Amplitude Encoding (Genlik Kodlama)\n\n**Fikir:** Normalize edilmiş klasik $N$-boyutlu veri noktasını, $n$-kübit durumunun **amplitüdlerine** yaz.\n\n$$|\\psi_x\\rangle = \\sum_{i=1}^{N} x_i \\, |i\\rangle$$\n\nBurada $N = 2^n$ ve $x$ normalize edilmiştir ($\\sum_i |x_i|^2 = 1$).\n\n**Avantaj:** Tüm veri setini (veya büyük bir vektörü) yalnızca $\\log_2(NM)$ kübit ile kodlama potansiyeli.  \n**Zorluk:** Genel amplitude loading devreleri **derin** olabilir; NISQ’ta pahalıdır.\n\nÖrnek: $x = \\dfrac{1}{\\sqrt{2}}(1, 0, 0, 1)$ → $|\\psi\\rangle = \\dfrac{1}{\\sqrt{2}}(|00\\rangle + |11\\rangle)$ (Bell benzeri)."
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f7993e4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 208
    },
    "id": "0f7993e4",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1787133335463,
     "user_tz": -180,
     "elapsed": 87,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "7048e1dc-df54-4436-9cf8-9423726c0781"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Amplitude encoding örneği:\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<Figure size 621.739x200.667 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": "desired_state = [\n    1 / math.sqrt(2),\n    0,\n    0,\n    1 / math.sqrt(2),\n]\n\nqc = QuantumCircuit(2)\nqc.initialize(desired_state, [0, 1])\nprint(\"Amplitude encoding örneği:\")\nqc.decompose().decompose().draw(\"mpl\", fold=-1)"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8038310d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8038310d",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1787133335479,
     "user_tz": -180,
     "elapsed": 14,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "aa093790-3dab-4dc4-d627-ca4bcfbed9d4"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Amplitüdler: [0.7071+0.j 0.    +0.j 0.    +0.j 0.7071+0.j]\n",
      "Olasılıklar: [0.5 0.  0.  0.5]\n"
     ]
    }
   ],
   "source": "sv = Statevector.from_instruction(qc)\nprint(\"Amplitüdler:\", np.round(sv.data, 4))\nprint(\"Olasılıklar:\", np.round(np.abs(sv.data)**2, 4))"
  },
  {
   "cell_type": "markdown",
   "id": "a9bf09f8",
   "metadata": {
    "id": "a9bf09f8"
   },
   "source": "**Not:** Amplitude encoding, tüm veri setini az kübitte toplama vaadi taşır; ancak state preparation maliyeti pratikte avantajı sınırlayabilir. VQC/QSVC uygulamalarında çoğu zaman **angle / feature map** encoding tercih edilir."
  },
  {
   "cell_type": "markdown",
   "id": "662a4767",
   "metadata": {
    "id": "662a4767"
   },
   "source": "---\n## 5. Angle Encoding (Açı Kodlama)\n\n**Fikir:** Her özelliği bir kübitin **dönme açısına** yaz. Tek seferde **bir veri noktası** kodlanır (tüm set değil).\n\n$$|x\\rangle = \\bigotimes_{i=1}^{N} \\Big( \\cos(x_i)|0\\rangle + \\sin(x_i)|1\\rangle \\Big)$$\n\nUnitary olarak:\n\n$$S_x = \\bigotimes_{i=1}^{N} U(x_i), \\qquad\nU(x_i) = \\begin{pmatrix} \\cos x_i & -\\sin x_i \\\\ \\sin x_i & \\cos x_i \\end{pmatrix}$$\n\n$Y$-ekseni etrafında tek kübit dönüşü:\n\n$$RY(\\theta) = \\begin{pmatrix} \\cos\\frac{\\theta}{2} & -\\sin\\frac{\\theta}{2} \\\\ \\sin\\frac{\\theta}{2} & \\cos\\frac{\\theta}{2} \\end{pmatrix}$$\n\nDolayısıyla $U(x_i) = RY(2x_i)$.\n\n**Avantaj:** $N$ kübit (veya daha az), **sabit derinlik** → mevcut donanıma uygun.  \n**Örnek:** $x = (0,\\, \\pi/4,\\, \\pi/2)$"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "edada049",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 272
    },
    "id": "edada049",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1787133335648,
     "user_tz": -180,
     "elapsed": 142,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "d6d3ee21-1e1e-4c05-e367-47f3e2a62e81"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Angle encoding: x = (0, π/4, π/2)\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<Figure size 203.885x284.278 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": "qc = QuantumCircuit(3)\nqc.ry(0, 0)                 # x0 = 0      → RY(0)\nqc.ry(2 * math.pi / 4, 1)   # x1 = π/4    → RY(π/2)\nqc.ry(2 * math.pi / 2, 2)   # x2 = π/2    → RY(π)\nprint(\"Angle encoding: x = (0, π/4, π/2)\")\nqc.draw(\"mpl\")"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3cefab0c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3cefab0c",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1787133335695,
     "user_tz": -180,
     "elapsed": 42,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "137f94ec-7c63-4f71-8b14-b74d216647ab"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Durum vektörü (yaklaşık):\n",
      "[0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j 0.7071+0.j 0.    +0.j\n",
      " 0.7071+0.j 0.    +0.j]\n"
     ]
    }
   ],
   "source": "sv = Statevector.from_instruction(qc)\nprint(\"Durum vektörü (yaklaşık):\")\nprint(np.round(sv.data, 4))"
  },
  {
   "cell_type": "markdown",
   "id": "f97d0bb8",
   "metadata": {
    "id": "f97d0bb8"
   },
   "source": "### Dense angle encoding\n\nAngle encoding’in hafif genellemesi: **kübit başına iki özellik** (genlik + göreli faz):\n\n$$|x\\rangle = \\bigotimes_{i=1}^{N/2}\n\\Big(\n\\cos(x_{2i-1})|0\\rangle + e^{i x_{2i}}\\sin(x_{2i-1})|1\\rangle\n\\Big)$$\n\nSinüs / üstel özel değildir; keyfi fonksiyonlarla veya parametreli unitary’lerle soyutlanabilir."
  },
  {
   "cell_type": "markdown",
   "id": "cbd215b9",
   "metadata": {
    "id": "cbd215b9"
   },
   "source": "---\n## 6. Arbitrary Encoding (Keyfi / Parametreli Kodlama)\n\n**Fikir:** $N$ özelliği, $n$ kübit üzerindeki $N$ parametreli kapının dönüş açılarına yaz ($n \\le N$).\n\n- Tek seferde bir veri noktası kodlar (angle encoding gibi)\n- Sabit derinlik + $n \\le N$ kübit → **NISQ uyumlu**\n- VQC ve quantum kernel yöntemlerinde en sık kullanılan aile\n\n### 6.1 EfficientSU2 ile 12 özellik → 3 kübit"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec46a9bf",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 283
    },
    "id": "ec46a9bf",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1787133335772,
     "user_tz": -180,
     "elapsed": 75,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "1a55cc04-c701-4cbc-806e-cb1693e25007"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Parametre sayısı: 12\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<Figure size 789.163x284.278 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": "if _FUNC:\n    circuit = efficient_su2(num_qubits=3, reps=1, insert_barriers=True)\nelse:\n    circuit = EfficientSU2(num_qubits=3, reps=1, insert_barriers=True)\n\nprint(f\"Parametre sayısı: {circuit.num_parameters}\")\ncircuit.decompose().draw(\"mpl\", fold=-1)"
  },
  {
   "cell_type": "markdown",
   "id": "83af3377",
   "metadata": {
    "id": "83af3377"
   },
   "source": "Veri noktası $x = [0.1, 0.2, \\ldots, 1.2]$ (12 özellik) — her parametreli kapı bir özelliği taşır:"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c96a930b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 266
    },
    "id": "c96a930b",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1787133335951,
     "user_tz": -180,
     "elapsed": 183,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "0be8e29e-8a7b-47de-d305-95e3150a8264"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<Figure size 789.163x284.278 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": "x = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2]\nencode = circuit.assign_parameters(x)  # Qiskit 2.x: assign_parameters (bind_parameters eski)\nencode.decompose().draw(\"mpl\", fold=-1)"
  },
  {
   "cell_type": "markdown",
   "id": "37417f2d",
   "metadata": {
    "id": "37417f2d"
   },
   "source": "### 6.2 ZZFeatureMap — 3 özellik, 3 kübit\n\nZZFeatureMap 3 kübitte yalnızca **3 özellik** kodlar (parametreli kapı sayısı daha fazla olsa da özellik boyutu kübit sayısına bağlıdır)."
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d16c05ad",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 255
    },
    "id": "d16c05ad",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1787133336157,
     "user_tz": -180,
     "elapsed": 207,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "4c611f06-ca45-4d4e-8d50-f25d75983d9a"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Parametre sayısı: 3\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<Figure size 1690.65x284.278 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": "if _FUNC:\n    circuit = zz_feature_map(3, reps=1, insert_barriers=True)\nelse:\n    circuit = ZZFeatureMap(3, reps=1, insert_barriers=True)\n\nprint(f\"Parametre sayısı: {circuit.num_parameters}\")\ncircuit.decompose().draw(\"mpl\", fold=-1)"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2fc50f85",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 255
    },
    "id": "2fc50f85",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1787133336319,
     "user_tz": -180,
     "elapsed": 158,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "784380ad-1fb4-459f-f460-e88bd28d8468"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<Figure size 1105.38x284.278 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": "x = [0.1, 0.2, 0.3]\nencode = circuit.assign_parameters(x)\nencode.decompose().draw(\"mpl\", fold=-1)"
  },
  {
   "cell_type": "markdown",
   "id": "fef9aeed",
   "metadata": {
    "id": "fef9aeed"
   },
   "source": "---\n## 7. Hızlı Quiz\n\n**Soru:** Parametreli bir kuantum devresinin **16 parametresi** var.  \nBu devre en fazla kaç özellik kodlayabilir?\n\n1. 4  \n2. 8  \n3. **16** ← doğru  \n4. 32  \n\n**Açıklama:** Arbitrary / angle-type encoding’de her parametre (genelde) bir özelliğe karşılık gelir.  \n16 parametre → en fazla 16 özellik. (ZZFeatureMap gibi yapılarda parametre sayısı özellik sayısından fazla olabilir; üst sınır yine parametre sayısı kadardır.)"
  },
  {
   "cell_type": "markdown",
   "id": "2386d1f7",
   "metadata": {
    "id": "2386d1f7"
   },
   "source": "---\n## 8. Özet Tablo — Hangi encoding ne zaman?\n\n| Yöntem | Ne kodlar? | Kübit | Derinlik | NISQ | VQC / QSVC ile |\n|--------|------------|-------|----------|------|----------------|\n| **Basis** | Bit string → taban durumu | ~N | Yüksek olabilir | Zayıf | Nadir |\n| **Amplitude** | Vektör → genlikler | $\\log_2 N$ | Yüksek (loading) | Zor | Teorik avantaj |\n| **Angle** | Özellik → $RY$ açısı | N | Sabit / düşük | İyi | Sık |\n| **Dense angle** | 2 özellik / kübit | N/2 | Sabit | İyi | Sık |\n| **Arbitrary (EfficientSU2, ZZ…)** | Özellik → parametreli kapılar | $n \\le N$ | Sabit | İyi | **En yaygın** |\n\n### VQC / QSVC bağlantısı\n- **VQC:** Encode (genelde angle / feature map) → trainable ansatz → ölçüm → loss → optimizer  \n- **QSVC:** Aynı feature map ile kernel/similarity üretilir; öğrenme classical SVM’de\n\n> Encoding seçimi, modelin ifade gücünü ve gürültüye duyarlılığını doğrudan etkiler.  \n> Product design case’lerinde “hangi representation?” sorusu çoğu zaman encoding seçimine iner."
  },
  {
   "cell_type": "markdown",
   "id": "b415331a",
   "metadata": {
    "id": "b415331a"
   },
   "source": "---\n## 9. Kaynaklar\n\n1. Maria Schuld & Francesco Petruccione, *Supervised Learning with Quantum Computers*, Springer 2018.  \n   [doi:10.1007/978-3-319-96424-9](https://www.springer.com/gp/book/9783319964232)\n\n2. Ryan LaRose & Brian Coyle, *Robust data encodings for quantum classifiers*, Phys. Rev. A 102, 032420 (2020).  \n   [doi:10.1103/PhysRevA.102.032420](https://journals.aps.org/pra/abstract/10.1103/PhysRevA.102.032420) · [arXiv:2003.01695](https://arxiv.org/abs/2003.01695)\n\n---\n*QML Study Companion · Veri Kodlama (Türkçe) · Qiskit 2.x*"
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "name": "python",
   "version": "3.10.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}