{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "91bfe1ad",
   "metadata": {
    "id": "91bfe1ad"
   },
   "source": "# Quantum Chemistry in a Nutshell + Practical Quantum Computing Examples\n\n**Kaynak Makale:**  \n- [Quantum Chemistry in a Nutshell I – For Quantum Computing Enthusiasts](https://www.quantum-computing-info.com/quantum-chemistry-in-a-nutshell/)  \n- [Quantum Chemistry in a Nutshell II – Post-Hartree-Fock methods](https://www.quantum-computing-info.com/post-hartree-fock/)\n\nBu notebook, yukarıdaki iki makalede anlatılan temel kuantum kimyası kavramlarını **görseller, diyagramlar ve grafikler** ile zenginleştirerek özetler ve **Qiskit** ile **PennyLane** kullanarak pratik örnek kodlar sunar. Özellikle H₂ molekülü üzerinden VQE uygulamaları yapılır.\n\n**Hedef Kitle:** Kuantum hesaplama meraklıları (kimya geçmişi zorunlu değil)\n\n---\n\n## İçindekiler\n1. Kurulum\n2. Makale Özeti – Temel Kavramlar + Görseller\n3. Atomik Orbitaller ve Diyagramlar\n4. Second Quantization ve Jordan-Wigner\n5. Qiskit ile H₂ VQE Örneği\n6. PennyLane ile H₂ VQE Örneği\n7. Potansiyel Enerji Yüzeyi (PES) ve Karşılaştırmalar\n8. İleri Konular ve Kaynaklar\n"
  },
  {
   "cell_type": "markdown",
   "id": "67f9521f",
   "metadata": {
    "id": "67f9521f"
   },
   "source": "## 1. Kurulum\n\nGoogle Colab'da çalıştırmak için aşağıdaki hücreyi çalıştırın. (Yerel ortamda da çalışır.)"
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3fc6f083",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3fc6f083",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1787416728104,
     "user_tz": -180,
     "elapsed": 55511,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "0d83f30e-f8d9-4b05-a5e4-fc45a25bd2dc"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.8/9.8 MB\u001b[0m \u001b[31m64.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m80.8 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[31m85.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m327.8/327.8 kB\u001b[0m \u001b[31m24.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m52.8/52.8 MB\u001b[0m \u001b[31m15.7 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[31m55.2 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[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m368.2/368.2 kB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
      "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
      "  \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
      "  \n",
      "  \u001b[31m×\u001b[0m \u001b[32mpip subprocess to install backend dependencies\u001b[0m did not run successfully.\n",
      "  \u001b[31m│\u001b[0m exit code: \u001b[1;36m2\u001b[0m\n",
      "  \u001b[31m╰─>\u001b[0m See above for output.\n",
      "  \n",
      "  \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
      "  Installing backend dependencies ... \u001b[?25l\u001b[?25herror\n",
      "\u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
      "\n",
      "\u001b[31m×\u001b[0m \u001b[32mpip subprocess to install backend dependencies\u001b[0m did not run successfully.\n",
      "\u001b[31m│\u001b[0m exit code: \u001b[1;36m2\u001b[0m\n",
      "\u001b[31m╰─>\u001b[0m See above for output.\n",
      "\n",
      "\u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
      "Kurulum tamamlandı!\n"
     ]
    }
   ],
   "source": "# Gerekli paketlerin kurulumu (Colab için)\n!pip install -q qiskit qiskit-nature qiskit-aer qiskit-algorithms pyscf\n!pip install -q pennylane pennylane-qchem\n!pip install -q matplotlib scipy\n\nprint(\"Kurulum tamamlandı!\")"
  },
  {
   "cell_type": "markdown",
   "id": "632df87c",
   "metadata": {
    "id": "632df87c"
   },
   "source": "## 2. Makale Özeti – Temel Kavramlar + Görseller\n\nMakaleler, kuantum hesaplama literatüründe sıkça geçen kuantum kimyası kavramlarını sade bir dille anlatır. Aşağıda en kritik noktaların **görsel destekli** özeti yer almaktadır.\n\n### 2.1 Neden Kuantum Kimyası?\n- Kuantum kimyası, kuantum hesaplamanın **en umut verici erken uygulama alanlarından** biridir.\n- Moleküler spin-orbitallerden qubitlere **son derece verimli bir eşleme** vardır.\n- Klasik yöntemler (özellikle Full CI) elektron sayısıyla üstel ölçeklenir.\n- Kimyasal doğruluk (chemical accuracy) ≈ **1 kcal/mol** (≈ 1.6 mHa) hedeflenir.\n- **1.36 kuralı**: Aktivasyon enerjisinde 1.36 kcal/mol hata, denge sabitinde 10 kat hataya yol açar.\n\n### 2.2 Atomlar, Spin ve Quantum Numbers\nHidrojen benzeri atomlar için Schrödinger denklemi tam çözülebilir. Kuantum sayıları:\n- $n$ (principal), $l$ (azimuthal), $m$ (magnetic), $m_s$ (spin)\n\n**Atomik orbitaller** (spatial) + spin → **spin-orbitaller**.\n"
  },
  {
   "cell_type": "markdown",
   "id": "30d41e27",
   "metadata": {
    "id": "30d41e27"
   },
   "source": "\n### Atomik Orbitallerin 3D Görünümü\n\nAşağıdaki görsel, hidrojen benzeri atomik orbitallerin (1s, 2s, 2p, 3s, 3p, 3d) olasılık yoğunluk bulutlarını göstermektedir. Renk, dalga fonksiyonunun fazını temsil eder.\n\n![Atomic orbital clouds](https://upload.wikimedia.org/wikipedia/commons/thumb/e/e7/Atomic-orbital-clouds_spd_m0.png/700px-Atomic-orbital-clouds_spd_m0.png)\n\n*Kaynak: Wikimedia Commons – Atomic-orbital-clouds spd m0 (CC BY-SA)*\n"
  },
  {
   "cell_type": "markdown",
   "id": "73a5539b",
   "metadata": {
    "id": "73a5539b"
   },
   "source": "\n### Gerçek ve Kompleks Orbitaller (Spherical Harmonics)\n\nSol taraf: Kimyada kullanılan **gerçek değerli** orbitaller (px, py, pz, dxy vb.).  \nSağ taraf: $m$-özdurumları (kompleks fazlı).\n\n![Atomic orbitals spdf](https://upload.wikimedia.org/wikipedia/commons/thumb/8/8c/Atomic_orbitals_spdf_m-eigenstates_and_superpositions.png/800px-Atomic_orbitals_spdf_m-eigenstates_and_superpositions.png)\n\n*Kaynak: Geek3 – Wikimedia Commons (CC BY-SA)*\n"
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b3b73416",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 468
    },
    "id": "b3b73416",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1787416728907,
     "user_tz": -180,
     "elapsed": 747,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "c41042ef-8bc9-452b-b492-1bae64676a3c"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Not: 1s orbitalinin maksimum olasılığı Bohr yarıçapı civarındadır (~1 a.u.).\n"
     ]
    }
   ],
   "source": "# Hidrojen atomu radyal dalga fonksiyonları ve olasılık yoğunlukları\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.special import genlaguerre, factorial\n\ndef radial_wavefunction(n, l, r, Z=1):\n    \"\"\"Hidrojen benzeri atom radyal dalga fonksiyonu R_nl(r)\"\"\"\n    rho = 2 * Z * r / n\n    norm = np.sqrt((2*Z/n)**3 * factorial(n-l-1) / (2*n * factorial(n+l)))\n    L = genlaguerre(n-l-1, 2*l+1)(rho)\n    return norm * np.exp(-rho/2) * rho**l * L\n\nr = np.linspace(0.01, 25, 500)\nfig, axes = plt.subplots(1, 2, figsize=(14, 5))\n\nfor n, l, label, color in [(1,0,\"1s\",\"C0\"), (2,0,\"2s\",\"C1\"), (2,1,\"2p\",\"C2\"), (3,0,\"3s\",\"C3\")]:\n    R = radial_wavefunction(n, l, r)\n    axes[0].plot(r, R, label=label, color=color, linewidth=2)\n\naxes[0].set_xlabel(r\"$r$ (a.u.)\")\naxes[0].set_ylabel(r\"$R_{nl}(r)$\")\naxes[0].set_title(\"Radyal Dalga Fonksiyonları\")\naxes[0].legend()\naxes[0].grid(True, alpha=0.3)\naxes[0].axhline(0, color=\"k\", linewidth=0.5)\n\nfor n, l, label, color in [(1,0,\"1s\",\"C0\"), (2,0,\"2s\",\"C1\"), (2,1,\"2p\",\"C2\"), (3,0,\"3s\",\"C3\")]:\n    R = radial_wavefunction(n, l, r)\n    P = 4 * np.pi * r**2 * R**2\n    axes[1].plot(r, P, label=label, color=color, linewidth=2)\n\naxes[1].set_xlabel(r\"$r$ (a.u.)\")\naxes[1].set_ylabel(r\"$4\\pi r^2 |R_{nl}|^2$\")\naxes[1].set_title(\"Radyal Olasılık Yoğunluğu\")\naxes[1].legend()\naxes[1].grid(True, alpha=0.3)\n\nplt.tight_layout()\nplt.show()\nprint(\"Not: 1s orbitalinin maksimum olasılığı Bohr yarıçapı civarındadır (~1 a.u.).\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "b1af0bd6",
   "metadata": {
    "id": "b1af0bd6"
   },
   "source": "\n### 2.3 Elektron Spini ve Bloch Küresi\n\nElektron spininin iki boyutlu temsili Pauli matrisleri ile tanımlanır. Spin durumları Bloch küresi üzerinde görselleştirilir.\n"
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cd6ae48c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 723
    },
    "id": "cd6ae48c",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1787416731809,
     "user_tz": -180,
     "elapsed": 2803,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "12aa5270-754e-4be6-fcd9-25a0d1dec94a"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 800x700 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ],
   "source": "# Bloch küresi görselleştirmesi\nfrom mpl_toolkits.mplot3d import Axes3D\n\nfig = plt.figure(figsize=(8, 7))\nax = fig.add_subplot(111, projection=\"3d\")\n\nu = np.linspace(0, 2*np.pi, 100)\nv = np.linspace(0, np.pi, 100)\nx = np.outer(np.cos(u), np.sin(v))\ny = np.outer(np.sin(u), np.sin(v))\nz = np.outer(np.ones(np.size(u)), np.cos(v))\nax.plot_surface(x, y, z, color=\"cyan\", alpha=0.15, edgecolor=\"none\")\n\nax.quiver(0,0,0, 1.3,0,0, color=\"r\", arrow_length_ratio=0.1, linewidth=2)\nax.quiver(0,0,0, 0,1.3,0, color=\"g\", arrow_length_ratio=0.1, linewidth=2)\nax.quiver(0,0,0, 0,0,1.3, color=\"b\", arrow_length_ratio=0.1, linewidth=2)\nax.text(1.4,0,0, r\"$|+>_x$\", color=\"r\")\nax.text(0,1.4,0, r\"$|+>_y$\", color=\"g\")\nax.text(0,0,1.4, r\"$|0> = |\\uparrow>$\", color=\"b\")\nax.text(0,0,-1.5, r\"$|1> = |\\downarrow>$\", color=\"b\")\n\ntheta, phi = np.pi/3, np.pi/4\nsx = np.sin(theta)*np.cos(phi)\nsy = np.sin(theta)*np.sin(phi)\nsz = np.cos(theta)\nax.quiver(0,0,0, sx, sy, sz, color=\"purple\", arrow_length_ratio=0.15, linewidth=3)\nax.scatter([sx],[sy],[sz], color=\"purple\", s=80)\n\nax.set_xlim([-1.5,1.5]); ax.set_ylim([-1.5,1.5]); ax.set_zlim([-1.5,1.5])\nax.set_xlabel(\"X\"); ax.set_ylabel(\"Y\"); ax.set_zlabel(\"Z\")\nax.set_title(\"Bloch Küresi – Elektron Spin Durumu\")\nax.set_box_aspect([1,1,1])\nplt.tight_layout()\nplt.show()\n"
  },
  {
   "cell_type": "markdown",
   "id": "b58f723d",
   "metadata": {
    "id": "b58f723d"
   },
   "source": "\n### 2.4 Moleküler Bağlar ve Born-Oppenheimer Yaklaşımı\n\n**Born-Oppenheimer yaklaşımı**: Çekirdekler elektronlara göre çok ağır olduğu için sabit kabul edilir. Elektronik Hamiltonian:\n\n$$\nH_{\\rm el} = T_e + V_{eN} + V_{ee} + V_{NN}\n$$\n\nAşağıda H + H₂ reaksiyonu için tipik bir potansiyel enerji yüzeyi örneği gösterilmiştir.\n"
  },
  {
   "cell_type": "markdown",
   "id": "58a5fef3",
   "metadata": {
    "id": "58a5fef3"
   },
   "source": "\n![H2 Potential Energy Surface](https://upload.wikimedia.org/wikipedia/commons/thumb/5/5a/H%2BH2_Potential_energy_surface.jpg/700px-H%2BH2_Potential_energy_surface.jpg)\n\n*Kaynak: Wikimedia Commons – H+H₂ Potential energy surface*\n"
  },
  {
   "cell_type": "markdown",
   "id": "90c322e4",
   "metadata": {
    "id": "90c322e4"
   },
   "source": "\n### 2.5 Hartree-Fock, Slater Determinantları ve Post-HF\n\n- **Hartree-Fock (HF)**: Ortalama alan yaklaşımı. Tek bir Slater determinantı kullanır.\n- **Post-Hartree-Fock** yöntemler: Korelasyon enerjisini kurtarmak için geliştirilmiştir.\n  - Full Configuration Interaction (FCI)\n  - Coupled Cluster (CCSD, CCSD(T) – “altın standart”)\n  - Møller-Plesset (MP2)\n  - MCSCF / CASSCF (çoklu referans sistemler için)\n  - DMRG\n\n**Slater Determinantı** (N elektron için antisimetrik dalga fonksiyonu):\n\n$$\n\\Psi(x_1,\\dots,x_N) = \\frac{1}{\\sqrt{N!}}\n\\begin{vmatrix}\n\\chi_1(x_1) & \\chi_2(x_1) & \\cdots & \\chi_N(x_1) \\\\\n\\chi_1(x_2) & \\chi_2(x_2) & \\cdots & \\chi_N(x_2) \\\\\n\\vdots & \\vdots & \\ddots & \\vdots \\\\\n\\chi_1(x_N) & \\chi_2(x_N) & \\cdots & \\chi_N(x_N)\n\\end{vmatrix}\n$$\n"
  },
  {
   "cell_type": "markdown",
   "id": "51034ae7",
   "metadata": {
    "id": "51034ae7"
   },
   "source": "## 3. Second Quantization → Qubit Eşlemesi (Jordan-Wigner)\n\nMakalede detaylı anlatıldığı üzere, fermiyonik operatörler qubit operatörlerine **Jordan-Wigner dönüşümü** ile eşlenir:\n\n$$\na_p^\\dagger = \\left( \\prod_{k<p} Z_k \\right) \\frac{X_p - i Y_p}{2}, \\quad\na_p = \\left( \\prod_{k<p} Z_k \\right) \\frac{X_p + i Y_p}{2}\n$$\n\nBu dönüşüm, lokal fermiyon operatörlerini non-lokal Pauli dizilerine çevirir. Alternatifler: Parity, Bravyi-Kitaev dönüşümleri.\n\nAşağıdaki hücrelerde hem Qiskit Nature hem de PennyLane bu eşlemeyi otomatik olarak yapar.\n"
  },
  {
   "cell_type": "markdown",
   "id": "46fabd65",
   "metadata": {
    "id": "46fabd65"
   },
   "source": "\n### Jordan-Wigner Dönüşümü Görsel Özeti\n\nFermiyonik yaratma/yok etme operatörleri, qubit Pauli operatörlerine şu şekilde eşlenir:\n\n$$\na_p^\\dagger = \\left( \\prod_{k<p} Z_k \\right) \\frac{X_p - i Y_p}{2}\n$$\n\nBu, lokal bir fermiyon operatörünü **non-lokal** bir Pauli dizisine çevirir.\n"
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "91b41e4e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 407
    },
    "id": "91b41e4e",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1787416732750,
     "user_tz": -180,
     "elapsed": 593,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "2d1d529d-dde0-4c21-b057-63dfc737a84a"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ],
      "image/png": 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     },
     "metadata": {}
    }
   ],
   "source": "# Jordan-Wigner mapping visualization (schematic)\nimport matplotlib.pyplot as plt\n\nfig, ax = plt.subplots(figsize=(12, 4))\nax.set_xlim(0, 10)\nax.set_ylim(0, 3)\nax.axis(\"off\")\nax.set_title(\"Jordan-Wigner Mapping (Schematic) – 4 Spin-Orbitals → 4 Qubits\", fontsize=14)\n\nfor i, label in enumerate([\"q0\", \"q1\", \"q2\", \"q3\"]):\n    circle = plt.Circle((1.5 + i*2, 1.5), 0.4, color=\"skyblue\", ec=\"navy\", lw=2)\n    ax.add_patch(circle)\n    ax.text(1.5 + i*2, 1.5, label, ha=\"center\", va=\"center\", fontsize=11, fontweight=\"bold\")\n\nax.annotate(\"\", xy=(1.5, 2.2), xytext=(1.5, 2.6),\n            arrowprops=dict(arrowstyle=\"->\", color=\"red\", lw=2))\nax.text(1.5, 2.75, \"Z\", ha=\"center\", color=\"red\", fontsize=12, fontweight=\"bold\")\n\nax.annotate(\"\", xy=(3.5, 2.2), xytext=(3.5, 2.6),\n            arrowprops=dict(arrowstyle=\"->\", color=\"red\", lw=2))\nax.text(3.5, 2.75, \"Z\", ha=\"center\", color=\"red\", fontsize=12, fontweight=\"bold\")\n\nax.annotate(\"\", xy=(5.5, 2.2), xytext=(5.5, 2.6),\n            arrowprops=dict(arrowstyle=\"->\", color=\"purple\", lw=2))\nax.text(5.5, 2.75, \"X/Y\", ha=\"center\", color=\"purple\", fontsize=12, fontweight=\"bold\")\n\nax.text(5.0, 0.4, r\"$a_2^\\dagger \\longleftrightarrow Z_0 Z_1 \\frac{X_2 - i Y_2}{2}$\",\n        fontsize=13, ha=\"center\",\n        bbox=dict(boxstyle=\"round\", facecolor=\"wheat\", alpha=0.8))\n\nplt.tight_layout()\nplt.show()\n"
  },
  {
   "cell_type": "markdown",
   "id": "2de5c08b",
   "metadata": {
    "id": "2de5c08b"
   },
   "source": "## 4. Qiskit ile H₂ Molekülü VQE Örneği\n\nH₂, kuantum kimyasında en basit ve en sık kullanılan test sistemidir (2 elektron, minimal baz setinde 4 spin-orbital → 4 qubit)."
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c496a945",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c496a945",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1787416733310,
     "user_tz": -180,
     "elapsed": 553,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "635f4976-eb7f-4f25-d8c8-b4175ddfd7a2"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Qiskit kütüphaneleri başarıyla yüklendi.\n",
      "Estimator tipi: <class 'qiskit.primitives.statevector_estimator.StatevectorEstimator'>\n",
      "AerEstimator tipi: <class 'qiskit_aer.primitives.estimator.Estimator'>\n"
     ]
    }
   ],
   "source": "import numpy as np\n\n# Qiskit Nature & Algorithms\nfrom qiskit_nature.second_q.drivers import PySCFDriver\nfrom qiskit_nature.second_q.mappers import JordanWignerMapper\nfrom qiskit_nature.second_q.circuit.library import HartreeFock, UCCSD\nfrom qiskit_nature.second_q.algorithms import GroundStateEigensolver\n\nfrom qiskit_algorithms import VQE, NumPyMinimumEigensolver\nfrom qiskit_algorithms.optimizers import SLSQP, SPSA\n\n# Primitives – Qiskit 1.x / 2.x uyumlu import\ntry:\n    # Eski yol (Qiskit < 1.0)\n    from qiskit.primitives import Estimator\nexcept ImportError:\n    try:\n        # Qiskit 1.x StatevectorEstimator\n        from qiskit.primitives import StatevectorEstimator as Estimator\n    except ImportError:\n        from qiskit.primitives import BackendEstimator as Estimator\n\nfrom qiskit_aer import AerSimulator\ntry:\n    from qiskit_aer.primitives import Estimator as AerEstimator\nexcept ImportError:\n    try:\n        from qiskit_aer.primitives import EstimatorV2 as AerEstimator\n    except ImportError:\n        # Fallback: StatevectorEstimator kullan\n        from qiskit.primitives import StatevectorEstimator as AerEstimator\n\nprint(\"Qiskit kütüphaneleri başarıyla yüklendi.\")\nprint(f\"Estimator tipi: {Estimator}\")\nprint(f\"AerEstimator tipi: {AerEstimator}\")\n"
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ac08660d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ac08660d",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1787416733703,
     "user_tz": -180,
     "elapsed": 292,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "562dc4bc-4e02-4fdf-ebb1-cdf13137b3a6"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Moleküler problem oluşturuldu.\n",
      "Elektron sayısı: (1, 1)\n",
      "Spin-orbital sayısı: 4\n"
     ]
    }
   ],
   "source": "# H2 molekülünü tanımla (bond length = 0.735 Angstrom – denge mesafesine yakın)\ndriver = PySCFDriver(\n    atom=\"H 0 0 0; H 0 0 0.735\",\n    basis=\"sto3g\",\n    charge=0,\n    spin=0\n)\n\nproblem = driver.run()\nprint(\"Moleküler problem oluşturuldu.\")\nprint(f\"Elektron sayısı: {problem.num_particles}\")\nprint(f\"Spin-orbital sayısı: {problem.num_spatial_orbitals * 2}\")"
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cec57597",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cec57597",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1787416734012,
     "user_tz": -180,
     "elapsed": 303,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "e5c373b9-0ec3-44b2-94b7-695002a8783c"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Qubit Hamiltonian terim sayısı: 15\n",
      "\n",
      "İlk birkaç terim:\n",
      "SparsePauliOp(['IIII', 'IIIZ', 'IIZI', 'IIZZ', 'IZII'],\n",
      "              coeffs=[-0.81054798+0.j,  0.17218393+0.j, -0.22575349+0.j,  0.12091263+0.j,\n",
      "  0.17218393+0.j])\n"
     ]
    }
   ],
   "source": "# Jordan-Wigner mapper\nmapper = JordanWignerMapper()\n\n# Hamiltonian'ı qubit operatörlerine çevir\nqubit_op = mapper.map(problem.hamiltonian.second_q_op())\nprint(f\"Qubit Hamiltonian terim sayısı: {len(qubit_op)}\")\nprint(\"\\nİlk birkaç terim:\")\nprint(qubit_op[:5] if hasattr(qubit_op, '__getitem__') else list(qubit_op)[:5])"
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a2a5a7b7",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 367
    },
    "id": "a2a5a7b7",
    "executionInfo": {
     "status": "error",
     "timestamp": 1787416734452,
     "user_tz": -180,
     "elapsed": 343,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "ed62a4f4-9663-49ad-ae13-e83ff4143d05"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Yeni API (mapper constructor'da yok) kullanıldı.\n"
     ]
    },
    {
     "output_type": "error",
     "ename": "ValueError",
     "evalue": "The qubit_mapper cannot be `None`.",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_1501/4095399816.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     36\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Eski API (mapper parametreli) kullanıldı.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Ansatz parametre sayısı : {ansatz.num_parameters}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     39\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Qubit sayısı            : {ansatz.num_qubits}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.13/dist-packages/qiskit/circuit/library/blueprintcircuit.py\u001b[0m in \u001b[0;36mnum_parameters\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    135\u001b[0m         \u001b[0;34m\"\"\"The number of parameter objects in the circuit.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    136\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_built\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 137\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_build\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    138\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_parameters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.13/dist-packages/qiskit/circuit/library/n_local/evolved_operator_ansatz.py\u001b[0m in \u001b[0;36m_build\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    455\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    456\u001b[0m         \u001b[0;31m# need to check configuration here to ensure the operators are not None\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 457\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_configuration\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    458\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    459\u001b[0m         \u001b[0mcoeff\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mParameter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"c\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.13/dist-packages/qiskit_nature/second_q/circuit/library/ansatzes/ucc.py\u001b[0m in \u001b[0;36m_check_configuration\u001b[0;34m(self, raise_on_failure)\u001b[0m\n\u001b[1;32m    350\u001b[0m         \u001b[0;31m# operators by getting them, and if we detect they are still None they\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    351\u001b[0m         \u001b[0;31m# will be built so that its valid check will end up passing in that regard.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 352\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_ucc_configuration\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mraise_on_failure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    353\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    354\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.13/dist-packages/qiskit_nature/second_q/circuit/library/ansatzes/ucc.py\u001b[0m in \u001b[0;36m_check_ucc_configuration\u001b[0;34m(self, raise_on_failure)\u001b[0m\n\u001b[1;32m    410\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqubit_mapper\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    411\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mraise_on_failure\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 412\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"The qubit_mapper cannot be `None`.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    413\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    414\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: The qubit_mapper cannot be `None`."
     ]
    }
   ],
   "source": "# Ansatz: Hartree-Fock başlangıç + UCCSD\n# Qiskit Nature güncel API uyumlu\n\nnum_particles = problem.num_particles\nnum_spatial_orbitals = problem.num_spatial_orbitals\n\n# 1) Önce mapper'sız dene (en yeni API)\ntry:\n    hf_state = HartreeFock(\n        num_spatial_orbitals=num_spatial_orbitals,\n        num_particles=num_particles,\n    )\n    ansatz = UCCSD(\n        num_spatial_orbitals=num_spatial_orbitals,\n        num_particles=num_particles,\n        initial_state=hf_state,\n    )\n    # Mapper uygula\n    if hasattr(ansatz, \"num_qubits\") and ansatz.num_qubits is None:\n        ansatz = mapper.map(ansatz)\n    print(\"Yeni API (mapper constructor'da yok) kullanıldı.\")\nexcept TypeError as e:\n    print(f\"Yeni API başarısız ({e}), eski API deneniyor...\")\n    # 2) Eski API (mapper parametresi var)\n    hf_state = HartreeFock(\n        num_spatial_orbitals=num_spatial_orbitals,\n        num_particles=num_particles,\n        mapper=mapper,\n    )\n    ansatz = UCCSD(\n        num_spatial_orbitals=num_spatial_orbitals,\n        num_particles=num_particles,\n        mapper=mapper,\n        initial_state=hf_state,\n    )\n    print(\"Eski API (mapper parametreli) kullanıldı.\")\n\nprint(f\"Ansatz parametre sayısı : {ansatz.num_parameters}\")\nprint(f\"Qubit sayısı            : {ansatz.num_qubits}\")\ntry:\n    print(f\"Ansatz derinliği        : {ansatz.decompose().depth()}\")\nexcept Exception:\n    pass\n"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97ba0164",
   "metadata": {
    "id": "97ba0164",
    "executionInfo": {
     "status": "aborted",
     "timestamp": 1787416736247,
     "user_tz": -180,
     "elapsed": 336,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    }
   },
   "outputs": [],
   "source": "# Ansatz devresini çiz (UCCSD)\nprint(\"UCCSD Ansatz Devresi:\")\ntry:\n    from IPython.display import display\n    # Eğer hâlâ fermionic ise önce map et\n    circuit_to_draw = ansatz\n    if not hasattr(circuit_to_draw, \"num_qubits\") or circuit_to_draw.num_qubits is None:\n        circuit_to_draw = mapper.map(ansatz)\n    display(circuit_to_draw.decompose().draw(output=\"mpl\", fold=80, scale=0.55))\nexcept Exception as e:\n    print(f\"Matplotlib drawer kullanılamadı ({type(e).__name__}: {e})\")\n    print(\"Text çıktısı:\")\n    try:\n        print(ansatz.decompose().draw(output=\"text\", fold=100))\n    except Exception:\n        print(ansatz)\n"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d55c7b9",
   "metadata": {
    "id": "3d55c7b9",
    "executionInfo": {
     "status": "aborted",
     "timestamp": 1787416736257,
     "user_tz": -180,
     "elapsed": 63817,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    }
   },
   "outputs": [],
   "source": "# VQE çalıştır (Aer / Statevector ile)\n# AerEstimator varsa onu kullan, yoksa StatevectorEstimator'a düş\ntry:\n    estimator = AerEstimator()\nexcept Exception:\n    from qiskit.primitives import StatevectorEstimator\n    estimator = StatevectorEstimator()\n\nvqe = VQE(\n    estimator=estimator,\n    ansatz=ansatz,\n    optimizer=SLSQP(maxiter=100)\n)\n\nsolver = GroundStateEigensolver(mapper, vqe)\nresult = solver.solve(problem)\n\nprint(\"\\n===== Qiskit VQE Sonuçları =====\")\nprint(f\"Hesaplanan toplam enerji : {result.total_energies[0]:.8f} Ha\")\nprint(f\"Nükleer itme enerjisi    : {problem.nuclear_repulsion_energy:.8f} Ha\")\nif hasattr(result, \"raw_result\") and hasattr(result.raw_result, \"eigenvalue\"):\n    print(f\"Raw eigenvalue           : {result.raw_result.eigenvalue.real:.8f} Ha\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "c253d9ad",
   "metadata": {
    "id": "c253d9ad"
   },
   "source": "\n> **Not:** Eğer yukarıdaki hücrede hâlâ `Estimator` / `VQE` ile ilgili hata alırsanız, aşağıdaki **alternatif modern API** hücresini kullanın. Qiskit 1.x / 2.x ile daha uyumludur.\n"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae6e7488",
   "metadata": {
    "id": "ae6e7488",
    "executionInfo": {
     "status": "aborted",
     "timestamp": 1787416736642,
     "user_tz": -180,
     "elapsed": 64201,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    }
   },
   "outputs": [],
   "source": "# ===== ALTERNATİF: Modern Qiskit API (EstimatorV2 / StatevectorEstimator) =====\n# Bu hücre, eski Estimator import'u çalışmazsa kullanılmalıdır.\n\nfrom qiskit.primitives import StatevectorEstimator\nfrom qiskit_algorithms import VQE\nfrom qiskit_algorithms.optimizers import SLSQP\nfrom qiskit_nature.second_q.algorithms import GroundStateEigensolver\n\nestimator = StatevectorEstimator()\n\nvqe = VQE(\n    estimator=estimator,\n    ansatz=ansatz,\n    optimizer=SLSQP(maxiter=100)\n)\n\nsolver = GroundStateEigensolver(mapper, vqe)\nresult = solver.solve(problem)\n\nprint(\"\\n===== Qiskit VQE Sonuçları (Modern API) =====\")\nprint(f\"Hesaplanan toplam enerji : {result.total_energies[0]:.8f} Ha\")\nprint(f\"Nükleer itme enerjisi    : {problem.nuclear_repulsion_energy:.8f} Ha\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "43c3c10c",
   "metadata": {
    "id": "43c3c10c"
   },
   "source": "### Klasik Referans (Exact Diagonalization)\nKarşılaştırma için klasik exact solver kullanalım."
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9773d7ac",
   "metadata": {
    "id": "9773d7ac",
    "executionInfo": {
     "status": "aborted",
     "timestamp": 1787416736644,
     "user_tz": -180,
     "elapsed": 64202,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    }
   },
   "outputs": [],
   "source": "from qiskit_algorithms import NumPyMinimumEigensolver\n\nnumpy_solver = NumPyMinimumEigensolver()\nexact_solver = GroundStateEigensolver(mapper, numpy_solver)\nexact_result = exact_solver.solve(problem)\n\nprint(\"===== Exact (NumPy) Sonuç =====\")\nprint(f\"Exact toplam enerji: {exact_result.total_energies[0]:.10f} Ha\")\nprint(f\"VQE hatası         : {abs(result.total_energies[0] - exact_result.total_energies[0]):.2e} Ha\")\n"
  },
  {
   "cell_type": "markdown",
   "id": "22231fec",
   "metadata": {
    "id": "22231fec"
   },
   "source": "## 5. PennyLane ile H₂ VQE Örneği\n\nPennyLane, diferansiyel programlama odaklı bir kütüphanedir ve kimya için `pennylane.qchem` modülünü sunar."
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13b0fbec",
   "metadata": {
    "id": "13b0fbec",
    "executionInfo": {
     "status": "aborted",
     "timestamp": 1787416736646,
     "user_tz": -180,
     "elapsed": 64203,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    }
   },
   "outputs": [],
   "source": "import pennylane as qml\nfrom pennylane import numpy as pnp\nfrom pennylane import qchem\n\nprint(f\"PennyLane versiyonu: {qml.__version__}\")"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3ab2df4",
   "metadata": {
    "id": "a3ab2df4",
    "executionInfo": {
     "status": "aborted",
     "timestamp": 1787416736649,
     "user_tz": -180,
     "elapsed": 64205,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    }
   },
   "outputs": [],
   "source": "# H2 molekülünü PennyLane ile tanımla\nsymbols = [\"H\", \"H\"]\ncoordinates = pnp.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.735])  # Angstrom\n\n# Moleküler Hamiltonian oluştur (STO-3G, Jordan-Wigner)\nH, qubits = qchem.molecular_hamiltonian(\n    symbols,\n    coordinates,\n    charge=0,\n    mult=1,\n    basis=\"sto-3g\",\n    method=\"pyscf\",\n    active_electrons=2,\n    active_orbitals=2\n)\n\nprint(f\"Qubit sayısı: {qubits}\")\nprint(f\"Hamiltonian terim sayısı: {len(H.ops)}\")"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "370204b2",
   "metadata": {
    "id": "370204b2",
    "executionInfo": {
     "status": "aborted",
     "timestamp": 1787416736652,
     "user_tz": -180,
     "elapsed": 64207,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    }
   },
   "outputs": [],
   "source": "# Hartree-Fock state ve UCCSD ansatz\nelectrons = 2\nhf_state = qchem.hf_state(electrons, qubits)\n\nsingles, doubles = qchem.excitations(electrons, qubits)\nprint(f\"Single excitations: {len(singles)}\")\nprint(f\"Double excitations: {len(doubles)}\")\n\ndev = qml.device(\"default.qubit\", wires=qubits)\n\n@qml.qnode(dev)\ndef circuit(params, wires):\n    qml.BasisState(hf_state, wires=wires)\n    qml.AllSinglesDoubles(params, wires, hf_state, singles, doubles)\n    return qml.expval(H)\n\n# Parametreleri başlat\nnp.random.seed(42)\nparams = pnp.random.normal(0, np.pi, len(singles) + len(doubles), requires_grad=True)\n\nprint(f\"Parametre sayısı: {len(params)}\")"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07577359",
   "metadata": {
    "id": "07577359",
    "executionInfo": {
     "status": "aborted",
     "timestamp": 1787416736655,
     "user_tz": -180,
     "elapsed": 64209,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    }
   },
   "outputs": [],
   "source": "# Optimizasyon\nopt = qml.GradientDescentOptimizer(stepsize=0.4)\n\nenergy_history = []\nmax_iterations = 100\nconv_tol = 1e-6\n\nfor n in range(max_iterations):\n    params, energy = opt.step_and_cost(circuit, params, wires=range(qubits))\n    energy_history.append(energy)\n\n    if n % 10 == 0:\n        print(f\"Iteration {n:3d}: Energy = {energy:.8f} Ha\")\n\n    if n > 0 and abs(energy_history[-1] - energy_history[-2]) < conv_tol:\n        print(f\"\\nYakınsama sağlandı (iteration {n})\")\n        break\n\nprint(\"\\n===== PennyLane VQE Sonuçları =====\")\nprint(f\"Son enerji: {energy:.10f} Ha\")"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9be45fa2",
   "metadata": {
    "id": "9be45fa2",
    "executionInfo": {
     "status": "aborted",
     "timestamp": 1787416736657,
     "user_tz": -180,
     "elapsed": 64209,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    }
   },
   "outputs": [],
   "source": "# Exact diagonalization (PennyLane)\nfrom pennylane import numpy as np\n\nH_matrix = qml.matrix(H)\nexact_energy = np.linalg.eigvalsh(H_matrix)[0]\n\nprint(f\"Exact enerji (PennyLane): {exact_energy:.10f} Ha\")\nprint(f\"VQE hatası               : {abs(energy - exact_energy):.2e} Ha\")"
  },
  {
   "cell_type": "markdown",
   "id": "e58e7ce5",
   "metadata": {
    "id": "e58e7ce5"
   },
   "source": "## 6. Sonuçların Karşılaştırması ve Görselleştirme"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff7c2f64",
   "metadata": {
    "id": "ff7c2f64",
    "executionInfo": {
     "status": "aborted",
     "timestamp": 1787416736696,
     "user_tz": -180,
     "elapsed": 64247,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    }
   },
   "outputs": [],
   "source": "import matplotlib.pyplot as plt\n\nplt.figure(figsize=(10, 5))\nplt.plot(energy_history, 'b-', linewidth=2, label='PennyLane VQE')\nplt.axhline(y=exact_energy, color='r', linestyle='--', label=f'Exact ({exact_energy:.6f} Ha)')\nplt.xlabel('Iteration')\nplt.ylabel('Energy (Ha)')\nplt.title('PennyLane VQE Convergence for H₂ (STO-3G)')\nplt.legend()\nplt.grid(True, alpha=0.3)\nplt.tight_layout()\nplt.show()\n\nprint(\"\\nNot: Qiskit ve PennyLane sonuçları çok yakın olmalıdır (farklı optimizör ve ansatz implementasyonlarından küçük farklar olabilir).\")"
  },
  {
   "cell_type": "markdown",
   "id": "f9dfe7d9",
   "metadata": {
    "id": "f9dfe7d9"
   },
   "source": "## 7. İleri Konular ve Kaynaklar\n\n### Makaleden Önemli İleri Konular (Part II)\n- **CASSCF / MCSCF**: Aktif uzay seçimi ve orbital optimizasyonu\n- **Coupled Cluster (CCSD(T))**: Klasik “altın standart”\n- **DMRG**: Quasi-1D sistemler için güçlü yöntem\n- **VQE’nin zorlukları**: Barren plateau, ansatz seçimi, ölçüm maliyeti\n\n### Önerilen Sonraki Adımlar\n1. Farklı bağ uzunluklarında potansiyel enerji yüzeyi (PES) çizmek\n2. LiH veya BeH₂ gibi biraz daha büyük moleküller denemek\n3. Farklı mapper’ları (Parity, Bravyi-Kitaev) karşılaştırmak\n4. Hardware-efficient ansatz vs UCCSD karşılaştırması\n5. Noise model ekleyerek NISQ etkilerini incelemek\n\n### Faydalı Kaynaklar\n- [Qiskit Nature Documentation](https://qiskit-community.github.io/qiskit-nature/)\n- [PennyLane Quantum Chemistry](https://pennylane.ai/qml/demos_qchem.html)\n- Makale Part I: https://www.quantum-computing-info.com/quantum-chemistry-in-a-nutshell/\n- Makale Part II: https://www.quantum-computing-info.com/post-hartree-fock/\n- McArdle et al., \"Quantum computational chemistry\" (Rev. Mod. Phys. 2020)\n"
  },
  {
   "cell_type": "markdown",
   "id": "c3a577d1",
   "metadata": {
    "id": "c3a577d1"
   },
   "source": "### Bonus: Potansiyel Enerji Yüzeyi (PES) – H₂\n\nFarklı bağ uzunluklarında enerji hesaplamak, kimyada **reaksiyon yollarını** ve **denge geometrisini** anlamak için kritiktir.\n"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b79d68f0",
   "metadata": {
    "id": "b79d68f0",
    "executionInfo": {
     "status": "aborted",
     "timestamp": 1787416736700,
     "user_tz": -180,
     "elapsed": 64250,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    }
   },
   "outputs": [],
   "source": "# Basit PES taraması (biraz zaman alabilir)\nbond_lengths = pnp.linspace(0.5, 1.5, 11)\nenergies_pes = []\n\nfor r in bond_lengths:\n    coords = pnp.array([0.0, 0.0, 0.0, 0.0, 0.0, float(r)])\n    H_r, qubits_r = qchem.molecular_hamiltonian(\n        symbols, coords, charge=0, mult=1, basis=\"sto-3g\",\n        method=\"pyscf\", active_electrons=2, active_orbitals=2\n    )\n\n    # Exact enerji (hızlı olsun diye)\n    H_mat = qml.matrix(H_r)\n    e = np.linalg.eigvalsh(H_mat)[0]\n    energies_pes.append(e)\n    print(f\"R = {r:.2f} Å → E = {e:.6f} Ha\")\n\nplt.figure(figsize=(8, 5))\nplt.plot(bond_lengths, energies_pes, 'o-', linewidth=2, markersize=8)\nplt.xlabel('Bond Length (Å)')\nplt.ylabel('Energy (Ha)')\nplt.title('H₂ Potential Energy Surface (STO-3G, Exact)')\nplt.grid(True, alpha=0.3)\nplt.tight_layout()\nplt.show()"
  },
  {
   "cell_type": "markdown",
   "id": "7bf03782",
   "metadata": {
    "id": "7bf03782"
   },
   "source": "---\n\n**Hazırlayan notu:**  \nBu notebook, [quantum-computing-info.com](https://www.quantum-computing-info.com) üzerindeki “Quantum Chemistry in a Nutshell” serisini temel alarak hazırlanmıştır. Hem teorik arka planı (görseller + diyagramlar) hem de çalıştırılabilir modern kuantum kimya kodlarını bir araya getirir.\n\nColab’da çalıştırmak için dosyayı Google Drive’a yükleyip Colab ile açmanız yeterlidir.\n"
  }
 ],
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  "language_info": {
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