quantum-plots.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quantum plots (beta)\n",
    "\n",
    "This notebook provides plotting functionality tailored for the use with the `quantumarticle` documentclass."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IMPORTANT\n",
    "\n",
    "In case this notebook does not run smoothly, please open an Issue on our GitHub page https://github.com/quantum-journal/quantum-journal/issues and append the following information:\n",
    "\n",
    "- The error message (you can include the whole output as .html).\n",
    "- The version of Jupyter notebook and IPython (as found in the menu under Help > About).\n",
    "- The version of Matplotlib and the current backend. This information can be found by running the following code:\n",
    "    ```python\n",
    "    print(mpl.__version__)\n",
    "    print(mpl.get_backend())\n",
    "    ```\n",
    "    \n",
    "You will help us tremendously in improving your experience.\n",
    "\n",
    "Should you have the problem that your figures are not displayed in the Jupyter notebook, you can still save them as PDF documents via `plt.savefig('filename.pdf')`. Afterwards you can use your favorite PDF viewer to check your figures. Nevertheless you should inform us so that the bug can be removed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Global routines\n",
    "\n",
    "The parameters for the plots are set in a way that the font sizes in the resulting graphic match those in the surrounding document."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setup methods loaded\n"
     ]
    }
   ],
   "source": [
    "import numpy             as     np\n",
    "import matplotlib        as     mpl\n",
    "import matplotlib.pyplot as     plt\n",
    "from   matplotlib        import rc\n",
    "from   cycler            import cycler\n",
    "\n",
    "_widths = {\n",
    "    # a4paper columnwidth = 426.79135 pt = 5.93 in\n",
    "    # letterpaper columnwidth = 443.57848 pt = 6.16 in\n",
    "    'onecolumn': {\n",
    "        'a4paper' : 5.93,\n",
    "        'letterpaper' : 6.16\n",
    "    },\n",
    "    # a4paper columnwidth = 231.84843 pt = 3.22 in\n",
    "    # letterpaper columnwidth = 240.24199 pt = 3.34 in\n",
    "    'twocolumn': {\n",
    "        'a4paper' : 3.22,\n",
    "        'letterpaper' : 3.34\n",
    "    }\n",
    "}\n",
    "\n",
    "_wide_widths = {\n",
    "    # a4paper wide columnwidth = 426.79135 pt = 5.93 in\n",
    "    # letterpaper wide columnwidth = 443.57848 pt = 6.16 in\n",
    "    'onecolumn': {\n",
    "        'a4paper' : 5.93,\n",
    "        'letterpaper' : 6.16\n",
    "    },\n",
    "    # a4paper wide linewidth = 483.69687 pt = 6.72 in\n",
    "    # letterpaper wide linewidth = 500.48400 pt = 6.95 in\n",
    "    'twocolumn': {\n",
    "        'a4paper' : 6.72,\n",
    "        'letterpaper' : 6.95\n",
    "    }\n",
    "}\n",
    "\n",
    "_fontsizes = {\n",
    "    10 : {\n",
    "        'tiny' : 5,\n",
    "        'scriptsize' : 7,\n",
    "        'footnotesize' : 8, \n",
    "        'small' : 9, \n",
    "        'normalsize' : 10,\n",
    "        'large' : 12, \n",
    "        'Large' : 14, \n",
    "        'LARGE' : 17,\n",
    "        'huge' : 20,\n",
    "        'Huge' : 25\n",
    "    },\n",
    "    11 : {\n",
    "        'tiny' : 6,\n",
    "        'scriptsize' : 8,\n",
    "        'footnotesize' : 9, \n",
    "        'small' : 10, \n",
    "        'normalsize' : 11,\n",
    "        'large' : 12, \n",
    "        'Large' : 14, \n",
    "        'LARGE' : 17,\n",
    "        'huge' :  20,\n",
    "        'Huge' :  25\n",
    "    },\n",
    "    12 : {\n",
    "        'tiny' : 6,\n",
    "        'scriptsize' : 8,\n",
    "        'footnotesize' : 10, \n",
    "        'small' : 11, \n",
    "        'normalsize' : 12,\n",
    "        'large' : 14, \n",
    "        'Large' : 17, \n",
    "        'LARGE' : 20,\n",
    "        'huge' :  25,\n",
    "        'Huge' :  25\n",
    "    }\n",
    "}\n",
    "\n",
    "_width         = 1\n",
    "_wide_width    = 1\n",
    "_quantumviolet = '#53257F'\n",
    "_quantumgray   = '#555555'\n",
    "\n",
    "# Sets up the plot with the fitting arguments so that the font sizes of the plot\n",
    "# and the font sizes of the document are well aligned\n",
    "#\n",
    "#     columns : string = ('onecolumn' | 'twocolumn')\n",
    "#         the columns you used to set up your quantumarticle, \n",
    "#         defaults to 'twocolumn'\n",
    "#\n",
    "#     paper : string = ('a4paper' | 'letterpaper')\n",
    "#         the paper size you used to set up your quantumarticle,\n",
    "#         defaults to 'a4paper'\n",
    "#\n",
    "#     fontsize : int = (10 | 11 | 12)\n",
    "#         the fontsize you used to set up your quantumarticle as int\n",
    "#\n",
    "#     (returns) : dict\n",
    "#         parameters that can be used for plot adjustments\n",
    "\n",
    "def global_setup(columns = 'twocolumn', paper = 'a4paper', fontsize = 10):\n",
    "    plt.rcdefaults()\n",
    "        \n",
    "    # Seaborn white is a good base style\n",
    "    plt.style.use(['seaborn-white', './quantum-plots.mplstyle'])\n",
    "    \n",
    "    try:        \n",
    "        # This hackery is necessary so that jupyther shows the plots\n",
    "        mpl.use(\"pgf\")\n",
    "        %matplotlib inline\n",
    "        plt.plot()\n",
    "        mpl.use(\"pgf\")\n",
    "    except:\n",
    "        print('Call to matplotlib.use had no effect')\n",
    "        \n",
    "    mpl.interactive(False) \n",
    "    \n",
    "    # Now prepare the styling that depends on the settings of the document\n",
    "    \n",
    "    global _width \n",
    "    _width = _widths[columns][paper]\n",
    "    \n",
    "    global _wide_width \n",
    "    _wide_width = _wide_widths[columns][paper]\n",
    "    \n",
    "    # Use the default fontsize scaling of LaTeX\n",
    "    global _fontsizes\n",
    "    fontsizes = _fontsizes[fontsize]\n",
    "    \n",
    "    plt.rcParams['axes.labelsize'] = fontsizes['small']\n",
    "    plt.rcParams['axes.titlesize'] = fontsizes['large']\n",
    "    plt.rcParams['xtick.labelsize'] = fontsizes['footnotesize']\n",
    "    plt.rcParams['ytick.labelsize'] = fontsizes['footnotesize']\n",
    "    plt.rcParams['font.size'] = fontsizes['small']\n",
    "    \n",
    "    return {\n",
    "            'fontsizes' : fontsizes,\n",
    "            'colors' : {\n",
    "                'quantumviolet' : _quantumviolet,\n",
    "                'quantumgray' : _quantumgray\n",
    "            }\n",
    "        }\n",
    "    \n",
    "\n",
    "# Sets up the plot with the fitting arguments so that the font sizes of the plot\n",
    "# and the font sizes of the document are well aligned\n",
    "#\n",
    "#     aspect_ratio : float\n",
    "#         the aspect ratio (width/height) of your plot\n",
    "#         defaults to the golden ratio\n",
    "#\n",
    "#     width_ratio : float in [0, 1]\n",
    "#         the width of your plot when you insert it into the document, e.g.\n",
    "#         .8 of the regular width\n",
    "#         defaults to 1.0\n",
    "#\n",
    "#     wide : bool \n",
    "#         indicates if the figures spans two columns in twocolumn mode, i.e.\n",
    "#         when the figure* environment is used, has no effect in onecolumn mode \n",
    "#         defaults to False\n",
    "#\n",
    "#     (returns) : matplotlib figure object\n",
    "#         the initialized figure object\n",
    "\n",
    "def plot_setup(aspect_ratio = 1/1.62, width_ratio = 1.0, wide = False):\n",
    "    width = (_wide_width if wide else _width) * width_ratio\n",
    "    height = width * aspect_ratio\n",
    "           \n",
    "    return plt.figure(figsize=(width,height), dpi=120, facecolor='white')\n",
    "    \n",
    "print('Setup methods loaded')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examples\n",
    "\n",
    "When setting up your plots you have to call `global_setup` once and set the global options of your `quantumarticle`. \n",
    "\n",
    "The routine `global_setup` returns a dictionary with fontsizes and custom colors that should be used instead of numeric values. This ensures consistency with the other font sizes used in your LaTeX document. This means \n",
    "```python\n",
    "fontsize = props['fontsizes']['large']\n",
    "```\n",
    "should be preferred over\n",
    "```python\n",
    "fontsize = 12\n",
    "``` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global props:\n",
      "fontsizes :\n",
      "     tiny :  6\n",
      "     scriptsize :  8\n",
      "     footnotesize :  9\n",
      "     small :  10\n",
      "     normalsize :  11\n",
      "     large :  12\n",
      "     Large :  14\n",
      "     LARGE :  17\n",
      "     huge :  20\n",
      "     Huge :  25\n",
      "colors :\n",
      "     quantumviolet :  #53257F\n",
      "     quantumgray :  #555555\n"
     ]
    }
   ],
   "source": [
    "props = global_setup(columns = 'twocolumn', paper = 'a4paper', fontsize = 11)\n",
    "\n",
    "print('Global props:')\n",
    "for key in props:\n",
    "    print(key, ':')\n",
    "    for subkey in props[key]:\n",
    "        print('    ', subkey, ': ', props[key][subkey])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting advice\n",
    "\n",
    "Plots should _always_ be saved as __PDF__ files via `plt.savefig('filename.pdf')`. This will result in the highest quality graphics that can seamlessly integrated in your publications. \n",
    "\n",
    "We further recommend that you use `tight_layout()` on your plots."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lineplot\n",
    "\n",
    "To set up the plotting environment for your figure, you have to use the function `plot_setup` which ensures the proper sizing of your plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 386.4x238.519 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plot_setup()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Energy Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "FCI_energy_vs_L_wide = np.loadtxt('./numerical_results/FCI_energy.txt')\n",
    "FCI_bond_length_wide = np.loadtxt('./numerical_results/FCI_length.txt')\n",
    "\n",
    "VQE_energy_vs_L_wide = np.loadtxt('./numerical_results/VQE_energy_vs_L_wide.txt')\n",
    "VQE_bond_length_wide = np.loadtxt('./numerical_results/VQE_length_wide.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "c_quantum = props['colors']['quantumviolet']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "FCI, = plt.plot(FCI_bond_length_wide, FCI_energy_vs_L_wide, color = props['colors']['quantumgray'], ls = '--')\n",
    "VQE_wide = plt.scatter(VQE_bond_length_wide, VQE_energy_vs_L_wide, color = c_quantum, marker = 'o', alpha=1, linewidths=4)\n",
    "\n",
    "plt.xlabel('Panjang Ikatan ($L$) [Å]')\n",
    "plt.ylabel('Energi $\\\\textit{Ground State}$ ($E_{gs}$) [Hartree]')\n",
    "plt.legend([FCI, VQE_wide], ['FCI', 'VQE'])\n",
    "#plt.title('$L = 0.1$ - $3.0$ Å')\n",
    "plt.tight_layout(pad = 0.1)\n",
    "\n",
    "plt.savefig('./plots/E_vs_L_wide.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "FCI_energy_vs_L_tight = np.loadtxt('./numerical_results/FCI_energy_tight.txt')\n",
    "FCI_bond_length_tight = np.loadtxt('./numerical_results/FCI_length_tight.txt')\n",
    "\n",
    "VQE_energy_vs_L_tight = np.loadtxt('./numerical_results/VQE_energy_vs_L_tight.txt')\n",
    "VQE_bond_length_tight = np.loadtxt('./numerical_results/VQE_length_tight.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "FCI, = plt.plot(FCI_bond_length_tight, FCI_energy_vs_L_tight, color = props['colors']['quantumgray'], ls = '--')\n",
    "VQE_tight = plt.scatter(VQE_bond_length_tight, VQE_energy_vs_L_tight, color = c_quantum, marker = 'o', alpha=1, linewidths=4)\n",
    "\n",
    "plt.xlabel('Panjang Ikatan ($L$) [Å]')\n",
    "plt.ylabel('Energi $\\\\textit{Ground State}$ ($E_{gs}$) [Hartree]')\n",
    "plt.legend([FCI, VQE_wide], ['FCI', 'VQE'])\n",
    "plt.tight_layout(pad = 0.1)\n",
    "#plt.title('$L = 0.6$ - $0.8$ Å')\n",
    "\n",
    "plt.savefig('./plots/E_vs_L_tight.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "FCI_energy_vs_L_crammed = np.loadtxt('./numerical_results/FCI_energy_crammed.txt')\n",
    "FCI_bond_length_crammed = np.loadtxt('./numerical_results/FCI_length_crammed.txt')\n",
    "\n",
    "VQE_energy_vs_L_crammed = np.loadtxt('./numerical_results/VQE_energy_vs_L_crammed.txt')\n",
    "VQE_bond_length_crammed = np.loadtxt('./numerical_results/VQE_length_crammed.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "FCI, = plt.plot(FCI_bond_length_crammed, FCI_energy_vs_L_crammed, color = props['colors']['quantumgray'], ls = '--')\n",
    "VQE_tight = plt.scatter(VQE_bond_length_crammed, VQE_energy_vs_L_crammed, color = c_quantum, marker = 'o', alpha=1, linewidths=4)\n",
    "\n",
    "plt.xlabel('Panjang Ikatan ($L$) [Å]')\n",
    "plt.ylabel('Energi $\\\\textit{Ground State}$ ($E_{gs}$) [Hartree]')\n",
    "plt.legend([FCI, VQE_wide], ['FCI', 'VQE'])\n",
    "plt.tight_layout(pad = 0.1)\n",
    "#plt.title('$L = 0.73$ - $0.75$ Å')\n",
    "plt.savefig('./plots/E_vs_L_crammed.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7348649729945989"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FCI_bond_length_crammed[np.argmin(FCI_energy_vs_L_crammed)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7352631578947368"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VQE_bond_length_crammed[np.argmin(VQE_energy_vs_L_crammed)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "VQE_energy_vs_iter = np.loadtxt('./numerical_results/VQE_energy_vs_iteration.txt')\n",
    "VQE_bond_length_iteration = np.loadtxt('./numerical_results/VQE_length_iteration.txt')\n",
    "\n",
    "\n",
    "# FCI energy for constant bond length = 0.7414$ Å\n",
    "FCI_energy_vs_iter = np.linspace(-1.1372701746609022, -1.1372701746609022, 600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "e_new = []\n",
    "bond_length_new = []\n",
    "\n",
    "for i in range(len(VQE_energy_vs_iter)):\n",
    "    if i%10 == 0:\n",
    "        e_new += [VQE_energy_vs_iter[i]]\n",
    "        bond_length_new += [i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "FCI, = plt.plot(FCI_energy_vs_iter, color = props['colors']['quantumgray'], ls = '--')\n",
    "VQE_tight = plt.scatter(bond_length_new, e_new, color = c_quantum, marker = 'x', alpha=1, s=20)\n",
    "\n",
    "plt.xlabel('Iterasi')\n",
    "plt.ylabel('Energi ($E(\\\\theta)$) [Hartree]')\n",
    "plt.legend([FCI, VQE_wide], ['$E_{gs}$ FCI', '$E_{\\\\theta}$ VQE'])\n",
    "plt.tight_layout(pad = 0.1)\n",
    "#plt.title('$L = 0.7414$ Å')\n",
    "plt.savefig('./plots/E_vs_iteration.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MAE Calculations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "FCI_wide = np.loadtxt('./numerical_results/FCI_energy_wide_30.txt')\n",
    "FCI_tight = np.loadtxt('./numerical_results/FCI_energy_tight_20.txt')\n",
    "FCI_crammed = np.loadtxt('./numerical_results/FCI_energy_crammed_20.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_error = np.sum(abs(FCI_wide - VQE_energy_vs_L_wide)) + np.sum(abs(FCI_tight - VQE_energy_vs_L_tight)) + np.sum(abs(FCI_crammed - VQE_energy_vs_L_crammed))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.524763142636202e-08"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_error/(len(FCI_wide) + len(FCI_tight) + len(FCI_crammed))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a291730c08>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AQwKBICoqKjc3Fx+nBOQIMBcaGioQCL799luUAl4s3B8xYyRJpqam8ng8lAKwHgHmrKysNBrN1q1bu7q6UA1AjgBDPB7P09MzNzf3aW9NBkCOwLMtXry4q6vr/PnzKAW8ELg/MhJQFJWSkoL9pQDrETCIi4vL+PHjT5w4gS58gBwBA+aSxWpoaNi7dy9KAcgRYC4+Pr6xsRHvKAHkCDDH4XA+/PDDzs5OlAKQI8Cck5NTVFTU/fv3kSaAHAGD/PTTTzk5OdiEEZAjwFx0dLSNjc3+/ftRCkCOAEMkSSYlJfX09OBNJYAcAeZ4PN6HH37I4XCqq6tRDXhhOaJ3YwvsdmFeGhsbP/nkk4cPH6IUYOwc0dsnvKGhISMjIyEhQSKRoHDmwsPDY8WKFTt37mxsbEQ1YJgMok94RUXFpk2bBAJBdHT0+++/P2bMGJTPLMycOVMul9+9e9fFxQXVAOOtR/T2CQ8PD6cDhcvl0l/ooE+4iZs7d25wcPBPP/2kUChQDTDSekRvn3CaWCxeu3YtRVH9n0SfcLNw+/btf/7znxkZGXw+H9WAYV+P6O0TrtFo8vLy5s+f7+/vj8KZo/fee8/d3X379u1KpRLVgGFfj+jtE25hYVFeXn7//n2tVrt58+axY8eifOaFJMnly5efPHkS73OFIT610Cd8FOrp6fniiy9iY2Otra1RDXh+6BMO/4fL5VpbW2/evLmlpQXVAMPh/ayj9AInNjZ23rx5WVlZ2D8NDIf9WUevBQsW+Pv783i85uZmR0dHFASwHgEmnJycuru7t2zZcurUKVQDkCPAkKWl5bZt2y5duiSRSHp7e1EQQI4AEw4ODlu3bnVyciJJEp/DBOQIMMTlcqOioths9ubNm7/77ju05gPkCDBEkmRKSsqFCxe2b9+OPwkDcgQYcnFx2bZtm4+PT2dnp0qlwsIEngf+7gsDURS1cOFCgiBOnz599erVpUuXjh8/HmUB5AgwERYWZmlpKRKJ/P39V61ahYIAcgQGjSTJoKCggICAu3fvEgRx7do1Hx+fAVvPABC4PwLPxOfzX331Va1We+vWrdTU1OLiYmyGBMgRYLg2WbFixY4dO2QyWVlZGUEQra2tKAvgugYGzdHRkb5RIpPJNmzYMGnSpDfffNPHx4ckSRQH6xGAwbGzs8vNzZ0+fXpBQcHly5fVanVTUxPKgvUIwODweLzQ0NDQ0FCtVltfX//RRx+5uLgEBATMmTPHxsYG9cF6BGAQSJJ86aWX9u7d+/bbb9fV1TU2NnZ1dX3zzTe1tbV4GxvWIwCDQFGUv78/vQe4TCaTyWS7du1SKBSrVq363e9+19ra6uDggCohRwCel52d3bJlywiCkEqlXC63vb1948aNbDbb29v7jTfe8PPz02q1uDWLHAF4LrqmAvn5+Y2NjVVVVVwuV6vVrlq1ys7OzsPDw8/Pb/bs2SqVisPhoFwjM0fUavWAfldPexLgmVxcXHSNQcVi8aNHj2pra+ntTj755JPa2lpnZ2cPD4/4+PjGxkalUikUCrGdvXnniFwuz8rK4vP548aNW7FixW88CcAAj8fz8vLy8vKiH2ZmZra3t9M3aAmCqKysPHv2rFQq7e3tzcnJUSgUJSUl9vb29vb2fn5+rq6udXV1VlZWY8aMwSrGpHNEb59wvU8CDAlbW1tbW1v666CgoKCgIIIgent7ORwOj8fz8/NrbW1tbGz08PAQCoUSiaSzs1Mul8+ePTs5OXnfvn0NDQ18Pt/GxiYxMfHOnTv37t3j8/k8Hm/mzJkURVVWVnK5XDab7ezsbGdnR2+tYmlpyWKx0KJ0GHOksrIyICCA+N8+4ZMnT37ak7RJkybRX6SkpKxevRplhSHB5XIJgrCxsXn99df7P5+dnU1/QTcGfOuttzo6OpRKJf1nZoqi1Gq1VCrt6emZMWOGQqH47rvvVCpVX19fUFDQG2+8kZ2drVAouru7vb29N2zYsHv37kuXLtH/w8LCwuvXrx84cIB+mJqa6uXllZGRQT8MDAyMiYkRiURtbW0EQdjZ2W3YsOHs2bPff/89/YJ169Z1dnbm5+fTD8PDw+fMmZOZmalbgsXGxh49erSqqoogCJIkMzMzKyoqzpw5Q7/+vffes7Ozk0gk9MOAgIDQ0NC8vDz6IwhCoTAhIeHbb7+9fv06/YI1a9Y0NTUdOXKEfvjWW29Nnz5927Zt9MPJkydHRkYWFhbW1NTQxdywYcOVK1dKS0vpFyxdutTCwsLCwsLw68dB9An/jebh6BMOL+bXIJtNEISbm5ubm5vuSW9vb29v7/4v27hxY/+HIpGo/8M1a9asWbNG93DmzJnTp0/XBRlFUXQQ0EsYgiCWLVtGX3/RV1WzZs3S/R61tbW1srJauXIl/ZD+U/fy5ctVKlVPTw/94xoQEDBlyhTdP+fh4fH222/TXwuFQh6Pp3tI36UODg6m99/m8XgEQUybNs3V1ZV+AZ/Pd3Z21r2eLoLu4ZgxYwiCmD17tq+vL0EQLBaLIAgvLy8rKyv6Bfb29hRFDcntTv05QvcJ9/T07N8nXO+TACMMh8MZcNvFycmp/0OhUCgUCnUP6Rs3/f/zV155pf/rPT09B/xw9X/o4OAw4J0106ZN6/9QF1I0V1dXXY7Q/9yA1w94OOBfHzD44b2u0dsnPCYmpv+TOOEAgDa4PuF6n0SfcIBRYmj6hKN5OAAMIkeGie5WtnnBsDFmDNuEcuTTTz81x3Jj2Bgzhm1COQIAuK4BABiINHynmejoaNQRYJTQ+/caEjtWAQCuawBglOUIvd+EWeg/1AHDNtmj+O1xmuOwzeWEMZdSD9OwjbcfmhltXyKVSg8fPvzgwYP9+/cPGLbJHkVDQ4NEImlpafHz80tJSTGXYdfU1Bw4cMDa2prP56elpfUfZ2xsrImfMCKRyMvLa968eWZRaoIgfvjhh3379jk5OTk6OsbFxQ3lsLXGUlRUVFZWptVqU1NT5XK51oSp1WqNRvOnP/3p18M22aM4efIkPZ6oqKiOjg5zGXZTU9OTJ0+0Wu2iRYsGVDs/P9+UT5irV69mZmZ+8cUX5lJqrVZ78eLFgoICvT+PBg7beNc1lZWV9EcP6e1LTPpij8XS7UI8YNgmexTh4eH0zlJcLlcgEJjLsB0dHe/evZuYmEjvC91/nOfPnzfZE0alUhUVFYWFhZnRGUI7c+ZMenp6aWnp0A7beDnyG9uXmLIBwzbxoxCLxWvXrqUoyoyGPWHChMTExHPnzmm12v7jpDfdMM0xHzp0aMGCBUqlUqFQ9PT0mEupPTw8EhMTs7OzRSKRUqkcwmEbL0fo7UsIgjCv7UsGDNtkj0Kj0eTl5c2fP5/uIGMuw1YoFFZWVr6+vkqlsq6urv84AwMDTfaEcXZ2rq6uLikpqaioIAjCLEpNEIRQKAwMDCRJ0tbW1snJaQiHTWVlZRnnGF566aU9e/Y8fPiQ7udoytkhk8n+9re/3bhxo6OjIzIysv+wTfYoDhw48K9//evu3btnz579/e9/P3HiRLMY9sWLF7/++uuKioru7u6YmJiXX35ZN85ly5aZ7Anj7e09c+ZMNpttb2+/ZMkSsyg1QRCnT58+efJkeXm5q6vr0qVLh3DYRn0fmt7tS0zfgGGby1GYy7CVSmVzc/O4cePoe1L9x4lSD7nu7u729nZ6R7UhHDbezwoAhsL7WQEAOQIAyBEAQI4AAHIEAAA5AgDIEQBAjgDA6PY/DOAn54Gt7cgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(VQE_energy_vs_iter-(-1.1372701746609022), color = props['colors']['quantumgray'], ls = '--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.845091665181769e-08"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abs(np.min(VQE_energy_vs_iter) - (-1.1372701746609022))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}