optimize_OCS_qubit.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8d07a216",
   "metadata": {},
   "source": [
    "# OCS Qubit\n",
    "\n",
    "## To Do:\n",
    "\n",
    "\n",
    "1. Parameter sweep of OCS qubit to get required $\\frac{E_j}{E_c} \\approx 20$ ⌛️ ->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "601adb26",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_metal.qlibrary.qubits.transmon_cross import TransmonCross\n",
    "import qiskit_metal as metal\n",
    "from qiskit_metal import designs, draw\n",
    "from qiskit_metal import MetalGUI, Dict, Headings\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f23f85aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Users\\lfl\\Anaconda3\\envs\\qiskit-metal\\lib\\site-packages\\pyproj\\__init__.py:89: UserWarning: pyproj unable to set database path.\n",
      "  _pyproj_global_context_initialize()\n"
     ]
    }
   ],
   "source": [
    "def launch_Metal_GUI():\n",
    "    design = designs.DesignPlanar()\n",
    "\n",
    "    design._chips['main']['size']['size_x'] = '5mm'\n",
    "    design._chips['main']['size']['size_y'] = '5mm'\n",
    "\n",
    "    design.variables['cpw_width'] = '10 um'\n",
    "    design.variables['cpw_gap'] = '6 um'\n",
    "\n",
    "    gui = MetalGUI(design)\n",
    "    return gui, design\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ca4a8248",
   "metadata": {},
   "outputs": [],
   "source": [
    "if __name__ == \"__main__\":\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "938784f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 500
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Allow running the same cell here multiple times to overwrite changes\n",
    "design.overwrite_enabled = True\n",
    "\n",
    "########## OCS QUBUIT ########\n",
    "options_d = dict(\n",
    "    cross_width = '30um',\n",
    "    cross_length = '180um',\n",
    "    cross_gap = '30um',\n",
    "    chip='main',\n",
    "    connection_pads=dict(\n",
    "        ground_pin  = dict(connector_location = '0', connector_type = '0', claw_width = '19.1um', claw_length = '35.9um', \n",
    "                           ground_spacing = '5um')\n",
    "    )\n",
    ")\n",
    "\n",
    "q_d = TransmonCross(design, 'Q1', options = dict(\n",
    "    pos_x='-1250um', pos_y='-1mm', orientation = '90', **options_d))\n",
    "\n",
    "######### REBUILD ##########\n",
    "\n",
    "gui.rebuild()\n",
    "gui.autoscale()\n",
    "gui.screenshot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ef235f4",
   "metadata": {},
   "source": [
    "# EPR Simulation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d570e32b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_metal.analyses.quantization import EPRanalysis\n",
    "eig_qb = EPRanalysis(design, \"hfss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fb01151f",
   "metadata": {},
   "outputs": [],
   "source": [
    "hfss = eig_qb.sim.renderer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3e1237d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 04:22PM [connect_project]: Connecting to Ansys Desktop API...\n",
      "INFO 04:22PM [load_ansys_project]: \tOpened Ansys App\n",
      "INFO 04:22PM [load_ansys_project]: \tOpened Ansys Desktop v2021.1.0\n",
      "INFO 04:22PM [load_ansys_project]: \tOpened Ansys Project\n",
      "\tFolder:    D:/Users/lfl/Documents/HFSS/\n",
      "\tProject:   Project42\n",
      "INFO 04:22PM [connect_design]: No active design found (or error getting active design).\n",
      "INFO 04:22PM [connect]: \t Connected to project \"Project42\". No design detected\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hfss.start()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cddc9031",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "04:22PM 55s WARNING [activate_ansys_design]: The design_name=ocs_test was not in active project.  Designs in active project are: \n",
      "[].  A new design will be added to the project.  \n",
      "INFO 04:22PM [connect_design]: \tOpened active design\n",
      "\tDesign:    ocs_test [Solution type: Eigenmode]\n",
      "WARNING 04:22PM [connect_setup]: \tNo design setup detected.\n",
      "WARNING 04:22PM [connect_setup]: \tCreating eigenmode default setup.\n",
      "INFO 04:22PM [get_setup]: \tOpened setup `Setup`  (<class 'pyEPR.ansys.HfssEMSetup'>)\n"
     ]
    }
   ],
   "source": [
    "hfss.activate_ansys_design(\"ocs_test\", 'eigenmode')  # use new_ansys_design() to force creation of a blank design"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1b6cae2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "hfss.render_design(['Q1'], [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9d9d45de",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 04:23PM [analyze]: Analyzing setup Setup\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Number of eigenmodes to find             = 1\n",
      "Number of simulation passes              = 10\n",
      "Convergence freq max delta percent diff  = 0.1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Analysis properties\n",
    "setup = hfss.pinfo.setup\n",
    "setup.passes = 10\n",
    "print(f\"\"\"\n",
    "Number of eigenmodes to find             = {setup.n_modes}\n",
    "Number of simulation passes              = {setup.passes}\n",
    "Convergence freq max delta percent diff  = {setup.delta_f}\n",
    "\"\"\")\n",
    "\n",
    "pinfo = hfss.pinfo\n",
    "pinfo.design.set_variable('Lj', '10 nH')\n",
    "pinfo.design.set_variable('Cj', '0 fF')\n",
    "\n",
    "setup.analyze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2ed38b43",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "de7c3e85",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:py.warnings:D:\\Users\\lfl\\Anaconda3\\envs\\qiskit-metal\\lib\\site-packages\\pyEPR\\ansys.py:1222: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
      "  df = pd.read_csv(io.StringIO(text2[3].strip()),\n",
      "\n",
      "04:27PM 37s INFO [get_f_convergence]: Saved convergences to C:\\Users\\lfl\\LL_OCS_Qubit\\hfss_eig_f_convergence.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "eig_qb.sim.convergence_t, eig_qb.sim.convergence_f, _ = hfss.get_convergences()\n",
    "eig_qb.sim.plot_convergences()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ede0327",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9a43c9ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Design \"ocs_test\" info:\n",
      "\t# eigenmodes    1\n",
      "\t# variations    1\n",
      "Design \"ocs_test\" info:\n",
      "\t# eigenmodes    1\n",
      "\t# variations    1\n"
     ]
    }
   ],
   "source": [
    "df1 = eig_qb.get_frequencies()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "bc6ab918",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "qubit frequency = 5.00255225064 GHz\n"
     ]
    }
   ],
   "source": [
    "qubit_freq  = df1[\"Freq. (GHz)\"][0]\n",
    "print(f\"qubit frequency = {qubit_freq} GHz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "51695ba8",
   "metadata": {},
   "outputs": [],
   "source": [
    "pinfo = hfss.pinfo\n",
    "pinfo.junctions['jj'] = {'Lj_variable': 'Lj', 'rect': 'JJ_rect_Lj_Q1_rect_jj', \n",
    "                             'line': 'JJ_Lj_Q1_rect_jj_',  'Cj_variable': 'Cj'}\n",
    "pinfo.validate_junction_info() # Check that valid names of variables and objects have been supplied\n",
    "pinfo.dissipative['dielectrics_bulk'] = ['main'] # Dissipative elements: specify"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cfc6f0b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyEPR as epr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1180ff53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Design \"ocs_test\" info:\n",
      "\t# eigenmodes    1\n",
      "\t# variations    1\n"
     ]
    }
   ],
   "source": [
    "eprd = epr.DistributedAnalysis(pinfo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "29712e90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Variation 0  [1/1]\n",
      "\n",
      "  \u001b[1mMode 0 at 5.00 GHz   [1/1]\u001b[0m\n",
      "    Calculating ℰ_magnetic,ℰ_electric\n",
      "       (ℰ_E-ℰ_H)/ℰ_E       ℰ_E       ℰ_H\n",
      "               99.0%  2.552e-25 2.585e-27\n",
      "\n",
      "    Calculating junction energy participation ration (EPR)\n",
      "\tmethod=`line_voltage`. First estimates:\n",
      "\tjunction        EPR p_0j   sign s_0j    (p_capacitive)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:py.warnings:D:\\Users\\lfl\\Anaconda3\\envs\\qiskit-metal\\lib\\site-packages\\pyEPR\\ansys.py:1250: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
      "  df = df.drop('Unnamed: 9', 1)\n",
      "\n",
      "WARNING:py.warnings:D:\\Users\\lfl\\Anaconda3\\envs\\qiskit-metal\\lib\\site-packages\\pyEPR\\ansys.py:1222: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
      "  df = pd.read_csv(io.StringIO(text2[3].strip()),\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t\tEnergy fraction (Lj over Lj&Cj)= 98.06%\n",
      "\tjj              0.989242  (+)        0.0195468\n",
      "\t\t(U_tot_cap-U_tot_ind)/mean=1.00%\n",
      "Calculating Qdielectric_main for mode 0 (0/0)\n",
      "p_dielectric_main_0 = 0.9211088376689041\n",
      "\n",
      "ANALYSIS DONE. Data saved to:\n",
      "\n",
      "C:\\data-pyEPR\\Project42\\ocs_test\\2022-11-16 16-30-56.npz\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(WindowsPath('C:/data-pyEPR/Project42/ocs_test/2022-11-16 16-30-56.npz'),\n",
       " ['0'])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eprd.do_EPR_analysis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "21032b66",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING 04:32PM [__init__]: <p>Error: <class 'IndexError'></p>\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t Differences in variations:\n",
      "\n",
      "\n",
      "\n",
      " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . \n",
      "Variation 0\n",
      "\n",
      "Starting the diagonalization\n",
      "Finished the diagonalization\n",
      "Pm_norm=\n",
      "modes\n",
      "0    1.020295\n",
      "dtype: float64\n",
      "\n",
      "Pm_norm idx =\n",
      "     jj\n",
      "0  True\n",
      "*** P (participation matrix, not normlz.)\n",
      "         jj\n",
      "0  0.970276\n",
      "\n",
      "*** S (sign-bit matrix)\n",
      "   s_jj\n",
      "0     1\n",
      "*** P (participation matrix, normalized.)\n",
      "      0.99\n",
      "\n",
      "*** Chi matrix O1 PT (MHz)\n",
      "    Diag is anharmonicity, off diag is full cross-Kerr.\n",
      "       188\n",
      "\n",
      "*** Chi matrix ND (MHz) \n",
      "       204\n",
      "\n",
      "*** Frequencies O1 PT (MHz)\n",
      "0    4815.001121\n",
      "dtype: float64\n",
      "\n",
      "*** Frequencies ND (MHz)\n",
      "0    4807.37006\n",
      "dtype: float64\n",
      "\n",
      "*** Q_coupling\n",
      "Empty DataFrame\n",
      "Columns: []\n",
      "Index: [0]\n"
     ]
    }
   ],
   "source": [
    "epra = epr.QuantumAnalysis(eprd.data_filename)\n",
    "sim_info = epra.analyze_all_variations(cos_trunc = 8, fock_trunc = 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "def2d7bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cjs</th>\n",
       "      <td>jj    2.000000e-15\n",
       "Name: 0, dtype: float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ljs</th>\n",
       "      <td>jj    1.000000e-08\n",
       "Name: 0, dtype: float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pm_normed</th>\n",
       "      <td>[[0.9899675506388271]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pm_raw</th>\n",
       "      <td>0    0.970276\n",
       "Name: jj, dtype: float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q_coupling</th>\n",
       "      <td>Empty DataFrame\n",
       "Columns: []\n",
       "Index: [0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Qs</th>\n",
       "      <td>0    inf\n",
       "Name: 0, dtype: float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZPF</th>\n",
       "      <td>[[0.38920959810716205]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>_Pm_cap_norm</th>\n",
       "      <td>modes\n",
       "0    0.488691\n",
       "dtype: float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>_Pm_norm</th>\n",
       "      <td>modes\n",
       "0    1.020295\n",
       "dtype: float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chi_ND</th>\n",
       "      <td>0\n",
       "0  203.923828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chi_O1</th>\n",
       "      <td>0\n",
       "0  187.55113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cos_trunc</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f_0</th>\n",
       "      <td>0    5002.552251\n",
       "Name: 0, dtype: float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f_1</th>\n",
       "      <td>0    4815.001121\n",
       "dtype: float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f_ND</th>\n",
       "      <td>0    4807.37006\n",
       "dtype: float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fock_trunc</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hfss_variables</th>\n",
       "      <td>_Cj     0fF\n",
       "_Lj    10nH\n",
       "Name: 0, dtype: object</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                             0\n",
       "Cjs                 jj    2.000000e-15\n",
       "Name: 0, dtype: float64\n",
       "Ljs                 jj    1.000000e-08\n",
       "Name: 0, dtype: float64\n",
       "Pm_normed                               [[0.9899675506388271]]\n",
       "Pm_raw                  0    0.970276\n",
       "Name: jj, dtype: float64\n",
       "Q_coupling              Empty DataFrame\n",
       "Columns: []\n",
       "Index: [0]\n",
       "Qs                            0    inf\n",
       "Name: 0, dtype: float64\n",
       "ZPF                                    [[0.38920959810716205]]\n",
       "_Pm_cap_norm                modes\n",
       "0    0.488691\n",
       "dtype: float64\n",
       "_Pm_norm                    modes\n",
       "0    1.020295\n",
       "dtype: float64\n",
       "chi_ND                                         0\n",
       "0  203.923828\n",
       "chi_O1                                          0\n",
       "0  187.55113\n",
       "cos_trunc                                                    8\n",
       "f_0                   0    5002.552251\n",
       "Name: 0, dtype: float64\n",
       "f_1                            0    4815.001121\n",
       "dtype: float64\n",
       "f_ND                            0    4807.37006\n",
       "dtype: float64\n",
       "fock_trunc                                                   7\n",
       "hfss_variables  _Cj     0fF\n",
       "_Lj    10nH\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyEPR.calcs import Convert\n",
    "\n",
    "df = pd.DataFrame(sim_info, columns=sim_info.keys())\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "411f9b1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Anharmonicity is -187.55112988438202\n"
     ]
    }
   ],
   "source": [
    "alpha = -df[\"0\"][\"chi_O1\"].values[0][0]\n",
    "print(f\"Anharmonicity is {alpha} MHz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1030c4c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "E_c is 187.55112988438202\n"
     ]
    }
   ],
   "source": [
    "E_c = -alpha\n",
    "print(f\"E_c is {E_c} MHz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f71e33d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "E_j is 16.34615128067812 GHz\n"
     ]
    }
   ],
   "source": [
    "E_j = Convert.Ej_from_Lj(10, 'nH', \"GHz\")\n",
    "print(f\"E_j is {E_j} GHz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a6d2f0a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "87.15570677049448"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ej / E_c*1e3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44f44a64",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b9e227c0",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Scattering Simulation\n",
    "<a id='simulation_scattering'></a>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9ca8b4e",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# This class lets us get S Y Z parameters\n",
    "from qiskit_metal.analyses.simulation import ScatteringImpedanceSim\n",
    "em1 = ScatteringImpedanceSim(design, \"hfss\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c04aaafc",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "em1.setup.sweep_setup.start_ghz = 4.5\n",
    "em1.setup.sweep_setup.stop_ghz = 5.5\n",
    "\n",
    "# Change Simulation Settings\n",
    "em1.setup.reuse_selected_design = False\n",
    "em1.setup.reuse_setup = False\n",
    "em1.setup.max_passes = 12\n",
    "em1.setup.min_passes = 5\n",
    "em1.setup.max_delta_s = 0.1\n",
    "em1.setup.min_converged = 5\n",
    "em1.setup.pct_refinement = 30\n",
    "em1.setup.sweep_setup.count = 25000\n",
    "em1.setup.sweep_setup.type = 'Interpolating'\n",
    "# example: update multiple settings\n",
    "\n",
    "em1.setup.vars.Lj = '10 nH'\n",
    "em1.setup.vars.Cj = '4.02 fF'\n",
    "\n",
    "em1.setup.junctions.jj.rect = 'JJ_rect_Lj_diss_rect_jj'\n",
    "em1.setup.junctions.jj.line = 'JJ_Lj_diss_rect_jj_'\n",
    "\n",
    "em1.setup\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2fb5a13c",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "q_d.options.hfss_inductance='10nH'\n",
    "gui.rebuild()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bfd24e47",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "resistance = 50 #Ohms (Signal Path Resistance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2d1a4ac",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "em1.run(name=\"scatter_test\", components=[], open_terminations=[], port_list=[('Q1', 'ground_pin', resistance)],\n",
    "           jj_to_port=[('Q1', 'rect_jj', resistance, True)], box_plus_buffer = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9459bec7",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b022af5",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "dataframe_scattering = em1.get_scattering(['S11'])\n",
    "dataframe_admittance = em1.get_admittance(['Y11'])\n",
    "\n",
    "df_s = dataframe_scattering[0]\n",
    "df_y = dataframe_admittance[0]\n",
    "\n",
    "\n",
    "def get_real_and_imag(a_ii):\n",
    "    return a_ii.values.real, a_ii.values.imag\n",
    "\n",
    "s11real, s11imag = get_real_and_imag(df_s['S11'])\n",
    "y11real, y11imag = get_real_and_imag(df_y['Y11'])\n",
    "\n",
    "\n",
    "dataframe_scattering[0]['Real(S11)'] = s11real\n",
    "dataframe_scattering[0]['Imag(S11)'] = s11imag\n",
    "dataframe_admittance[0]['Real(Y11)'] = y11real\n",
    "dataframe_admittance[0]['Imag(Y11)'] = y11imag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c13c6c7",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "518dc883",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Reference to current axis.\n",
    "magnitude = plt.figure('Real S11, Imag S21')\n",
    "axis = plt.gca() # Get current axis.\n",
    "dataframe_scattering[0].plot(kind = 'line', y='Real(S11)', color = 'green', ax = axis)\n",
    "dataframe_scattering[0].plot(kind = 'line', y='Imag(S11)', color = 'blue', ax = axis)\n",
    "plt.title(f'S-Parameter Amplitude')\n",
    "plt.xlabel(f'frequency [GHZ]')\n",
    "plt.ylabel(f're(S11), im(S11) Amp')\n",
    "magnitude.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74fb0388",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## Scattering Analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ecd180e2",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "import numpy as np\n",
    "import pyEPR.calcs\n",
    "from pyEPR.calcs import Convert\n",
    "\n",
    "sys.path.append(r'C:/Users/lfl/fitTools/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68089532",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from fitTools.Resonator import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75568967",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "reals11 = dataframe_scattering[0]['Real(S11)']\n",
    "imags11 = dataframe_scattering[0]['Imag(S11)']\n",
    "freqs = np.array(list(reals11.keys())) * 1e9\n",
    "I = np.array(list(reals11.values))\n",
    "Q = np.array(list(imags11.values))\n",
    "phase = np.angle(I+1j*Q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "661b6c54",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "res = Resonator(f_data=freqs,z_data=I + 1j*Q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e735f6f1",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "plt.scatter(freqs,phase,s=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1dcb70d2",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "delay = 0.8/18e9\n",
    "phase_corr = np.angle(np.exp(-2j*np.pi*delay)*(I+1j*Q))\n",
    "plt.scatter(freqs,phase_corr,s=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab31156b",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e6b141e",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "res.plotrawdata()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "628e3341",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "f_c = freqs[np.argmin(np.abs(I+1j*Q))]\n",
    "span = 20e6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b666d1c",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "f_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b92b96d4",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "res.autofit(electric_delay= 0.8/18e9,  fcrop=(f_c-span/2,f_c+span/2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c24bfeb0",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "res.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cba2f009",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def cal_Ec_from_freq(freq,Lj):\n",
    "    omega = freq * 2*np.pi * 1e9\n",
    "    e = 1.60217663e-19 # in coulombs\n",
    "    C = 1/(Lj * 1e-9 * omega ** 2)\n",
    "    h = 6.62607015 *  1e-34\n",
    "    Ec = e ** 2/2/C/h/1e6 # in MHz, linear freq\n",
    "    print(f'Ec = {Ec:.0f} MHz')\n",
    "    return Ec\n",
    "\n",
    "def cal_g_in_linear_freq(res,Lj, w_c = 6.2e9):\n",
    "    # Lj in nH\n",
    "    kappa = res.kappa\n",
    "    Z_0 = 50\n",
    "    omega_0 = res.f0 * 2 * np.pi\n",
    "    Lj = Lj*1e-9\n",
    "    e = 1.60217663e-19 # in coulombs\n",
    "    hbar = 1.05457182e-34 # in m^2*kg/s\n",
    "    C_c = np.sqrt(kappa / (Lj*Z_0)) * (1/omega_0**2)\n",
    "    g = C_c/(1/(Lj * omega_0**2)) * np.sqrt(Z_0*e**2/(np.pi*hbar)) *w_c/1e6\n",
    "    print(f'g/2pi = {g:.1f} MHz')\n",
    "    return g\n",
    "\n",
    "\n",
    "def cal_gamma_in_linear_freq(res):\n",
    "    gamma = res.f0/res.Qi/1e6\n",
    "    print(f'gamma/2pi = {gamma:.1f} MHz')\n",
    "    return gamma\n",
    "\n",
    "def print_design_parameters(options):\n",
    "    keys1 = ['cross_width','cross_length','cross_gap']\n",
    "    keys2 = ['finger_count','finger_length','cap_gap','cap_gap_ground','cap_width']\n",
    "    keys3 = ['termination_finger_count', 'termination_finger_length','termination_cap_gap','termination_cap_gap_ground','termination_cap_width']\n",
    "    keys4 = ['hfss_inductance']\n",
    "    params = [key + ' = ' + \"'\"+ str(value) + \"'\" for key,value in options.items() if key in keys1 + keys2 +keys3 + keys4] \n",
    "    print(',\\n'.join(params)+',')\n",
    "    \n",
    "def print_simulated_results(res, Lj):\n",
    "    print(f'f = {res.f0/1e9:.2f} GHz')\n",
    "    cal_Ec_from_freq(res.f0/1e9,Lj)\n",
    "    cal_g_in_linear_freq(res,Lj)\n",
    "    cal_gamma_in_linear_freq(res) \n",
    "\n",
    "def get_anharmonicity(Ec):\n",
    "    print(f\"E_c = {Ec} MHz\")\n",
    "    a = 2*np.pi*(-Ec)\n",
    "    print(f\"Anharmonicity = {a} MHz\")\n",
    "    return "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0f7e9db",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "f_c/1e9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "127c432a",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "Lj = float(q_d.options['hfss_inductance'][:-2])\n",
    "Ej = Convert.Ej_from_Lj(Lj, 'nH', \"GHz\")\n",
    "#Ec = cal_Ec_from_freq(res.f0/1e9,Lj)\n",
    "Ec = cal_Ec_from_freq(f_c/1e9,Lj)\n",
    "\n",
    "print(f\"Ej = {Ej} GHz\")\n",
    "#print_simulated_results(res,Lj)\n",
    "print(f\"\\nEj/Ec = {Ej/Ec*1e3}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e7f0089",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "print_design_parameters(q_d.options)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7c2a6ad",
   "metadata": {
    "hidden": true
   },
   "source": [
    "![alpha](anharmonicity.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "042a5cb5",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "get_anharmonicity(Ec)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5de90162",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# LOM Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b48205a5",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from qiskit_metal.analyses.quantization import LOManalysis\n",
    "c1 = LOManalysis(design, \"q3d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ced1b067",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "q3d = c1.sim.renderer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "669da143",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "q3d.start()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "712a7c9d",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "q3d.activate_ansys_design(\"test1\", 'capacitive')  # use new_ansys_design() to force creation of a blank design"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7da23880",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "q3d.clean_active_design()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf6b28d4",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "q3d.render_design(['Q1'], [('Q1','ground_pin')],box_plus_buffer=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "303db4a9",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "q3d.analyze_setup(\"Setup\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ed2b0f6",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "q3d.save_screenshot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e29f58a6",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "c1.sim.capacitance_matrix, c1.sim.units = q3d.get_capacitance_matrix()\n",
    "c1.sim.capacitance_all_passes, _ = q3d.get_capacitance_all_passes()\n",
    "c1.sim.capacitance_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce5103bb",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "q3d.stop()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89bd5c39",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## LOM Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9962c4b3",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "c1.setup.junctions=Dict(Lj=10, Cj=2)\n",
    "c1.setup.freq_readout = 6.0\n",
    "c1.setup.freq_bus = []\n",
    "\n",
    "c1.run_lom()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0e896cc",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "c1.lumped_oscillator_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce5392b9",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "c1.plot_convergence();\n",
    "c1.plot_convergence_chi()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44b837cc",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "c1.sim.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d58e3170",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef47edf7",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a6389f0",
   "metadata": {
    "hidden": true
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   "source": []
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   "execution_count": null,
   "id": "97937024",
   "metadata": {
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  },
  {
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   "execution_count": null,
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  },
  {
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   "execution_count": null,
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  },
  {
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   "execution_count": null,
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