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    "# Tutorial 6: From a Lumped Oscillator Model to readout parameters\n",
    "\n",
    "Tutorials 1 and 2 fit a lumped `L`–`C` model to a CPW resonator and check how\n",
    "accurately it reproduces the full distributed response. \n",
    "\n",
    "In this tutorial, you will learn how to turning that `L` and `C` into quantities \n",
    "you might actually use for designing a superconducting circuit. We will use the two helpers, `resonator_readout_params` and\n",
    "`extract_f0_kappa`, to do the following:\n",
    "\n",
    "1. Convert a fitted model into a resonance frequency $f_r$, characteristic\n",
    "  impedance $Z_r$, and zero-point voltage and current fluctuations\n",
    "2. Measure the loaded linewidth $\\kappa$ and get the quality factor $Q$ \n",
    "\n",
    "3. Design a symmetric $C_c$ to match a target $Q$\n"
   ]
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   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import skrf as rf\n",
    "\n",
    "import simpleLOMs as sl\n",
    "\n",
    "sl.apply_style()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89e4e8c3",
   "metadata": {},
   "source": [
    "## 1. Fit a resonator and read off its `L` and `C`\n",
    "\n",
    "We use a 7 mm CPW resonator on an ultracold-silicon chip. The  `fit_lom(d, model=...)`  returns effective `L` and `C` for any of\n",
    "the three models (`\"optimized\"`, `\"foster\"`, `\"analytical\"`). In this tutorial, we use Optimized, which is the\n",
    "default and the most accurate. However, you can  switch `MODEL` below if you want a faster\n",
    "Foster or Analytical fit.\n"
   ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitted lumped model (optimized):\n",
      "  L = 0.5605 nH\n",
      "  C = 633.14 fF\n"
     ]
    }
   ],
   "source": [
    "cpw = sl.CPWParams(ep_r=11.45)                       # ultracold-silicon substrate\n",
    "freq = rf.Frequency(4e9, 12e9, 8001, unit=\"Hz\")\n",
    "\n",
    "MODEL = \"optimized\"                               # or \"foster\" / \"analytical\"\n",
    "L, C = sl.fit_lom(7e-3, model=MODEL, cpw_params=cpw, freq=freq)\n",
    "\n",
    "print(\"Fitted lumped model ({}):\".format(MODEL))\n",
    "print(\"  L = {:.4f} nH\".format(L * 1e9))\n",
    "print(\"  C = {:.2f} fF\".format(C * 1e15))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbd25461",
   "metadata": {},
   "source": [
    "## 2. Convert fitted `L`, `C` to relevant circuit parameters.\n",
    "\n",
    "One of the benefits of lumped oscillator models is that they can be easily used to convert to Hamiltonian parameters using standard circuit quantization techniques.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dce7d250",
   "metadata": {},
   "source": [
    "### 2a. Unloaded quantities\n",
    "\n",
    "To convert `L, C` to frequency, impedance, and zero-point fluctuations, use `resonator_readout_params(L, C)` to get these values based on the fitted `L, C` quantities.  \n",
    "\n",
    "$$\n",
    "\\omega_r = \\frac{1}{\\sqrt{LC}}, \\qquad\n",
    "Z_r = \\sqrt{\\frac{L}{C}}, \\qquad\n",
    "V_\\mathrm{zpf} = \\sqrt{\\frac{\\hbar\\omega_r}{2C}}, \\qquad\n",
    "I_\\mathrm{zpf} = \\sqrt{\\frac{\\hbar\\omega_r}{2L}}.\n",
    "$$\n",
    "\n",
    "$Z_r$ and the zero-point fluctuations depend **only** on `L` and `C`, so we can\n",
    "compute them before saying anything about loss or coupling.\n"
   ]
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    {
     "name": "stdout",
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     "text": [
      "Resonator readout parameters\n",
      "----------------------------\n",
      "  f_r        =     8.4486 GHz\n",
      "  Z_r        =      29.75 Ohm\n",
      "  L          =     0.5605 nH\n",
      "  C          =   633.1379 fF\n",
      "  V_zpf      =      2.103 uV\n",
      "  I_zpf      =     70.667 nA\n"
     ]
    }
   ],
   "source": [
    "rp = sl.resonator_readout_params(L, C)\n",
    "print(rp.summary())\n"
   ]
  },
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   "cell_type": "markdown",
   "id": "6c3d8ec5",
   "metadata": {},
   "source": [
    "$V_\\mathrm{zpf}$ is on the order of a few $\\mu$V and $I_\\mathrm{zpf}$ is on the order of\n",
    "tens of nA, as expected for a standard coplanar readout resonator.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc02ee14",
   "metadata": {},
   "source": [
    "### 2b. Loaded quantities ($\\kappa$, $Q$)\n",
    "\n",
    "The quality factor $Q$ is not a property of the bare mode. Instead it is set\n",
    "by how strongly we couple the mode to the environment. \n",
    "\n",
    "In this geometry, the coupling capacitor $C_c$ ties the resonator to the 50 $\\Omega$ feedline and\n",
    "gives the mode a finite linewidth. We measure it using a circle fit via `extract_f0_kappa`, which\n",
    "locates the resonance on a coarse grid, then re-simulates a narrow window on a\n",
    "fine grid and reads $f_0$ and $\\kappa$ from a circle fit (a single wide grid\n",
    "cannot resolve a sub-MHz linewidth).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e58f796b",
   "metadata": {},
   "source": [
    "Beginning with a 6 fF coupling capacitor, we'll extract $f_0$ and $\\kappa$ fo the CPW system.\n"
   ]
  },
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    {
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     "output_type": "stream",
     "text": [
      "measured:  f0 = 8.3697 GHz    kappa/2pi = 2.4586 MHz\n",
      "\n",
      "Resonator readout parameters\n",
      "----------------------------\n",
      "  f_r        =     8.4486 GHz\n",
      "  Z_r        =      29.75 Ohm\n",
      "  L          =     0.5605 nH\n",
      "  C          =   633.1379 fF\n",
      "  V_zpf      =      2.103 uV\n",
      "  I_zpf      =     70.667 nA\n",
      "  kappa/2pi  =     2.4586 MHz\n",
      "  Q (loaded) =       3436\n",
      "  tau_photon =       64.7 ns\n"
     ]
    }
   ],
   "source": [
    "Cc = 6e-15                                       \n",
    "f0, kappa = sl.extract_f0_kappa(cpw, d=7e-3, Cc=Cc)\n",
    "print(\"measured:  f0 = {:.4f} GHz    kappa/2pi = {:.4f} MHz\".format(f0/1e9, kappa/1e6))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "532b3ae7",
   "metadata": {},
   "source": [
    "### 2c. Summarize the results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dbe27a64",
   "metadata": {},
   "outputs": [],
   "source": [
    "rp = sl.resonator_readout_params(L, C, kappa_Hz=kappa)\n",
    "print()\n",
    "print(rp.summary())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "385f87fb",
   "metadata": {},
   "source": [
    "Because this chip is lossless (`tand = 0`, superconducting metal), the internal\n",
    "quality factor is effectively infinite and the loaded $Q$ is set entirely by the\n",
    "coupling — i.e. here $Q \\approx Q_c$, the **coupling quality factor**. On a real\n",
    "device the loaded $Q$ combines with the internal $Q_i$ as\n",
    "$Q_\\ell^{-1} = Q_c^{-1} + Q_i^{-1}$.\n",
    "\n",
    "The photon lifetime $\\tau = 1/(2\\pi\\kappa) \\approx 65$ ns is the readout\n",
    "resonator's ring-down time.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "653a8245",
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   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5e4ca1c",
   "metadata": {},
   "source": [
    "## 3. Setting a design to match a target parameter. \n",
    "\n",
    "Here we will use the example of showing how the coupling capacitor $C_c$ sets $\\kappa$ and $Q$ to show how you can use sweeps like this to correlate geometric features of your circuit design to actual desired parameters.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8bd33a8",
   "metadata": {},
   "source": [
    "### 3a. Set up the sweep\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d482c7b7",
   "metadata": {},
   "source": [
    "We'll use 8 Cc values with all else fixed, and use `extract_f0_kappa` to get $\\kappa$ an subsequently $Q$ \n"
   ]
  },
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     "text": [
      "Cc =  2.0 fF    kappa/2pi =   0.279 MHz    Q =    30272\n",
      "Cc =  4.0 fF    kappa/2pi =   1.104 MHz    Q =     7656\n",
      "Cc =  6.0 fF    kappa/2pi =   2.459 MHz    Q =     3436\n",
      "Cc =  8.0 fF    kappa/2pi =   4.328 MHz    Q =     1952\n",
      "Cc = 10.0 fF    kappa/2pi =   6.696 MHz    Q =     1262\n",
      "Cc = 12.0 fF    kappa/2pi =   9.547 MHz    Q =      885\n",
      "Cc = 14.0 fF    kappa/2pi =  12.865 MHz    Q =      657\n",
      "Cc = 16.0 fF    kappa/2pi =  16.636 MHz    Q =      508\n"
     ]
    }
   ],
   "source": [
    "Cc_values = np.linspace(2e-15, 16e-15, 8)\n",
    "f0s, kappas, Qs = [], [], []\n",
    "for Cc in Cc_values:\n",
    "    f0, kappa = sl.extract_f0_kappa(cpw, d=7e-3, Cc=Cc)\n",
    "    rp = sl.resonator_readout_params(L, C, kappa_Hz=kappa)\n",
    "    f0s.append(f0 / 1e9)\n",
    "    kappas.append(kappa / 1e6)\n",
    "    Qs.append(rp.Q)\n",
    "\n",
    "f0s, kappas, Qs = map(np.array, (f0s, kappas, Qs))\n",
    "for cc, k, q in zip(Cc_values, kappas, Qs):\n",
    "    print(\"Cc = {:4.1f} fF    kappa/2pi = {:7.3f} MHz    Q = {:8.0f}\".format(cc*1e15, k, q))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "613b6911",
   "metadata": {},
   "source": [
    "### 3b. (Optional) Validate the data\n",
    "\n",
    "We already know approximately what to expect from this data based on approximate formulas. For \n",
    "a capacitively coupled resonator we expect that expectation is $\\kappa \\propto C_c^2$ and\n",
    "therefore $Q \\propto 1/C_c^2$. To ensure that this pattern occured, we can plot the two of them in log scale:\n"
   ]
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",
      "text/plain": [
       "<Figure size 2117.1x1308.44 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(<Figure size 2117.1x1308.44 with 2 Axes>,\n",
       " array([<Axes: xlabel='Coupling capacitance, $C_c$ (fF)', ylabel='Linewidth, $\\\\kappa/2\\\\pi$ (MHz)'>,\n",
       "        <Axes: xlabel='Coupling capacitance, $C_c$ (fF)', ylabel='Loaded quality factor, $Q$'>],\n",
       "       dtype=object))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sl.plot_curve_pair(\n",
    "    Cc_values * 1e15, kappas, Qs,\n",
    "    xlabel=sl.LABEL_CC,\n",
    "    ylabel0=sl.LABEL_KAPPA,\n",
    "    ylabel1=sl.axis_label(\"Loaded quality factor\", \"Q\"),\n",
    "    xscale=\"log\", yscale=\"log\",\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e828fc8",
   "metadata": {},
   "source": [
    "Indeed the graph matches our expectations. To quantify it, we can fit a line in log-log space.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d3a5309",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-19T04:23:59.238620Z",
     "iopub.status.busy": "2026-07-19T04:23:59.238511Z",
     "iopub.status.idle": "2026-07-19T04:23:59.240944Z",
     "shell.execute_reply": "2026-07-19T04:23:59.240563Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kappa  ~  Cc^+1.97   (expected +2)\n",
      "Q      ~  Cc^-1.97   (expected -2)\n"
     ]
    }
   ],
   "source": [
    "slope_k = np.polyfit(np.log(Cc_values), np.log(kappas), 1)[0]\n",
    "slope_Q = np.polyfit(np.log(Cc_values), np.log(Qs), 1)[0]\n",
    "print(\"kappa  ~  Cc^{:+.2f}   (expected +2)\".format(slope_k))\n",
    "print(\"Q      ~  Cc^{:+.2f}   (expected -2)\".format(slope_Q))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf287783",
   "metadata": {},
   "source": [
    "The fitted exponents sit close to $\\pm 2$, confirming that $\\kappa$ and $Q$ scale with $C_c$ as expected. \n",
    "\n",
    "This fitted curve is now the curve we can design against. WE can pick the $C_c$ that lands the\n",
    "readout resonator at the $\\kappa$ (equivalently $Q$) we want. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c72c257c",
   "metadata": {},
   "source": [
    "## 3c. Reading off a coupling for a target $Q$\n",
    "\n",
    "Because $Q$ is monotonic in $C_c$, you can interpolate the sweep to size the\n",
    "coupling capacitor for a chosen quality factor. Here, let's set the target $Q$ to be $5000$.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7e9a982",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-19T04:23:59.242203Z",
     "iopub.status.busy": "2026-07-19T04:23:59.242072Z",
     "iopub.status.idle": "2026-07-19T04:23:59.244282Z",
     "shell.execute_reply": "2026-07-19T04:23:59.243957Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For a loaded Q of 5000, choose  Cc ~ 4.96 fF\n"
     ]
    }
   ],
   "source": [
    "target_Q = 5000\n",
    "Cc_target = sl.invert_monotonic(Cc_values, Qs, target_Q, log=True)\n",
    "print(\"For a loaded Q of {}, choose  Cc ~ {:.2f} fF\".format(target_Q, Cc_target * 1e15))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b683cedd",
   "metadata": {},
   "source": [
    "Now as described in Tutorial 4, we can invert the $Cc$ graph to match the target.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f93daf02",
   "metadata": {},
   "outputs": [],
   "source": [
    "Cc_target = sl.invert_monotonic(Cc_values, Qs, target_Q, log=True)\n",
    "print(\"For a loaded Q of {}, choose  Cc ~ {:.2f} fF\".format(target_Q, Cc_target * 1e15))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "710b7319",
   "metadata": {},
   "source": [
    "We now have a way to convert lumped parameters into design targets.\n",
    "\n",
    "## Next up\n",
    "\n",
    "In Tutorial 7 we will learn how to use these principles taught in this tutorial to hit a target frequency and target $Q$ simultaneously by\n",
    "choosing the resonator length and coupling together.\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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