{ "cells": [ { "cell_type": "raw", "id": "35199c25", "metadata": { "tags": [ "remove-cell" ] }, "source": [ "/// script\n", "requires-python = \">=3.12,<3.15\"\n", "dependencies = [\n", " \"qpdk[models] @ git+https://github.com/gdsfactory/quantum-rf-pdk.git\",\n", "]\n", "///" ] }, { "cell_type": "markdown", "id": "d55407d4", "metadata": {}, "source": [ "# CPW parametric amplifiers: layout and circuit models\n", "\n", "::::{admonition} Required extras\n", ":class: tip\n", "\n", "Needs `models` and the external\n", "[VACASK](https://codeberg.org/arpadbuermen/VACASK) simulator:\n", "`uv sync --extra models` (or `pip install \"qpdk[models]\"`). Install VACASK,\n", "put `vacask` on `PATH` or set `VACASK`; it compiles Verilog-A with the bundled\n", "`openvaf-r`, and Linux also needs `binutils`. See the\n", "{ref}`extras reference `.\n", "::::\n", "\n", "A flux-pumped JPA uses a SQUID to modulate the resonance of a distributed\n", "resonator at twice its frequency\n", "{cite:p}`yamamotoFluxdrivenJosephsonParametric2008`. To simulate that in a\n", "circuit solver, represent the linear part as a subcircuit and solve the\n", "pumped nonlinear part with harmonic balance\n", "{cite:p}`kundertSimulationNonlinearCircuits1986,maasNonlinearMicrowaveRF2003`.\n", "\n", "This notebook checks the models with released VACASK. It compares two\n", "line models against SAX. It then uses them to measure resonance against flux,\n", "the single-mode threshold, gain against pump amplitude, and the higher-mode\n", "conversion that breaks the simple signal-idler balance. The\n", "{doc}`TWPA notebook `\n", "covers traveling-wave amplification. The\n", "{doc}`lumped companion `\n", "runs lumped Kerr and SQUID examples.\n", "\n", "The analyses differ in what they assume. AC linearizes the circuit around its\n", "DC operating point. ACSP returns S-parameters with the pump off. HB finds the\n", "nonlinear periodic pump. HBAC finds infinitesimal signal and idler waves\n", "around that pump. The {ref}`vacask-analysis-guide` in the lumped companion\n", "compares them in detail." ] }, { "cell_type": "code", "execution_count": 1, "id": "d7da0825", "metadata": { "execution": { "iopub.execute_input": "2026-10-21T07:06:38.790071Z", "iopub.status.busy": "2026-10-21T07:06:38.789972Z", "iopub.status.idle": "2026-10-21T07:06:38.793710Z", "shell.execute_reply": "2026-10-21T07:06:38.793071Z" }, "tags": [ "hide-input", "hide-output" ] }, "outputs": [], "source": [ "import sys\n", "\n", "if \"google.colab\" in sys.modules:\n", " import subprocess\n", "\n", " print(\"Running in Google Colab. Installing dependencies...\")\n", " subprocess.check_call([\n", " sys.executable,\n", " \"-m\",\n", " \"pip\",\n", " \"install\",\n", " \"-q\",\n", " \"qpdk[models] @ git+https://github.com/gdsfactory/quantum-rf-pdk.git\",\n", " ])" ] }, { "cell_type": "code", "execution_count": 2, "id": "52455541", "metadata": { "execution": { "iopub.execute_input": "2026-10-21T07:06:38.795156Z", "iopub.status.busy": "2026-10-21T07:06:38.795057Z", "iopub.status.idle": "2026-10-21T07:06:40.814281Z", "shell.execute_reply": "2026-10-21T07:06:40.813861Z" }, "tags": [ "hide-input" ] }, "outputs": [], "source": [ "import os\n", "import time\n", "from pathlib import Path\n", "\n", "import jax\n", "import jax.numpy as jnp\n", "from jax.typing import ArrayLike\n", "from matplotlib import font_manager, pyplot as plt\n", "from matplotlib.collections import PolyCollection\n", "from sax.models import rf\n", "\n", "import qpdk.colormaps # ruff: ignore[unused-import] - registers the style colormap\n", "from qpdk import PDK, logger\n", "from qpdk.cells.resonator import resonator\n", "from qpdk.config import PATH\n", "from qpdk.models.constants import Φ_0\n", "from qpdk.models.cpw import get_cpw_dimensions, get_cpw_substrate_params\n", "from qpdk.models.junction import squid_junction\n", "from qpdk.simulation.vacask import (\n", " Netlist,\n", " Vacask,\n", " VacaskError,\n", " cpw_tline_params,\n", " format_value,\n", " vector_fit,\n", " vector_fit_subckt,\n", ")\n", "\n", "# Agg suppresses notebook figure outputs in headless CI.\n", "if \"ipykernel\" in sys.modules:\n", " from IPython import get_ipython\n", " from matplotlib_inline.backend_inline import set_matplotlib_formats\n", "\n", " if (ipython := get_ipython()) is not None:\n", " ipython.run_line_magic(\"matplotlib\", \"inline\")\n", " set_matplotlib_formats(\"png\", \"svg\")\n", "\n", "jax.config.update(\"jax_enable_x64\", True)\n", "PDK.activate()\n", "\n", "# The checkout stylesheet is absent from installed wheels.\n", "for _style in (PATH.docs / \"qpdk.mplstyle\", \"qpdk\"):\n", " try:\n", " plt.style.use(_style)\n", " except OSError:\n", " continue\n", " break\n", "\n", "# The stylesheet names fonts that only the docs build ships; register them so\n", "# they resolve without docs/ipython_config.py.\n", "_font_dir = os.environ.get(\"QPDK_DOCS_FONTS_DIR\")\n", "for _fonts in (Path(_font_dir) if _font_dir else None, PATH.repo / \"build/docs-fonts\"):\n", " if _fonts is not None and _fonts.is_dir():\n", " for _font in sorted(_fonts.iterdir()):\n", " if _font.suffix in {\".otf\", \".ttf\"}:\n", " font_manager.fontManager.addfont(str(_font))\n", " break" ] }, { "cell_type": "markdown", "id": "9a1bb7bb", "metadata": {}, "source": [ "## Finding VACASK\n", "\n", "{meth}`Vacask.find() ` checks the\n", "`VACASK`/`QPDK_VACASK` environment variables and `PATH`. The notebook stops\n", "if the binary is missing." ] }, { "cell_type": "code", "execution_count": 3, "id": "6876fa98", "metadata": { "execution": { "iopub.execute_input": "2026-10-21T07:06:40.815337Z", "iopub.status.busy": "2026-10-21T07:06:40.815170Z", "iopub.status.idle": "2026-10-21T07:06:40.817234Z", "shell.execute_reply": "2026-10-21T07:06:40.816928Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using VACASK at \n" ] } ], "source": [ "try:\n", " vacask = Vacask.find()\n", "except VacaskError as error:\n", " print(error)\n", " raise SystemExit(\n", " \"VACASK is not installed; skipping the rest of this notebook.\"\n", " ) from None\n", "print(f\"Using VACASK at {vacask.binary}\")" ] }, { "cell_type": "markdown", "id": "3f62b7da", "metadata": {}, "source": [ "Every netlist uses `tolscale=1e-6 reltol=1e-6`. The default tolerances shift\n", "the gain by more than the differences resolved here. Port conventions:\n", "\n", "- A port is a Norton source $I_\\text{s}$ in parallel with $R=50\\,\\Omega$.\n", " So $V_\\text{inc}=I_\\text{s}R/2$ and $\\Gamma=2V/(I_\\text{s}R)-1$\n", " {cite:p}`m.pozarMicrowaveEngineering2012`.\n", "- VACASK reports a `sine` source of amplitude $A$ as $-\\text{j}A$, so the\n", " incident HB phasor is $-\\text{j}I_\\text{s}R/2$.\n", "- `hbac` writes the response at $f_\\text{s}+k f_\\text{p}$ as `node;k`." ] }, { "cell_type": "code", "execution_count": 4, "id": "e94b1509", "metadata": { "execution": { "iopub.execute_input": "2026-10-21T07:06:40.817958Z", "iopub.status.busy": "2026-10-21T07:06:40.817918Z", "iopub.status.idle": "2026-10-21T07:06:40.819995Z", "shell.execute_reply": "2026-10-21T07:06:40.819753Z" }, "tags": [ "hide-input" ] }, "outputs": [], "source": [ "R_PORT = 50.0\n", "I_SIG = 1e-9\n", "OPTIONS = \"options tolscale=1e-6 reltol=1e-6\"\n", "SMALL_SIGNAL = f\"mag=1e-9 spur={{[0]}} smag=[{I_SIG}]\"\n", "\n", "\n", "def db(x: ArrayLike) -> jax.Array:\n", " r\"\"\"Power gain in dB of a wave amplitude ratio, :math:`20\\log_{10}|A|`. See :cite:p:`m.pozarMicrowaveEngineering2012`.\"\"\"\n", " return 20 * jnp.log10(jnp.abs(x))\n", "\n", "\n", "def reflection(v: ArrayLike, source_current: float) -> jax.Array:\n", " r\"\"\"Reflection coefficient of a Norton port from its node voltage.\n", "\n", " Uses :math:`\\Gamma=2V/(I_\\text{s}R)-1`\n", " :cite:p:`m.pozarMicrowaveEngineering2012`.\n", "\n", " Returns:\n", " Complex reflected wave amplitude divided by the incident amplitude.\n", " \"\"\"\n", " return 2 * jnp.asarray(v) / (source_current * R_PORT) - 1\n", "\n", "\n", "def conversion(v: ArrayLike, source_current: float) -> jax.Array:\n", " r\"\"\"Conversion gain into a sideband other than the one driven.\n", "\n", " The sideband amplitude is :math:`2V/(I_\\text{s}R)`, as in\n", " frequency-conversion analysis :cite:p:`maasNonlinearMicrowaveRF2003`.\n", "\n", " Returns:\n", " Complex sideband wave amplitude divided by the incident amplitude.\n", " \"\"\"\n", " return 2 * jnp.asarray(v) / (source_current * R_PORT)\n", "\n", "\n", "def show_plot() -> None:\n", " \"\"\"Show the current figure with Outfit titles, as the docs config does.\"\"\"\n", " fig = plt.gcf()\n", " for text in [ax.title for ax in fig.axes] + list(fig.texts):\n", " if text.get_text():\n", " text.set_fontfamily(\"Outfit\")\n", " text.set_fontweight(\"bold\")\n", " plt.show()" ] }, { "cell_type": "markdown", "id": "1fb1c2d4", "metadata": {}, "source": [ "## SQUID-terminated quarter-wave resonator\n", "\n", "Following {cite:t}`yamamotoFluxdrivenJosephsonParametric2008`, a 4680 µm CPW\n", "($\\lambda/4$ at 6.5 GHz) is shorted through the SQUID. It is coupled to the\n", "port by $C_\\text{c}=20$ fF, using the PDK `cpw` cross-section. The pump\n", "modulates the SQUID flux at $f_\\text{p}\\approx2f_0$. A signal at\n", "$f_\\text{s}$ is amplified with an idler at $f_\\text{p}-f_\\text{s}$. This is\n", "three-wave mixing.\n", "\n", "The symmetric SQUID is one junction with\n", "$I_\\text{c,tot}\\cos(\\pi\\Phi/\\Phi_0)$. This neglects loop inductance, valid for\n", "$\\beta_L=2L_\\text{loop}I_\\text{c}/\\Phi_0\\ll1$\n", "{cite:p}`tescheDcSQUIDNoise1977`. Its flux terminal is driven in units of\n", "$\\Phi_0$." ] }, { "cell_type": "markdown", "id": "c05fce6c", "metadata": {}, "source": [ "(vacask-cpw-layout)=\n", "## Use the same geometry for layout and models\n", "\n", "Make a QPDK meander with the `cpw` cross-section. Read its realized centreline length after grid snapping. Use that length and cross-section for both the SAX line model and VACASK's line impedance and delay. Here the realized length is 4680 µm, the strip width is 10 µm and the gap is 6 µm.\n", "\n", "The two end ports connect to the ideal coupling capacitor and SQUID in the circuit. Those end devices are not drawn here. The line model uses a uniform cross-section approximation {cite:p}`simonsCoplanarWaveguideCircuits2001,ghioneAnalyticalFormulasCoplanar1984`. It does not include coupling between meanders, bend corrections or end-pad parasitics. For those effects, export this geometry to a field solver as in the {doc}`CPW field simulation notebook `.\n", "\n", "The plot below comes from the layout polygons. Changing the cross-section or length changes both the geometry and the model inputs." ] }, { "cell_type": "code", "execution_count": 5, "id": "63f31f7a", "metadata": { "execution": { "iopub.execute_input": "2026-10-21T07:06:40.820735Z", "iopub.status.busy": "2026-10-21T07:06:40.820697Z", "iopub.status.idle": "2026-10-21T07:06:41.117361Z", "shell.execute_reply": "2026-10-21T07:06:41.116951Z" }, "lines_to_next_cell": 2, "tags": [ "hide-input", "cpw-layout" ] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[32m2026-10-21 10:06:40.902\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m\u001b[0m:\u001b[36m14\u001b[0m - \u001b[1mLayout length 4680.0 µm, Z0 49.3128 Ω, delay 38.4457 ps\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "findfont: Failed to find font weight bold, now using 400.\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "LINE_CROSS_SECTION = \"cpw\"\n", "line_layout = resonator(\n", " length=4680.0,\n", " meanders=6,\n", " cross_section=LINE_CROSS_SECTION,\n", " open_start=False,\n", " open_end=False,\n", ")\n", "LENGTH = float(line_layout.info[\"length\"])\n", "C_COUPLING = 20e-15\n", "width, gap = get_cpw_dimensions(LINE_CROSS_SECTION)\n", "h_sub, t_metal, ep_r, _ = get_cpw_substrate_params()\n", "z0_line, td_line = cpw_tline_params(LENGTH, cross_section=LINE_CROSS_SECTION)\n", "logger.info(\n", " \"Layout length {} µm, Z0 {:.4f} Ω, delay {:.4f} ps\", LENGTH, z0_line, td_line * 1e12\n", ")\n", "\n", "fig, (ax_layout, ax_values) = plt.subplots(\n", " 1, 2, figsize=(8, 5), gridspec_kw={\"width_ratios\": [1, 1.2]}\n", ")\n", "polygons = line_layout.get_polygons_points(by=\"name\")\n", "for layer_name, color, label in (\n", " (\"M1_ETCH\", \"C1\", \"Etch\"),\n", " (\"M1_DRAW\", \"C0\", \"Metal\"),\n", "):\n", " ax_layout.add_collection(\n", " PolyCollection(\n", " polygons[layer_name], facecolor=color, edgecolor=\"none\", label=label\n", " )\n", " )\n", "for port_name, label in ((\"o1\", \"Coupler\"), (\"o2\", \"SQUID\")):\n", " port = line_layout.ports[port_name]\n", " ax_layout.annotate(\n", " label, port.center, xytext=(0, 12), textcoords=\"offset points\", ha=\"center\"\n", " )\n", "ax_layout.autoscale_view()\n", "ax_layout.margins(0.25, 0.08)\n", "ax_layout.set_aspect(\"equal\")\n", "ax_layout.set(xlabel=\"x (µm)\", ylabel=\"y (µm)\", title=\"QPDK line geometry\")\n", "ax_layout.legend(loc=\"center left\", bbox_to_anchor=(0, 0.5))\n", "ax_values.axis(\"off\")\n", "parameters = [\n", " [\"Cross-section\", LINE_CROSS_SECTION],\n", " [\"Length (µm)\", f\"{LENGTH:.3f}\"],\n", " [\"Strip width (µm)\", f\"{width:g}\"],\n", " [\"Gap (µm)\", f\"{gap:g}\"],\n", " [\"Impedance (Ω)\", f\"{z0_line:.3f}\"],\n", " [\"Delay (ps)\", f\"{td_line * 1e12:.3f}\"],\n", "]\n", "parameter_table = ax_values.table(\n", " cellText=parameters, colLabels=[\"Input\", \"Value\"], loc=\"center\"\n", ")\n", "parameter_table.auto_set_font_size(False)\n", "parameter_table.set_fontsize(10)\n", "parameter_table.scale(1, 1.8)\n", "ax_values.set_title(\"Shared model inputs\")\n", "fig.tight_layout()\n", "show_plot()" ] }, { "cell_type": "markdown", "id": "315ff891", "metadata": { "lines_to_next_cell": 2 }, "source": [ "## A distributed line and a rational fit\n", "\n", "- **Route A**: an ideal `tline_ideal` with $Z_0$ and delay from\n", " {func}`~qpdk.simulation.vacask.cpw_tline_params`. This is exact for a\n", " lossless TEM line.\n", "- **Route B**: a rational fit of any linear SAX model by\n", " {func}`~qpdk.simulation.vacask.vector_fit` (vector fitting with passivity\n", " enforcement {cite:p}`gustavsenRationalApproximationFrequency1999`),\n", " emitted by {func}`~qpdk.simulation.vacask.vector_fit_subckt`.\n", "\n", "Both are compared with SAX after renormalizing to 50 Ω through ABCD matrices\n", "{cite:p}`m.pozarMicrowaveEngineering2012`. The loss tangent is zero, so all\n", "three describe the same lossless line.\n", "\n", "The line comparison and the fit check answer different questions. The CPW\n", "dispersion comes from the SAX coplanar-waveguide model\n", "{cite:p}`gopplCoplanarWaveguideResonators2008a`. The rational fit tests only\n", "how well Route B reproduces that model." ] }, { "cell_type": "code", "execution_count": 6, "id": "1c6e2ef2", "metadata": { "execution": { "iopub.execute_input": "2026-10-21T07:06:41.118198Z", "iopub.status.busy": "2026-10-21T07:06:41.118141Z", "iopub.status.idle": "2026-10-21T07:06:41.737257Z", "shell.execute_reply": "2026-10-21T07:06:41.736953Z" }, "tags": [ "hide-input" ] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " RuntimeWarning: divide by zero encountered in matmul\n", " prod_neg = np.matmul(np.matmul(B, inv_neg), C)\n", " RuntimeWarning: overflow encountered in matmul\n", " prod_neg = np.matmul(np.matmul(B, inv_neg), C)\n", " RuntimeWarning: invalid value encountered in matmul\n", " prod_neg = np.matmul(np.matmul(B, inv_neg), C)\n", " RuntimeWarning: divide by zero encountered in matmul\n", " prod_pos = np.matmul(np.matmul(B, inv_pos), C)\n", " RuntimeWarning: overflow encountered in matmul\n", " prod_pos = np.matmul(np.matmul(B, inv_pos), C)\n", " RuntimeWarning: invalid value encountered in matmul\n", " prod_pos = np.matmul(np.matmul(B, inv_pos), C)\n", " RuntimeWarning: divide by zero encountered in matmul\n", " P = np.matmul(A - prod_neg, A - prod_pos)\n", " RuntimeWarning: overflow encountered in matmul\n", " P = np.matmul(A - prod_neg, A - prod_pos)\n", " RuntimeWarning: invalid value encountered in matmul\n", " P = np.matmul(A - prod_neg, A - prod_pos)\n", " UserWarning: The fitted network is passive, but the vector fit is not passive. Consider running `passivity_enforce()` to enforce passivity before using this model.\n", " vf.vector_fit(n_poles_real=n_poles_real, n_poles_cmplx=n_poles_cmplx)\n", " UserWarning: Passivity enforcement: The dc point in the model is not passive. Cannot preserve the dc point during passivity enforcement. \n", " vf.passivity_enforce(n_samples=n_samples, f_max=float(f.max()))\n", "\u001b[32m2026-10-21 10:06:41.699\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mqpdk.simulation.vacask.network\u001b[0m:\u001b[36mvector_fit\u001b[0m:\u001b[36m169\u001b[0m - \u001b[1mVector fit: 11 poles, RMS error 2.37e-03\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "rational fit: 11 poles, max |ΔS| = 2.3e-03, 0.6 s\n" ] } ], "source": [ "def s_to_abcd(s: jax.Array, z0: float) -> jax.Array:\n", " \"\"\"ABCD matrices of two-port S-matrices ``(..., 2, 2)`` referenced to ``z0``.\n", "\n", " Standard S-to-ABCD conversion :cite:p:`m.pozarMicrowaveEngineering2012`.\n", "\n", " Returns:\n", " ABCD matrices with the same leading axes as the input.\n", " \"\"\"\n", " s11, s12, s21, s22 = s[..., 0, 0], s[..., 0, 1], s[..., 1, 0], s[..., 1, 1]\n", " a = ((1 + s11) * (1 - s22) + s12 * s21) / (2 * s21)\n", " b = z0 * ((1 + s11) * (1 + s22) - s12 * s21) / (2 * s21)\n", " c = ((1 - s11) * (1 - s22) - s12 * s21) / (2 * s21 * z0)\n", " d = ((1 - s11) * (1 + s22) + s12 * s21) / (2 * s21)\n", " return jnp.stack([jnp.stack([a, b], -1), jnp.stack([c, d], -1)], -2)\n", "\n", "\n", "def abcd_to_s(abcd: jax.Array, z0: float) -> jax.Array:\n", " \"\"\"Two-port S-matrices of ABCD matrices, referenced to ``z0``.\n", "\n", " Standard ABCD-to-S conversion :cite:p:`m.pozarMicrowaveEngineering2012`.\n", "\n", " Returns:\n", " Two-port S-matrices with the same leading axes as the input.\n", " \"\"\"\n", " a, b, c, d = abcd[..., 0, 0], abcd[..., 0, 1], abcd[..., 1, 0], abcd[..., 1, 1]\n", " den = a + b / z0 + c * z0 + d\n", " s11 = (a + b / z0 - c * z0 - d) / den\n", " s12 = 2 * (a * d - b * c) / den\n", " s22 = (-a + b / z0 - c * z0 + d) / den\n", " return jnp.stack([jnp.stack([s11, s12], -1), jnp.stack([2 / den, s22], -1)], -2)\n", "\n", "\n", "def coupled_line_abcd(f: jax.Array) -> jax.Array:\n", " \"\"\"ABCD matrix of the coupling capacitor followed by the CPW.\n", "\n", " The line is the SAX coplanar-waveguide model\n", " :cite:p:`gopplCoplanarWaveguideResonators2008a`. The coupling capacitor is\n", " a series impedance.\n", "\n", " Returns:\n", " ABCD matrices of the series coupling capacitor and line.\n", " \"\"\"\n", " line = rf.coplanar_waveguide(\n", " f=f,\n", " length=LENGTH,\n", " width=width,\n", " gap=gap,\n", " thickness=t_metal,\n", " substrate_thickness=h_sub,\n", " ep_r=ep_r,\n", " tand=0.0,\n", " )\n", " s = jnp.stack(\n", " [\n", " jnp.stack([line[\"o1\", \"o1\"], line[\"o1\", \"o2\"]], -1),\n", " jnp.stack([line[\"o2\", \"o1\"], line[\"o2\", \"o2\"]], -1),\n", " ],\n", " -2,\n", " )\n", " one, zero = jnp.ones_like(f), jnp.zeros_like(f)\n", " z_cap = 1 / (2j * jnp.pi * f * C_COUPLING)\n", " abcd_cap = jnp.stack([jnp.stack([one, z_cap], -1), jnp.stack([zero, one], -1)], -2)\n", " return abcd_cap @ s_to_abcd(s, z0_line)\n", "\n", "\n", "def coupled_line_sdict(f: jax.Array) -> dict:\n", " \"\"\"50 Ω S-parameters of the coupling capacitor followed by the CPW.\"\"\"\n", " s = abcd_to_s(coupled_line_abcd(f), R_PORT)\n", " return {(f\"o{i + 1}\", f\"o{j + 1}\"): s[:, i, j] for i in range(2) for j in range(2)}\n", "\n", "\n", "f_fit = jnp.linspace(10e6, 70e9, 3500)\n", "start = time.perf_counter()\n", "fit = vector_fit(coupled_line_sdict(f_fit), f_fit, ports=[\"o1\", \"o2\"])\n", "reference_fit = coupled_line_sdict(f_fit)\n", "fit_error = max(\n", " jnp.max(jnp.abs(fit.get_model_response(i, j, f_fit) - reference_fit[pi, pj]))\n", " for i, pi in enumerate((\"o1\", \"o2\"))\n", " for j, pj in enumerate((\"o1\", \"o2\"))\n", ")\n", "pole_count = 0 if fit.poles is None else len(fit.poles)\n", "print(\n", " f\"rational fit: {pole_count} poles, max |ΔS| = {fit_error:.1e}, \"\n", " f\"{time.perf_counter() - start:.1f} s\"\n", ")" ] }, { "cell_type": "markdown", "id": "30518fae", "metadata": {}, "source": [ "Route A is a hand-written subcircuit. Route B is generated. Both have\n", "terminals `p1` (port side) and `p2` (far end of the line).\n", "\n", "The check uses ACSP. It gives the cold multiport S-parameters at the fixed DC\n", "bias with small port excitations. No pump is solved." ] }, { "cell_type": "code", "execution_count": 7, "id": "6d892f19", "metadata": { "execution": { "iopub.execute_input": "2026-10-21T07:06:41.738262Z", "iopub.status.busy": "2026-10-21T07:06:41.738216Z", "iopub.status.idle": "2026-10-21T07:06:41.746122Z", "shell.execute_reply": "2026-10-21T07:06:41.745848Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "// Rational model of ports o1, o2, 11 poles\n", "subckt ccline (p1 p2)\n", " model vccs vccs\n", " r1 (p1 0) resistor r=50\n", " g2 (0 p1 b0 0) vccs gain=0.04\n", " r3 (a0 0) resistor r=1\n", " g4 (0 a0 p1 0) vccs gain=1\n", " g5 (0 a0 b0 0) vccs gain=-1\n", " r6 (b0 0) resistor r=1\n", " r7 (p2 0) resistor r=50\n", " g8 (0 p2 b1 0) vccs gain=0.04\n", " r9 (a1 0) resistor r=1 \n", "...\n" ] } ], "source": [ "ROUTE_A = f\"\"\"\n", " subckt ccline (p1 p2)\n", " cc (p1 1) capacitor c={C_COUPLING}\n", " t1 (1 0 p2 0) tline_ideal z0={z0_line} td={td_line}\n", " ends\n", "\"\"\"\n", "ROUTE_B = vector_fit_subckt(fit, \"ccline\")\n", "print(\"\\n\".join(ROUTE_B.splitlines()[:12]), \"\\n...\")" ] }, { "cell_type": "code", "execution_count": 8, "id": "54688c12", "metadata": { "execution": { "iopub.execute_input": "2026-10-21T07:06:41.746968Z", "iopub.status.busy": "2026-10-21T07:06:41.746923Z", "iopub.status.idle": "2026-10-21T07:06:42.568263Z", "shell.execute_reply": "2026-10-21T07:06:42.567927Z" }, "tags": [ "hide-input", "cpw-passive" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "s(1,1): Route A max|ΔS| = 8.0e-16, Route B max|ΔS| = 1.1e-03\n", "s(2,1): Route A max|ΔS| = 6.1e-16, Route B max|ΔS| = 1.2e-03\n", "s(2,2): Route A max|ΔS| = 3.8e-15, Route B max|ΔS| = 1.1e-03\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f_check = jnp.linspace(1e9, 20e9, 1901)\n", "LINE_LOADS = [\"capacitor.osdi\", \"resistor.osdi\", \"tline_ideal.osdi\"]\n", "ports = [\"vp1\", \"rp1\", \"vp2\", \"rp2\"]\n", "\n", "\n", "def two_port_netlist(route: str) -> Netlist:\n", " \"\"\"S-parameters of the coupled line, Route A or Route B.\"\"\"\n", " return Netlist(\n", " f\"Coupled CPW, Route {route}\",\n", " loads=LINE_LOADS,\n", " subckts=[ROUTE_A if route == \"A\" else ROUTE_B],\n", " circuit=f\"\"\"\n", " model vsource vsource\n", " model resistor resistor\n", " model capacitor capacitor\n", " model tline_ideal tline_ideal\n", "\n", " x1 (1 2) ccline\n", " vp1 (a1 0) vsource dc=0\n", " rp1 (a1 1) resistor r={R_PORT}\n", " vp2 (a2 0) vsource dc=0\n", " rp2 (a2 2) resistor r={R_PORT}\n", " \"\"\",\n", " control=f\"\"\"\n", " {OPTIONS}\n", " analysis sp acsp ports={format_value(ports)} values={format_value(f_check)}\n", " \"\"\",\n", " )\n", "\n", "\n", "results = {route: vacask.run(two_port_netlist(route))[\"sp\"] for route in \"AB\"}\n", "reference = coupled_line_sdict(f_check)\n", "for key, (i, j) in {\"s(1,1)\": (0, 0), \"s(2,1)\": (1, 0), \"s(2,2)\": (1, 1)}.items():\n", " ref = reference[f\"o{i + 1}\", f\"o{j + 1}\"]\n", " errors = {\n", " route: jnp.max(jnp.abs(jnp.asarray(results[route][key]) - ref))\n", " for route in \"AB\"\n", " }\n", " print(\n", " f\"{key}: Route A max|ΔS| = {errors['A']:.1e}, \"\n", " f\"Route B max|ΔS| = {errors['B']:.1e}\"\n", " )\n", "\n", "fig, (ax, ax_err) = plt.subplots(1, 2, figsize=(10, 4))\n", "ax.plot(f_check / 1e9, db(reference[\"o2\", \"o1\"]), label=\"SAX\")\n", "ax.plot(f_check / 1e9, db(results[\"A\"][\"s(2,1)\"]), \"--\", label=\"Route A (tline_ideal)\")\n", "ax.plot(f_check / 1e9, db(results[\"B\"][\"s(2,1)\"]), \":\", label=\"Route B (rational fit)\")\n", "ax.set_xlabel(\"Frequency (GHz)\")\n", "ax.set_ylabel(r\"$|S_{21}|$ (dB)\")\n", "ax.legend()\n", "for route in \"AB\":\n", " error = jnp.abs(jnp.asarray(results[route][\"s(2,1)\"]) - reference[\"o2\", \"o1\"])\n", " ax_err.semilogy(f_check / 1e9, error, label=f\"Route {route}\")\n", "ax_err.set_xlabel(\"Frequency (GHz)\")\n", "ax_err.set_ylabel(r\"$|S_{21} - S_{21}^\\text{SAX}|$\")\n", "ax_err.legend()\n", "fig.tight_layout()\n", "show_plot()" ] }, { "cell_type": "markdown", "id": "ff6baabd", "metadata": {}, "source": [ "Route A matches SAX to rounding error. Route B differs by about $10^{-3}$.\n", "That is acceptable for linear S-parameters, but not for a high-Q resonator.\n", "\n", "## Resonance versus flux\n", "\n", "The SQUID has $I_\\text{c,tot}=4$ µA. The resonance is the peak of the\n", "reflection group delay, $\\tau=-\\text{d}\\arg\\Gamma/\\text{d}\\omega$\n", "{cite:p}`m.pozarMicrowaveEngineering2012`. For a lossless, overcoupled\n", "one-port resonator $\\tau_\\text{peak}=4/\\kappa$, so the width\n", "$4/(2\\pi\\tau_\\text{peak})$ is the linewidth only in that case\n", "{cite:p}`gopplCoplanarWaveguideResonators2008a`. The SAX reference terminates\n", "the coupled-line ABCD matrix with the admittance of\n", "{func}`~qpdk.models.junction.squid_junction` {cite:p}`tescheDcSQUIDNoise1977`.\n", "\n", "AC linearizes the circuit around its DC operating point. With zero pump, it\n", "gives the unpumped resonance." ] }, { "cell_type": "code", "execution_count": 9, "id": "60ae958d", "metadata": { "execution": { "iopub.execute_input": "2026-10-21T07:06:42.569275Z", "iopub.status.busy": "2026-10-21T07:06:42.569231Z", "iopub.status.idle": "2026-10-21T07:06:45.870646Z", "shell.execute_reply": "2026-10-21T07:06:45.870355Z" }, "tags": [ "hide-input", "cpw-flux" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Φ = 0.00 Φ0: VACASK 6.08328 GHz, SAX 6.08328 GHz\n", "Φ = 0.10 Φ0: VACASK 6.07064 GHz, SAX 6.07064 GHz\n", "Φ = 0.20 Φ0: VACASK 6.02580 GHz, SAX 6.02580 GHz\n", "Φ = 0.25 Φ0: VACASK 5.98322 GHz, SAX 5.98322 GHz\n", "Φ = 0.30 Φ0: VACASK 5.91600 GHz, SAX 5.91600 GHz\n", "Φ = 0.35 Φ0: VACASK 5.80267 GHz, SAX 5.80267 GHz\n", "Φ = 0.40 Φ0: VACASK 5.58455 GHz, SAX 5.58455 GHz\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "IC_SQUID = 4e-6\n", "\n", "\n", "def cpw_jpa_netlist(\n", " control: str,\n", " *,\n", " route: str = \"A\",\n", " flux: float = 0.3,\n", " pump: float = 0.0,\n", " f_pump: float = 1e9,\n", ") -> Netlist:\n", " \"\"\"Quarter-wave CPW JPA netlist using Route A or Route B for the line.\"\"\"\n", " return Netlist(\n", " \"Quarter-wave CPW JPA\",\n", " loads=[*LINE_LOADS, \"squid.va\"],\n", " subckts=[ROUTE_A if route == \"A\" else ROUTE_B],\n", " circuit=f\"\"\"\n", " model isource isource\n", " model vsource vsource\n", " model resistor resistor\n", " model capacitor capacitor\n", " model tline_ideal tline_ideal\n", " model squid squid\n", "\n", " iin (0 in) isource dc=0 {SMALL_SIGNAL}\n", " rport (in 0) resistor r={R_PORT}\n", " x1 (in 2) ccline\n", " s1 (2 0 fl) squid ic_tot={IC_SQUID}\n", " vfl (fl 0) vsource type=\"sine\" sinedc={flux} ampl={pump} freq={f_pump}\n", " \"\"\",\n", " control=f\"{OPTIONS}\\n{control}\",\n", " )\n", "\n", "\n", "def resonance(f: jax.Array, gamma: jax.Array) -> tuple[jax.Array, jax.Array]:\n", " r\"\"\"Peak-delay frequency and 4/(2π tau).\n", "\n", " Group delay is :math:`-\\text{d}\\arg\\Gamma/\\text{d}\\omega`\n", " :cite:p:`m.pozarMicrowaveEngineering2012`. The second value is a linewidth\n", " only for a lossless one-port resonator\n", " :cite:p:`gopplCoplanarWaveguideResonators2008a`.\n", "\n", " Returns:\n", " Peak-delay frequency and effective delay width, both in Hz.\n", " \"\"\"\n", " grad = jnp.gradient(jnp.unwrap(jnp.angle(gamma)), 2 * jnp.pi * f)\n", " tau = -jnp.asarray(grad[0] if isinstance(grad, list) else grad)\n", " k = tau.argmax()\n", " return f[k], 4 / tau[k] / (2 * jnp.pi)\n", "\n", "\n", "def sax_reflection(f: jax.Array, flux: float) -> jax.Array:\n", " \"\"\"Reflection of the SAX coupled line terminated by the SAX SQUID.\n", "\n", " The SQUID admittance follows :cite:p:`tescheDcSQUIDNoise1977`.\n", "\n", " Returns:\n", " Complex port reflection coefficients.\n", " \"\"\"\n", " abcd = coupled_line_abcd(f)\n", " squid = squid_junction(\n", " f=f, ic_tot=IC_SQUID, capacitance=0.0, resistance=1e12, flux=flux * Φ_0\n", " )\n", " y_load = 1 / squid[\"o1\", \"o1\"] - 1\n", " z_in = (abcd[:, 0, 0] / y_load + abcd[:, 0, 1]) / (\n", " abcd[:, 1, 0] / y_load + abcd[:, 1, 1]\n", " )\n", " return (z_in - R_PORT) / (z_in + R_PORT)\n", "\n", "\n", "fluxes = [0.0, 0.1, 0.2, 0.25, 0.3, 0.35, 0.4]\n", "ac = vacask.run(\n", " cpw_jpa_netlist(f\"\"\"\n", " sweep flux instance=\"vfl\" parameter=\"sinedc\" values={format_value(fluxes)}\n", " analysis ac1 ac from=5.4e9 to=6.2e9 mode=\"lin\" points=160001\n", " \"\"\")\n", ")[\"ac1\"]\n", "f_grid = jnp.linspace(5.4e9, 6.2e9, 160001)\n", "f_res = jnp.array([\n", " resonance(jnp.asarray(g[\"frequency\"].real), reflection(g[\"in\"], I_SIG))[0]\n", " for g in ac.split(\"flux\")\n", "])\n", "f_sax = jnp.array([\n", " resonance(f_grid, sax_reflection(f_grid, flux))[0] for flux in fluxes\n", "])\n", "for flux, f0, f0_sax in zip(fluxes, f_res, f_sax, strict=True):\n", " print(f\"Φ = {flux:.2f} Φ0: VACASK {f0 / 1e9:.5f} GHz, SAX {f0_sax / 1e9:.5f} GHz\")\n", "\n", "fluxes_fine = jnp.linspace(0, 0.4, 41)\n", "f_sax_fine = jnp.array([\n", " resonance(f_grid, sax_reflection(f_grid, flux))[0] for flux in fluxes_fine\n", "])\n", "fig, ax = plt.subplots()\n", "ax.plot(fluxes_fine, f_sax_fine / 1e9, \"-\", label=\"SAX\")\n", "ax.plot(fluxes, f_res / 1e9, \"o\", label=\"VACASK\")\n", "ax.set_xlabel(r\"Flux bias ($\\Phi_0$)\")\n", "ax.set_ylabel(r\"$f_0$ (GHz)\")\n", "ax.legend()\n", "show_plot()" ] }, { "cell_type": "markdown", "id": "77c6be9b", "metadata": {}, "source": [ "## Operating point and the single-mode threshold\n", "\n", "For $\\omega_0(t)=\\omega_0+\\delta\\omega\\cos(2\\omega_0t)$, single-mode theory\n", "{cite:p}`royIntroductionParametricAmplification2016` gives the threshold\n", "$\\delta\\omega=\\kappa$ and the center gain\n", "\n", "```{math}\n", ":label: eq:vacask-3wm-gain\n", "G_0 = \\left(\\frac{1 + r^2}{1 - r^2}\\right)^2,\n", "\\qquad r = \\frac{\\delta\\omega}{\\kappa}\n", " = \\frac{|\\text{d}f_0/\\text{d}\\Phi|\\,\\Phi_\\text{p}}{\\kappa / 2\\pi}.\n", "```\n", "\n", "We measure $f_0$, $\\kappa$ and $\\text{d}f_0/\\text{d}\\Phi$ at\n", "$\\Phi=0.3\\,\\Phi_0$ from AC sweeps. These runs have no pump, so the delay gives\n", "the unpumped linewidth." ] }, { "cell_type": "code", "execution_count": 10, "id": "95bcefc2", "metadata": { "execution": { "iopub.execute_input": "2026-10-21T07:06:45.871708Z", "iopub.status.busy": "2026-10-21T07:06:45.871661Z", "iopub.status.idle": "2026-10-21T07:06:46.682903Z", "shell.execute_reply": "2026-10-21T07:06:46.682578Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "f0 = 5.916001 GHz, κ/2π = 10.242 MHz, Q = 578\n", "df0/dΦ = -1.7005 GHz/Φ0\n", "single-mode threshold Φp = 0.00602 Φ0\n" ] } ], "source": [ "FLUX_BIAS = 0.3\n", "operating_fluxes = [FLUX_BIAS - 1e-3, FLUX_BIAS, FLUX_BIAS + 1e-3]\n", "ac = vacask.run(\n", " cpw_jpa_netlist(f\"\"\"\n", " sweep flux instance=\"vfl\" parameter=\"sinedc\" values={format_value(operating_fluxes)}\n", " analysis ac1 ac from=5.85e9 to=6.0e9 mode=\"lin\" points=150001\n", " \"\"\")\n", ")[\"ac1\"]\n", "operating = [\n", " resonance(jnp.asarray(g[\"frequency\"].real), reflection(g[\"in\"], I_SIG))\n", " for g in ac.split(\"flux\")\n", "]\n", "(f_lo, _), (F0, KAPPA), (f_hi, _) = (tuple(map(float, x)) for x in operating)\n", "DF_DPHI = (f_hi - f_lo) / 2e-3\n", "PUMP_THRESHOLD = KAPPA / abs(DF_DPHI)\n", "print(f\"f0 = {F0 / 1e9:.6f} GHz, κ/2π = {KAPPA / 1e6:.3f} MHz, Q = {F0 / KAPPA:.0f}\")\n", "print(f\"df0/dΦ = {DF_DPHI / 1e9:.4f} GHz/Φ0\")\n", "print(f\"single-mode threshold Φp = {PUMP_THRESHOLD:.5f} Φ0\")" ] }, { "cell_type": "markdown", "id": "abbd253f", "metadata": {}, "source": [ "## Gain versus pump amplitude\n", "\n", "Pump at $f_\\text{p}=2f_0$ below threshold and compare with\n", "Eq. {eq}`eq:vacask-3wm-gain`.\n", "\n", "`hbac` first solves the pump state, then finds the infinitesimal signal and\n", "idler around it. By default it also solves the pump HB, so no separate HB run\n", "is needed. See the {ref}`vacask-analysis-guide`.\n", "\n", "At $f_\\text{s}=f_\\text{p}/2$ the signal and idler share one frequency and the\n", "gain depends on the signal phase. The sweep skips that point, so each curve\n", "is the phase-preserving gain of a slightly detuned signal." ] }, { "cell_type": "code", "execution_count": 11, "id": "54dda149", "metadata": { "execution": { "iopub.execute_input": "2026-10-21T07:06:46.683922Z", "iopub.status.busy": "2026-10-21T07:06:46.683869Z", "iopub.status.idle": "2026-10-21T07:06:47.362515Z", "shell.execute_reply": "2026-10-21T07:06:47.362177Z" }, "tags": [ "hide-input", "cpw-gain" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "r = 0.3: peak gain 1.26 dB (single-mode 1.57 dB), |S_ss|² - |S_is|² = 0.932\n", "r = 0.5: peak gain 3.42 dB (single-mode 4.44 dB), |S_ss|² - |S_is|² = 0.786\n", "r = 0.7: peak gain 6.53 dB (single-mode 9.31 dB), |S_ss|² - |S_is|² = 0.492\n", "r = 0.8: peak gain 8.46 dB (single-mode 13.17 dB), |S_ss|² - |S_is|² = 0.241\n", "r = 0.9: peak gain 10.70 dB (single-mode 19.58 dB), |S_ss|² - |S_is|² = -0.136\n" ] }, { "data": { "image/png": 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], "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "F_PUMP_CPW = 2 * F0\n", "ratios = jnp.array([0.3, 0.5, 0.7, 0.8, 0.9])\n", "# The 1.0013 keeps the 200-point grid off the degenerate point f_s = F0.\n", "plot = vacask.run(\n", " cpw_jpa_netlist(\n", " f\"\"\"\n", " sweep pump instance=\"vfl\" parameter=\"ampl\" \\\\\n", " values={format_value(ratios * PUMP_THRESHOLD)}\n", " analysis hbac1 hbac freq=[{F_PUMP_CPW}] nharm=5 outspur={{[0],[-1]}} \\\\\n", " from={F0 - 1.0013 * KAPPA} to={F0 + 1.0013 * KAPPA} mode=\"lin\" points=200\n", " \"\"\",\n", " f_pump=F_PUMP_CPW,\n", " )\n", ")[\"hbac1\"]\n", "\n", "fig, ax = plt.subplots()\n", "for group, r in zip(plot.split(\"pump\"), ratios, strict=True):\n", " detuning = (jnp.asarray(group[\"frequency\"].real) - F0) / KAPPA\n", " gain = db(reflection(group[\"in;0\"], I_SIG))\n", " idler = db(conversion(group[\"in;-1\"], I_SIG))\n", " k = jnp.argmin(jnp.abs(detuning))\n", " analytic = 20 * jnp.log10((1 + r**2) / (1 - r**2))\n", " print(\n", " f\"r = {r:.1f}: peak gain {gain.max():5.2f} dB (single-mode {analytic:5.2f} dB), \"\n", " f\"|S_ss|² - |S_is|² = {10 ** (gain[k] / 10) - 10 ** (idler[k] / 10):.3f}\"\n", " )\n", " (line,) = ax.plot(detuning, gain, label=f\"{r:.1f}\")\n", " ax.axhline(float(analytic), color=line.get_color(), ls=\":\", lw=1)\n", "ax.set_xlabel(r\"Signal detuning $(f_\\text{s} - f_0)/(\\kappa/2\\pi)$\")\n", "ax.set_ylabel(\"Signal gain (dB)\")\n", "ax.legend(title=r\"$\\Phi_\\text{p}$ / threshold\")\n", "ax.set_title(\"Solid: VACASK hbac; dotted: single-mode peak gain\")\n", "show_plot()" ] }, { "cell_type": "markdown", "id": "d317b2fa", "metadata": {}, "source": [ "The CPW JPA gives less gain than Eq. {eq}`eq:vacask-3wm-gain`, and the\n", "two-mode balance $|S_\\text{ss}|^2-|S_\\text{is}|^2$ falls below 1. Photons\n", "leave through other sidebands. A quarter-wave line has modes near\n", "$(2n+1)f_0$, spaced by $2f_0=f_\\text{p}$. Every sideband\n", "$|f_\\text{s}+k f_\\text{p}|$ then lands near a mode. For $k=+1$ and $k=-2$ that\n", "is the $3\\lambda/4$ mode, and for $k=+2$ and $k=-3$ the $5\\lambda/4$ mode. The\n", "pump couples the signal to these modes by frequency conversion ($k>0$) and by\n", "further amplification ($k<0$). The SQUID's lumped inductance shifts the\n", "higher modes slightly away from the odd harmonics, which limits this\n", "conversion without removing it. A single-mode model cannot capture this.\n", "{cite:t}`wustmannNondegenerateParametricResonance2017` analyse such intermode\n", "processes.\n", "\n", "A lossless pumped circuit conserves photons. That is the Manley-Rowe relation\n", "{cite:p}`manleyGeneralPropertiesNonlinear1956`:\n", "\n", "```{math}\n", ":label: eq:vacask-manley-rowe\n", "\\sum_k \\operatorname{sign}(f_k)\\,|S_{k0}|^2\\,\\frac{f_\\text{s}}{|f_k|} = 1,\n", "\\qquad f_k = f_\\text{s} + k f_\\text{p}.\n", "```\n", "\n", "Negative-frequency sidebands enter with a minus sign because they are\n", "amplified, not converted {cite:p}`clerkIntroductionQuantumNoise2010`. The next\n", "cell locates the $3\\lambda/4$ mode and evaluates the sum over every sideband\n", "that `hbac` retains." ] }, { "cell_type": "code", "execution_count": 12, "id": "f0781fac", "metadata": { "execution": { "iopub.execute_input": "2026-10-21T07:06:47.363371Z", "iopub.status.busy": "2026-10-21T07:06:47.363327Z", "iopub.status.idle": "2026-10-21T07:06:49.050978Z", "shell.execute_reply": "2026-10-21T07:06:49.050676Z" }, "tags": [ "hide-input", "cpw-photon-balance" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "modes between 12 and 22 GHz: 17.787 GHz\n", "f_s + f_p at f_s = f_0: 17.748 GHz\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "r = 0.5: -3: -0.0003, -2: -0.0630, -1: -1.4071, +0: +2.1934, +1: +0.2758, +2: +0.0012; sum = 0.999998\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "r = 0.9: -3: -0.0250, -2: -1.7248, -1: -11.8197, +0: +11.6938, +1: +2.8344, +2: +0.0412, +3: +0.0001; sum = 0.999997\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "r = 0.9, nharm 5 -> 9: |ΔS_ss| = 1.3e-10\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ac = vacask.run(\n", " cpw_jpa_netlist('analysis ac1 ac from=12e9 to=22e9 mode=\"lin\" points=100001')\n", ")[\"ac1\"]\n", "f_ac = jnp.asarray(ac[\"frequency\"].real)\n", "v_mode = jnp.abs(jnp.asarray(ac[\"2\"]))\n", "peaks = jnp.flatnonzero((v_mode[1:-1] > v_mode[:-2]) & (v_mode[1:-1] > v_mode[2:])) + 1\n", "print(\n", " \"modes between 12 and 22 GHz:\", \", \".join(f\"{f_ac[p] / 1e9:.3f} GHz\" for p in peaks)\n", ")\n", "print(f\"f_s + f_p at f_s = f_0: {(F0 + F_PUMP_CPW) / 1e9:.3f} GHz\")\n", "\n", "NHARM = 5\n", "sidebands = list(range(-NHARM, NHARM + 1))\n", "f_signal = F0 + 0.01 * KAPPA\n", "photon_flux = {}\n", "for r in (0.5, 0.9):\n", " group = vacask.run(\n", " cpw_jpa_netlist(\n", " f\"\"\"\n", " analysis hbac1 hbac freq=[{F_PUMP_CPW}] nharm={NHARM} \\\\\n", " outspur={format_value([[k] for k in sidebands])} values=[{f_signal}]\n", " \"\"\",\n", " pump=r * PUMP_THRESHOLD,\n", " f_pump=F_PUMP_CPW,\n", " )\n", " )[\"hbac1\"]\n", " photons = []\n", " for k in sidebands:\n", " f_k = f_signal + k * F_PUMP_CPW\n", " amplitude = (reflection if k == 0 else conversion)(group[f\"in;{k}\"][0], I_SIG)\n", " photons.append(jnp.sign(f_k) * jnp.abs(amplitude) ** 2 * f_signal / abs(f_k))\n", " photon_flux[r] = jnp.array(photons)\n", " parts = \", \".join(\n", " f\"{k:+d}: {p:+.4f}\"\n", " for k, p in zip(sidebands, photons, strict=True)\n", " if abs(p) > 1e-4\n", " )\n", " print(f\"r = {r}: {parts}; sum = {photon_flux[r].sum():.6f}\")\n", "\n", "refined = vacask.run(\n", " cpw_jpa_netlist(\n", " f\"\"\"\n", " analysis hbac1 hbac freq=[{F_PUMP_CPW}] nharm={NHARM + 4} \\\\\n", " outspur={{[0]}} values=[{f_signal}]\n", " \"\"\",\n", " pump=0.9 * PUMP_THRESHOLD,\n", " f_pump=F_PUMP_CPW,\n", " )\n", ")[\"hbac1\"]\n", "basis_change = jnp.abs(\n", " reflection(refined[\"in;0\"][0], I_SIG) - reflection(group[\"in;0\"][0], I_SIG)\n", ")\n", "print(f\"r = 0.9, nharm {NHARM} -> {NHARM + 4}: |ΔS_ss| = {basis_change:.1e}\")\n", "\n", "fig, (ax_modes, ax_bars) = plt.subplots(1, 2, figsize=(10, 4))\n", "ax_modes.semilogy(f_ac / 1e9, v_mode)\n", "ax_modes.axvline((F0 + F_PUMP_CPW) / 1e9, color=\"gray\", ls=\":\")\n", "ax_modes.annotate(\n", " r\"$f_0 + f_\\text{p}$\",\n", " ((F0 + F_PUMP_CPW) / 1e9, 0.95),\n", " xycoords=(\"data\", \"axes fraction\"),\n", ")\n", "ax_modes.set_xlabel(\"Frequency (GHz)\")\n", "ax_modes.set_ylabel(r\"$|V_\\text{SQUID}|$ for 1 nA drive (V)\")\n", "x = jnp.arange(len(sidebands))\n", "for offset, (r, photons) in zip((-0.2, 0.2), photon_flux.items(), strict=True):\n", " ax_bars.bar(x + offset, photons, width=0.4, label=f\"r = {r}\")\n", "ax_bars.set_xticks(x, [f\"{k:+d}\" for k in sidebands])\n", "# |k| > 3 carries under 1e-4 of the flux.\n", "ax_bars.set_xlim(NHARM - 3.6, NHARM + 3.6)\n", "ax_bars.axhline(0, color=\"black\", lw=0.8)\n", "ax_bars.set_xlabel(\"Sideband $k$\")\n", "ax_bars.set_ylabel(\"Weighted photon flux\")\n", "ax_bars.legend()\n", "fig.tight_layout()\n", "show_plot()" ] }, { "cell_type": "markdown", "id": "fb0bb635", "metadata": {}, "source": [ "Eq. {eq}`eq:vacask-manley-rowe` holds to $3\\times10^{-6}$ at both drives.\n", "At $r=0.9$ the signal and idler alone would give $-0.13$. The $3\\lambda/4$\n", "sidebands $k=+1$ and $k=-2$ carry most of the difference, and the\n", "$5\\lambda/4$ sidebands contribute $+0.041$ ($k=+2$) and $-0.025$ ($k=-3$). So the gain lost\n", "relative to Eq. {eq}`eq:vacask-3wm-gain` is fully accounted for by intermode\n", "conversion. Raising `nharm` from 5 to 9 changes the signal reflection by\n", "less than $10^{-9}$, so the harmonic basis is converged.\n", "\n", "```{warning}\n", "`hbac` linearizes around the pumped state whether or not it is stable. Above\n", "threshold that state is unstable and the reported gain is not physical.\n", "```\n", "\n", "## Route B with the SQUID attached\n", "\n", "The rational subcircuit contains only linear elements, so it works in\n", "harmonic balance. Its residual fit error appears as loss at the high-Q\n", "resonance." ] }, { "cell_type": "code", "execution_count": 13, "id": "574e66a6", "metadata": { "execution": { "iopub.execute_input": "2026-10-21T07:06:49.052107Z", "iopub.status.busy": "2026-10-21T07:06:49.052063Z", "iopub.status.idle": "2026-10-21T07:06:50.612924Z", "shell.execute_reply": "2026-10-21T07:06:50.612603Z" }, "tags": [ "cpw-fit-residual" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Route A: f0 = 5.91600 GHz, κ/2π = 10.24 MHz, min |Γ| = 1.000\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Route B: f0 = 5.91566 GHz, 4/(2π τ_peak) = 9.08 MHz, min |Γ| = 0.463\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "for route in \"AB\":\n", " ac = vacask.run(\n", " cpw_jpa_netlist(\n", " 'analysis ac1 ac from=5.85e9 to=6.0e9 mode=\"lin\" points=150001',\n", " route=route,\n", " )\n", " )[\"ac1\"]\n", " f = jnp.asarray(ac[\"frequency\"].real)\n", " gamma = reflection(ac[\"in\"], I_SIG)\n", " f0, delay_width = resonance(f, gamma)\n", " metric = \"κ/2π\" if route == \"A\" else \"4/(2π τ_peak)\"\n", " print(\n", " f\"Route {route}: f0 = {f0 / 1e9:.5f} GHz, {metric} = {delay_width / 1e6:.2f} MHz, \"\n", " f\"min |Γ| = {jnp.abs(gamma).min():.3f}\"\n", " )\n", " ax.plot(f / 1e9, jnp.abs(gamma), label=f\"Route {route}\")\n", "ax.set_xlabel(\"Frequency (GHz)\")\n", "ax.set_ylabel(r\"$|\\Gamma|$\")\n", "ax.legend()\n", "show_plot()" ] }, { "cell_type": "markdown", "id": "7e4a09fc", "metadata": {}, "source": [ "Route B gives min $|\\Gamma|=0.463$ at resonance. Its $10^{-3}$ S-parameter\n", "error introduces appreciable loss in a $Q\\approx580$ resonator. Its\n", "$4/(2\\pi\\tau_\\text{peak})=9.08$ MHz is an effective group-delay quantity, not\n", "a loaded linewidth. The lossless identity used for Route A no longer applies.\n", "Use Route A, or tighten the fit near resonance, when the line sets amplifier\n", "$Q$.\n", "\n", "## Limitations\n", "\n", "- **Classical.** HB gives classical gain and conversion. It does not give\n", " added noise or squeezing ({cite:t}`clerkIntroductionQuantumNoise2010`;\n", " {cite:t}`royIntroductionParametricAmplification2016`). The junction shunt\n", " conductance is noiseless.\n", "- **Threshold and stability.** The single-mode threshold comes from AC scans.\n", " It assumes a purely phase-modulated resonance. `hbac` linearizes around the\n", " pumped state whether or not it is stable. Above threshold the reported gain\n", " is not physical.\n", "- **Route B fit loss.** A $10^{-3}$ rational fit adds appreciable loss in this\n", " $Q\\approx580$ resonator. Its group delay cannot be read as a lossless\n", " linewidth. The fit covers 10 MHz to 70 GHz.\n", "- **Multimode gain.** Eq. {eq}`eq:vacask-3wm-gain` ignores the higher line\n", " modes, so it overestimates the gain of this resonator. Its threshold is\n", " also only an estimate. A design that needs single-mode behaviour should\n", " detune the $3\\lambda/4$ mode from $3f_0$, for example with a larger\n", " SQUID participation or a stepped-impedance line.\n", "- **Junction and SQUID models.** RCSJ keeps only the first Josephson harmonic\n", " {cite:p}`McCumber1968,tinkhamIntroductionSuperconductivity2015`; real\n", " junctions show higher ones\n", " {cite:p}`willschObservationJosephsonHarmonics2024`. The SQUID model\n", " neglects loop inductance and asymmetry.\n", "\n", "## References\n", "\n", "```{bibliography}\n", ":filter: docname in docnames\n", "```" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.12.15" } }, "nbformat": 4, "nbformat_minor": 5 }