{ "cells": [ { "attachments": {}, "cell_type": "markdown", "id": "5ca1ec25", "metadata": {}, "source": [ "# Running Eigenmode Simulations & EPR Analysis with `QHFSSEigenmodePyaedt`\n", "\n", "This notebook is a tutorial on how to use `QHFSSEigenmodePyaedt` class.\n", "\n", "First, what is an eigenmode solver? \"The eigenmode solver is used to calculate the frequencies and the corresponding electromagnetic field patterns (eigenmodes), when no excitation is applied.\" From [MIT - Eigenmode Solver Overview](https://space.mit.edu/RADIO/CST_online/mergedProjects/3D/special_overview/special_overview_eigenmodesolverover.htm).\n", "\n", "This is all fine, but our circuit contains quantum effects and Ansys uses a purely classical electromagnetic solver. How can we predict qubit anharmonicity and dispersive shift? Well, we can use [Energy Participation Ratio (EPR) analysis](https://www.nature.com/articles/s41534-021-00461-8) to quantize the circuit. Luckily for us, Quantum Metal integrates `pyEPR`, allowing for easy implementation of EPR analysis.\n", "\n", "This notebook simulates a single transmon qubit and runs EPR analysis on it." ] }, { "cell_type": "markdown", "id": "eigenmode-badges", "metadata": {}, "source": [ "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/qiskit-community/qiskit-metal/blob/main/docs/tut/4-Analysis/pyaedt-multiplanar/Eigenmode-pyaedt-multiplanar.ipynb)\n", "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/qiskit-community/qiskit-metal/main?labpath=docs%2Ftut%2F4-Analysis%2Fpyaedt-multiplanar%2FEigenmode-pyaedt-multiplanar.ipynb)" ] }, { "cell_type": "markdown", "id": "044c6807", "metadata": {}, "source": [ "> 💡 **Desktop GUI or inline.** `metal.gui(design)` opens the desktop `MetalGUI` when Qt and a display are available and falls back to an inline viewer (Colab, Binder, a server) with the same API, so `gui.rebuild()` and `gui.screenshot()` work in both. See [1.1 Quick start](https://qiskit-community.github.io/qiskit-metal/tut/1-Overview/1.1-Quick-start.html) and [`docs/headless-usage.rst`](https://qiskit-community.github.io/qiskit-metal/headless-usage.html).\n", ">\n", "> The design and its screenshot run anywhere. From \"Launch eigenmode analysis\" on, the cells drive Ansys HFSS through pyaedt and pyEPR; they need Ansys Electronics Desktop and are shown here without outputs." ] }, { "cell_type": "markdown", "id": "simulation-pathways", "metadata": {}, "source": [ "> 🧭 **Other simulation pathways.** This notebook uses Ansys, which needs a license. Without one, the same kind of result can come from the open-source ElmerFEM path for capacitance (tutorial 4.19) or the pip-installable gmsh + scikit-fem solver for eigenmodes, couplings and capacitance (tutorials 4.43–4.45); AWS Palace is available through SQDMetal, with a native integration in progress. [Simulation pathways](https://qiskit-community.github.io/qiskit-metal/simulation-pathways.html) summarizes what each solver computes, how to install it, and which tutorials use it." ] }, { "cell_type": "code", "execution_count": 2, "id": "ed098aee", "metadata": {}, "outputs": [], "source": [ "import qiskit_metal as metal\n", "from qiskit_metal.designs.design_multiplanar import MultiPlanar\n", "from qiskit_metal.qlibrary.qubits.transmon_cross import TransmonCross\n", "from qiskit_metal.renderers.renderer_ansys_pyaedt.hfss_renderer_eigenmode_aedt import (\n", " QHFSSEigenmodePyaedt,\n", ")" ] }, { "attachments": {}, "cell_type": "markdown", "id": "0cc7eb37", "metadata": {}, "source": [ "## Use Multiplanar design, for Ansys HFSS Eigenmode simulation using pyaedt renderer\n", "\n", "**DISCLAIMER:** For now, this might only work with the full version (paid) of Ansys and not the student version. This issue is fixable and requires more testing. Please run the notebook and test it out on student license, if you're interested to make a contribution!" ] }, { "cell_type": "code", "execution_count": 3, "id": "a8cff190", "metadata": {}, "outputs": [], "source": [ "design = MultiPlanar(metadata={}, overwrite_enabled=True)\n", "\n", "# Check if layer data is usable, if so launch GUI\n", "ls_unique = design.ls.is_layer_data_unique()\n", "if not ls_unique:\n", " raise ValueError(\"Layer data in `MultiPlanar` design is not unique\")\n", "\n", "gui = metal.gui(design)" ] }, { "attachments": {}, "cell_type": "markdown", "id": "25c18b3b", "metadata": {}, "source": [ "## A single transmon" ] }, { "attachments": {}, "cell_type": "markdown", "id": "48e0f860", "metadata": {}, "source": [ "## Create the design\n", "The design is a single Xmon-style transmon with one readout pad." ] }, { "cell_type": "code", "execution_count": 4, "id": "82652491", "metadata": {}, "outputs": [ { "output_type": "display_data", "metadata": { "image/png": { "width": 500 } }, "data": { "image/png": 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"text/plain": "" } } ], "source": [ "xmon_options = dict(\n", " connection_pads=dict(readout=dict()),\n", ")\n", "\n", "TransmonCross(design, \"transmon\", options=xmon_options)\n", "\n", "gui.rebuild()\n", "gui.zoom_on_components([\"transmon\"])\n", "gui.screenshot()" ] }, { "attachments": {}, "cell_type": "markdown", "id": "1a4501d8", "metadata": {}, "source": [ "## Launch eigenmode analysis\n", "This uses pyaedt to connect to Ansys. As of May 2023 only the paid version of Ansys was supported." ] }, { "cell_type": "code", "execution_count": null, "id": "7fadad46", "metadata": { "tags": [ "requires-ansys" ] }, "outputs": [], "source": [ "em1_aedt = QHFSSEigenmodePyaedt(\n", " design, \"Eigenmode_Tutorial\", \"Qubit ONLY\", initiate=True\n", ")\n", "\n", "# Path to access Hfss pyAEDT object\n", "hfss_aedt = em1_aedt.current_app" ] }, { "attachments": {}, "cell_type": "markdown", "id": "a7e35353", "metadata": {}, "source": [ "### Render Design to ANSYS" ] }, { "cell_type": "code", "execution_count": null, "id": "2d57ee54", "metadata": { "tags": [ "requires-ansys" ] }, "outputs": [], "source": [ "em1_aedt.render_design()" ] }, { "attachments": {}, "cell_type": "markdown", "id": "fba2608a", "metadata": {}, "source": [ "### Add Setup\n", "\n", "To create a setup in ANSYS, we call the method `add_hfss_em_setup`, it has the arguments:\n", "\n", "```\n", "Create a solution setup in Ansys HFSS Driven-Modal solution type. If user does not provide\n", " arguments, they will be obtained from QHFSSDrivenmodalPyaedt.default_setup dict.\n", "\n", " Args:\n", " name (str, optional): _description_. Defaults to None.\n", " MinimumFrequency (float, optional): Minimum frequency in GHz. Defaults to self.default_setup.\n", " NumModes (int, optional): Number of modes. Defaults to self.default_setup.\n", " MaxDeltaFreq (float, optional): Maximum difference in freq between consecutive passes.\n", " Defaults to self.default_setup.\n", " MaximumPasses (int, optional): Maximum number of passes. Defaults to self.default_setup.\n", " MinimumPasses (int, optional): Minimum number of passes.Defaults to self.default_setup.\n", " MinimumConvergedPasses (int, optional): Minimum number of converged passes.\n", " Defaults to self.default_setup.\n", " PercentRefinement (int, optional): Percent refinement. Defaults to self.default_setup.\n", " BasisOrder (int, optional): Basis order. Defaults to self.default_setup.\n", "\n", " Returns:\n", " new_setup (pyaedt.modules.SolveSetup.SetupHFSS): pyAEDT simulation setup object.\n", "```\n", "\n", "In Eigenmode simulations, we can directly find the resonant frequency. This is in comparison to a DrivenModal simualtion, where we need to sweep the frequency space and find the resonant response.\n", "\n", "Since the transmon has a resonant frequency, we are ONLY simulating a transmon. We have 1 resonant mode. Hence `NumModes = 1`." ] }, { "cell_type": "code", "execution_count": null, "id": "46614c6e", "metadata": { "tags": [ "requires-ansys" ] }, "outputs": [], "source": [ "setup_name = \"Setup_QubitONLY\"\n", "\n", "# Add Setup\n", "setup_options = dict(\n", " name=setup_name,\n", " MinimumFrequency=3, # GHz\n", " NumModes=1, # THIS IS ONLY FOR A SINGLE TRANSMON\n", " MaxDeltaFreq=0.1,\n", " MaximumPasses=5,\n", " MinimumPasses=1,\n", " MinimumConvergedPasses=2,\n", " PercentRefinement=30,\n", " BasisOrder=None,\n", ")\n", "\n", "em1_aedt.add_hfss_em_setup(**setup_options)" ] }, { "attachments": {}, "cell_type": "markdown", "id": "df27523c", "metadata": {}, "source": [ "### Analyze Setup (run ANSYS simulation)" ] }, { "cell_type": "code", "execution_count": null, "id": "e7809891", "metadata": { "tags": [ "requires-ansys" ] }, "outputs": [], "source": [ "em1_aedt.analyze_setup(setup_name)" ] }, { "attachments": {}, "cell_type": "markdown", "id": "2a0d2321", "metadata": {}, "source": [ "## Run EPR Analysis\n", "\n", "From here, we use `pyEPR` to run the EPR analysis.\n", "\n", "```\n", "Format of results:\n", " f_0 [MHz] : Eigenmode frequencies computed by HFSS; i.e., linear freq returned in GHz\n", " f_1 [MHz] : Dressed mode frequencies (by the non-linearity; e.g., Lamb shift, etc. ).\n", " If numerical diagonalizaiton is run, then we return the numerically diagonalizaed\n", " frequencies, otherwise, use 1st order pertuirbation theory on the 4th order\n", " expansion of the cosine.\n", " f_ND [MHz] : Numerical diagonalizaiton\n", " chi_O1 [MHz] : Analytic expression for the chis based on a cos trunc to 4th order, and using 1st\n", " order perturbation theory. Diag is anharmonicity, off diag is full cross-Kerr.\n", " chi_ND [MHz] : Numerically diagonalized chi matrix. Diag is anharmonicity, off diag is full\n", " cross-Kerr.\n", "\n", "Args:\n", " cos_trunc (int, optional): Truncation of the cosine. Defaults to 8.\n", " fock_trunc (int, optional): Truncation of the fock. Defaults to 7. \n", " print_results (bool, optional): Print results of EPR analysis. Defaults to True.\n", " numeric (bool, optional): Use numerical diagonalization. Defaults to True.\n", "\n", " Returns:\n", " self.epr_quantum_analysis.data (dict): all results of EPR analysis\n", "```" ] }, { "cell_type": "code", "execution_count": null, "id": "fff33812", "metadata": { "tags": [ "requires-ansys" ] }, "outputs": [], "source": [ "epr_data = em1_aedt.run_epr(print_result=True)" ] }, { "cell_type": "code", "execution_count": null, "id": "878a2589", "metadata": { "tags": [ "requires-ansys" ] }, "outputs": [], "source": [ "print(epr_data)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "name": "python", "version": "3.9.9" } }, "nbformat": 4, "nbformat_minor": 5 }