{ "cells": [ { "cell_type": "markdown", "id": "4ce6adc9", "metadata": {}, "source": [ "## Exploiting higher modes\n", "\n", "QuLTRA simulations allow the CPWs to be exploited in their entirety, including the utilization of their higher-order resonant modes. To illustrate this approach, we consider a qubit coupled to a resonator designed such that one of its admittance pole lies at the qubit frequency, while the resonator’s second-order mode is used for readout.\n", "\n", "Suppose the qubit has a frequency of around 6 GHz and an anharmonicity of 200 MHz. In order to have an admittance pole at the qubit frequency, the λ/4 resonator should be 9.8 mm longer" ] }, { "cell_type": "code", "execution_count": null, "id": "0cf19ef1", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import qultra as qu\n", "\n", "e = 1.60217657e-19 # electron charge\n", "h = 6.62606957e-34 # Plank's\n", "\n", "fq=6e9\n", "alpha=200e6\n", "\n", "Cj=e**2/2/h/alpha #qubit capacitance given the anharmonicity constraint\n", "Lj=1/Cj/(2*np.pi*fq)**2 # qubit inductance given the qubit frequency\n", "Cg=20e-15 #coupling capacitance\n", "l=9.8e-3 #lambda/4 length resonator\n", "\n", "Ck=12e-15 #coupling capacitance resonator feedline\n", "\n" ] }, { "cell_type": "markdown", "id": "4a55ed7d", "metadata": {}, "source": [ "Since the qubit modes will differ slightly from the bare qubit frequency, the resonator length should be fine-tuned. To do this, we can vary the length slightly around approximately 9.8 mm by running a loop that plots the Purcell decay time as a function of the resonator length. We expect to observe a peak at a certain length, indicating optimal Purcell suppression.\n", "\n", "**Note**: Since we are searching for a zero that is close to a pole, it is necessary to refine the grid to avoid missing it. We can achieve this by adjusting the global step parameter in constants.py " ] }, { "cell_type": "code", "execution_count": 11, "id": "bcd7a8cf", "metadata": {}, "outputs": [], "source": [ "from qultra import constants\n", "\n", "constants.step=0.001\n", "\n", "variations = np.linspace(0, 0.25, 35)\n", "Tp=[]\n", "for var in variations:\n", " l_var=l+l*var\n", " net=[qu.C(0,1,Cj),qu.J(0,1,Lj),qu.C(1,2,Cg),qu.CPW(0,2,l_var),qu.C(2,3,Ck),qu.R(3,0,50)]\n", " circuit_with_filter=qu.QCircuit(net,2,12)\n", " #circuit_with_filter.show_modes()\n", " k=circuit_with_filter.kappa()\n", " Tp.append(1/2/np.pi/(k[1]*1e6)) #take qubit kappa\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 12, "id": "31ccf013", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "plt.plot(variations*100, Tp, marker='o')\n", "plt.xlabel('Percentage resonator length variation',fontsize='18')\n", "plt.ylabel('Qubit Purcell decay time [s]',fontsize='18')\n", "#plt.title('Qubit Purcell decay vs Resonator Length Variation')\n", "plt.xticks(fontsize='18')\n", "plt.yticks(fontsize='18')\n", "plt.grid(True)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 13, "id": "b01eb52d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+------+------------+-----------+-----------+\n", "| Mode | Freq [GHz] | k [MHz] | Q |\n", "+------+------------+-----------+-----------+\n", "| 1 | 2.68e+00 | -3.48e-01 | -7.70e+03 |\n", "| 2 | 5.46e+00 | -1.66e-08 | -3.29e+11 |\n", "| 3 | 8.08e+00 | -3.14e+00 | -2.57e+03 |\n", "+------+------------+-----------+-----------+\n", "Chi matrix [MHz]:\n", "+------+----------+----------+----------+\n", "| Mode | 1 | 2 | 3 |\n", "+------+----------+----------+----------+\n", "| 1 | 9.39e-05 | 2.47e-01 | 2.09e-03 |\n", "| 2 | 2.47e-01 | 1.63e+02 | 2.75e+00 |\n", "| 3 | 2.09e-03 | 2.75e+00 | 1.16e-02 |\n", "+------+----------+----------+----------+\n", "Purcell decay time for the optimal resonator length: 9.597532503213579 s\n" ] } ], "source": [ "i=np.argmax(np.array(Tp))\n", "l_final=l+l*variations[i]\n", "\n", "net=[qu.C(0,1,Cj),qu.J(0,1,Lj),qu.C(1,2,Cg),qu.CPW(0,2,l_final),qu.C(2,3,Ck),qu.R(3,0,50)]\n", "circuit_with_filter=qu.QCircuit(net,2,10)\n", "circuit_with_filter.show_all()\n", "\n", "print('Purcell decay time for the optimal resonator length: ', Tp[i], 's')" ] }, { "cell_type": "code", "execution_count": null, "id": "533b2a5f", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.9" } }, "nbformat": 4, "nbformat_minor": 5 }