{ "cells": [ { "cell_type": "markdown", "id": "01ae46e8", "metadata": {}, "source": [ "## Fast multiplexed superconducting qubit readout with intrinsic Purcell filtering\n", "\n", "Here we aim to reproduce the main results presented in [this paper](https://arxiv.org/abs/2409.04967) by using QuLTRA. The core idea is to design a notch filter at the qubit frequency by coupling the readout resonator and the filter resonator through a mutual inductive (MTL) coupling. The article provides almost all the necessary elements to recreate this circuit in QuLTRA. We extract the qubit capacitance, inductance, and coupling capacitance from the mode frequency and anharmonicity. The lengths of the resonators and the coupler, along with the widths and gaps of the transmission lines, are explicitly reported in the paper.\n", "\n", "In the first task, after defining all the design parameters, we will show the Purcell decay as a function of the qubit inductance variation. The results demonstrate that when the qubit frequency is aligned with the notch frequency, the Purcell decay becomes significantly enhanced.\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "64b06a10", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import qultra as qu" ] }, { "cell_type": "code", "execution_count": 2, "id": "263bbf31", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "59.41788181803965\n", "6.608086177178166\n" ] } ], "source": [ "e = 1.60217657e-19 # electron charge\n", "h = 6.62606957e-34 # Plank's\n", "\n", "alpha=326e6\n", "f=8.032e9\n", "\n", "C=e**2/2/h/alpha #Cj+Cg\n", "L=1/C/(2*np.pi*f)**2 #Lj\n", "\n", "print(C/1e-15)\n", "print(L/1e-9)\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "97574980", "metadata": {}, "outputs": [], "source": [ "Cj=55.5e-15\n", "L=6.6e-9\n", "Cg=4e-15\n", "Ck=10e-15 #coupling to the feedline\n", "\n", "#all these values are available in the original article\n", "Z0=66\n", "gap=[7.5,7.5,7.5,7.5]\n", "width=[5,5.5,5]\n", "\n", "l_r0=974e-6\n", "l_rs=1617e-6\n", "l_p0=759e-6\n", "l_ps=1659e-6\n", "l_c=318e-6\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "9e2cb000", "metadata": {}, "outputs": [], "source": [ "variations = np.linspace(-0.5, 0.5, 60) #percentage variation\n", "Tp=[]\n", "for var in variations:\n", " Lj=L+L*var\n", " net=[qu.C(0,1,Cj),qu.J(0,1,Lj),qu.C(1,2,Cg),qu.CPW(2,3,l_r0,Z0),qu.CPW_coupler([3,4,5,6],gap,width,l_c),qu.CPW(4,0,l_rs,Z0),qu.CPW(5,7,l_p0,Z0),qu.CPW(6,0,l_ps,Z0),qu.C(7,8,Ck),qu.R(8,0,50)]\n", " complete_notch=qu.QCircuit(net,5,12)\n", " k=complete_notch.kappa()\n", " Tp.append(1/2/np.pi/(k[0]*1e6)) #take qubit kappa" ] }, { "cell_type": "code", "execution_count": 5, "id": "3a6049d2", "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 qubit inductance variation', fontsize=18)\n", "plt.ylabel('Qubit Purcell decay [s]',fontsize=18)\n", "#plt.title('Qubit Purcell decay vs Qubit inductance Variation',fontsize=18)\n", "plt.xticks(fontsize=18)\n", "plt.yticks(fontsize=18)\n", "plt.grid(True)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 6, "id": "8fe3a555", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+------+------------+-----------+-----------+\n", "| Mode | Freq [GHz] | k [MHz] | Q |\n", "+------+------------+-----------+-----------+\n", "| 1 | 8.19e+00 | -1.94e-09 | -4.21e+12 |\n", "| 2 | 1.01e+01 | -6.40e-02 | -1.57e+05 |\n", "| 3 | 1.05e+01 | -1.89e+01 | -5.53e+02 |\n", "+------+------------+-----------+-----------+\n", "Chi matrix [MHz]:\n", "+------+----------+----------+----------+\n", "| Mode | 1 | 2 | 3 |\n", "+------+----------+----------+----------+\n", "| 1 | 3.19e+02 | 6.65e+00 | 2.80e-02 |\n", "| 2 | 6.65e+00 | 3.47e-02 | 2.92e-04 |\n", "| 3 | 2.80e-02 | 2.92e-04 | 6.14e-07 |\n", "+------+----------+----------+----------+\n", "Purcell decay time for the optimal resonator length: 81.84390238332135 s\n" ] } ], "source": [ "i=np.argmax(np.array(Tp))\n", "Lj=L+L*variations[i]\n", "\n", "net=[qu.C(0,1,Cj),qu.J(0,1,Lj),qu.C(1,2,Cg),qu.CPW(2,3,l_r0,Z0),qu.CPW_coupler([3,4,5,6],gap,width,l_c),qu.CPW(4,0,l_rs,Z0),qu.CPW(5,7,l_p0,Z0),qu.CPW(6,0,l_ps,Z0),qu.C(7,8,Ck),qu.R(8,0,50)]\n", "complete_notch=qu.QCircuit(net,5,12)\n", "complete_notch.show_all()\n", "\n", "print('Purcell decay time for the optimal resonator length: ', Tp[i], 's')" ] }, { "cell_type": "markdown", "id": "d91070b5", "metadata": {}, "source": [ "The article shows $ Z_{12} $ of the two-port composed only of the two coupled resonators. In the graph, the resonant frequencies of the two resonators and the notch frequency can be clearly identified. Here, we aim to reproduce the same result. After constructing the circuit, we will compute the total admittance matrix, from which we will extract the impedance matrix of the two-port, in order to demonstrate that QuLTRA provides consistent results.\n" ] }, { "cell_type": "code", "execution_count": 7, "id": "a7b985fe", "metadata": {}, "outputs": [], "source": [ "Z0=66\n", "gap=[7.5,7.5,7.5,7.5]\n", "width=[5,5.5,5]\n", "\n", "l_r0=974e-6\n", "l_rs=1617e-6\n", "l_p0=759e-6\n", "l_ps=1659e-6\n", "l_c=318e-6\n", "\n", "net=[qu.CPW(1,2,l_r0,Z0),qu.CPW_coupler([2,3,4,5],gap,width,l_c),qu.CPW(3,0,l_rs,Z0),qu.CPW(4,6,l_p0,Z0),qu.CPW(5,0,l_ps,Z0)]\n", "eth_notch=qu.QCircuit(net,6,12)" ] }, { "cell_type": "code", "execution_count": 10, "id": "aeb9c7da", "metadata": {}, "outputs": [], "source": [ "frequencies=np.arange(6,12,0.001)\n", "#frequencies=np.linspace(6,12,5001)\n", "port=[0,5]\n", "Z_12=[]\n", "for f in frequencies:\n", " Y=eth_notch.build_total_Y_matrix(1j*2*np.pi*1e9*f)\n", " Z=np.linalg.inv(Y)\n", " Z_submatrix=np.zeros((len(port),len(port)),dtype=complex)\n", " for i in range(len(port)):\n", " for j in range(len(port)):\n", " Z_submatrix[i,j]=Z[port[i],port[j]]\n", "\n", " Z_12.append(20*np.log10((abs(Z_submatrix[1,0].imag))))" ] }, { "cell_type": "code", "execution_count": 11, "id": "4de97a3c", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "plt.figure(figsize=(8, 5))\n", "plt.plot(frequencies, Z_12, '-', color='blue')\n", "\n", "\n", "# Etichette e legenda\n", "plt.xlabel('Frequency [GHz]', fontsize='18')\n", "plt.ylabel('$Z_{12}$ [dB]', fontsize='18')\n", "plt.grid(True)\n", "#plt.tight_layout()\n", "plt.xticks(fontsize='18')\n", "plt.yticks(fontsize='18')\n", "\n", "\n", "# Mostra il grafico\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "b2e6452e", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "40fdf276", "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 }