{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "![](Rydiqule_Logo_Transparent_300.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Calculating SNR\n", "\n", "This notebook can be downloaded [here](https://github.com/QTC-UMD/rydiqule/blob/main/docs/source/examples/Calculate_SNR.ipynb)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Rydiqule contains the function `rydiqule.get_snr()` function that will take a Sensor or Cell and calculate the expected SNR for one axis of the solve. Below we demonstrate the use of this function to numerically confirm the analytic results of Meyer et. al. PRA 104, 043103 (2021) Eqs. 12 & 13:\n", "\n", "$$ \\Omega_p^\\text{(opt)}\\approx\\sqrt{\\Gamma(2\\gamma+\\Gamma_r+\\Gamma_c)}$$\n", "\n", "$$ \\Omega_c^\\text{(opt)}\\approx\\sqrt{2}\\Omega_p^\\text{(opt)}$$\n", "\n", "These show the optical probe and coupling Rabi frequencies for resolving Rydberg state shifts in a 2-color Rydberg EIT measurement, in an optically-thin with no Doppler broadening." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "tags": [] }, "outputs": [], "source": [ "import numpy as np\n", "import rydiqule as rq\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Manually define representative kappa and eta constants for a Rb85 sensor. These are necessary to find the SNR in experimental units and must be supplied by the user when calculating using a Sensor. If using a Cell, these constants are automatically calculated and do not need to be passed to `get_snr`.\n", "\n", "The definition of these numerical factors is found in Meyer et. al. PRA 104, 043103 (2021) Eqs. 5 & 7.\n", "\n", "$$ \\kappa = \\frac{\\omega_p n \\mu^2}{2c\\epsilon_0\\hbar} $$\n", "\n", "$$ \\eta = \\sqrt{\\frac{\\omega\\mu^2}{2c\\epsilon_0\\hbar A}}$$" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "tags": [] }, "outputs": [], "source": [ "kappa = 28974.8787\n", "eta = 0.00135882\n", "probe_freq = 2.416e9" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1D Optimum" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we demonstrate calculating the SNR for resolving a phase shift due to an RF Rydberg coupling vs probe Rabi frequency. We have chosen a far-detuned RF coupling to ensure Stark shifts are linear." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "tags": [] }, "outputs": [], "source": [ "##Set up a simplified Rb Sensor\n", "basis_size = 4\n", "Rb_sensor = rq.Sensor(basis_size)\n", "Rb_sensor.set_experiment_values(probe_freq = probe_freq,\n", " kappa = kappa, eta = eta, cell_length = .000001)\n", "\n", "red_rabi = np.linspace(0.1,6,100)\n", "blue_rabi = np.linspace(0.1,4,101)\n", "blue_rabi_1 = 1\n", "my_step = np.array([1, 1.1])\n", "probe = {'states': (0,1), 'rabi_frequency': red_rabi, 'detuning': 0, 'label': 'probe'}\n", "couple = {'states': (1,2), 'rabi_frequency':blue_rabi_1, 'detuning': 0, 'label':'couple'}\n", "rf = {'states': (2,3), 'rabi_frequency': my_step, 'detuning':20, 'label': 'rf'}\n", "\n", "Rb_sensor.add_couplings(probe,couple, rf)\n", "\n", "#simplify the gamma matrix to match predictions\n", "gam = np.zeros((basis_size, basis_size))\n", "gam[2,0] = 0.1\n", "gam[3,0] = 0.01\n", "gam[1,0] = 6.0\n", "Rb_sensor.set_gamma_matrix(gam)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To calculate the SNR vs a specific parameter, that parameter must be list-like with at least two elements. So calculated vs RF Rabi frequency, we have specified two Rabi frequency values very close to each other to measure the local linear sensitivity. More values can be added to this list to see if sensitivity changes for larger changes in the parameter, which indicates nonlinear response." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rq.draw_diagram(Rb_sensor)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We call `get_snr` with the Sensor to calculate with, the label of the swept parameter to calculate SNR against, the tuple of the probing transition to get measurable parameters from, which quadrature the probing field is being detected in, and the kappa and eta numerical factors." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "tags": [] }, "outputs": [], "source": [ "snrs, param_mesh = rq.get_snr(Rb_sensor, param_label = 'rf_rabi_frequency',\n", " phase_quadrature = True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using `Sensor.axis_labels()` we can identify which axis rydiqule has used for the swept parameters. This allows us to correctly index out the appropriate solutions for analysis. In particular, we need to index the sensitivity axis to get the sensitivity at the second RF Rabi frequency in the list (relative to the first)." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "['probe_rabi_frequency', 'rf_rabi_frequency']" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#print the axis labels\n", "Rb_sensor.axis_labels()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "tags": [] }, "outputs": [], "source": [ "snrs_final = snrs[:,1]\n", "param_mesh_final=np.array(param_mesh)[:,:,1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can plot the SNR as a function of probe rabi frequency. The vertical line represents the analytic optimum value for the probe Rabi frequency." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fix, ax = plt.subplots(figsize = (2,2))\n", "ax.plot(param_mesh_final[0], snrs_final)\n", "ax.set_xlabel(\"Red Rabi Freq [Mrad/s]\")\n", "ax.set_ylabel(\"SNR in 1 sec.\")\n", "ax.set_title('RF LO = 1 Mrad/s \\n RF Signal = 0.1 Mrad/s')\n", "ax.axvline(blue_rabi_1/np.sqrt(2),0,3, color = 'k')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2D Optimum - Fnd Optimized $\\Omega_p$ and $\\Omega_c$ for best SNR" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also calculate the SNR versus many different axis. Here we calculate versus both the probe and coupling Rabi frequencies." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "tags": [] }, "outputs": [], "source": [ "couple = {'states': (1,2), 'rabi_frequency': blue_rabi, 'detuning': 0, 'label':'couple'}\n", "\n", "Rb_sensor.add_couplings(probe,couple, rf)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "tags": [] }, "outputs": [], "source": [ "snrs, param_mesh = rq.get_snr(Rb_sensor, param_label = 'rf_rabi_frequency', phase_quadrature = True)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "['probe_rabi_frequency', 'couple_rabi_frequency', 'rf_rabi_frequency']" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Rb_sensor.axis_labels()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "tags": [] }, "outputs": [], "source": [ "snrs_final = snrs[:,:,1]\n", "param_mesh_final=np.array(param_mesh)[:,:,:,1]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Predicted optimum probe Rabi frequency: 0.775 Mrad/s\n", "Predicted optimum coupling Rabi frequency: 1.095 Mrad/s\n" ] } ], "source": [ "predictedOptimumProbe = np.sqrt(gam[1,0]*gam[2,0])\n", "predictedOptimumCouple = np.sqrt(2*gam[1,0]*gam[2,0])\n", "print(f'Predicted optimum probe Rabi frequency: {predictedOptimumProbe:.3f} Mrad/s')\n", "print(f'Predicted optimum coupling Rabi frequency: {predictedOptimumCouple:.3f} Mrad/s')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We plot the SNR versus both Rabi frequencies using a contour plot. We have overlaid the analytic predictions for the optimal SNR. Compare Figure 5(a) of Meyer et. al." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "Text(60, -10, 'prediction from\\narXiv:2105.10494 Eqs 12,13')" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize = (6,4))\n", "CS = ax.contourf(param_mesh_final[0], param_mesh_final[1], snrs_final)\n", "fig.colorbar(CS)\n", "ax.set_xlabel('probe rabi frequency (Mrad/s)')\n", "ax.set_ylabel('coupling rabi frequency (Mrad/s)')\n", "ax.plot(predictedOptimumProbe, predictedOptimumCouple, '*', color = 'C4', markersize = 10)\n", "ax.plot([0,10,20], [0,np.sqrt(2)*10,np.sqrt(2)*20 ],'--', color = 'black')\n", "ax.set_title(\"SNR vs Probe and Coupling (off res field)\")\n", "ax.set_ylim((0,4))\n", "ax.set_xlim((0,4))\n", "\n", "ax.annotate(\"prediction from\\narXiv:2105.10494 Eqs 12,13\",\n", " xy=(predictedOptimumProbe, predictedOptimumCouple), xycoords='data',\n", " xytext=(60,-10), textcoords='offset points',\n", " arrowprops=dict(arrowstyle='->'),\n", " bbox=dict(boxstyle='round')\n", " )\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " Rydiqule\n", " ================\n", " \n", "Rydiqule Version: 2.2.0.dev49+g12b02b0ad.d20260120\n", "Installation Path: ~\\src\\rydiqule_public\\src\\rydiqule\n", "\n", " Dependencies\n", " ================\n", " \n", "NumPy Version: 2.2.6\n", "SciPy Version: 1.15.3\n", "Matplotlib Version: 3.10.8\n", "ARC Version: 3.9.0\n", "Python Version: 3.10.19\n", "Python Install Path: ~\\src\\rydiqule_public\\.venv\\Scripts\n", "Platform Info: Windows (AMD64)\n", "CPU Count and Freq: 16 @ 3.91 GHz\n", "Total System Memory: 256 GB\n" ] } ], "source": [ "rq.about()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "rydiqule_public (3.10.19)", "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": "3.10.19" } }, "nbformat": 4, "nbformat_minor": 4 }