comparing_QEC_setups.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# This Notebook contains the code to create (or load) the data and plots for Figures 4.6 and 4.8\n",
    "\n",
    "Start by importing the necessary modules"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "# Standard modules\n",
    "import numpy as np\n",
    "import scipy\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# Local packages\n",
    "from simulator_program.custom_noise_models import standard_times\n",
    "from simulator_program import data_analysis_tools"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Adding custom device equivalences\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/home/alexion/anaconda3/lib/python3.8/site-packages/qiskit/aqua/__init__.py:86: DeprecationWarning: The package qiskit.aqua is deprecated. It was moved/refactored to qiskit-terra For more information see <https://github.com/Qiskit/qiskit-aqua/blob/main/README.md#migration-guide>\n",
      "  warn_package('aqua', 'qiskit-terra')\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Part 1: Creating Figure 4.6"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "# Simulation parameters\n",
    "n_cycles = 15\n",
    "n_shots = 1024*8*3\n",
    "\n",
    "# Noise parameters\n",
    "T1 = 40e3\n",
    "T2 = 60e3"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "# Run simulations (TAKES UP TO AN HOUR TO RUN, DATA CAN BE LOADED BELOW INSTEAD)\n",
    "fid_L_s, P_L_s, time_s = data_analysis_tools.fidelity_from_scratch(n_cycles, n_shots, \n",
    "    gate_times=standard_times, T1=T1, T2=T2, encoding=False, project=True)\n",
    "print('Standard run done')\n",
    "fid_L_f, P_L_f, time_f = data_analysis_tools.fidelity_from_scratch(n_cycles, n_shots,\n",
    "    gate_times={'feedback': 5000}, T1=T1, T2=T2, encoding=False, project=True)\n",
    "print('Extra feedback time done')\n",
    "fid_L_d, P_L_d, time_d = data_analysis_tools.fidelity_from_scratch(n_cycles, n_shots,\n",
    "    gate_times={'delay': 5000}, T1=T1, T2=T2, encoding=False, project=True)\n",
    "print('Added delay time time done')"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Standard run done\n",
      "Extra feedback time done\n",
      "Added delay time time done\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "# Reformat as np arrays\n",
    "fid_L_standard = np.array(fid_L_s)\n",
    "fid_L_feedback = np.array(fid_L_f)\n",
    "fid_L_delay = np.array(fid_L_d)\n",
    "P_L_standard = np.array(P_L_s)\n",
    "P_L_feedback = np.array(P_L_f)\n",
    "P_L_delay = np.array(P_L_d)\n",
    "\n",
    "# Generate x-values from times dict\n",
    "t_standard = np.array([time_s['dm_'+str(i)] for i in range(n_cycles+1)])\n",
    "t_feedback = np.array([time_f['dm_'+str(i)] for i in range(n_cycles+1)])\n",
    "t_delay    = np.array([time_d['dm_'+str(i)] for i in range(n_cycles+1)])\n",
    "\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "source": [
    "# SAVE DATA \n",
    "# Note: The standard run can be generated from the file active_qec.ipynb.\n",
    "#       Therefore we do not save it from this file\n",
    "\n",
    "# 5mus feedback run\n",
    "with open('data/feedback_5mus_data.npy', 'wb') as f:\n",
    "    np.save(f, (fid_L_feedback, P_L_feedback, t_feedback))\n",
    "\n",
    "# 5mus delay run\n",
    "with open('data/delay_5mus_data.npy', 'wb') as f:\n",
    "    np.save(f, (fid_L_delay, P_L_delay, t_delay))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "# LOAD DATA\n",
    "# Standard run (Note that not all the data is used)\n",
    "with open('data/base_qec_data.npy', 'rb') as f:\n",
    "    fid_L_standard, P_L_standard, t_standard = np.load(f)\n",
    "with open('data/feedback_5mus_data.npy', 'rb') as f:\n",
    "    fid_L_feedback, P_L_feedback, t_feedback = np.load(f)\n",
    "with open('data/delay_5mus_data.npy', 'rb') as f:\n",
    "    fid_L_delay, P_L_delay, t_delay = np.load(f)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "# Calculate fidelity by F = F_L * P_L\n",
    "fid_standard = np.array(fid_L_standard)\n",
    "fid_standard[1:] = fid_standard[1:]*np.mean(P_L_standard)\n",
    "fid_feedback = np.array(fid_L_feedback)\n",
    "fid_feedback[1:] = fid_feedback[1:]*np.mean(P_L_feedback[1:])\n",
    "fid_delay = np.array(fid_L_delay)\n",
    "fid_delay[1:] = fid_delay[1:]*np.mean(P_L_delay[1:])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "# Perform curve fitting\n",
    "def monoExp(t, T, c, A):\n",
    "    return (A-c) * np.exp(-t/T) + c\n",
    "p0 = (40e3, 0, 0.9)  # start with values near those we expect\n",
    "pars_s, cov_s = scipy.optimize.curve_fit(\n",
    "    monoExp, t_standard[1:], fid_standard[1:], p0)\n",
    "pars_f, cov_f = scipy.optimize.curve_fit(\n",
    "    monoExp, t_feedback[1:], fid_feedback[1:], p0)\n",
    "pars_d, cov_d = scipy.optimize.curve_fit(\n",
    "    monoExp, t_delay[1:], fid_delay[1:], p0)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "# %% Plotting\n",
    "fig, ax = plt.subplots(1, 1, figsize=(7, 5))\n",
    "\n",
    "# Plot data points\n",
    "ax.plot(t_standard, fid_standard, 'o', color='C0', label='Standard times')\n",
    "ax.plot(t_feedback, fid_feedback, '^', color='C1', label='Feedback, 5 μs')\n",
    "ax.plot(t_delay, fid_delay, 'v', color='C2', label='Delay, 5 μs')\n",
    "\n",
    "# Perform curve fits for plotting\n",
    "time_list = [t_standard, t_feedback, t_delay]\n",
    "fid_list = [fid_standard, fid_feedback, fid_delay]\n",
    "P_list = [np.mean(P_L_standard[1:]), np.mean(\n",
    "    P_L_feedback[1:]), np.mean(P_L_delay[1:])]\n",
    "color_list = ['C0', 'C1', 'C2']\n",
    "lifetimes = []\n",
    "errs = []\n",
    "t = np.linspace(0, 140e3, 100)\n",
    "for i in range(len(time_list)):\n",
    "    p0 = (40e3, 0, 0.9)  # start with values near those we expect\n",
    "    x = time_list[i]\n",
    "    y = fid_list[i]\n",
    "    pars, cov = scipy.optimize.curve_fit(monoExp, x[1:], y[1:], p0)\n",
    "    T, c, A = pars\n",
    "    lifetimes.append(T)\n",
    "    errs.append(np.sqrt(cov[0][0]))\n",
    "    ax.plot(t, monoExp(t, *pars), ':', color=color_list[i])\n",
    "ax.text(65000, 0.38,\n",
    "        rf'$T_L ={lifetimes[0]/1000:.1f}$ μs,  $P_L={P_list[0]:.2f}$', color='C0')\n",
    "ax.text(88000, 0.34,\n",
    "        rf'$T_L ={lifetimes[1]/1000:.1f}$ μs,  $P_L={P_list[1]:.2f}$', color='C1')\n",
    "ax.text(45000, 0.56,\n",
    "        rf'$T_L ={lifetimes[2]/1000:.1f}$ μs,  $P_L={P_list[2]:.2f}$', color='C2')\n",
    "ax.set_xticklabels([\"0\", \"0\", \"20\", \"40\", \"60\", \"80\", \"100\", \"120\", \"140\"])\n",
    "\n",
    "ax.set_xlabel('Time [μs]')\n",
    "ax.set_ylabel(r'Probability of remaining in the initial state, $F$')\n",
    "ax.legend()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "<ipython-input-7-53228306b5a4>:33: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels([\"0\", \"0\", \"20\", \"40\", \"60\", \"80\", \"100\", \"120\", \"140\"])\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
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     },
     "metadata": {},
     "execution_count": 7
    },
    {
     "output_type": "display_data",
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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Part 2: Creating Figure 4.8\n",
    "The procedure is identical to part 1. Here we instead compare the fidelity and\n",
    "lifetime for three setups: The hexagonal layout, double diamond layout and the\n",
    "pipelined hexagonal layout. "
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "# Simulation parameters\n",
    "n_cycles = 15\n",
    "n_shots = 1024*8*3\n",
    "\n",
    "# Noise parameters\n",
    "T1 = 40e3\n",
    "T2 = 60e3"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "# Generate data\n",
    "# With the number of cycles and shots used, each simulation takes approximately\n",
    "# 30 minutes, resulting in a total of 3 hours for all six. Instead, data can\n",
    "# be loaded further down\n",
    "\n",
    "# QEC, no delay, hexagonal layout (Identical to the standard run in part 1)\n",
    "fid_L_hex, P_L_hex, time_hex = data_analysis_tools.fidelity_from_scratch(n_cycles, n_shots,\n",
    "    gate_times=standard_times, T1=T1, T2=T2, encoding=False, transpile=True, project=True)\n",
    "\n",
    "# QEC, no delay, square layout\n",
    "fid_L_DD, P_L_DD, time_DD = data_analysis_tools.fidelity_from_scratch(n_cycles, n_shots,\n",
    "    gate_times=standard_times, T1=T1, T2=T2, encoding=False, transpile=False, project=True, \n",
    "    device='double_diamond')\n",
    "\n",
    "# QEC, no delay, pipelined (hexagonal layout)\n",
    "fid_L_pipe, P_L_pipe, time_pipe = data_analysis_tools.fidelity_from_scratch(n_cycles, n_shots,\n",
    "    gate_times=standard_times, T1=T1, T2=T2, encoding=False, transpile=False, project=True, \n",
    "    pipeline=True, include_barriers=False)\n",
    "\n",
    "# QEC, with delay, hexagonal layout (Identical to the delay run in part 1)\n",
    "fid_L_hex_d, P_L_hex_d, time_hex_d = data_analysis_tools.fidelity_from_scratch(n_cycles, n_shots,\n",
    "    gate_times={'delay': 5000}, T1=T1, T2=T2, encoding=False, transpile=True, project=True)\n",
    "\n",
    "# QEC, with delay, square layout\n",
    "fid_L_DD_d, P_L_DD_d, time_DD_d = data_analysis_tools.fidelity_from_scratch(n_cycles, n_shots,\n",
    "    gate_times={'delay': 5000}, T1=T1, T2=T2, encoding=False, transpile=False, project=True, \n",
    "    device='double_diamond')\n",
    "\n",
    "# QEC, with delay, pipelined (hexagonal layout)\n",
    "fid_L_pipe_d, P_L_pipe_d, time_pipe_d = data_analysis_tools.fidelity_from_scratch(n_cycles, n_shots,\n",
    "    gate_times={'delay': 4000}, T1=T1, T2=T2, encoding=False, transpile=False, project=True, \n",
    "    pipeline=True, include_barriers=False)\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "# Reformat outputs as np arrays\n",
    "fid_L_hex = np.array(fid_L_hex)\n",
    "fid_L_DD = np.array(fid_L_DD)\n",
    "fid_L_pipe = np.array(fid_L_pipe)\n",
    "fid_L_hex_d = np.array(fid_L_hex_d)\n",
    "fid_L_DD_d = np.array(fid_L_DD_d)\n",
    "fid_L_pipe_d = np.array(fid_L_pipe_d)\n",
    "P_L_hex = np.array(P_L_hex)\n",
    "P_L_DD = np.array(P_L_DD)\n",
    "P_L_pipe = np.array(P_L_pipe)\n",
    "P_L_hex_d = np.array(P_L_hex_d)\n",
    "P_L_DD_d = np.array(P_L_DD_d)\n",
    "P_L_pipe_d = np.array(P_L_pipe_d)\n",
    "\n",
    "# Generate x-values\n",
    "t_hex = np.array([time_hex['dm_'+str(i)] for i in range(n_cycles+1)])\n",
    "t_DD = np.array([time_DD['dm_'+str(i)] for i in range(n_cycles+1)])\n",
    "t_pipe = np.array([time_pipe['dm_'+str(i)] for i in range(n_cycles+1)])\n",
    "t_hex_d = np.array([time_hex_d['dm_'+str(i)] for i in range(n_cycles+1)])\n",
    "t_DD_d = np.array([time_DD_d['dm_'+str(i)] for i in range(n_cycles+1)])\n",
    "t_pipe_d = np.array([time_pipe_d['dm_'+str(i)] for i in range(n_cycles+1)])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "# SAVE DATA\n",
    "# Note: The standard run can be generated from the file active_qec.ipynb.\n",
    "#       Therefore we do not save it from this file.\n",
    "#       The hexagonal 5mus delay run can also be generated in part 1, and is saved there\n",
    "\n",
    "# Double diamond run\n",
    "with open('data/QEC_square_standard_times.npy', 'wb') as f:\n",
    "    np.save(f, (fid_L_DD, P_L_DD, t_DD))\n",
    "\n",
    "# Pipeline run\n",
    "with open('data/QEC_pipeline_standard_times.npy', 'wb') as f:\n",
    "    np.save(f, (fid_L_pipe, P_L_pipe, t_pipe))\n",
    "\n",
    "# Double diamond with delay\n",
    "with open('data/QEC_square_5mus_delay.npy', 'wb') as f:\n",
    "    np.save(f, (fid_L_DD_d, P_L_DD_d, t_DD_d))\n",
    "\n",
    "# Pipeline with delay\n",
    "with open('data/QEC_pipeline_5mus_delay.npy', 'wb') as f:\n",
    "    np.save(f, (fid_L_pipe_d, P_L_pipe_d, t_pipe_d))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "source": [
    "# LOAD DATA\n",
    "# Standard run \n",
    "with open('data/base_qec_data.npy', 'rb') as f:\n",
    "    fid_L_hex, P_L_hex, t_hex = np.load(f)\n",
    "\n",
    "# Double diamond run\n",
    "with open('data/QEC_square_standard_times.npy', 'rb') as f:\n",
    "    fid_L_DD, P_L_DD, t_DD = np.load(f)\n",
    "\n",
    "# Pipeline run\n",
    "with open('data/QEC_pipeline_standard_times.npy', 'rb') as f:\n",
    "    fid_L_pipe, P_L_pipe, t_pipe = np.load(f)\n",
    "\n",
    "# Standard run \n",
    "with open('data/delay_5mus_data.npy', 'rb') as f:\n",
    "    fid_L_hex_d, P_L_hex_d, t_hex_d = np.load(f)\n",
    "\n",
    "# Double diamond run\n",
    "with open('data/QEC_square_5mus_delay.npy', 'rb') as f:\n",
    "    fid_L_DD_d, P_L_DD_d, t_DD_d = np.load(f)\n",
    "\n",
    "# Pipeline run\n",
    "with open('data/QEC_pipeline_5mus_delay.npy', 'rb') as f:\n",
    "    fid_L_pipe_d, P_L_pipe_d, t_pipe_d = np.load(f)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "source": [
    "# Calculate fidelity by F = F_L * P_L\n",
    "fid_hex = np.ones(n_cycles+1)\n",
    "fid_DD = np.ones(n_cycles+1)\n",
    "fid_pipe = np.ones(n_cycles+1)\n",
    "fid_hex_d = np.ones(n_cycles+1)\n",
    "fid_DD_d = np.ones(n_cycles+1)\n",
    "fid_pipe_d = np.ones(n_cycles+1)\n",
    "fid_hex[1:] = fid_L_hex[1:]*np.mean(P_L_hex[1:])\n",
    "fid_DD[1:] = fid_L_DD[1:]*np.mean(P_L_DD[1:])\n",
    "fid_pipe[1:] = fid_L_pipe[1:]*np.mean(P_L_pipe[1:])\n",
    "fid_hex_d[1:] = fid_L_hex_d[1:]*np.mean(P_L_hex_d[1:])\n",
    "fid_DD_d[1:] = fid_L_DD_d[1:]*np.mean(P_L_DD_d[1:])\n",
    "fid_pipe_d[1:] = fid_L_pipe_d[1:]*np.mean(P_L_pipe_d[1:])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "source": [
    "# Perform curve fits\n",
    "p0 = (40e3, 0, 0.9)  # start with values near those we expect\n",
    "pars_hex, cov_hex = scipy.optimize.curve_fit(\n",
    "    monoExp, t_hex[1:], fid_L_hex[1:], p0)\n",
    "pars_DD, cov_DD = scipy.optimize.curve_fit(monoExp, t_DD[1:], fid_L_DD[1:], p0)\n",
    "pars_pipe, cov_pipe = scipy.optimize.curve_fit(\n",
    "    monoExp, t_pipe[1:], fid_L_pipe[1:], p0)\n",
    "pars_hex_d, cov_hex_d = scipy.optimize.curve_fit(\n",
    "    monoExp, t_hex_d[1:], fid_L_hex_d[1:], p0)\n",
    "pars_DD_d, cov_DD_d = scipy.optimize.curve_fit(\n",
    "    monoExp, t_DD_d[1:], fid_L_DD_d[1:], p0)\n",
    "pars_pipe_d, cov_pipe_d = scipy.optimize.curve_fit(\n",
    "    monoExp, t_pipe_d[1:], fid_L_pipe_d[1:], p0)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "source": [
    "# %% Plot figures\n",
    "fig, ax = plt.subplots(1, 1, figsize=(7, 5))\n",
    "\n",
    "# No delay plots (Commented out in Figure 4.8)\n",
    "ax.plot(t_hex, fid_hex, 'o', color='C0',\n",
    "       label=rf'Hexagonal, $T_L ={pars_hex[0]/1000:.1f}$ μs')\n",
    "ax.plot(t_DD, fid_DD, '>', color='C1',\n",
    "       label=rf'Square, $T_L ={pars_DD[0]/1000:.1f}$ μs')\n",
    "ax.plot(t_pipe, fid_pipe, '^', color='C2',\n",
    "       label=rf'Pipeline (Hexagonal), $T_L ={pars_pipe[0]/1000:.1f}$ μs')\n",
    "\n",
    "# With-delay plots\n",
    "ax.plot(t_hex_d[:9], fid_hex_d[:9], 'D', color='C3',\n",
    "        label=rf'Hexagonal, 5 μs delay, $T_L ={pars_hex_d[0]/1000:.1f}$ μs')\n",
    "ax.plot(t_DD_d[:9], fid_DD_d[:9], '<', color='C4',\n",
    "        label=rf'Square, 5 μs delay, $T_L ={pars_DD_d[0]/1000:.1f}$ μs')\n",
    "ax.plot(t_pipe_d[:9], fid_pipe_d[:9], 'v', color='C5',\n",
    "        label=rf'Pipeline, 5 μs delay, $T_L ={pars_pipe_d[0]/1000:.1f}$ μs')\n",
    "\n",
    "# Plot fitted curves\n",
    "t = np.linspace(0, 70e3, 100)\n",
    "ax.plot(t, monoExp(t, *pars_hex)*np.mean(P_L_hex[1:]), ':', color='C0')\n",
    "ax.plot(t, monoExp(t, *pars_DD)*np.mean(P_L_DD[1:]), ':', color='C1',zorder=-5)\n",
    "ax.plot(t, monoExp(t, *pars_pipe)*np.mean(P_L_pipe[1:]), ':', color='C2',zorder=-5)\n",
    "ax.plot(t, monoExp(t, *pars_hex_d) *\n",
    "        np.mean(P_L_hex_d[1:]), ':', color='C3', zorder=-5)\n",
    "ax.plot(t, monoExp(t, *pars_DD_d) *\n",
    "        np.mean(P_L_DD_d[1:]), ':', color='C4', zorder=-5)\n",
    "ax.plot(t, monoExp(t, *pars_pipe_d) *\n",
    "        np.mean(P_L_pipe_d[1:]), ':', color='C5', zorder=-5)\n",
    "\n",
    "# Axes settings\n",
    "ax.set_xticklabels([\"0\", \"0\", \"20\", \"40\", \"60\", \"80\", \"100\", \"120\", \"140\"])\n",
    "ax.set_xlabel('Time [μs]')\n",
    "ax.set_ylabel(r'Probability of remaining in the initial state $F$')\n",
    "ax.legend()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "<ipython-input-19-f9b1ba056f45>:36: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels([\"0\", \"0\", \"20\", \"40\", \"60\", \"80\", \"100\", \"120\", \"140\"])\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f411c61cfa0>"
      ]
     },
     "metadata": {},
     "execution_count": 19
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
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"
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