lab6_ex1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image](resources/qgss-header.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab 6: Qubit Drive: Rabi & Ramsey Experiments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this lab, you will take what you learned about qubit drive to perform Rabi and Ramsey experiment on a Pulse Simulator. The goal of this lab is to familiarize yourself with the important concepts of manipulating qubit states with microwave pulses."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Installing Necessary Packages\n",
    "Before we begin, you will need to install some prerequisites into your environment. Run the cell below to complete these installations. At the end, the cell outputs will be cleared."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -U -r grading_tools/requirements.txt\n",
    "\n",
    "from IPython.display import clear_output\n",
    "clear_output()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulating the Transmon as a Duffing Oscillator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you learned in Lecture 6, the transmon can be understood as a Duffing oscillator specified by a frequency $\\nu$, anharmonicity $\\alpha$, and drive strength $r$, which results in the Hamiltonian\n",
    "$$\n",
    "   \\hat{H}_{\\rm Duff}/\\hbar = 2\\pi\\nu a^\\dagger a + \\pi \\alpha a^\\dagger a(a^\\dagger a - 1) + 2 \\pi r (a + a^\\dagger) \\times D(t),\n",
    "$$\n",
    "\n",
    "where $D(t)$ is the signal on the drive channel for the qubit, and $a^\\dagger$ and $a$ are, respectively, the creation and annihilation operators for the qubit. Note that the drive strength $r$ sets the scaling of the control term, with $D(t)$ assumed to be a complex and unitless number satisfying $|D(t)| \\leq 1$. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Qiskit Pulse Overview\n",
    "\n",
    "As a brief overview, Qiskit Pulse schedules (experiments) consist of Instructions (i.e., Play) acting on Channels (i.e., the drive channel). Here is a summary table of available Instructions and Channels:\n",
    "\n",
    "![image](resources/pulse-instr.png)\n",
    "\n",
    "For more detail, this table summarizes the interaction of the channels with the actual quantum hardware:\n",
    "\n",
    "![image](resources/pulse-user-device-layers.png)\n",
    "\n",
    "However, we find it is more instructive to begin with guided programming in Pulse. Below you will learn how to create pulses, schedules, and run experiments on a simulator. These lessons can be immediately applied to actual pulse-enabled quantum hardware, in particular [`ibmq_armonk`](https://www.ibm.com/blogs/research/2019/12/qiskit-openpulse/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's get started!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In most of the cells below, nothing needs to be modified. **However, you will need to execute the cells by pressing `shift+Enter` in each code block**. In order to keep things tidy and focus on the important aspects of Qiskit Pulse, the following cells make use of methods from the `helper` module. For the gory details, please refer back to the [Lab 6 notebook](lab6-drive-ham-rabi-ramsey.ipynb). Before coming to Exercise 1a, the following code blocks\n",
    "\n",
    "- create backend pulse simulator and instantiate the transmon as a Duffing oscillator of frequency $\\sim 5$ GHz\n",
    "- import libraries for numerics and visualization, and define helpful constants\n",
    "- create the channels for the pulse schedule and define measurment schedule (we will only work with the drive channel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# our backend is the Pulse Simulator\n",
    "from resources import helper\n",
    "from qiskit.providers.aer import PulseSimulator\n",
    "backend_sim = PulseSimulator()\n",
    "\n",
    "# sample duration for pulse instructions \n",
    "dt = 1e-9\n",
    "\n",
    "# create the model\n",
    "duffing_model = helper.get_transmon(dt)\n",
    "\n",
    "# get qubit frequency from Duffing model\n",
    "qubit_lo_freq = duffing_model.hamiltonian.get_qubit_lo_from_drift()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# visualization tools\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('dark_background')\n",
    "\n",
    "# unit conversion factors -> all backend properties returned in SI (Hz, sec, etc)\n",
    "GHz = 1.0e9 # Gigahertz\n",
    "MHz = 1.0e6 # Megahertz\n",
    "kHz = 1.0e3 # kilohertz\n",
    "us = 1.0e-6 # microseconds\n",
    "ns = 1.0e-9 # nanoseconds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Instantiate channels and create measurement schedule\n",
    "\n",
    "We will use the same measurement schedule throughout, whereas the drive schedules will vary. This must be built for the simulator, for a real backend we can ask for its default measurement pulse."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import pulse\n",
    "from qiskit.pulse import Play, Acquire\n",
    "from qiskit.pulse.pulse_lib import GaussianSquare\n",
    "\n",
    "# qubit to be used throughout the notebook\n",
    "qubit = 0\n",
    "\n",
    "### Collect the necessary channels\n",
    "drive_chan = pulse.DriveChannel(qubit)\n",
    "meas_chan = pulse.MeasureChannel(qubit)\n",
    "acq_chan = pulse.AcquireChannel(qubit)\n",
    "\n",
    "# Construct a measurement schedule and add it to an InstructionScheduleMap\n",
    "meas_samples = 1200\n",
    "meas_pulse = GaussianSquare(duration=meas_samples, amp=0.025, sigma=4, width=1150)\n",
    "measure_sched = Play(meas_pulse, meas_chan) | Acquire(meas_samples, acq_chan, pulse.MemorySlot(qubit))\n",
    "\n",
    "inst_map = pulse.InstructionScheduleMap()\n",
    "inst_map.add('measure', [qubit], measure_sched)\n",
    "\n",
    "# save the measurement/acquire pulse for later\n",
    "measure = inst_map.get('measure', qubits=[qubit])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graded Exercise 1a: Rabi Schedule\n",
    "\n",
    "Add code to the method below in order to build a Rabi pulse schedule. A Rabi experiment consists of a drive pulse at the qubit frequency, followed by a measurement. A list of Rabis schedules will vary the drive amplitude each time. For a review of creating pulse schedules, see [Lab 6 notebook](lab6-drive-ham-rabi-ramsey.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.pulse import pulse_lib\n",
    "\n",
    "def build_rabi_pulse_schedule(drive_duration, drive_amp, drive_sigma):\n",
    "    ### create a Rabi schedule (already done)\n",
    "    ### create a Gaussian Rabi pulse using pulse_lib\n",
    "    ### play Rabi pulse on the Rabi schedule and return\n",
    "    \n",
    "    rabi_schedule = pulse.Schedule(name='rabi_experiment')\n",
    "    \n",
    "    ### WRITE YOUR CODE BETWEEN THESE LINES - START\n",
    "    rabi_pulse = pulse_lib.gaussian(duration=drive_duration, amp=drive_amp, \n",
    "                                    sigma=drive_sigma, name='rabi_experiment')\n",
    "    rabi_schedule += Play(rabi_pulse, drive_chan)\n",
    "    ### WRITE YOUR CODE BETWEEN THESE LINES - END\n",
    "    \n",
    "    # add measurement to rabi_schedule\n",
    "    # << indicates time shift the beginning to the start of the schedule\n",
    "    rabi_schedule += measure << rabi_schedule.duration\n",
    "    return rabi_schedule"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From the Rabi schedule of Exercise 1a, create a list of schedules for the experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gaussian pulse parameters, with varying amplitude\n",
    "drive_duration = 128\n",
    "num_rabi_points = 41\n",
    "drive_amps = np.linspace(0, 0.9, num_rabi_points)\n",
    "drive_sigma = 16\n",
    "\n",
    "# now vary the amplitude for each drive amp\n",
    "rabi_schedules = []\n",
    "for drive_amp in drive_amps:\n",
    "    rabi_schedules.append(build_rabi_pulse_schedule(drive_duration, drive_amp, drive_sigma))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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in6TKRx99pCOOOELjx4/XwoULVVpaqsLCQsXjcX399dd64YUXdNFFF+nWW29NrHPTTTfpkksuafR6fh9//LEeffRRSdITTzyhY445pt73Hz9+vK644gr9/e9/l8fjUTgc1sqVK/Vf//VfOu+88xQKhepdb9WqVTr22GN1880368MPP1RxcbHat28vj8ejb775RrNmzdIVV1yROEEnE+3Zs0fnnHOOLr74Yr3xxhv67rvvlJOTo7y8PG3btk3vvfeebr75Zh122GFJf+9Zs2bp6quv1sKFC+X3+3XooYfqsMMO2+9JOgBSz1FVBQJAxpgwYYJ+//vfa8GCBY2e+AIAqIs9fwAAABYh/gAAACxC/AEAAFiE27sBlps9e7ZOO+20Zq1z2WWXafHixSmaCACQSpzwAVhu/vz5GjZsWLPWGTZsmD799NPUDAQASCniDwAAwCIc8wcAAGAR4g8AAMAixB8AAIBFiD8AAACLEH8AAAAWIf4AAAAsQvwBAABYhPgDAACwCPEHAABgEeIPAADAIsQfAACARYg/AAAAixB/AAAAFiH+AAAALEL8AQAAWIT4AwAAsEiW2wM0RywWkzHG7TEAAAD2a+/evTr44IPdHqOOjIo/Y4z8gaDbYwAAAOzXt99+6/YI9eJrXwAAAIsQfwAAABYh/gAAACxC/AEAAFiE+AMAALAI8QcAAGAR4g8AAMAixB8AAIBFiD8AAACLEH8AAAAWIf4AAAAsQvwBAABYhPgDAACwCPEHAABgEeIPAADAIsQfAACARYg/AAAAixB/AAAAFiH+AAAALEL8AQAAWIT4AwAAsAjxBwAAYBHiDwAAwCLEHwAAgEWIPwAAAIsQfwAAABYh/gAAACxC/AEAAFiE+AMAALAI8QcAAGAR4g8AAMAixB8AAIBFiD8AAACLEH8AAAAWIf4AAAAsQvwBAABYhPgDAACwCPEHAABgEeIPAADAIsQfAACARYg/AAAAixB/AAAAFiH+AAAALEL8AQAAWIT4AwAAsAjxBwAAYBHiDwAAwCLEHwAAgEWIPwAAAIsQfwAAABYh/gAAACxC/AEAAFiE+AMAALAI8QcAAGAR4g8AAMAixB8AAIBFiD8AAACLEH8AAAAWIf4AAAAsQvwBAABYhPgDAACwCPEHAABgEeIPAADAIsQfAACARYg/AAAAixB/AAAAFslyewDbGWMUCYfdHgMAAFgi7eNvzJgxGjt2rCTJcRyXp0m+9evXadOmzW6PAQAAkuyIw/u4PUK9HEnG7SGaKhqNyh8Iuj1GUq1ZvVpLly3VoYce6vYoAAAgSXbv3q3ThwzRySef7PYodaT9nj8b5Ofnq7Cw0O0xAABAkoRCIbdHaBAnfAAAAFiE+AMAALAI8QcAAGAR4g8AAMAixB8AAIBFiD8AAACLEH8AAAAWIf4AAAAsQvwBAABYhPgDAACwCPEHAABgEeIPAADAIsQfAACARYg/AAAAixB/AAAAFiH+AAAALEL8AQAAWIT4AwAAsAjxBwAAYBHiDwAAwCLEHwAAgEWIPwAAAIsQfwAAABYh/gAAACxC/AEAAFiE+AMAALAI8QcAAGAR4g8AAMAixB8AAIBFiD8AAACLEH8AAAAWIf4AAAAsQvwBAABYhPgDAACwCPEHAABgEeIPAADAIsQfAACARYg/AAAAixB/AAAAFiH+AAAALEL8AQAAWIT4AwAAsAjxBwAAYBHiDwAAwCLEHwAAgEWIPwAAAIsQfwAAABYh/gAAACxC/AEAAFiE+AMAALAI8QcAAGAR4g8AAMAixB8AAIBFiD8AAACLEH8AAAAWIf4AAAAsQvwBAABYhPgDAACwCPEHAABgEeIPAADAIsQfAACARYg/AAAAixB/AAAAFiH+AAAALEL8AQAAWIT4AwAAsAjxBwAAYBHiDwAAwCLEHwAAgEWIPwAAAIsQfwAAABYh/gAAACxC/AEAAFiE+AMAALAI8QcAAGAR4g8AAMAixB8AAIBFiD8AAACLEH8AAAAWIf4AAAAsQvwBAABYhPgDAACwCPEHAABgkSy3B9ifMWPGaOzYsZIkx3FcngYAACCzpX38TZs2TdOmTZMkRaNRl6cBAADIbHztCwAAYBHiDwAAwCLEHwAAgEWIPwAAAIsQfwAAABYh/gAAACxC/AEAAFiE+AMAALAI8QcAAGAR4g8AAMAixB8AAIBFiD8AAACLEH8AAAAWIf4AAAAsQvwBAABYhPgDAACwCPEHAABgEeIPAADAIsQfAACARYg/AAAAixB/AAAAFiH+AAAALEL8AQAAWIT4AwAAsAjxBwAAYBHiDwAAwCLEHwAAgEWIPwAAAIsQfwAAABYh/gAAACxC/AEAAFiE+AMAALAI8QcAAGAR4g8AAMAixB8AAIBFiD8AAACLEH8AAAAWIf4AAAAsQvwBAABYhPgDAACwCPEHAABgEeIPAADAIsQfAACARYg/AAAAixB/AAAAFiH+AAAALEL8AQAAWIT4AwAAsAjxBwAAYBHiDwAAwCLEHwAAgEWIPwAAAIsQfwAAABYh/gAAACxC/AEAAFiE+AMAALAI8QcAAGAR4g8AAMAixB8AAIBFiD8AAACLEH8AAAAWIf4AAAAsQvwBAABYhPgDAACwCPEHAABgEeIPAADAIsQfAACARYg/AAAAixB/AAAAFiH+AAAALEL8AQAAWIT4AwAAsAjxBwAAYBHiDwAAwCLEHwAAgEWIPwAAAIukRfxdc801WrFihfx+v3bu3Klp06apa9eubo8FAADQ5rgef7fffrtmzpyp0tJS3XbbbXruued05ZVXasGCBWrXrp3b4wEAALQpWW6+eZcuXfTAAw9o6dKlOvvssxWPxyVJy5Yt0zvvvKPbbrtNkydPdnNEAACANiUle/4KCgo0adIkLVmyREVFRQoGg/rnP/+pyZMnKy8vL7HcJZdcovz8fD355JOJ8JOkd999Vxs2bNDVV1+divEAAACslZI9fz169NDo0aM1e/ZsvfLKK4pGoxo6dKjuvvtunXTSSTrvvPMkSQMHDpQkLV68uM42lixZol/+8pfKz89XZWVlKsYEAACwTkrib+PGjerVq5ei0Wjiuaefflr333+/7rvvPg0cOFDLli1T9+7dJUnbtm2rs41t27bJ4/Goe/fu+uc//5mKMQEAAKyTkq99I5FIIvy8Xq8KCwvVpUsXffTRR5KkQYMGSVLihI5QKFRnG8FgsNYyAAAAOHApO+Hj17/+tW688UYdd9xx8nq9tV7r1KmTJMnv90uScnJyErFXLTc3t9YyklRaWqp58z5M1cgJoViujDzK8fjlOKl9r3AkrN27dikcDqf2jQAAQKspKSnRiSec4PYY9UpJ/N1xxx16/PHH9cEHH2jKlCnavn27wuGwevTooZdeekkeT9UOx+3bt0uqOkZww4YNtbbRo0cPxeNxnXvuuXrllVckVX2d/MtfXpmKkQEAAJLqZxdf7PYI9UpJ/F1zzTXatGmTzj//fBljEs+fe+65tZZbtmyZfvWrX2nw4MF14m/QoEFav369nnrqKT311FOSpHA4rO07dqZi5ITvin36tsinSNRRzy4RHdU99XvkfrhnFAAAZL7vvt3s9gj1Skn8xWIxGWPkOE4i/rxer8aPH19rublz52rKlCm65ZZb9MorryQu93LhhRfqiCOO0G9/+9tay3s8HhUUFKRi5IRosVeOx6PcXMl4spSbl60s2gwAALQRKYm/N998Uw899JDef/99vfXWW+rQoYNGjRqlSCRSa7ni4mLdd999euyxx/TRRx/p1VdfVY8ePTRu3DitXbtWTzzxRCrGa1AsLsViUrsco+wsKRp3FImK+AMAAG1GSuLv0UcfleM4+s///E/96U9/0s6dO/Xaa69p+vTpWrt2ba1lH3/8ce3Zs0d33HGHpkyZorKyMr3++usaP358q1/frzLoKBB25HGkLK9RRdBRmd9RXo7Z/8oAAAAZwJGUMWUTjUblDwT3v2ALlVY6Wr3Jq/Z5Rr4saWeJo+N7x3RIYcZ8RAAAIE2sX7c2cUOLdJKS6/xlqn0VjuI1Os8YqaiUjwgAALQdlE0NcSP5Q07iGL9YzFE05u5MAAAAyUT8/UCOzyQu7Jyfa6q+GAcAAGgjiL/vVe/1q/N8XApH61kBAAAgAxF/3wtHpIpA1Zm+1bK8VWcA1xeFAAAAmYj4q8Ho+696v5ebbWQ40RcAALQhxB8AAIBFiL/vlVY6+v7ucgmOqo4F3FPG174AAKBtIP6+F4078oekXN+/n/N6pEDIyZyrYAMAAOwH8VeD1yN5anwijiPl+gxXewEAAG0G8QcAAGAR4u97pZUN79/zh+seDwgAAJCJiD9VndQRCEuRWN0AdBypMuAoFHFhMAAAgCQj/r5njNQ+t+6pHQW5hhM+AABAm0H8AQAAWIT4AwAAsAjxJ8kflOKm4RM+4nGp1M8FXwAAQOYj/iSFIlUXeM7Lrnt0X45PqgxKsTjxBwAAMh/xV0OWt+5zHk/9zwMAAGQi4g8AAMAixB8AAIBFiD9J+yocmUYu5mcklVRwzB8AAMh8xJ+kSFQKhh15Gvg04nFHgZC4xRsAAMh4xJ8kOVK7nIZ3/RXUc+cPAACATET8AQAAWIT4AwAAsAjxJykcbdrJHHz5CwAAMp318VcZlMKRxs/2dRwpFHU44xcAAGQ86+MvbqRwRCrIa7j+8nOMwtGqZQEAADJZSuOvW7dumjNnjrZs2SJjjIYOHbrfdS688EKtW7dOlZWV+uKLL3TCCSekcsQET2M79djhBwAA2oiUxl88Hte8efM0cuRIlZaW7nf5ww8/XK+++qruuusuderUSW+99Zbeffdd5ebmpnJMAAAAaxxw/PXs2VNFRUU6//zzJUler1eLFi3Sww8/rN27d+vpp5/WkiVLZBo7qO571157rRYsWKB33nlH4XBYjzzyiBzH0XnnnXegYwIAAEBJiL+tW7dq9OjRmjFjhrp166aJEyfK5/Pp3nvvbfa2+vXrp5UrVyb+3RijVatWqV+/fgc6ZoPCkaZ/pxsI8/0vAADIbFnJ2MjcuXM1YsQIzZs3Tz179tSAAQMUjUabvZ327dvX+Xq4pKRE7du3T8aY9SoPOgpEpIK8hpdxVHU5mPIA8QcAADJb0o75mzp1qvr27avZs2dr48aNLdpGeXm5OnbsWOu5wsJClZeXJ2PEejmSHDnK8jayjCPl+oy40h8AAMh0SYm/rKwsTZ8+XTNnztTIkSM1ZMiQFm1n9erVOvHEE2s9d8IJJ2jNmjXJGLNBjrP/qHPY6QcAANqApMTfgw8+qGg0qhtuuEHjxo3TrFmzVFhYKEnKyclRTk6OJCk7O1s5OTlyGiipmTNnavjw4brgggvk8/l01113SZLef//9ZIwJAABgvQOOvxEjRmj06NEaNWqUYrGYXnzxRS1ZskQvvPCCJCkYDCoYDKqwsFDz5s1TMBjUmWeeKUk6/fTTa32lu2HDBo0aNUqPP/64SktL9Ytf/EIXXXSRgsHggY4JAAAAVR3yljEHskWjUfkDyQ3B9Vu92rbH0SGFjX8Me8oddcg3OuGwGF8BAwCA/Vq/bq0GDhzo9hh1WH17t2is6t6+0fj+a84YKRR2VMlOSAAAkMGsjr+4kSIxRx0aua9vtfZ5RpGoFDfs9gMAAJnL6vir1qSzfVthDgAAgFQj/gAAACxC/AEAAFiE+AMAALCI1fFXUuEoGmvi8XyOFI1Le8o4+g8AAGQuq+MvbqRgRMrL3v+yPq8UCDlyrP7EAABAprM+ZRxJniZ8Co4j5fgy5nrYAAAA9bI+/gAAAGxC/AEAAFiE+AMAALCI1fFXUulRc4/iqww4Mhz6BwAAMpTV8RcIS+Fo8y7dUhFyFI6maCAAAIAUszr+HEntc5u+G68g16jZuwoBAADSiNXxBwAAYBviDwAAwCLEHwAAgEWsjr943O0JAAAAWpe18VfmdxSJNe9MX8eRwlFpb3nz1gMAAEgX1sZfLC4Fw1J+M872zcuWQpEUDgUAAJBi1sZfNU8zduI5TvOWBwAASDfWxx8AAIBNiD8AAACLEH8AAAAWsTb+yvwtO3jPSCqptPZjAwAAGc7aiglHJX/IkbeZn0Ak6igQSs1MAF3rSyYAACAASURBVAAAqWZt/ElSXraR08wdgO3zmn5pGAAAgHRjdfwBAADYhvgDAACwCPEHAABgEWvjr7n39a2Jo/4AAECmsjL+QhEpEJLiLay4SNRRaSX3eQMAAJnHyviLxaVQxFF+bvPrLz/XKBip2gYAAECmsTL+qjX3Gn+S5GGHHwAAyGBWxx8AAIBtiD8AAACLEH8AAAAWsTL+yv0HfuBeCWf7AgCADGRl/EVijsoDUnZW89f1OFIg5CgSTf5cAAAAqWZl/EmSz9uys30dR8rL5jLPAAAgM7Vg31frGjNmjMaOHStJchy+agUAADgQaR9/06ZN07Rp0yRJ0SjftQIAABwIa7/2BQAAsJGV8VcecHSgR+2FIk6L7w0MAADgFuviLxaX/CEpGjuw4wcDYUeBUJKGAgAAaCXWxZ8kxeOOOuS1fLdd+zyjaDyJAwEAALQSK+MPAADAVsQfAACARYg/AAAAi1gXf/EkHqt3oCeNAAAAtDbr4q+k0lEoInk8LT/hw+ORQuGqbQEAAGQS6+JPksIxKT+n5etnZ0mxuCOPlZ8eAADIZORLC3n55AAAQAYiYQAAACxC/AEAAFjEuvgLhJN3koY/mLRNAQAAtArr4q+00lEkeuABGI1JlUFHMW7zBgAAMoh18SdJuT4j5wD7r+r+vo5My68YAwAA0OqsjD9PEr75PdB4BAAAcIOV8QcAAGAr4g8AAMAiVsVfNKakn6ARjCR3ewAAAKlkVfz5Q44qA05S7s6RnWVUGZT8QQ7+AwAAmcOq+JOkuKS8nAM/RTfHJ870BQAAGce6+AMAALAZ8QcAAGARq+IvnoKvaVOxTQAAgFSxKv6KyxxFosm7QHMsLhWVWvURAgCADGdVuWR5pFDEkc+bnO15HPb8AQCAzGJV/ElSji95tZbtk7jQCwAAyCTWxR8AAIDNiD8AAACLWBN/sbhUHkjyl7RGisSkYDi5mwUAAEgVa+IvEpUqg46yknSyh1R1/KA/6CgY4cg/AACQGayJv2q5yTzhIytpmwIAAGgV1sUfAACAzYg/AAAAi1gTf6V+R7F48u7uIUlypJiR9pZzzB8AAMgM1sRfLC4FQlKuL3nbzPJIgRDhBwAAMoc18SdJHk/VI1kcJ7l3DAEAAEg1q+IPAADAdtbEXyicuq9ngyncNgAAQDJZE3+lfkfhaPIjLW4kf1gKRZK+aQAAgKSzJv6icSk/N/nH53XIM4pEHRkO/QMAABnAmviTJG8Kvp1N6qVjAAAAUsyq+AMAALCdFfFXEZTicUep+mY2HpdKKtkFCAAA0p8V8ReKOPKHpLzs5Odfrk/yh6RYnPgDAADpz4r4q5blTf42PR7Ja9WnCAAAMhnZAgAAYBEr4m9PmaN4Ci/FYoy0t5zLvQAAgPRnRfzF41V3+EjV17PGVF3kmfYDAADpzor4kyO1y0ldmrXLSdmmAQAAksqK+ItzJi4AAIAkC+IvGJYCYaX0eDzHkSIxR6Vc6w8AAKS5Nh9/0ZgUDDsp/trXKBiuOrYQAAAgnbX5+KuWimv8VfOwww8AAGSINh9/lcHWK7NSPxUIAADSW5uPv1DUUUUwtXv+HKfqq+VgOHXvAQAAkAxtPv4kyedN7S3YnBRfSgYAACBZ2nz8VQRa76vYcCy1dxIBAAA4UG06/qIxyR+Woq1wnT/HkYIhR/5gyt8KAACgxdp0/BkjxeKOOuSl/hosBblG0Ri3eAMAAOmtTcdfOFr1v1vzHFx/iDN+AQBA+mrT8VcRcBQISb6s1L+X1yP5Q1IgTPwBAID01abjT07VXTdyfKl/qyyvlJXVunsZAQAAmqtNx9/uEk+rnn1r4lKZ3+E2bwAAIG216fiLxqV4K5zpWy3LW3XMX5T4AwAAaarNxl8sXnW2b2tefDk3u+q9wpFWe0sAAIBmabPxVx5wFAg58rTiQXg+b9VJHxWteD9hAACA5miz8RcKV+39a5fbenv+cnxVJ5gEuNwLAABIU202/koDjkIRteqeP0mKxByV+ok/AACQntpk/BkjhcJVX/l6W/knzM81iser9joCAACkmzYZf8FI1cWWHRd2wHmdqvcuY+8fAABIQ20y/soDjmJxqUNe699pN//7e/yWVBJ/AAAg/bTJ+KsMOir3t85t3X6o6jZvjvyc8QsAANJQq8ffpk2bNH/+/JRtP26k0kpHkqMsb8repkGOI+X4jAIRKRhu/fcHAABoTNrs+TvllFP04YcfqqysTKWlpXr//fd1wgknNHs7FQFHoaijvOzW/8q3WkGuUSjkaG8Fe/8AAEB6SYv4GzRokD799FP9+Mc/1u9+9ztNmDBBRx55pBYuXKjjjz++WdvaVeLIH5QKXDjer1petlQRkvaVp8XHCwAAkODCUXF1TZkyReFwWGeeeaa2b98uSXr99de1du1aPfbYYzr33HObtJ1oTKoMOQqGHWV3dC/+PB7J4zjyh6TKoJSf69ooAAAAtaRs11TPnj312muvqaSkRKWlpfrrX/+qPn361Fnu8MMP1ymnnKI33ngjEX6StH37dr3xxhs655xzdMghhzTpPXfu86jS76hDO/fCr1qX9nFVBKWtxez9AwAA6SMlZdKxY0d99tlnuuyyy/Tyyy9r/Pjx8vv9mj9/vvLz82stO3DgQEnS4sWL62xnyZIl8ng86t+//37fMxyV9pY7KgukR/zl+KRg2FF5kGv+AQCA9JGS+Lv77rv14x//WGPGjNGtt96qZ555RldeeaXeeustHXTQQbWW7d69uyRp27ZtdbZT/VyPHj0afT9jpH9u82pfhaMOeabVb+nWkIM6xlVS4WhLkYc7fgAAgLSQkvi75JJLtHPnTs2cObPW8w8//HCdZdu1aydJCoVCdV4LBoO1lqlPPC59/Z1XpX5H/pCjwgL39/pVy8uuurdwcZmj/9viVTjq9kQAAMB2KTnho0+fPlq2bJni8dq7u3bu3Kl9+/bVes7v90uScnJy6mwnNze31jI1BULSjn0eVQYdlVY6Kg846tk1/XavHdzRaNtej/aUSWvjXuX6pO5dYirIlSu3nwMAAHZL2dm+xtS/B875QfFUn+RR31e71c/dddddGjt2rFauXJk4RhAAACCdrVyzye0R6pWS+Nu4caOOOuooeTyeWnv/unXrpsLCwlrLLlu2TJI0ePBgvfjii7VeO/XUUxWPx3XGGWdo165d2lEU0CMzvqm1TK6LF3NuiWC4Zbv72pVU6JAN25VTGVA025fkqQAAwP44xsh4HBX/6BCVHNrF7XFaLCXxN3fuXN1zzz269tprNWPGjMTzv/nNb+osu2HDBi1btkwjR47Ufffdpx07dkiSDj30UI0cOVKffPKJdu3aJUk6qJNPN/68eypGTnuRNdsV9uTK27ObnJy0uDwjAABWMXGj6De75Ts2Xzmn779H1q9b2wpTNV9KKuKRRx7RqFGjNG3aNPXv319ff/21hg0bpsGDB6uoqKjO8rfddpvmz5+vhQsX6sknn5Qk3XrrrfJ4PBo3blwqRsw8HkdyHMIPAACXOB5HnvzsqsuMZLCUnO1bUlKiM844Q3PmzNG1116rRx55RO3atdPw4cNVWVlZZ/nFixdr2LBh2rx5sx544AFNmjRJ//rXv3TmmWdq9erVqRgxM3GGCAAA7nIcKcP/OnYkZUy+RqNR+QNBt8dwReTrHQov36KsH3d2exQAAKwV21oi7486Kef0w/e77Pp1a9PyRFXuPQYAAGAR4g8AAMAixB8AAIBFiD8AAACLEH8AAAAWIf4yRTT97lsMAICVMvzvZOIvA5hoXLHdFVIss3/ZAADIeMYo7g8rXp65l54j/jJBLC4TCMvJz3Z7EgAArOZ0yJOpDEuRmNujtBjxl0G4tRsAAO5yfJmfTpn/EwAAAKDJiD8AAACLEH8AAAAWIf4AAAAsQvwBAABYhPgDAACwCPEHAABgEeIPAADAIsQfAACARYg/AAAAixB/AAAAFiH+AAAALEL8AQAAWIT4AwAAsAjxBwAAYBHiLwOYWNztEQAAQA0mFHN7hBYj/jJAfFe5jD8sefnjAgDAVV6PTEVY8d3lbk/SYtREhjCBqDwdc90eAwAAqzk5WVXfyHkct0dpMeIvU/AnBQBAWnB8XrdHOCAkBQAAgEWIPwAAAIukNP66deumOXPmaMuWLTLGaOjQoY0uf9xxx+m9997Trl27ZIxR7969UzkeAACAdVIaf/F4XPPmzdPIkSNVWlq63+UjkYhmz56tyy+/PJVjAQAAWOuA469nz54qKirS+eefL0nyer1atGiRHn74Ye3evVtPP/20lixZImPMfrf1zTff6MUXX9SqVasOdCwAAADU44Djb+vWrRo9erRmzJihbt26aeLEifL5fLr33nuTMR8AAACSKCsZG5k7d65GjBihefPmqWfPnhowYICi0WgyNg0AAIAkStoxf1OnTlXfvn01e/Zsbdy4MVmbBQAAQBIlJf6ysrI0ffp0zZw5UyNHjtSQIUOSsVkAAAAkWVLi78EHH1Q0GtUNN9ygcePGadasWSosLJQk5eTkKCcnR5KUnZ2tnJwcOU7Dt0SpuXzNfwYAAMCBO+D4GzFihEaPHq1Ro0YpFovpxRdf1JIlS/TCCy9IkoLBoILBoAoLCzVv3jwFg0GdeeaZkqTTTz9d5eX/vjFy7969FQwGtWvXLknS+vXrFQwGud4fAABAkjiS9n8NljQRjUblDwTdHqPVRTftUeizfynrx13cHgUAAOtFN+1V9sk95evbvdHl1q9bq4EDB7bSVE3H7d0AAAAsQvwBAABYhPgDAACwCPEHAABgEeIPAADAIsQfAACARYg/AAAAixB/AAAAFiH+MkE0JsXdHgIAAEiSjJGJxNyeosWIvwwQ/W6fFKP+AABIC7G4YsWVGRuAxF8miEvK5o8KAIB04HTMlQJhKZqZO2YoikzgSE6Oz+0pAACAJMfndXuEA0L8AQAAWIT4AwAAsAjxBwAAYBHiDwAAwCLEHwAAgEWIPwAAAIsQfwAAABYh/gAAACxC/AEAAFiE+AMAALAI8QcAAGAR4g8AAMAixB8AAIBFiD8AAACLEH8AAAAWIf4AAAAsQvwBAABYhPgDAACwCPEHAABgEeIPAADAIsQfAACARYg/AAAAixB/ac4YIxOLuz0GAAD4ARM3bo/QIsRfmosXV0qBiNtjAACAal6P4v6I4jvL3J6kRYi/dBc3igci8nRu5/YkAABAktM+RwpGJMftSVqG+AMAAGgGx3EkT4aWn4g/AAAAqxB/AAAAFslye4D9GTNmjMaOHSvp+92sAAAAaLG0j79p06Zp2rRpkqRoNOryNAAAAJmNr30BAAAsQvwBAABYhPgDAACwCPEHAABgEeIPAADAIsQfAACARYg/AAAAixB/AAAAFiH+AAAALEL8AQAAWIT4AwAAsAjxBwAAYBHiDwAAwCLEHwAAgEWIPwAAAIsQfwAAABYh/gAAACxC/KW5+M4yKW7cHgMAANQUl6JbS9yeokWIvzQXj8RkSgNSttftUQAAwPdMJCoFIm6P0SLEXwZw2ufIcRy3xwAAAN/zdMpze4QWI/4AAAAsQvwBAABYhPgDAACwCPEHAABgEeIPAADAIsQfAACARYg/AAAAixB/AAAAFiH+AAAALEL8AQAAWIT4AwAAsAjxBwAAYBHiDwAAwCLEHwAAgEWIPwAAAIsQfwAAABYh/gAAACxC/AEAAFiE+EtjJhyV2ReQjNuTAACAWoxkIjHFSwNuT9JsxF8aM6Go4mUBOXlZbo8CAABqcApyFC8Lyvgjbo/SbMRfBnDaZbs9AgAAqMHJztwdM8QfAACARYg/AAAAixB/AAAAFiH+AAAALEL8AQAAWIT4AwAAsAjxBwAAYBHiDwAAwCLEHwAAgEWIPwAAAIsQfwAAABYh/gAAACxC/AEAAFiE+Etj8X0Bt0cAAACNiBeVuz1CsxF/acz4wzL7AnJyfG6PAgAAasryyJQEFQ9E3J6k2Yi/dOZI8nnkZHvdngQAANTgeBw5HXLcHqNFiD8AAACLEH8AAAAWIf4AAAAsQvwBAABYhPgDAACwCPEHAABgEeIPAADAIsQfAACARYi/dBZ3ewAAANAoY9yeoNmIvzRl4kax7SUyUQoQAIC0FDeKlwZlAmG3J2kW4i9dGSMTiMrJyXJ7EgAAUA9P+xyZipBMOOb2KM1C/KU5p53P7REAAEB9MnQHDfEHAABgEeIPAADAIsQfAACARYg/AAAAixB/6SrGJV4AAMgEnO2LpIjtKpfxh+R4+CMCACAdOV6PTGVY8d3lbo/SLJRFGjPBqJzCPLfHAAAA9XDyfFIkLmXYjpq0mfaoo47S22+/rb1796qiokKfffaZhg8f7vZYAAAADctKm5RqsrSYuE+fPvr88881ePBgPfLII7rrrrtUUFCgDz74QGeffbbb4wEAALQZaXFp6smTJ6uwsFD9+/fXP/7xD0nSzJkz9fXXX2vq1Kk65phjXJ6w9cWLK90eAQAANEG8qFz6ySFuj9FkB7zn77rrrpMxRmeddZbuu+8+bd68WX6/X0uWLNGgQYMkSWeeeaYWLlyoiooKbd++Xb/97W8T67dr104XX3yxFixYkAg/SaqsrNQLL7ygo48+WgMHDjzQMTOOKQvK+MOS4/YkAACgQeGY4hUhmUjmnPGbtK99H3roIV1yySX605/+pIkTJ6pPnz764IMP9LOf/UxvvfWWFi5cqDvvvFPr1q3TpEmTdNVVV0mS+vXrp9zcXC1evLjONpcsWSJJVsafHEeejrlyHOoPAIB05enSToobt8dolqR97ev1enXqqacqEolIkv7v//5Pf/3rX/Xmm29q8ODBWr58uSTpxRdf1Lfffqubb75Zs2bNUvfu3SVJ27Ztq7PN6ud69OiRrDEzggmEFQ+E3R4DAAA0RdwoXhKQ96ACtydpkqTt+XvmmWcS4SdJCxculFS19646/CQpEolo6dKlOvLIIyVVfe0rSaFQqM42g8FgrWVsESuulCkJyMn1uT0KAABohNM+R/ESv+I7y9wepcmStudv48aNtf69pKREkrRp06Y6y+7bt09du3aVJPn9fklSTk5OneVyc3NrLRMPR1W5eWeyRk4PJvE/qv7VH1Z0817Fdu2Rc2gHOXsiDa4KAADcZhTbU6LwBq+yIgF5Ov17h1W6HriVtPiLxeo/0LGh56tt375dUv1f7VY/d9VVV2nEiBH6x9dr7Dz+DwAAZJx/Lv/H/hdygeuXelmzZo2CwaAGDx5c57VTTz1VknT55Zdr6dKlqty1V6umvtHaI6acCcUkY6p2AHoceQqy5Wmf6/ZYAACgiUwgolhJIPH3uePzyG+ibo9VL9fjr7KyUu+8844uu+wy9evXT6tXr5Yk5efna/To0frmm2+0dOlSSVJOlw7qc+35bo4LAADQJOvXrXV7hHq5Hn+SdM899+jss8/WvHnz9Mc//lFlZWUaM2aMevTooQsuuMDt8QAAANqMtIi/DRs2aMiQIXrooYc0fvx4ZWdna8WKFTrvvPP08ccfuz0eAABAm+Go5qmmaS4ajcofCLo9BgAAwH6tX7c2LU9UTdp1/gAAAJD+iD8AAACLEH8AAAAWIf4AAAAsQvwBAABYhPgDAACwCPEHAABgEeIPAADAIsQfAACARYg/AAAAixB/AAAAFiH+AAAALEL8AQAAWIT4AwAAsAjxBwAAYBHiDwAAwCLEHwAAgEWIPwAAAIsQfwAAABYh/gAAACxC/AEAAFiE+AMAALAI8QcAAGAR4g8AAMAixB8AAIBFiD8AAACLEH8AAAAWIf4AAAAsQvwBAABYJGnxN3bsWBljdNttt9V6/tRTT9WKFSvk9/v11Vdf6ayzzmp0O81dHgAAAE2XlPjr1q2b7r77bq1Zs6bW8x07dtS7776rZ599VoWFhZo8ebLmzJmjbt261bud5i4PAACA5tlv/PXs2VNFRUU6//zzJUler1eLFi3Sww8/nFhm6tSpmjRpkvbs2VNr3csuu0w7duzQ888/r3A4rFmzZumrr77SlVdeWe97NXd5AAAANM9+42/r1q0aPXq0ZsyYoW7dumnixIny+Xy69957JVUFW6dOnfTSSy/VWbdfv35auXJlredWrFihfv361ftezV0eAAAAzZPVlIXmzp2rESNGaN68eerZs6cGDBigaDSqjh076n//93913nnn1bte+/btVVpaWuu5kpISHXLIIUlZHgAAAM3T5GP+pk6dqr59+2r27NnauHGjJOmRRx7RjBkz9M0339S7Tnl5uTp27FjrucLCQpWXlydleQAAADRPk+IvKytL06dP18yZMzVy5EgNGTJEkjRixAjdcsst2rFjh3bs2KHTTjtN999/v2bPni1JWr16tU488cRa2zrppJPqnBhSrbnLAwAAoHmaFH8PPvigotGobrjhBo0bN06zZs1SYWGhBg4cqL59++rEE0/UiSeeqOXLl+vRRx/V6NGjJUlvv/22unfvrtGjR8vn8+nKK69U37599Ze//KXe92nu8gAAAGie/cbfiBEjNHr0aI0aNUqxWEwvvviilixZohdeeEHFxcXatWtX4hEOh1VeXq59+/ZJqjpe76KLLtLNN9+s0tJS3Xfffbr00ku1Y8cOSVKvXr1UXl6uXr16NWl5AAAAHBhHknF7iKaKRqPyB4JujwEAALBf69et1cCBA90eow5u7wYAAGAR4g8AAMAixB8AAIBFiD8AAACLEH8AAAAWIf4AAAAsQvwBAABYhPgDAACwCPEHAABgEeIPAADAIsQfAACARYg/AAAAixB/AAAAFiH+AAAALEL8AQAAWIT4AwAAsAjxBwAAYBHiDwAAwCLEHwAAgEWIPwAAAIsQfwAAABYh/gAAACxC/AEAAFiE+AMAALBIltsDNIff79f69evdHgMZoGvXriouLnZ7DGQIfl/QVPyuoDmOPvpot0eoV0bF3/r16zVw4EC3x0AGWLZsGb8raDJ+X9BU/K6gOZYtW+b2CPXia9//b+/cg6Ks/j/+XsTQxbwEKiRqQQlloIxjaaNmqeWlKW5dFJ0ExUSdBjECv2peEQ2dGDQv4SUmy0CR0i5mKmUolokkXiARjQRWkIvcFhbW9+8Pf7vjugss3pbi85p5z8A5n3Oe8zzPZ8/zec5znvMIgiAIgiC0IST4EwRBEARBaEO0A7DE0o1oCenp6ZZugvAvQXxFaAniL4K5iK8ILaE1+osCAC3dCEEQBEEQBOHBII99BUEQBEEQ2hAS/AmCIAiCILQhLBr8KRQKhISE4Pz581Cr1cjLy8OaNWugVCofSHnBcvTr1w87duzAuXPnUF5ejurqapw/fx5r166Fg4ODgW1oaChSUlJQUFCA2tpaFBQU4PDhw/Dy8jKq98knn8TSpUuRlpaGoqIiVFRU4NSpU/jf//7XYr8YN24cjh49iqqqKpSUlCAxMRGPPfbY3ey2cBd069YN0dHRuHDhAtRqNYqKinD48GEMGzbMyHby5MlITU3F9evXUVlZiczMTCxcuLDJ+t3d3aHRaEASvr6+ZrdL+qHWR48ePbBx40bk5eWhrq4Of//9N2JiYtClSxcjWz8/P/3vvKKiAkeOHMG4ceMarbslftgYU6ZMQXp6OmpqaqBSqRAXFwd7e/s72lfBPCIiIpCYmIiLFy+CJC5dutSobVRUFI4ePYqrV6+itrYWeXl52LdvH1544QUjW09PT0RHR+PkyZMoLS1FaWkpfv/9dwQHB8Pa2vRqemPHjsXBgwdRWFiIqqoqZGVlITo6Gj169DB7f/r164fk5GSUlpaiqqoKR44cwYsvvmh2eeDmnD+LKCYmhiSZlJTE6dOnc+3atdRoNDx06BAVCsV9Ly+ynF566SUeOnSIkZGRDA4OZlBQEGNjY1lZWcn8/Hx2795db/vVV19x+/btDAsLY0BAAOfNm8fjx4+TJBcuXGhQb1RUFCsqKrhjxw7OmTOH7777Lr/66iuSZEZGBjt06GBW+7y9vanVapmens7g4GBGRERQpVIxPz+fjo6OFj9+bU19+vRhbm4ui4qKGBUVxYCAAIaEhHDbtm186623DGy3bt3KhoYGJiQkMDg4mDNmzGBUVBTj4uIarV+hUPD48eOsqKggSfr6+prdNumHWpe6d+/OS5cusa6ujrGxsZwxYwZjY2NZV1fH9PR0duzYUW/7wQcfkCRPnjzJ0NBQzp07lydPnqRWq+WkSZPuyg8bU0hICEkyJSWFQUFBXLp0KSsrK3nmzBkqlUqLH7//qkjy2rVrPHDgAEtKSnjp0qVGbX/++Wdu2rSJoaGhDAgI4Pz583n27FmS5OTJkw1sd+7cyeLiYsbFxXHmzJmcPXs2f/jhB5Lk/v37jeqePn06SfLEiRMMCQlhUFAQP/30U9bX1/PixYtm+YCzszOvXbtGlUrFiIgIBgcHMz09nRqNhqNGjTL3mFjmRDz99NPUarXcvXu3QfqcOXNIkhMnTryv5UWtU35+fiTJsLCwJu3atWvHjIwMVlRU0MrKSp8+aNAgdu7c2ch++fLlJMnZs2c32wZra2teuXKFly9fpq2trT59wIABbGho4ObNmy1+nNqajhw5wry8PDo4ODRpFxgYaLKDbk7vvfceKysruWjRohYFf9IPtT59/PHHJMm3337bIP3tt98mSS5YsIAA2KNHD9bW1vL06dO0trbW21lbW/P06dMsKSnhww8/bFCHuX7YmOzs7FhVVcXffvvNoN969dVXSZLz58+3+PH7r+rxxx/X/52Zmdlk8GdKtra2VKlUPHv2rEH6888/TxsbGyP7zz//nCQ5YcIEg/SsrCzm5+cbldFdo15//fVm25KQkMCGhgYOGDDAoH2XL19mVlaWuftkmROh29Fhw4YZpNvY2LCqqorffffdfS0vap0aPHgwSXLlypXN2n733XfUarUmf3i365lnniFJbty4g+bBhwAADFpJREFUsVnbUaNGmRxVBMCDBw+yvLzc4GIhur8aPnw4SXLOnDkEbl6cbx29uVUXLlzgH3/8of+/U6dOzdbv5OTEiooKzps3j++8806Lgj/ph1qfMjIyWF1dbZSuUChYU1PDnJwcAqCXlxdJMjw83Mg2IiLC6CaiJX7YmKZNm9bozUlOTo5RYCG6P7qT4A8Az549y4KCArNsdQH97f5VUFDAM2fOGNkHBweTJEePHt1kvUqlkmq1mgcPHjTKW7hwIUly8ODBzbbPYnP+Bg8eDK1Wi99//90gva6uDhkZGc1+PuduywutAxsbG9jZ2aFXr14YM2YMNm/eDAD4/vvvjWy7desGe3t7uLm5YdGiRRg7dixSUlJQV1fX7HacnJwAAFevXm3WVuc7aWlpRnnHjx9Hly5d0K9fv2brEe4N48ePBwDk5eVh7969UKvV+u98+/v76+1cXV3xxBNP4NixY1i4cCGuXbuGyspKlJeXY+PGjbC1tTVZ/4YNG5Cbm4uYmJgWt036odaHjY0NamtrjdJJQq1Ww8XFBXZ2drCxsQFw85vxt6NLGzJkiD7NXD9siub6Fjc3t0b9VHjw2NnZoXv37vDw8MC6devw9NNPm7w2maKxa86PP/6I/v37Y82aNXBzc4OTkxO8vb2xaNEi/Pzzzzh8+HCT9Xp4eKBDhw6N+hAAs/sdi0Tep0+fpkqlMpmXkJBAkmzfvv19Ky9qHZo9ezZvJTc31+RcGwAsLi7W22k0GiYmJtLe3r7ZbVhZWfHYsWPUaDTs169fs/axsbEkSTc3N6M83d3ZmDFjLH7s2or27NlDkrx69SpTU1M5adIkBgQEMDMzkyQ5depUAuBrr72mtysrK+O8efPo4+PDbdu2kSQPHz5sVPebb75JrVbLIUOGEECLR/6kH2p92r17N0kaPBIDbk7b0OHp6al/GpCcnGxUR3JyMknym2++abEfNqW9e/eSpMm5x6tXryZJPvnkkxY/hv91mTPyZ2tra3Btqq6u5qZNm8yak2dra8uLFy+yrKyM3bp1M8jr3LkzExISWF9fb1D/1q1bzXqi5OPjQ5KcOXOmUd5TTz1FkoyMjGy2HtOvojwAlEployM2urs2pVKJ69ev35fyQuvg66+/RlZWFjp16gRPT0+89tpr6N69u0lbHx8fdOjQAb169cIbb7yBjh07onPnzrh27VqT24iJicHQoUMxf/58/PXXX822SfeWpin/utW3hAfDww8/DACorKzEiy++iPr6egBAcnIycnNzsXLlSsTHx+vtevTogdGjR+PQoUMAgD179kChUGDq1KkYO3Ys9u/fDwDo0qULYmJiEBcXp79jbinSD7U+YmJi4OXlhcTERISEhODMmTPo378/YmJioNFo8NBDD0GpVOLo0aM4cOAAvLy8sHr1amzfvh0AMHXqVP3bvrf+zs31Q5KNtk36ln8ParUao0ePhrW1Nfr27Qt/f3906tQJSqXS5GixDisrK+zYsQPOzs6YOHEiysrKDPLr6+uRl5eH5ORk7Nu3DzU1NXjllVcQGBgIrVaLGTNmNNmue+lDFom8ZeRPZEru7u6sra1lREREs7ZffvklCwoK2LVr10Ztli1bRpLctGmT2W2Qkb/WJd1oyfLly43yPvvsM/250t0R//PPP0Z2I0eOJEmuWrVKnxYXF0eVSmXgPzLy99+Qn58fCwoK9KMq9fX13LhxI5OSkkiS7u7uBMCuXbty9+7d1Gq1etvc3Fz93LykpKQW+2FT7ZKRv9ahO5nzZ2VlxSNHjhi9IHSrFAqF3hdMvbyjUCj4yy+/8OjRo0Z5UVFRJNns27r3auTPYnP+CgoKYG9vj4ceesgor1evXiguLtbfWd2P8kLrJDMzE6dOncKsWbOatY2Pj4ejoyN8fHxM5i9evBiLFi3Ctm3bMHPmTLPbUFBQAOCmH92OLi0/P9/s+oS748qVKwAAlUpllFdYWAjg5nxQc+2Am2tzBQYGYv369bCzs4OLiwtcXFz062w5ODjAxcXFZP9yK9IPtU52794NJycnDBw4EMOHD8ejjz6K4OBgODk5ob6+Hjk5OQCA8vJy+Pn5wdHREcOHD4enpydcXFz0fUBWVpa+zpb4V2M017fcuHFDbyO0Lm7cuIEvvvgC7u7uGDFihEmbLVu24J133sGSJUsQFRVllD9s2DCMGDECSUlJRnm7du0CAJNrCd7Kvbo+WSz4O3HiBNq1a4dnn33WIN3GxgYDBw7EH3/8cV/LC62Xjh074pFHHjHLDoBJ2w8//BBLlixBfHw8pk+f3qLtnzhxAgAwdOhQo7whQ4bg+vXrZj0+Fu4NupcpdBOob0WXVlRUhMzMTNTU1JjsFG+1A4A+ffrAysoKy5cvR05Ojl4fffQRAGD9+vXIycmBu7t7k22Tfqj1cuPGDfz5559ITU1FcXExevbsCU9PT/zyyy9Qq9UGtkVFRUhNTUVGRgZI6l/uuHVyv7l+2BRN9S3PPfccsrOzUV1d3YK9FB4kTV1ztmzZgsDAQCxfvhxLly41WV7XN7Vr184oT7cgdGMLQ+vIzMxEbW1to9cnAGb3OxYZdn3mmWeaXB/L399fn+bs7ExXV9c7Li9qferZs6fJ9JEjR7KhoUH/GrtSqTRYa08nKysr/vTTTySNl9nQrdUWHx/f7CK7Dg4OdHV1NViywdramvn5+Ubr/Hl4eLChoaHJxYJF915du3bl9evX+c8//xicDwcHB1ZWVjI7O1ufpltby8vLy6AO3UsAQ4cOJQA6OjrS19fXSOvWrSNJRkdH09fX1+CRsPRD/14pFAomJCRQq9Vy5MiRTdoOGjSIarWaKSkpd+yHANi7d2+6uroaPCK0t7dndXU1jx8/bnKdP90ahKL7q6Ye+3bt2tXkVA2lUsnMzEw2NDSwT58+BnlxcXEkyRUrVjS5XQ8PD5I3Pzhw+6PjDRs2kCS9vb31aZ07d6arqyvt7OwMbBMTE9nQ0EAPDw99mm6dv9v9sAlZ7gTo5lYlJSVx2rRpXLNmDTUaDVNSUgwu2pcuXSJvzqK9o/Ki1qc9e/YwLS2NkZGRnDFjBt977z3Gx8ezrq6OZWVl+jf1BgwYwPLycm7ZsoVhYWGcNm0aP/zwQ54/f54kuX37doN6Z82aRZK8fPkyp0yZQn9/fwPdvobS9u3bSZIvvPCCQbqfn5/BFz7Cw8OpUqlYWFjIRx991OLHr60pKCiIJJmZmcm5c+cyPDycly9fZl1dncH8y969e7OwsJA1NTVcvXo1g4ODuW/fPpLkZ5991ux2mprzJ/3Qv0O2trY8e/YsV6xYwWnTpjE0NJQnTpwwOQ9r2bJl3LlzJ+fOncvAwECuX79evxagk5PTHfshAKakpJAk+/bta5AeGhpK8ubb50FBQVyyZAkrKyt57tw5kze6onujyZMnc8GCBVywYAFVKhVLS0v1/9+67uLrr79OlUrFTz75hHPnzuX06dMZGRnJvLw8kuTixYsN6o2OjiZJnjp1yuh64+/vr19JQKddu3bpA8D333+fs2bN4jfffEOSPHbsmMFNga4/un2bLi4uLCkpoUqlYnh4uP4LH/X19Xz55ZfNPSaWOxlWVlYMDQ1lVlYWa2treeXKFa5du9boB9BYp2tueVHr0xtvvMFvv/2WeXl5VKvVrKmp4fnz5xkbG8vevXvr7ezs7Lh+/XpmZGSwpKSEGo2GxcXFPHDggMklYXTBXGPcfjffWPAHgBMmTGBaWhqrq6tZWlrKXbt20dnZ2eLHrq3K29ubaWlprKqqYkVFBX/88Uc+//zzRnZ9+/bljh07WFRUxLq6Op47d47z5s0zKxC7k+BP+qHWpfbt23Pnzp3Mzc2lWq1mSUkJ9+/fb/Ki6O3tzWPHjrGkpIRqtZrZ2dlctWoVu3Tpctd+2Fjwp/OzjIwMqtVqXr16lVu3bjX4pKXo3kt3Ppq7Ljg7O3PLli08d+4cy8vLqdFoWFhYyL1793L8+PEtqpc0HqBo3749w8LCmJGRwZqaGtbW1jI7O5uRkZFGy8g0FvwBoJubG7/++muWlZWxurqav/76a0s+7UbF//8hCIIgCIIgtAEs9sKHIAiCIAiC8OCR4E8QBEEQBKENIcGfIAiCIAhCG0KCP0EQBEEQhDaEBH+CIAiCIAhtCAn+BEEQBEEQ2hAS/AmCIAiCILQhJPgTBEEQBEFoQ0jwJwiCIAiC0IaQ4E8QBEEQBKEN8X+79blLr6UkMwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x864 with 1 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rabi_schedules[-1].draw() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# assemble the schedules into a Qobj\n",
    "from qiskit import assemble\n",
    "\n",
    "rabi_qobj = assemble(**helper.get_params('rabi', globals()))\n",
    "answer1a = rabi_qobj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# run the simulation\n",
    "rabi_result = backend_sim.run(rabi_qobj, duffing_model).result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# retrieve the data from the experiment\n",
    "rabi_values = helper.get_values_from_result(rabi_result, qubit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit Results and Plot Rabi Experiment\n",
    "Once the rough frequency of the qubit is know, the Rabi experiment determines the amplitude of a $\\pi$-pulse, that is, the strength of a pulse needed to rotate the qubit around the Bloch sphere from the $|0\\rangle$ to $|1\\rangle$ states (or vice versa). We assume the rotation axis to be the $x$-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QWCh5bdvK3tq1TfdUllzOVkpLS5OxY8dKcHCwaJrm1A8wb948eeeddwybGThwoOzYsUMyMjIkISFBAJk2bZoMGjRIQM9QWrNmjWzatEk2btwo/fr1c/UEDSkFFRzKUhDIruLrU1Z2jydlZtnYbDY50727FEVGij0jQwUGpXLllB79ww/6oLc33rDsfeNycMjNzZXrrrvO8XufPn0c7/Xr108OHz5s5RM0pBRUcChLA9BrDXea9Plml83txeff3wJloWRt2e32v6rVQ4aI1K8vcuaMZZMYXO6QPnDgAHXr1gX0Kbp79OjheK9FixZGDqHwYSYCB4EvzTZiEouBw8B4s40oLI8jWSEjA5YsgQkTICzMJ5MYDHVI//DDD3Tv3p2vv/6axMREXn75ZVq2bMm5c+cYNmyYyvn2YyKBm4CXgAKTvZhFAfqkfw8DDYE/zbWjsDBZWVlER0fDnDkQEgL33+/Y7otUWPW4/PLLpW3bto7XkyZNkjVr1sj69evlpZdekho1ali2aqTkmp4D0UCiLODFTF2F3rT0Twt4UbKubDab5Bw/LtKwocgtt1g+icGj02dYQSo4eEZVQA6ALLGAFyvoJ5A0C/hQsrZe7t5dn2Lmq68sP+WKR6fP8BceKf75iqkurMUtQCPMH/thlbKZi9681B1YbbIXhXVpsXo1B4Gmt9yCZrYZFyizQ3r9+vW0adPG8IGCgoJYv349V155pVuMeZubi6X4i4mAHX10p5lYpWw+A06iOqYVZdMQ/V79CHw6MEA5HdLt27enfv36hIWFGTtQaCjt27enRo0abjOnMI+rgN7AY0CRyV6swhngE/TRrg+hBwqF4kJGoT9UPzDbiBsot1kpJSXlkg528TxJCt9lApCPWirzYuYC9wMjgbdN9qKwHmOBX4A0s424gTKDw5gxlZsN5Px03ArfpQb6us2L0PP7FX+xEdgAxKOCg8KZOKAt+r3hD5QZHD766CNv+jAdNSPrX9wB1AXeMdtIMVYrm0T0TvpOwHqTvSisw1j0pscFZhtxE2oN6WJuLJYC7gbSgTVmGynGamWThP4QUB3TivOEATb0pIXTJntxFyo4KJx4YPBgrgdaTJ2K3W7HZrOZbclynERvchuB3gSnUNwK1MG/+uhUcCjmyWIFMjabjVc6dgQg+K67HMtimh0grFg2iUBt4E6zjShMwWazYbfb0TQNu93O45dfzm7gJ7ONuRnTR+m5KjUrq3tk371b5KqrRLp3t9SymFYtm20gayzgQ8m7cpqWW/8HEQFZ+H//Z7q3S5XblglV+DdRhw9DWhrcdZfzdh+cUdIbzAW6AcYWyVX4C9OnTyf8wtUQ582DoCC6/sfsuQTcS5nZSkbXhT7P9u3bXTajMJect9+mdrVqcMcdTtt9dUZJT5N7663I11+z9e9/Z8/DD5OQkKBmKA4AnL4sFRXpwaFvX5pcc41pnjxFqVUKTdOksLCwQp3fr6zjeEOqWcl1hYKcrFZNCm691evLgPpi2TiaFoYO1WfgLCiwxLVS8rycFvRZsUIERJKSTG9+rYwqNfFe7969y3rLLzlqtgGTGQDUPneOWUePckdmJlFRUWRlZVni27AVy8bRtDBqFHz+OaxYQfiAAUyfPt3066XwLAkJCSQmJurl/8EHULcuuf36kfDgg2ZbczumRy9Xpabsdl0LQQ6h1yDM9uIL0jRN/+aYlydSt67IqFEiIqJpmunelDwvm80mezZvlqLq1eXkqFE+W2N063oOQUFBEhYWVkIWPkGlClQX5CzIvy3gxVfk1LQQHy8SHi6Sk+OTTQtKldO96AtAdbSAl8rKLdlKjz76KOnp6RQUFHD69OkS8nWmFysQuQOoDsw320gZWLFsEhISyM3N1V+MHAm5ueQtXEhCQoK5xhReYyzwO/pcW/5KhdHloYcekmPHjsljjz0mmqbJtGnT5Omnn5Zt27ZJRkaGjB071rLRz6hSsF6np7f0E8hWC/jwtbKx2Wxit9tFKyiQgiuukA1XXGG6JyXvqCV6reEfFvDiilxuVvrjjz/kwQcflODgYNE0TTp06CCgNzF9/fXX8uKLL1r5BA0pBWs+gDytGPSb/DELePHlsnkRpAAkwgJelDyvp9DXVr/CAl5ckcvNSjExMWzatImioiIKCgqoW7cuACLC22+/zejRo40cRmERLhz6v37yZIrQF7FRVJ5P0AcNDTPbiMIrjAB+APab7MOTGAoOR48epWbNmoA+IKpDhw6O9+rVq2d4tTiF+dhsNhITE4mOjiY4KIh6S5YgPXpwnZpgzyW2oLc/jzTbiMLjdEBfKfFTs414mHJXgjvPzz//TFxcHMuWLePTTz9l6tSpXHbZZeTn5/P3v/+dlStXetqnx8k224CXcBr6/8svsGsXIU8+yfRevSybn+8rZfMJMANoDuw22YvCc4xAXyXxc7ONeIEK26Vat24tvXv3FkCqVq0q//73vyU7O1uOHj0qycnJ0qBBA8u2myk5y5GfLyIyYYJIWJjIqVMqP98NikTvv3nSAl6UPKNgkGyQLy3gxR1y6zgHK0oFB+Ny5Ofn5YnUqycyYoQlZl71F6WAbLeADyXPqCf6F4A7LODFHVKzshrgtWL5O478/KVL4fhxuPtucnNzLZ2f70tl8wl6e3TCDTc4zfdv9poYCvcwAn2lt6/NNuIFDPU5AAwdOpTbbruNpk2bUr169RLvX3311W415m3am23AS5zvV5h75AhhjRqxp0ULEuLjLdvfAL5VNp8BbwcHM7VVK6pERwM4Fk0CLH2dFeVTFbgd+BLrrWvuCQzVHJ555hkWLlxIbGwse/fuZevWrSVklP79+5OWlkZ6ejpTpkwpdZ877riDrVu3smXLFj75RCVZuptlSUmELl/OawcPEtOqlXpguZETQH7fvlT5/HPQNMf28PBwpk+32jhvxaXQH7gM/89SupAK26WysrLkhRdecLl9Kzg4WDIyMiQmJkaqVKkimzZtktjYWKd9WrZsKRs2bJC6desKYKizWw2CuzSNQW837WwBL/5YNtqCBfo0zt9/7zT9uer0920l4X+TU7rc51CrVi23pKt26dKFjIwM7HY7BQUFJCcnM2TIEKd94uPjmT17NidOnADg8OHDLn+uwpnhQAbwm9lG/JS9f/sb1K4NH3/stF0tmuS71AQGAwuBQpO9eAtDwSE5OZkBAwa4/GFNmjRh7969jtfZ2dk0adLEaZ/WrVvTunVr1qxZwy+//EL//v1LPVZ8fDypqamkpqYSERHhsredxfJ3GgB9gGSzjVwCvlY2j0+bRsEtt8AXX8CZMwCW7/RXlM8QoAaB1aRkqEN65cqVzJgxg4iICJYvX+74Vn8hy5Ytq/A4QUFBJbaJiLOh0FBatWpFr169aNq0KatXr6Zdu3acPHnSab/ExERHJ19qaqqR0yiX+1w+gm9wBxCCbwUHXyubpKQk2vTuzZM5ORQtXkzWNddYYtEkReUZAdiBX8w24kUMBYcFCxYAetZFafMoiQihoRUfKjs7m8jISMfrpk2bsn///hL7rF27lsLCQjIzM9mxYwetWrXit99UI4g7GI4+1YPxFAJFZXgmJYUxwPoRIxhS4d4KKxMB3ADMNNuIlzEUHGJiYtzyYampqbRq1Yro6Gj27dvH8OHDGTFihNM+X331FTabjQ8//JD69evTunVrdu/2/GQEc4p/+tq31EshEugOPGG2kUvEF8umCEgCHkbPcDlmrh2FC9yB/qAMpCal83i1d3zgwIGyY8cOycjIkISEBAFk2rRpMmjQIMc+r7zyimzdulV+//13GTZsmEs97kaVgm9lxFRGj6BnKbWwgJdAKJv2xdc73gJelC5NjrU6NE3Odu4se+rUMd2TJ1TeszOo+JdyubAp6GKKioo4deqUqavBpaamEhcX59IxUop/9nbdjmX5DdAAXxuu6MtlkwbsQ08CUPgG52cuDg8Ph8xMiIkhf9o07klL87t+o4qenRVGF03TpLCwsFzt3r1bJk2aZLnoZ1Qp+Oa3U6Nqhf4tdrIFvARS2UxFXxSmkQW8KBmT0/rgL76oj1mx2/1y/rHynp2G+hxGjBjBjBkz2LJlC0uWLOHw4cM0aNCAIUOG0K5dO6ZPn07nzp2ZOVPvsvn3v/9t5LAKLzIcvR18gdlGAowFwDPo7dZvmuxFYYyoqKi/Xnz6KVx7LURHE1VUZJ4pk6gwuiQmJsobb7xR6ntvvPGGfPjhhwLIa6+9JmlpaZaKfkb1WrG87d1b2gbygwV8BGLZbAJZYwEfSsbkqDn88Ydea3jrLb+dudjlKbtPnDghffr0KfW9vn37yokTJwSQG264Qc6ePWu1Ewx4/R96k9J9FvASiHqs+PpHWcCLUsWy2WySk5MjkpAgEhIicuiQ5OTkiM1mM92bu+Xy9Bl5eXl069at1Pe6detGXl4eoA9yy83NNXJIhRexoQ/5D4SVq6zI+aa8O011oTBKUlIS8ePHU/DJJ8j115N55gzxFp+52FNUGF2effZZKSgokNdff1369u0rf/vb36Rv377y5ptvSkFBgUydOlVAT0lduXKlpaKfUc0vlre9e0IXpuHZd++WQ+Hh8o0FfAVy2fwK8psFfCgZUyf02t4YC3jxpNyyEtykSZNk3759jswlTdNk3759ThlKbdq0kWbNmlntBA0pBd/NiLlQjirxeX75RQTk7auvNt1bIJfNZPSHTUsLeFGqWLNAzoHUtYAXT8pty4QGBQVJVFSUdOnSRaKioiQoKMj0kzNwgoaUgu8/gOCiNDwRkYcfFqlWTTI3bzbdWyCXTRP04PCEBbwola8gkD0giy3gxdNyOZX1PCJCVlaWmnrYwjil4WkaLFgAN95IZLt25plSsA/4CT2l+AWTvSjK5xogCnjcbCMmU2ZwmDhxIosWLeLIkSNMnDix3IOICO+8847bzSkunaysLKKLl6fkp5/g4EEYPlwFdAuQDLwNtEVNfGhlhqMvA7rEbCMWoNQqhaZpEhcX5/i9PBUWFlq2amRU04tl5nm4Q059DvfeKxIeLjl//unTaXj+UjYNQQpBnrOAF6XSFQxyAGShBbx4Q27rc7Cq1DgHZ9lsNrHv2CFFl10mp4cM8enA4G/6HiTdAj6USldv9L6hoRbw4g2p4BCAGoh+k99sAS9Kf2lscbl0tIAXpZKaA3IKJMwCXrwhlwfBXXfddQwePNjxun79+nzyySds3LiRl19+2dBCP1bns2L5C8OB48B3ZhtxA/5UNl8C+ejlo7AWocBQYDF6n0OgYyg4zJw5k3YXZLu8/vrr9OnTh7Vr13LPPfcwbdo0jxn0FvWL5Q9UB25BHxFdYLIXd+BPZXM+YA8DSi6aqzCTvuj3mZqcUsdQcLjyyitZv349AGFhYdx66608/PDDTJw4kUcffZRhw4Z51KTi0hgI1Ma31okOJJLRUyX3LVqEpmnY7XZsNpvZtgKe87Xt7802YhEMBYeqVas65k/q1q0boaGhLF26FICdO3fSuHFjzzlUXDI24BDwg8k+FKVT7fbbkerVafzjjwQHBxMdHU1iYqIKECZSDbgV+AK92U9hMDikpaUxYMAAAEaOHMkvv/xCTk4OAFdccQXHjqkVcq1CTeBmYCH6qm8K6/H0rFkE3XQTLFqkD1QEwsPDmT59usnOApfztW3VpPQXhnqSn332WRYtWsS4ceOoU6cOQ4YMcbw3YMAANm7c6DGD3mKl2QbcxGAgDP9qUvKXsjlPVFQUDB8On38OP/4I11//13aFKQwDDgOrzDZiMQylPMXExMhtt90mrVq1ctoeHx8vV5s8qZtKZf1LS9DnhQmygBel0mW320Vyc0Vq1hQZP94xDZY/LibjC6oBkgMy2wJevC01ziFAVA99JsmZFvCiVLYco9hHjhSpV0/k3Dm/XUzGFzQMfexJDwt48bZcHucQCHxTLF/mVqAq/tWkBP5RNheSlJREfHw8h66/Ho4f59D8+QG7mIyZ2Gw27HY7SUOGUHj55TRVWZclMD16uSo1Zbeu70F2WsCHu+UPZVOaqoAcAfnYAl4CTY7a24kTIlWrikyaFJC1N1VzCAAaAtfjf7UGf6YAfeT3EKCGyV4CjenTpxMeHg5ffQX5+TBsmCdPcX4AACAASURBVMoYuwgVHPyEO4AQQDVM+BZJ6OnHg8w2EmA4MsOSk6FZM7j6auftChUc/IXhwO/AdrONKC6J1egLAanhb94lKysLDh+G5cv1tOKgoL+2K4Byxjm89957l3SgcePGuWzGTL4224ALRALXAQlmG/EQvlw2FVGEPvDqAaAucMJcOwFDQkICH8TFUU3TYORIAHJzc0lI8Nf/ospRamfEunXrnHTw4EHRNE0OHDggmzdvlgMHDoimaXLw4EH59ddfLdupEgh6BD0Vr7kFvChdujoVl99YC3gJJKVFRMi5K68UTdPEbrcHXGc0uGGcw8033ywZGRlyzTXXOG2/9tprJT09XQYNGmTlE/R7pYL8agEfSpXXTpDlFvARKGqGHpAfs4AXM+VycNiyZYsMHz681PdsNpts27bNsJn+/ftLWlqapKeny5QpU8rcb+jQoSIi0qlTJ1dP0JBS8K10SZvNJna7XbS0NBGQ+R06mO7JU/K1sqmMpqEvIXq5BbwEgqagB4doC3gxUy6nsjZv3pwzZ86U+t6ZM2f+WtC+AoKDg5k9ezYDBw6kTZs22Gw2YmNjS+xXs2ZNHnroIdauXWvouIGGzWYjMTGR6OhoghctAuC25GQ1q6cPk4SebXan2UYChBHAz0CmyT6sjKHgsGHDBqZOnUqjRo2ctjdu3JipU6c61nqoiC5dupCRkYHdbqegoIDk5GSnSfzO89xzzzFz5kzHNOEKZxw52qCn4nXvTo3WrVWOtg+TBmxCZS15g3bA/wM+NduIxTEUHO69914aNmxIZmYmP//8M19++SU///wzdrudhg0bMmHCBEMf1qRJE/bu3et4nZ2dTZMmTZz2ad++PZGRkY71IsoiPj6e1NRUUlNTiYiIMPT5/oIjF3vLFti6FYprDCpH27dJAq4Bok324e+MAAqBRWYbsTiGgsO2bdto0aIFkydPZseOHVSrVo0dO3YwefJkWrRowdatWw19WFBQyYURRcTp/ddee41HHnmkwmMlJiYSFxdHXFwcR44cMfT5/oIjFzspCUJCYOhQ5+0Kn+T86Ha1vrTnCEKvnS1Hn6JbUT5e6/zo2rWrfPvtt47Xjz32mDz22GOO17Vr15bDhw+L3W4Xu90uZ8+elX379lXYKe2ODumJxfLm9aisbDab5Jw+LRIdLXLDDSIifj0vjC+VjataA7LZAj78Vdeid0SPsoAXK8htU3YPGDBAnnzySZkzZ45ERkYKIN27d5fGjRsb+vuQkBDZtWuXREdHS5UqVWTTpk3Spk2bMvdPSUnxWraSr2lq374iINq8eQGbo+2P+jv6w6utBbz4o94COQNS0wJerCCXg0PDhg1l7dq1UlBQIOnp6VJYWCgdilMn33//fXn77bcNmxk4cKDs2LFDMjIyJCEhQQCZNm1aqWMlvBkcwopldmEZ1TvoC5SEW8CLp+VrZeOKGqKntD5vAS/+plCQP0GSLeDFKnI5OCxYsEC2bNkiLVq0kJCQENE0zREcRowYITt27LDyCRpSCr6TS18V5BjIRxbw4g35Utm4Q9+BZFjAh79pAHqtbLAFvFhFLo9zGDBgAE8++SS7du1y6kCG0jOOFJ7lJqAe8LHZRhQe4VOgBdDFbCN+xgjgOPCt2UZ8BMOzsmqaVur2iIgIzp496zZDiooZBRwAVpptROERQoYORapWZe1DD2G329XgRjcQhr5S4mdAvslefAVDwWH16tU8+OCDBAf/tfv5GsTYsWNZtWqVZ9wpSlAPuBk9J770cK3wZWw2G298+CFBN91E0MKFREdGkpiYqAKEiwxCXzdDDXy7NCpsl2rbtq0cP35ctm3bJi+88IIUFhbK7Nmz5ccff5SjR49Kq1atLNtuZlQp+Ea79n3o7abtLeDFW/KVsnGH7Ha7iIjIokUiIPL99yIiYrfbTffmy/oKJBsk2AJerCS3pLI2b95cPvroI9m3b5+cO3dODhw4IB9//LG0bNnS6idoSKOLZfa5VKTVIH9YwIc35Stl4w5pmqYHh7NnRerWFRk5UkRENE0z3Zuvqh7IOZBZFvBiNbkUHIKCguSKK66Q8PBw00+kkifoN4pBrzVMsYAXJc/IUXMQEZkwQSQsTOTkSVVzcEHj0f9vOljAi9XkUrZScHAwmZmZXHfddRXt6tPUL5aVGVX88xNTXXgfXygbd5GQkEBubq7+4p574OxZzn38sVqhzAVGok9suNFsIz5GmcuEnkfTNPbs2UONGjW84cc0Piv+2dtUF+UzCkgBss024mV8oWzcRVJSEqDPvBvVuTNa8+bsnjqVpMNqJqDK0AToAUw12YcvYihbacaMGTzxxBMBN/uplegCtAbmm21E4XGSkpKIiYkhJDSUp3bvJvbwYZqbbcpHGYX+kEsy24gPUmHNAeCGG26gcePGZGZmsn79eg4dOuQ0GE5EGD5czSXpSe4CzgKfm21E4VU+BqYDd6O+/VaGscBPQIbZRnwQQ8EhIiKCHTt2OL1WeI9Q9GmclwCnTPai8C77gBXAaGAaek+hwhjd0GvbagmsymEoOFx//fWe9qEohwFABKpJKVCZhz54qwfwo7lWfIqxwGn+6rNSXBqGgkMg8B+zDZTDKPSFSb4z24hJWLlsvMFX6DXG0ajgYJRw9PW4k4Fck734KoaCw4wZMyrcZ8qUKS6bMZOFZhsog9rAECARfWnDQMSqZeMtzgIL0FcwexD1sDPCHejTZdy0aBHabbeRlZVFQkKCIxtMYYwKB0rs3r27hI4fPy6apsmxY8dk165dlh3IYVRNi2XmeZSmsegDeOIs4MUsWbVsvKlu6PfBXRbw4gva3qCBaK1aiRQVOcYU+vNqiZWV21aCu1hdunSRTZs2SdeuXa18goaUgvXm77HZbHKma1cpatVK7Lt3B+yNbcWyMUPpICst4MPqaoUeSOWll+Ri1EhzZ3ksOAAycuRI+e2336x8goaUgrUeQDabTXK3bdNv8mefDehvPlYrG7P0JPpDL8oCXqysF0CKQkJE9u8vERzUHFXOcnmxn/I4evQoV155pauHUVzE9OnTqZGUBEFBcNddAISHhzN9ukrMC1TOZ6vdbaoLaxOC3nF/tmdPaNy4xPtZWVle9+SrGAoOYWFhJVSnTh26du3Ks88+y9atWz3tM+CIatwY5s6FgQMhOvqv7VFR5plSmMoeYBUqOJTHDehTZszJz/9rjqpicnNz1RxVl0iFVQ9N06SwsLCENE2TrKws6dixo2WrRkaVgrWaLg69847epLR4ccC3mVqtbMzU3ehNS9dawIsVtQjkT5Aq6E2zdrtdNE0Tu90ekE2yFam8Z6ehVNaxY8eWWDs6Ly+P7Oxs1q1bR2Gh7ydZvmK2gYs4MHUqEVdcQfCNNzq2Beo3H6uVjZl8DswG7gH+Z64Vy1EfGAy8BRSgz1GlUlddw/To5ar8bT2H8+s2LGrXTn3zUSqhD0BOgIRZwIuV9BD6/01bC3jxFblcc2jQoAHh4eFkZmY6tsXHx9OmTRtWrlzJ119/beQwlqZ18c+dprrQiUdfH3rSli3si4kx247pWKlsrMCH6DWHW1CzjV7IOGAdoHpA3UeF0WXp0qXy+uuvO15PmzZNCgoKZPv27VJQUCCjR4+2bPQzqhSs0a5dBeQg+pq3ZnuxiqxSNlZREMhukFUW8GIVdUSvNUywgBdfksuprB07dmTVqlUABAUFMXHiRBISEoiNjeWFF15g0qRJRg6jMMAQ4HJgjtlGFJZluM1GnSlT6A1kL1uGzWYz25Jp2Gw27HY7v91/P0XVqlEwdKjZlvyKCqPLmTNn5LrrrhNAOnfuLIWFhXLFFVcIID169JDTp09bNvoZVQrW+Ha6AsSuR23TvVhFVikbK8hms0lOTo7IkSMi1auL3HtvwA6OdFyLs2dF6tYVGTEiYK9FZeVyzSE7O5s2bdoAcNNNN5GWlsb+/fsBqFOnDnl5eUYOo6iAlkAf9En2ikz2orAm06dPJzw8HOrXhxEj4OOPCc/PD8jBkY5r8dVXcOIEjB2rBoq6EUPB4f3332fmzJksXLiQRx99lHfffdfxXteuXdm+fbvHDAYS96Kn4L1vthGFZXEaBPngg3DmDHzwQUAOjnSc89y50KwZ9O7tvF3hEoaylV566SX27dtHXFwcDz74IO+//9fj67LLLmPu3LkeM+gtnjf586sCY4DFwEGTvVgNs8vGSmRlZRF9fsR8+/Zw3XUwezZZQ4aY6ssMsrKyiD59GlauhBdfhOBgx3aFezC93ctV+cM4Bxt6tkVfC3hRsq4c7eznWbBABGRmjx6mezPjWuTfdZdIjRoiR48G9OSUlZXbZmUNCQmRmJgYiY2NLSGjx+jfv7+kpaVJenq6TJkypcT7kydPlq1bt8rmzZtlxYoVEhUV5eoJGtLfimVWIf0AkoGepmj2DWM1mV02VpPTtBA7d8rRsDBZZgFf3lZDkPzgYDk5apQaKFpJuRwcQkND5e2335bc3NxS51gqLCw0ZCQ4OFgyMjIkJiZGqlSpIps2bSoRWHr16iVhYWECyIQJEyQ5OdnVEzSkFMzLiLkKvdbwqAVuFivKzLLxBZ2fyru1Bbx4U88Un3crC3jxVbmcrfT0009z8803M27cOIKCgnjggQcYM2YMK1euJDMzk0GDBhk5DF26dCEjIwO73U5BQQHJyckMuait9IcffuDs2bMArF27lqZNmxo6ti9zL5APfGC2EYVP8i5wDvi72Ua8SDXgfuC/QLrJXvwVQ8HhzjvvZOrUqSxcqK/mu27dOubPn0///v1Zs2ZNiQd8WTRp0oS9e/c6XmdnZ9OkSZMy9x83bhzLli0r9b34+HhSU1NJTU0lIiLC0OdbkeroUyF8ARw214rCR/kTfZ3te9DXTQ4ERgINgVfNNuLHGAoOkZGR7Ny5k6KiIvLy8qhXr57jvU8++YShBkclBgUFldh28Wyv5xk5ciSdO3dm1qxZpb6fmJhIXFwccXFxHDlyxNDnW5E7gHqoEdEK13gLqE3grPUwGdgE/GCyD3/GUHA4cOAAdevWBcBut9OjRw/Hey1atDD8YdnZ2URGRjpeN23a1DGY7kL69OnDE088weDBg8nPzzd8fF9kEpCGuskVrrGuWA+YbcQL9APaAa+ZbSQAqLDTYu7cuTJjxgwB5OGHH5aCggL55JNP5P3335fc3FyZO3euoc6PkJAQ2bVrl0RHRzs6pNu0aeO0T/v27SUjI0Natmzplk4Vo7qmWK4e51I0EL1DbbQFOqasLDPKxhc1isBIh/4GZD9IVQt48XW5nK10+eWXS9u2bR2vJ02aJGvWrJH169fLSy+9JDVq1DBsZuDAgbJjxw7JyMiQhIQEAX2W10GDBgkgy5cvl4MHD8rGjRtl48aNsnjxYldP0LL6H/o8SqEW8KLk+6oKcgj/ntE3Fj0APmEBL/4gt41zsKp8sebQG/0mv88C18/qUjUH43oORAOJtoAXT2gOyBmQ+hbw4g9yOZX1PLGxsYwaNYrHH3+cyy+/HND7HGrW9P0cienF8hZPAvuBeV78TF/F22XjyxwcMoSgkBB2P/IIdrvdr6bzrg/cBXwEHDXZS6BQYXQJDw+XBQsWSGFhoeTl5UlhYaF06NBBAFmwYIHMmjXLstHPqFLw3kCra9FrDR916KCWAbVY2fiyHFNr3HGHSL16IqdO+dV0Ek+g/99cZQEv/iKXm5XmzJkje/fulV69ekmVKlVE0zRHcBg9erT88ccfVj5BQ0rBew+gpSAnq1WTnEOH5EL86R/ZnfJm2fiy7Ha7fiP9+qsIiDz3nIiI2O120725qqogB9A7o8324k9yOTgcPnxYRowYIaBPgXFhcOjVq5ecOnXKyidoSCl45wHUAf3bz7F//UtKwx/+kd0tb5WNr0vTtL9upFtuEaldW+TIEdE0zXRvrupuAiMTy9tyuc8hLCyMo0dLb+WrVasWmqYZOYwCeAI4DtRJSCj1fTUXvaKyOE1V/dxzcPo0zJzpF1NYTwb+AFaYbSSAMBQcUlNTufvu0sde3n777fzvf/9zqykzmFQsT9IGGAq8AWSdOFHqPv7wj+xuvFE2/kBCQgK5ubn6i3btYORI5I03mOnja7zP6NGD9kDbd9/1u052q1Nh1aNbt25y9uxZWb58udx7771SWFgoTzzxhHz00Udy5swZ6dy5s2WrRmbrwumVTw8ZImdCQ+UySpmXX/U5KLlBF95ve3/8UQqCgmS2BXxVViOHDRPtyitFWrUSOXdO/Z+4WW4Z53DttdfKTz/9JPn5+aJpmhQWFsqaNWvk2muvtfoJGlKfYrnTl1MA2LlTJDhYzk2a5LixneblV9lKXi2bQNFskHyQGAt4qYyOPPec3rn+1Veqb84DcusguOrVq0vjxo0day5YQVbtkHZkj4iIjB0rUr26yIED6sa2QNkEihqB5IJ8ZAEvl6raIEURESK9eokUFTkFB3/oZLeC3DYIDiAvL48DBw441lxQlI2jczkrCz76COLjoVEj1ems8BoHgTfRp7hua7KXSyUB4OhReOUVuGhGZ9U353lCy3rjqaeeMnwQEeH559Uy8BfjWAx+5kz95v7XvxzbFQpvMQOYADwP3GqyF6NEoych/BQdTecrryT8gvdyc3NJKCPbT+FeSq1SaJomOTk5cujQIfnzzz/L1aFDhyxbNTKqFNzfdGGz2SR3yxaRatVE4uNVZ5qFyibQdH508ZP9+vlEP1cSenPYFai+OU+qUn0O6enpcu7cOfnmm2/k7rvvllq1apl+IpU8QUNKwTMPoM2NGokWHi5aZqa6sS1WNoGkmiAnqlWTwp49LZ8h1xU9kE21gBd/V6U7pDt16iSzZs2SPXv2SG5urnz++edy++23S/Xq1U0/qUs4QUNqjfsXaB+NfpNPtMA18mV5omwCUUeeflrP/FmxwtKZP/8D2QcSbgEv/i63ZCt1795dZs+eLQcPHpRTp07Jxx9/LN27dzf95AycoClqCHIU5CeQIAv4UVLScnNFIiNFunRxyv6xUubPnehfqMZYwEsgyK2prFWqVJGZM2dKQUGBfP7556afnIETNKSbi+UuTwtAzqK+8bpD7i6bQJXdbhd57z299vDZZ5arOVRDX/xqI0iwBfwEgtw2CO6NN96Q/fv3y+nTpyUpKUl69Ohh+skZOEFDSsF97dpD0L/9PG6Ba+MPcmfZBLJsNpvknDgh8v/+n0iDBiL79lmiz8HR4fzSSyIgz/fubfq1ChRVOjh06NBBZsyYIZmZmXL27Fn58ssvZdiwYZYaAGfgBA0pBfc8gOqgt5duRC3/6S65q2yU9Adx9vLlUlSjhpy55hoZMWyY6X5ycnJEDh3SZ5G9+WZLBKxAUaWCQ1pampw7d06WLVsmo0ePVtlKBvUuSCFIRwtcF3+Ru8pG6S/dQ3Fqq8k+7Ha73v9x990iISEi27dbqqnL31Wp4KBpmuTm5jrGMVQkC5+gIaVQuQfQhTnYBz79VARkhgUK3Z9U2bJRKl/z0b/IdDfRg6ZpIm+8ofeDPPmkJTvJ/VnlPTvLHCE9bdq0st5SFGOz2UhMTCQ8PBzOnKHRU09R1KIF2zp2hEWLzLanUJTLRKAL8CnQHnPWZf7z009pNHkyDB4MFzxz1CwC1sD06OWq3FFzaFqsS/kbp4n1/vlP/dtPSoqqErtZlSkbJWNqD5IHsgTENny4V0citwTJqVJFtDZtRE6dsvTAPH+VW1NZrSizxjk4lmVct04kONgxRYaqEiv5kh5A73/Ie+klr42erg2yDeQwyEODBqnpMUySCg4GdGexLuVv7Ha7yPr1IhERIk2bihw/rjrTPKDKlI3SpSnnhhtEqlQRSU31+OjpYJCl6OtM9LTAuQeyVHAwoBQuvdPz6b59pah2bZGoKJH0dFUl9pAqUzZKlybt8GH9Pm7eXOTECY92DM9Er6ncZ4HzDnS5dT0HhU4f4NEVKzgowt5PP6WoeXMyMzOJj48nKSnJbHsKxSWRlZMDSUmwZw+MGwf5+fp2N3cM3w38C5gNzHHrkRWewPTo5ao8XXO4eMrgWd27Sx7IZpDLLXD+/q7yykbJPXIMRnv5ZT2xoksXyf3jD7fWgruid36vRA0QtYpUs5IBpVD6A8hpHWgRkU8/laKQEEmvX1/qWeDcA0FllY2Se+X4ErRggWi1aklulSpyh5uOPat7dymsX1+KmjeXPRs2qKZXi0gFBwNKofQHkFO6amKiSFCQSM+ekvnHH6afd6CorLJR8pyaoU+dLSBzQMKo3KI7V6GvaSIgEhsrsm2b6puzkCwVHPr37y9paWmSnp4uU6ZMKfF+1apVJTk5WdLT02Xt2rXSrFkzV0+wQtlsNtmzfr1ohw6VuOk1TRM5eVLkhRf0G3zgQJHcXJWu6kXVL5bZPgJNoSAvogeIrDp1JHfdujJTXS8OHGOHDpVX0DOStFq1RP79b5H8fEuvIxGIskxwCA4OloyMDImJiZEqVarIpk2bJDY21mmfiRMnyn/+8x8BZNiwYZKcnOzqCZarEs1GxTf9w4MGyUMgZ7p101P8QGToUJFz59SNrRRQ6gdSWL++SFiYyLvvihQUOD3gnf6HNE1k7lwpiogQDb3WoR08KKWhvmCZL8sEh65du8q3337reP3YY4/JY4895rTPt99+K127dhVAQkJC5PDhw66eYLlyNBu9957I44+LPPqoSJs2ejAA2Vu7tpybPFlk9WrHAimqSuxdjS6W2T4CWdq+fSJ9+zr+L6RePZFWraTommskt08fkTFjRP71L5FOnfT3u3WTff/9r8BFTbOq5mApVWpuJU/QpEkT9u7d63idnZ3N1VdfXeY+mqZx8uRJ6tevz9GjzjO/xMfHc++99wIQERFRaU9RUVH6L1Onwt69EBoKPXtSFB9Pq8mT2X3qFLaDB5netClRImTt2UNCQoJKV/Ui9xT//NBMEwFOVn4+0d99B8nJsHMnHDkCR46Ql51N2JEjkJamb2vYED75BGw2GokAkJCQ8NccZMXk5uaSkJBg1ukoDOK1KHX77bdLYmKi4/WoUaPkjTfecNpny5Yt0qRJE8frjIwMueyyyyod/SqS41tN5856jaF4AJD6VmMdpaA6pM1WWc2v5/saRESvWV+w/OiF/0OV6cxW8rwsMwguOzubyMhIx+umTZuyf//+MvcJCQmhTp06HDt2zGOeEhISyM3NhfBwaNAA6tRR32oUiotISkoiPj6ezMxMioqKnAZ8Ov6HgoJ0UbJmkJSURExMDCEhIcTExKiat4/gtSgVEhIiu3btkujoaEeHdJs2bZz2uf/++506pBcsWOBS9DMim80mZ6++Wop69lTfaiyoFFTNwepSNQPflGU6pAEZOHCg7NixQzIyMiQhIUEAmTZtmgwaNEgAqVatmixcuFDS09Pl119/lZiYGFdP0JBSUA8gq0qVjZKSZ1TeszOo+BefJjU1lbi4OJeOEVb886zrdhRuRpWNQuEZynt2ejVbycqoB491UWWjUHgfNStrMROLpbAeqmwUCu+jgkMxdxZLYT1U2SgU3kcFB4VCoVCUQAUHhUKhUJRABQeFQqFQlEAFB4VCoVCUwC/GOfz555/s2bPH5eNERERw5MgRNzjyfdS10FHXQUddh7/wp2vRrFkzGjZsWOb7po/Ss4rcMdLaX6SuhboO6joE9rVQzUoKhUKhKIEKDgqFQqEoQQgw1WwTVmLDhg1mW7AM6lroqOugo67DXwTCtfCLDmmFQqFQuBfVrKRQKBSKEqjgoFAoFIoSBGRw6N+/P2lpaaSnpzNlypQS71etWpXk5GTS09NZu3YtzZo1M8Gl56noOkyePJmtW7eyefNmVqxYQVRUlAkuvUNF1+I8Q4cORUTo1KmTF915DyPX4Y477mDr1q1s2bKFTz75xMsOvUNF1yEyMpJVq1axYcMGNm/ezMCBA01w6XlMz6f1poKDgyUjI0NiYmIcS5XGxsY67TNx4kSnpUqTk5NN923GdejVq5eEhYUJIBMmTPDL62D0WgBSs2ZN+fHHH+WXX36RTp06me7bjOvQsmVL2bBhg9StW1cAadCggem+zbgOc+bMkQkTJgggsbGxYrfbTfft9utAgNGlSxcyMjKw2+0UFBSQnJzMkCFDnPYZMmQIH374IQCfffYZffr0McOqRzFyHX744QfOntWX2lm7di1NmzY1w6rHMXItAJ577jlmzpxJXl6eCS49j5HrEB8fz+zZszlx4gQAhw8fNsOqRzFyHUSE2rVrA1CnTh32799vhlWPEnDBoUmTJuzdu9fxOjs7myZNmpS5j6ZpnDx5kvr163vVp6cxch0uZNy4cSxbtswb1ryOkWvRvn17IiMjWbp0qbfteQ0j16F169a0bt2aNWvW8Msvv9C/f39v2/Q4Rq7D1KlTGTVqFHv37uWbb77hwQcf9LZNjxNwy4QGBQWV2CYil7yPr3Mp5zhy5Eg6d+5Mz549PW3LFCq6FkFBQbz22mvcc889XnTlfYzcE6GhobRq1YpevXrRtGlTVq9eTbt27Th58qS3bHocI9fBZrMxb948Xn31Vbp27cr8+fNp166dXz0nAq7mkJ2dTWRkpON106ZNS1QJL9wnJCSEOnXqcOzYMa/69DRGrgNAnz59eOKJJxg8eDD5+fnetOg1KroWtWrVol27dvzwww/Y7Xa6du3KkiVL/K5T2uj/xuLFiyksLCQzM5MdO3bQqlUrb1v1KEauw7hx41i4cCGgN7lWr16diIgIr/r0BqZ3fHhTISEhsmvXLomOjnZ0NrVp08Zpn/vvv9+pQ3rBggWm+zbjOrRv314yMjKkZcuWpvs1+1pcqJSUFL/skDZyHfr37y/z5s0TQOrXry9ZWVly2WWXme7d29fhm2++kdGjRwsgV111lezbt8903x6Q6Qa8roEDB8qOHTskIyNDEhISBJBp06bJoEGDBJBq1arJdz7PaAAACHlJREFUwoULJT09XX799VeJiYkx3bMZ12H58uVy8OBB2bhxo2zcuFEWL15sumezrsWF8tfgYPQ6vPLKK7J161b5/fffZdiwYaZ7NuM6xMbGypo1a2TTpk2yceNG6devn+me3S01fYZCoVAoShBwfQ4KhUKhqBgVHBQKhUJRAhUcFAqFQlECFRwUCoVCUQIVHBQKhUJRAhUcFF7lmWeeQUQQETRN49ixY6xbt47nn3+eyy+/3PBx7HY7s2bN8qBT6/PBBx84ruVrr71mmo+ePXs6fPjjXEuBSsBNn6EwnxMnTjBgwABAn7SsY8eOTJw4kXvvvZcBAwYYWoLx1ltv5ejRo562anm2b9/OmDFjOHDggGkeNmzYQNeuXRk/fjy33HKLaT4U7sf0wRZKgaNnnnlGDh8+XGJ7nTp1ZPPmzZKeni7BwcFl/n316tVNPwer6IMPPpDU1FTTfVRUtkq+KdWspLAEJ0+e5NFHH6Vly5b069cPgGbNmiEijBgxgg8//JDjx4/z3//+F3BuVrrnnnvIy8ujTp06Tsds06YNIsL111/v2DZ48GBSU1M5e/YsBw4cYMaMGYSGll+BvvHGG/n+++85dOgQJ0+e5JdffnF4PM8zzzzD4cOH6dKlC6mpqZw5c4bVq1cTHR1NgwYN+PLLLzl9+jTbtm2jd+/eTn97/lyefPJJDhw4wOnTp/n4448dU0JfKl27dmXx4sXs27ePnJwcNm7cyIgRIyr8u5SUFBYtWuS07XyTUdu2bSvlReG7qOCgsAwpKSkUFBTQtWtXp+0vv/wyp0+f5o477mD69Okl/u6LL74A9KamCxk2bBiHDh3ihx9+APQVzL744gvWrVvH4MGDmTZtGvfeey8vvvhiub5iYmL473//y1133cXQoUP53//+x7Jly7j22mud9qtRowbvvvsur732GjabjaioKObPn09SUhJr1qzhtttuY9++fSxatIiwsDCnv7XZbPTt25f4+Hj+8Y9/cNNNNzF37lxD1+1imjVrxs8//8z48eMZNGgQn3/+OR988AHDhw+v1PEUgYvp1RelwFFFTQ/79++Xt99+WwBp1qyZiIh88cUXJfaz2+0ya9Ysx+uvvvpKli1b5rRPWlqavPnmm47XmZmZ8v777zvtM2bMGDlz5ozhyeOCgoIkJCREvv32W3nvvfeczktEpEePHo5tEydOFBGRp556yrEtNjZWREQGDBjgdC5Hjx6V8PBwx7YRI0aIpmly1VVXlenFaLNSSEiIvPPOO7Jy5cpy90tJSZFFixY5bevZs6eIiLRt29blslXyLamag8JSlDaXvpEFdhYsWECfPn0cizL97W9/48orr2TBggWAvkhNs2bNWLhwISEhIQ6tWrWKsLAw2rVrV+axmzRpwrx588jOzqawsJDCwkL69+9P69atnfY7d+4cq1evdrzOyMgAYNWqVSW2Xbx4zPLly8nNzXW8/uKLLwgODiYuLq7Cc7+YunXr8vrrr5OZmUlBQQGFhYXcd999JfwqFOWhspUUlqFatWrUr1+fQ4cOOW2/+HVpLFmyhIKCAm677TYSExMZNmwY2dnZrFmzBsAx135Zq9ldOH//hQQFBbFkyRJq1arF008/TUZGBrm5uTz77LM0bNjQad/Tp087LfZyfv2L80tqAhQUFABQvXp1p7/9888/nV7n5eVx+vRpGjduXOG5X8y8efPo2rUrzz33HNu2bePUqVNMnDix1KVPFYqyUMFBYRl69+5NlSpV+OWXX5y2G1ldKzc3l6VLlzJs2DASExO58847HYuxAI7FmuLj49m4cWOJv7fb7aUet2XLlnTs2JEBAwbw3XffObZf3GfgKhcHmurVq1OrVq1LTlGtVq0aN910Ew888ABz5sxxbA8OrriRIC8vj6pVqzptu+yyyy7p8xX+gwoOCktQp04dZsyYQXp6OitWrKjUMZKTk1mwYAE333wzLVq0IDk52fHejh07yM7OJjo6+pI6es8HgXPnzjm2RUVF0a1bN37//fdK+SyNfv36ER4e7mhauu222ygqKuK33367pONUq1aN0NBQJ781a9Zk8ODBFQbZ7OxsevToUcKXIjBRwUHhdUJDQ7n66qsBfQnOTp06MXHiRGrUqMGAAQMoKiqq1HGXLl3KmTNnmDNnDrt37yY1NdXxnojwyCOPMH/+fGrXrs2yZcvIz8+nefPm3HLLLdx+++2cPXu2xDHT0tLYu3cvr7zyCk899RS1atVi2rRp7Nu3r3InXwZnz55l6dKlzJo1i8aNGzNr1iy+/PJLtm/ffknHOXXqFOvWrePpp5/m1KlTFBUV8dhjj3Hy5Emn1NioqCh27drF2LFjmT9/PgBffvkl48eP59VXX2Xp0qX07t2b/v37l/iM9PR0fvzxR8aPH+/aSSssjQoOCq9Tt25d1q5dS1FREadOnSIjI4OPP/6YN99801D/QlmcO3eOJUuWMGrUqFLTUxcuXMipU6dISEhg7NixaJrG7t27+frrr8tcHzs/P5/bbruN2bNn89lnn5Gdnc0LL7xAr169yu3EvlSSk5M5ffo07733HjVr1mTJkiVMnDixUscaMWIE7777Lh999BFHjx7lrbfeokaNGjzwwAOOfYKCgggNDXVqbvrmm294/PHHuf/++xk/fjyLFy9m0qRJLFmyxOn4oaGhhISEVO5EFT6F6SlTSkqBrIvTco3qfCprSEiIBAUFmXoOISEhMm3aNJXK6kdSqawKhQ/TuXNnCgsLefXVV03z0LNnTwoLC3n66adN86BwP6pZSaHwUaZOncpbb70FGEv39RTr16+nc+fOwF+pugrfJwi9CqFQKBQKhQPVrKRQKBSKEqjgoFAoFIoSqOCgUCgUihKo4KBQKBSKEqjgoFAoFIoS/H+7YZrX3E5WdgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pi pulse amplitude is 0.310160\n"
     ]
    }
   ],
   "source": [
    "fit_params, y_fit = helper.fit_sinusoid(drive_amps, rabi_values, [1, 0, 0.5, 0])\n",
    "\n",
    "plt.scatter(drive_amps, rabi_values, color='white')\n",
    "plt.plot(drive_amps, y_fit, color='red')\n",
    "\n",
    "drive_period = fit_params[2] # get period of rabi oscillation\n",
    "\n",
    "plt.axvline(0, color='red', linestyle='--')\n",
    "plt.axvline(drive_period/2, color='red', linestyle='--')\n",
    "plt.xlabel(\"Drive amp [a.u.]\", fontsize=15)\n",
    "plt.ylabel(\"Measured signal [a.u.]\", fontsize=15)\n",
    "plt.show()\n",
    "\n",
    "print(\"Pi pulse amplitude is %f\"%float(drive_period/2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The $\\pi$ pulse amplitude is half the period of the sinusoid (a full period of $360^\\circ$ brings it back to zero, but we wish to take the qubit from the zero to one state). For the following experiment, we want a $\\pi/2$ pulse: we wish to place the qubit on the equator of the Bloch sphere. The following creates a pulse that rotates the qubit $\\pi/2$ ($90^\\circ$ degrees) around the Bloch sphere:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# x_90 is a concise way to say pi_over_2; i.e., an X rotation of 90 degrees\n",
    "x90_pulse = pulse_lib.gaussian(duration=drive_duration,\n",
    "                               amp=drive_period/4, \n",
    "                               sigma=drive_sigma,\n",
    "                               name='x90_pulse')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ramsey Experiment\n",
    "\n",
    "The Ramsey experiment reveals the time dynamics of driving the qubit off-resonantly. In particular, we vary the delay between two $\\pi/2$-pulses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ramsey experiment parameters\n",
    "time_max_us = 0.4\n",
    "time_step_us = 0.0035\n",
    "times_us = np.arange(0.1, time_max_us, time_step_us)\n",
    "# Convert to units of dt\n",
    "delay_times_dt = times_us * us / dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graded Exercise 1b: Ramsey Schedule\n",
    "\n",
    "Add code to the method below in order to build a Ramsey pulse schedule. For a review of creating pulse schedules, see [Lab 6 notebook](lab6-drive-ham-rabi-ramsey.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_ramsey_pulse_schedule(delay):\n",
    "    ### create a Ramsey pulse schedule (already done)\n",
    "    ### play an x90 pulse on the drive channel\n",
    "    ### play another x90 pulse after delay\n",
    "    ### add measurement pulse to schedule\n",
    "    \n",
    "    ramsey_schedule = pulse.Schedule(name='ramsey_experiment')\n",
    "    \n",
    "    ### HINT: include delay by adding it to the duration of the schedule\n",
    "    ###       round delay to nearest integer with int(delay)\n",
    "    \n",
    "    ### WRITE YOUR CODE BETWEEN THESE LINES - START\n",
    "    ramsey_schedule += Play(x90_pulse, drive_chan)\n",
    "    ramsey_schedule += Play(x90_pulse, drive_chan) << ramsey_schedule.duration + int(delay)\n",
    "    ramsey_schedule += measure << ramsey_schedule.duration\n",
    "    \n",
    "    ### WRITE YOUR CODE BETWEEN THESE LINES - END\n",
    "    \n",
    "    return ramsey_schedule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create schedules for Ramsey experiment \n",
    "ramsey_schedules = []\n",
    "for delay in delay_times_dt:\n",
    "    ramsey_schedules.append(build_ramsey_pulse_schedule(delay))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x864 with 1 Axes>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ramsey_schedules[-1].draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# assemble the schedules into a Qobj\n",
    "# the helper will drive the pulses off-resonantly by an unknown value\n",
    "ramsey_qobj = assemble(**helper.get_params('ramsey', globals()))\n",
    "answer1b = ramsey_qobj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# run the simulation\n",
    "ramsey_result = backend_sim.run(ramsey_qobj, duffing_model).result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# retrieve the data from the experiment\n",
    "ramsey_values = helper.get_values_from_result(ramsey_result, qubit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive is off-resonant by 8.666402 MHz\n"
     ]
    }
   ],
   "source": [
    "# off-resonance component\n",
    "fit_params, y_fit = helper.fit_sinusoid(times_us, ramsey_values, [1, 0.7, 0.1, 0.25])\n",
    "_, _, ramsey_period_us, _, = fit_params \n",
    "del_f_MHz = 1/ramsey_period_us # freq is MHz since times in us\n",
    "\n",
    "plt.scatter(times_us, np.real(ramsey_values), color='white')\n",
    "plt.plot(times_us, y_fit, color='red', label=f\"df = {del_f_MHz:.6f} MHz\")\n",
    "plt.xlim(np.min(times_us), np.max(times_us))\n",
    "plt.xlabel('Delay between X90 pulses [$\\mu$s]', fontsize=15)\n",
    "plt.ylabel('Measured Signal [a.u.]', fontsize=15)\n",
    "plt.title('Ramsey Experiment', fontsize=15)\n",
    "plt.legend(loc=3)\n",
    "plt.show()\n",
    "\n",
    "print(\"Drive is off-resonant by %f MHz\"%float(del_f_MHz))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now grade your solutions by running the cell below. **Provide always the same name and email, as the one you wrote during the course sign up.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grading...\n",
      "lab6/ex1a - 🎉 Correct\n",
      "Grading...\n",
      "lab6/ex1b - 🎉 Correct\n",
      "🎊 Hurray! You have a new correct answer! Let's submit it.\n",
      "Submitting the answers for lab6...\n",
      "📝 Our records, so far, are:\n",
      "Correct answers: lab1:ex1, lab2:ex1, lab3:ex1, lab4:ex1, lab5:ex1, lab6:ex1a, lab6:ex1b\n"
     ]
    }
   ],
   "source": [
    "name = 'First Last'\n",
    "email = 'first.last@domain.com'\n",
    "\n",
    "from grading_tools import grade\n",
    "grade(answer1a, name, email, 'lab6', 'ex1a')\n",
    "grade(answer1b, name, email, 'lab6', 'ex1b')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Help us improve our educational tools by submitting your code**<br>\n",
    "If you would like to help us learn how to improve our educational materials and offerings, you can opt in to send us a copy of your Jupyter notebook. By executing the cell below, you consent to sending us the code in your Jupyter notebook. All of the personal information will be anonymized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import display, Javascript;display(Javascript('IPython.notebook.save_checkpoint();'));\n",
    "from grading_tools import send_code;send_code('ex1.ipynb')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Additional Resources\n",
    "\n",
    "- The Qiskit textbook sections that cover this material are\n",
    "  - [Introduction to Transmon Physics](https://qiskit.org/textbook/ch-quantum-hardware/transmon-physics.html)\n",
    "  - [Calibrating Qubits with Qiskit Pulse](https://qiskit.org/textbook/ch-quantum-hardware/calibrating-qubits-openpulse.html)\n",
    "\n",
    "- Watch the videos\n",
    "  - [Quantum Coding with Lauren Capelluto](https://www.youtube.com/watch?v=ZvipHRY-URs)\n",
    "  - [\"Qiskit Pulse: Programming Quantum Computers Through the Cloud with Pulses\"](https://www.youtube.com/watch?v=V_as5PufUiU) webinar at CQT by yours truly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}