examples/two_qubits.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup of a two-qubit chip with $C^3$\n",
    "\n",
    "In this example we will set-up a two qubit quantum processor and define a simple gate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "# System imports\n",
    "import copy\n",
    "import numpy as np\n",
    "import time\n",
    "import itertools\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import tensorflow_probability as tfp\n",
    "\n",
    "# Main C3 objects\n",
    "from c3.c3objs import Quantity as Qty\n",
    "from c3.experiment import Experiment as Exp\n",
    "from c3.system.model import Model as Mdl\n",
    "from c3.generator.generator import Generator as Gnr\n",
    "\n",
    "# Building blocks\n",
    "import c3.generator.devices as devices\n",
    "import c3.signal.gates as gates\n",
    "import c3.system.chip as chip\n",
    "import c3.signal.pulse as pulse\n",
    "import c3.system.tasks as tasks\n",
    "\n",
    "# Libs and helpers\n",
    "import c3.libraries.algorithms as algorithms\n",
    "import c3.libraries.hamiltonians as hamiltonians\n",
    "import c3.libraries.fidelities as fidelities\n",
    "import c3.libraries.envelopes as envelopes\n",
    "import c3.utils.qt_utils as qt_utils\n",
    "import c3.utils.tf_utils as tf_utils"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model components\n",
    "We first create a qubit. Each parameter is a Quantity (Qty()) object with bounds and a unit. In $C^3$, the default multi-level qubit is a Transmon modelled as a Duffing oscillator with frequency $\\omega$ and anharmonicity $\\delta$ :\n",
    "$$ H/\\hbar = \\omega b^\\dagger b - \\frac{\\delta}{2}                        \\left(b^\\dagger b - 1\\right) b^\\dagger b \n",
    "$$\n",
    "The \"name\" will be used to identify this qubit (or other component) later and should thus be chosen carefully."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "qubit_lvls = 3\n",
    "freq_q1 = 5e9 * 2 * np.pi\n",
    "anhar_q1 = -210e6 * 2 * np.pi\n",
    "t1_q1 = 27e-6\n",
    "t2star_q1 = 39e-6\n",
    "qubit_temp = 50e-3\n",
    "\n",
    "q1 = chip.Qubit(\n",
    "    name=\"Q1\",\n",
    "    desc=\"Qubit 1\",\n",
    "    freq=Qty(\n",
    "        value=freq_q1,\n",
    "        min=4.995e9 * 2 * np.pi,\n",
    "        max=5.005e9 * 2 * np.pi,\n",
    "        unit='Hz 2pi'\n",
    "    ),\n",
    "    anhar=Qty(\n",
    "        value=anhar_q1,\n",
    "        min=-380e6 * 2 * np.pi,\n",
    "        max=-120e6 * 2 * np.pi,\n",
    "        unit='Hz 2pi'\n",
    "    ),\n",
    "    hilbert_dim=qubit_lvls,\n",
    "    t1=Qty(\n",
    "        value=t1_q1,\n",
    "        min=1e-6,\n",
    "        max=90e-6,\n",
    "        unit='s'\n",
    "    ),\n",
    "    t2star=Qty(\n",
    "        value=t2star_q1,\n",
    "        min=10e-6,\n",
    "        max=90e-3,\n",
    "        unit='s'\n",
    "    ),\n",
    "    temp=Qty(\n",
    "        value=qubit_temp,\n",
    "        min=0.0,\n",
    "        max=0.12,\n",
    "        unit='K'\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the same for a second qubit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "freq_q2 = 5.6e9 * 2 * np.pi\n",
    "anhar_q2 = -240e6 * 2 * np.pi\n",
    "t1_q2 = 23e-6\n",
    "t2star_q2 = 31e-6\n",
    "q2 = chip.Qubit(\n",
    "    name=\"Q2\",\n",
    "    desc=\"Qubit 2\",\n",
    "    freq=Qty(\n",
    "        value=freq_q2,\n",
    "        min=5.595e9 * 2 * np.pi,\n",
    "        max=5.605e9 * 2 * np.pi,\n",
    "        unit='Hz 2pi'\n",
    "    ),\n",
    "    anhar=Qty(\n",
    "        value=anhar_q2,\n",
    "        min=-380e6 * 2 * np.pi,\n",
    "        max=-120e6 * 2 * np.pi,\n",
    "        unit='Hz 2pi'\n",
    "    ),\n",
    "    hilbert_dim=qubit_lvls,\n",
    "    t1=Qty(\n",
    "        value=t1_q2,\n",
    "        min=1e-6,\n",
    "        max=90e-6,\n",
    "        unit='s'\n",
    "    ),\n",
    "    t2star=Qty(\n",
    "        value=t2star_q2,\n",
    "        min=10e-6,\n",
    "        max=90e-6,\n",
    "        unit='s'\n",
    "    ),\n",
    "    temp=Qty(\n",
    "        value=qubit_temp,\n",
    "        min=0.0,\n",
    "        max=0.12,\n",
    "        unit='K'\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A static coupling between the two is realized in the following way. We supply the type of coupling by selecting int_XX $(b_1+b_1^\\dagger)(b_2+b_2^\\dagger)$ from the hamiltonian library. The \"connected\" property contains the list of qubit names to be coupled, in this case \"Q1\" and \"Q2\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "coupling_strength = 20e6 * 2 * np.pi\n",
    "q1q2 = chip.Coupling(\n",
    "    name=\"Q1-Q2\",\n",
    "    desc=\"coupling\",\n",
    "    comment=\"Coupling qubit 1 to qubit 2\",\n",
    "    connected=[\"Q1\", \"Q2\"],\n",
    "    strength=Qty(\n",
    "        value=coupling_strength,\n",
    "        min=-1 * 1e3 * 2 * np.pi,\n",
    "        max=200e6 * 2 * np.pi,\n",
    "        unit='Hz 2pi'\n",
    "    ),\n",
    "    hamiltonian_func=hamiltonians.int_XX\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the same spirit, we specify control Hamiltonians to drive the system. Again \"connected\" connected tells us which qubit this drive acts on and \"name\" will later be used to assign the correct control signal to this drive line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "drive = chip.Drive(\n",
    "    name=\"d1\",\n",
    "    desc=\"Drive 1\",\n",
    "    comment=\"Drive line 1 on qubit 1\",\n",
    "    connected=[\"Q1\"],\n",
    "    hamiltonian_func=hamiltonians.x_drive\n",
    ")\n",
    "drive2 = chip.Drive(\n",
    "    name=\"d2\",\n",
    "    desc=\"Drive 2\",\n",
    "    comment=\"Drive line 2 on qubit 2\",\n",
    "    connected=[\"Q2\"],\n",
    "    hamiltonian_func=hamiltonians.x_drive\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SPAM errors\n",
    "In experimental practice, the qubit state can be mis-classified during read-out. We simulate this by constructing a _confusion matrix_, containing the probabilities for one qubit state being mistaken for another."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "m00_q1 = 0.97 # Prop to read qubit 1 state 0 as 0\n",
    "m01_q1 = 0.04 # Prop to read qubit 1 state 0 as 1\n",
    "m00_q2 = 0.96 # Prop to read qubit 2 state 0 as 0\n",
    "m01_q2 = 0.05 # Prop to read qubit 2 state 0 as 1\n",
    "one_zeros = np.array([0] * qubit_lvls)\n",
    "zero_ones = np.array([1] * qubit_lvls)\n",
    "one_zeros[0] = 1\n",
    "zero_ones[0] = 0\n",
    "val1 = one_zeros * m00_q1 + zero_ones * m01_q1\n",
    "val2 = one_zeros * m00_q2 + zero_ones * m01_q2\n",
    "min = one_zeros * 0.8 + zero_ones * 0.0\n",
    "max = one_zeros * 1.0 + zero_ones * 0.2\n",
    "confusion_row1 = Qty(value=val1, min=min, max=max, unit=\"\")\n",
    "confusion_row2 = Qty(value=val2, min=min, max=max, unit=\"\")\n",
    "conf_matrix = tasks.ConfusionMatrix(Q1=confusion_row1, Q2=confusion_row2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following task creates an initial thermal state with given temperature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "init_temp = 50e-3\n",
    "init_ground = tasks.InitialiseGround(\n",
    "    init_temp=Qty(\n",
    "        value=init_temp,\n",
    "        min=-0.001,\n",
    "        max=0.22,\n",
    "        unit='K'\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We collect the parts specified above in the Model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "model = Mdl(\n",
    "    [q1, q2], # Individual, self-contained components\n",
    "    [drive, drive2, q1q2],  # Interactions between components\n",
    "    [conf_matrix, init_ground] # SPAM processing\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Further, we can decide between coherent or open-system dynamics using set_lindbladian() and whether to eliminate the static coupling by going to the dressed frame with set_dressed()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "model.set_lindbladian(False)\n",
    "model.set_dressed(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Control signals\n",
    "With the system model taken care of, we now specify the control electronics and signal chain. Complex shaped controls are often realized by creating an envelope signal with an arbitrary waveform generator (AWG) with limited bandwith and mixing it with a fast, stable local oscillator (LO)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "sim_res = 100e9 # Resolution for numerical simulation\n",
    "awg_res = 2e9 # Realistic, limited resolution of an AWG\n",
    "lo = devices.LO(name='lo', resolution=sim_res)\n",
    "awg = devices.AWG(name='awg', resolution=awg_res)\n",
    "mixer = devices.Mixer(name='mixer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Waveform generators exhibit a rise time, the time it takes until the target voltage is set. This has a smoothing effect on the resulting pulse shape."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "resp = devices.Response(\n",
    "    name='resp',\n",
    "    rise_time=Qty(\n",
    "        value=0.3e-9,\n",
    "        min=0.05e-9,\n",
    "        max=0.6e-9,\n",
    "        unit='s'\n",
    "    ),\n",
    "    resolution=sim_res\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In simulation, we translate between AWG resolution and simulation (or \"analog\") resolultion by including an up-sampling device."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "dig_to_an = devices.Digital_to_Analog(\n",
    "    name=\"dac\",\n",
    "    resolution=sim_res\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Control electronics apply voltages to lines, whereas in a Hamiltonian we usually write the control fields in energy or frequency units. In practice, this conversion can be highly non-trivial if it involves multiple stages of attenuation and for example the conversion of a line voltage in an antenna to a dipole field coupling to the qubit. The following device represents a simple, linear conversion factor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "v2hz = 1e9\n",
    "v_to_hz = devices.Volts_to_Hertz(\n",
    "    name='v_to_hz',\n",
    "    V_to_Hz=Qty(\n",
    "        value=v2hz,\n",
    "        min=0.9e9,\n",
    "        max=1.1e9,\n",
    "        unit='Hz 2pi/V'\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The generator combines the parts of the signal generation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "generator = Gnr([lo, awg, mixer, v_to_hz, dig_to_an, resp])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gates-set\n",
    "It remains to write down what kind of operations we want to perform on the device. For a gate based quantum computing chip, we define a gate-set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "gateset = gates.GateSet()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We choose a gate time and a gaussian envelope shape with a list of parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "t_final = 7e-9   # Time for single qubit gates\n",
    "sideband = 50e6 * 2 * np.pi\n",
    "gauss_params_single = {\n",
    "    'amp': Qty(\n",
    "        value=0.5,\n",
    "        min=0.4,\n",
    "        max=0.6,\n",
    "        unit=\"V\"\n",
    "    ),\n",
    "    't_final': Qty(\n",
    "        value=t_final,\n",
    "        min=0.5 * t_final,\n",
    "        max=1.5 * t_final,\n",
    "        unit=\"s\"\n",
    "    ),\n",
    "    'sigma': Qty(\n",
    "        value=t_final / 4,\n",
    "        min=t_final / 8,\n",
    "        max=t_final / 2,\n",
    "        unit=\"s\"\n",
    "    ),\n",
    "    'xy_angle': Qty(\n",
    "        value=0.0,\n",
    "        min=-0.5 * np.pi,\n",
    "        max=2.5 * np.pi,\n",
    "        unit='rad'\n",
    "    ),\n",
    "    'freq_offset': Qty(\n",
    "        value=-sideband - 3e6 * 2 * np.pi,\n",
    "        min=-56 * 1e6 * 2 * np.pi,\n",
    "        max=-52 * 1e6 * 2 * np.pi,\n",
    "        unit='Hz 2pi'\n",
    "    ),\n",
    "    'delta': Qty(\n",
    "        value=-1,\n",
    "        min=-5,\n",
    "        max=3,\n",
    "        unit=\"\"\n",
    "    )\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we take gaussian_nonorm() from the libraries as the function to define the shape."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "gauss_env_single = pulse.Envelope(\n",
    "    name=\"gauss\",\n",
    "    desc=\"Gaussian comp for single-qubit gates\",\n",
    "    params=gauss_params_single,\n",
    "    shape=envelopes.gaussian_nonorm\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also define a gate that represents no driving."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "nodrive_env = pulse.Envelope(\n",
    "    name=\"no_drive\",\n",
    "    params={\n",
    "        't_final': Qty(\n",
    "            value=t_final,\n",
    "            min=0.5 * t_final,\n",
    "            max=1.5 * t_final,\n",
    "            unit=\"s\"\n",
    "        )\n",
    "    },\n",
    "    shape=envelopes.no_drive\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We specify the drive tones with an offset from the qubit frequencies. As in experiment, we will later adjust the resonance by modulating the envelope function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "lo_freq_q1 = 5e9 * 2 * np.pi + sideband\n",
    "carrier_parameters = {\n",
    "    'freq': Qty(\n",
    "        value=lo_freq_q1,\n",
    "        min=4.5e9 * 2 * np.pi,\n",
    "        max=6e9 * 2 * np.pi,\n",
    "        unit='Hz 2pi'\n",
    "    ),\n",
    "    'framechange': Qty(\n",
    "        value=0.0,\n",
    "        min= -np.pi,\n",
    "        max= 3 * np.pi,\n",
    "        unit='rad'\n",
    "    )\n",
    "}\n",
    "carr = pulse.Carrier(\n",
    "    name=\"carrier\",\n",
    "    desc=\"Frequency of the local oscillator\",\n",
    "    params=carrier_parameters\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the second qubit drive tone, we copy the first one and replace the frequency. The deepcopy is to ensure that we don't just create a pointer to the first drive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "lo_freq_q2 = 5.6e9 * 2 * np.pi + sideband\n",
    "carr_2 = copy.deepcopy(carr)\n",
    "carr_2.params['freq'].set_value(lo_freq_q2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Instructions\n",
    "We define the gates we want to perform with a \"name\" that will identify them later and \"channels\" relating to the control Hamiltonians and drive lines we specified earlier. As a start we write down 90 degree rotations in the positive $x$-direction and identity gates for both qubits. Then we add a carrier and envelope to each."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "X90p_q1 = gates.Instruction(\n",
    "    name=\"X90p\",\n",
    "    t_start=0.0,\n",
    "    t_end=t_final,\n",
    "    channels=[\"d1\"]\n",
    ")\n",
    "X90p_q2 = gates.Instruction(\n",
    "    name=\"X90p\",\n",
    "    t_start=0.0,\n",
    "    t_end=t_final,\n",
    "    channels=[\"d2\"]\n",
    ")\n",
    "QId_q1 = gates.Instruction(\n",
    "    name=\"Id\",\n",
    "    t_start=0.0,\n",
    "    t_end=t_final,\n",
    "    channels=[\"d1\"]\n",
    ")\n",
    "QId_q2 = gates.Instruction(\n",
    "    name=\"Id\",\n",
    "    t_start=0.0,\n",
    "    t_end=t_final,\n",
    "    channels=[\"d2\"]\n",
    ")\n",
    "\n",
    "X90p_q1.add_component(gauss_env_single, \"d1\")\n",
    "X90p_q1.add_component(carr, \"d1\")\n",
    "QId_q1.add_component(nodrive_env, \"d1\")\n",
    "QId_q1.add_component(copy.deepcopy(carr), \"d1\")\n",
    "\n",
    "X90p_q2.add_component(copy.deepcopy(gauss_env_single), \"d2\")\n",
    "X90p_q2.add_component(carr_2, \"d2\")\n",
    "QId_q2.add_component(copy.deepcopy(nodrive_env), \"d2\")\n",
    "QId_q2.add_component(copy.deepcopy(carr_2), \"d2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When later compiling gates into sequences, we have to take care of the relative rotating frames of the qubits and local oscillators. We do this by adding a phase after each gate that realigns the frames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "QId_q1.comps['d1']['carrier'].params['framechange'].set_value(\n",
    "    (-sideband * t_final) % (2*np.pi)\n",
    ")\n",
    "QId_q2.comps['d2']['carrier'].params['framechange'].set_value(\n",
    "    (-sideband * t_final) % (2*np.pi)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The remainder of the gates-set can be derived from the X90p gate by shifting its phase by multiples of $\\pi/2$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "Y90p_q1 = copy.deepcopy(X90p_q1)\n",
    "Y90p_q1.name = \"Y90p\"\n",
    "X90m_q1 = copy.deepcopy(X90p_q1)\n",
    "X90m_q1.name = \"X90m\"\n",
    "Y90m_q1 = copy.deepcopy(X90p_q1)\n",
    "Y90m_q1.name = \"Y90m\"\n",
    "Y90p_q1.comps['d1']['gauss'].params['xy_angle'].set_value(0.5 * np.pi)\n",
    "X90m_q1.comps['d1']['gauss'].params['xy_angle'].set_value(np.pi)\n",
    "Y90m_q1.comps['d1']['gauss'].params['xy_angle'].set_value(1.5 * np.pi)\n",
    "Q1_gates = [QId_q1, X90p_q1, Y90p_q1, X90m_q1, Y90m_q1]\n",
    "\n",
    "\n",
    "Y90p_q2 = copy.deepcopy(X90p_q2)\n",
    "Y90p_q2.name = \"Y90p\"\n",
    "X90m_q2 = copy.deepcopy(X90p_q2)\n",
    "X90m_q2.name = \"X90m\"\n",
    "Y90m_q2 = copy.deepcopy(X90p_q2)\n",
    "Y90m_q2.name = \"Y90m\"\n",
    "Y90p_q2.comps['d2']['gauss'].params['xy_angle'].set_value(0.5 * np.pi)\n",
    "X90m_q2.comps['d2']['gauss'].params['xy_angle'].set_value(np.pi)\n",
    "Y90m_q2.comps['d2']['gauss'].params['xy_angle'].set_value(1.5 * np.pi)\n",
    "Q2_gates = [QId_q2, X90p_q2, Y90p_q2, X90m_q2, Y90m_q2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the single qubit gates in place, we can combine them to get all possible combinations of simultaneous gates on both qubits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "all_1q_gates_comb = []\n",
    "for g1 in Q1_gates:\n",
    "    for g2 in Q2_gates:\n",
    "        g = gates.Instruction(\n",
    "            name=\"NONE\",\n",
    "            t_start=0.0,\n",
    "            t_end=t_final,\n",
    "            channels=[]\n",
    "        )\n",
    "        g.name = g1.name + \":\" + g2.name\n",
    "        channels = []\n",
    "        channels.extend(g1.comps.keys())\n",
    "        channels.extend(g2.comps.keys())\n",
    "        for chan in channels:\n",
    "            g.comps[chan] = {}\n",
    "            if chan in g1.comps:\n",
    "                g.comps[chan].update(g1.comps[chan])\n",
    "            if chan in g2.comps:\n",
    "                g.comps[chan].update(g2.comps[chan])\n",
    "        all_1q_gates_comb.append(g)\n",
    "\n",
    "for gate in all_1q_gates_comb:\n",
    "    gateset.add_instruction(gate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The experiment\n",
    "Finally everything is collected in the experiment that provides the functions to interact with the system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "exp = Exp(model=model, generator=generator, gateset=gateset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Simulation\n",
    "With our experiment all set-up, we can perform simulations. We first decide which basic gates to simulate, in this case only the 90 degree rotation on one qubit and the identity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp.opt_gates = ['X90p:Id', 'Id:Id']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The actual numerical simulation is done by calling exp.get_gates(). \n",
    "*WARNING:* This is resource intensive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "gates = exp.get_gates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After this step the unitaries or process matrices are stored in the exp object. We can look at their names and matrix representations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'X90p:Id': <tf.Tensor: shape=(9, 9), dtype=complex128, numpy=\n",
       " array([[ 5.39819078e-01+5.54603216e-02j, -1.14694617e-03+1.16680948e-03j,\n",
       "          6.35464026e-05+5.22498534e-05j, -1.29292989e-01-8.22160640e-01j,\n",
       "         -1.13478696e-03-6.07863135e-04j,  5.07695559e-05-1.14803968e-04j,\n",
       "          1.04660103e-01+4.35502228e-02j, -5.21547147e-05-4.34546648e-05j,\n",
       "         -3.68545017e-06+2.99133416e-05j],\n",
       "        [-3.20946391e-02-1.49349729e-02j,  5.40690046e-01+3.38742176e-02j,\n",
       "          1.00683905e-03+1.03637963e-03j,  3.80517379e-03-2.30238976e-02j,\n",
       "         -1.28256906e-01-8.22334399e-01j,  5.39849908e-04+2.19723020e-03j,\n",
       "         -2.74290405e-03-1.24604716e-03j,  1.00399646e-01+4.37250681e-02j,\n",
       "         -4.12720421e-03+3.17144911e-04j],\n",
       "        [ 4.55862271e-05-3.70138706e-04j, -7.57408825e-02-3.62399352e-02j,\n",
       "         -2.25247681e-01-4.89874576e-01j, -2.36477353e-04+3.46466216e-05j,\n",
       "          1.01775803e-02-5.35513529e-02j, -6.17938712e-01+5.52587890e-01j,\n",
       "         -3.65692613e-04+2.38745771e-03j, -5.90804365e-03-3.14899019e-03j,\n",
       "         -1.62137216e-02-1.08391318e-01j],\n",
       "        [-2.13173067e-01-8.02586019e-01j, -1.37813294e-02-1.87935551e-02j,\n",
       "          3.06672692e-05-1.39087365e-04j,  4.77164475e-01-2.75205001e-01j,\n",
       "         -2.34975084e-02+2.67177710e-02j,  1.11373304e-04-7.85038499e-05j,\n",
       "         -1.87452849e-02-6.94519191e-02j,  2.54992539e-04-2.23837831e-03j,\n",
       "          1.27832515e-04+1.24361693e-04j],\n",
       "        [ 3.09187803e-03-5.78285161e-03j, -2.09209142e-01-7.99985116e-01j,\n",
       "         -2.33869397e-02+5.05069138e-02j, -4.10740441e-03-3.06221218e-04j,\n",
       "          4.85877479e-01-2.51366387e-01j,  8.27593912e-02+1.18327787e-02j,\n",
       "         -2.50311809e-02+3.61203973e-02j, -1.33138780e-02-6.81031163e-02j,\n",
       "         -9.63660252e-03-2.62639405e-03j],\n",
       "        [-1.52871789e-04+2.93090910e-05j,  5.22828939e-03-9.93229693e-03j,\n",
       "         -5.58844458e-01+6.12082603e-01j, -4.05590405e-05-1.06944909e-04j,\n",
       "         -6.89644744e-03+3.99138893e-04j, -4.96319891e-01-2.32411113e-01j,\n",
       "         -1.37630797e-05-6.73547047e-06j, -5.53989995e-02+6.84254343e-02j,\n",
       "         -5.05319019e-02+4.71057293e-02j],\n",
       "        [ 1.17690460e-01-2.79314065e-02j, -2.14679039e-02+3.02114983e-02j,\n",
       "          1.34413111e-03+1.85253979e-04j, -4.29786862e-02-3.87326669e-02j,\n",
       "         -2.26691568e-02-9.49668429e-03j, -2.79930688e-04-2.02718511e-03j,\n",
       "         -9.86225905e-01+8.59470337e-02j, -1.94376532e-03+2.26914899e-03j,\n",
       "          5.08189786e-05+5.73244432e-04j],\n",
       "        [-2.12480689e-03-6.58499213e-04j,  1.13957037e-01-2.51644745e-02j,\n",
       "          7.39473147e-02+3.54664176e-03j,  2.09964348e-04-1.55939335e-03j,\n",
       "         -3.85782380e-02-3.97851959e-02j,  1.22781042e-02+4.76891587e-02j,\n",
       "         -2.10992908e-04-1.91367437e-04j, -9.87561368e-01+9.94482769e-03j,\n",
       "          4.15215766e-03+1.16047421e-03j],\n",
       "        [-1.59167982e-05-7.11388976e-06j, -4.39740046e-03-5.28533714e-03j,\n",
       "         -8.18589077e-02-8.39623463e-02j,  7.22702367e-07+9.44157411e-06j,\n",
       "          6.69026147e-03-6.89924152e-03j, -1.37767242e-02+5.32407490e-02j,\n",
       "          1.22096659e-04+4.88962214e-05j, -1.13439824e-03-2.00491931e-03j,\n",
       "          5.47457063e-01+8.26648046e-01j]])>,\n",
       " 'Id:Id': <tf.Tensor: shape=(9, 9), dtype=complex128, numpy=\n",
       " array([[ 9.99996157e-01+1.66312075e-03j,  3.13939806e-19+4.84770178e-19j,\n",
       "          1.28032909e-04+1.04971168e-04j,  3.34063860e-20+1.35529392e-20j,\n",
       "         -1.19100366e-03-1.86229446e-03j, -7.36426897e-21-9.06076966e-22j,\n",
       "          3.59665445e-05-6.85292544e-05j,  4.95759587e-22-1.55637415e-22j,\n",
       "         -4.92362244e-06+1.47624912e-06j],\n",
       "        [-3.03649014e-19+4.03179031e-19j,  9.98867604e-01-2.69161261e-02j,\n",
       "         -2.53962433e-16-1.47417663e-16j,  2.40032050e-02-3.08638872e-02j,\n",
       "          1.42839145e-17-2.88039109e-17j, -1.17055348e-03+2.98027845e-03j,\n",
       "          3.28404661e-16-2.37012724e-17j,  1.86788099e-04-1.07950540e-04j,\n",
       "          2.97091753e-18-5.86171744e-18j],\n",
       "        [ 9.31012050e-05-1.36907042e-04j, -2.61305302e-16-1.25520294e-16j,\n",
       "         -5.14475037e-01-8.52615045e-01j, -2.18372661e-16-1.20675725e-16j,\n",
       "          5.79580964e-02-7.07049140e-02j, -1.14689033e-18+1.06929193e-19j,\n",
       "         -5.35018127e-05+2.12336639e-03j, -1.32338599e-18+6.87189335e-19j,\n",
       "          1.09212124e-04-3.44334610e-04j],\n",
       "        [-2.96792921e-21-2.24978288e-20j, -2.37928470e-02-3.10263406e-02j,\n",
       "         -2.10217378e-16-1.20221724e-16j,  9.98661613e-01+3.37046928e-02j,\n",
       "          2.47809754e-18-1.78302974e-17j,  2.08584254e-04+3.37408485e-05j,\n",
       "          1.28876238e-17-6.42580666e-18j,  2.04506898e-03+2.45273369e-03j,\n",
       "         -9.97968160e-18+1.73851896e-17j],\n",
       "        [ 1.30266429e-03-1.78597206e-03j, -2.29874716e-18-1.43711610e-17j,\n",
       "         -3.59856665e-02+8.40437834e-02j,  1.15968527e-18+3.54160449e-18j,\n",
       "          9.92931635e-01+6.34350158e-02j,  2.93531758e-18-2.69204719e-18j,\n",
       "         -1.91009009e-02+3.62406105e-02j, -4.92295584e-20+2.00396899e-19j,\n",
       "          1.39550739e-03-4.40029880e-03j],\n",
       "        [ 9.93918174e-23-2.12791778e-21j,  1.80867614e-03-2.64215145e-03j,\n",
       "         -1.75513969e-18+1.19757425e-18j,  2.75600177e-05-2.09490721e-04j,\n",
       "          1.16077808e-18+5.51121158e-18j, -5.38424445e-01-8.38697200e-01j,\n",
       "          4.13863066e-17-7.98392804e-17j, -4.33455593e-02+6.92472174e-02j,\n",
       "         -2.56929853e-16-3.74976245e-16j],\n",
       "        [-4.55991113e-05+6.25345674e-05j,  3.27189867e-16-3.20043748e-17j,\n",
       "          2.05185014e-03+5.49052087e-04j,  2.05879439e-17-5.37415590e-18j,\n",
       "         -2.20160651e-02-3.45473462e-02j,  3.30736050e-17-8.25031184e-17j,\n",
       "         -9.88830082e-01+1.43290951e-01j, -3.84528365e-18+5.59702243e-18j,\n",
       "         -5.49763454e-05+1.35720195e-04j],\n",
       "        [-6.75601057e-23+1.72845410e-22j, -1.91269334e-04+9.97958510e-05j,\n",
       "         -3.68934401e-19+1.79772687e-18j,  1.96636032e-03-2.51627686e-03j,\n",
       "         -4.37681990e-20-3.84579475e-19j, -3.00284638e-02+7.59756673e-02j,\n",
       "         -2.27527015e-18+5.73777955e-18j, -9.94585448e-01+6.41428607e-02j,\n",
       "          1.64647981e-17+3.00896221e-17j],\n",
       "        [-1.43374319e-06-4.93623496e-06j,  4.16834705e-18-7.29913867e-18j,\n",
       "         -1.42437386e-04+3.31976636e-04j, -8.90175362e-18+1.78659560e-17j,\n",
       "          2.92908664e-03-3.56806088e-03j, -2.57093213e-16-3.75257130e-16j,\n",
       "          1.41997996e-04+3.57698051e-05j,  1.48460042e-17+2.53974741e-17j,\n",
       "          5.93404557e-01+8.04875378e-01j]])>}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp.unitaries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To investigate dynamics, we define an initial state with finite temperature we set earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "psi_init = [[0] * 9]\n",
    "psi_init[0][0] = 1\n",
    "init_state = tf.transpose(tf.constant(psi_init, tf.complex128))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(9, 1), dtype=complex128, numpy=\n",
       "array([[1.+0.j],\n",
       "       [0.+0.j],\n",
       "       [0.+0.j],\n",
       "       [0.+0.j],\n",
       "       [0.+0.j],\n",
       "       [0.+0.j],\n",
       "       [0.+0.j],\n",
       "       [0.+0.j],\n",
       "       [0.+0.j]])>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "init_state"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we stored the process matrices, we can now relatively inexpesively evaluate sequences. We start with just one gate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "barely_a_seq = ['X90p:Id']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and plot system dynamics. The \"debug\" options shows the plot in this notebook. Otherwise the plot is stored as a file in a given directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "exp.plot_dynamics(init_state, barely_a_seq, debug=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see a bad, un-optimized gate. The labels indicate qubit states in the product basis. Next we increase the number of repetitions of the same gate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['X90p:Id',\n",
       " 'X90p:Id',\n",
       " 'X90p:Id',\n",
       " 'X90p:Id',\n",
       " 'X90p:Id',\n",
       " 'X90p:Id',\n",
       " 'X90p:Id',\n",
       " 'X90p:Id',\n",
       " 'X90p:Id',\n",
       " 'X90p:Id']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "barely_a_seq * 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9nszMTLRa1x8SKomuk1tZV5FWK4179lK/dg2qyChiLrwATXy8l9S1pqdaPeGTLfY0B56kX/aF3u4Y3ieG9Fg9u4+3vkb9UWtXeFuvccdOar/4AoC0xx51a9/I00+n3xuvc+SGGzl6++30ff31Vt8BpWwbsI6/uLiY6OhosrOzu5wCbzAYeq3ijqqkFq1axaCUKI/270qrlJKKigqKi4vp37/rlL69xc6dO5kxY4ZH+zbu2cOhSy9rtaz0ySfRDR1K1ssvoU3vuDKVp/REq6esLigjKyGcvH7u/5j5Qq8rDE+PYWVBGTabRKWyf+/8VWtneFtv+UsvARB78ZxWD3NdJWLCBDKe/QfH7rmX4t/eRt8330Cl1yuitZmAHeppbGwkMTHRr4pUR+k0NJgsWKw2r7cthCAxMbHbOxx/R9psFN9xZyunrx89moiJE1FFRtK0dy8HZp5J/U/rfaiy50gp2XW8limDkvzqGu0puf3iqW4ws/Fwz2eqBwOmI0eoW7ECgPSnn/a4nZjzziPt8ccwbtlCySOPIj2Y5esOAev4Ab9LdhUXYR+CaTR75vi70+pvDiQlJcWt7aWUHLn6GgzffmtfoFIxaMX39P/4I/oteIshm/Lp8/e/A1A0bx41ixf7TGtP+amwkhqjmVEZcR7t39t6XeWqCVmAPelcM/6qtTO8qbf4jjsB0A3rvIymq8RfeSUJN95I7RdfcPS225Emk2K2DWjH7wp6xy2TEhiNRqZPn47VagXg4w/e5aKp4xk9YigLFizodv/KykrOPvtscnJyOPvsszEa7dESS5Ys4dFH3Rsr9AWDBw92a/uq9993xi0n3nwzQ3ftbDekE3vhBeSsXYNuyBCOP/gQtd986xOtPeWbnSUAXNBJMfXu6G29rpIYpSM5WtdqnN9ftXaGt/SaT56kae9eALLfe9crbaY++ADJd95J/Zo1FF52Gf2jPBs27o6gd/x1dXWKtf3GG29w2WWXoVarqays5Mk//YkPlizn46++54knnqCqqusIn6effppZs2axf/9+Zs2axZ/+9CcALrjgAr744gsaHDHB/sratWtd3tZUVETpn/4MQOyll5Jy912d9pA0SUn0ffUVdAMGcOyeezBu3dqrWr3BnhID4/vFuzVbtyW9rdcdhqXH8MX24zSa7R0ef9baEd7Se2DadADC+vdHFRHhlTYBkm69hT5/+xvmI0Uc/MVV2OrrvdZ2M4o6fiHEbCHEPiHEASHEAx2s7yuEWCGE2CKE2C6EOF9JPd7mvffe4+KLLwbg22+/5eyzzyY7IxVdZAxnnXUW33zzTZf7f/7559x4440A3HjjjSxZsgSwD+nMmDHD+T7QsVRWcvCcc53v0//4RLf7aJKTyXr1FVTh4RTfdTfmkhIlJXqVRrOVzUVVDO+mvGKgcsGoNMxW2Wq45+dGS2fc9803vN5+7EUX0nfBW9TNuQhVZKTX21dsAFwIoQb+DZwNFAMbhRCLpZS7W2z2MPCxlPJlIcRw4Csg291jPfHFrnYhZs1YrVbUarW7TTK8TwyPXdR5PK7JZKKwsJDs7GwAjh07RlZWFhFhasqBtD4ZHDvWddWi0tJS0h1DHWlpaZSVlTnX5eXlsWbNGq688kq3tfcWroSaWSor2X/6Gc73A75YjHAxHFWblkbGP5+n+NbfcPy+++2zHcPCFNPqLRZvO47FJjlzqOfjs/4cInnGIHuyuYJSAxOyE/xaa0d4Q+/BCy8C7IEJ2jRlKqpF5OYimzqugdBTlOzxnwYckFIWSilNwIfAxW22kUBztygW8Pr0NU+cviuUl5cTFxfXbrleaz+eyeLeA14hRKuhj5SUFI738mw+d5k8eXKX66XZzP4pU53vE+bPR+fmhJSoM84g7bFHacjPp/Rvf/dIJ3Sv1ZvsKaklXKtm+uBkj9voTb3ukhEXTmSYms1HqgH/1toR3tBrcdyBpj7QbiDDqyhlWyVDXjKAlokmioGJbbZ5HFgqhPgdEAmc1VFDJSUlDBkyBLD/Ws+bN48ZM2ZgMBhQq9U8euHwVmP50dHR1NfXY7PZsNlsREVFYTabMZvNzjaEEM7QSI1Gg16vd7YhhCAqKsrZBkBkZCQmk8nZhlqtprGxEYPBnrskOTmZH374AZOxHgEcPlLEmBHnUFdX50xlGxkZSVNTExZHPu6UlBSOHDlCQkICJ06cICkpCZvNRn19PVVVVc6JWi3bkFKye/duTp60Z0kcOXIkTU1N7N+/H4CsrCxSU1PJz88HICYmhtzcXNauXes87rRp09i1axcVFfaJZmPGjMFgMFBYWAhAdnY2CQkJziIQ8fHxjBkzhlWrViGlRAjB9OnTWbZsmTMSKTc3l8rKSg4fPgzAgAED4KOPwGE/GRFB0h3/x8qVK502nzJlCps3b3ZOWsvLy6O0tNSZnyQnJwedTsfOpCSip06Bd94hbOQItsfGOs/j5MmTyc/Pd567iRMnUlxc7LzbGjJkCGq1mvz8fCIiIkhLS6N///6sW7cOgPDwcCZOnMj69eudD9cnT57MoUOHOHHCnpBs+PDhWK1W9u3bB0BGRgaZmZmsX28POY2KiiIvL49169bR1NTET3uMDEqJYu/ePR6fp4aGBmbPnu2V87Rt2zbn86aOzlN0dDTbmh+6JyYyYsQIZ5Hvzs7T8BQ9X2wr5vykSrQaNcOHD2fnzp3O63rw4MHOsXR3ztPu3fYBASXP0zfffOMM+pgyZQoFBQVunafMNWsAsKSlsaGmmmk2m2Lnaffu3URERLh8nh5++GEWLlyIg87zgEspFXkBc4HXWry/HnixzTZ3A/c4/p8M7AZUbdsaP368bMvu3bvbLeuI2tpal7bzhMzMTGk0GqWUUlZUVMjs7GxZWVkpdxYWy4ysvrK8vFxKKeX1118v169f327/e++9Vz711FNSSimfeuopeccddzjX/f3vf3eua4mrn7s3WLFiRafrjHv2yN1Dhjpflh6eB2t9vTx40Ry5e/gIWZ+f7/b+XWn1JkUV9bLf75fIP3y6vUft9JZeT1mUf1T2+/0Sub/U4Pda29ITvTabzXlNl7/2mvdEdUJPtAL5shP/rORQzzEgq8X7TMeylvwS+BhASrkO0NPVr5Sfcc455zh7NQkJCTzyyCNMmDCBC8+axs133E9MnH3G5vbt2+nTp0+7/R944AG+++47cnJyWLZsGXfddZdz3YoVK7jgggt654O0QVosGL7/nn2nTWTP0GHO1+FfXEXdqlXd7m9YsYJDl1zqfD/wm69R93D2tCoign7vvYs2LY3iO+7E0k3ElK9YV2jv9V07sZ+PlShL/2T7A8fD5d6POPFnqt5/3/l/3C9+4UMlPUPJoZ6NQI4Qoj92h38VcE2bbYqAWcBbQohh2B1/GV4kUoEn4s3cdtttPPfcc5x1ln2Eav78+cyfPx9Do5lD5fXUNVlQWxrJyckhMzOz3f6JiYksX77c+b55WKm0tBSj0cioUaMU094ZpqNHOXTxJdg6CCU1btvG0VtuBaDP3/9O7qCBSKsV4XiO0rBlC0euvc45vAOQcv/9hDkegPcUdXQ0mS++wKErf8Gxu++m7+uvuzxFfuLEtqOMynCkoh6NSpDjYdqOZnpLr6cMSLJ/r77fd5JHZvu31rZ4altbY6MzJDny9NNRKxRj3xKlrgPFevxSSgtwO/AtsAd79M4uIcQfhRBzHJvdA/xaCLEN+AC4yXGL4jVMCk59zs3NZebMmc4JXM1EhNl/Tw1GCzExMS3H3LqkWWtRURH/+Mc/vCvWBWqWfMnBs8/B1tBA2MCB9PnbM6T98QliL277TB6O33svxy65lL0jRrJn6DD2TTiNI1df08rpi/BwEufP86pG/bBhpD30IA3rfqL0yb+4XAqwuLjYqzo6Y98JA1kJEWjUPftq9ZZeT4mLCGPa4GSW7jrh91rb4qneyrdOTcpMffhhb8npEqVsq2g+AynlV9hDNFsue7TF/7uBM9ru503MZrOis3fnz5/fbplaJYjRa2m0WDvYo3OatU6YMMFb8lymetEiSh5+BISg37vvEDF+vHNd/JVX0uevT2OpqODAmbM6DDGzGVoX6BBhYQxZ/5MiWuOuuoqm/Qeoeu89UKlIfejBbqfLHzt2TPH0wSdqGlmxr4w5Y9oP67lLb+jtKVMGJbK6oIyCw8V+r7Ulnti26eBByp5/HgBNn3R0A3onUaJS10HQz9z1FTqtiiaLLSCKUxtWrqTk4UfQDR9Gzo8/tHL6LdEkJjJk8yZSfv/7LttLuu02hmzb6nHMfXcIIUj9w0PEX3cdVe+8Q/kLL/iFnfeeqMVqk1yZl9X9xkFAv0T7cM/JBu8nJfQnpJQcv+9+5/t+b7/tQzXeIWDTMruKryaX6DRqpJQ0mKxE6lwzsy+0NhYUcPyee9FmZtLv7be7HbcUajWJ824icd5NHNu7l7Blyyl/8UUAwseOpe/rryky07AjHal/eAhzSQnlL72M+dgxkm67jbC+fTvcvjkcWEmOVtqfiwxI7vnn7w29PWVwqv2B/SGz+/UGfIm7tjUfPUqjI8w07uqrCOvgeZ1SKHUdBH2P31cZLaMczt6dUoy9rdVcUkLxb29D6PX0W/CW2w+rtAkJJN9+G8P27mHY3j1kf/hBrzj9ZoQQZPzj72gzM6n5fDGFl1yKYdmyDrdVaiJfS3Yes0/cSonu+Q94b+jtKf2TIhmTFcePh5UvdORN3LVtq3Qjjz3mbTldotR1EPSO31f568M0KvRaNU1upGjuTa3SYuH47x/AXFxMxnPPos3IcLuN5sk2vkSl1zPwu6UMXPotYRkZFN95F4bvv2+3ndJabTbJlztKGJsV55UfcH+wrStM7J/A4UojVpvvh9pcxR3bVrx+Kg9P37feVEJOlyh1HQS941eStmmZFyxYQE5ODjk5OSxYsACdxj7O3xkLFy5kxIgRqFQq58xAgB07dnDTTTcpqr38lVdo2LCBlAd+T+Rppyl6LKURQhDWty/93nsX3eAcjt11N3U//NCrGo7XGKlrsnCRFx7sBhKDkqOw2E4NcwULUkqqP/mUk3/7m3NZ5KRJPlTkXYLe8StZiKVtWuYnnniC9evXs2HDBp544gmMdTU0WayYO6nINXLkSD755BOmTZvWSuuoUaMoLi6mqKhIEd2Ne/ZQ/sKLRE6dSmIPfmDSFEpO5SnqmBj6vvoq2qxMjv3u/2jcV+Bcp7TWwjL7RCZvjO+D/9m2M4am28f5P9/q33mlWtKdbW319RTNn0/JQw85lw1auUJpWR2i1HUQ9I5fyQemHaVlTkhIID4+nrPPPpt1K+1DDoZOxvmHDRvW6uFNS60XXXQRH374odc1m0tPUvTrm1HFxJD+pz/2qC1/qf3bEk1iIn1ffRWA4/feg7W6GlBea3OKYm85fn+0bUeMyoglPVbH1qP+OZO6I7qyrbWungNnzqJh3alQ5MRf/VKxDJzdodR1EBxRPV8/ACd2dLjKZrWgUnvwMdNGwXmd19DsLC1zM5mZmZSfLEEI0eVwT0vq6+udxdbz8vJ4+umnuf/++7vZy3Wkzcbxe+7BWlND9jtv9/hiXrdunV8W2damp5P+5z9x7O57qHjtNVLuvVdxrV9sO05GXDjJUd7paPirbdsihCAr3EJhAKVu6Mi25tKTVL3zNhWvvd5qeexll5F8zz29qK41Sl0HweH4fUBnaZlbIoQgTK1yO0UzKJOWufzFf9OQn0/yXXcRPnasV9v2N2LOP5+qjz6m4rXXib38ckWPZbVJCsvquemMbL+ri9wbpEeq2HCwgUaz1ZmWPFCwGgycfOZvVHcwu77/Z5+iHzrUB6qUJzgcfxc988a6OqIUyKkRHh7eKgonIyPDmXIY7FOtZ8yYQZhGRaPZ5ky/2hUt1zc2NhIeHu41vQ1btlD+0ktEzZpF4q9+6ZU2valPCfo8/RQHzpzFicefIPw3typ2nOPVRkxWG/2TvBfK6u+2bcmARD0cNPPFtuNcEQCT18LDw5FWK9ULF3Li8fbV4MJzc8n894to4uN9oK6NFoWug6Af41fC6YM9p7bVanU6/3PPPZelS5dSVVVFVVUVS5cu5dxzzyVKp+He23/N6h/WuaW1oKCAkSNHekWrta6OkgcfQhUVRfrjjzmTqvUUf08kpk1PJ/6662hYv54RVvfSZ7hDQak9XUV2ovccv7/btiW/u2QqYRoVW49W+1qKS+T27cveESNbOX2h15N4880M+HIJ2e+/5xdOHwIwSZu/oGSx9c7SMk+YMIFHH32UhIQEYsO17N+zi4Tk1Hb7f/rpp2RmZrJu3TouuOACZ5ZP8F5aZmmxcPTWWzEdPkyfZ/6KJtnzqlBtaS5w4c8k3/ZbRHg4Rx54ULG0Dp9tPY5GJRjex3s1dgPBts1s3LiBURmxHDip3HfNWxiWLePAzDOd7yOnTWXwT+sYunULKXffhW7gQB+qa49S10HQO34lc7jcdtttLFhwKmPf/PnzOXDgAAcOHGDePHtWSmO9gb79B5KY2j6++9JLL6W4uJimpiZKS0v59NNPAWhqaiI/P5/zzjuvR/psJhPHH3gQY/4mUh96kOgzz+x+JzdoroTkz6jj4kj7w0OoysqoeOVVRY6x70QtU3OSiA13rZawKwSCbZsxGo0MSo5iw+FKbH48kav6f59QfPvvAHvqhcH5+fR95RXU3Tyr8yVKXQdB7/iVpLO0zC2JjY3lxdffwWhyfaihqKiIp59+ukdzEKTNRslDf6B2yRISfjmfhBtu8LitQCf24osx9+lDxWuvIb2cpttmkxypaGBQD/PvBzqjMmORElYWnPS1lA6p/e47Sv7wBwCq7ryD9MceQx3Ve+lF/I2gd/xKFmIBey+/u3waOo2KepMFi63r6J5mrTk5OT0K4ZJWKyf/+ldqlywh/obrSb3vPo/b6opAKbIttFoy778Pm8FA2b/+5dW2T9Q20mSx0deL4/sQOLYFu9ZLx9lTfuwpMXSzde/TsGkTx++7H6HVMmjVKsbP826NCCVR6joIesff1EHu+N4mPtKenri7vD091Wo6coTD117HgZlnUrngbWIvnkPqgw/2qM2uOHTokGJte5uT2dnox4ym5oslXh3++2pHCQDD0npWWrItgWTbQ4cOEanTkBKt87tSjOYTJyi+406ETkf/zz9Dm5oScLZVgqB3/BaLxdcS0GnsZu5uIldPtFrr6jgybx7GTZsQOh19nvkr6U8/rWhc+YkTJxRr29ucOHGCmHPOwVJaSu2SL73W7paiapKiwhjfz7tRIIFmW7BHNe08Xus34/w2k4ljd9+DtbyczH/+E92AAUBg2tbbBL3j9wfC1CoEgiazMiGF9gv8bizHS8h8+SUGfbeU2DlzfpaTiboi/rrrUMXEYPh+efcbu8jhinpGZsSGbA1MyUliT0kt2x3pK3yJlJITjz2OcfNm0h57lMhJgRMe2xsEveNXsuyiqwghCNOous3N76nWsn/+k/rVa0i+6y6iZ870qA1PGD58eK8dq6cMHz4clU5H1NSpGL7+BpMXEuBJaX+w2y8hwgsKWxNotgW4ZKx9nH/fCd/n56/59DNqPv2U2LmXE3/11a3WBaJtvU3QO34lwzm7S8vckphwDU0WW6sHvPfddx9Dhw5l9OjRXHrppVRV2RNduZOWuf6nn6h8/Q2izz2XpFtu9s4Hc5Guopn8jWatCTfao5tqv/qqq81dYndJLXVNFgYkez+iJxBtmxEfjk6j8nk8v/nECU489hi64cNIf/TRdusD0bbeJugdv5IPd7tLy9zsyAEiwuyhmS3z9px99tns3LmT7du3M3jwYJ566inA9bTM0maj7F8voI6PJ/3JPyvwCbtm3759vX5MT2nWGj56NLqcQZS/9DK2Hha+WbbbHrp41vD2k/N6SiDaVq0S9E+KZMW+Mp/qKXn0UaTVSsbf/9Fh3edAtK23CXrHryTdpWX+5ptvnNs2P+Bt6fjPOeccZ6z+pEmTOHbsmHOdK2mZ61aswLh5M/FXX+V22cSfMzFz5iBNJup7WKzlSGU96bF6MuICJ6+O0kzITuDAyTpKa31T+a7y/fepX72GxF//Gt2AwEht7QuCIknbXzf8lb2VeztcZ7PZUKnc/30bmjCU35/2+07Xu5KWuaUjD1PbNVQ3mImLaN8Lab57aKa7tMzSYuHkc8+hjo0l6be/deuzeYsMD8o1+oqWWhNuvJHyf71A2b9eIOrMMz1+MFtU0UBfBcb3IXBte/6odN756QgFpQZSY3r3+VrT/v2U/uUpIiZMIKmLpHyBaltvEvQ9fk+cviu4kpa5tQ5BRJgGYweRPU8++SQajYYbWsyu7S4ts2HFCkwHDpJ4660IBauMdUVmZqZPjusJLbWqwsKInD6Npn37MB854lF7Ukr2njB4NTFbSwLVts0zmHt7nN9SVcWxu+9BFRZG+p/+iKqLQIlAta03CYoef1c9c4PB4Cxu4k1cTcvckhi9hhO1jVhtErXK3st86623WLJkCcuXL6ehocGptau0zNJqpfSpp1DFxBB/7TXe/WBusH79+oAoFgLttabccQd1y5ZT/elnpNx1p9vtrd5fTl2ThTFZcV7T2JJAtW1SVBix4VqW7znJvDN6b6il9E9/ounAATJf+BdhjrvwzghU23qToO/xK4WraZkBbrjhBjZs2NBinN/e6//mm2945plnWLx4MRERrYcMukrLXPvVV1iOl5By152oOnh4FaJ7wgYORBUdTdX77yO7SaXREfsdqZjPGxkYtXF7CyEEE/snsOFwZae1pr2N4fvvqf3qa+KvvoroFhluQ3RO0Dt+pYZ6wLW0zADbt2+nT58+hGnsOX1qG+0zdG+//XYMBgNnn302Y8eO5c4773S23VVa5opXXwMgds4cpT6aSyhV60AJ2moVKhVJt9yMzWDAXFzsdntFlQ1E6zXERXgvI2dLAtm2545Iw2SxUVTZoPixzSUlHL/vfrR9+pD6wAMu7RPItvUWQTHU0xVKJmm77bbbeO6555x59OfPn8/8+fNbbVNbW0tOTg6ZmZnYpEQlBPVNdsd/4MCBDtttTsv8/PPPt1tna2ykqaCA1D/8AZXCCei6Iy8vz6fHd4eOtEZMmABAzWefk/x/v3OrvSOOB7tKzdgNZNu2HOcfqMAch2ak1cqxe+7F1tREv/+83GHoZkcEsm29RdD3+JUsxOJKWuaYmBgWOup5qoQgNlzbac6eZq1dpWW21dWhiooi5sKeF2npKevWdV9VzF/oSKtuyBAQgtov3cvdU9to5qfCCganev/ZUTOBbNuBDse/ZHuJYseUUnLsrrsxbt5Myl13oh882OV9A9m23iLoHb+SM3fBtbTMLdFpVJitNqwdJLJq1tpZWmZptSJNJuIuv9wvSsP5Q+ZTV+lIq0qvJ+m22zAVFWFzo+DFzmM1NFlsnK3AxK1mAtm2UToNp2UnsOFQhWLHLP3TnzEsXUrir39NQpu77O4IZNt6C5ccvxBCJ4S4RgjxkBDi0eaXC/vNFkLsE0IcEEJ0OAAnhLhSCLFbCLFLCPG+ux8g0Gh+wNs83OMOljL7jMjIKVO8qunnjC4nB6Sk5osvXN7nqGPselRGrFKyAp5Zw1IorW2iusF7hW+k48765LPPUfX++8RecgnJd90ZSpDnAa72+D8HLgYsQH2LV6cIIdTAv4HzgOHA1UKI4W22yQEeBM6QUo4A7nRHvCv424OcCJ19+MbQgePvTqutoQHUaiKnnKGINneZEkA/QJ1pjRifC4ChxSzr7jhaaUStEqTHKjdBKdBtO9hRn+Cnwsoety+l5Ng997J3xEjKXniRildeIeb880j/0x8RHgRvBLptvYGrVsuUUv5CSvmMlPIfza9u9jkNOCClLJRSmoAPsf94tOTXwL+llFUAUkqv121r7GE+Fm+jVauICNPQ2MFErq602oxGbA0NqPR6v+nhFBQU+FqCy3SmVZOUROzll9G41/WcKDuP15AZH45GrdxIaaDbdmQf+93Qlzt6Ps5f9c67zucw5f/+N1HTp5P+5JMIrWcRVYFuW2/g6pX7oxBilJttZwBHW7wvdixryWBgsBDiByHET0KI2W4eo1v8oRBLW3QaVaucPc10pdVaY89xropQJkWAJ5w86Z/1VTuiK636IUOwVlZiWN59nv7aRjNr9pczVqGJW80Eum2To3WcOTSFnT3Mzd+wcSOlf/kLEaedRsYL/yJ70SIyX34JVSeTG10h0G3rDVwN55wC3CSEOAQ0AQKQUsrRXjh+DjADyARWCyFGSSmrW25UUlLCkCFDANDpdMybN48ZM2ZgMBhQq9WEh4e3it6Jjo6mvr4em82G1WrFarViNpsxm83ONoQQzh62RqNBr9c72xBCEBUV5WwD7GGhJpOpVRuNjY2cf/75LFmyBJ1OxyWXXML69euZNGkSixYtIioqirq6OudD28jISPvDGpsZs1VSXWvg17+cz6ZNm0hISOCNN95g5MiRbNiwgRdeeIFXXnmFyMhI6urqUFdWIsN0YDGze/du5wUxcuRImpqa2L9/PwBZWVmkpqaSn58P2KOKcnNzWbt2rfOHZdq0aezatYuKCvvDtzFjxmAwGCgsLAQgOzubhIQENm/eDNgnq40ZM4ZVq1YhpUQIwfTp0zEajc7Zyrm5uVRWVnL48GEABgwYQHR0NNu2bQMgMTGRESNGsHr1aqfNp0yZwubNm6mttedvz8vLo7S0lKNH7f2FnJwcdDodO3fuBOxpLAYPHuycO6HT6Zg8eTL5+fnOczdx4kSKi4udeZKGDBmCWq2mrq6OlStXkpaWRv/+/Z3REuHh4eTOmkXpX55i/9vvUKtWM3nyZA4dOuSsfjR8+HCsViv79u3jSK0Vq00yMyfB+dmjoqLIy8tj3bp1zodxU6ZMoaCgwOPzVFdXh81m88p52rZtmzNTrBLnyWQyUV5e3u48xViqOFxuZuWaH5kx9XSXztPu3bsBSEtLo19aGoV33ImICKf0xhvoN2sW69evx+jQ1tV5Avts+szMTNavX9/qPNXX1zvPXU/PE3jv+9TReWq+bl09Tw8//LAzihBIojOklN2+gH4dvbrZZzLwbYv3DwIPttnmP8C8Fu+XAxPatjV+/HjZlt27d7db1hEmk8ml7TzhxRdflM8//7zz/bJly+TixYvlBRdc0OV+tUaT3Ha0Sv712X/KW265RUop5QcffCDnzp3r3GbWrFnyyJEjUkopLTU1smHHDmkqKXH5c/cGZWVlvpbgMt1pPXLzzXL/Oed0287XO47Lfr9fIncUV3tLWocEg22/3lEi+/1+ifxoY5FH7R67//dy95Ch0rB6dU/ktSMYbOsKQL7sxD+7NNQjpTwCxAEXOV5xjmVdsRHIEUL0F0KEAVcBi9ts8xn23j5CiCTsQz+FrmhyFalgOGfLtMwAs2bNcikvUJROg1ol+PKLxdx4440AzJ07lxUrVjj1tkzLbHX0kjQpKd7+CD0imMLi9EOGYj5SRN2atV1ud6TCHtGTpVBWzmaCwbbTByejErDuoPthnaajR6n9+mtiL7uMqKlTeyqxFcFg257i0lCPEOIO7A9iP3EselcI8YqU8oXO9pFSWoQQtwPfAmrgDSnlLiHEH7H/Ei12rDtHCLEbsAL3SSndvkpO/OUvNO3pOC2zxWpF40acfTO6YUNJe+ihTte3TcvsDkIIdBo1JSXHnamcNRoN0dHRVFRUkJSU5EzLfN9dd2GtrEQVEelRBIOS7N+/P2BS3HanNf7aa6h45RXqf/yRqKmdR1J8s+sEqTE6YsOVSdXQTDDYNjxMzfTByewpcb8UY9lzz4FaTeL8ed6Q2IpgsG1PcXWM/5fARCllPYAQ4q/AOqBTxw8gpfwK+KrNskdb/C+Bux2vgMLdtMxt0WlU2GTndyTNaZltDfYepiYxweNjhegebWoquuHDMDrGYTvCZLGx7Wg1103q14vKApuh6TGs2FdGjdHs8o+lqaiI2q++Ju7KK9ENGqSwwp8nrjp+gb1H3ozVscwv6Kpn3tjYqEjB9bZpmd1Fp1GRkprO/sLDZGVlYbFYMBgMJCYmAqfSMlsqK0EIVAqklu4pLQvP+DuuaNXnDKbm889p3LcPvSOYoCXHqo3YZO9M3AoW2+b1s88wX7L9ONdOdO0HszkJYcIN1/dcXAcEi217gqtjB28C64UQjwshHgd+Al5XRJGX0XoY69sdbdMyd8WDDz7Ip59+2nr/yDBmnD2bBW+/DcCiRYuYOXOmM0a/oKCAEcOHY6uvRx0V5XfDPACpqcqlLPA2rmiNv+46gE57/Ucq7HMWs5OUT44XLLY9c2gK8RFal8M6mwoPUb1wIdHnzVastx8stu0Jrj7cfRaYB1Q6XvOklM8rosjLNDQolxq2ZVpmgKlTp3LFFVewfPlyMjMz+fbbbwHYsWMHaWmt87Zr1SrmXnMDFeUVDBo0iGeffZZHHnnEuX7FihWc58j6qU7qPCrLlzSHuAUCrmjVjxyBCA/H5Ah1bEvzQ8oBveD4g8W2QgiGpsWwu8TgUltV774DQPL//Z9XtHVEsNi2J3Tp+IUQMY6/CcBh4F3H64hj2c+a2267jQULFjjfr1mzhrKyMoxGI8XFxc5CLGazmcmTJ7fbPyYqkudeWcCBAwfYsGED/fvbKxY1p2U+Z8JpAKh0ul74NCGEEIRlZVG9cBGyg8l0y/aUMjw9hsSo0Plwh6Hp0Ww7Ws2Jmq7vjpsOHaLq/Q+ImjkTXf9QoXQl6a7H35w0bROQ3+LV/N7vUbIQiytpmQFnz78tOo0Ko9nqrFTUrLWoqIinnnoKVaMRoVL5rKZud8TExPhagsu4qjVy8iRsDQ00tokSs9okRyuNTM3pnbuvYLLtBaPSAVhV0PUsVMPS7wBIuf8+7wjrhGCyrad06RWllBc6/vaXUg5o8eovpRygiCIvo2QhFnA/LXNL4iPshSMaTPYfjmatOTk5TJ8yBWm1ovHj8cjc3FxfS3AZV7XGXXklAKbDh1otL6kxYrLaemV8H4LLtrl944kMU7P7eOdhnVJKaj77DN3QoYr39oPJtp7ialrmdklMOlrmjxgMro0t+oLwMPsPRnPCtpZabY7cPEKBiCRv0fL5hr/jqlatI4rCuH1Hq+VbiqoB6N9Ljj+YbKtSCYalx7B0d2mn4cumgwcxHTpE3JVXKCGxFcFkW0/pboxf7xjLTxJCxAshEhyvbNonXAvhJmqVIEytorbR3G6dMylbD5JRKY0/JsDrDFe1qsLC0GZmUvXuu8787wBf7yxBp1H1Wg7+YLPtaf0TKKlpZFcnvf7KBfbotuhZs7yqrSOCzbae0F2P/xbs4/lDHX+bX58DLyqi6GeGTqvGaLJisZ3K1imlxNbYhDo+3i/DOIOd2DkXgZSYioqcywrL6pmak0Skzj+ft/g710zsC8CWoqp266x19VR/+inRs2ej9eOhzWCiuzH+f0op+wP3thjb7y+lHCOlDAjH72+FWNqSFGUf528025xabQYDSJtfpWDuiGnTpvlagsu4ozXSkRumOaxTSsmRigb6JfZecftgs21GXDhJUTo+23q83Trj1q1gsRB3+WUKqGtPsNnWE1yN439BCDHSUSbxhuaXIoq8jNGNWqqetD19+nRnVM/s2bOJi4vjwgsvdGn/1atXM23yRHKzk/h44UKn1hMHDzLn1ltRKfxguqfs2rXL1xJcxh2tYY78S5VvvgVAUWUDRrOV7MTe+yEONtsKIThjUCKbjlRxsrZ1WGflm28itFr0I0cqJbEVwWZbT3D14e5j2PPyvADMBJ4B5iiiyMt0F2rZE9544w0uu+wyZ1TPfffdxzvvvOPy/n379uWtt97k/EvmUtdocWpNjIykT3o66zZuVES3t2jOPx4IuKNVHReHJjWVhk2bkFLyuaOXmpfde1NXgtG2N52eDcCmI6eGe2yNjdRv2EDcFVegiY9XQl47gtG27uLqAPJcYBZwQko5DxgD/OwrTXualrmZ7OxsxowZg06rwWyV2KTEZjQiTSbmXHgR7733nhKyQ3SDEILEm38NViuWk2XsKzWQERfOsPTAif/2R0b0iSVMo+I/q09lXjd89x2YzURMnuRDZT8/XH1SZZRS2oQQFsds3pOA32Q6WvNxAeVH6zpcJx0VbtwlKSuKqVcO7nR9T9Iyt0WrVgEStVaPpbIMgNOmnMHjf/9bj9tWkjFjxvhagsu4q7U5lrxuxQqKKjIYkNy7w27BaNswjYpzR6Tx1Y4SGkwWIsI0VH3wIerERCInn66wylMEo23dxdUef74QIg54FXtUz2bsaZn9HokyhVh6mpa5JWqV/YfJ0GhGNjWhioggrW9fjh9v/yDMn/DnORJtcVerzpGds/qT/3G4vJ5+vTi+D8Fr28tyM7DaJJuOVGEzGjFu3kzsJRejjuq9H9Zgta07uNTjl1L+1vHvf4QQ3wAxUsrtiijygK565gaDwa3hF1fpaVrmltgdv6DOaCKuoQF1fIIzLbM/U1hYSN++fX0twyXc1apJTCT6vNnUrP0RwwALw9N7d2QzWG2bmxWPELDgx8MMM24CIHJS+zxWShKstnWH7iZw5bZ9AQmAxvH/z5aepmVuS5Reg95oT/urjoqkoKCAkb0U5RCiY8JHjEBlqCXSZGTO2D6+lhMUxEZoOW9kGusLK6nfmE9Y//5dVjwLoQzd9fj/0cU6CZzpRS2KEBYWpljbzWmZz3KkT546dSp79+6lrq6OzMxMXn/9dc4991x27NjBnDntg6A2btzIpZdeSlVVFYu/+IJnExLZuvhz1LGxrFixggsuuEAx7d7AG883egtPtDaHdV5Vmk+Ubq53BXVDMNt21tBUfsg/QMMPPzhzI/UmwWxbV+nS8UspZypy1F5Eo2Bmy9tuu43nnnvO6fjXrFnT4XadpWWeMGECxcXFADSYLBgLD2F1jPcvXryYzz//XCHl3iEhIXAyc3uiNcIxBDG6+oi35XRLMNt28sBEZh61F7uJmjnD+4K6IZht6yquxvHf0NFLEUVeRslCLD1Ny9wSnbQSYWmiSa2lrKyMu+++m/heimv2lM1d1Kf1NzzRetwk+CF9JGmGcgUUdU0w27ZPXDhTVVVUh8cQPbP3+5bBbFtXcbU7PKHF/3rsMf2bgbe9rijAmD9/vlfakfX28f0abTj9k5K45JJLvNJuCM/5qbCCExEJnF683+Ow4BDtkVYrg/dvZmt0JprDlUzoxYlxIey4GtXzu5bvHaGdHyohyNt4miu/t5EmEyCoV4XRZLGh1/q/bn+/I2mJJ1oPlNVxMjoZ0dSE4bvviDnnHAWUdUww27b+x3VojPUcyMpky/qiXnf8wWxbV/E09WM9EBC10SL8PNFZM9baWgjTIoHqBpOv5bhEsE+E2Xa0moO50wFo2Ni7BeeC2baNe/cA0HDpL/hqp30yV28SzLZ1FVfH+L8QQix2vL4E9gFdxyf6CYEwWcPW2Ig0mbBptYRr1c6KXP7OqlWrfC3BZdzVeqzayE+FlYwckIpu2DBMRb37gDeYbVv/449o0tKYM2UojWYb768v6n4nLxLMtnUVV8f4/97ifwtwREpZrICenyWyqQkAW0wMESoNFfVN2GwSlcq/x5Q7q6bkj7irtblM4Ny8TLQr+zhTNPcWwWpb84kTNKz7ichpU5mak8zg1Ci+3FHC/DP699r1Hqy2dQdX0zKvwt7Lj8U+gStwStgoSE/TMj/77LMMHz6csZMnc/6vfkXR8eNE6tRUVpRz9rnnKindKwTSw053tRaU2u8Uh6ZFE5aZhenAQRr37VNCWocEq20bd+4EIOH66wGYOz6TLUXVfLPrhCLaOiJYbesOrg71/ArYAFyGPVPnT0II74SzKIwS6Rqa6Wla5nHjxrFx40Y2fPwxl557Ln/885+JDdeSkpxCYnIqP/zwg1LSvcL06dN9LcFl3NEqpeTrnSVkxocTrdcSe7F98l3D+vVKyWtHsNq2bu1aEIKI8eMB+OWUASRGhvHqmsJe64kHq23dwdWHu/cB46SUN0kpbwTGA79XRJGXUTKOv6dpmWfOnEm4VgvApDPOoKioCCEEkTo1Z8w6j3fffdfrmr3Jtm3bfC3BZdzRWl5nYuexWmaPSANAN3QoqqgoTEVHlZLXjmC0rTSZqP7wI7RZWc7qcmqV4MLR6WwpqmbHsRolZToJRtu6i6tj/BVAy6ekBscyv2DFW69w8khhh+usFitqjfuhkSn9BjDzpps7Xe+ttMzN4/sL/vc/5wzgaL2W4aPH8t9n/9KjtpWmqqp9/VR/xR2t+0/aL/UZQ1IA++22NiurV8f5g9G2TQcOABD/i9ZpGu48azDvri/ijbWHeP6qcV7X15ZgtK27uNrjPwCsF0I87qjG9RNQIIS4WwhxtyLK/BxvpWW2lJbywRdfsGnrVu644w4A4iK0JCWnUFJS0uP2Q7jP1qPVAOSknqrXHNa3L/Vr12I+0Xtj0cFG7bdLAYia2TrFV3xkGFNzkli87TgnDd7JeBuia1zt8R90vJppTiKj3AC6G3TVM7darYpM4vJGWmYpJctWreKZ115j9bp1zskaKiFQSzNhOj02KVH56cOo3NzASdDqjtZFm4rJiAsnJVrnXBZz3nkYvv2Wxp070aalKSGxFcFo2+pFiwAIy+7Xbt1D5w9j5b4yXl97iAfPG+ZVfW0JRtu6i6tRPU9IKZ/Anq3zH83vWyzvECHEbCHEPiHEASHEA11sd7kQQgoh8tz/CF1jsSgTgOSNtMybN27kd48/zqcffEBKSkorrWXFhxk0ZBj1Tf4bQFVZWelrCS7jqtYGk4XCsnouz81oFVERebo9YZvpSO/E8webbS1VVVgrKkiYPx+hau92BqdGM7F/Au+sO0Kdwtd8sNnWE1yN6hkphNgC7AJ2CSE2CSFGdLOPGvg3cB4wHLhaCDG8g+2igTsARUImTCblZsE2p2VuZurUqVxxxRUsX76czMxMZ3K2HTt2kNZBL/H++++nvqGBq3/1K8aOHcull17qXLfpp7VMnXWOXzv+w70c294TXNV68KQ9Z9LwPq3r66pjYlDHxtKwqXcSfAWbbY1btwIQeXrnJRbvnz2UBpOVuz/a6h1hnRBstvUEV4d6XgHullKuABBCzMBehrGrQpmnAQeklIWOfT4ELgZ2t9nuT8BfsUcOBRQ9Tcv81RtvYDMa0Q8dilCrW80yXvLFF/zj1XeprDeRGqMPqNjjQObLHfbnKkPT2hdW1/brR93332Otq+/VUoHBQPnL/wGVivCRnfcXx/eL5zczBvLyyoN8taOE80el96LCnxeuOv7IZqcPIKVcKYTo7srPAFrGvxUDE1tu4KjilSWl/FII0anjLykpYYijBqpOp2PevHnMmDEDg8GAWq0mPDycurpTxdajo6Opr6/HZrNhs9mwWq2YzWbMZrOzDSGEc5hGo9Gg1+udbQghiIqKcrYBEBkZiclkatXGqFGjmDx5MtXV1eh0OnQ6HfWOLJvNbdTV1bFo0SIMBgORkZE0NTU5h3Q0TU2ImBjqHCGnKpUKm83GkSNH+M1vfkNGUjzlRitVtXVoVU7bs3v3bk6ePAnAyJEjaWpqYv/+/QBkZWWRmppKfr49t0xMTAy5ubmsXbvWedxp06axa9cuKirsgVljxozBYDBQWGiPjMrOziYhIcGZEjY+Pp4xY8awatUqZ5bK6dOno9VqWblyJWAfi6ysrHT2UAYMGEB0dLQzHC0xMZERI0awevVqp82nTJnC5s2bqa21z5LNy8ujtLSUo0ftl01OTg46nY6djkk/KSkpDB482HmXpdPpmDx5Mvn5+c5zN3HiRIqLizl27BgAQ4YMQa1WYzKZWLlyJWlpafTv35916+wlo8PDw5k4cSLr16/HaDSyeFMDQ1KjMJYVsXJnKQDDhw/HarVycuRIYrZv5+DqVWTNnMl6R1x/VFQUeXl5rFu3jiZHlNaUKVMoKCjw+DyZTCZsNptXztO2bduc0SFKnKf4+HjKy8s7P09hYSQUFmKZNIk1jp5/Z+dpjKaEpHDBHz/fzml9Y9i5ZWOH5wlg8uTJHDp0iBOOB+7N52mfY6JdRkYGmZmZ7c6TzWZzXrc9PU/gve9TR+ep+bp19Tw9/PDDLFy4EAdJdIaUstsX9rw8jwDZjtfDwKfd7DMXeK3F++uBF1u8VwErgWzH+5VAXkdtjR8/XrZl9+7d7ZZ1hNlsdmm73sZmsciGHTuk6eRJ57K2WhtNFrntaJU8UWN0LnP1c/cGlZWVvpbgMq5obTRb5MAHv5TPfLOnw/XGvfvk7iFDZfWSJd6W145gsq25okLuHjJUVixY4FJ7y/eckP1+v0T+7v3N3pDXjmCybVcA+bIT/+xqOOd8IBn4BPif45eku5m7x4CsFu8zHcuaiQZGAiuFEIeBScBibz/gbe4d+BtWR+9J1aI0ZFutYRoVeq2aijqTX+YXCbaJMPmHq7DYJEM6GOYBCMvKBMDYC+P8wWTbGkdgQ1j/AS61d+bQVOadkc3ibcdZse9kj/W1JZhs6yndFVvXCyHuxD4OvwuYKKUcL6W8U0rZ3cyCjUCOEKK/ECIMuApY3LxSSlkjpUySUmZLKbOxzw2YI6Xs3fy3PsLmGBJSRXY+YiaEIClKh8Vmo9EcGBk7A5kPN9qHLs4YmNjhelVEBJrkZGq++MIvf4j9lYb8TQBE5I13eZ/fzx5KcrSO+xZu41B5vVLSfrZ01+NfAOQBO7BH5/zN1YallBbgduBbYA/wsZRylxDij0KI9pXHFcJfC7HIpiZUkZGIFjWBO9IardegEoLj1f43sSUxsWMH6Y+4onXb0WrOH5VGYpSu023ifvELbAYD1upqL6prTzDZtungQaJnz0YVHu5ym3qtmldvyKPJYuOK//xIcZX3Uq8Ek209pTvHP1xKeZ2U8r/Yx+ynudO4lPIrKeVgKeVAKeWTjmWPSikXd7DtDCV6++FuXGy9hZTS7vh1rR1MR1q1ahUp0TrqTRaMfpanf8SILiN6/YrutO4vNVBU2cDozLgut9MPt0ckm48qm7cnWGxrqajAXFSEbtAgt9sdmxXHf68fT22jhQtfWMumI96JaQ8W2/aE7hy/ufkfRw8+4GgZ7eNtWqZl3rp1K5MnT2bEiBGMHj2ajz76qNP9bLW1SJsNE/CLX/yCQYMGMXHiRHbt2gXY4/5vuukm5/ZxEfZEbv42nb05oiAQ6E7re45iIHPG9OlyO21GBgCmQ4e8I6wTgsW2Ve+9D0DEeM9moJ4+MIlPfnM6GpXg8pfXMefFtXy6pRibzfOhtmCxbU/ozvGPEULUOl4GYHTz/0KIWkUUBRAt0zJHRETw9ttvs2vXLr755hvuvPNOqjsZDrA12B/ivvW//xEfH8+BAwe46667eOyxxwAYNWoUxcXFFBXZnVGYRk1iZBg1RjPWHlzwITpnc1EVkwYk0Ceu6zvEsMwMhF5P1ccLu9wuhJ2mwkLUsbFETJrkcRsjM2JZetd0fnfmIIwmK3d9tI3ffbgl9NyrB3Tp+KWUailljOMVLaXUtPi/49CHnxEt0zIPHjyYnJwcAPr06UNKSgplZWUd7idNTaj0ehYvWcKNN94IwNy5c1m5cqXzoeFFF13Ehx+eqmcfH2mP/lF6Ors7aDSuTgPxPV1p3VFcw/biGk5zoei3KjKSyClnYDp4sNtte0Kw2LZx927CJ+T1eAJiQmQY95wzhK/umMr/zcrhy+0l/HLBRo++D8Fi2x61q0irvUz1FwcxHe/8yb8nAZ1hfSKJu2hgp+u7Ssu8YcMGTCYTAwe2319KibWuDnV0DMeOHSMryx7xqtFoiIuLo6KigqSkJPLy8nj66ae5//77AYgI0xCl01DuGOsPD/P9Q+spU6b4WoLLdKX1W0f1pxtPz3aprYixY6lbthyrwYBaoUI/wWDb+p9+wlxURKyLFelcQatWcffZg8mI0/PQpzu59tWfePdXE4nWa11uIxhs21NcjeMPWGw2ZW4HO0vLXFJSwvXXX8+bb76JqoNkVDaDAaRE6NtHjrQMEUxJSeH48eOt1idEhmG1wUcbe7c4dWc0z0QMBLrSuuFwJSP6xHQZzdMSbaY9nt9crFzZ6WCwbXMYZ9yVV3j9mL+Y0Jd/XzOOXcdrufXdTW6F1waDbXtKUPT4u+qZGwwGRcovdpSWuba2lgsuuIAnn3ySSZ2Madqa00QkJpKRkcHRo0fJzMzEYrFQU1PjDN9qbGxsF+UTFxGGVi14/8cirpyQRUSYb09f8xT+QKAzrSdqGtl4uJJbpnV+DbVF67hLq/70U9KGKZNCOBhs21RQQFi/foqlsZ49Mp37Zzfwl6/28uL3B/jdrByX9gsG2/aUoO/xK0XbtMwmk4lLL72UG264gblz57batmVaZmkyIbRahFrNnDlzWLBgAQCLFi1i+vTpzrHQgoICRo4c2e640XotBaV1LPixd9IDBzsr951ESrhkXNfRPC3RDxuGOjmJxu07FFQW2JhPnMCwdCm64crm1p93Rn9Oy07gX9/vZ2cvlW4MBoLe8Uc4ansqQcu0zB9//DGrV6/mrbfeYuzYsYwdO5atjoRUzWmZpdWKtboa4Yjf/+Uvf0lFRQWDBg3i2Wef5a9//auz7RUrVnDBBRe0/zxhavL6xfOPpfvYU+LbnktentfLJyhGR1qtNsnb647QJ1bPkFTX7wqFSkX0jBmYFIzlD3TbNmyyD/PEnH22osfWqlU8M3c0eq2a//tgC02W7od2A9223iDoHX9zNk0luO2225w99uuuuw6z2czWrVudr7Fjxzo1TJ48GZsjF4/akaZBr9ezcOFCDhw4wIYNG5wPepuamsjPz+e8887r8Lj/vjYXvVbNbe9txmK1Kfb5uqO0tNRnx3aXjrTuPFbD7pJa5k/p73bUiTYzC2tFBbYG780obUmg27apYD9oNEQ7UpYrSXZSJH+6eCSF5fU8vrht1vf2BLptvUHI8feA3NxcZs6cidXadS+juSBLc2F1VSe1epu1FhUV8fTTT3caypUao+fB84dSWF7PC98f8FB9zzmq8OxVb9KR1pX77OG2F452fZinmeaEbbVffdUzYZ0QyLaVVitV772Hrn9/RIskhEpyybgMbpjcjw82FLG6oOMw6mYC2bbeIugdv9LMnz/f5XxANqMRoVK1ys/TETk5OcyYMaPLba6e0JezhqXwz+X7WV9Y4arcEA4azVZeW1PI4NQo0mL1bu8fOW06AA0bN3pbWsBjOnQIW10d4Y473t7iwfOG0S8xgse/2BWa3NUNQe/4dTrXQvSUxjm+r++8mpY7WlUqwTNzxxAbruX+/23H0KjcnU1nNE9YCwTaav121wkMTRbuPGuwR+2poyKJOO00TEeUCa0NZNs2HbBPbou/+qpe1REepub3s4dSWFbP0t2dD5EEsm29RdA7fn8pWSgdtX/VsbGdbuOu1oTIMP5+xRiOVDRwwxsbqGnoXefvLz+qrtBSq5SSfywtIFqnYeaQFI/b1PbNUuwBb6DaFhx3QUIQ1r9/r2s5d0QaqTE6nvlmb6e9/kC2rbcIesffNtbeV9iax/e7yL/vidazh6fy50tGsqWomsv/8yMlNb1XeKa51F4g0FJr/pEqiiobuOOsnB7NgA7r1w9rRQWNe/Z4Q2IrAtW2UkqqFy1Cm5XlVhpmb6FWCe4+ezDFVUZ+PFje4TaBaltvEvSO319oLryixMOu6yb149Ub8jhUXs8l//6Bfy0PxTR3xT+X7SdMo+Ly3MwetRM7x15WwrBiRTdb/nywHD+ObGoi7vLLfabh4rEZxEVoeeSzXZh9GPXmzwS941cyIZOraZmllNhqalHp9YgWaRxWr15Nbm4uGo2GRYsWObWWlZUxe/Zst7ScPTyV9381kSidhme/K+DCF9Zy/6JtLsU1e0pKiufDJL1Ns9YDJw38cLCc30wf6Ex85yna1FTUyUmYi491v7GbBKJtAZoO2KPM3Km25W30WjVPzBnBsWojS3e1H+sPVNt6k6B3/Hq9+xEbruJyWmaLBWmzom4Txtm3b1/eeustrrnmmlZak5OTSU9P54cffnBLz8QBiSy/ZwabHj6L6yf14+P8Yq55dT0VdU09/agdMniwZw9GfUGz1v+sKkSvUXPNxL5eaTesbz9MR7w/izoQbQtQueBtAHQ+foB64eg+9E2I4Nnv9rXL3R+otvUmQe/4lSzE4mpaZpvjwa7Qtf4Rys7OZvTo0c5kbi21XnLJJbz33nse6UqM0vGnS0byz6vGsr24miv+u06RuqXNs5YDgbVr17LxcCWLNhVzWW4GqTHe6RDoBudg3LoVc5uEej0l0GwL9jvbxl270A0ZgjrGt1nb1SrBnWflcLCsntX7W8f1B6JtvU1QJGn7+uuvOXHiRIfrrFarR3V309LSOp05C+6lZbbW2MfbO8rI2Rl5eXk8/PDD7oluw8VjM0iM1PGb9zZx1Svr+PiWyfRL7PzhcrDz7NICYsO1HodwdkTcxRdT/cGH1G/YQNwll3it3UDEUlaGtaaGpNtv97UUAM4flc5fv9nLP5fvZ0YPoreCkaDv8SuFO2mZbQYDKp0OlbbrnOEtwzk7SsvsCVNykvjo5sk0mKzc8s4m6r1YyCWQwuK2VwjWFVZw9Wl9SY72nm7d8OEgBOaj3k3RHEi2bdZa+6V9FrOvh3ma0WvV/HrqALYUVbcKdghE23qboOjxd9UzVwpX0zJLqxVpNqNOSe22zaioKOf/HaVl9pThfWL451Vj+eWCfP74xW7+One0V9qdPHmyV9pRmuoGE+/tM9MnVs9dZ3vXKanCwtCmp2M6fNir7QaKbeGUVsPSpQCEj/HO9eUNLs/N5O9L9/HuT0d4+nK7rkC0rbcJ+h5/fb33x7bB9bTM0mTi0eefZ/HSb93S2llaZk85c2gqv5rSn4/yj7J4m3fGo/Pz873SjpJIKXn0812U1jbxtyvGoNN4v3KZbsgQ6lavxurF50mBYNtm8vPzkTYbjXv2EH/D9T6J3++M+MgwTh+YxJr95c6HvIFmWyUIesdvsykXx+tKWmZrdTU79+8nzZF5syUbN24kMzOThQsXcsstt7RKwdpZWuaecO+5QxiQFMkfPtlBaW3PJ7Yp+eDcW3y00f5Dd9FALWcMSlLkGLGXXoLNYKBxd/eZIV0lEGzbTF1dHY279yAbG9ENGuRrOe04d0Qqx6qNLNpkH44LNNsqQdA7fiVxJS2zrb4ei5ScMX16u/0nTJhAcXEx9fX1VFRUsGHDBue6xYsXc91113lVr06j5oVrxtFgtrpdri4QWXewgkcX7+K0/glcMtD1mqzuoh86FABzAGV99DbVH30EQhDph8MoV+ZlER+h7XQm78+RoHf8kV2kSOgp3aVlllJia2riqxaTubqiWWtZWRl333038fHxXtPazIg+sTxywTC2FFXz2ppDPWpr4sSJXlLlfY5XG7n57Xxi9BpeuHocp0/uuBSmN9D26YPQamk6WOi1Nv3Ztm2ZOHEixh07iDzjDMI6uLP1NUIIpuYk89WOExRVNAScbZUg6B2/yRFDrxRdpWWWjY32wuouPplv1pqcnMwlCoYGXj85m7FZcby08gB1PYjyKVaw2HhPKCyr47rX1mO22XjvV5NIjdErqlVoNOiGDaPm88+RXqr/4K+27Yijhw7RtH+/30TzdMT/zRqE2Wbj4/yjAWVbpbQGveNXshBLd1irqgCBqkW0Tlf0ltbmyS1VDWaeXVrgcTvHjnk/VUFPkFLyzk9HmP38GiobTCyYdxpD0uwlFZXWGnfZZVgrKjCXlHilPX+zbVdULVwIVqtP0zR0x6CUaCYPSOSzrcc4GkCOX6nrIOgdvy+xNTaiighH1UtViNxhxpAUzh+Vxps/HuJ4de9l9FSK8rom7vl4G498tpNh6dEsvm0KEwck9trxdQMHACiWn9+f0R4sRJOSQvSsWb6W0iWXjsuguMpIQVUocVvQO35fTtaQTSaXh3mg97X+dsYgpMTjsf4hQ4Z4WZF7NPfwpz2zgglPLuOzrceYd0Y2i35zOn0TI1ptq7TWMEc0i3HHdq+052vbukNUZSW6ANB77sg0VAL2N0b7WorLKHUdBL3j91UhFmtdHdJqQaVzPSdMb2sdmRHL9MHJvPHDIY9y+XiSCsNbGBrN3PHhVh75bCdxEVruOmswS++azmMXjUCrbn9ZK61VEx+PbuhQqhcu8kp7vrStOzTuK8B64AD6If6f+CxGr2VkRiyLd5X7pGKdJyh1HSjq+IUQs4UQ+4QQB4QQD3Sw/m4hxG4hxHYhxHIhRD9va1CyEEtXaZk/fO89EAJ1fFyn+z/77LMMHz6c0aNHM2vWLAoK7OPtnqRl9pQHz7eHIr7oQdH23V6MW3eH2kYz1722nsXbjnPr9IH87zen83+zchiU0vmzlN7QGnPeeVhKSrDW9XzSoK9s6y71a9cAEO/IMOvv3HvOEMxWSf7hKl9LcQmlrgPFHL8QQg38GzgPGA5cLYQY3mazLUCelHI0sAh4Rik9StBVWuZ7H3mE2qYmRBe/2OPGjSM/P5/t27czd+5cHn30UcDztMyeMDQthrOHp/K/zcUcViCDp7epb7Jwy9ub2FZcw9/mjuaB84Z22MP3BWGOhH3eTt/gzzQVFGCNj0fbp4+vpbjEhOwENALe+KFnocyBjpLfmNOAA1LKQimlCfgQuLjlBlLKFVLKBsfbn4CelUTqACULsXSWljk9LY3khATKu5l1N3PmTCIi7GPRkyZNapWUrSdpmd3liTkjAPjb0n1u7ZeWlqaEnE6RUvLQpztYV1jBYxcN54o812PGe0Orfpj97qn644973FZv29YTbEYjdWt/QN3XO7UNeoPwMDWzh8Sw9kC5VxMWKoVS14GSSdoygJZTGYuBrmYj/BL42pMDFRT8CUNdJ3VPpQQPxs6jo4YxePAjna7vKi3zT6tWYTKZyHEj187rr7/O+eef73zvjbTMrtInLpwZQ5L5akcJ5XVNJEW59pC5fy8X0351TSGfbz3OL/KymHeGe8fuDa1hffsSPn489T/91OO2etu2nmDcuhVrRQUp993rayluMWf8AJbs3cqa/eXMHunfP7BKXQd+kZ1TCHEdkAe0z2uAPdVx89NtnU7HvHnzmDFjBgaDAbVajQSs1lO/3mq1xjGbViKl/QGJlBIp7WFcKqECcSqPjxAClUrVYgau/Yeivr7euU1kZCQmk8kZa19RUUFsbCwGgwGw31nodDoOHjzIjb/6Fa/+6U9oY2Opq6tzpkaIjIykqakJi8WuVa/XI6VkwYIFrF+/ni+//BKbzUZ9fT3h4eHOO4CWbUgp2b17NydPngRg5MiRNDU1sX//fgCysrJITU11JneKiYkhNzeXtWvXOo87bdo0du3aRUVFBQBjxozhmlExrNxXxv0LVvLoBUNISEhg8+bNgD0h3ZgxY1i1ahVSSoQQTJ8+ne+//96ZQXTsoJHUbC2hrLYSY6Kk37ABREdHs23bNgASExMZMWIEq1evdtprypQpbN68mdraWsD+Y1daWspRR+qDnJwcdDodO3fuZOtJC89vbuK07HjOTqhg5cqV6HQ6Jk+eTH5+vjOnycSJEykuLnbGPw8ZMgS1Ws2GDRuIiooiLS2N/v37s27dOsCeZXXixImsX78eo9Ee1jp58mQOHTrkrPEwfPhwrFYr+/bZ74gyMjLIzMxk/fr1gD2ral5eHuvWrUOTnkbk5s2Y6uo4UFTk8Xmqq6vj/PPPb3eeDAYDhYX2GcLZ2dkunadt27ZRVWUf087NzaWyspLDjuGoAQM8P08VX35FNLDFamVsebmzMHhKSgqDBw925rFy5zw1j2kreZ4Mh7YSroG/fbGFs4aeTUFBgde/T946Tzt37iQqKsrl8/Twww+zcOFCHHSenMruEL3/AiYD37Z4/yDwYAfbnQXsAVI6a2v8+PGyLbt37263rCNqa2td2s5dKisrZb9+/Votq6mpkePGjZMfvPSSNO7d61I73333nRw6dKgsLS1tpbW2tlZmZGS0297Vz+0uVqtNzvrHSjn68W+lxWpzaZ8VK1ZIKaVs2F0uj/5+datX1ZeFXtNWXNUgxz7xrRz/p+9krdHkURvNWpWm5uuv5e4hQ2V9fn6P2uktvT3h4KWXyn1TpgSE1pasWLFC/nnJLpnzh69kk9nqazld0hPbAvmyE5+q5Bj/RiBHCNFfCBEGXAUsbrmBEGIc8F9gjpTypBIilAqR7Cwt8/XXXcfFU6eialHr98EHH+TTTz9t18aWLVu45ZZbWLx4MSkpKa20ejstc3eoVIL/m5VDjdHMFy6mbQ4PD8dmslKxoH3kQd3qYuo2eGcW6+3vb8bQaOGNm/KI1nuWbM1btQ26Pc64XNBoqP54Yfcbd9WOH6U27ghrTQ1Nu/cQkZfn91rbEh4ezvh+CZgsNj7a2PWEO2udCcOaYhr3VSLNvT/xSynbKub4pZQW4HbgW+w9+o+llLuEEH8UQsxxbPY3IApYKITYKoRY3ElzHhPlYroET+goLfOCt95i4ty5TLjwQrZu3QrAjh07OnxIc99991FXV8cVV1zB2LFjnUXXQZm0zN0xY0gyeq2KN3887NL2EydO5MQzG08taHM1VX9yAGnrWQbQT7cUs6WomgfOG8rozDiP2+mtxFza1BQiJ0+mcb/nqTDA/5O0GR3DOvFXXOH3WtsyceJEZgxJJiEyjM+2dt7JMR2ro+TP66n58hDlb+7i2CM/YNxd0YtKAzRJm5TyKynlYCnlQCnlk45lj0opFzv+P0tKmSqlHOt4zem6RfdRMvd2R2mZ87//nvWLFrF182bGjh0L2HPwdFRJZ9myZZSWljrTOL///vvOdUqkZe6OGL2W30wfxLaj1ewpqe12+03LfsJWZ3/mkXjdMDL/MpXMp6eSdv8E5zYVC3Z5rKey3sTfvy2gb0IE10/u2RSP5nHe3kA3aBCm/QeQPUgQ2Jt6PaF60SIQAv3IkX6vtS3r169Hr1VzeW4Gm45UdRjdY61p4uQLW9otr3h7N/WbSntDJqDcdeAfAdAKIhXMOd9RWmZLZSVCq0W0qK/77bfdV9+CU1qVTMvcHXPG2uOx/7vqYLfbpi6zO339sATCR556jqRJ0JN88ygAGvdVIa2e3SL/c1kBx6qN/PmSkT2unNX8QLA3iJgwAWk2U9WD4Z7e1OsuUkrqf1yHftgw1DExfq21I5r1TslJBuzRYi2RUnLylVOpN1RRrYcXqxYWYC5rQGmklNTVK3OcoHf8StMyLbO0WJAmk8vZODtD6bTMXdE/KZLLxmXw2dbjVNQ1dbqdcW+l8/+4iwe2Wx/WP5awLHtOlMqF7g977D5ey4J1Rzh/VBrTBie7vb8viZo5A01KCkbHUF+wYT5yBFtNDXFXX+VrKT1i+uBkhqZFs+5g6+Gbxn1VWCvsz+4Srx9On4cnkfHUFBLnjXBuU/P1YUW1GRrN3LdoO//Y1IjR1HG9j54Q9I5fyUIsbbE5HvSqY2M92r83tXbF3Dz7PLoF6450uk3FW/YhnLg5A9HEtc9HJIQg5txsAIxby7C5cfGaLDYeX7wLtUrw6IUjut/BBXqzwLYQAv2wYT0qxejPBcGr//c/AMLHjAH8W2tHtNR7+sAk1h+q5GCZfUhYWqXz2g4fkUj4CHuGVyEE4UMSSL7FXrC9cXcFpmKDIvqW7jrBWc+uYtGmYsYN7INO4303HdCO35VhnKamznut3sbqiMFtGdHjDt1pVXLYqiWnD0xifL94XltTSKO5vcM27jvV24+c0PkEGP2gOPTDEgCoXe56uuIvdxxnw+FK7j1nCGmxntmyLYcO9e4U/YjTJmAqLKTOw7Qbva3XHerWrEXbt6+z8Io/a+2IlnqvmWif/f3ZFvt8gurPT+WsSrh2WLt9df1j0Q+xD8Ead3q3lKOUkqe+2sPN72wiXKvmvV9N5PrhOlQq70cmBqzj1+v1VFRUdOsMmydZKI2UElt9PSq9HuFhmoiutEopqaioQO/hj4q7XDQ6nQaTlW92nmi3rm6N/UtSOsqG0HZ9CUVNybDvs7rYpbF+k8XGk1/uJTVGx83TBnigvGOaJ/n0FvHXXIPQ6ahbucqj/Xtbr6vY6utpKigg9sILneHH/qq1M1rqHZQSzaiMWD7ceBSr1Ub9Bvu6tPvyEJ043ISr7ak5DCuLsVR6Jwmk1SZ54H87+O/qQi4Ync5Xd0zljEFJitnWL2buekJmZibFxcWUlZV1uV1jY2OvOEtptWIpLUUdG4tqTyfpI7qhO616vZ7MTK+nM+qQGyZn88/l+3ltbSGXjMtwLrdUNdJ0oBpNSgSGPt3f6uoHxqFJCsdSbqRhaxmR41O73H7RpmLK65p49soxqBXo6fQWqvBwwseMoX7dj76W4lWqFi4Em42ICXm+luI1Th+UyH9XFbLnwz3EYQ9W0CR2Hj+v0mvQZkRhPlZHY0EVUZPSe3R8q03y0Cc7+Cj/KFef1pc/XzJS8Ws/YB2/Vqt1KY/FyZMnSUlJUVxP2Yv/pvzFF8le+DHhw9rfIrpCb2l1BZVKMCE7gaW7S9l7opahaTEAGLfbb2/1Q+IZPsK1JGnxVwym7OVtmIoNXTr+JouVv3y1h0EpUVw0xrvZHocPb5sYVnkiTjuN8hdfpHHvXvRDh7q1ry/0uoLhu2Xohw8nYtKp4vX+qrUz2uq9Y1YOr605RNwO+xBm3IXd32km3zKa44/+SPVnB4gYk4wq3DNXarVJHv7M7vSvnWh3+i0ncipl24Ad6nGVlqGWSlK3ejWatDT0Izx/GNlbWl3liYvtn+V/m07VKK3fXIrQqog9r7/LenX97D8a9etKsFR0Hvr32ppD1DVZeOh876da9oVt466YC0CDB7HY/nYtgP2utmnPHsLHjm3lnPxRa1e01RsRpuHCBHsEmm5yepe9/WZUYWrUsfaSqo0Hqj3W8rdv9/HBBntP/8lLR7XLNKCUbYPe8TcnbFISW2MjjXv2EHvRhQiV5ybtDa3ukB4bTnZiBG/+cJhGsxVTsQFLaQPqBD1CJdzSGz3TfndgKup4eMhqk7y+9hAZceFMGeT98E1f2FabmoomLQ3DsuVu7+tv1wJAzWefYWtoIDw3t9Vyf9TaFR3pvcVsj9U/GOP6fJGU340DoPK9PR4FXnyx7Tj/WXWQyQMS+culHadnUcq2Qe/4e4PqjxeC2Uz4+PG+luJ1bps5CItNsvFwJU2H7LN5E+a6X2YvZpY9Z3vVp/s7/JK8vPIAlfUmHjx/KGEKhK/5iqipU2nYuBHT0aPdb+zn1K1ajToxkZjZ5/paileRFhsxNWZ2YeWf+12flauOCkOE2X8orFXuRQ8eOFnH3R9vJSMunNdvyuv1sqvB8w3rhIyMjO436iF1P6xFHRtL1NSpPWqnN7S6yznD0whTq3j2m73UfGmf4dg8McsdvUKjQoRrkCYb1jaRECaLjf+uLmRoWjSzRyiTH91Xtk244XoAGvI3ubWfv10L0mKhYfNmIiZMaBe15m9au6Ot3rof7fl6CmI1/HSoghqj6/V4k35p76lXvOd6QIfNJpn/1kZ0GnvIZkRY588HlLJt0Dt+paNgbE1N1P/wI9HnnNNlmUVX6K2IHXeIjdBy4+n9qD9mn+CiTTs1ycxdvUnX2x96m461zp/0/LICDI0W7piVg0ahMoq+sm3YwIGoYmKodOR0chV/uxZqFn+BtbycqBntS2b4m9buaKlX2iR1P9mzyI67JAcp4fu9rvf6w/rYvw/mY3UuJyT8+9J9FFU28JfLRpGd1PWkTaVsG/SOX+kEUtWLFoHFQtTMmT1uy1+TXV2Wm8m92B94Jd54KsrAXb1hWfaHvJXv73UO95TWNvLGD4cY3y+e80b1LCyuK3xlW6FSkXDdtTTt3YupyPVJbP52LRi3bEYVE0PsxRe3W+dvWrujpV7j9jKslY1E5KYwpn8CYWoVL63oPk9VM0KrJt4x9NnYYmJjZ7y/voiXVh7k3BGpXDS6++s9lKTNT2lYvwF1YiLRZ/bc8fsrg2PCGY6aJiTmSM8jgIVWhS4nDgBrtX1M9N2fjtBotnHnWTnekOqXxDjSa9d+5VFlUZ9jNRgwfLeM8DFjen0sWmkattnnAcVeMIBovZbbZg5i/8k6TtS4PjErrJ996LP228NdbldQauAPn+0gPVbPM3N9a8ugd/xK5uM3nzyJYelSoqZM8Up7SmrtCQ2b7be+D9DAX785FWXgid7Yc7IBqPnqEMeqjbzw/QHy+sUzNUfZRGy+tG3YgAHoR4+m4vXXXU7V7E/XQt2KFVirq4nvJCmbP2l1hWa9pqMGGvdUosuJQx1pj+qZNcw+j+aZb/a63J42OQLd4HgsFY2dDvc0mq389r3NSAmv3ZhHbLhrBYWUsm3QO/68POVmGBqWLQMg7hdXeqU9JbV6ipSS2u/tESm29Eg+3XIMsyP1gid6ten2Mc3GvZV8vME+9OHN1Ayd4UvbCiFI+s2t2AwGal1M0e1P10L9Dz8iIiKImt5hSWy/0uoKzXobD1YDEDv71ETQkRmxzBySzNc7T2Bzo4hQxKgkpNnWaU6q/64q5MDJOm6ZPoARfVxP4qiUbYPe8TcXa/Y2Ukrqli1H26cP4ePGeaVNpbT2BGtNE9JoIXpmFhflZVJjNPPmD/YkV57oFRoV8VcMRpptbPv+MEPTojlHoUielvjatpFnnIEmOZnKt99xaXtf623GUllJzZdfEjl5cqfBC/6i1VXWrVuHrcFM7TeH0SSHE5bRuld94eg+GM1Wnlvmejrx8FH2ehRNhdXt1hVXNfDcsgIGJkfywGz3ZnArZdugd/xKZec0bt5M/Y8/EnvJJV4bq+vNTKKuUvW//YD9wr5+cjbJ0TpeWnkQk8XmsV7dAHuP5xp0PHi+Z+kt3MXXtlWFhRF/7bU07thBowu5nHytt5n6H34Ei4XEX/2y0238RaurNDU10XTYPicl6vT2qUHmjO1DRlw4768vwuJiESGVXkPU6X0wHaqlqehU9TopJbe9b6/k9fTlo932FUrZNugdv1LUrV4DQpBw4w2+lqIYUkpMxXWEZUUT1icKtUpw1YQsqhvMfLK5uPsGOqE2TMVCmhiOmil947wn2M+JvehC0Gopf/k/vpbiEtJmo+yFF9AkJxM+erSv5XgPCTXfHgaNIDKvfe4orVrF1adlUVFv4oMNrkdiRYyzPx9ozvAJ8N/VhWw7Ws01E/syITuhx9K9RdA7/ileevDaElt9PVXvvkv4mDEeF13pCCW09oSGTaVIo4XI004Nxdx99mCSo3U8+OkOck+b1MXenXPLO/nkY89BUvtN58VevIk/2FabkUH8FXMxLF3abWinP+ht2rsXc1ERyXfe0eUcFX/Q6g6Tho3HUtpA5PhUhLbjz/XraQOI0ml45PNdLlfACsuKRj80gYb8Uiw1Tew8VsPTX+9laFo0j1/kWQ4vpWwb9I6/oMD9sn/dUbv0O2z19STddptX21VCa0+o33QSoVU5xy/B/qDyhkn9kBJe/mar220u31PKxsNVWLOj0SSH03SoxouKO8dfbBt/vX0mb/l//9vldv6gt/zllwGInNL1jHR/0OoOxxfbh9qaa0V0hE6jZv4Z2QB8tvWYy2039/prNp7glnfss7X/c914j9OQKGXboHf8J0+e9Gp7tsZGSp9+mrD+/Yk83bsl57yttSeYTzZgOlRD5KR0VPrWsfu3zRxESrSOlzdUUNPg+vR2k8XGnR9uBeDZq8YRMTYFS2kDxt0VXe/oBfzFtrr+/Ym9+GJq/vcJxh07O93O13qtBgOG5d8Td8VctKldpwr3tVZ3sBpMaAsb0Q9NQJsc0eW2d509mKyEcB78ZEeX9adbEjEmGU1KOBXfF1FWbeThC4Z1Ozu3K5SybdA7fm9T89ln2GpqSH3ooR6naPBnmsPSIie2n12oUgkePN8enfDb913PQfPo5zsxNFl48tKR9IkLtz9YE/YhpZ8TKffdiwgPp/Tpp5Fm1384e5OKV14Fm63DmbqBTPXigwgEMef063ZbIQS/nTEIgHsWbnP5GPtiNETY4JaMJH45pfuaIb4g6B3/yJEdpzv1BGm1Uv7fV9BmZRE5aaLX2m3Gm1p7grXWhHFXORF5qWiTOs5NfsnYDPrG6/nhQIVLD8C+3XWCDzceJSMunKsm2DN1qsI1ROalYdxVgbmswaufoS3+YlsATVISybffjnHTJspffbXDbXypV9psVH/yCeHjx7uUcbYjrVJKGraXUfLXDRQ/sMb5Kv33Vgyri7E19k5J1JZYqhox7ihHNTyGsD6uTYy6+rS+pMXoWbmvjBV7u+99f7+3lPkHjtGI5Gq1rqeSFbsOgt7xezMcqvzl/2ApKSHlnrsRWtdm3nWHpbKRyoUFFD+whsa/76H4gTUc/+M6yt7Y2StDIB1Rs/Qw2CTR0zpPECWE4IVLBwLw4Cc72FHc+Vj9vhMG53jnhzdPalVWLmpaBqjA4EYxdk/wt5DDxF/OJ3LaVMpf/HeH4Z2+1Fv55ltYKypIuPYal8IPW2qVVolxVznHHlxL5ft726UrNh81UPPVIY7/+SeqPtmPzcUHp96geok9u6wtN8at/T697XQA5r21kX0nOi83+sW248x/yx62oBmagK3IgPl4vcd6IRTO6TH79+/3SjvW6moqXn+dyDPOIPqcc3rUlpSS+k2lHP/jOk48s7HdUIetwUJTQRUVb++m+ME1GNYcQ5pdiyfuKY0FVTTklxKRm4o2pesx0Krjh3nzpgkAXPTiWnYdb+/8txdXc+7zqwF446Y8shJat6lNjiAiN5WGrWWYjnZfw9dTXLkObCYrlnIjTYXVNBXVYnPj+YUn9HnqKVQRERy95VYsVVWt1nnrunUXm8lE5VtvoRs2jOjzznNpn2atVoOJkr/8RMU79h8ydUwYSb8cSZ8nJpPx5zNIf2QSKb8bR0ReKlgk9RtOcPyP66j76TjS6n4hE3do2F5G464KomdkcaDcvUiy9NhwnnQUSjn3+dXtsnearTae/novv/vAHq//zi9PI+tye+K2ujWehz2DctdBwNbc7W1Kn3oaaTSSdNtve1Rly1zWQNl/tmGrP3WrG3VGHyInprNu2wbOmHQ60mzDUmGk9rsjmIoM1HxZSO3Sw0RNySB6ZhaqMGWeLUizjeolhagiNMRfPMilfWYOTeGZy0dz//+2c8G/1nLVhCx+PW0AJ2oaeWfdEb7ZZY9pfmLOCM4c2nG93dhz+tGwqZTaZUdIvHEEopeKrEuLjYYd5dStOor5RNdDTVHTMog6vQ+aOL3Xjq9JTCTr5Zc4ctM8jt9zD1mvv+7zJGgV//kvlrIyUh99xC0tDTvKqHx/L0jQpkWQcO2wdg9P1RoV6kgtCXMHE3fRQIzbyqj+6hDVnx3EsKqYhCuHoOvvvfDoZmwmKzXfHEaTqCf6zCz48bDbbVw7sR+VdSb+8V0B89/KR6sW/GbGIHYfr2XZnlM/BAtvneyM19cPT6RhaxlRUzPbzQ72NcKTkmG9TV5enszPz/do34MHDzJw4MAeHb960SJKHn6E+GuvJe2Rhz1qQ1ps1H5fhOH7U5WYUm4bizYzyvkF60irzWSl/qcSDGuPYas1oUnUE3NuNuGjkrzuJGpXFFH77RHiLh1EVAcPddvSUu/yPaX8ckHH5+hfV49jTjfF02u+PYxhxVHiL88hcoL3Uzg0a5VS0nSgmvqNJzDuKIeWl79agFWCRgWWju+wVBEa4i/PQT8kAeGlSmHlr75K2T+eJWbORaQ/9hiqyEivXLfu0rivgEOXXkrklDPo+8orLu0jrZKiBZtQFxhBJUi4eigRLcJ/u93fJqn/qYSabw4jLTaiJqUTNT0TTWzPx8eb26/+7AD1G06QeP1wwkck9si2u47XcMG/1rZbPjozlvd/PYko3am+tLXORMlTG9AkhZN6Z65H39eeaBVCbJJSdpjsJ+gdf11dXY8y3DVs2cKRa64lfPRo+r69AJXO/QuyqaiW8jd3IY32Xn783Bwi89o7t660Nn9BapcdwdZgQZseSfT0TMJHJ3ulh9x0qIay/25HPzSBxBuHu3SRttV7vNrIv5bv58vtJYRpVNx0eja/mjqAcBfuUKRNUvr8Jqw1JtJ/PwFVhHeeobTUqj1poXpxIeYT9nFXTXI4+px4Ik/v0+lDbFuTBfOxOgxrj9PY5pmLfkg8cRcNRNPJvq4ibTbKnnueildfRZcziIznnsOcltarWS9tjY0cvvoaTIWFDPjyS8Iyu6/8ZGuyUrFgF02FNWjTI0n65UjUUWEeHd9aa6Lq8wM07qoAjYqo09OJnpbpcXvN1Cw9jOH7o0ROTCP+Unvq7576BLPVxsbDlSzfc5K4cC3XTepHfGTHOg2ri6n56hAx5/Qj5sy+bh+rJ1p/1o5/5cqVzJgxw6N9GzZvpmjefFTR0fRf+DHadPcKhdhMVmq/PUzdD/bSbhG5KcRfltNpT9EVrbYmC/U/naBu3XGs1U2oE/XEntXPfgfgYQ/UfLKBk//eilAL0u7Nc9np9sS2HdFUVEvZS9vQD08k8bphXvlBs9abadh8kpOrDqKrE4gwNZGT04memuGRU2ncX4VhdTFN+6udy/QjEomanI5uYFyP7sIMy5dT8tAfsDU2UjNnDqc9/livhQyXPPEE1R98SNofnyD+yu6zzZpP1FP+zm6sFY1U97Ux4tZpXjlf5tJ6ar8/inFbmfNcRU1Ic/vHVdokNV8dom7tMcJHJpJw7TDnufH2ddulDouNsld3YDpWR8qtownLjHZr/55o7crxh8b4O6F+wwaKb7sddWws/d59xy2nL6WkIb+Umm8PY6szE9Y3moSrhqJJ6Pn4sEqnIXp6JlFTMzBuL6N2xVEqP9qH6stC9EMSCB+WgDYtEnWcrtsfAiklxh3lVH9+AAQk3zLa6z1td9D1jSHm7H7UfneEmiWFxF44wCNnYq0307S/CuPuChr3VNofjEfbi21E5qWiCvf8stfnxKPPiUdabDTuraT6q0M07qqgcVcFQq8mMjeViHEprYbwXCV61iz0nw7j+EN/QC5cyKFt20j89a+JPvssVHrvPVtoS9XChVR/8CExF1zQrdOXZht1Px6n5rsjCJUg4dphHKjY6bXnMtrUSBKvHoppeia1y4qoW11M3api592ZOkGPJl5PWFYUquiwdjaWFhuN+6uoXXoEc0k9EeNTib9skM+enQiNioQrB3PypW2Uv7mTlP/L9dowVk9Q1PELIWYD/wTUwGtSyqfbrNcBbwPjgQrgF1LKw97UEBPjXuiWtbqaitdeo+KtBWjT0uj7+muE9XXtFk2abTRsL8Ow8iiWMiOqaC3xVwwmIjfFpQvPHa1CJYgYm0L46GQa91TQsLUM467yUxFCAtTxesIyo9CmR6FNDkcdr0cVrsHWYMZ0rI6GTaWYigxoUiNIvHZYt1E8PdHrKtEzs7DWNFH343GajtQSf3lOpzHXUkqsNU1YyhuRjRZMJfU0FVRhKjaAtI/Hh49JJmpiOjvL9jMw13uFq4VGRfjIJMJHJmGpaqR+wwmMuyqo+/E4dT8eRx2rI6x/DPqBcegGxaGJd81xa/v0oe+bb7D9hRfQfvklx++7D1VMDNFnn0X0mWcSMX486rg4r3wGKSXVH37IiT8/iX7MaPo89ZfOt7VJjDvLqf32MJaKRnSD4oifOxhNnI6Yzd6/DsL6RJF0w3As1Y0Yd1bQuK+SuvUl9mcwDoRegyZehypKiypMjc1ovwak0YIqXEP8FYOIHBULplqwWcBqAquJZFU1lO4GmxmsjpfNbN/GZgNpBWkDm7XN/7ZTL6ECoQYhQKW2/9/8V621v1RaUIehUWtIvDCcso9rqXhjC4lz09HEh4NKY29HpXb87/jbInhEie8YKDjUI4RQAwXA2UAxsBG4Wkq5u8U2vwVGSylvFUJcBVwqpfxF27Z6MtTjCtJkwrhtG9WffErt118jGxuJmXMRaY88gjq681szaZPY6syYig007qvEuLMcW70FdWwYUaf3IeqMDK89AHTpc1htmI4asJQ3Yq1uxFxSj+lYnbPMYVtUMXad0Wf06TRZlS+QUtKwsZTqJYVIkxXdILvzVIVrQIKl0oj5eD3mkrpW0VEAYZkR6AdFo8+JRZsRZR8qEaoWL2F/KYS5rAHjznKaCmswFRmQTfY4dVWUFk16JJrUSFRJ4agS9BAdhtQKkGCzSmw2ab+mHP8jJcZtO6hbvZqGTZuQjfZygKrYOLSZGahjYlHFxKBNTUWTnoYmPR1NQiJCrcL5tZat/jQbGPOxY1S9/z6NO3cSPnwYqffejSYqAqESqFQCoRIIAbK6EWthDeadFdgqG1HF64ic2Rf9yERUapVjWxzbK9irLj+AfP8qbE1gsaRgMmdhsaZjtSZgtcUhpQ4hGtCqitCrthIu1iCEf82KbrCeTpX5bgCi1J8TpVmCWrQO47XKaMy2/pgZiFlmI9XxJD5xh0fH88kYvxBiMvC4lPJcx/sHAaSUT7XY5lvHNuuEEBrgBJAs24jy1PF/8uzNWG1W1KKN85WATaKySbBKRItKOzJMg02vgXZjqwKVVKFBjVqqUUsNGqlBOJuUNKoaqVPXY1S5Mgu1/ZfEbDaj7WximKunyWax91JaHkmq0MgwNGhQocImbJgxYxbNZQA9+8JarTbUbo1Bu3ccFSqibDFEEIGmxc2pDYkFMyb7p8BsMWDDikVasGFzHMdxrFZ2a7G8g2Wy1XrhfMm2y6TjfyFaONjW+0okoEKHGp1aS5hQE6ZSoxXqVr87NimxSRtWwCZt2KREOva2v2Sbj9HRhSC6WNc1zXs0t6BChUqASqgIEypUju+OyWbBYDHRYDOdOkqnh5MgJELaGxbOT0MHf9svE87/W7+XaJCoWvzAyDanUzqOR5vT2Hq51WZFo3Fct0K02lacMsQpWv2gtfxfnpJua14iaXHyWn9MG2ikhnhiiMB+B2jCglXaEIBaqNFy6vtkxYbJYuO8u/6CULv/HfXVGH8GcLTF+2KgbZ4D5zZSSosQogZIBMpbblRSUsKQIUMA0Ol0zJs3j3GOqleJiYmMGDGC1avtk4Q0Gg1Tpkxh8+bNRI1ahVqt3NTwtudV53iFUBa14xUMtvaf+6yu0QKepxoL0ZbmYGENrZ1wyyBiAYgmPavWnMuAAQOIjo5m2zZ7zqDO/N7DDz/MwoULm5voNK5WyR7/XGC2lPJXjvfXAxOllLe32GanY5tix/uDjm1aOX5Pe/zLX/sLVbXVxMXFOsbihH38TAjQqlv/kgtHz6rlIgHOPldzz6GLkRvR9r/ufqTbrD9RWkp6amqn67tsytlrUUN4rAv7drOBaL1Fu60FFB0pom+/TpJdiS7f2mk5HiFo8SvapgfboremEm32EcIxTqrp8hMVHipkQP8uavt2sbPocqWrJ8kGNukYM7b/FUjajck4/j1y5DD9+mXTamG77aTj+C3uNpzXp+O9yQwNjfZttVpEVGSLXm7LLm6LNpyL2rSN465IqBwy7N+NwsJCBgwYQMsecHPPt/njuepnOt7MxX1d2gqOHD5Mv+zsUwe0SfswvqMnJx3vO2yw7bIWI4g4hsfsl6Q4dX32gKIjxVx/1R882tdXPf5jQFaL95mOZR1tU+wY6onF/pDXK8z61UPYbDZUPZhp25sEklYILL2T8wJHK8DEyYGjd1Ju4GgFsE0MHL22icqkalHy028EcoQQ/YUQYcBVwOI22ywGbnT8Pxf4vu34fk/ZtWuXN5tTlEDSCoGlN5C0QmDpDSStEFh6ldKqWI/fMWZ/O/At9qHMN6SUu4QQfwTypZSLgdeBd4QQB4BK7D8OXqWiwjcZLj0hkLRCYOkNJK0QWHoDSSsEll6ltCoaxy+l/Ar4qs2yR1v83whcoaSGECFChAjRmsAY6OoBY8aM8bUElwkkrRBYegNJKwSW3kDSCoGlVymtQe/4/9tNUWt/IpC0QmDpDSStEFh6A0krBJZepbQGfZK2IUOGsG/fPi8rUoZA0gqBpTeQtEJg6Q0krRBYenuitatwzqDv8YcIESJEiNYERI9fCFEGHPFw9yTazAT2YwJJKwSW3kDSCoGlN5C0QmDp7YnWflLK5I5WBITjDxEiRIgQ3iM01BMiRIgQPzNCjj9EiBAhfmYEreMXQswWQuwTQhwQQjzgaz3dIYQ4LITYIYTYKoRQrviAhwgh3hBCnHQk1mteliCE+E4Isd/xN96XGpvpROvjQohjDvtuFUKc70uNzQghsoQQK4QQu4UQu4QQdziW+6ttO9Prd/YVQuiFEBuEENscWp9wLO8vhFjv8A0fOVLK+KvWt4QQh1rYdaxXjheMY/yuFIHxN4QQh4G8tplJ/QUhxDSgDnhbSjnSsewZoFJK+bTjxzVeSvl7X+p06OpI6+NAnZTy777U1hYhRDqQLqXcLISIBjYBlwA34Z+27UzvlfiZfYU9cX+klLJOCKEF1gJ3AHcDn0gpPxRC/AfYJqV82U+13goskVIu8ubxgrXHfxpwQEpZKKU0AR8CF/tYU0AjpVyNPZ9SSy4GFjj+X4DdAficTrT6JVLKEinlZsf/BmAP9joV/mrbzvT6HdJOneOt1vGSwJlAsyP1C9t2oVURgtXxd1QExi8vzhZIYKkQYpMQ4mZfi3GRVCllieP/E0BqVxv7AbcLIbY7hoL8YuikJUKIbGAcsJ4AsG0bveCH9hVCqIUQW4GTwHfAQaBaStlcoclvfENbrVLKZrs+6bDrc8Jep7zHBKvjD0SmSClzgfOA2xzDFQGDI522P48bvgwMBMYCJcA/fKqmDUKIKOB/wJ1SytqW6/zRth3o9Uv7SimtUsqx2OuBnAYM9a2izmmrVQgxEngQu+YJQALgleG+YHX8rhSB8SuklMccf08Cn2K/SP2dUseYb/PY70kf6+kUKWWp44tlA17Fj+zrGNP9H/CelPITx2K/tW1Hev3ZvgBSympgBTAZiHMUfgI/9A0ttM52DK1JKWUT8CZesmuwOn5XisD4DUKISMeDMoQQkcA5wM6u9/ILWhbSuRH43IdauqTZiTq4FD+xr+Oh3uvAHinlsy1W+aVtO9Prj/YVQiQLIeIc/4djD/bYg92pznVs5he27UTr3hY//gL7swiv2DUoo3oAHOFkz3OqCMyTvlXUOUKIAdh7+WCvkfC+v+kVQnwAzMA+hbwUeAz4DPgY6Is9pcaVUkqfP1TtROsM7MMQEjgM3NJiDN1nCCGmAGuAHZyqtf0Q9nFzf7RtZ3qvxs/sK4QYjf3hrRp7J/djKeUfHd+3D7EPnWwBrnP0qH1GF1q/B5KxV/DdCtza4iGw58cLVscfIkSIECE6JliHekKECBEiRCeEHH+IECFC/MwIOf4QIUKE+JkRcvwhQoQI8TMj5PhDhAgR4mdGyPGHCBEixM+MkOMPEdQIIRJbpLQ90SJ1cJ0Q4iUFjtecRvdWD/df4dDWYZHsECG8gab7TUKECFyklBXYJxb1Zmrm+zxNoyulnCmEWOllPSFCtCLU4w/xs0QIMUMIscTx/+NCiAVCiDVCiCNCiMuEEM8Ie2Gcbxy5aRBCjBdCrHJkUP22TZqCzo7zlhDiX0KIH4UQhUKIuY7l6UKI1Y67j51CiKnKfuIQIU4RcvwhQtgZiD1P+xzgXWCFlHIUYAQucDj/F4C5UsrxwBuAq2k10oEpwIXA045l1wDfOrIxjsE+HT9EiF4hNNQTIoSdr6WUZiHEDuz5Ur5xLN8BZANDgJHAd/Z8Waixpx92hc8cWSt3CyGa8+pvBN5w/KB8JqXc6pVPESKEC4R6/CFC2GkCcDhoszyVxMqGvYMkgF1SyrGO1ygp5TnutO1AOI6zGpiGPSXwW0KIG7zxIUKEcIWQ4w8RwjX2AclCiMlgz0kvhBjhaWNCiH5AqZTyVeA1INc7MkOE6J7QUE+IEC4gpTQ5Hsz+SwgRi/278zywy8MmZwD3CSHM2AvDh3r8IXqNUFrmECG8iBDiLWCJp+GcjjZWAvdKKfO9pStEiJaEhnpChPAuNcCfejKBCxgAmL2qKkSIFoR6/CFChAjxMyPU4w8RIkSInxkhxx8iRIgQPzNCjj9EiBAhfmaEHH+IECFC/MwIOf4QIUKE+Jnx/9OMj+hsFZVTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "exp.plot_dynamics(init_state, barely_a_seq * 5, debug=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "exp.plot_dynamics(init_state, barely_a_seq * 10, debug=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that at this point, we only multiply already computed matrices. We don't need to solve the equations of motion again for new sequences."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Open-loop optimal control\n",
    "\n",
    "In order to improve the gate, we create the optimizer object for open-loop optimal control. We notice some leakage into the $|2,0>$ state and enable a DRAG option. Details on DRAG can be found here: https://arxiv.org/abs/1809.04919 The main principle is adding a phase-shifted component proportional to the derivative of the original signal. With automatic differentiation, our AWG can perform this operation automatically for arbitrary shapes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator.devices['awg'].enable_drag_2()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At the moment there are two implementations of DRAG, variant 2 is independent of the AWG resolution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To define which parameters we optimize, we write the gateset_opt_map, a nested list of tuples that identifies each parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt_gates = [\"X90p:Id\"]\n",
    "gateset_opt_map=[\n",
    "    [\n",
    "      (\"X90p:Id\", \"d1\", \"gauss\", \"amp\"),\n",
    "    ],\n",
    "    [\n",
    "      (\"X90p:Id\", \"d1\", \"gauss\", \"freq_offset\"),\n",
    "    ],\n",
    "    [\n",
    "      (\"X90p:Id\", \"d1\", \"gauss\", \"xy_angle\"),\n",
    "    ],\n",
    "    [\n",
    "      (\"X90p:Id\", \"d1\", \"gauss\", \"delta\"),\n",
    "    ]\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can look at the two parameters this opt_map specified with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Tensor: shape=(), dtype=float64, numpy=0.5>,\n",
       " <tf.Tensor: shape=(), dtype=float64, numpy=-333008821.28051805>,\n",
       " <tf.Tensor: shape=(), dtype=float64, numpy=-4.440892098500626e-16>,\n",
       " <tf.Tensor: shape=(), dtype=float64, numpy=-1.0>]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp.gateset.get_parameters(gateset_opt_map)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More human friendly output is generated by"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X90p:Id-d1-gauss-amp                  : 500.000 mV \n",
      "X90p:Id-d1-gauss-freq_offset          : -53.000 MHz 2pi \n",
      "X90p:Id-d1-gauss-xy_angle             : -444.089 arad \n",
      "X90p:Id-d1-gauss-delta                : -1.000  \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(exp.gateset.print_parameters(gateset_opt_map))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from c3.optimizers.c1 import C1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The C1 object will handle the optimization for us. As a fidelity function we choose average fidelity as well as LBFG-S (a wrapper of the scipy implementation) from our library. See those libraries for how these functions are defined and how to supply your own, if necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt = C1(\n",
    "    dir_path=\"/tmp/c3log/\",\n",
    "    fid_func=fidelities.average_infid_set,\n",
    "    fid_subspace=[\"Q1\", \"Q2\"],\n",
    "    gateset_opt_map=gateset_opt_map,\n",
    "    opt_gates=opt_gates,\n",
    "    algorithm=algorithms.lbfgs,\n",
    "    options={\"maxfun\" : 10},\n",
    "    run_name=\"better_X90\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we supply our defined experiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt.set_exp(exp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Everything is in place to start the optimization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "opt.optimize_controls()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After a few steps we have improved the gate significantly, as we can check with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.003798"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opt.current_best_goal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And by looking at the same sequences as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "exp.plot_dynamics(init_state, barely_a_seq, debug=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "exp.plot_dynamics(init_state, barely_a_seq * 5, debug=True)"
   ]
  }
 ],
 "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": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}