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.parametermap import ParameterMap as PMap\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\n",
    "anhar_q1 = -210e6\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_val=4.995e9 ,\n",
    "        max_val=5.005e9 ,\n",
    "        unit='Hz 2pi'\n",
    "    ),\n",
    "    anhar=Qty(\n",
    "        value=anhar_q1,\n",
    "        min_val=-380e6 ,\n",
    "        max_val=-120e6 ,\n",
    "        unit='Hz 2pi'\n",
    "    ),\n",
    "    hilbert_dim=qubit_lvls,\n",
    "    t1=Qty(\n",
    "        value=t1_q1,\n",
    "        min_val=1e-6,\n",
    "        max_val=90e-6,\n",
    "        unit='s'\n",
    "    ),\n",
    "    t2star=Qty(\n",
    "        value=t2star_q1,\n",
    "        min_val=10e-6,\n",
    "        max_val=90e-3,\n",
    "        unit='s'\n",
    "    ),\n",
    "    temp=Qty(\n",
    "        value=qubit_temp,\n",
    "        min_val=0.0,\n",
    "        max_val=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\n",
    "anhar_q2 = -240e6\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_val=5.595e9 ,\n",
    "        max_val=5.605e9 ,\n",
    "        unit='Hz 2pi'\n",
    "    ),\n",
    "    anhar=Qty(\n",
    "        value=anhar_q2,\n",
    "        min_val=-380e6 ,\n",
    "        max_val=-120e6 ,\n",
    "        unit='Hz 2pi'\n",
    "    ),\n",
    "    hilbert_dim=qubit_lvls,\n",
    "    t1=Qty(\n",
    "        value=t1_q2,\n",
    "        min_val=1e-6,\n",
    "        max_val=90e-6,\n",
    "        unit='s'\n",
    "    ),\n",
    "    t2star=Qty(\n",
    "        value=t2star_q2,\n",
    "        min_val=10e-6,\n",
    "        max_val=90e-6,\n",
    "        unit='s'\n",
    "    ),\n",
    "    temp=Qty(\n",
    "        value=qubit_temp,\n",
    "        min_val=0.0,\n",
    "        max_val=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\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_val=-1 * 1e3 ,\n",
    "        max_val=200e6 ,\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_val = one_zeros * 0.8 + zero_ones * 0.0\n",
    "max_val = one_zeros * 1.0 + zero_ones * 0.2\n",
    "confusion_row1 = Qty(value=val1, min_val=min_val, max_val=max_val, unit=\"\")\n",
    "confusion_row2 = Qty(value=val2, min_val=min_val, max_val=max_val, 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_val=-0.001,\n",
    "        max_val=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 elimin_valate 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_val=0.05e-9,\n",
    "        max_val=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.DigitalToAnalog(\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.VoltsToHertz(\n",
    "    name='v_to_hz',\n",
    "    V_to_Hz=Qty(\n",
    "        value=v2hz,\n",
    "        min_val=0.9e9,\n",
    "        max_val=1.1e9,\n",
    "        unit='Hz/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(\n",
    "        devices={\n",
    "            \"LO\": devices.LO(name='lo', resolution=sim_res, outputs=1),\n",
    "            \"AWG\": devices.AWG(name='awg', resolution=awg_res, outputs=1),\n",
    "            \"DigitalToAnalog\": devices.DigitalToAnalog(\n",
    "                name=\"dac\",\n",
    "                resolution=sim_res,\n",
    "                inputs=1,\n",
    "                outputs=1\n",
    "            ),\n",
    "            \"Response\": devices.Response(\n",
    "                name='resp',\n",
    "                rise_time=Qty(\n",
    "                    value=0.3e-9,\n",
    "                    min_val=0.05e-9,\n",
    "                    max_val=0.6e-9,\n",
    "                    unit='s'\n",
    "                ),\n",
    "                resolution=sim_res,\n",
    "                inputs=1,\n",
    "                outputs=1\n",
    "            ),\n",
    "            \"Mixer\": devices.Mixer(name='mixer', inputs=2, outputs=1),\n",
    "            \"VoltsToHertz\": devices.VoltsToHertz(\n",
    "                name='v_to_hz',\n",
    "                V_to_Hz=Qty(\n",
    "                    value=1e9,\n",
    "                    min_val=0.9e9,\n",
    "                    max_val=1.1e9,\n",
    "                    unit='Hz/V'\n",
    "                ),\n",
    "                inputs=1,\n",
    "                outputs=1\n",
    "            )\n",
    "        },\n",
    "        chain=[\n",
    "            \"LO\", \"AWG\", \"DigitalToAnalog\", \"Response\", \"Mixer\", \"VoltsToHertz\"\n",
    "        ]\n",
    "    )"
   ]
  },
  {
   "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.\n",
    "\n",
    "We choose a gate time and a gaussian envelope shape with a list of parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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 \n",
    "gauss_params_single = {\n",
    "    'amp': Qty(\n",
    "        value=0.5,\n",
    "        min_val=0.4,\n",
    "        max_val=0.6,\n",
    "        unit=\"V\"\n",
    "    ),\n",
    "    't_final': Qty(\n",
    "        value=t_final,\n",
    "        min_val=0.5 * t_final,\n",
    "        max_val=1.5 * t_final,\n",
    "        unit=\"s\"\n",
    "    ),\n",
    "    'sigma': Qty(\n",
    "        value=t_final / 4,\n",
    "        min_val=t_final / 8,\n",
    "        max_val=t_final / 2,\n",
    "        unit=\"s\"\n",
    "    ),\n",
    "    'xy_angle': Qty(\n",
    "        value=0.0,\n",
    "        min_val=-0.5 * np.pi,\n",
    "        max_val=2.5 * np.pi,\n",
    "        unit='rad'\n",
    "    ),\n",
    "    'freq_offset': Qty(\n",
    "        value=-sideband - 3e6 ,\n",
    "        min_val=-56 * 1e6 ,\n",
    "        max_val=-52 * 1e6 ,\n",
    "        unit='Hz 2pi'\n",
    "    ),\n",
    "    'delta': Qty(\n",
    "        value=-1,\n",
    "        min_val=-5,\n",
    "        max_val=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": 16,
   "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": 17,
   "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_val=0.5 * t_final,\n",
    "            max_val=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": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "lo_freq_q1 = 5e9  + sideband\n",
    "carrier_parameters = {\n",
    "    'freq': Qty(\n",
    "        value=lo_freq_q1,\n",
    "        min_val=4.5e9 ,\n",
    "        max_val=6e9 ,\n",
    "        unit='Hz 2pi'\n",
    "    ),\n",
    "    'framechange': Qty(\n",
    "        value=0.0,\n",
    "        min_val= -np.pi,\n",
    "        max_val= 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": 19,
   "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  + 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": 20,
   "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": 21,
   "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": 22,
   "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": 23,
   "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)"
   ]
  },
  {
   "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": [
    "parameter_map = PMap(instructions=all_1q_gates_comb, model=model, generator=generator)"
   ]
  },
  {
   "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(pmap=parameter_map)"
   ]
  },
  {
   "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.set_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": [
    "unitaries = 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([[ 5.398e-01+5.545e-02j, -3.113e-03-3.722e-04j,\n",
       "         -1.311e-05+1.611e-04j, -1.293e-01-8.222e-01j,\n",
       "         -4.278e-04-2.100e-03j,  2.404e-04-4.793e-05j,\n",
       "          1.047e-01+4.361e-02j, -2.087e-04-1.827e-04j,\n",
       "         -4.107e-05+2.128e-05j],\n",
       "        [-1.159e-02-5.395e-02j, -1.815e-01+5.097e-01j,\n",
       "         -4.628e-03-6.518e-04j,  3.250e-02-1.698e-02j,\n",
       "          8.247e-01+1.035e-01j, -2.077e-03-2.468e-03j,\n",
       "         -1.069e-03-4.639e-03j, -6.987e-02+8.462e-02j,\n",
       "          5.632e-04-4.182e-03j],\n",
       "        [ 8.213e-04-9.283e-05j,  1.285e-01+1.009e-02j,\n",
       "         -7.079e-02+5.307e-01j, -2.726e-04-4.666e-04j,\n",
       "          1.436e-02+8.505e-02j,  8.117e-01-1.362e-01j,\n",
       "         -5.353e-03+6.375e-05j,  1.065e-02+1.152e-03j,\n",
       "         -4.298e-02+9.923e-02j],\n",
       "        [-2.139e-01-8.013e-01j,  1.059e-02-3.502e-02j,\n",
       "          4.669e-04-1.790e-05j,  4.762e-01-2.754e-01j,\n",
       "         -5.547e-02-4.144e-03j,  2.071e-04+2.477e-04j,\n",
       "         -1.880e-02-6.934e-02j,  2.529e-03-9.596e-04j,\n",
       "         -1.397e-05+2.876e-04j],\n",
       "        [ 1.038e-02-1.483e-03j,  8.198e-01+1.397e-02j,\n",
       "          5.435e-03-8.492e-02j, -3.586e-03-5.871e-03j,\n",
       "          1.101e-01+5.309e-01j, -1.262e-01+2.859e-02j,\n",
       "         -6.835e-02+2.923e-03j,  6.857e-02+5.508e-03j,\n",
       "          1.341e-02-1.008e-02j],\n",
       "        [-1.571e-04-4.215e-04j, -1.743e-03+1.872e-02j,\n",
       "          7.945e-01-2.190e-01j,  2.457e-04-1.519e-04j,\n",
       "          1.176e-02-4.125e-03j,  2.944e-01+4.585e-01j,\n",
       "         -5.332e-06-3.621e-05j,  4.207e-02-1.304e-01j,\n",
       "          6.735e-02-1.307e-02j],\n",
       "        [ 1.174e-01-2.808e-02j, -5.773e-02+1.742e-03j,\n",
       "          4.725e-05+2.984e-03j, -4.300e-02-3.854e-02j,\n",
       "         -9.572e-03-3.703e-02j,  4.314e-03-1.314e-03j,\n",
       "         -9.847e-01+8.711e-02j, -4.830e-03-3.058e-04j,\n",
       "         -6.965e-04+4.606e-04j],\n",
       "        [-1.622e-03-4.074e-03j, -6.455e-03+1.156e-01j,\n",
       "         -1.096e-01+3.615e-02j,  2.368e-03-1.571e-03j,\n",
       "          4.804e-02-2.628e-02j, -4.466e-02-6.218e-02j,\n",
       "         -4.671e-06-3.408e-04j,  2.553e-01-9.482e-01j,\n",
       "         -9.153e-03+2.901e-04j],\n",
       "        [ 4.007e-05-7.092e-06j,  5.160e-03+3.744e-03j,\n",
       "          2.498e-02+1.143e-01j, -1.611e-05-2.341e-05j,\n",
       "         -6.733e-03+4.599e-03j,  3.958e-02-3.760e-02j,\n",
       "         -2.696e-04+2.000e-05j,  2.733e-04+2.067e-03j,\n",
       "         -2.804e-02-9.912e-01j]])>,\n",
       " 'Id:Id': <tf.Tensor: shape=(9, 9), dtype=complex128, numpy=\n",
       " array([[ 1.000e+00+1.656e-03j, -1.078e-18+5.490e-19j,\n",
       "         -1.520e-04+9.832e-05j, -8.330e-21+1.665e-20j,\n",
       "          2.220e-03-6.643e-04j, -2.091e-21-1.033e-20j,\n",
       "          6.912e-05+5.739e-05j,  3.521e-22+7.971e-22j,\n",
       "          5.531e-06+1.166e-06j],\n",
       "        [-8.133e-19-4.790e-20j, -2.485e-01+9.678e-01j,\n",
       "          1.859e-17-2.331e-18j,  2.308e-02+3.156e-02j,\n",
       "          1.260e-16+3.841e-16j,  2.693e-03-2.005e-03j,\n",
       "         -2.026e-16+1.071e-16j, -2.312e-04+2.119e-06j,\n",
       "          2.557e-18+5.882e-19j],\n",
       "        [ 1.115e-04+1.426e-04j,  5.213e-17+1.514e-17j,\n",
       "         -1.366e-02+9.957e-01j,  3.788e-17+1.493e-18j,\n",
       "         -8.657e-02+2.941e-02j, -5.225e-17-6.841e-17j,\n",
       "          1.167e-03-1.775e-03j,  3.113e-18+5.732e-18j,\n",
       "         -1.531e-04-3.659e-04j],\n",
       "        [ 3.950e-20-7.341e-21j,  3.637e-02-1.435e-02j,\n",
       "          3.446e-17+8.755e-18j, -3.068e-01+9.510e-01j,\n",
       "         -3.794e-19+8.647e-18j, -1.642e-04-1.547e-04j,\n",
       "         -1.959e-18+5.613e-18j, -4.621e-04-3.317e-03j,\n",
       "         -2.907e-18-1.825e-18j],\n",
       "        [ 1.425e-03+1.827e-03j,  1.475e-16+3.803e-16j,\n",
       "          7.496e-02-5.235e-02j,  1.871e-17+9.014e-18j,\n",
       "         -8.095e-01-5.784e-01j, -1.406e-18+6.116e-18j,\n",
       "          3.536e-02-2.068e-02j,  8.826e-20-5.470e-19j,\n",
       "         -2.012e-03-4.401e-03j],\n",
       "        [-5.729e-22+3.611e-21j, -3.074e-03+1.350e-03j,\n",
       "         -5.101e-17-6.924e-17j, -1.498e-04+1.687e-04j,\n",
       "          5.416e-19-6.184e-18j, -9.621e-01-2.603e-01j,\n",
       "         -1.490e-16-5.651e-17j,  1.435e-02+8.042e-02j,\n",
       "         -1.224e-16-2.586e-16j],\n",
       "        [-5.999e-05-6.688e-05j, -2.006e-16+7.977e-17j,\n",
       "         -1.452e-03-1.550e-03j, -8.021e-18+5.492e-18j,\n",
       "          4.403e-04+4.096e-02j, -1.506e-16-4.983e-17j,\n",
       "          9.153e-01+4.008e-01j,  1.056e-17+5.277e-18j,\n",
       "          6.982e-05+1.538e-04j],\n",
       "        [ 6.840e-23-2.540e-22j,  2.310e-04+7.850e-06j,\n",
       "          4.162e-18+4.970e-18j, -3.145e-03+1.151e-03j,\n",
       "         -3.280e-21+3.816e-19j,  2.874e-02+7.647e-02j,\n",
       "          1.118e-17+3.671e-18j, -6.942e-01+7.151e-01j,\n",
       "          9.310e-18+2.224e-17j],\n",
       "        [-1.214e-06+5.521e-06j,  5.431e-19+2.649e-18j,\n",
       "          1.165e-04+3.792e-04j, -3.206e-18-6.723e-19j,\n",
       "         -2.351e-04-4.833e-03j, -1.066e-16-2.658e-16j,\n",
       "          1.427e-04-9.033e-05j,  8.159e-18+1.898e-17j,\n",
       "         -4.600e-01+8.879e-01j]])>}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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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\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",
    "]\n",
    "parameter_map.set_opt_map(gateset_opt_map)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can look at the parameters this opt_map specified with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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": [
    "parameter_map.print_parameters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt = C1(\n",
    "    dir_path=\"/tmp/c3log/\",\n",
    "    fid_func=fidelities.average_infid_set,\n",
    "    fid_subspace=[\"Q1\", \"Q2\"],\n",
    "    pmap=parameter_map,\n",
    "    algorithm=algorithms.lbfgs,\n",
    "    options={\"max_valfun\" : 10},\n",
    "    run_name=\"better_X90\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we supply our defined experiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp.set_opt_gates(opt_gates)\n",
    "opt.set_exp(exp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Everything is in place to start the optimization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C3:STATUS:Saving as: /tmp/c3log/better_X90/2020_11_26_T_12_17_45/open_loop.log\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nico/repos/c3po/c3/libraries/algorithms.py:275: OptimizeWarning: Unknown solver options: max_valfun\n",
      "  return minimize(\n"
     ]
    }
   ],
   "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": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.513 "
      ]
     },
     "execution_count": 43,
     "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": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEDCAYAAAAyZm/jAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAABUEElEQVR4nO2deXyUxf2An9ndZHNDThISIBxJOAVCIESRQ0RRFMUfHrUqglrr1VpbW23Rqq2VWg9aj1YrCt4V1IqoeANCMRDAAAlyyBkkCSRA7t3s7vz+2M2ShBxLsm92Z/M+n89C9j1mn503+e7svDPfEVJKdHR0dHS6DwZfC+jo6OjodC164NfR0dHpZuiBX0dHR6eboQd+HR0dnW6GHvh1dHR0uhl64NfR0dHpZph8LeAJcXFxMjU1tUPnHjlyhKSkJO8KaYRKrqCWr+6qHSr5quQKnfPdtGnTMSllfEv7lAj8qamp5OXldejc0aNHd/jcrkYlV1DLV3fVDpV8VXKFzvkKIQ60ti/gu3rsdruvFTxGJVdQy1d31Q6VfFVyBe18Az7wWywWXyt4jEquoJav7qodKvmq5Ara+QZ84L/kkkt8reAxKrmCWr66q3ao5KuSK2jnK1TI1ZOVlSU72s+1f/9+OnpjuKtRyRXU8tVdtUMlX5VcoXO+QohNUsqslvZp1uIXQrwshCgVQmxvZb8QQvxDCLFHCLFVCJGphUdMTIwWxWqCSq6glq/uqh0q+arkCtr5atnVsxiY3sb+i4A01+NnwD+1kNi8ebMWxWqCSq6glq/uqh0q+arkCtr5ahb4pZRrgPI2DrkMeFU6+RboKYTw6gBbh0PikBIVurN0dHR0ugpfjuNPBg41el7k2nbEWy8w9I8rqat3wKcfN9kuBAhACIFoss25o/k20WSb65zm2xqV6dqNEIJgoyDYZHA/zCYjwUZD021GA+YgAxXHYV11oXO70eg6vvG5Bve5ZpOxyfbwYBPhZiPhZhNmk8HtoSXR0dGav4a30F21QyVflVxBO18lJnAdOXKEjIwMAMxmM3PnzmX06NEAxMbGMmzYMNasWQOAyWRiwoQJbN68mRmpRuzSSFJSb6qqq6g4WYEEomNiMBqNlJYeBSAsLIzomBgOHSoCwGg00rt3b44UF2O1WpFAUmISlZWVVFZVOcuIjkYIQVlZORJJWFgYkZFRFJcUgwSjyURcfAI/FpdgqbdQ74DI8GhOVFZTVud8bgwyY7U7qKmzUu8AhxTY9h/AYnPg6MSXFJNBEGqSRAQJwoJNxEdH4qitJDJIEh4kGJo2AGvFMULs1fQIFmSNHIaw1XFw/z7AOWEuJibG/TUzOjqakSNHsnr1aqSUCCGYNGkSAKtWrQIgMzOT8vJy9u/fD8CAAQOIjIwkPz+/3etUUVEBQFZWFiUlJRw65GwPpKWlYTab2b7deZsoISGB9PR01q5d6/5dyMnJIS8vj6qqKgCys7MpKiri8OHDAGRkZGA0Gjl+/DirVq0iMTGR/v37s379egBCQ0PJzs4mNzeX2tpaAHJycti3bx/FxcUADB06FLvdzs6dOwFITk4mJSWF3NxcACIiIsjKymL9+vXu4XcTJkxg165dlJaWAjB8+HAsFgu7d+8GoE+fPvTq1cs9OScqKorMzEzWrl2LzWZj1apVTJw4kYKCAsrKygAYOXIklZWV7N2794yuU35+PsePH9f0Oh07dswr16mwsBBAmesEaHqdGn5vPb1O8+fPZ+nSpbiIoxU0HdUjhEgFVkgph7ew7wVglZTyLdfzncBkKeVpLf7OjOpZvXq1O0j5O41dbXYHVrsDq835sNhOPbfYTm232u1Y6h1UW+1U1dU7/7fYOFlbz4kaKzVWO9UWG+XVVsqqrZyoqW/xtU0GQWpcOKmxYYSbTcSGmxmUEEFarwj6RIfRMyyIkCBjq77+ju6qHSr5quQKnfNta1SPL1v8y4E7hRBvA9nAyZaCfmdRqX+/savJaMBkNBAW7N3XcDgkNfV2SirqOFReQ3m1lco6G8UVdewpreJQeQ01VjtHKy3U1p+aNSgEDIgL56yUnqTGhhMVaqL6qI3MunoiQ4K8K6kBqv4eqIBKviq5gna+mgV+IcRbwGQgTghRBPwRCAKQUv4L+Bi4GNgD1ABzNfLQolhN6ApXg0EQYTYRER/BwPiIVo9zOCSHT9Sy52gVP56o5Wilhe2HK1i35xjvbznsPu7JTZ/RLyaMiBATgxOjmJQezzmD4ogOC/Kruvcnl/ZQyRXU8lXJFbTzDfgJXDrep97u4GRtPTuOVLD5wAl2lVRSabGxteiEuysp2GggIcrM+UN6cfnoZAYnRp7WVaSjo6Md/trV0yXk5+czcuRIX2t4hCquQUYDcRFmomp+5Jfnn/K1OyTfHTrO5gMnKKu2svdoFW9uOMji/+0HoEdoEGcPjOUn4/oyYVAcBkPXtb5UqVtQyxXU8lXJFbTzDfjA33CXXAVUcoXTfY0GwZh+MYzpd2q24cnaelbtLOVQeQ2HT9Sycnsxn2wvxmwykNQjhCFJUVw3vh9nD4zV9Gu4SnWrkiuo5auSK2jnG/CBX8e39AgN4rJRye7nD80cxmcFJWw7fJIfT9Sy/ocyPtleTHLPUBJ7hJCRGMm8c1IZlBDpQ2sdncAm4Pv4KyoqiIqK8rKRNqjkCt7xrau3s2LrEb76voTj1fVsOXQcq83B1CG96BcTRr/YMC45qzfR4Z0b3qRS3arkCmr5quQKnfPt1n385eXlylxolVzBO74hQUZmj0lh9pgUZ5nVVl5Y/QMfbTvCN7uPUlfv4PFPd/KL89KYlBFPbHgwsRFmn7h2FSq5glq+KrmCdr4Bn4+/YYaiCqjkCtr4xoQHc//FQ1j7u/PY8ch0Pv7FuYzuG82jH+/ggqfXMObPX3D9olz2Hav2uatWqOQKavmq5Ara+QZ8i19HXYQQDO0dxZK5Yyn4sYK9x6rZd7Sal9bu5aK/r+HyUckkRJqZlBHf5Iayjo5O2wR84B8wYICvFTxGJVfoOl8hBMOTezA8uQcA14zrw59WFLKyoJiTtfX846s9XDE6mYcvG9bqLGKV6lYlV1DLVyVX0M434AN/ZKQ6o0NUcgXf+faKCuHZa53r9lRbbLy4Zi/PfLWbVbuOMjA+nIHxEfxqWjq9okJ87toRVHIFtXxVcgXtfAO+j78hm50KqOQK/uEbbjbxq2npLP15DpPS4zEIwftbDjN94RrW7DrqPs4fXD1FJVdQy1clV9DON+Bb/Drdg8YTx344WsWtr23ihpc3EBseTHykmYkJNib7VlFHx28I+MAfGxvrawWPUckV/Nd3YHwEH9xxDu9tOUzhjyfZfriCF7dasEYUcP/FgzGb/DtnkL/Wa2uo5KuSK2jnG/ATuBwOBwaDGj1aKrmCOr42u4PHPtnBorX7MRoEMeHBXDC0Fw9cMtQvE8epUq8NqOSrkit0zretCVzq1EAHaVihRgVUcgV1fE1GA+dGHOX1m7K5bdJAcgbE8uaGg1zz4reUV1t9rXcaqtRrAyr5quQK2vkGfFePjk4DE9LimJDmXI1uxllJ3PXWFs5e8CX9YsIZ2juKP8wYQlwHZgXr6KhGwLf4TSZ1PttUcgW1fJu7XjgskfdvP5trxvalb2wYH287wpX/Ws+PJ2p9ZHgKleoV1PJVyRW08w34Pn4dHU/I21/O3Fc2EhFi4sJhifSJCWP2mBR6hPr/spI6Oi3Rrfv4G1a2VwGVXEEt3/Zcs1JjePOW8fTuGcqyTUX8aUUhs//5P45WWrrI8BQq1Suo5auSK2jnG/CBv6KiwtcKHqOSK6jl64nriJQevHvb2Wx/+ELeuDmbouO1/PSlb8k/dIITNV13E1ilegW1fFVyBe18Az7w6+h0hHMGxbFoThaHymu57Ll1jHrkc+Yt3kiN1eZrNR2dThPwffxVVVVERER42UgbVHIFtXw76lpSUcemA8cp/LGC51ft4ZxBcbw0J0vTSWAq1Suo5auSK3TOt1v38ZeUlPhawWNUcgW1fDvq2isqhItHJPGbCzP46/+dxTe7jzHruf8x/7/beH9LEVo0nFSqV1DLVyVX0M434AP/oUOHfK3gMSq5glq+3nC9MqsPC68ehcEAy7/7kV/9J5+/fLzDC3ZNUaleQS1flVxBO1+1BrXq6PiYy0cnc/noZKSUPLS8gH9/s4+okCB+NmmA3+cA0tFpIOADf1pamq8VPEYlV1DL19uuQgj+eOkwTtTW8+Tnu3jy8130jQnj4ZnDmDI4oVNlq1SvoJavSq6gna+ygb++vp6ioiLq6uraPM5utyszhKs915CQEFJSUggK8o9JRWazOukNtHA1GARPXTWKC4clsqukkk+2FfPz1zfx5i3ZnVoKUqV6BbV8VXIF7XyVDfxFRUVERkaSmpqKEKLV4yorK5VZdactVyklZWVlFBUV0b9//y42a5nt27czefJkX2t4hFauRoPg4hFJXDwiiRtyUrni+XXctCSP3180hJToULJSYwg2ndmtNJXqFdTyVckVtPNV9uZuXV0dsbGxbQb9QEIIQWxsbLvfcHR8R0x4MK/OyyYyxMRv393KtS/lMuv5dVTU1ftaTUenCcoGfsCjoK9SUqb2XP3tQy4hoXN92V1JV7n2jQ3jq19P5ot7JvHUVSPZWVzJnW9uod7u8LgMleoV1PJVyRW081U68HtCSEhI+wd1kNraWiZNmoTdbgdgyZIlpKWlkZaWxpIlS9o9v7y8nGnTppGWlsa0adOorXVmhlyxYgUPPvigZt7eIj093dcKHtOVrkFGA4MSIrgiM4U/Xz6cNbuOMuflDTz12U427Ctv93yV6hXU8lXJFbTzDfjAX1VVpVnZL7/8MldccQVGo5Hy8nIefvhhcnNz2bBhAw8//DDHjx9v8/wFCxYwdepUdu/ezdSpU/nTn/4EwIwZM/jwww+pqanRzN0brF271tcKHuMr12vG9eXBS4ayq6SSZ77ew1UvrOe1bw+0eY5K9Qpq+arkCtr5ahr4hRDThRA7hRB7hBD3tbC/rxDiayHEFiHEViHExVr6eJs33niDyy67DIBPP/2UadOmERMTQ3R0NNOmTWPlypVtnv/BBx8wZ84cAObMmcOKFSsAZ5fO5MmT3c911GbehP7kzZ9G4cPTOX9IAg9+sJ3/bjnsay2dboxmHeBCCCPwHDANKAI2CiGWSykLGx02H3hHSvlPIcRQ4GMg9Uxf6+EPCyj8seVhkHa7HaPxzCfWDO0dxR8vHdbqfqvVyt69e0lNTQXg8OHD9OnTx70/JSWFw4fb/uMuKSkhKSkJgMTERI4ePerel5WVxTfffMNVV111xu5dhUpD4/zBNTTYyLPXZjL3lY38emk+nxUWMyg+gp9NGkiE+dSfoj+4ngkq+arkCtr5atniHwfskVLulVJagbeBy5odI4Eo1889gB+9LdGRoO8Jx44do2fPnl4rTwjR5OZtQkICP/7o9erwKjk5Ob5W8Bh/cQ0JMvLvOVlMH57I90ec3T83LMrFYrO7j/EXV09RyVclV9DOV8shL8lA40QTRUB2s2MeAj4TQtwFhAPnt1TQkSNHyMjIAJyfgHPnzmXy5MlUVlZiNBp58JKhTfryIyMjqa6uxuFw4HA4iIiIoL6+nvr6encZQgj30EiTyURISIi7DCEEERER7jIAwsPDsVqt7jKMRiN1dXVUVlYCEB8fz7p169zP9+3bx7Rp06iqqnIn8goPD8disWCzOVP7JiQkcODAAWJiYiguLiYuLg6Hw0F1dTXHjx93T9RqXIaUksLCQkpLSwEYPnw4FouF3bt3A9CnTx969epFQzbTqKgoMjMzWbt2rft1J06cSEFBAWVlZQCMHDmSyspK9u7dC0BqaioxMTHuRSCio6MZOXIkq1evRkqJEIJJkybxxRdfuEciZWZmUl5ezv79+wEYMGAAkZGR5OfnAxAbG8uwYcPci0ebTCYmTJjA5s2b3ZPWsrKyKCkpcecnSUtLw2w2s337dnd9paenu/s9zWYzOTk55OXlua9ddnY2RUVF7m9bGRkZGI1G8vLyCAsLIzExkf79+7N+/XoAQkNDyc7OJjc3131zPScnh3379lFcXAzA0KFDsdvt7Ny5E4Dk5GRSUlLIzc0FICIigqysLNavX4/F4ly4ZcKECezatavV63RPdh969Urn+RXf8vx3J/j5S6t5+dYprFu3joqKCsLCwrx2nfLz8933m7S4TgaDgaFDh3rlOhUWOjsEtLpO4Jz86el16sq/p5auU2FhIWFhYR5fp/nz57N06VJcxNEaUkpNHsBs4KVGz68Hnm12zD3Ar10/5wCFgKF5WWPGjJHNKSwsPG1bS1RUVHh0XEdISUmRtbW1Ukopy8rKZGpqqiwvL5fl5eUyNTVVlpWVSSmlvP7662Vubu5p5//mN7+Rjz32mJRSyscee0z+8pe/dO974okn3Psa4+n77gq+/vprXyt4jD+7Pvnp97Lf71bImc98I+94Y5N86f0vfK10Rvhz3TZHJVcpO+cL5MlW4rOWXT2HgT6Nnqe4tjXmJuAdACnleiCEtj6l/IwLLrjA3aqJiYnhgQceYOzYsYwdO5YHH3yQmBjntP2tW7fSu3fv086/7777+Pzzz0lLS+OLL77gV7/6lXvf119/zYwZM7rmjej4lLvPT+eeaekEmwys/6GMBRvq2FZ00tdaOoFMa58InX3g7EbaC/QHgoF8YFizYz4BbnT9PARnH79oXlZnWvx2u/0MPiPPjE2bNsnrrruuzWNOnjwpZ8+e7VF5Da7FxcXyvPPOa/EYf2rx19TU+FrBY1RxPVpZJ8f/5XOZ85cv5NHKOl/reIQqdSulWq5Sds4XX7T4pZQ24E7gU2AHztE7BUKIR4QQM12H/Rq4RQiRD7zl+hDw6soWVqt2a6VmZmYyZcoU9wSuloiKimrc59YmDa4HDx7kySef9IqjlhQVFflawWNUcY2LMPPA5F6UVVuZ9fw67l2az9K8Q5os+OItVKlbUMsVtPPVdBy/lPJjKWW6lHKglPJR17YHpZTLXT8XSinPkVKOlFKOklJ+5m2HhpuxWjFv3jyvjRxqcB07diyjRo3ySpla0t5wVX9CJdcwSxn/viGL5J6hfPl9Kfcu28qzX+3xtVarqFS3KrmCdr7qJLLR0elGTEyPZ2J6PFJK7nknn6e+2MXQ3lFMHdLL12o6AUDAB36VJmyo5Aq4h9iqgKquQggeu2IEu0qcyd4uGNaLtIQIrs9JpUeof6zLoGrdqoBWvgGfq8ffMlq2hUquoN3kOC1Q2TUkyMhLc7KYnBFP3v7jPPHZLm54eQN19a3fW+pKVK5bf0cr34AP/Crlr1fJFXBPtlEB1V2TeoTyz+vGsO6+8/jXdWPIP3SC37+3zS9u+qpet/6MVr4BH/i1pLNpmZcuXcqwYcMwGAzumYEA27Zt48Ybb9RKW0dxpg9P5J5p6by35TBXv/AtDy0v4LtDJ3ytpaMQAR/4tVyIpbNpmYcPH857773HxIkTm7iOGDGCoqIiDh48qJm7N0hMTPS1gscEmutd5w3i19PSqam38eaGg1z9wnqfBf9Aq1t/QivfgA/8Wt4w7Wxa5iFDhjS5edPY9dJLL+Xtt9/WRtxL+Mvav54QaK5CCO6amsaKu85l/X3nER9p5mev5lFS0fXdhYFWt/6EVr6BMarnk/ugeFuLuxx2GwZjB95m4gi4aEGru72Rlrk51dXV7sXWs7KyWLBgAb/97W/P3L2LWL9+vTILVweya2yEmZfmZHHF8/9jxj/WMjA+nKlDErjl3AFdMmAgkOvW12jlG/Atfq3wdlrm5qiQllnHfxicGMXiuePI7NuTaquNv3z8PY9/utPXWjp+SmC0+NtomddVVbnTsXqT0NDQJqNwkpOTWbVqlft5UVHRGX9SN26d1dXVERoa2llNTfF3v8Z0B9dx/WMY1z8GKSV/+O92/rnqBwYnRnLZqGQvGzalO9Str9DKN+Bb/FoEfXDm1Lbb7e7gf+GFF/LZZ59x/Phxjh8/zmeffcaFF14IwA033MCGDRvOyHXXrl0MHz5cE3dvkZ3dfHkF/6U7uQoheOjSYYxLjeHX7+Qz6/l1/PqdfMqqLF4ybEp3qtuuRivfgA/8Wi623tm0zO+//z4pKSmsX7+eGTNmcP75p9ahUSEtc8MCFyrQ3VyDTQb+df0Yrhrbh/BgEx/m/8gtr+ZpMumru9VtV6KVb8AHfi0nuNxxxx1NxuvPmzePPXv2sGfPHubOnQtARUUFaWlppKSknHb+rFmzKCoqwmKxUFJSwvvvvw+AxWIhLy+Piy66SDN3b9CwEpIKdEfXmPBg/jJrBK/fnM3Ca0ax+eAJ/vLxDq+U3ZjuWLddhVa+gdHH7yMap2VubWr1maRlbuDgwYMsWLBA0zkIOt2Li0ckcdOE/ixau49NB46TEGnm9imDGJsa42s1HR8g/GHKd3tkZWXJxjNbAXbs2MGQIUPaPdfhcGAwqPHFxhNXT993V2CxWJRJLKe7gt0heWHND/xvTxm7SiqptthYdtvZDEmK6lS5et1qR2d8hRCbpJRZLe1TIyJ2goZFlVVAJVdwLiivCrorGA2C2ycP4vWbs1l+5wQiQkzc9vomKuo6t2aFXrfaoZVvwAd+m83mawWPUckVoLi42NcKHqO7NiWxRwjPXptJ0fFazntiFZc9u5anPt+Fw3HmPQB63WqHVr4BH/h1dHRaZmxqDK/fnE3OwDiCjAb+8eVuHvvE+zd/dfyPgL97GBIS4msFj1HJFWDo0KG+VvAY3bVlxg+IZfyAWKSUPLS8gH9/s4++MWFcNbYPwUaDRykf9LrVDq18A77Fr+XN686mZb733nsZPHgwZ511FrNmzXJn81QlLXNbi8z7G7pr2wgheOCSoUxMj+eBDwrImL+SnMe+YsO+8nbP1etWO7TyDfjAr+UN086mZZ42bRrbt29n69atpKen89hjjwHqpGXeuVOdXDC6a/uYjAYWzcni6atH8utp6YQGG7lp8Ua+L65o8zy9brVDK9+AD/xa0tm0zBdccIF7rP748eObZPNUIS2zTuARZDQwa3QKd01N442bswkzG7lpcR7v5B3iyx0lWGxqtZh1WiYg+vj/uuGvfF/+fYv7OjqOf3DMYH437net7vd2WuaGbw8NqJCWOTlZ2+Rf3kR3PXN69wxl0Zyx3Lwkj98u2wrAyD49efPmbMLNp0KHv/h6gkquoJ1vwLf4tZq85c20zI8++igmk4kbbrjBvU2FtMwtpaHwV3TXjjE8uQff/G4KX/9mMn+bfRbbD5/kzjc3Y2807NOffNtDJVfQzjcgWvxttcwrKyvdi5t4E2+lZV68eDErVqzgyy+/pKamxu2qQlrm3NxcZRa10F07TpDRQP+4cPrHhWOxOZj/3+3Men4dfWPCODctjoSqH5gyZYqvNT3C3+q2PbTyDfgWv1Z4Iy3zypUrefzxx1m+fDlhYWFN9qmQllmn+3Hd+H78ZdYIBLDpwHF+9+42PtnfuZm/Ol1PQLT420LLPD0NaZnPP//8JmmZAY/SMt95551YLBamTZsGwJgxY1i0aBGgRlpmrdY60ALd1Xtcm92Xa7P74nBI7np7C+9sPULOliIuH5XcJUs9dgZ/r9vmaOUb8EnatGTz5s08/fTTvPbaa60eU1FRwU033XRGGTotFguTJk1i7dq1p2Xo9If3raPTQF29neteyiXvgHPocnLPUBZeM0rP+ukHdOskbVouxNI4LXNrnEla5gZXVdIyr1+/3tcKHqO7akNIkJG7RsCCK0bwi6lpmIyCeR6M/fcVKtUtaOfr35HFC2j9jWbevHleK6vBtWH2r7+jUjZR3VU7pM3KNRP6AnBVVgr/98//cf2iDfxkXF/iI4K5eEQSsRH+kQpZtbrVytejFr8QwiyEuFYI8XshxIMNDw/Omy6E2CmE2COEuK+VY64SQhQKIQqEEG+e6RvQ0dHxH1Kiw3h1XjY9Q4P4x5e7eeCDAi59Zi0Hyqp9rabTCI/6+IUQK4GTwCbA3a8hpXyyjXOMwC5gGlAEbAR+IqUsbHRMGvAOcJ6U8rgQIkFKWdq8rM708Usp/f6GUwOeuPpTH7/NZvP77qgGdFftaM3XZneQX3SSm5ZsJDTIyIL/O4vePULoGxuG2dTyinVaEyh16wne6ONPkVJeLaV8XEr5ZMOjnXPGAXuklHullFbgbeCyZsfcAjwnpTwO0FLQ7yyNx9r7Oyq5gnPIqSrortrRmq/JaGBMv2jeumU89XYHc17ewLSn1zDx8a/ZfvhkF1s6CZS67SyeBv7/CSFGnGHZycChRs+LXNsakw6kCyHWCSG+FUJMP8PXaBeVFjdRyRWgtNTrn9OaobtqR3u+Q5Ki+Oo3k1kybxxPXTUSk8HAT178lq93llJZV9+hxV86SqDVbUfx9DvEBOBGIcQ+wAIIQEopz/LC66cBk4EUYI0QYoSU8kTjg44cOUJGRgYAZrOZuXPnMnnyZCorKzEajYSGhjYZvRMZGUl1dTUOhwO73Y7dbqe+vp76+np3GUIIdwvbZDIREhLiLkMIQUREhLsMgPDwcKxWa5My6urquPjii1mxYgVms5nLL7+c3Nxcxo8fz7Jly4iIiKCqqsp90zY8PByLxeIO8EII5s6dy6ZNm4iJieHll19m+PDhbNiwgWeeeYYXX3yR8PDwJmVIKSksLHT/QgwfPhyLxcLu3bsB6NOnD7169aKhaywqKorMzEzWrl3rft2JEydSUFBAWVkZACNHjqSyspK9e/cCkJqaSkxMDJs3bwack9VGjhzJ6tWr3d1RkyZNora21j1bOTMzk/Lycvbv3w/AgAEDiIyMJD8/H4DY2FiGDRvGmjVr3HU+YcIENm/eTEWFcwRIVlYWJSUlHDrkbC+kpaVhNpvZvn074ExjkZ6eztq1a93XICcnh7y8PPe1y87OpqioyJ0nKSMjA6PRSFVVFatWrSIxMZH+/fu7R0uEhoaSnZ1Nbm4utbW1AOTk5LBv3z736kdDhw7Fbre7MyUmJyeTkpJCbm4u4BxrnZWVxfr169034yZMmMCuXbs6dJ0aXL11nfLz892ZYrW4TlarlWPHjrV7ncIrDiCrqrhnJDxfEMzcVzY6/y6C4PfT+jNtcByFhc6eYK2uE+C16+Ttv6eWrlPD74Kn12n+/PmNRxHG0RpSynYfQL+WHu2ckwN82uj5/cD9zY75FzC30fMvgbHNyxozZoxsTmFh4WnbWsJqtXp0XEd49tln5cKFC93Pv/jiC7l8+XI5Y8YMj85/7rnn5K233iqllPKtt96Ss2fPdu+bOnWqPHDgwGnnePq+u4KjR4/6WsFjdFft6IhvZV29XJp3SL64+gc589m1MvW+FfK19fulw+HQwPAU3aFuGwDyZCvx2aOuHinlAaAncKnr0dO1rS02AmlCiP5CiGDgGmB5s2P+i7O1jxAiDmfXz15PnDxFajics3FaZoCpU6eeUV6gDz74gDlz5gAwe/Zsvv76a7evCmmZVRoap7tqR0d8I8wmZo9J4ZaJA3j7lvFMTo9n/n+3kzF/JeMe/YKFX+xqkgjOl66+RCtfj7p6hBC/xHkj9j3XpteFEC9KKZ9p7RwppU0IcSfwKWAEXpZSFgghHsH5SbTcte8CIUQhztFC90opy870TRT/5S9YdrScltlmt2MynvkIAvOQwST+/vet7m+elrkjNE7lbDKZiIyMpKysjLi4OCXSMu/evVuZNLe6q3Z01jc02MiLN2SxbFMR+8uq2VNSxcIvdrO16CQLrxlFVEiQ37h2NVr5etrHfxOQLaWsBhBC/BVYD7Qa+AGklB8DHzfb9mCjnyVwj+uhFN5My9wSKqRl1tHxFkFGAz8Z19f9/LVvD/Dw8gLOWfAVMeHBZPWL4cFLh9Ij1HsfAt0ZTwO/oNH4fdfPfjM4vq2WeV1dnSaLmDdPy9wRkpOTOXToECkpKdhsNiorK4mNjQXUSMvceOEZf0d31Q4tfK8f34/BiZG8t7mIk7X1LM8/TH7RCV65cSx9YsLaL6AV9Lp14mngfwXIFUK873p+ObBIEyMvExSkTQuhcVrm9j5Y7r//fsaNG8esWbOabJ85cyZLliwhJyeHZcuWMWXKFPcELhXSMvfq1cvXCh6ju2qHVr5jU2Pcyd7+98Mxfv7aJqY+uZrUuDCSe4ZybXY/pg09s9fW69aJpzd3nwLmAuWux1wp5UJNjLxMTU2NZmU3pGVu4Nxzz+XKK6/kyy+/JCUlhU8//RSAbdu2kZiYeNr5N910E2VlZQwaNIinnnqKBx54wL1PhbTMzWdT+zO6q3Z0he/ZA+NYfucE5pzdj9TYcPYcreKWV/P4w/vbWL3rKOt/KMNqc/iFqzfRyrfNFr8QIkpKWSGEiAH2ux4N+2KklOWaWCnCHXfcwdNPP835558PwDfffNPicfX19eTk5Jy2PSQkpEnmzsrKSsB5Jz8vL4+FCxd6X1pHR1FS48L5w4yhAFhtDh5f+T0vrd3HG7kHAegbE8bDlw1jSkaCLzWVoL2unjeBS3Dm6Gk8tkq4ng/QyMtraLkQS+O0zMY2Rg41tPzbo8FVlbTMUVFRvlbwGN1VO3zhG2wyMP+Sodx87gAOltdQVmXhyc93MfeVjQxOjCTYZCCzbzS3Tx5IQtSprli9bp3oC7EoRnd93zo67VFXb2fR2n1sPnCcOpudjfuPYzYZuGPKIJJ6hBAXYSZnQCwGg9+MS9GUTidpE0J86ck2f6Sh+0QFVHIFmtzf8Hd0V+3wF9+QICN3TBnEohvH8sbN4/n07okMTYpiwSff88u3v+OnL+Uy7fFP2V2izt+ZVnXbXh9/CBAGxAkhojk1hDOK0xOu6XQzVEoqp7tqh7/69o8L5+2fjefIyTpqrDY2HzzBwx9sZdrTa4iLMBNkFJw/pBe/mpZOTHiwr3VbRKu6ba8T+VbgbqA3zn7+hsBfATyriZGOjo6OlxBC0Luncz7MoIRIzGV7+NHcl4PlNVTU1vPWhoOs2PojkzMSMJsM9IkJ4yfj+vrtB4G38HQhlrvaSs+gNfpCLKfwpz5+h8Oh6c1zb6K7aodKvs1dvy+uYOHnu8kvOoHdISmttBAebGTqkF70CA0iJMjAtKGJjE2N9kkc6UzddrqPX0r5jBBiuGuZxBsaHh2y6WIaUrhqVfakSZPci61Pnz6dnj17cskll3h0/po1a8jMzMRkMrFs2TK369GjR5k+3etLE3idgoICXyt4jO6qHSr5NncdnBjFv64fw/r7p7LhD+fz+a8mcuHwRDYdOM5H246wZP0BrnphPVOeWMVPXvyWn770LX/79Hv2HeuapSS1qltPk7T9EWcWzaE4c+9cBKwFXtXEyos0BGUtePnll7niiivcQznvvfdeampqeOGFFzw6v2/fvixevJgnnniiiWt8fDxJSUmsW7eOc845Rxt5L9CQf1wFdFftUMm3Pde0XpE8ddUo9/Maq42Pth7hk+3FVNbVY7U6+NfqvTz39Q/ERZixORykxoYzY0QS04cnYjQIws0mr+UU0qpuPR0oPhsYCWyRUs4VQvQCXtfESCHeeOMN3nzz1PrwU6dOdS9M4gkNmT1b+ip3+eWX88Ybb/h14NfRCXTCgk1cmdWHK7NO5cwprajjvS2HOVBWjUEItv9YwaMf7+DRj3e4jxmeHMVFw5PoERqEQQhSY8MYltzDb5LMeRr4a6WUDiGETQgRBZQCfpPt6Jt3dnHsUFWL+zraxx/XJ4Jzr0pvdb830jI3p3FStqysLObPn++1srVg5MiRvlbwGN1VO1Ty9YZrQlQIP580sMm2PaWV5O4rxygEZdVWPiss4W+f7jzt3KQeIRiEwBxkYEBcBAPjwwkJMiKBlJ6h9I8xEyNs2KQgtvxHMsq1SY7gaeDPE0L0BP6Nc3RPFc60zH6PRCI0SCSqRVpmu93unq2rQlrmyspKoqOjfa3hEbqrdqjkq5XroIRIBiWcWoTpjimDOFZlweGQ2BySH45WsWPHQU7s2IlBgiyvgO/2EVFchMlWj0E6iKw+hrn6GFaHs8v3KFATHErCzMswms58TZG28CjwSylvd/34LyHESiBKSrnVqyadoK2WeWVl5RmtiuUp3kjL3Byr1YrZbAbUSMu8d+9e+vbt2/6BfoDuqh0q+XaFq6OujoqVK7GuX4+9ohLpsJO0dx9xrjWKG2NKSsIQHg5CYB+UwcmE8ygL74lROjhi7kmuCGOMl4M+tD+BK7OtfVLKzV43UgRvpGVuCxXSMuvodGeklGC3U7NxIyeWLqV26zYQAltpKdJiwRQfjzEuDgSEDBlC9DVXYx48GBEUhCE0FPOAAc6g3wa9zuCe4ZnQXov/yTb2SeA8L7poQnCwdhMxGtIyN2TnPPfcc/n++++pqqoiJSWFRYsWceGFF7Jt2zZmzpx52vkbN25k1qxZHD9+nA8//JBevXpRWFgIqJGW2Zv3N7RGd9UOlXw76irtdhxVVTiqqjj50cecfP99rAcOgMOZCtrYsyfhZ+eAyYQpOpqIKecRlj2u02P/tarbNgO/lHKKJq/ahWiZ4bKzaZnHjh1LUVGR+3njoafLly/ngw8+8LKxd4mJifG1gsfortqhkq+nrvaqahxVlVgPHuT4G29StXo1slHXblh2NpHTpiFCzAT36UPkBRdgcHXT+sL3TPF0HH+Lk7WklH4/jr+mpkaTPn7wflrmBtejR49yzz33+P0Ns82bNzN58mRfa3iE7qodKvm25SqlpOqrrzj23PPUub55Axiiouj5f/9HcN8+iOBgQseMISS99fuKXeXbGTxtDo9t9HMIMBXYjAITuLRm3rx5Xi8zPj6eyy+/3Ovl6ujoNKU2P5+jf/8HtVu34qipAYeD4H79iL/7bowx0RijehAx8VwMYR1f59cf8XRUz12Nn7uGdr6thZC3aasl7m+o5Ar4/TeSxuiu2qGSb7TZzIn33se6fz/1hw9T8cknmOLi6DHzUgxRUZgHpRE1/UKEnyyCpFXddvTdVQP9vSmiFWEKfVKr5Ardb+JOV6GSK6jhK6WkJjeXsD/8gSM/HgGTCWNkJNHXXkv8L3+BUaPu4M6iVd16uhDLh0KI5a7HR8BO4H1NjLyMSoubqOQKsHr1al8reIzuqh3+6uuoqaFkwV/ZPXES3w8bzsEb51JXb6Pvq0sYvG0r6ev/R+L8P/ht0Aft6tbTFv8TjX62AQeklEWtHazTPVBh2c4GdFft8Cff+h9/pGbTJmxlZRx/6y3qDx4icto0ggf0JygxkfyYGEaMG+drTY/Rqm49Tcu8GmcrvwcQgzP4d3s6m5b5qaeeYujQoZx11llMnTqVgwcPAuqkZVZlnQPQXbXEH3wdViulTzzBnqnn8+O9v6V0wV8RCPq+8gop//g7CXffTfQ11yA0GHKpJVrVrafDOW8GHgS+wrkK1zNCiEeklC9rYuVFtBrKCZ1Pyzx69Gjy8vIICwvjn//8J4888gj/+c9/lEnLPGnSJF8reIzuqh2+8rX88APlixdj2b0H6+Ei7EeP0fPKK4m+7jpMCfEYe/RANMt8q9etE0+XdrkXGC2lvFFKOQcYA/xOEyMvU1NTo1nZb7zxBpdddpn7+dSpU8/og2bKlCnuG7rjx493t/jhVFpmfyY/P9/XCh6ju2pHV/tKKTm5fDn7Zl/JyY8+RoSEED52LH3+/W+S/vQIIRnpmKKjTwv6vnDtLFr5etrHXwY0vvNY6drmF3y9+EVKD+xtcZ/dZu9QZruEfgOYcuPPWt3v7bTMixYtcs8ABjXSMh8/ftzXCh6ju2pHV/hKh4Njzz3Pyf/+F1tZGbKujtDMTJIXPk1QQoLH5eh168TTwL8HyBVCfIAzR89lwFYhxD0AUsqnNLHzY7yZlvn1118nLy+PFStWuLepkJZZR6crcFgsHHngASqWf0j4xHOJvOACzIMG0ePyyxCKzX3xFzwN/D+4Hg00JJHxi3FQbbXM20un0FG8lZb5iy++4NFHH2X16tVNJmuokJY5M7PV5K1+h+6qHVr4OqqrKX16IdXffEN9aSmytpb4u+8m9tafdeqGp163TjydufswgBAiwvW85eWumiGEmA78HTACL0kpF7Ry3P8By4CxUso8T8r2FJvNpkng90Za5i1btnDrrbeycuVKEhISsFgsblcV0jKXl5cTFRXlaw2P0F21w9u+9qoqDv3859Ru3kLk1POImDyZ8InnEuGFgQ7dvW4b8HQC13AhxBagACgQQmwSQgxr5xwj8BzOhdmHAj8RQgxt4bhI4JdA7pnKe4LVatWiWOBUWuYGzj33XK688kq+/PJLUlJS3MnZtm3bRmJi4mnn33vvvVRVVXHllVcyatSoJh8MKqRl3r9/v68VPEZ31Q5v+NorKzn67HMU3fUL9s6cSe2W7+j9t8dJeeYZet1/n1eCPnTPum0JT7t6XgTukVJ+DSCEmIxzGcaz2zhnHLBHSrnXdc7bOO8NFDY77k/AX3GOHFKKzqZl/uKLL5o8bzxzV4W0zDo63sBWXs7BOXOw7PmB4AEDMA8YSO/HFhCerc5EK9XwNPCHNwR9ACnlKiFE20vHQDLQeK2xIiC78QGuFb76SCk/EkK0GviPHDlCRkYGAGazmblz5zJ58mQqKysxGo2EhoZSVXWq9ykyMpLq6mocDgcOhwO73U59fT319fXuMoQQ7j56k8lESEiIuwwhBBEREe4yAMLDw7FarU3KGDFiBDk5OZw4cQKz2YzZbKa6urpJGVVVVSxbtozKykrCw8OxWCzYbM75byEhIUgpsVgsABgMBhwOBwcOHOC2225zLyJTVVXlnsEnpaSwsJDS0lIAhg8fjsViYffu3QD06dOHXr16kZfn7DGLiooiMzOTtWvXul934sSJFBQUUFbmHJg1cuRIKisr2bvXOTIqNTWVmJgYNm92LrAWHR3NyJEjWb16tXvx+kmTJhEUFMQq1wpBmZmZlJeXu1soAwYMIDIy0j0cLTY2lmHDhrFmzRp3nU+YMIHNmzdTUVEBOEcylZSUcMi1RF1aWhpms5nt27cDzhve6enp7m9ZZrOZnJwc8vLy3NcuOzuboqIiDh8+DEBGRgZGoxGr1cqqVatITEykf//+rF/vXDI6NDSU7OxscnNzqa2tBSAnJ4d9+/ZRXFwMwNChQ7Hb7ezc6Vw8Ozk5mZSUFHJznV9SIyIiyMrKYv369e5rOWHCBHbt2tWh69Tg6q3rlJ+f7x4dosV1io6O5tixY2d0ncYOG0bRe+9TsWcPIev/h6myiqinnmJ3qLPbtKZHFP0tFq9fpwEDBnjtOnn776ml69Twu+DpdZo/fz5Lly7FRRytIDyZEiyEeB9nGubXXJuuA8ZIKVtdS1AIMRuYLqW82fX8eiBbSnmn67kB54SwG6WU+4UQq4DftNTHn5WVJRsqvoEdO3YwZMiQdt1tNpumi7F4E09cPX3fXcHx48eVycyou2rHmfpaDx7k4I1zqXeNWgtOTaX3gscIHTVKI8NTBHrdNkYIsUlKmdXSPk8ncM0D4oH3gHdxfpK0l4j+MNCn0fMU17YGIoHhwCohxH5gPLBcCNGiaEdpaB2ogEquoNZkGN1VOzzxlfX11O3YQcUnn3Dg+htw1NTQd8kSMrbmM3DlJ10S9CEw67YjtLfYegjwc2AQsA34tZSy3sOyNwJpQoj+OAP+NcC1DTullCdp9FWkrRa/jo6OulgPHuTgTTdT7+oaCkpJoc+LLxDi6r7V6Xra6wNZAtQD3+AcnTMEuNuTgqWUNiHEncCnOIdzviylLBBCPALkSSmXd9j6DFBpcROVXMHZz6gKuqt2tOVr2buPgzfeiLRa6f3XBQT364c5IwODj+aoBFLddoY2+/iFENuklCNcP5uADVLKLp8B0Zk+/oabJyrgias/9fE7HA4MLeRD8Ud0V+1o7ltfWkrpgr9i2bcP6759GMLC6PvKK4RkdM06tW2het2eCZ3p43d360gplUzF3Hi0j7dpnJb5u+++Iycnh2HDhnHWWWfxn//8p93zLRYLV199NYMGDSI7O5uCggLAOe7/xhtv1MzbWzSMKFAB3VU7GvvaT57k0E03U/nVVwQlJNDziitIfecdvwj6oHbdepP2unpGCiEqXD8LINT1XABSSqnOFDgNaJyWOSwsjFdffZW0tDR+/PFHxowZw4UXXthmPp9FixYRHR3Nnj17ePvtt/njH//Iu+++y4gRIygqKuLgwYP07du3696Qjk4HqS8txVZcTMljC7Du30+fF18gvIW5Kzr+QZstfimlUUoZ5XpESilNjX7u1kEfmqZlTk9PJy0tDYDevXuTkJDA0aNH2zz/gw8+YM6cOQDMnj2bVatWucfrX3rppbz9tn+vZ6/KMFnQXbUkYtVq9kyazP6rrqauoIDeTzzht0FftbrVyletWmiFEx/+gPXH6lb3d2SQZHDvcHpeOrDV/W2lZd6wYQNWq5WBA1s/H+Dw4cP06eMc8WoymejZsydlZWXExcWRlZXFggUL+O1vf9sB+65hwoQJvlbwGN1VG8pff4Pwt98mYupUes66nJDhwwlqIT2Jv6BS3YJ2vurc5eggDoddk3JbS8t85MgRrr/+el555ZUzvinT+Ea7CmmZG2YiqoDu6j0se/dR/uqrHHnwj5T8+c/Ys7JIWfg0keef79dBH/y/bpujlW9AtPjbaplXVlZqsvxiS2mZKyoqmDFjBo8++ijjx49vt4zk5GQOHTpESkoKNpuNkydPuodvqZCWuWEKvwrort6hOncDh265BWm1QlAQPa+8kp2TJiKCgnyt5hH+XLctoZVvwLf4taJxWmZwdv3MmjWLG264gdmzZzc59v777+f9998/rYyZM2eyZMkSAJYtW8akSZPcwzlVSMus072o27WLojvuIKhPHwZ+8QWDv9tC0p8eAcX6zXUCpMXfFg1r2mpBQ1rm888/n3feeYc1a9ZQVlbG4sWLAVi8eDGjRo1i27ZtzJw587Tzb7rpJq6//noGDRpETExMkzV2VUjLnJXl1ewamqK7dgz7yZOULlyIZecuLN9/jyEigr4v/ZugpCT3Mf7k2x4quYJ2vgEf+Ovr6zWbEds4LfN1113Hdddd16pDS2mZQ0JCGmfSc397sFgs5OXlsXDhQk28vUVJSQkRERG+1vAI3fXMcdTUcOhnt1JbUEDY6NFEXjSduFtvbRL0wX98PUElV9DON+C7ehrSKGtBZmYmU6ZMwW5v+wZyw4Is7dHgevDgQRYsWOD3Q88a0vKqgO7qOQ6rFcvefRTdfTe127aRsvBp+r32Kr0ffZTgFuaV+Nr3TFDJFbTz9e/IogDz5rWXpPTMSUtLc88J0NHpSo796wWOPvcc1NeDECQ+9BCRroWGdAKHgA/8ZrPZ1woeo5IroNSHk+7aPuVvvsnRhQuJOH8qkVPPJ3T4MMweuOh1qx1a+QZ84FclQRuo5QpqfVDpri1jr6igZtMm6goKOfb880RMmULKwoWIM+hm1OtWO7TyDfg+/uZj7f0ZlVwB91J7KqC7no61qIi9My6h6LbbOfbss4SPH0/yk0+cUdAHvW61RCvfgG/x6+jonI69qpqi227HYbXS56WXCBkyGJNiuep1Ok7At/i1HBnT2bTMa9asITMzE5PJxLJly9yuR48eZfr06Zp5e4uEhARfK3iM7uqk9rvvKHvpJQ7dcguWvXtJefopIiac06mgr9etdmjlG/At/pCQEM3K7mxa5r59+7J48WKeeOKJJq7x8fEkJSWxbt06zjnnHM38O0t6un/kWPcE3dWZUK3kz38GwNCjB0mP/pnws8/udLl63WqHVr4B3+LXciGWzqZlTk1N5ayzznInc2vsevnllzeZyeuPrF271tcKHtPdXau//ZaSxx4jYsoU0jfkkpH7LT0vv9wrZXf3utUSrXwDosX/ySefUFxc3OI+u93eoZm7iYmJXHTRRa3u90Za5rbIyspi/vz5HT5fR8dRV8fJDz/EuncfJ959l+D+qfT+2+MYFZq5qqMNARH4fUF7aZmXLFlyxmmZGw/nVCEts0pD47qbq8Nq5eBNN1O7aRMiOJiQIUPo/eQTmgT97la3XYlWvgER+NtqmWuFN9IyN6dxTg4V0jK3lH/IX+lOrlJKih96mNpNm+j9+F+JuvRSTeeIdKe67Wq08g34Pv7q6tZX5uoM3kjL3JarCmmZ8/LyfK3gMd3BtW7HDkoeW8Chm27m5HvvEXf7bfSYOVPziYHdoW59hVa+AR/4HQ6HZmU3pGUG3GmZG1Ixjxo1iu+++w6Abdu2kdjCykQbN24kJSWFpUuXcuuttzZJwapCWmYtb5x7m0B3rdm8mf1XXc3x//yH+sOHib3t58TddZcGdqcT6HXrS7TyDYiuHl/R2bTMY8eOpaioyP28srLS/fPy5cv54IMPvC+tE3BYDx2i6K5fYOqdROrbb2OKjva1ko6fE/CBPzw8XLOyG6dlbmvkkKdpmRtcjx49yj333EO0n/8BZ2dn+1rBYwLNVVqtHHvpJWo2bsSy43uklPR5/nmfBP1Aq1t/QivfgO/qsVqtmpY/b948ry300uAaHx/P5V4aY60ljb+t+DuB5Cql5Mf7f8+xfzyDo6qasPHj6bf4FcydGD7cGQKpbv0NrXwDPvBruRCLt1HJFeDw4cO+VvCYQHI99vzzVHz0EfF3/5L+S98hZeHThAwZ0kV2pxNIdetvaOUb8F09OjqBQO3WrZQ8/jjW/QewHztGj8suI/bWW32tpaMoAR/4VZqwoZIrQEZGhq8VPEZlV+uBAxy86WYMERFETJ5ESMZgoq+52m/Wb1C5bv0drXwDPvD7yx+HJ6jkCmi2iL0WqOpqO3aMQ7fdjjAY6PfaawSnJPvQrGVUrVsV0MpX0z5+IcR0IcROIcQeIcR9Ley/RwhRKITYKoT4UgjRz9sOWi5u0tm0zE899RRDhw7lrLPOYurUqezatQtQJy1zYWGhrxU8RinX7dspfXohO8dls3vCudQfPkzKs8/4ZdAHxepWIVfQzlezwC+EMALPARcBQ4GfCCGGNjtsC5AlpTwLWAY8rpWPFrSUlrmgoICVK1dy9913c+LEiTbPHz16NHl5eWzdupXZs2fz4IMPAk3TMut0P8JXfkrZCy8QPn488b/8Banv/IewsWN9raUTQGjZ4h8H7JFS7pVSWoG3gcsaHyCl/FpKWeN6+i2Q4m0JLRdi6Wxa5ilTphAWFgbA+PHjmyRlUyEtc0uzkf0Vf3eVDgeWffsoW7yYiOXLibrkEpL/vpC4224jxM/7pf29bhujkito56tlH38ycKjR8yKgrdkINwGfdOSFdu36E5VVO1reKSV0oO88MmII6ekPtLrf22mZFy1axMUXX+x+rkJa5v79+/tawWP82bW+uJiD827CuncvACHjxpH05z8pc8/Hn+u2OSq5gna+fnFzVwhxHZAFTGpp/5EjR9x3t81mM3PnzmXy5MlUVlZiNBqRgN1ucx9vNJqw2+2ARErnDRIpJVI68/YYhAHEqTw+QggMBoPrHADnH1x1dbX7mPDwcKxWq3usfVlZGT169HCnWTCZTJjNZn744Qd++tOf8sILL2AwGKiqqkJK6S7DYrFgszldQ0JCkFKyZMkScnNz+eijj3A4HFRXVxMaGur+BtC4DCklhYWFlJaWAjB8+HAsFgu7d+8GoE+fPvTq1cud3CkqKorMzEzWrl3rft2JEydSUFBAWVkZACNHjqSyspK9rsCTmppKTEwMmzdvBpwJ6UaOHMnq1auRUiKEYNKkSXz11VfuDKKZmZmUl5ezf/9+AAYMGEBkZCT5+fkAxMbGMmzYMNasWeOurwkTJrB582YqKioA54ddSUkJhw452wtpaWmYzWb3gtMJCQmkp6e78yOZzWZycnLIy8tz5zTJzs6mqKjIPf45IyMDo9HIhg0biIiIIDExkf79+7N+/XrAmWU1Ozub3NxcamtrAWdGxH379rnXeBg6dCh2u52dO3cCkJycTEpKCrm5uYAzq2pWVhbr16/HYrEAMGHCBHbt2tXuddr0zTdE/+0JTMfLSXroIb6vt1LWowcHvv3Wa9cpPz+f48ePa3adrFYrmZmZXrlODX3aWl2nqqoqzGbzGV+nrvh7auk6bd++nYiICI+v0/z581m6dCku4mgNZ0D0/gPIAT5t9Px+4P4Wjjsf2AEktFbWmDFjZHMKCwtP29YSFRUVHh13ppSXl8t+/fo12Xby5Ek5evRouXTpUo/L+fzzz+XgwYNlSUlJE9eKigqZnJx82vGevu+u4Ouvv/a1gsf4m2ttYaE8vnSp3HvVVbJw2HBZ9b//uff5m2t7qOSrkquUnfMF8mQrMVXLFv9GIE0I0R84DFwDXNv4ACHEaOAFYLqUslQLCa2+LjdOyxwSEtJuWuZx48Yxa9asJtu3bNnCrbfeysqVK0lISGiSiU+FtMz+vl5AY/zJ9eg/nuHY888DYAgPJ/lvjxPeKImfP7l6gkq+KrmCdr6a3dyVUtqAO4FPcbbo35FSFgghHhFCzHQd9jcgAlgqhPhOCLHc2x4RGi4z19m0zPfeey9VVVVceeWVjBo1imuvPfW5qEJaZpUSXvmLa/mbb3Ls+efpcdllDPz8M9I35BLVbCEhf3H1FJV8VXIFDX1b+yrgT4/OdPVUVlZ6dFxH2LRpk7zuuuvaPe6CCy7wqLzGrueee64sLy8/7Rh/6ur59ttvfa3gMb50rfthryx+/HF54JZbZOHgIfLgrT+Xjvr6Vo9XqV6lVMtXJVcpO+eLj7p6/ALpuimqBd5Oy9zgqkpa5oabbCrgK9e6Xbs4cN31OGprMaemEn3ttST85teINoYZq1SvoJavSq6gnW/AB36tmTdvntfLVCUts07bWA8e5NDNt2AICaH/e+8SnOL1aSo6Oh0i4AO/lguxeBuVXEGthau7ytVeVU3xgw9Ss3EjtvJyDBER9Hvt1TMK+irVK6jlq5Ir6Iutt4gn3TgN43VVoD1XLbutOsK+fft8reAxXeHqqKuj6LbbqPj0U8LPziHu1p/R/913CUlPP6NyVKpXUMtXJVfQzlfZwB8SEkJZWVm7wbBhkoUKtOUqpaSsrIyQkJAuNGqbhokzKqCla11hISc/XMGhW39OTV4evRcsoPdf/0r8L37RocRqKtUrqOWrkito56tsV09KSgpFRUXt5sNpGGevAu25hoSEkKL3E/sV5UuWUPLYAgBEaChJf/kLPS69xMdWOjpto2zgDwoK8iiPRWlpKQkJCV1g1HlUcgXnFHlV8LardDg4+vd/UPbCC0RecAHxv/wFQUlJGFxJ9zqDSvUKavmq5Ara+Sob+D3lVP4d/0clV1DL11uuJ5Yt4/ibb2EtKsJRUUHPK68k8aE/Iry4YIZK9Qpq+arkCtr5KtvH7ykNCZtUQCVXUMvXG67H/v1vjsx/AIQg6qKL6P3kEyQ+8rBXgz6oVa+glq9KrqCdb8C3+HV0OkPt9gJq8jZSV1hIxfIPiZoxg95/XdDmBCwdHX8n4H97k5P9c7m6llDJFdTy7Yjr8bffpvihhwEwhIUR/dOf0uv++zQP+irVK6jlq5IraOcb8IFfpVEwKrmCWr6eujpqaqg/fJiKzz7j2DPPEjF5MkmP/hljTEyXLYyiUr2CWr4quYJ2vgHfx9+wCIMKqOQKavl64lr+5pvsyjmbvZfO5NgzzxJ18UWk/OPvmGJju3Q1LJXqFdTyVckVtPMN+Ba/jk57SKuVo88/T9m/XiB84rn0mHkZwf36EjJ8uDLLH+ronAkBH/i1zMfvbVRyBbV8m7tKu51jzz3HiaXLsLkmAfa8cjaJf/yjz2/cqlSvoJavSq6gna/wt/wvLZGVlSUb1rzU0eks9hMn+PG++6latYqI884jZMgQQoYNI2LKZL2FrxMwCCE2SSmzWtoX8H38DYs1q4BKrqCW77cffUTRL+9mZ+YYdo3Poeqbb+j1wHz6PP8c8XfdSeR5U/wm6KtUr6CWr0quoJ1vwHf1BFJ2Tn/D332llNhPnKD2u++IePgRqm02oi69lKDEXkRMnkzIkCG+VmwRf6/X5qjkq5IraOcb8IFfp3tS8cknFP/5UexlZQDIxERSX/o35kGDfGymo+N7Ar6P32azYVJklqVKruBfvtJup+7777GVlFCx4iMqPv6YkLPOosclMzAl9CLknLMJjoz0taZH+FO9eoJKviq5Qud8u3Uf/65du3yt4DEquYL/+Fr37+fAtT9l///Npuj2O6j8+mvibr+N1DdeJ+aGG4iafiF7Dh3ytabH+Eu9eopKviq5gna+AR/4S0tLfa3gMSq5gm99q9asoeRvf+PwPb9m72WXY9m/n8SHHiJ16TukrV5F/C9+gQgK8gvXM0UlV1DLVyVX0M5Xne88Ot0eabNhPXiQ0qeeouqLLxHBwRijo4m69BLi77qLoF69fK2oo6MEAR/4hw8f7msFj1HJFbrGV9psnFj2LmUvv0z9wYMACLOZhN/8mpg5c5q06ttCpbpVyRXU8lXJFbTzDfjAr9LwLZVcQRtfKSU1ublUfv4FtvIy6rZuo/7wYUJHj6bHJZdgio8jYupUgs5wpTKV6lYlV1DLVyVX0Idzdpjdu3crk4pVJVfwnq+16DDVa9diP3GCqtWrqd2yBUNYGKaEBIIHDiDhvt8Ref75nZpgpVLdquQKavmq5Ara+QZ84NfxP+qPHKGusJD64mKq166jatUqcA0rDurbl17z59PzytkYzGbfiuroBCgBH/j79OnjawWPUckVPPe1HjxIfVER9UeOUPHJSqrXrXMHemNsLLG3/oyes2ZhSkzULNirVLcquYJaviq5gna+AR/4eyk00kMlV2jZ115VRV1hIfby41j376Pio4+x7N7t3m9KSiLu9tuJmDyJoMREjLGxCIP2o4pVqluVXEEtX5VcQTvfgA/8eXl5TJ482dcaHqGSK8Dmr75idEQElt17cFRVUbt1KzWbNoHN5j4mdPRoev3hD5gz0jHFxxPct6/XFyf3BJXqViVXUMtXJVfQzjfgA79Ox5FSYisupv5IMbZjR6krLKRuewH1R45grzhJ/NFjFDUcbDBgTk8ndu6NhI3LxpSQgCkhHlN0tC/fgo6OTgtoGviFENOBvwNG4CUp5YJm+83Aq8AYoAy4Wkq535sOUVFR3ixOU7rSVdrt2EpKsJUfx1FVia20FMueH6g/fBhHbS220lKse/fiqKk5dZLRiDktDfPAgRiiIjlmNjNoxgxCBg9GhIb6TVrjllD990BKibTacdTZT220OXDU2JAOCQKQ4Ki1gV0iggxgEDhq6nHU2hBBBkSQ0VlGdT0YBAaz85uXvbreeY7ZiAgy4Ki1Ia12RLARg9mItEkctfUIowERYkQYnccgJYZQEwnloVTlHkFaHRhCjBhCnWHFYbEjggwYQk2IIAOy3gGAIcSECDGCQyJt8tQ5RgPS5nC5GjT5fVLp9wC089UsSZsQwgjsAqYBRcBG4CdSysJGx9wOnCWl/LkQ4hpglpTy6uZl6QuxOJFSQn09Dms9st6KtLoeFguOmhocVVU4amqwV1XhqKjAfuKkc3tNDfbj5djKyrGfPImjuhrbsWNNumQAMJkISkrCEBGBKbonwQMHYR44gKDkZIzRMZgHDcQQEuKbN98FSCnBATgczmBql6f+t0tnoHI4/8cVkxx1dhx1Nmj4M7I5cFjtyDq78xwB2J0B2mG1I4RwBvFamzOISwlC4LDYkbX1znMMAmlz4KiwIm0Ol1wXV4Zo9prNn2uNSbg/dDAIDGEm9wcKEucHitmICDaCQSCMAmE2YnA9R+D84HJ9UCGc5wiz6dSHilE4fw42IkwGhEm4/jc49/lxQ8YT2krSpmWLfxywR0q51yXxNnAZUNjomMuAh1w/LwOeFUII6aVPo3devoTgqHJvFKUeYa5HqzTLVOn+Ha9yPYqB752bSl2Pnd4UbBvRwk+tH9f+H6ho9xjhSTEdp/mvdBDIoEbbTkscKk+Ls6dtkS0f11JJp+rp1PHCvb/ls5pWSPPnp2j8mdD0erR8TovHiObHtHReO9tsrkfDts5ezzbCUPu17tkxzc849a+T+opYrpz74RmW0z5aBv5koHFKxCIgu7VjpJQ2IcRJIBY41vigI0eOkJGRAYDZbGbu3LmMHj0agNjYWIYNG8aaNWsAMJlMTJgwgc2bNxNmCEcaKrz/znS6Dun+p+1D2j1G0u6fYqPXOu24Vk5sucSWtvo2/XlL78qT0NX289O3nv46HtaEFtUjW2oWtPSs6QdPe40JTxoRwqPjTn/t5hhFKKtWrWLAgAFERkaSn58PtB735s+fz9KlSxtOj2v1VTXs6pkNTJdS3ux6fj2QLaW8s9Ex213HFLme/+A6pkng72hXT3Xe92zbuo0RZ40A4bwUzhfG+TWu4eHa1nCM+xte42NObWx6fpPjGr1/1xBFERyMCApqeySL67xvv/2W8ePHt/CLcFpzqLU9Z3Ruax6t7296wLp16zjnnHNa2tXuuW2+lkEgDOLU/15g1apVyozmUMkV1PL1V1cppfPDz+H83/lc8s03a5l0/uQOldlWV4+WgT8HeEhKeaHr+f0AUsrHGh3zqeuY9UIIE87+hfjmXT2d6eN3OBwYumCcuDdQyRXU8tVdtUMlX5VcoXO+vlqIZSOQJoToL4QIBq4Bljc7Zjkwx/XzbOArb/XvN1BQUODN4jRFJVdQy1d31Q6VfFVyBe18Nevjd/XZ3wl8inM458tSygIhxCNAnpRyObAIeE0IsQcox/nh4FXKXGuuqoBKrqCWr+6qHSr5quQK2vlqOo5fSvkx8HGzbQ82+rkOuFJLBx0dHR2dpqjT2dVBRo4c6WsFj1HJFdTy1V21QyVflVxBO9+AD/wvvPCCrxU8RiVXUMtXd9UOlXxVcgXtfDUb1eNNOjOqJyMjg507u3DmUSdQyRXU8tVdtUMlX5VcoXO+vhrVo6Ojo6PjhyjR4hdCHAUOdPD0OJrNBPZjVHIFtXx1V+1QyVclV+icbz8pZXxLO5QI/Do6Ojo63kPv6tHR0dHpZuiBX0dHR6ebEbCBXwgxXQixUwixRwhxn6992kII8bIQotSVtM6vEUL0EUJ8LYQoFEIUCCF+6WunthBChAghNggh8l2+D/vaqT2EEEYhxBYhxApfu7SHEGK/EGKbEOI7IYRfL5ohhOgphFgmhPheCLHDlU/M7xBCZLjqs+FRIYS426uvEYh9/J4sAuNPCCEm4kyC/6qUcrivfdpCCJEEJEkpNwshIoFNwOV+XLcCCJdSVgkhgoC1wC+llN/6WK1VhBD3AFlAlJTyEl/7tIUQYj+Q1Tyjrj8ihFgCfCOlfMmVPyxMSnnCx1pt4oplh3FmLe7oAJfTCNQWv3sRGCmlFWhYBMYvkVKuwZmryO+RUh6RUm52/VwJ7MC5roJfIp1UuZ4GuR5+29oRQqQAM4CXfO0SSAghegATceYHQ0pp9feg72Iq8IM3gz4EbuBvaREYvw1OqiKESAVGA7k+VmkTV9fJdzjXEftcSunPvguB3+JcBFIFJPCZEGKTEOJnvpZpg/7AUeAVVzfaS0KIcF9LecA1wFveLjRQA7+OxgghIoB3gbullH69zJmU0i6lHAWkAOOEEH7ZnSaEuAQolVJu8rXLGTBBSpkJXATc4eq29EdMQCbwTynlaKAa8Pd7f8HATGBpe8eeKYEa+A8DfRo9T3Ft0/ECrr7yd4E3pJTv+drHU1xf7b8GpvtYpTXOAWa6+s3fBs4TQrzuW6W2kVIedv1fCryPs5vVHykCihp921uG84PAn7kI2CylLPF2wYEa+D1ZBEanA7huli4Cdkgpn/K1T3sIIeKFED1dP4fivOH/vU+lWkFKeb+UMkVKmYrzd/YrKeV1PtZqFSFEuOsGP65ukwsAvxyZJqUsBg4JITJcm6YCfjkgoRE/QYNuHtA4H7+vaG0RGB9rtYoQ4i1gMhAnhCgC/iilXORbq1Y5B7ge2ObqNwf4vWvtBX8kCVjiGh1hAN6RUvr9MElF6AW872wLYALelFKu9K1Sm9wFvOFqDO4F5vrYp1VcH6TTgFs1KT8Qh3Pq6Ojo6LROoHb16Ojo6Oi0gh74dXR0dLoZeuDX0dHR6WbogV9HR0enm6EHfh0dHZ1uhh74dXR0dLoZeuDXCWiEELGN0tsWCyEOu36uEkI8r8HrLRZC7BNC/LyD53/tcmtxkWwdHW8QkBO4dHQakFKWAaMAhBAPAVVSyic0ftl7pZTLOnKilHKKEGKVl310dJqgt/h1uiVCiMkNC50IIR4SQiwRQnwjhDgghLhCCPG4a4GRla7cRAghxgghVrsyUX7qWpugvddZLIT4hxDif0KIvUKI2a7tSUKINa5vH9uFEOdq+451dE6hB34dHScDgfNwZkN8HfhaSjkCqAVmuIL/M8BsKeUY4GXgUQ/LTgImAJcAC1zbrgU+dWUNHQl85523oaPTPnpXj46Ok0+klPVCiG048zs15JzZBqQCGcBw4HNXbhojcMTDsv8rpXQAhUKIXq5tG4GXXR8o/5VSfueVd6Gj4wF6i19Hx4kFwBWg6+WpJFYOnA0kARRIKUe5HiOklBecSdkuhOt11uBcEeowsFgIcYM33oSOjifogV9HxzN2AvENC3QLIYKEEMM6WpgQoh9QIqX8N85lFv09N7xOAKF39ejoeICU0uq6MfsP1/qtJpzLJHY03fdk4F4hRD1QBegtfp0uQ0/LrKPjRYQQi4EVHR3O6SpjFfAbKWWet7x0dBqjd/Xo6HiXk8CfOjOBCxgA1HvVSkenEXqLX0dHR6ebobf4dXR0dLoZeuDX0dHR6WbogV9HR0enm6EHfh0dHZ1uhh74dXR0dLoZ/w+90GlYm8k5cAAAAABJRU5ErkJggg==\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": 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 * 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
}