examples/two_qubit_entangling_gate.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Entangling gate on two coupled qubits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "!pip install -q -U pip\n",
    "!pip install -q matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-03-31 13:54:57.682485: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n"
     ]
    }
   ],
   "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",
    "from typing import List\n",
    "from pprint import pprint\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.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.libraries.chip as chip\n",
    "import c3.signal.pulse as pulse\n",
    "import c3.libraries.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\n",
    "\n",
    "# Qiskit related modules\n",
    "from c3.qiskit import C3Provider\n",
    "from c3.qiskit.c3_gates import RX90pGate\n",
    "from qiskit import QuantumCircuit, Aer, execute\n",
    "from qiskit.tools.visualization import plot_histogram"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model components\n",
    "The model consists of two qubits with 3 levels each and slightly different parameters:"
   ]
  },
  {
   "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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-03-31 13:55:00.301743: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1\n",
      "2022-03-31 13:55:00.388479: E tensorflow/stream_executor/cuda/cuda_driver.cc:328] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n",
      "2022-03-31 13:55:00.388552: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (pop-os): /proc/driver/nvidia/version does not exist\n",
      "2022-03-31 13:55:00.389478: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "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(value=freq_q1, min_val=4.995e9, max_val=5.005e9, unit='Hz 2pi'),\n",
    "    anhar=Qty(value=anhar_q1, min_val=-380e6, max_val=-120e6, unit='Hz 2pi'),\n",
    "    hilbert_dim=qubit_lvls,\n",
    "    t1=Qty(value=t1_q1, min_val=1e-6, max_val=90e-6, unit='s'),\n",
    "    t2star=Qty(value=t2star_q1, min_val=10e-6, max_val=90e-3, unit='s'),\n",
    "    temp=Qty(value=qubit_temp, min_val=0.0, max_val=0.12, unit='K')\n",
    ")\n",
    "\n",
    "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(value=freq_q2, min_val=5.595e9, max_val=5.605e9, unit='Hz 2pi'),\n",
    "    anhar=Qty(value=anhar_q2, min_val=-380e6, max_val=-120e6, unit='Hz 2pi'),\n",
    "    hilbert_dim=qubit_lvls,\n",
    "    t1=Qty(value=t1_q2, min_val=1e-6, max_val=90e-6,unit='s'),\n",
    "    t2star=Qty(value=t2star_q2, min_val=10e-6, max_val=90e-6, unit='s'),\n",
    "    temp=Qty(value=qubit_temp, min_val=0.0, max_val=0.12, unit='K')\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a static coupling in x-direction between them: $(b_1+b_1^\\dagger)(b_2+b_2^\\dagger)$"
   ]
  },
  {
   "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 = 50e6\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": [
    "and each qubit has a 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": [
    "drive1 = 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": [
    "All parts are collected in the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "model = Mdl(\n",
    "    [q1, q2], # Individual, self-contained components\n",
    "    [drive1, drive2, q1q2],  # Interactions between components\n",
    ")\n",
    "model.set_lindbladian(False)\n",
    "model.set_dressed(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Control signals\n",
    "The devices for the control line are set up"
   ]
  },
  {
   "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": [
    "sim_res = 100e9 # Resolution for numerical simulation\n",
    "awg_res = 2e9 # Realistic, limited resolution of an AWG\n",
    "v2hz = 1e9\n",
    "\n",
    "lo = devices.LO(name='lo', resolution=sim_res)\n",
    "awg = devices.AWG(name='awg', resolution=awg_res)\n",
    "mixer = devices.Mixer(name='mixer')\n",
    "dig_to_an = devices.DigitalToAnalog(name=\"dac\", resolution=sim_res)\n",
    "v_to_hz = devices.VoltsToHertz(\n",
    "    name='v_to_hz',\n",
    "    V_to_Hz=Qty(value=v2hz, min_val=0.9e9, max_val=1.1e9, unit='Hz/V')\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The generator combines the parts of the signal generation and assignes a signal chain to each control line."
   ]
  },
  {
   "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": [
    "generator = Gnr(\n",
    "        devices={\n",
    "            \"LO\": lo,\n",
    "            \"AWG\": awg,\n",
    "            \"DigitalToAnalog\": dig_to_an,\n",
    "            \"Mixer\": mixer,\n",
    "            \"VoltsToHertz\": v_to_hz\n",
    "        },\n",
    "        chains={\n",
    "            \"d1\": {\n",
    "                \"LO\": [],\n",
    "                \"AWG\": [],\n",
    "                \"DigitalToAnalog\": [\"AWG\"],\n",
    "                \"Mixer\": [\"LO\", \"DigitalToAnalog\"],\n",
    "                \"VoltsToHertz\": [\"Mixer\"],\n",
    "            },\n",
    "            \"d2\": {\n",
    "                \"LO\": [],\n",
    "                \"AWG\": [],\n",
    "                \"DigitalToAnalog\": [\"AWG\"],\n",
    "                \"Mixer\": [\"LO\", \"DigitalToAnalog\"],\n",
    "                \"VoltsToHertz\": [\"Mixer\"],\n",
    "            }\n",
    "        }\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gates-set and Parameter map\n",
    "Following a general cross resonance scheme, both qubits will be resonantly driven at the frequency of qubit 2 with a Gaussian envelope. We drive qubit 1 (the control) at the frequency of qubit 2 (the target) with a higher amplitude to compensate for the reduced Rabi frequency."
   ]
  },
  {
   "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": [
    "t_final_2Q = 45e-9\n",
    "sideband = 50e6\n",
    "gauss_params_2Q_1 = {\n",
    "    'amp': Qty(value=0.8, min_val=0.2, max_val=3, unit=\"V\"),\n",
    "    't_final': Qty(value=t_final_2Q, min_val=0.5 * t_final_2Q, max_val=1.5 * t_final_2Q, unit=\"s\"),\n",
    "    'sigma': Qty(value=t_final_2Q / 4, min_val=t_final_2Q / 8, max_val=t_final_2Q / 2, unit=\"s\"),\n",
    "    'xy_angle': Qty(value=0.0, min_val=-0.5 * np.pi, max_val=2.5 * np.pi, unit='rad'),\n",
    "    'freq_offset': Qty(value=-sideband - 3e6, min_val=-56 * 1e6, max_val=-52 * 1e6, unit='Hz 2pi'),\n",
    "    'delta': Qty(value=-1, min_val=-5, max_val=3, unit=\"\")\n",
    "}\n",
    "\n",
    "gauss_params_2Q_2 = {\n",
    "    'amp': Qty(value=0.03, min_val=0.02, max_val=0.6, unit=\"V\"),\n",
    "    't_final': Qty(value=t_final_2Q, min_val=0.5 * t_final_2Q, max_val=1.5 * t_final_2Q, unit=\"s\"),\n",
    "    'sigma': Qty(value=t_final_2Q / 4, min_val=t_final_2Q / 8, max_val=t_final_2Q / 2, unit=\"s\"),\n",
    "    'xy_angle': Qty(value=0.0, min_val=-0.5 * np.pi, max_val=2.5 * np.pi, unit='rad'),\n",
    "    'freq_offset': Qty(value=-sideband - 3e6, min_val=-56 * 1e6, max_val=-52 * 1e6, unit='Hz 2pi'),\n",
    "    'delta': Qty(value=-1, min_val=-5, max_val=3, unit=\"\")\n",
    "}\n",
    "\n",
    "gauss_env_2Q_1 = pulse.Envelope(\n",
    "    name=\"gauss1\",\n",
    "    desc=\"Gaussian envelope on drive 1\",\n",
    "    params=gauss_params_2Q_1,\n",
    "    shape=envelopes.gaussian_nonorm\n",
    ")\n",
    "gauss_env_2Q_2 = pulse.Envelope(\n",
    "    name=\"gauss2\",\n",
    "    desc=\"Gaussian envelope on drive 2\",\n",
    "    params=gauss_params_2Q_2,\n",
    "    shape=envelopes.gaussian_nonorm\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We choose a single qubit gate time of 7ns and a gaussian envelope shape with a list of parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_final_1Q = 7e-9   # Time for single qubit gates\n",
    "sideband = 50e6 \n",
    "gauss_params_single = {\n",
    "    'amp': Qty(\n",
    "        value=0.5,\n",
    "        min_val=0.2,\n",
    "        max_val=0.6,\n",
    "        unit=\"V\"\n",
    "    ),\n",
    "    't_final': Qty(\n",
    "        value=t_final_1Q,\n",
    "        min_val=0.5 * t_final_1Q,\n",
    "        max_val=1.5 * t_final_1Q,\n",
    "        unit=\"s\"\n",
    "    ),\n",
    "    'sigma': Qty(\n",
    "        value=t_final_1Q / 4,\n",
    "        min_val=t_final_1Q / 8,\n",
    "        max_val=t_final_1Q / 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": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "gauss_env_1Q = 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 (used for the single qubit gates)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "nodrive_env = pulse.Envelope(\n",
    "    name=\"no_drive\",\n",
    "    params={\n",
    "        't_final': Qty(\n",
    "            value=t_final_1Q,\n",
    "            min_val=0.5 * t_final_1Q,\n",
    "            max_val=1.5 * t_final_1Q,\n",
    "            unit=\"s\"\n",
    "        )\n",
    "    },\n",
    "    shape=envelopes.no_drive\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The carrier signal of each drive for the 2 Qubit gates is set to the resonance frequency of the target qubit."
   ]
  },
  {
   "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": [
    "lo_freq_q1 = freq_q1 + sideband\n",
    "lo_freq_q2 = freq_q2 + sideband\n",
    "\n",
    "carr_2Q_1 = pulse.Carrier(\n",
    "    name=\"carrier\",\n",
    "    desc=\"Carrier on drive 1\",\n",
    "    params={\n",
    "        'freq': Qty(value=lo_freq_q2, min_val=0.9 * lo_freq_q2, max_val=1.1 * lo_freq_q2, unit='Hz 2pi'),\n",
    "        'framechange': Qty(value=0.0, min_val=-np.pi, max_val=3 * np.pi, unit='rad')\n",
    "    }\n",
    ")\n",
    "\n",
    "carr_2Q_2 = pulse.Carrier(\n",
    "    name=\"carrier\",\n",
    "    desc=\"Carrier on drive 2\",\n",
    "    params={\n",
    "        'freq': Qty(value=lo_freq_q2, min_val=0.9 * lo_freq_q2, max_val=1.1 * lo_freq_q2, unit='Hz 2pi'),\n",
    "        'framechange': Qty(value=0.0, min_val=-np.pi, max_val=3 * np.pi, unit='rad')\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We specify the drive tones for the 1Q gates with an offset from the qubit frequencies. As is done in experiment, we will later adjust the resonance by modulating the envelope function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "carr_1Q_1 = pulse.Carrier(\n",
    "    name=\"carrier\",\n",
    "    desc=\"Frequency of the local oscillator\",\n",
    "    params={\n",
    "    'freq': Qty(\n",
    "        value=lo_freq_q1,\n",
    "        min_val=0.9 * lo_freq_q1 ,\n",
    "        max_val=1.1 * lo_freq_q1 ,\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",
    ")\n",
    "\n",
    "carr_1Q_2 = pulse.Carrier(\n",
    "    name=\"carrier\",\n",
    "    desc=\"Frequency of the local oscillator\",\n",
    "    params={\n",
    "    'freq': Qty(\n",
    "        value=lo_freq_q2,\n",
    "        min_val=0.9 * lo_freq_q2 ,\n",
    "        max_val=1.1 * lo_freq_q2 ,\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",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Instructions\n",
    "The instruction to be optimised is a CNOT gates controlled by qubit 1."
   ]
  },
  {
   "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": [
    "# CNOT comtrolled by qubit 1\n",
    "cnot12 = gates.Instruction(\n",
    "    name=\"cx\", targets=[0, 1], t_start=0.0, t_end=t_final_2Q, channels=[\"d1\", \"d2\"],\n",
    "    ideal=np.array([\n",
    "        [1,0,0,0],\n",
    "        [0,1,0,0],\n",
    "        [0,0,0,1],\n",
    "        [0,0,1,0]\n",
    "    ])\n",
    ")\n",
    "cnot12.add_component(gauss_env_2Q_1, \"d1\")\n",
    "cnot12.add_component(carr_2Q_1, \"d1\")\n",
    "cnot12.add_component(gauss_env_2Q_2, \"d2\")\n",
    "cnot12.add_component(carr_2Q_2, \"d2\")\n",
    "cnot12.comps[\"d1\"][\"carrier\"].params[\"framechange\"].set_value(\n",
    "    (-sideband * t_final_2Q) * 2 * np.pi % (2 * np.pi)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also add some typical single qubit gates to the instruction set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "rx90p_q1 = gates.Instruction(\n",
    "    name=\"rx90p\", targets=[0], t_start=0.0, t_end=t_final_1Q, channels=[\"d1\", \"d2\"]\n",
    ")\n",
    "rx90p_q2 = gates.Instruction(\n",
    "    name=\"rx90p\", targets=[1], t_start=0.0, t_end=t_final_1Q, channels=[\"d1\", \"d2\"]\n",
    ")\n",
    "\n",
    "rx90p_q1.add_component(gauss_env_1Q, \"d1\")\n",
    "rx90p_q1.add_component(carr_1Q_1, \"d1\")\n",
    "\n",
    "\n",
    "rx90p_q2.add_component(gauss_env_1Q, \"d2\")\n",
    "rx90p_q2.add_component(carr_1Q_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": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "rx90p_q1.add_component(nodrive_env, \"d2\")\n",
    "rx90p_q1.add_component(copy.deepcopy(carr_1Q_2), \"d2\")\n",
    "rx90p_q1.comps[\"d2\"][\"carrier\"].params[\"framechange\"].set_value(\n",
    "    (-sideband * t_final_1Q) * 2 * np.pi % (2 * np.pi)\n",
    ")\n",
    "\n",
    "rx90p_q2.add_component(nodrive_env, \"d1\")\n",
    "rx90p_q2.add_component(copy.deepcopy(carr_1Q_1), \"d1\")\n",
    "rx90p_q2.comps[\"d1\"][\"carrier\"].params[\"framechange\"].set_value(\n",
    "    (-sideband * t_final_1Q) * 2 * np.pi % (2 * np.pi)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The experiment\n",
    "All components are collected in the parameter map and the experiment is set up."
   ]
  },
  {
   "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": [
    "parameter_map = PMap(instructions=[cnot12, rx90p_q1, rx90p_q2], model=model, generator=generator)\n",
    "exp = Exp(pmap=parameter_map)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dynamics\n",
    "\n",
    "The system is initialised in the state $|0,1\\rangle$ so that a transition to $|1,1\\rangle$ should be visible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(\n",
      "[[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]], shape=(9, 1), dtype=complex128)\n"
     ]
    }
   ],
   "source": [
    "psi_init = [[0] * 9]\n",
    "psi_init[0][0] = 1\n",
    "init_state = tf.transpose(tf.constant(psi_init, tf.complex128))\n",
    "print(init_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_dynamics(exp, psi_init, seq):\n",
    "        \"\"\"\n",
    "        Plotting code for time-resolved populations.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        psi_init: tf.Tensor\n",
    "            Initial state or density matrix.\n",
    "        seq: list\n",
    "            List of operations to apply to the initial state.\n",
    "        \"\"\"\n",
    "        model = exp.pmap.model\n",
    "        exp.compute_propagators()\n",
    "        dUs = exp.partial_propagators\n",
    "        psi_t = psi_init.numpy()\n",
    "        pop_t = exp.populations(psi_t, model.lindbladian)\n",
    "        for gate in seq:\n",
    "            for du in dUs[gate]:\n",
    "                psi_t = np.matmul(du.numpy(), psi_t)\n",
    "                pops = exp.populations(psi_t, model.lindbladian)\n",
    "                pop_t = np.append(pop_t, pops, axis=1)\n",
    "\n",
    "        fig, axs = plt.subplots(1, 1)\n",
    "        ts = exp.ts\n",
    "        dt = ts[1] - ts[0]\n",
    "        ts = np.linspace(0.0, dt*pop_t.shape[1], pop_t.shape[1])\n",
    "        axs.plot(ts / 1e-9, pop_t.T)\n",
    "        axs.grid(linestyle=\"--\")\n",
    "        axs.tick_params(\n",
    "            direction=\"in\", left=True, right=True, top=True, bottom=True\n",
    "        )\n",
    "        axs.set_xlabel('Time [ns]')\n",
    "        axs.set_ylabel('Population')\n",
    "        plt.legend(model.state_labels)\n",
    "        pass\n",
    "\n",
    "def getQubitsPopulation(population: np.array, dims: List[int]) -> np.array:\n",
    "    \"\"\"\n",
    "    Splits the population of all levels of a system into the populations of levels per subsystem.\n",
    "    Parameters\n",
    "    ----------\n",
    "    population: np.array\n",
    "        The time dependent population of each energy level. First dimension: level index, second dimension: time.\n",
    "    dims: List[int]\n",
    "        The number of levels for each subsystem.\n",
    "    Returns\n",
    "    -------\n",
    "    np.array\n",
    "        The time-dependent population of energy levels for each subsystem. First dimension: subsystem index, second\n",
    "        dimension: level index, third dimension: time.\n",
    "    \"\"\"\n",
    "    numQubits = len(dims)\n",
    "\n",
    "    # create a list of all levels\n",
    "    qubit_levels = []\n",
    "    for dim in dims:\n",
    "        qubit_levels.append(list(range(dim)))\n",
    "    combined_levels = list(itertools.product(*qubit_levels))\n",
    "\n",
    "    # calculate populations\n",
    "    qubitsPopulations = np.zeros((numQubits, dims[0], population.shape[1]))\n",
    "    for idx, levels in enumerate(combined_levels):\n",
    "        for i in range(numQubits):\n",
    "            qubitsPopulations[i, levels[i]] += population[idx]\n",
    "    return qubitsPopulations\n",
    "\n",
    "def plotSplittedPopulation(\n",
    "    exp: Exp,\n",
    "    psi_init: tf.Tensor,\n",
    "    sequence: List[str]\n",
    ") -> None:\n",
    "    \"\"\"\n",
    "    Plots time dependent populations for multiple qubits in separate plots.\n",
    "    Parameters\n",
    "    ----------\n",
    "    exp: Experiment\n",
    "        The experiment containing the model and propagators\n",
    "    psi_init: np.array\n",
    "        Initial state vector\n",
    "    sequence: List[str]\n",
    "        List of gate names that will be applied to the state\n",
    "    -------\n",
    "    \"\"\"\n",
    "    # calculate the time dependent level population\n",
    "    model = exp.pmap.model\n",
    "    dUs = exp.partial_propagators\n",
    "    psi_t = psi_init.numpy()\n",
    "    pop_t = exp.populations(psi_t, model.lindbladian)\n",
    "    for gate in sequence:\n",
    "        for du in dUs[gate]:\n",
    "            psi_t = np.matmul(du, psi_t)\n",
    "            pops = exp.populations(psi_t, model.lindbladian)\n",
    "            pop_t = np.append(pop_t, pops, axis=1)\n",
    "    dims = [s.hilbert_dim for s in model.subsystems.values()]\n",
    "    splitted = getQubitsPopulation(pop_t, dims)\n",
    "\n",
    "    # timestamps\n",
    "    dt = exp.ts[1] - exp.ts[0]\n",
    "    ts = np.linspace(0.0, dt * pop_t.shape[1], pop_t.shape[1])\n",
    "\n",
    "    # create both subplots\n",
    "    titles = list(exp.pmap.model.subsystems.keys())\n",
    "    fig, axs = plt.subplots(1, len(splitted), sharey=\"all\")\n",
    "    for idx, ax in enumerate(axs):\n",
    "        ax.plot(ts / 1e-9, splitted[idx].T)\n",
    "        ax.tick_params(direction=\"in\", left=True, right=True, top=False, bottom=True)\n",
    "        ax.set_xlabel(\"Time [ns]\")\n",
    "        ax.set_ylabel(\"Population\")\n",
    "        ax.set_title(titles[idx])\n",
    "        ax.legend([str(x) for x in np.arange(dims[idx])])\n",
    "        ax.grid()\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "sequence = [cnot12.get_key()]\n",
    "plot_dynamics(exp, init_state, sequence)\n",
    "plotSplittedPopulation(exp, init_state, sequence)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualisation with qiskit circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">     ┌────────────┐     \n",
       "q_0: ┤ Rx90p(π/2) ├──■──\n",
       "     └────────────┘┌─┴─┐\n",
       "q_1: ──────────────┤ X ├\n",
       "                   └───┘</pre>"
      ],
      "text/plain": [
       "     ┌────────────┐     \n",
       "q_0: ┤ Rx90p(π/2) ├──■──\n",
       "     └────────────┘┌─┴─┐\n",
       "q_1: ──────────────┤ X ├\n",
       "                   └───┘"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc = QuantumCircuit(2)\n",
    "qc.append(RX90pGate(), [0])\n",
    "qc.cx(0, 1)\n",
    "qc.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "c3_provider = C3Provider()\n",
    "c3_backend = c3_provider.get_backend(\"c3_qasm_physics_simulator\")\n",
    "c3_backend.set_c3_experiment(exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No classical registers in circuit \"circuit-0\", counts will be empty.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Result from unoptimized gates:\n",
      "{'(0, 0)': 0.08226171680112239,\n",
      " '(0, 1)': 0.1890913397763435,\n",
      " '(0, 2)': 1.424154452538259e-05,\n",
      " '(1, 0)': 0.36099243186572805,\n",
      " '(1, 1)': 0.3555090482677506,\n",
      " '(1, 2)': 4.815852947615158e-06,\n",
      " '(2, 0)': 0.008136069767852147,\n",
      " '(2, 1)': 0.003990097959792763,\n",
      " '(2, 2)': 2.3816370114933619e-07}\n"
     ]
    }
   ],
   "source": [
    "c3_job_unopt = c3_backend.run(qc)\n",
    "result_unopt = c3_job_unopt.result()\n",
    "res_pops_unopt = result_unopt.data()[\"state_pops\"]\n",
    "print(\"Result from unoptimized gates:\")\n",
    "pprint(res_pops_unopt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_histogram(res_pops_unopt, title='Simulation of Qiskit circuit with Unoptimized Gates')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Open-loop optimal control\n",
    "\n",
    "Now, open-loop optimisation with DRAG enabled is set up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cx[0, 1]-d1-gauss1-amp                : 800.000 mV \n",
      "cx[0, 1]-d1-gauss1-freq_offset        : -53.000 MHz 2pi \n",
      "cx[0, 1]-d1-gauss1-xy_angle           : -444.089 arad \n",
      "cx[0, 1]-d1-gauss1-delta              : -1.000  \n",
      "cx[0, 1]-d1-carrier-framechange       : 4.712 rad \n",
      "cx[0, 1]-d2-gauss2-amp                : 30.000 mV \n",
      "cx[0, 1]-d2-gauss2-freq_offset        : -53.000 MHz 2pi \n",
      "cx[0, 1]-d2-gauss2-xy_angle           : -444.089 arad \n",
      "cx[0, 1]-d2-gauss2-delta              : -1.000  \n",
      "cx[0, 1]-d2-carrier-framechange       : 0.000 rad \n",
      "\n"
     ]
    }
   ],
   "source": [
    "generator.devices['AWG'].enable_drag_2()\n",
    "\n",
    "opt_gates = [cnot12.get_key()]\n",
    "exp.set_opt_gates(opt_gates)\n",
    "\n",
    "gateset_opt_map=[\n",
    "    [(cnot12.get_key(), \"d1\", \"gauss1\", \"amp\")],\n",
    "    [(cnot12.get_key(), \"d1\", \"gauss1\", \"freq_offset\")],\n",
    "    [(cnot12.get_key(), \"d1\", \"gauss1\", \"xy_angle\")],\n",
    "    [(cnot12.get_key(), \"d1\", \"gauss1\", \"delta\")],\n",
    "    [(cnot12.get_key(), \"d1\", \"carrier\", \"framechange\")],\n",
    "    [(cnot12.get_key(), \"d2\", \"gauss2\", \"amp\")],\n",
    "    [(cnot12.get_key(), \"d2\", \"gauss2\", \"freq_offset\")],\n",
    "    [(cnot12.get_key(), \"d2\", \"gauss2\", \"xy_angle\")],\n",
    "    [(cnot12.get_key(), \"d2\", \"gauss2\", \"delta\")],\n",
    "    [(cnot12.get_key(), \"d2\", \"carrier\", \"framechange\")],\n",
    "]\n",
    "parameter_map.set_opt_map(gateset_opt_map)\n",
    "\n",
    "parameter_map.print_parameters()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a fidelity function we choose unitary fidelity as well as LBFG-S (a wrapper of the scipy implementation) from our library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import tempfile\n",
    "from c3.optimizers.optimalcontrol import OptimalControl\n",
    "\n",
    "log_dir = os.path.join(tempfile.TemporaryDirectory().name, \"c3logs\")\n",
    "opt = OptimalControl(\n",
    "    dir_path=log_dir,\n",
    "    fid_func=fidelities.unitary_infid_set,\n",
    "    fid_subspace=[\"Q1\", \"Q2\"],\n",
    "    pmap=parameter_map,\n",
    "    algorithm=algorithms.lbfgs,\n",
    "    options={\n",
    "        \"maxfun\": 25\n",
    "    },\n",
    "    run_name=\"cnot12\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Start the optimisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C3:STATUS:Saving as: /tmp/tmpbyj0t9_h/c3logs/cnot12/2022_03_31_T_13_55_06/open_loop.c3log\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-03-31 13:55:17.891977: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)\n",
      "2022-03-31 13:55:18.008600: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2199995000 Hz\n"
     ]
    }
   ],
   "source": [
    "exp.set_opt_gates(opt_gates)\n",
    "opt.set_exp(exp)\n",
    "opt.optimize_controls()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The final parameters and the fidelity are"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cx[0, 1]-d1-gauss1-amp                : 2.327 V \n",
      "cx[0, 1]-d1-gauss1-freq_offset        : -53.243 MHz 2pi \n",
      "cx[0, 1]-d1-gauss1-xy_angle           : 523.431 mrad \n",
      "cx[0, 1]-d1-gauss1-delta              : -761.526 m \n",
      "cx[0, 1]-d1-carrier-framechange       : -705.444 mrad \n",
      "cx[0, 1]-d2-gauss2-amp                : 52.793 mV \n",
      "cx[0, 1]-d2-gauss2-freq_offset        : -53.163 MHz 2pi \n",
      "cx[0, 1]-d2-gauss2-xy_angle           : 22.085 mrad \n",
      "cx[0, 1]-d2-gauss2-delta              : -546.271 m \n",
      "cx[0, 1]-d2-carrier-framechange       : 586.902 mrad \n",
      "\n",
      "0.0047978066863104685\n"
     ]
    }
   ],
   "source": [
    "parameter_map.print_parameters()\n",
    "print(opt.current_best_goal)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Results of the optimisation\n",
    "Plotting the dynamics with the same initial state:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_dynamics(exp, init_state, sequence)\n",
    "plotSplittedPopulation(exp, init_state, sequence)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Now we plot the dynamics for the control in the excited state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(\n",
      "[[0.+0.j]\n",
      " [0.+0.j]\n",
      " [0.+0.j]\n",
      " [0.+0.j]\n",
      " [1.+0.j]\n",
      " [0.+0.j]\n",
      " [0.+0.j]\n",
      " [0.+0.j]\n",
      " [0.+0.j]], shape=(9, 1), dtype=complex128)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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6yVoOnSzg1csHkNAwyuyQAkISlI85HdFG32MV/4jGtjx14/Bqnlco6SFBa1j/qdFD9IPxcHR31ftYxbN94NnexvTP/zEGuXumF8pVi8Yf95RLaLN/Md5jW0Czbr6LU1hT0kQYeiOsfL12V9lB7M1fdvPz9kz+MakH/do2MjucgAn+a0Qru/R/xjM/VSnINhpMzL+1bNm+5dC4g99Dq7OiCtWTK8qag0cUHPb+OJFx8I9jRovHiOB58FB4aeyDRuOh+bcaA4aG8FXzjkPZPPndVsYmteCSwW3NDieg5AoqkK5e6Dnfbmj12z+W4JmcwOjyB+DwVqMaLf+E+XX1H11iXN25nHAirWx5jucYXN22VtI7fGVKHni22SQ5hSp7mNGZs80Bn19j/v9xkxQ7Xdz+2Tqiw+08ekGvoOi+qDYkQQVS2zOMp+dPx45FxvsLg41qtMfblrWI8xVncdU9su9bXpYkwXi4cusCY3rFa8YAdSUOrPHYtdHx9d6dX3qCEADxicaDvPvXhGxPE8/9uIN1qcf559ReNI+1/gCDviYJKtB6XWA0mHjguO+OmVdhfJ3N8yF1Rd2P9+5k+Gfzyte9eR787+qy+QXlBvvLPujZhP79C707X6P2xsOaQlTUY7LR08TSJ+HITrOjCai1qcd5fvEOzu+XwMTeoTkasSQoM8S2LGvhVxdV9Y6eexRWvQWfXGoMBVKe1qfeH9IavpxtXBWVt3fZqcet2Krw8FbIOui5rK6faepL0Hs6XP1d2bhNQpQY9xiERRtfhupz69ZaKHK6uPt/62kWE8GDk3uaHY5pJEFZSVzCqfepKvPzfypf/uHFnvesyhfmhffBv1pC3vGyZYU5xhXPm+dVfjxnkdFk/N8dYPlLnuv2LoPMCk3nN3n50G2/yz3nI+KM97ZDZNwmcarYlnDW/bBrifcPdtdz7y/fy9aDWTw4uSfxUWFmh2MaSVBmiy43bMdlnxv3qWqyuJIevguyIK1CtZ6zyHhPXw3LXzCmczLLbVDJt9HySW3r12VP9P/wsOd28/8KqsJ/nyPbawwdgIhYaNq1bD4sNJ7pEKdh4NXQvKfxRaswt+bt67HDWQX836JtjOjclPN6tjA7HFNJgjJb23It+ZonGe/RlYw1VZPHEk9dtvxFbM4CeG1M2bLyD9Ee21O2fI+7Ws9j/KWTcCLdmC7Og0ObPY9fvsVebQy40khSJUK4CbHwkt0BE56EE6nwSxU1CEHiP4u2kVvo5IFJPUKu1V5FkqDMNvi6U5cNu9k3x/7+AWKyKzzY+8GFZQ/Rfn1X2fK3Jxj3tg5uKFv21Y3GuEwlXqxwdVe+o9faaNYNJj5Tt31F6Go/3BhaZdl/y75kBZm0LBefrNzHZWe0o0uL2Jp3CHKSoMzWYZTxXj4pte7nuc3YB6HbhDodvv+auytf4Sw+tc+/3182Xr6WMODUZc17gC0Mzv6H788ngtfYB43GOBWrnIPEp1sLiYlwcMvZXcwOxRKkJwmzKQUPnvBc1nFU2fTfM42HFsF4GNZXHqmkGnHh33x3/PIu/gBOpLH7hzfocNZVxjK7A/6RWf1+QlQUnwCDr4df/wvDb4ZWfcyOyGd+3n6Y9ZlO7puQRKMgGxm3ruQKyqomPgPjnyxLTvVZXCtoM4i97WcYLfWEOB1n3gER8fBTFWOH1UNFThcPzN1IsyjFFcNkjLMSkqCsauBVMOR6z2U9L/Cc/5NFbhZPerbqdRfKMN7CxyLjYfC1sGU+HNpidjQ+8eWadHZl5jCtWzgRDi8GPg0RkqDqkxEVBjJ0WLTrk1H3wGVfQIte0PP8mrcXoraG/AUcUVWPwFyPOF2al5bspHvLWAa2kORUniSo+qRV77KHWsHofWHKC+bFU6JJZ8/5M++EzmfDX5aBTQqc8IOYZjDoGqNfyAPrzI7mtHyz4QC7M3O4YUxnbCHerLwiSVD1zUVvlk3bwyq/SVzd4Ij+0H6E57xd2t6IABh5u9EF0rJqqpgtTmvNq0t30bZxNH9KbmV2OJYjCaq+6eLuYy/ePS5My+RTt7EFQcMKIWoS3Rj6Xgobv6y3Hcn+uOUQ69NOMHtUJ2w2uXqqSBJUffTAcbi13NAVsyt07tqgkibk99ax14fKyLNLwiqG32x8IfvtebMjqTWtNf/9cQcJDaOYNrCSnmCEfxOUUmqcUmqrUmqHUuqeStbHK6XmKaXWKaU2KqWu8mc8QUMpz57DW/Yqm06eXvk+tmqq3e7wsg89gNjWMOK2U5fP/MR473WR98cS4nQ1bAvJF8G6T4yuueqRH7ccYl3qcW48qzNhdrlWqIzffipKKTvwAjAe6AHMVEr1qLDZDcAmrXUfYDTwtFJKnlA7Hd3cQ8jfVaGLI3s1P9aYKsZ+Km/U3XD5HLh9s5EcKyapbuOMzm6nvlTp7kL4zaBroSgH1n5odiS18vziHbRrEs0F/RPMDsWy/Jm2BwM7tNa7tNaFwMfAlArbaCBWGT0ixgBHgWI/xhS8Lv0cOo+FXu5BAqMbe6632Y313hjxV5j2jueyMX+DTuU6nR10rfFefoTgzmNluAwReAn9oe0wYzTnetLT+YrdR1mz7zhXDWsvzz1VQ2k/DQCmlLoIGKe1vtY9fzkwRGt9Y7ltYoGvgO5ALHCx1npBxWO1bNlSx8eXdfMzceJEJk2aVDqfnZ1NTEyMXz5HbVkplv5/3EFcllF9t2T0XNCa6NxU2u39jBaHlgKQ0eIstiTdwuglZd8dloyeC1C6bHf7mUYvEKfJSj8b8G888+bNY/78+QBs27Ztr9a6vV9O5BbqZaThsfX0Xfd3tnb9CwdajzM9npo8tyafrUedPD0qmghHWXW9lX5PYIEyorX2ywuYBrxebv5y4LkK21wE/B+ggM7AbiCu4rEGDBigq7N48eJq1weSlWLRx/Zp/UCc1gvvr3z9/rVaFxcZ0w/Elb1KHN6u9evnal2Y55NwLPWz0YGLB1il/VTOtJQRg8ul9csjtX5uoDFtdjzV2HckR7e/Z75+ZN5G02OpidllxJ9VfGlAm3LzicD+CttcBXzhjnGHO0F192NMoaVhG3468ws495HK17fqU/0zS007wzULIcyiPVYIUUIpoxPZzG2wzYtRqU306apUtIarRnQwOxTL82eCWgl0UUp1cDd8mIFRnVfePuBsAKVUC6AbsMuPMYUc7W1PDuc9ZrxX16+eEFaWPB0aNIflL5odSZUKip18tGIfZ3VvTkJDGUm6Jn575F9rXayUuhFYCNiBN7XWG5VSs93rXwYeAd5WSqVgVPPdrbWWMRjMcMZfoONoaFGxoaUQ9YQj3Gi8s+RROLzVGBjTYuau2U9mdiFXDJUey73h1z5ptNZfA19XWPZyuen9wLn+jEF4SSlJTqL+GzALlj5pjLo71QL9VJajtebNZbvp0jyGUV2bmR1OvSBPhwkhgkdsS+h3GaR8CrlHzY7Gw++7j7IlI4trRnRASaewXpEEJYQILoOuBWchrP/U7Eg8vL1sDw2jw5jSVx7M9ZYkKCFEcGnZC1r3h9XvgJ+e86ytAyfyWLT5IBcPakNUuDyY6y1JUEKI4DNgFhzaBOl/mB0JAB+vSMXp0swc1NbsUOoVSVBCiODT60IIawB/vG12JBQ7Xby/fC9jk5rTvmkDs8OpVyRBCSGCT0Qs9LoANnwBBVmmhvLrziMcySnkwv4ypEZtSYISQgSn/rOMXs43fGFqGJ+uSiU+KoxR3aRpeW1JghJCBKfEgdAsyWgsYZLM7AIWbsxgYu9WRIf79bHToCQJSggRnJQyGkuk/wEZG0wJ4au1+ylyai4dIj1H1IUkKCFE8Op9sTFY5+p3A35qrTUfrdhHz9Zx9GgdF/DzBwNJUEKI4BXdGJImw/qPoSgvoKfeuP8k2w9lc8kQaVpeV5KghBDBrf8VkH8CUj4L6Gk/X51GuN3GxOTWAT1vMJEEJYQIbu1HQotesOLVgJ2yyOniq7X7GdujOfHRYQE7b7CRBCWECG42G/S7HDJS4NCWgJzyp62H5dknH5AEJYQIfj3PB2WDDf8LyOm+WJNGkwbhnCnDapwWSVBCiOAX2wI6jDLuQ/m5A9kTuUV8v+kQk/u2Jswuf2JPh/z0hBChofd0OLYHdi/162m+XJNGodPFBf2keu90SYISQoSGnudDeCys/8Svp/nf6jSSWsXRK0GefTpdkqCEEKEhLAp6TIGNX0Jhrl9OsSczhw3pJ5nYu5WMmusDkqCEEKGj93QoyoVt3/jl8HPX7kcpmNJXnn3yBUlQQojQ0W44xLaClM99fmitNZ+vTmNQu8YkNor2+fFDkSQoIUTosDuMro+2LoDcoz499Op9x9h3NJdpA6VxhK9IghJChJae5xvvm+b49LCfrEwlKszOuF4tfXrcUCYJSggRWtqeAU27wXrf9c2XX+Tk2w0ZjO/VkthI6drIVyRBCSFCi1KQPA32/Qon9/vkkIu3HOJkfjFT+iX45HjCIAlKCBF6ekwx3jfP98nhvtmQQcPoMIZ3auKT4wmDJCghROhp1hWadYfNX532ofKLnPy45RDn9WiJQ7o28in5aQohQlPSZNi7jLDCE6d1mF93ZpJdUMz4ZGkc4WuSoIQQoanHZNAummb+flqH+SYlg5gIB0Oles/nJEEJIUJTi17QuCPNDi+r8yGyC4r5ZkMG5/RoQYTD7sPgBEiCEkKEKqUgaTINj6dA3rE6HWLptsNkFxTLw7l+4vDnwZVS44BnATvwutb68Uq2GQ08A4QBmVrrUf6MSdQ/RUVFpKWlkZ+f79PjxsfHs3nzZp8dLzIyksTERMLC5DmYeqPHZGzLnoEtX0O/S2u9+4L1B4iPCmNw+8a+j60WgrWMeJWglFIRwIVA+/L7aK0frmYfO/ACcA6QBqxUSn2ltd5UbpuGwIvAOK31PqVUc6+iFiElLS2N2NhY2rdv79MeorOysoiNjfXJsbTWHDlyhLS0NDp06HDax6tLmRN10Lo/eZEtiVr/ca0TVE5BMT9uOcRFAxJNb70XrGXE25/qXGAKUAzklHtVZzCwQ2u9S2tdCHzsPkZ5lwBfaK33uT/AIS/jESEkPz+fJk2aWHr4AqUUTZo08eU32LqUOVFbSpHR8ixjEMPj+2q165dr0skrcjKxdys/Bee9YC0j3lbxJWqtx9UyngQgtdx8GjCkwjZdgTCl1BIgFnhWa/1uLc8jQoCVC14JH8dYlzIn6uBQ8+F02POhUc13xmyv91u06SDtmkQzpKM1Wu8FYxnxNkH9qpRK1lqn1CaWSpbpSs4/ADgbiAJ+U0ot11pvK79RWloa3bp1K52fOHEikyZNKp3Pzs5myZIltQjNf6wUC1grnrrGEh8fT1ZWls/jcTqdPj9ufn4+S5YsYd68ecyfX9pLQdM6HKpWZU7KSN1luxqSE92WwuXvsy6/u3f7FGp+3p7Lue3CfPpZpIxUoLWu8QVsAgqBrcB6IAVYX8M+Q4GF5ebvBe6tsM09wIPl5t8AplU81oABA3R1Fi9eXO36QLJSLFpbK566xrJp0ybfBuJ28uTJWm3/zTff6K5du+pOnTrpxx57rNJtKosVWKW9KGf6NMqclJG6W7x4sdYL79P6oSZaZ2d6tc8Xq1N1u7vn6zX7jvk+ljoI1jLi7RXUeG+zYzkrgS5KqQ5AOjAD455TeXOB55VSDiAcowrw/+pwLiH8yul0csMNN7Bo0SISExMZNGgQkydPpkePHv46ZV3KnKir5Gnw63Ow4X8w5M81bv7thgyax0bQOyE+AMHVD/4oI14lKK31XqVUH2Cke9HPWut1NexTrJS6EViI0cz8Ta31RqXUbPf6l7XWm5VS32J8Q3RhNEXfUNcPI4LfQ/M2smn/SZ8cy+l0Yrfb6dE6jgcm9ax22xUrVtC5c2c6duwIwIwZM5g7d67fElRdypw4Da36QLMk2DyvxgR1JLuAHzYf4vKh7bDZrHffJ5jKiFet+JRStwAfAM3dr/eVUjfVtJ/W+mutdVetdSet9b/cy17WWr9cbpsntdY9tNa9tNbP1OlTCOFn6enptGnTpnQ+MTGR9PR0v52vrmVOnIakSbB3GeRkVrvZwo0HKXZppg1oU+12ocYfZcTbKr5rgCFa6xwApdQTwG/Ac6d1diFqqaZvcbVRm2c8jGpyT35uNSVlLtB6TIal/4YtC2DArCo3+3HLQRIbRZHUyjfPB/laMJURb5+DUoCz3LyTylvpCRGUEhMTSU0te2oiLS2N1q1b+/OUUuYCrUUvaNS+2iE48oucLNtxhLO6N68XzboDyR9lxNsrqLeA35VSX7rnp2K0uBMiJAwaNIjt27eze/duEhIS+Pjjj/nwww/9eUopc4GmlFHNt/xlyDsOUQ1P2WTJ1kPkFTk5O6lFwMOzOn+UEa+uoLTW/wGuAo4Cx4Cr5H6RCCUOh4Pnn3+e8847j6SkJKZPn07Pnr6rSqlIypxJkqaAqwi2Lax09aer0mgdH8kwGVrjFP4oI9VeQSml4rTWJ5VSjYE97lfJusZa66OndXYh6pEJEyYwYcIEv55DypzJEgZAbCujmq/PxR6rcgqK+W3nEaYNTCRMRs6tlK/LSE1VfB8CE4E/8OwFQrnnO/osEiEESJkzl81mVPOtfg8KcyC8QemqJVsPk1fkZFxPGTk3UKpNUFrrie730++eWQhRIylzFpA0CVa8Cju+hx5l/VvPW7efuEgHgzqYO7RGKPH2OagfvFkmhPANKXMmajsMopvAprmli/IKnfy45RBT+yVI9V4A1XQPKhKIBpoqpRpR1sw1DvBrG1shQpGUOQuwO6DHVFj9LhRkQ0QMP207TKHTxVndZci6QKrpq8CfMerCu7vfS15zMQYjFEL4lpQ5K0i+yGjNt8Xobfu7TRnER4UxrFNdOqYXdVXTPahngWeVUjdpreUJdiH8TMqcRbQdCnGJsHkexb2m89PWw5zZtRnhDqneCyRvn4N6TinVSyk1XSl1RcnL38EJYRVXX301zZs3p1evXgE5n5Q5kykF3cbD9kWs2baHIzmFjO8lrfeq448y4m0jiQcw+gB7DhgD/BuY7LMohLC4K6+8km+//TZg55MyZwG9LwZnAWkr5hJmV4zsItV71fFHGfG2q6OLgD7AGq31VUqpFsDrPo1ECG98cw9k1GZg56pFOYuNG+Itk2H849Vue+aZZ7Jnzx6fnNdLUubM1rovOi6B1nvnMKLzk8RGhpkdkXeCqIx4W6Gap7V2AcVKqTjgEPLAoBD+JGXObPYwMjtOZaBzHVO7hJsdTUjy9gpqlVKqIfAaRouibGCFv4ISoko1fIurjbxaDCVgAilzFjCv+AyuVi8w1rYK6Gt2ON4JojLi7Yi6/889+bJ7BNw4rfV6/4UlRGiTMmc+rTVv7mjAnxytabFjPgy71uyQQk5ND+r2r26d1nq170MSInRJmbOOlPQTpB3P52jP8bTY9TbkHoVo6eYokGq6gnq6mnUaOMuHsQhhWTNnzmTJkiVkZmaSmJjIQw89xDXXXOOPU0mZs4gFKQdw2BSJw6bDzjdg6zfQ71Kzw7Isf5SRmh7UHXNaRxciSHz00UcBOY+UOWvQWvNNSgbDOzcltsNAiEuArV9LgqqGP8qIV/egqnpAUGv9rm/DEUKAlDmzbdx/kn1Hc7lhTCdjCI4u58L6T04ZgkP4l7et+AaVm44EzgZWA1JYhPAPKXMmmrduP3abYmzJ0O49psAfb8H2RdBzqqmxhRJvW/HdVH5eKRUPvOeXiIQQUuZMpLVm3rr9DOvUhCYxEcbC9iMhMh62LJAEFUB17fkwF+jiy0CEENWSMhcguzJz2H8in3N7tChbaHdA94lGQ4niQvOCCzHe3oOaR9nw03YgCfjUX0EJEeqkzJln4cYMAEZ3qzD2U7cJsPYDY6Td7hNMiCz0eHsP6qly08XAXq11mh/iEUIYpMyZZOHGg/RJjKdN42jPFV3OgQbNYN1HkqACxNvhNn4CtgLxQGOMAiNEyEhNTWXMmDEkJSXRs2dPnn32Wb+eT8qcOQ6ezGdd6nHO7VnJ0BqOCOMqatcScMqvoyJ/lBFvh9u4FqMfsAswellerpS6+rTPLkQ94XA4ePrpp9m8eTPLly/nhRdeYNOmTX47n5Q5c3y5Jh2A83q2qHyDzmOh4CRs/y6AUdUP/igj3lbx3Qn001ofAVBKNQF+Bd48rbMLUUtPrHiCLUe3+ORYTqcTu91O98bduXvw3dVu26pVK1q1agVAbGwsSUlJpKen06NHD5/EUgkpcyZYsP4AvRPj6dy8ig5Su02ABs2NZ6IsWs0XTGXE21Z8aUBWufksILXOZxWiHtuzZw9r1qxhyJAh/jyNlLkA23ckl5T0E/wpuVXVG9kdkDTJuIIqzA1ccPWMr8qIt1dQ6cDvSqm5GC2LpgArlFK3AWit/3NaUQjhpZq+xdVGVh2GEsjOzubCCy/kmWeeIS4uzmexVELKXIB9s+EAABOqS1BgPAe16g3Ysch4gNdigqmMeJugdrpfJea63y07mI4QvlZUVMSFF17IpZdeygUXXODv00mZC7CvU4zqvVNa71XUdhhEN4VNcy2ZoMzk6zLibU8SDwEopWKNWZ3tzX5KqXHAsxjPcbyuta50JC2l1CBgOXCx1vp/3hxbiEDSWnPNNdeQlJTEbbfdFojz1anMibpJPZrLurQT3D2ue80b2x2QNBHWfwZFeRAW5f8A6wF/lBFvW/H1UkqtATYAG5VSfyiletawjx14ARgP9ABmKqVOuVvm3u4JYGFtgxciUJYtW8Z7773Hjz/+SN++fenbty9ff/21385XlzIn6u6rdfsBmJBcSfPyyvSYCkU5xkO7AvBPGfG2iu9V4Dat9WIApdRojKGoh1Wzz2Bgh9Z6l3ufjzHq0Su2O7wJ+BzPzjGFsJQRI0agta55Q9+pS5kTdfTlmnT6t21IuyZe9lTefiRENTaq+ZIm+Te4esIfZUR5c0Cl1DqtdZ+allVYfxEwTmt9rXv+cmCI1vrGctskAB9iDML2BjC/siq+li1b6vj4+NL5iRMnMmlS2X+K7OxsYmJiavwcgWClWMBa8dQ1lvj4eDp37uzzeEqa0PrSjh07OHHiBPPmzWP+/PkAbNu2ba/Wun1tjlPbMidlpO72ZGbz4CrFxd3CGd8hzOv9um59nuaHfuHXYe/isof7JBYpIxVorWt8AV8Cfwfau1/3A3Nq2Gcaxn2nkvnLgecqbPMZcIZ7+m3gosqONWDAAF2dxYsXV7s+kKwUi9bWiqeusWzatMm3gbidPHnS58esLFZglfainOnTKHNSRuru9jcW6nZ3z9d7MrNrt+P2RVo/EKf15gU+i0XKiOfL2+egrgaaAV+4X02Bq2rYJw1oU24+EdhfYZuBwMdKqT0YT8u/qJSa6mVMIoTowFav1YmPY6xLmRN1sPqgk14Jcd5X75XoMMoYgmPzPP8EVkvBWEaqvQellIoEZgOdgRTgdq11kZfHXgl0UUp1wHimYwZwSYVgO5Q719sYVXxzvA1ehIbIyEiOHDlCkyZNUEqZHU6ltNYcOXKEyMjI0zrOaZY5UUt7MnPYdcLF3UNb135ne5gxBMfGOTDpGaOvPpMEaxmpqZHEO0AR8DNGa7wk4FYvgylWSt2I0TrPDryptd6olJrtXv+y11GKkJaYmEhaWhqHDx/26XHz8/NPO6GUFxkZSWJi4ukeps5lTtTeghTj4dzJfeuQoMBoILH2A6MD2a7n+S6wWgrWMlJTguqhtU4GUEq9gdF5pde01l8DX1dYVmli0lpfWZtji9ARFhZGhw4dat6wlpYsWUK/fv18ftzTdFplTtTOvHX76dzQRkLDOj7L1GGU0ZpvzXumJqhgLSM13YMqrVrQWkv/8kL4n5S5ANlxKJstGVkMbunt0zaVCI+GXhfC9u+hMMd3wQmg5gTVRyl10v3KAnqXTCulTgYiQCFCjJS5AFmw/gBKwaCWp9mMuscUKM6DbdLXgK9V+9VBa+3bBvBCiGpJmQucBSn7GdSuMY0iC07vQO2GGSPtbpoLvfzeR2NI8baZuRBCBI1tB7PYdjCbiX1q6LncGza7DMHhJ5KghBAhZ/66/dgUjOvlZd97NekxFYpyjSE4hM9IghJChBStNfNTDjCkQxOax/qoCXW74cYQHBvn+OZ4ApAEJYQIMWtTj7PrcI5vqvdKlIy0u2UB5B713XFDnCQoIURI+XhFKlFhdiYm1/Hh3Kr0vwKcBbBpjm+PG8IkQQkhQkZ+kZOFmzI4O6k58dHe91zuldb9oGk3WP+pb48bwiRBCSFCxtJthzmeW8T5/RJ8f3CloPd02PcbHNnp++OHIElQQoiQ8fnqNOIiHZzZtZl/TtB7uvG+4XP/HD/ESIISQoSEk/lFLN5ymAv6JxJm99OfvoZtoUWyJCgfkQQlhAgJ89cdoNDpqnvP5d4aMAsOb4H9a/x7nhAgCUoIERLmrE2nY7MG9GvT0L8n6j0dHJGw9kP/nicESIISQgS91KO5rNh9lKl9E/w/oF9kPHQbDyn/g+JC/54ryEmCEkIEvXnr9wMwta8fWu9Vps8lkHdUuj46TZKghBBBzeXSfPj7Pga3b0zbJtGBOWmns4weztd9FJjzBSlJUEKIoLYu7Thpx/KYMbhN4E5qd0DydNj6LeQcCdx5g4wkKCFEUPt2YwZ2m+Ks7s0De+J+l4GrCNZ/HNjzBhFJUEKIoFVY7OKTlamc3b05DaPDA3vyFj0gYQCseA20Duy5g4QkKCFE0FqQsp/juUWBrd4rb9B1cGw37F5qzvnrOUlQQoig9c6ve+nYtAFndvFT10Y16TEZIhvCH2+Zc/56ThKUECIobdp/krWpx5k+qA0Of3VtVJPwBsaDu1sWQE6mOTHUY5KghBBB6a1lu4kOt3PxQJOq90r0uwychTJOVB1IghJCBJ38IicLN2YwrmdLGjUIcOOIilr2hmZJ0liiDiRBCSGCzsKNGZzML+aC/olmh2KME3XGbKMD2bSVZkdTr0iCEkIEnQ9+30dCwyiGdWpidiiGnhdAeCyseNXsSOoVSVBCiKCy90gOK3Yf5ZIhbbHZ/NwxrLci44x7URvnwMkDZkdTb0iCEkIElY9WpGJTcKEVqvfKG3I9uIph1RtmR1JvSIISQgSNrPwiPl65j7FJLWgZH2l2OJ4ad4RuE2DVm1CUb3Y09YIkKCFE0PhqndFzxOzRncwOpXJD/gy5RyDlM7MjqRf8mqCUUuOUUluVUjuUUvdUsv5SpdR69+tXpVQff8YjhAhexU4Xry7dRfeWsf4fNbeuOpxpNDn//RVpcu4FvyUopZQdeAEYD/QAZiqlelTYbDcwSmvdG3gEkCYuQog6+XHLIfYeyeX/jens/1Fz60opGHwdHEyB1N/Njsby/HkFNRjYobXepbUuBD4GppTfQGv9q9b6mHt2OWCxu5pCiPritZ930SIugnE9W5odSvV6XwyOKFjzvtmRWJ4/E1QCkFpuPs29rCrXAN/4MR4hRJBKSTvByj3HuGp4B8IdFr+1HhEDvafBuo/h5H6zo7E0pf1UD6qUmgacp7W+1j1/OTBYa31TJduOAV4ERmitTxl+smXLljo+Pr50fuLEiUyaNKl0Pjs7m5iYGN9/iDqwUixgrXisFAv4N5558+Yxf/58ALZt27ZXa93eLydyC/Uy8sq6fNYccvL06GgahNWues+Mn01U7gGGrJjN7vaXsrf9dFNjqY7pZURr7ZcXMBRYWG7+XuDeSrbrDewEulZ1rAEDBujqLF68uNr1gWSlWLS2VjxWikXrwMUDrNJ+KmdayojefjBLd7hnvn7oq42WiMdr756v9ePttM49an4sVTC7jPjzWngl0EUp1UEpFQ7MAL4qv4FSqi3wBXC51nqbH2MRQgSpl3/aSbjDxg1jLNq0vCpjH4S8Y/DbC2ZHYll+S1Ba62LgRmAhsBn4VGu9USk1Wyk1273ZP4AmwItKqbVKqVX+ikcIEXz2H89jzpp0ZgxqS5OYCLPDqZ1WvSFpMix/GfKOmx2NJTn8eXCt9dfA1xWWvVxu+lrgWn/GIIQIXq//vBuAa0d2MDmSOjrzDtj8FSx/Ccbca3Y0lmPx5i5CCFG5YzmFfLRiH5P7tiaxUbTZ4dRNqz7QdRysfA0Kc82OxnIkQQkh6qW3f91DXpGT2aPq2b2niobfYnR/JJ3InkISlBCi3jl0Mp9Xl+7inB4t6Noi1uxwTk+7YdB+JPz6HDZnodnRWIokKCFEvfP6L7vJL3Zy97huZofiGyNvg+yDtMxYZHYkliIJSghRrxzJLuD95XuZkNyKzs3r+dVTiY5joO0wOuz+SFr0lSMJSghRrzy/eAf5RU5uObuL2aH4jlIw/nHCirPgl/+YHY1lSIISQtQbuzNzePe3vUwb0Kb+33uqqFUfDjYfBSteg2N7zI7GEiRBCSHqBa01D83bSKTDxu3ndTU7HL/Y1fEyUHZYcIfZoViCJCghRL2waNNBlmw9zF/P6UrzWIsN5+4jBZHNYdSdsGMR7Pje7HBMJwlKCGF5+UVOHlmwiS7NY5g1rL3Z4fjX4D9D446w8H5wFpkdjakkQQkhLO+NX3aTejSPByf3JMwe5H+2wiLhnIfh8GbjflQIC/LftKjOmkNryC7MNjsMIaqVejSX537czrieLRneuanZ4QRG94nQeSz8+E84utvsaEwjCSrIrT9cTEGxE4AlqUt4ae1L/Pm9VZzz/Cdc8c0VDP1oKAAbMzeS/E4yqw+uBuC+X+4j+Z1kXln3CgBFriKS30nm8q8vLz32rhO7OFl4MrAfSIQUrTV/n7sBm1L8fVIPs8MJHKVg4jNgc8AX14Oz2OyITCEJKki4XJr8IqfHsn/O38R//iig2/3fAnDTjzfx4roXWbRjLemOt0q301ozY8EMAGZ9OwuAr3YaQ3c9v/Z5vt2wn/7v9Qdg7eG1AKQcTmHKnCkM/2g4AKknU0l+J5k/Dv7hvw8pQs7nq9NZsvUwt53TlYSGUWaHE1gN28DE/0DaCvj5KbOjMYUkqCDR8W9f0/3v35ZeLR3NP8obv60novnXqLBM8ovzS7d1xG7EHpVeOl/s8vx2lnoiw2P+pgWeA6odyj3EFzu+8Fg24csJAFz57ZWly4yBMoWom31Hcnlg7gYGtW/EVcPr6XAapyv5IkieDj/9G9JD78ufJKggsO1gFir8MNHtX+CB+cv4audXjPpkFDFd/0V4k6XEdH6KQR8MKt0+opln89Ufd3uOEzlhzjke846YrR7z015Zwv+2/a/amJ5f8zy93+3NrhO76vKRRIgrcrq49ZM12JTi/y7ui92mzA7JPBOehJgW8OXskBuSQxJUPfPC4h10uP8DnvzhF4qcRRQ4C5jwxjPEdHoae1QqC07cwH2/3FerY96x7Ppq11dMUPsL13jMZxVmnbLPK+uNe1fXf1f9sYWozNPfbWP1vuM8ekFy/R3ryVeiGsLUFyBzG3xzF4RQzYRfR9QVvvHwbw/z2bbPeHb4Jzy5cDexSY/zbhq8+76xPiohsPFEtpzvMf/tnm895nceOlE6fTD3YOn03GNzSd+czqVJl/o3QFGvLdyYwcs/7WTm4DZM6tPa7HCsodNZMPJ2+PlpSBwIA640O6KAkCsoi3r95120v2cBC9Yf4LNtnwFwy7KLsTfYZnJkpzqUe8hj/tyX3/KYz8zLZGPmRr4/+T2Pr3gcAKfLyYD3BvDcmucCFqewvr1Hcrjtk7X0TozngUk9zQ7HWkb/zRg3asHtsD00huWQBGUxTpeTz7Z9xtPr70KFHeWmL+Z4rI9u+6Y5gVXj5XUve8xHtXnPY/7LTb+WthIEOFFwguUHllPoKuTV9a8GJEZhfSfyirju3VXYlOL5mf2JDLObHZK12B1w8fvQPAk+uwoObTY7Ir+TBGWyEwUnSH4nmRHvTwTgrM/O4uHfHsYRs5WYzv+mQYcXTYvNmdvWY167wr3aT9k8RwV9NuV+j/m3N77NkyufLJ0/UWBUCa7MWEluUWjdBBaGgmIn1727ip2Hc3jpsgG0bRLi952qEtUQZn4C4dHwwXTIOljjLvWZJCgTvLD2BT7d+ikAl391GwAnnHvJOJHP0fyjAYujONtzPJ3ezXp7zBceG+oxn729do0vSijleVN3z4k97Dyxs3T+UO4hFu5ZyNULr2bIh0PqdA5Rfzldmts/XceK3Ud54sLejOgSIr1F1FV8Asz8GHIz4f0LIDdwfzMCTRJUAB3OKuCDP1bz8rqXeWT5IxS7itmdu6J0/ciXHvP5OduFtyudztr8uMe6nva/esy/fu7rPDHyidJ5Z06FIQ1cER6zo5tcV6eYvt/n2cx989HN3PHTqcMLaK1PeUZLBJcip4s7P1vH/PUHuHd8dy4akGh2SPVDQn+juu/wVnh7Ipw8YHZEfiEJys+01jhdxsOzg/71PQ//XPbQ6ze7v/HYNrKV58OvtTE6cTSPDPsXruIGZG15hKzNj7P28vXc3vJ2Hhv5GNnb/wbAua2uAGD15at588ozyNr8OGH7/kPKrBSiHFFM6Dih9Jg7H5lW7TkfOfsaj/n4wrEe8wWZo7yK/f5fPKsAS64up82bRr/3+pGalerVcUT9klfo5C/v/8EXa9L569iu/HlUJ7NDql86nw0zPoTje+Gt8XA0+J45lATlY3muPH5K/al0vve7ven7Xl+S30kGILxR2RVTxcYF3rqy55XkZ0whL+0SsrffQ9bWh3ju7OeY2mUyn43/DnQYAHabQinFxI4T+fCqcxnVtRlPnH07KbNSCLOFERcZxvybRvDzXWM8jv+vEf/ipn43YbMZ+5bY8/ifSqfDbeE0jGrgsd8tI8/ymC867lldp3XlD1tqPKsAH1n+CCmHU9h6zHj+avKXkwH4bf9vzP5+Ni7tqvFnJKwtp6CYWW+t4Icth/jn1F7cMjaIhm8PpK7nwuVfQt4xeO1s2LPM7Ih8ShKUj92Vehc3/ngjXZ++hSnP/+KxzhG7zmN+X9a+Ko9TlFXWMWb+wQnkpRnPDp2bMIPbB97O55feQXFWb3RxQz64+szSbXu0jmP9g+ey+eFxHscb2qkJ71w9GEeFoQp6JcTTIMLzcbjJnSZzfW/jAdvLki4DYEqnKQBM6jgJgKUzlgKw6KKy5q4XdZtY/jBop2cCy919U5Wft6JLvr6kdLpYG9V81y+6nmXpy3gj5Q2vjyOsJzPPxfRXfmPVnqM8O6Mfl53RruadRNXaDIbrfoToJvDuFFj7odkR+YwkqNO0P3s/D/76IMWuYo/7JRFNf2TDsRUe20YlfuTVMQsOnUv+/uml80VHR1Kc1Yvs7ffy1NlGVV3vxIbsefxP7Hn8T6cMQRAXGUZUuG+a6PZs2pMnz3yS+84wGkg8OvJRUmal0CDMSD4tG7QkZVYKKbNSUEpxTa+yar+193smrH8OqnsVToGzoHT6v2v+C0CRs6i0BaCoHxZvPcSDv+axJzOHN2YNYrI8iOsbTTrBtYug3TCY8xejB/S842ZHddokQdVSRk4GRe5RLk/mF3He5+fx+fbPufTrS8nMy/TYNrzJEq+O2TSyBS+cXXZvqvDocB6aOIBHhj/Cc2c9x23ndGNsUgt2PDwTpQLfJ9m4DuOIcnjXk/TsPrNLpxtGhzMiYUTpfOsYz/9uz4551usY+rxw9SnL+r/fnxEfj2DWN7O8Po4wR25hMX+fs4Gr3lpJXIRi/s0jGdO9udlhBZeoRnDZ53DmXZDyP3hxKGz8sl53jSRdHdWg0FlIuN14/mftobVc/o0xHtK6K9bR/z9PlHYztOnIptJeEko4GhgDjYUVdqEofHvZMY8OJbzxb6Xzb417nfbx7cnedj/YCkBHuIe1bg/A6Db++Wz+EOmIZOWlK0vn7xx0J7+k/8LTo56GPXBd8nW8lmKMEnpW27No2aAlGTlG7+n5GRNP6UapRFj8eo/5D9b9UDq9+tBqjuQdISY8hoHvDwRg/RXrTUnmwpPWmu83H+LBrzaSfjyPa0d0YEj0QTo0bVDzzqL27GFw1n3Q9TyYfyt8dqXRTdK4J6BZ15r2thy5gqrGb/t/Y8D7A0h+J5n29ywoTU4A9y/9J1EJn3hs/8O+HyoeAoBje6Z7zM/qdis5u28AoG/YHbSPbw/Atoen8cC4kWx5ZFzFQ9QrkY5IIh2RAHSM70jKrBTObX8uADf3v5m5U+ey5nKjw9lPJ35aut/3f5nNv8/8d+m8M7/FKc9qlXh87a0e86Pfn1aanMBIWgAfbfmIxfsWn/6HErWiteanbYeZ9vJvXPfuKqLD7Xw2eyj3T+xBWCj3TB4oiQPhuiUw/t+QtgpeHAKfX1vvep8I6QRVvgk4wN+X/Z3kd5Lp+MAbHMku4PpF5XriVp69I8zb81mVx83ZeZvneZyxFB3vB0DfZn25e1wSrvw2ZG1+nLcuLkt6YXYbVw7vEPRdvHSM74jDZly8N4psxItnv8gzY56hXVw7zmt/Xul2i2Z8yoJLnvHYt/BYFQ/yhh/2mL3y2yu5c+ErPPr7o9y8+GYW7FoAgEu7+HHfjx7jYwnfKXa6mLs2nQn//YVZb64g9Vgu/5zaiwU3j2RQ+8Zmhxda7A4Y8me4aTUMvRG2fgMvngHvTILV79WLe1QhVcV3IPsAjaMaE2GPYOW+NK5ePB6AZTOXkZ6VzpwdcwBo0PEZBj4eRUy5L+8NOlZ9v6Rv1FWszSvrIHX2sKG85+6BRGvjO0D+gYuZf8U/6NSwE0opUh48F5eLU1rVhaKRiSNLp23KxlOjnuJY/jES4hujdaPSdc+OeZbBzUcy9JP+pcuyNj9GbNK9lR7324znS6fv+fkeUvcn8MLOsi8Ez7V7jkJnIXcvvZvre19PUpMkX36skKG1ZvOBLOat38+Xq9PJOJlPp2YN+PdFvZnaN4Fwh/wfN1VMMzj3ERh+K6x8HdZ/DF/daHQ62+Uc6DEF2o+AOOs1WPFrglJKjQOeBezA61rrxyusV+71E4Bc4Eqt9eq6nOt4/nFyi4po0aApdpvizp/u5Ns933JJwn955ftc4ro+gbYfA+Cxfgu4+/fLsbm7lisZtry8mC6evTrYwo8AUHSiD/YG27E5jD7jbh9wOzO7X8rAD4wElbP7Bm6/vBt3qvWsyFhBUsM+vPTTHm46qzPR4WU/7tjIsLp8zJBQ/ipKKcW6K9aRX5xPdJjRP9srY1/hz9//mYu6XsQDsyaS/E5ZgsrefjcxXZ445ZiAR3ICmJ3yGmF7jabv3+/7ngvb3MVfBk9k7OdGs/03zn2Dwa0G43Q52X1id+mXi1CntSb9eB4r9xxl2Y4j/LI9k4yT+dhtijO7NOXhKT0Zm9QCm1TlWUuDJjD6bhh1F+xfDes/NRpRbHHf941vA22HQsIAaNoZmnRGlathMoPfEpRSyg68AJwDpAErlVJfaa03ldtsPNDF/RoCvOR+r5XFJ37hpk+MPzQFh8dyXpcBLDlujFH0YfrNOGIvK01OAPeu+VNpcqqocP+lhLf+oHQ+a8sjxHb/e+l8fsYU0A5iu/8DgFk9Z6GUYtWla3lgbgr9Bh0tHf1zSCvjo9w9rnttP5Iox6ZspckJYFjCMFJmpZTO/zbzN4Z+NJQ7Bt7BrFmXsSGzLzMXzATg/IS7eW+JIqZL2XejvP0XEtX6c8LiPBtefJ76bz5PLbsHds13nj1lAHSJOo+WMQ35+bBx//GSzjdz5xnXBN2VsMulOZZbyMGTBWSczGNPZi67MrPZeSiHLRknOZZrtGRtGB3GsE5NGNW1GWOTWtAkJqKGI/tHfnE+Tu3Epmxod6u1SEckNhVcvxefUMpIQgkD4LxHISMF9v4Kqcth90+QUnZfeKRywIZ2xtVVVCPjWavoxhARB+ENwBEB9nDPl63kZ66Mc6GgeQ+IbVH7ULWfmiAqpYYCD2qtz3PP3wugtX6s3DavAEu01h+557cCo7XWHh1LDRw4UK9a5TksOUDq8Uxu+vZxdhYs9FiutQ2lXLgKm5Re+QDk7LqJBh3Lxh+6ud/NfLkym1SHMYRF4dGhbL7lFfq8Z3SaWnjsDAoypvLxn/tx3dLzuH3A7czodjn3z9nA1SPa0q1FHHab5/2iJUuWMHr06Fr+tPzHSvEEMhattcfVzvd7v+evS/7K3Klz6RjfkVt/uIcf0oz7Ullb/0Fki/mENSy7eHcVxWILO3Wk4MokR8/kw2l/q3K9UuoPrfXAKjfwgarKyB/pO3lo6fNkZecQFR2FS7vQWuPSGhfaPe2i2OWi2Gm8FzqdFDmN7Sjt6FcTZocGEXZiIhzERNqJi3IQ5jCe/3NpF9p9PLvNjl0ZL5uyYbPZsGHDruygIONwBtFx0RQ4C8gpyiG7KJv84nwUCrvNXtqzSKQ9kpiwmNJWtGG2MOw2O/nF+eQU5VDkKqLQWUhucS55xXmnfHaHchAXEUeEPQKFIjosmkh7JBpNgbOA/OJ8NJrjuccp0AU4tbM05pLPYFM2HDaHsUyduqxkuXb/A3C5XNhsNsJsYTiUg0hHJFGOKMLt4ThsDuzKjkLhwlX6+3BqJ8WuYg5nHqZp06ZlP7ty5yj/blM2FHW7Qq3Yc8spinIh/yQUnCQrcz+xYRqKctHFBVCcD86CWjddv2DQX0k+45Yq11dVRvyZoC4Cxmmtr3XPXw4M0VrfWG6b+cDjWutf3PM/AHdrrT1KWsuWLXV8fHzp/MSJE5k0aRK5zkLu2vt3ogo7ckWzy+jQUPNu5vuk5WbR4MgltIlqSuNWS1iRu4xhMcM5eXAMW05mcrj5YwyIHsCVza4E4IUdy9mav5lHu84iJtz4Blaki9A6jHAbtarWyc7OJiYmpq4/Np+zUjxWigVOjSfHmUOeK4+mYcaDz6mFqXx34jvOb3g+cfZGbDx5gCW584hTTRgSMQEnxSzK+oaJTUbQNcZzWON58+Yxf75RdbJt27a9Wuv2/vwsVZWRVUf38c7xFzH+Jrm/zYL7j5tyT7nnlDFlUwqbAjs2bDZwKIXDZsOuyrZWGN1oOZQDB47Sfcv/4S39A4zGhTsx4sKu7UTaIwlTYUTYIoi0RRKhIozttKskRIpcReTr/NKeRIp1MS5cxh99FWmcWzmIUBHE2GOwYUOjS/9w57nyyHHlGGUZTYGroPRYYbYwwghDKYWt2EZcRBx2ZcepnaXxlv8MHu+VfL6yn6lxxe/SLooxEnehLqRQF1Ksi8uOr12lScb4mduwY0e7NMqmPGKobNpff7crqvhFTynl/r+k3S9Q6HLLKjej4RR6xQ3zWOZVGdFa++UFTMO471QyfznwXIVtFgAjys3/AAyoeKwBAwboqhQUFerFixdXuT7QrBSL1taKx0qxaB24eIBV2k/lTHtRRrS21s/eSrFoba14rBSL1uaXEX9W0KYB5R8xTQT212GbaoU7pLGBEEIEI38mqJVAF6VUB6VUODAD+KrCNl8BVyjDGcAJXeH+kxBCiNDkt1Z8WutipdSNwEKMZuZvaq03KqVmu9e/DHyN0cR8B0Yz86v8FY8QQoj6xa/PQWmtv8ZIQuWXvVxuWgM3+DMGIYQQ9ZM8JCCEEMKSgiJBzZs3z+wQSlkpFrBWPFaKBawXjz9Z6bNaKRawVjxWigXMjycoElRJW3orsFIsYK14rBQLWC8ef7LSZ7VSLGCteKwUC5gfT1AkKCGEEMHHbz1J+JJS6jCwt5pNmgKZ1awPJCvFAtaKx0qxQODiaae1bubPE0gZOS1WisdKsYDJZaReJCghhBChR6r4hBBCWJIkKCGEEJYkCUoIIYQl1esEpZQap5TaqpTaoZS6x4Tzv6mUOqSU2lBuWWOl1CKl1Hb3e6PqjuHDWNoopRYrpTYrpTYqpW4xOZ5IpdQKpdQ6dzwPmRmP+9x2pdQa9zAvpsYSKFJGPGKRMlJzTJYqI/U2QZUbsXc80AOYqZTqEeAw3gbGVVh2D/CD1roLxvAhgfqjUAzcrrVOAs4AbnD/PMyKpwA4S2vdB+gLjHN3CGxWPAC3AJvLzZsZi99JGTmFlJGaWauMVDYGR314AUOBheXm7wXuNSGO9sCGcvNbgVbu6VbAVpN+PnOBc6wQDxANrAaGmBUPxlAuPwBnAfOt9Lvy42eWMlJ9XFJGPGOwXBmpt1dQQAKQWm4+zb3MbC20e8gQ93vzQAeglGoP9AN+NzMed3XBWuAQsEhrbWY8zwB3Aa5yy0z/XfmZlJEqSBmp1DNYrIzU5wRV2TjsIf9Ql1IqBvgcuFVrfdLMWLTWTq11X4xvZoOVUr3MiEMpNRE4pLX+w4zzm0jKSCWkjJzKqmWkPieo0x6N108OKqVaAbjfDwXqxEqpMIyC94HW+guz4ymhtT4OLMG4F2FGPMOByUqpPcDHwFlKqfdNiiWQpIxUIGWkSpYsI/U5QXkzYq8ZvgJmuadnYdRz+51SSgFvAJu11v+xQDzNlFIN3dNRwFhgixnxaK3v1Vonaq3bY/w/+VFrfZkZsQSYlJFypIxUzbJlJNA3A318U28CsA3YCdxnwvk/Ag4ARRjfVq8BmmDcaNzufm8coFhGYFTfrAfWul8TTIynN7DGHc8G4B/u5abEUy6u0ZTdADY1lgB9XikjZbFIGfEuLsuUEemLTwghhCXV5yo+IYQQQUwSlBBCCEuSBCWEEMKSJEEJIYSwJElQQgghLEkSlBBCCEuSBFUPKKWaKKXWul8ZSql093S2UupFP5zvbaXUbqXU7Druv9gd20BfxyZEZaSMBCeH2QGImmmtj2B0x49S6kEgW2v9lJ9Pe6fW+n912VFrPUYptcTH8QhRJSkjwUmuoOoxpdTocgOLPaiUekcp9Z1Sao9S6gKl1L+VUilKqW/dfZChlBqglPpJKfWHUmphST9bNZznbaXUf5VSvyqldimlLnIvb6WUWur+prpBKTXSv59YiNqRMlK/SYIKLp2APwFTgPeBxVrrZCAP+JO7AD4HXKS1HgC8CfzLy2O3wugqZiLwuHvZJRjjDfUF+mB0HSOElUkZqUekii+4fKO1LlJKpQB24Fv38hSMQeO6Ab2ARUa/mdgx+knzxhyttQvYpJRq4V62EnjTXajnaK3X+uRTCOE/UkbqEbmCCi4FAO5CUqTLOlp0YXwZUcBGrXVf9ytZa31ubY7tptznWQqcCaQD7ymlrvDFhxDCj6SM1COSoELLVqCZUmooGGPjKKV61vVgSql2GIOcvYYxjEF/34QphGmkjFiIVPGFEK11ofvm7X+VUvEYv/9ngI11PORo4E6lVBGQDci3Q1GvSRmxFhluQ5xCKfU2xngwdWpC6z7GEuAOrfUqX8UlhFVIGQkMqeITlTkBPHI6DyECHTEGqRMiGEkZCQC5ghJCCGFJcgUlhBDCkiRBCSGEsCRJUEIIISxJEpQQQghL+v82m84vR55H7wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "psi_init = [[0] * 9]\n",
    "psi_init[0][4] = 1\n",
    "init_state = tf.transpose(tf.constant(psi_init, tf.complex128))\n",
    "print(init_state)\n",
    "\n",
    "plot_dynamics(exp, init_state, sequence)\n",
    "plotSplittedPopulation(exp, init_state, sequence)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "As intended, the dynamics of the target is dependent on the control qubit performing a flip if the control is excited and an identity otherwise."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Optimizing the single qubit gate on Qubit 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rx90p[0]-d1-gauss-amp                 : 500.000 mV \n",
      "rx90p[0]-d1-gauss-freq_offset         : -53.000 MHz 2pi \n",
      "rx90p[0]-d1-gauss-xy_angle            : -444.089 arad \n",
      "rx90p[0]-d1-gauss-delta               : -1.000  \n",
      "rx90p[0]-d1-carrier-framechange       : 0.000 rad \n",
      "\n"
     ]
    }
   ],
   "source": [
    "opt_gates = [rx90p_q1.get_key()]\n",
    "gateset_opt_map=[\n",
    "    [\n",
    "      (rx90p_q1.get_key(), \"d1\", \"gauss\", \"amp\"),\n",
    "    ],\n",
    "    [\n",
    "      (rx90p_q1.get_key(), \"d1\", \"gauss\", \"freq_offset\"),\n",
    "    ],\n",
    "    [\n",
    "      (rx90p_q1.get_key(), \"d1\", \"gauss\", \"xy_angle\"),\n",
    "    ],\n",
    "    [\n",
    "      (rx90p_q1.get_key(), \"d1\", \"gauss\", \"delta\"),\n",
    "    ],   \n",
    "    [\n",
    "      (rx90p_q1.get_key(), \"d1\", \"carrier\", \"framechange\"),\n",
    "    ]\n",
    "]\n",
    "parameter_map.set_opt_map(gateset_opt_map)\n",
    "parameter_map.print_parameters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt_1Q = OptimalControl(\n",
    "    dir_path=log_dir,\n",
    "    fid_func=fidelities.unitary_infid_set,\n",
    "    fid_subspace=[\"Q1\", \"Q2\"],\n",
    "    pmap=parameter_map,\n",
    "    algorithm=algorithms.lbfgs,\n",
    "    options={\n",
    "        \"maxfun\": 25\n",
    "    },\n",
    "    run_name=\"rx90p_q1\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C3:STATUS:Saving as: /tmp/tmpbyj0t9_h/c3logs/rx90p_q1/2022_03_31_T_13_55_40/open_loop.c3log\n"
     ]
    }
   ],
   "source": [
    "exp.set_opt_gates(opt_gates)\n",
    "opt_1Q.set_exp(exp)\n",
    "opt_1Q.optimize_controls()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rx90p[0]-d1-gauss-amp                 : 386.167 mV \n",
      "rx90p[0]-d1-gauss-freq_offset         : -52.985 MHz 2pi \n",
      "rx90p[0]-d1-gauss-xy_angle            : -148.032 mrad \n",
      "rx90p[0]-d1-gauss-delta               : -960.910 m \n",
      "rx90p[0]-d1-carrier-framechange       : -293.001 mrad \n",
      "\n",
      "0.005569018116779345\n"
     ]
    }
   ],
   "source": [
    "parameter_map.print_parameters()\n",
    "print(opt_1Q.current_best_goal)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before running the qiskit simulation, we must call `set_opt_gates()` to ensure propagators are calculated for all the required gates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp.set_opt_gates([rx90p_q1.get_key(), cnot12.get_key()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No classical registers in circuit \"circuit-0\", counts will be empty.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Result from gates:\n",
      "{'(0, 0)': 0.5031205851405698,\n",
      " '(0, 1)': 0.0007523237769721514,\n",
      " '(0, 2)': 3.7724719197046883e-07,\n",
      " '(1, 0)': 0.0019480561541019474,\n",
      " '(1, 1)': 0.4910946730272626,\n",
      " '(1, 2)': 3.094004564620503e-05,\n",
      " '(2, 0)': 0.0014615201616086152,\n",
      " '(2, 1)': 0.0015911982277944904,\n",
      " '(2, 2)': 3.262185985404622e-07}\n"
     ]
    }
   ],
   "source": [
    "c3_job_opt = c3_backend.run(qc)\n",
    "result_opt = c3_job_opt.result()\n",
    "res_pops_opt = result_opt.data()[\"state_pops\"]\n",
    "print(\"Result from gates:\") \n",
    "pprint(res_pops_opt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_histogram(res_pops_opt, title='Simulation of Qiskit circuit with Optimized Gates')"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "8fc56ae400e717d872a76f4d6b257151d16696a9d0a72e6998d355f9b43887c7"
  },
  "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}