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examples/two_qubit_entangling_gate.ipynb

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{
 "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": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "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": [],
   "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": [],
   "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.EnvelopeDrag(\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.EnvelopeDrag(\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.EnvelopeDrag(\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 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "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": "stdout",
     "output_type": "stream",
     "text": [
      "Initial state not specified. Using ground state as the initial state.\n",
      "You can use model.set_init_state() method to set the initial state.\n",
      "Result from unoptimized gates:\n",
      "{'(0, 0)': 0.07007758389634004,\n",
      " '(0, 1)': 0.18932191689037392,\n",
      " '(0, 2)': 1.4780353876119443e-05,\n",
      " '(1, 0)': 0.37563257275091777,\n",
      " '(1, 1)': 0.36365218004523736,\n",
      " '(1, 2)': 4.98764686152771e-06,\n",
      " '(2, 0)': 0.0010749838407441766,\n",
      " '(2, 1)': 0.00022096533218863963,\n",
      " '(2, 2)': 2.9243270773363447e-08}\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 700x500 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": [
    "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.cma_pre_lbfgs,\n",
    "    options = {\n",
    "    \"cmaes\": {\n",
    "        \"maxfevals\" : 50, # 2000 recommended\n",
    "        \"tolfun\" : 0.01, # 0.001 recommended\n",
    "        \"spread\" : 0.25,\n",
    "        \"popsize\": 10, # 15 recommended\n",
    "    },\n",
    "    \"lbfgs\": {\n",
    "        \"maxfun\": 50, # 500 recommended\n",
    "        \"disp\": True}},\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: /var/folders/13/ryq3f6n9279chp5c9f7sb5s80000gn/T/tmp1yu52t2c/c3logs/cnot12/2022_08_30_T_15_48_45/open_loop.c3log\n",
      "(7_w,15)-aCMA-ES (mu_w=4.5,w_1=34%) in dimension 10 (seed=1032808, Tue Aug 30 15:48:45 2022)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-30 15:48:48.336436: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iterat #Fevals   function value  axis ratio  sigma  min&max std  t[m:s]\n",
      "    1     15 3.946493280132269e-01 1.0e+00 2.49e-01  2e-01  3e-01 0:03.4\n",
      "    2     30 6.611342656560064e-01 1.2e+00 2.35e-01  2e-01  2e-01 0:04.0\n",
      "    3     45 7.045065506725673e-01 1.3e+00 2.16e-01  2e-01  2e-01 0:04.7\n",
      "    8    120 5.108037765288014e-01 1.7e+00 2.18e-01  2e-01  2e-01 0:08.0\n",
      "   14    210 1.370751854049885e-01 2.1e+00 1.59e-01  1e-01  2e-01 0:12.1\n",
      "   22    330 6.334008308980010e-02 3.1e+00 1.07e-01  7e-02  1e-01 0:17.5\n",
      "   31    465 3.043918660535172e-02 4.7e+00 7.55e-02  4e-02  1e-01 0:24.2\n",
      "   40    600 1.779741795363798e-02 5.5e+00 6.03e-02  2e-02  9e-02 0:31.8\n",
      "   52    780 1.355044279082640e-02 9.6e+00 5.87e-02  2e-02  1e-01 0:40.1\n",
      "   66    990 7.918952469183838e-03 1.5e+01 3.40e-02  8e-03  7e-02 0:49.7\n",
      "   81   1215 5.396935257488078e-03 2.5e+01 3.86e-02  6e-03  9e-02 1:00.3\n",
      "   97   1455 4.886533469408927e-03 3.4e+01 1.36e-02  2e-03  3e-02 1:11.6\n",
      "  100   1500 4.821291107448111e-03 3.5e+01 1.39e-02  2e-03  3e-02 1:13.8\n",
      "  118   1770 4.510508925774648e-03 9.9e+01 3.84e-02  3e-03  1e-01 1:27.0\n",
      "  137   2055 4.316642774862656e-03 9.4e+01 1.64e-02  1e-03  4e-02 1:41.5\n",
      "  155   2325 4.246921086021205e-03 1.0e+02 1.12e-02  7e-04  2e-02 1:56.8\n",
      "  169   2535 4.157837394042074e-03 1.3e+02 3.90e-02  2e-03  8e-02 2:13.3\n",
      "  179   2685 4.062696549598455e-03 2.1e+02 3.77e-02  2e-03  1e-01 2:30.6\n",
      "  189   2835 4.009492166882844e-03 1.9e+02 2.69e-02  1e-03  6e-02 2:49.3\n",
      "  200   3000 3.992084476519331e-03 1.4e+02 1.84e-02  6e-04  3e-02 3:07.2\n",
      "  203   3045 3.990827685644738e-03 1.4e+02 1.33e-02  4e-04  2e-02 3:11.7\n",
      "termination on tolfun=0.0001\n",
      "final/bestever f-value = 3.990828e-03 3.988018e-03\n",
      "incumbent solution: [ 0.51523064  0.98047977 -0.71134385 -0.13283853 -0.6615916  -0.89409322\n",
      "  2.2012431  -0.7722151  ...]\n",
      "std deviations: [0.00140563 0.01346235 0.00196054 0.01156252 0.0018333  0.00043471\n",
      " 0.02274043 0.00217716 ...]\n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =           10     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f=  3.98802D-03    |proj g|=  7.29130D-03\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    1    f=  3.98628D-03    |proj g|=  1.00262D-03\n",
      "\n",
      "At iterate    2    f=  3.98622D-03    |proj g|=  5.47438D-04\n",
      "\n",
      "At iterate    3    f=  3.98619D-03    |proj g|=  5.16104D-04\n",
      "\n",
      "At iterate    4    f=  3.98612D-03    |proj g|=  4.36483D-04\n",
      "\n",
      "At iterate    5    f=  3.98608D-03    |proj g|=  3.13000D-04\n",
      "\n",
      "At iterate    6    f=  3.98596D-03    |proj g|=  6.40404D-04\n",
      "\n",
      "At iterate    7    f=  3.98579D-03    |proj g|=  1.34995D-03\n",
      "\n",
      "At iterate    8    f=  3.98535D-03    |proj g|=  2.34862D-03\n",
      "\n",
      "At iterate    9    f=  3.98469D-03    |proj g|=  2.88149D-03\n",
      "\n",
      "At iterate   10    f=  3.98468D-03    |proj g|=  2.87118D-03\n",
      "\n",
      "At iterate   11    f=  3.98468D-03    |proj g|=  2.87103D-03\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "   10     11     33      1     0     0   2.871D-03   3.985D-03\n",
      "  F =   3.9846830674972189E-003\n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " Warning:  more than 10 function and gradient\n",
      "   evaluations in the last line search.  Termination\n",
      "   may possibly be caused by a bad search direction.\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                : 2.323 V \n",
      "cx[0, 1]-d1-gauss1-freq_offset        : -52.000 MHz 2pi \n",
      "cx[0, 1]-d1-gauss1-xy_angle           : -215.019 mrad \n",
      "cx[0, 1]-d1-gauss1-delta              : -1.533  \n",
      "cx[0, 1]-d1-carrier-framechange       : -1.035 rad \n",
      "cx[0, 1]-d2-gauss2-amp                : 50.773 mV \n",
      "cx[0, 1]-d2-gauss2-freq_offset        : -54.499 MHz 2pi \n",
      "cx[0, 1]-d2-gauss2-xy_angle           : -508.327 mrad \n",
      "cx[0, 1]-d2-gauss2-delta              : 2.871  \n",
      "cx[0, 1]-d2-carrier-framechange       : 1.082 rad \n",
      "\n",
      "0.003984683067497219\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 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "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 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "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\": 50, # 500 recommended\n",
    "        \"disp\": True,\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: /var/folders/13/ryq3f6n9279chp5c9f7sb5s80000gn/T/tmp1yu52t2c/c3logs/rx90p_q1/2022_08_30_T_15_52_12/open_loop.c3log\n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            5     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f=  6.99553D-02    |proj g|=  4.52155D-01\n",
      "\n",
      "At iterate    1    f=  6.20960D-02    |proj g|=  4.73291D-01\n",
      "\n",
      "At iterate    2    f=  5.51651D-02    |proj g|=  3.19800D-01\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    3    f=  4.54454D-02    |proj g|=  1.49159D-01\n",
      "\n",
      "At iterate    4    f=  4.16067D-02    |proj g|=  1.38300D-01\n",
      "\n",
      "At iterate    5    f=  2.15375D-02    |proj g|=  3.56219D-01\n",
      "\n",
      "At iterate    6    f=  9.64701D-03    |proj g|=  2.75074D-01\n",
      "\n",
      "At iterate    7    f=  5.84659D-03    |proj g|=  6.78595D-02\n",
      "\n",
      "At iterate    8    f=  5.72482D-03    |proj g|=  4.97609D-03\n",
      "\n",
      "At iterate    9    f=  5.72003D-03    |proj g|=  5.10557D-03\n",
      "\n",
      "At iterate   10    f=  5.71669D-03    |proj g|=  5.07548D-03\n",
      "\n",
      "At iterate   11    f=  5.67894D-03    |proj g|=  1.26464D-02\n",
      "\n",
      "At iterate   12    f=  5.63142D-03    |proj g|=  1.79047D-02\n",
      "\n",
      "At iterate   13    f=  5.61334D-03    |proj g|=  2.68212D-02\n",
      "\n",
      "At iterate   14    f=  5.57531D-03    |proj g|=  5.55997D-03\n",
      "\n",
      "At iterate   15    f=  5.56991D-03    |proj g|=  3.20967D-03\n",
      "\n",
      "At iterate   16    f=  5.56903D-03    |proj g|=  2.71387D-04\n",
      "\n",
      "At iterate   17    f=  5.56902D-03    |proj g|=  4.62534D-05\n",
      "\n",
      "At iterate   18    f=  5.56902D-03    |proj g|=  4.58852D-05\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    5     18     21      1     0     0   4.589D-05   5.569D-03\n",
      "  F =   5.5690181167756814E-003\n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \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",
      "0.005569018116775681\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": "stdout",
     "output_type": "stream",
     "text": [
      "Result from gates:\n",
      "{'(0, 0)': 0.4883145868820096,\n",
      " '(0, 1)': 0.0007598915340775902,\n",
      " '(0, 2)': 2.055312854017028e-07,\n",
      " '(1, 0)': 0.0007093665471084265,\n",
      " '(1, 1)': 0.508428462733987,\n",
      " '(1, 2)': 2.911978199867078e-05,\n",
      " '(2, 0)': 0.0009442059049129742,\n",
      " '(2, 1)': 0.0008136823959958936,\n",
      " '(2, 2)': 4.786886302915569e-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 700x500 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": {
  "kernelspec": {
   "display_name": "Python 3.9.13 ('c3-dev')",
   "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": "3.9.13"
  },
  "vscode": {
   "interpreter": {
    "hash": "a0b03eaf8c8ce3e409cea7c8959f8534c7373313420530ac00542abdcb44e9ec"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}