examples/two_qubits.ipynb

{
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  {
   "cell_type": "markdown",
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   "source": [
    "### Setup of a two-qubit chip with $C^3$\n",
    "\n",
    "In this example we will set-up a two qubit quantum processor and define a simple gate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
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   "outputs": [],
   "source": [
    "# System imports\n",
    "import copy\n",
    "import numpy as np\n",
    "import time\n",
    "import itertools\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import tensorflow_probability as tfp\n",
    "\n",
    "# Main C3 objects\n",
    "from c3.c3objs import Quantity as Qty\n",
    "from c3.parametermap import ParameterMap as PMap\n",
    "from c3.experiment import Experiment as Exp\n",
    "from c3.system.model import Model as Mdl\n",
    "from c3.generator.generator import Generator as Gnr\n",
    "\n",
    "# Building blocks\n",
    "import c3.generator.devices as devices\n",
    "import c3.signal.gates as gates\n",
    "import c3.system.chip as chip\n",
    "import c3.signal.pulse as pulse\n",
    "import c3.system.tasks as tasks\n",
    "\n",
    "# Libs and helpers\n",
    "import c3.libraries.algorithms as algorithms\n",
    "import c3.libraries.hamiltonians as hamiltonians\n",
    "import c3.libraries.fidelities as fidelities\n",
    "import c3.libraries.envelopes as envelopes\n",
    "import c3.utils.qt_utils as qt_utils\n",
    "import c3.utils.tf_utils as tf_utils"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model components\n",
    "We first create a qubit. Each parameter is a Quantity (`Qty()`) object with bounds and a unit. In $C^3$, the default multi-level qubit is a Transmon modelled as a Duffing oscillator with frequency $\\omega$ and anharmonicity $\\delta$ :\n",
    "$$ H/\\hbar = \\omega b^\\dagger b - \\frac{\\delta}{2}                        \\left(b^\\dagger b - 1\\right) b^\\dagger b \n",
    "$$\n",
    "The \"name\" will be used to identify this qubit (or other component) later and should thus be chosen carefully."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "qubit_lvls = 3\n",
    "freq_q1 = 5e9\n",
    "anhar_q1 = -210e6\n",
    "t1_q1 = 27e-6\n",
    "t2star_q1 = 39e-6\n",
    "qubit_temp = 50e-3\n",
    "\n",
    "q1 = chip.Qubit(\n",
    "    name=\"Q1\",\n",
    "    desc=\"Qubit 1\",\n",
    "    freq=Qty(\n",
    "        value=freq_q1,\n",
    "        min_val=4.995e9 ,\n",
    "        max_val=5.005e9 ,\n",
    "        unit='Hz 2pi'\n",
    "    ),\n",
    "    anhar=Qty(\n",
    "        value=anhar_q1,\n",
    "        min_val=-380e6 ,\n",
    "        max_val=-120e6 ,\n",
    "        unit='Hz 2pi'\n",
    "    ),\n",
    "    hilbert_dim=qubit_lvls,\n",
    "    t1=Qty(\n",
    "        value=t1_q1,\n",
    "        min_val=1e-6,\n",
    "        max_val=90e-6,\n",
    "        unit='s'\n",
    "    ),\n",
    "    t2star=Qty(\n",
    "        value=t2star_q1,\n",
    "        min_val=10e-6,\n",
    "        max_val=90e-3,\n",
    "        unit='s'\n",
    "    ),\n",
    "    temp=Qty(\n",
    "        value=qubit_temp,\n",
    "        min_val=0.0,\n",
    "        max_val=0.12,\n",
    "        unit='K'\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the same for a second qubit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "freq_q2 = 5.6e9\n",
    "anhar_q2 = -240e6\n",
    "t1_q2 = 23e-6\n",
    "t2star_q2 = 31e-6\n",
    "q2 = chip.Qubit(\n",
    "    name=\"Q2\",\n",
    "    desc=\"Qubit 2\",\n",
    "    freq=Qty(\n",
    "        value=freq_q2,\n",
    "        min_val=5.595e9 ,\n",
    "        max_val=5.605e9 ,\n",
    "        unit='Hz 2pi'\n",
    "    ),\n",
    "    anhar=Qty(\n",
    "        value=anhar_q2,\n",
    "        min_val=-380e6 ,\n",
    "        max_val=-120e6 ,\n",
    "        unit='Hz 2pi'\n",
    "    ),\n",
    "    hilbert_dim=qubit_lvls,\n",
    "    t1=Qty(\n",
    "        value=t1_q2,\n",
    "        min_val=1e-6,\n",
    "        max_val=90e-6,\n",
    "        unit='s'\n",
    "    ),\n",
    "    t2star=Qty(\n",
    "        value=t2star_q2,\n",
    "        min_val=10e-6,\n",
    "        max_val=90e-6,\n",
    "        unit='s'\n",
    "    ),\n",
    "    temp=Qty(\n",
    "        value=qubit_temp,\n",
    "        min_val=0.0,\n",
    "        max_val=0.12,\n",
    "        unit='K'\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A static coupling between the two is realized in the following way. We supply the type of coupling by selecting `int_XX` $(b_1+b_1^\\dagger)(b_2+b_2^\\dagger)$ from the hamiltonian library. The \"connected\" property contains the list of qubit names to be coupled, in this case \"Q1\" and \"Q2\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "coupling_strength = 20e6\n",
    "q1q2 = chip.Coupling(\n",
    "    name=\"Q1-Q2\",\n",
    "    desc=\"coupling\",\n",
    "    comment=\"Coupling qubit 1 to qubit 2\",\n",
    "    connected=[\"Q1\", \"Q2\"],\n",
    "    strength=Qty(\n",
    "        value=coupling_strength,\n",
    "        min_val=-1 * 1e3 ,\n",
    "        max_val=200e6 ,\n",
    "        unit='Hz 2pi'\n",
    "    ),\n",
    "    hamiltonian_func=hamiltonians.int_XX\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the same spirit, we specify control Hamiltonians to drive the system. Again \"connected\" connected tells us which qubit this drive acts on and \"name\" will later be used to assign the correct control signal to this drive line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "drive = chip.Drive(\n",
    "    name=\"d1\",\n",
    "    desc=\"Drive 1\",\n",
    "    comment=\"Drive line 1 on qubit 1\",\n",
    "    connected=[\"Q1\"],\n",
    "    hamiltonian_func=hamiltonians.x_drive\n",
    ")\n",
    "drive2 = chip.Drive(\n",
    "    name=\"d2\",\n",
    "    desc=\"Drive 2\",\n",
    "    comment=\"Drive line 2 on qubit 2\",\n",
    "    connected=[\"Q2\"],\n",
    "    hamiltonian_func=hamiltonians.x_drive\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SPAM errors\n",
    "In experimental practice, the qubit state can be mis-classified during read-out. We simulate this by constructing a _confusion matrix_, containing the probabilities for one qubit state being mistaken for another."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "m00_q1 = 0.97  # Prop to read qubit 1 state 0 as 0\n",
    "m01_q1 = 0.04  # Prop to read qubit 1 state 0 as 1\n",
    "m00_q2 = 0.96  # Prop to read qubit 2 state 0 as 0\n",
    "m01_q2 = 0.05  # Prop to read qubit 2 state 0 as 1\n",
    "one_zeros = np.array([0] * qubit_lvls)\n",
    "zero_ones = np.array([1] * qubit_lvls)\n",
    "one_zeros[0] = 1\n",
    "zero_ones[0] = 0\n",
    "val1 = one_zeros * m00_q1 + zero_ones * m01_q1\n",
    "val2 = one_zeros * m00_q2 + zero_ones * m01_q2\n",
    "min_val = one_zeros * 0.8 + zero_ones * 0.0\n",
    "max_val = one_zeros * 1.0 + zero_ones * 0.2\n",
    "confusion_row1 = Qty(value=val1, min_val=min_val, max_val=max_val, unit=\"\")\n",
    "confusion_row2 = Qty(value=val2, min_val=min_val, max_val=max_val, unit=\"\")\n",
    "conf_matrix = tasks.ConfusionMatrix(Q1=confusion_row1, Q2=confusion_row2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following task creates an initial thermal state with given temperature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "init_temp = 50e-3\n",
    "init_ground = tasks.InitialiseGround(\n",
    "    init_temp=Qty(\n",
    "        value=init_temp,\n",
    "        min_val=-0.001,\n",
    "        max_val=0.22,\n",
    "        unit='K'\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We collect the parts specified above in the Model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "model = Mdl(\n",
    "    [q1, q2], # Individual, self-contained components\n",
    "    [drive, drive2, q1q2],  # Interactions between components\n",
    "    [conf_matrix, init_ground] # SPAM processing\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Further, we can decide between coherent or open-system dynamics using set_lindbladian() and whether to eliminate the static coupling by going to the dressed frame with set_dressed()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "model.set_lindbladian(False)\n",
    "model.set_dressed(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Control signals\n",
    "With the system model taken care of, we now specify the control electronics and signal chain. Complex shaped controls are often realized by creating an envelope signal with an arbitrary waveform generator (AWG) with limited bandwith and mixing it with a fast, stable local oscillator (LO)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "sim_res = 100e9 # Resolution for numerical simulation\n",
    "awg_res = 2e9 # Realistic, limited resolution of an AWG\n",
    "lo = devices.LO(name='lo', resolution=sim_res)\n",
    "awg = devices.AWG(name='awg', resolution=awg_res)\n",
    "mixer = devices.Mixer(name='mixer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Waveform generators exhibit a rise time, the time it takes until the target voltage is set. This has a smoothing effect on the resulting pulse shape."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "resp = devices.Response(\n",
    "    name='resp',\n",
    "    rise_time=Qty(\n",
    "        value=0.3e-9,\n",
    "        min_val=0.05e-9,\n",
    "        max_val=0.6e-9,\n",
    "        unit='s'\n",
    "    ),\n",
    "    resolution=sim_res\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In simulation, we translate between AWG resolution and simulation (or \"analog\") resolultion by including an up-sampling device."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "dig_to_an = devices.DigitalToAnalog(\n",
    "    name=\"dac\",\n",
    "    resolution=sim_res\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Control electronics apply voltages to lines, whereas in a Hamiltonian we usually write the control fields in energy or frequency units. In practice, this conversion can be highly non-trivial if it involves multiple stages of attenuation and for example the conversion of a line voltage in an antenna to a dipole field coupling to the qubit. The following device represents a simple, linear conversion factor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "v2hz = 1e9\n",
    "v_to_hz = devices.VoltsToHertz(\n",
    "    name='v_to_hz',\n",
    "    V_to_Hz=Qty(\n",
    "        value=v2hz,\n",
    "        min_val=0.9e9,\n",
    "        max_val=1.1e9,\n",
    "        unit='Hz/V'\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The generator combines the parts of the signal generation and assignes a signal chain to each control line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "generator = Gnr(\n",
    "        devices={\n",
    "            \"LO\": devices.LO(name='lo', resolution=sim_res, outputs=1),\n",
    "            \"AWG\": devices.AWG(name='awg', resolution=awg_res, outputs=1),\n",
    "            \"DigitalToAnalog\": devices.DigitalToAnalog(\n",
    "                name=\"dac\",\n",
    "                resolution=sim_res,\n",
    "                inputs=1,\n",
    "                outputs=1\n",
    "            ),\n",
    "            \"Response\": devices.Response(\n",
    "                name='resp',\n",
    "                rise_time=Qty(\n",
    "                    value=0.3e-9,\n",
    "                    min_val=0.05e-9,\n",
    "                    max_val=0.6e-9,\n",
    "                    unit='s'\n",
    "                ),\n",
    "                resolution=sim_res,\n",
    "                inputs=1,\n",
    "                outputs=1\n",
    "            ),\n",
    "            \"Mixer\": devices.Mixer(name='mixer', inputs=2, outputs=1),\n",
    "            \"VoltsToHertz\": devices.VoltsToHertz(\n",
    "                name='v_to_hz',\n",
    "                V_to_Hz=Qty(\n",
    "                    value=1e9,\n",
    "                    min_val=0.9e9,\n",
    "                    max_val=1.1e9,\n",
    "                    unit='Hz/V'\n",
    "                ),\n",
    "                inputs=1,\n",
    "                outputs=1\n",
    "            )\n",
    "        },\n",
    "        chains= {\n",
    "            \"d1\": [\"LO\", \"AWG\", \"DigitalToAnalog\", \"Response\", \"Mixer\", \"VoltsToHertz\"],\n",
    "            \"d2\": [\"LO\", \"AWG\", \"DigitalToAnalog\", \"Response\", \"Mixer\", \"VoltsToHertz\"]\n",
    "        }\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gates-set and Parameter map\n",
    "It remains to write down what kind of operations we want to perform on the device. For a gate based quantum computing chip, we define a gate-set.\n",
    "\n",
    "We choose a gate time of 7ns and a gaussian envelope shape with a list of parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "t_final = 7e-9   # Time for single qubit gates\n",
    "sideband = 50e6 \n",
    "gauss_params_single = {\n",
    "    'amp': Qty(\n",
    "        value=0.5,\n",
    "        min_val=0.4,\n",
    "        max_val=0.6,\n",
    "        unit=\"V\"\n",
    "    ),\n",
    "    't_final': Qty(\n",
    "        value=t_final,\n",
    "        min_val=0.5 * t_final,\n",
    "        max_val=1.5 * t_final,\n",
    "        unit=\"s\"\n",
    "    ),\n",
    "    'sigma': Qty(\n",
    "        value=t_final / 4,\n",
    "        min_val=t_final / 8,\n",
    "        max_val=t_final / 2,\n",
    "        unit=\"s\"\n",
    "    ),\n",
    "    'xy_angle': Qty(\n",
    "        value=0.0,\n",
    "        min_val=-0.5 * np.pi,\n",
    "        max_val=2.5 * np.pi,\n",
    "        unit='rad'\n",
    "    ),\n",
    "    'freq_offset': Qty(\n",
    "        value=-sideband - 3e6 ,\n",
    "        min_val=-56 * 1e6 ,\n",
    "        max_val=-52 * 1e6 ,\n",
    "        unit='Hz 2pi'\n",
    "    ),\n",
    "    'delta': Qty(\n",
    "        value=-1,\n",
    "        min_val=-5,\n",
    "        max_val=3,\n",
    "        unit=\"\"\n",
    "    )\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we take `gaussian_nonorm()` from the libraries as the function to define the shape."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "gauss_env_single = pulse.Envelope(\n",
    "    name=\"gauss\",\n",
    "    desc=\"Gaussian comp for single-qubit gates\",\n",
    "    params=gauss_params_single,\n",
    "    shape=envelopes.gaussian_nonorm\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also define a gate that represents no driving."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "nodrive_env = pulse.Envelope(\n",
    "    name=\"no_drive\",\n",
    "    params={\n",
    "        't_final': Qty(\n",
    "            value=t_final,\n",
    "            min_val=0.5 * t_final,\n",
    "            max_val=1.5 * t_final,\n",
    "            unit=\"s\"\n",
    "        )\n",
    "    },\n",
    "    shape=envelopes.no_drive\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We specify the drive tones with an offset from the qubit frequencies. As is done in experiment, we will later adjust the resonance by modulating the envelope function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "lo_freq_q1 = 5e9  + sideband\n",
    "carrier_parameters = {\n",
    "    'freq': Qty(\n",
    "        value=lo_freq_q1,\n",
    "        min_val=4.5e9 ,\n",
    "        max_val=6e9 ,\n",
    "        unit='Hz 2pi'\n",
    "    ),\n",
    "    'framechange': Qty(\n",
    "        value=0.0,\n",
    "        min_val= -np.pi,\n",
    "        max_val= 3 * np.pi,\n",
    "        unit='rad'\n",
    "    )\n",
    "}\n",
    "carr = pulse.Carrier(\n",
    "    name=\"carrier\",\n",
    "    desc=\"Frequency of the local oscillator\",\n",
    "    params=carrier_parameters\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the second qubit drive tone, we copy the first one and replace the frequency. The deepcopy is to ensure that we don't just create a pointer to the first drive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "lo_freq_q2 = 5.6e9  + sideband\n",
    "carr_2 = copy.deepcopy(carr)\n",
    "carr_2.params['freq'].set_value(lo_freq_q2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Instructions\n",
    "We define the gates we want to perform with a \"name\" that will identify them later and \"channels\" relating to the control Hamiltonians and drive lines we specified earlier. As a start we write down 90 degree rotations in the positive $x$-direction and identity gates for both qubits. Then we add a carrier and envelope to each."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "rx90p_q1 = gates.Instruction(\n",
    "    name=\"rx90p\", targets=[0], t_start=0.0, t_end=t_final, channels=[\"d1\", \"d2\"]\n",
    ")\n",
    "rx90p_q2 = gates.Instruction(\n",
    "    name=\"rx90p\", targets=[1], t_start=0.0, t_end=t_final, channels=[\"d1\", \"d2\"]\n",
    ")\n",
    "\n",
    "rx90p_q1.add_component(gauss_env_single, \"d1\")\n",
    "rx90p_q1.add_component(carr, \"d1\")\n",
    "\n",
    "\n",
    "rx90p_q2.add_component(copy.deepcopy(gauss_env_single), \"d2\")\n",
    "rx90p_q2.add_component(carr_2, \"d2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When later compiling gates into sequences, we have to take care of the relative rotating frames of the qubits and local oscillators. We do this by adding a phase after each gate that realigns the frames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "rx90p_q1.add_component(nodrive_env, \"d2\")\n",
    "rx90p_q1.add_component(copy.deepcopy(carr_2), \"d2\")\n",
    "rx90p_q1.comps[\"d2\"][\"carrier\"].params[\"framechange\"].set_value(\n",
    "    (-sideband * t_final) * 2 * np.pi % (2 * np.pi)\n",
    ")\n",
    "\n",
    "rx90p_q2.add_component(nodrive_env, \"d1\")\n",
    "rx90p_q2.add_component(copy.deepcopy(carr), \"d1\")\n",
    "rx90p_q2.comps[\"d1\"][\"carrier\"].params[\"framechange\"].set_value(\n",
    "    (-sideband * t_final) * 2 * np.pi % (2 * np.pi)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The remainder of the gates-set can be derived from the RX90p gate by shifting its phase by multiples of $\\pi/2$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "ry90p_q1 = copy.deepcopy(rx90p_q1)\n",
    "ry90p_q1.name = \"ry90p\"\n",
    "rx90m_q1 = copy.deepcopy(rx90p_q1)\n",
    "rx90m_q1.name = \"rx90m\"\n",
    "ry90m_q1 = copy.deepcopy(rx90p_q1)\n",
    "ry90m_q1.name = \"ry90m\"\n",
    "ry90p_q1.comps['d1']['gauss'].params['xy_angle'].set_value(0.5 * np.pi)\n",
    "rx90m_q1.comps['d1']['gauss'].params['xy_angle'].set_value(np.pi)\n",
    "ry90m_q1.comps['d1']['gauss'].params['xy_angle'].set_value(1.5 * np.pi)\n",
    "single_q_gates = [rx90p_q1, ry90p_q1, rx90m_q1, ry90m_q1]\n",
    "\n",
    "\n",
    "ry90p_q2 = copy.deepcopy(rx90p_q2)\n",
    "ry90p_q2.name = \"ry90p\"\n",
    "rx90m_q2 = copy.deepcopy(rx90p_q2)\n",
    "rx90m_q2.name = \"rx90m\"\n",
    "ry90m_q2 = copy.deepcopy(rx90p_q2)\n",
    "ry90m_q2.name = \"ry90m\"\n",
    "ry90p_q2.comps['d2']['gauss'].params['xy_angle'].set_value(0.5 * np.pi)\n",
    "rx90m_q2.comps['d2']['gauss'].params['xy_angle'].set_value(np.pi)\n",
    "ry90m_q2.comps['d2']['gauss'].params['xy_angle'].set_value(1.5 * np.pi)\n",
    "single_q_gates.extend([rx90p_q2, ry90p_q2, rx90m_q2, ry90m_q2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With every component defined, we collect them in the parameter map, our object that holds information and methods to manipulate and examine model and control parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "parameter_map = PMap(instructions=single_q_gates, model=model, generator=generator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The experiment\n",
    "Finally everything is collected in the experiment that provides the functions to interact with the system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "exp = Exp(pmap=parameter_map)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Simulation\n",
    "With our experiment all set-up, we can perform simulations. We first decide which basic gates to simulate, in this case only the 90 degree rotation on one qubit and the identity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp.set_opt_gates(['rx90p[0]'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The actual numerical simulation is done by calling `exp.compute_propagators()`. \n",
    "This is the most resource intensive part as it involves solving the equations of motion for the system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "unitaries = exp.compute_propagators()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After this step the unitaries or process matrices are stored in the exp object. We can look at their names and matrix representations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'rx90p[0]': <tf.Tensor: shape=(9, 9), dtype=complex128, numpy=\n",
       " array([[ 5.398e-01+5.545e-02j, -1.212e-05-5.610e-05j,\n",
       "          4.382e-08+4.135e-07j, -1.293e-01-8.222e-01j,\n",
       "         -1.807e-04-5.352e-05j,  8.982e-07-7.047e-07j,\n",
       "          1.046e-01+4.360e-02j,  4.930e-05-1.399e-04j,\n",
       "         -7.742e-07+1.237e-06j],\n",
       "        [ 7.985e-06-7.234e-05j,  5.412e-01+3.448e-02j,\n",
       "         -1.372e-03+6.024e-04j, -1.660e-04+1.034e-04j,\n",
       "         -1.275e-01-8.232e-01j,  3.454e-04+6.123e-04j,\n",
       "          4.316e-05+3.034e-05j,  1.003e-01+4.393e-02j,\n",
       "         -4.326e-03+2.609e-04j],\n",
       "        [ 4.729e-08-3.429e-07j, -5.836e-04-1.513e-03j,\n",
       "         -2.235e-01-4.938e-01j,  8.724e-08-1.001e-07j,\n",
       "         -1.297e-03-9.361e-06j, -6.244e-01+5.517e-01j,\n",
       "          7.548e-06+2.385e-06j,  2.013e-04+1.554e-04j,\n",
       "         -1.498e-02-1.086e-01j],\n",
       "        [-2.142e-01-8.031e-01j,  6.753e-05+8.724e-05j,\n",
       "          4.465e-08+1.306e-07j,  4.773e-01-2.760e-01j,\n",
       "         -4.660e-06-2.805e-05j, -7.131e-07-4.391e-07j,\n",
       "         -1.884e-02-6.949e-02j, -1.314e-05-1.729e-04j,\n",
       "         -1.386e-06-1.734e-07j],\n",
       "        [ 1.454e-05-9.292e-05j, -2.143e-01-8.039e-01j,\n",
       "          2.266e-04+1.483e-03j, -1.646e-05-1.365e-05j,\n",
       "          4.875e-01-2.553e-01j,  5.858e-04-6.572e-04j,\n",
       "          2.132e-05-9.747e-06j, -1.371e-02-6.842e-02j,\n",
       "         -4.056e-03-6.929e-03j],\n",
       "        [-1.017e-06+1.327e-06j, -4.339e-04+6.305e-04j,\n",
       "         -5.640e-01+6.118e-01j,  6.646e-07+2.999e-07j,\n",
       "          2.670e-04+9.552e-04j, -4.972e-01-2.357e-01j,\n",
       "          1.002e-05-3.270e-06j, -2.552e-04+8.399e-06j,\n",
       "         -5.087e-02+4.688e-02j],\n",
       "        [ 1.178e-01-2.813e-02j,  1.683e-05-2.309e-05j,\n",
       "         -1.054e-05+2.155e-06j, -4.312e-02-3.874e-02j,\n",
       "         -3.441e-05-3.187e-05j, -7.390e-06+2.235e-06j,\n",
       "         -9.871e-01+8.721e-02j,  1.419e-05-1.491e-04j,\n",
       "         -8.367e-07-2.418e-07j],\n",
       "        [ 1.726e-04+6.156e-05j,  1.143e-01-2.586e-02j,\n",
       "          3.968e-05-1.855e-04j,  1.460e-04+3.544e-05j,\n",
       "         -3.908e-02-3.982e-02j,  9.950e-05+2.962e-04j,\n",
       "         -1.166e-05-1.297e-04j, -9.914e-01+1.471e-02j,\n",
       "         -1.953e-03+3.631e-04j],\n",
       "        [ 6.502e-07-8.689e-07j, -1.071e-03-4.250e-03j,\n",
       "         -8.181e-02-8.413e-02j,  1.037e-06+3.546e-07j,\n",
       "          6.506e-03-4.602e-03j, -1.405e-02+5.324e-02j,\n",
       "          5.829e-07+1.478e-07j, -8.613e-04-1.901e-03j,\n",
       "          5.475e-01+8.267e-01j]])>}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unitaries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dynamics\n",
    "\n",
    "To investigate dynamics, we define the ground state as an initial state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "psi_init = [[0] * 9]\n",
    "psi_init[0][0] = 1\n",
    "init_state = tf.transpose(tf.constant(psi_init, tf.complex128))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(9, 1), dtype=complex128, numpy=\n",
       "array([[1.+0.j],\n",
       "       [0.+0.j],\n",
       "       [0.+0.j],\n",
       "       [0.+0.j],\n",
       "       [0.+0.j],\n",
       "       [0.+0.j],\n",
       "       [0.+0.j],\n",
       "       [0.+0.j],\n",
       "       [0.+0.j]])>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "init_state"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we stored the process matrices, we can now relatively inexpesively evaluate sequences. We start with just one gate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "barely_a_seq = ['rx90p[0]']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and plot system dynamics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_dynamics(exp, psi_init, seq, goal=-1):\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",
    "        goal: tf.float64\n",
    "            Value of the goal function, if used.\n",
    "        debug: boolean\n",
    "            If true, return a matplotlib figure instead of saving.\n",
    "        \"\"\"\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 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_dynamics(exp, init_state, barely_a_seq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see an ill-defined un-optimized gate. The labels indicate qubit states in the product basis. Next we increase the number of repetitions of the same gate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['rx90p[0]',\n",
       " 'rx90p[0]',\n",
       " 'rx90p[0]',\n",
       " 'rx90p[0]',\n",
       " 'rx90p[0]',\n",
       " 'rx90p[0]',\n",
       " 'rx90p[0]',\n",
       " 'rx90p[0]',\n",
       " 'rx90p[0]',\n",
       " 'rx90p[0]']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "barely_a_seq * 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_dynamics(exp, init_state, barely_a_seq * 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_dynamics(exp, init_state, barely_a_seq * 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that at this point, we only multiply already computed matrices. We don't need to solve the equations of motion again for new sequences."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Open-loop optimal control\n",
    "\n",
    "In order to improve the gate, we create the optimizer object for open-loop optimal control. We notice some leakage into the $|2,0>$ state and enable a DRAG option. Details on DRAG can be found here: https://arxiv.org/abs/1809.04919 The main principle is adding a phase-shifted component proportional to the derivative of the original signal. With automatic differentiation, our AWG can perform this operation automatically for arbitrary shapes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator.devices['AWG'].enable_drag_2()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At the moment there are two implementations of DRAG, variant 2 is independent of the AWG resolution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To define which parameters we optimize, we write the gateset_opt_map, a nested list of tuples that identifies each parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt_gates = [\"rx90p[0]\"]\n",
    "gateset_opt_map=[\n",
    "    [\n",
    "      (\"rx90p[0]\", \"d1\", \"gauss\", \"amp\"),\n",
    "    ],\n",
    "    [\n",
    "      (\"rx90p[0]\", \"d1\", \"gauss\", \"freq_offset\"),\n",
    "    ],\n",
    "    [\n",
    "      (\"rx90p[0]\", \"d1\", \"gauss\", \"xy_angle\"),\n",
    "    ],\n",
    "    [\n",
    "      (\"rx90p[0]\", \"d1\", \"gauss\", \"delta\"),\n",
    "    ]\n",
    "]\n",
    "parameter_map.set_opt_map(gateset_opt_map)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can look at the parameters this opt_map specified with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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",
      "\n"
     ]
    }
   ],
   "source": [
    "parameter_map.print_parameters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from c3.optimizers.c1 import C1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The C1 object will handle the optimization for us. As a fidelity function we choose average fidelity as well as LBFG-S (a wrapper of the scipy implementation) from our library. See those libraries for how these functions are defined and how to supply your own, if necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import tempfile\n",
    "\n",
    "# Create a temporary directory to store logfiles, modify as needed\n",
    "log_dir = os.path.join(tempfile.TemporaryDirectory().name, \"c3logs\")\n",
    "\n",
    "opt = C1(\n",
    "    dir_path=log_dir,\n",
    "    fid_func=fidelities.average_infid_set,\n",
    "    fid_subspace=[\"Q1\", \"Q2\"],\n",
    "    pmap=parameter_map,\n",
    "    algorithm=algorithms.lbfgs,\n",
    "    options={\"maxfun\" : 10},\n",
    "    run_name=\"better_X90\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we supply our defined experiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp.set_opt_gates(opt_gates)\n",
    "opt.set_exp(exp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Everything is in place to start the optimization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C3:STATUS:Saving as: /tmp/tmp_uw1t4va/c3logs/better_X90/2021_03_04_T_16_05_04/open_loop.log\n"
     ]
    }
   ],
   "source": [
    "opt.optimize_controls()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After a few steps we have improved the gate significantly, as we can check with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00063 "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opt.current_best_goal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And by looking at the same sequences as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_dynamics(exp, init_state, barely_a_seq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_dynamics(exp, init_state, barely_a_seq * 5)"
   ]
  }
 ],
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  "kernelspec": {
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