{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7cee0c3f",
   "metadata": {},
   "source": [
    "# Conditional Playback"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9341ff14",
   "metadata": {},
   "source": [
    "In this tutorial we show how our Cluster Feedback capability (see section [Feedback](https://qblox-qblox-instruments.readthedocs-hosted.com/en/master/cluster/feedback.html)) can be used to send pulses dependent on the measurement outcome. Concretely, this tutorial will show how to perform active reset, which means sending a $\\pi$-pulse if (and only if) the qubit state equals $\\left|1\\right\\rangle$, to flip the qubit back to the $\\left|0\\right\\rangle$ state.\n",
    "\n",
    "This tutorial should be ran on a Cluster with at least two modules. A QRM or QRM-RF module is used to emulate qubit readout. For this tutorial we connect $\\text{O}^{1}$ and $\\text{O}^{2}$ to $\\text{I}^{1}$ and $\\text{I}^{2}$ of the QRM. Depending on the result acquired by this QRM, a second module (called QCM in this tutorial) is used to conditionally generate a $\\pi$-pulse. The output of this QCM can be visualized on an oscilloscope to verify the behavior of the system.\n",
    "\n",
    "To display the behaviour of the QRM, its marker 1 should be used to trigger an oscilloscope. Marker 2 and marker 4 need to be connected to the oscilloscope when running the section `Active reset`.\n",
    "\n",
    "The tutorial will go through the following steps:\n",
    "- Calibrate the time of flight (TOF) from QRM output to QRM input.\n",
    "- Set up the thresholding to separate the $\\left|0\\right\\rangle$ and $\\left|1\\right\\rangle$ state.\n",
    "- Sending the qubit state through the feedback infrastructure.\n",
    "- Performing conditional pulses depending on the qubit state.\n",
    "\n",
    "To synchronize the modules, we use the SYNQ technology, as demonstrated in the synchronization tutorial. We advise familiarizing yourself with that tutorial before continuing.\n",
    "\n",
    "To run this tutorial please make sure you have installed and enabled ipywidgets:\n",
    "```\n",
    "pip install ipywidgets\n",
    "jupyter nbextension enable --py widgetsnbextension\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33379b52",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "First, we are going to import the required packages and connect to the instrument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d4b5c3d9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:47.395216Z",
     "iopub.status.busy": "2023-11-08T11:49:47.394210Z",
     "iopub.status.idle": "2023-11-08T11:49:48.377892Z",
     "shell.execute_reply": "2023-11-08T11:49:48.376812Z"
    }
   },
   "outputs": [],
   "source": [
    "# Import ipython widgets\n",
    "\n",
    "import contextlib\n",
    "\n",
    "import ipywidgets as widgets\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Set up the environment.\n",
    "import numpy as np\n",
    "from IPython.display import display\n",
    "from qcodes import Instrument\n",
    "\n",
    "from qblox_instruments import Cluster, PlugAndPlay"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b35d7864",
   "metadata": {},
   "source": [
    "### Scan For Clusters\n",
    "\n",
    "We scan for the available devices connected via ethernet using the Plug & Play functionality of the Qblox Instruments package (see [Plug & Play](https://qblox-qblox-instruments.readthedocs-hosted.com/en/master/api_reference/tools.html#api-pnp) for more info)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a06258bd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:48.382798Z",
     "iopub.status.busy": "2023-11-08T11:49:48.381690Z",
     "iopub.status.idle": "2023-11-08T11:49:50.512415Z",
     "shell.execute_reply": "2023-11-08T11:49:50.511415Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Devices:\n",
      " - 10.10.200.33: cluster_mm 0.4.0 with name \"PE-Cluster\" and serial number 00015_2321_004\n",
      " - 10.10.200.42: cluster_mm 0.6.1 with name \"QAE_1\" and serial number 00015_2320_004\n",
      " - 10.10.200.50: cluster_mm 0.6.1 with name \"cluster-mm\" and serial number 00015_2219_003\n",
      " - 10.10.200.51: pulsar_qcm 0.11.1 with name \"pulsar-qcm\" and serial number 00012_2039_005\n",
      " - 10.10.200.52: pulsar_qrm 0.11.1 with name \"pulsar-qrm\" and serial number 00013_2120_008\n",
      " - 10.10.200.53: cluster_mm 0.6.1 with name \"cluster-mm\" and serial number 00015_2320_003\n"
     ]
    }
   ],
   "source": [
    "!qblox-pnp list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c8f0895b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:50.517493Z",
     "iopub.status.busy": "2023-11-08T11:49:50.516975Z",
     "iopub.status.idle": "2023-11-08T11:49:50.527720Z",
     "shell.execute_reply": "2023-11-08T11:49:50.526556Z"
    }
   },
   "outputs": [],
   "source": [
    "cluster_ip = \"10.10.200.42\"\n",
    "cluster_name = \"cluster0\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75675c05",
   "metadata": {},
   "source": [
    "### Connect to Cluster\n",
    "\n",
    "We now make a connection with the Cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8a216329",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:50.531858Z",
     "iopub.status.busy": "2023-11-08T11:49:50.531858Z",
     "iopub.status.idle": "2023-11-08T11:49:51.116505Z",
     "shell.execute_reply": "2023-11-08T11:49:51.114470Z"
    },
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "from qblox_instruments import Cluster, ClusterType\n",
    "\n",
    "try:  # Close the chosen QCodes instrument to prevent name clash\n",
    "    Cluster.find_instrument(cluster_name).close()\n",
    "except KeyError:\n",
    "    pass\n",
    "\n",
    "cluster = Cluster(\n",
    "    name=cluster_name,\n",
    "    identifier=cluster_ip,\n",
    "    dummy_cfg={\n",
    "        2: ClusterType.CLUSTER_QCM,\n",
    "        4: ClusterType.CLUSTER_QRM,\n",
    "        6: ClusterType.CLUSTER_QCM_RF,\n",
    "        8: ClusterType.CLUSTER_QRM_RF,\n",
    "    }\n",
    "    if cluster_ip is None\n",
    "    else None,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edaa2cba",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "#### Get connected modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cff0ee00",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:51.121195Z",
     "iopub.status.busy": "2023-11-08T11:49:51.120175Z",
     "iopub.status.idle": "2023-11-08T11:49:51.131091Z",
     "shell.execute_reply": "2023-11-08T11:49:51.129851Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_connected_modules(cluster, filter_fn=None):\n",
    "    def checked_filter_fn(mod):\n",
    "        if filter_fn is not None:\n",
    "            return filter_fn(mod)\n",
    "        return True\n",
    "\n",
    "    return {\n",
    "        mod.slot_idx: mod for mod in cluster.modules if mod.present() and checked_filter_fn(mod)\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fe1b4dad",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:51.135996Z",
     "iopub.status.busy": "2023-11-08T11:49:51.134776Z",
     "iopub.status.idle": "2023-11-08T11:49:51.239153Z",
     "shell.execute_reply": "2023-11-08T11:49:51.238124Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{4: <QcmQrm: cluster0_module4 of Cluster: cluster0>}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# QRM baseband modules\n",
    "readout_modules = get_connected_modules(cluster, lambda mod: mod.is_qrm_type and not mod.is_rf_type)\n",
    "readout_modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d9b22dd1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:51.242592Z",
     "iopub.status.busy": "2023-11-08T11:49:51.242592Z",
     "iopub.status.idle": "2023-11-08T11:49:51.255060Z",
     "shell.execute_reply": "2023-11-08T11:49:51.253464Z"
    }
   },
   "outputs": [],
   "source": [
    "readout_module = readout_modules[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "20a4a7f2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:51.260139Z",
     "iopub.status.busy": "2023-11-08T11:49:51.259125Z",
     "iopub.status.idle": "2023-11-08T11:49:51.361526Z",
     "shell.execute_reply": "2023-11-08T11:49:51.360523Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{2: <QcmQrm: cluster0_module2 of Cluster: cluster0>}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# QCM baseband modules\n",
    "control_modules = get_connected_modules(\n",
    "    cluster, lambda mod: not mod.is_qrm_type and not mod.is_rf_type\n",
    ")\n",
    "control_modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8bb37313",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:51.366371Z",
     "iopub.status.busy": "2023-11-08T11:49:51.366371Z",
     "iopub.status.idle": "2023-11-08T11:49:51.378485Z",
     "shell.execute_reply": "2023-11-08T11:49:51.376365Z"
    },
    "lines_to_next_cell": 0
   },
   "outputs": [],
   "source": [
    "control_module = control_modules[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d008f543",
   "metadata": {},
   "source": [
    "## Sequencer configuration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33ad0b35",
   "metadata": {},
   "source": [
    "Define all waveforms necessary for this tutorial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3142bad4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:51.382104Z",
     "iopub.status.busy": "2023-11-08T11:49:51.382104Z",
     "iopub.status.idle": "2023-11-08T11:49:51.393321Z",
     "shell.execute_reply": "2023-11-08T11:49:51.392318Z"
    }
   },
   "outputs": [],
   "source": [
    "waveform_length = 120\n",
    "t = np.arange(-80, 81, 1)\n",
    "sigma = 20\n",
    "# waveforms for readout pulses\n",
    "waveforms = {\n",
    "    \"zero\": {\"data\": [0.0] * 1024, \"index\": 0},\n",
    "    \"one\": {\"data\": [1.0] * 1024, \"index\": 1},\n",
    "    \"state0_I\": {\n",
    "        \"data\": [0.001] * waveform_length,\n",
    "        \"index\": 2,\n",
    "    },  # I waveform emulating a qubit in the 0 state\n",
    "    \"state0_Q\": {\n",
    "        \"data\": [0.001] * waveform_length,\n",
    "        \"index\": 3,\n",
    "    },  # Q waveform emulating a qubit in the 0 state\n",
    "    \"state1_I\": {\n",
    "        \"data\": [0.001] * waveform_length,\n",
    "        \"index\": 4,\n",
    "    },  # I waveform emulating a qubit in the 1 state\n",
    "    \"state1_Q\": {\n",
    "        \"data\": [0.003] * waveform_length,\n",
    "        \"index\": 5,\n",
    "    },  # Q waveform emulating a qubit in the 1 state\n",
    "    \"gauss\": {\"data\": list(np.exp(-(0.5 * t**2 / sigma**2))), \"index\": 6},\n",
    "    \"empty\": {\"data\": list(0.0 * t), \"index\": 7},\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21aaecad",
   "metadata": {},
   "source": [
    "Specify acquisitions that are used in the tutorial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ce376192",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:51.397971Z",
     "iopub.status.busy": "2023-11-08T11:49:51.397971Z",
     "iopub.status.idle": "2023-11-08T11:49:51.408008Z",
     "shell.execute_reply": "2023-11-08T11:49:51.407022Z"
    }
   },
   "outputs": [],
   "source": [
    "acquisitions = {\n",
    "    \"state0\": {\"index\": 0, \"num_bins\": 1024},\n",
    "    \"state1\": {\"index\": 1, \"num_bins\": 1024},\n",
    "    \"single\": {\"index\": 2, \"num_bins\": 1},\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ac140f6",
   "metadata": {},
   "source": [
    "Set up sequencer parameters for both QRM and QCM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b20ecea5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:51.412099Z",
     "iopub.status.busy": "2023-11-08T11:49:51.412099Z",
     "iopub.status.idle": "2023-11-08T11:49:51.562919Z",
     "shell.execute_reply": "2023-11-08T11:49:51.561278Z"
    }
   },
   "outputs": [],
   "source": [
    "integration_length = 120\n",
    "readout_module.sequencer0.integration_length_acq(integration_length)\n",
    "readout_module.sequencer0.sync_en(True)\n",
    "readout_module.sequencer0.nco_freq(50e6)\n",
    "readout_module.sequencer0.mod_en_awg(True)\n",
    "readout_module.sequencer0.demod_en_acq(True)\n",
    "\n",
    "control_module.sequencer1.sync_en(False)  # We will turn on the sync enable of the QCM later\n",
    "control_module.sequencer1.nco_freq(50e6)\n",
    "control_module.sequencer1.mod_en_awg(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c56c1c9c",
   "metadata": {},
   "source": [
    "## Calibrate TOF"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e4a0665",
   "metadata": {},
   "source": [
    "To ensure proper measurements, we must calibrate the time-of-flight (TOF). This is the time it takes for a signal to travel from the output to the input of the module. To do so, we first play an excitation pulse and acquire it back into"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f972d5ed",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:51.567939Z",
     "iopub.status.busy": "2023-11-08T11:49:51.566942Z",
     "iopub.status.idle": "2023-11-08T11:49:51.577607Z",
     "shell.execute_reply": "2023-11-08T11:49:51.576573Z"
    }
   },
   "outputs": [],
   "source": [
    "prog = \"\"\"\n",
    "play    1, 0, 4     # start readout pulse\n",
    "acquire 2, 0, 16384 # start the 'single' acquisition sequence and wait for the length of the scope acquisition window\n",
    "stop\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b092b6d1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:51.583697Z",
     "iopub.status.busy": "2023-11-08T11:49:51.582697Z",
     "iopub.status.idle": "2023-11-08T11:49:51.759684Z",
     "shell.execute_reply": "2023-11-08T11:49:51.757483Z"
    }
   },
   "outputs": [],
   "source": [
    "readout_module.sequencer0.sequence(\n",
    "    {\"waveforms\": waveforms, \"program\": prog, \"acquisitions\": acquisitions, \"weights\": {}}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "55353fe6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:51.762774Z",
     "iopub.status.busy": "2023-11-08T11:49:51.762774Z",
     "iopub.status.idle": "2023-11-08T11:49:51.803983Z",
     "shell.execute_reply": "2023-11-08T11:49:51.802912Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: STOPPED, Flags: FORCED_STOP, ACQ_SCOPE_DONE_PATH_0, ACQ_SCOPE_DONE_PATH_1, ACQ_BINNING_DONE\n"
     ]
    }
   ],
   "source": [
    "# Arm and start sequencer.\n",
    "readout_module.arm_sequencer(0)\n",
    "readout_module.start_sequencer()\n",
    "\n",
    "# Wait for the sequencer and acquisition to finish with a timeout period of one minute.\n",
    "readout_module.get_acquisition_state(0, 1)\n",
    "readout_module.store_scope_acquisition(0, \"single\")\n",
    "# Print status of sequencer.\n",
    "print(readout_module.get_sequencer_state(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1f2292d0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:51.809352Z",
     "iopub.status.busy": "2023-11-08T11:49:51.808359Z",
     "iopub.status.idle": "2023-11-08T11:49:51.942481Z",
     "shell.execute_reply": "2023-11-08T11:49:51.941223Z"
    }
   },
   "outputs": [],
   "source": [
    "p0 = np.array(readout_module.get_acquisitions(0)[\"single\"][\"acquisition\"][\"scope\"][\"path0\"][\"data\"])\n",
    "p1 = np.array(readout_module.get_acquisitions(0)[\"single\"][\"acquisition\"][\"scope\"][\"path1\"][\"data\"])\n",
    "# Determine when the signal crosses half-max for the first time (in ns)\n",
    "t_halfmax = np.where(np.abs(p0) > np.max(p0) / 2)[0][0]\n",
    "\n",
    "# The time it takes for a sine wave to reach its half-max value is (in ns)\n",
    "correction = 1 / readout_module.sequencer0.nco_freq() * 1e9 / 12\n",
    "\n",
    "tof_measured = t_halfmax - correction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "47f107c7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:51.946668Z",
     "iopub.status.busy": "2023-11-08T11:49:51.945645Z",
     "iopub.status.idle": "2023-11-08T11:49:52.270370Z",
     "shell.execute_reply": "2023-11-08T11:49:52.269249Z"
    }
   },
   "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 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r = readout_module.get_acquisitions(0)[\"single\"][\"acquisition\"][\"scope\"]\n",
    "plt.plot(r[\"path0\"][\"data\"], \".-\")\n",
    "plt.plot(r[\"path1\"][\"data\"], \".-\")\n",
    "plt.axvline(tof_measured, c=\"k\")\n",
    "plt.xlim(\n",
    "    tof_measured - 10 / readout_module.sequencer0.nco_freq() * 1e9,\n",
    "    tof_measured + 10 / readout_module.sequencer0.nco_freq() * 1e9,\n",
    ")\n",
    "plt.ylabel(\"Amplitude (V)\")\n",
    "plt.xlabel(\"Time (ns)\")\n",
    "plt.show()\n",
    "\n",
    "plt.plot(r[\"path0\"][\"data\"], \".-\")\n",
    "plt.plot(r[\"path1\"][\"data\"], \".-\")\n",
    "plt.axvline(1024 + tof_measured, c=\"k\")\n",
    "plt.xlim(\n",
    "    1024 + tof_measured - 10 / readout_module.sequencer0.nco_freq() * 1e9,\n",
    "    1024 + tof_measured + 10 / readout_module.sequencer0.nco_freq() * 1e9,\n",
    ")\n",
    "plt.ylabel(\"Amplitude (V)\")\n",
    "plt.xlabel(\"Time (ns)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e6da9a77",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:52.274969Z",
     "iopub.status.busy": "2023-11-08T11:49:52.273936Z",
     "iopub.status.idle": "2023-11-08T11:49:52.287356Z",
     "shell.execute_reply": "2023-11-08T11:49:52.285808Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Measured TOF: 144.33333333333334\n",
      "Rounded TOF:  144\n"
     ]
    }
   ],
   "source": [
    "tof = int(tof_measured)  # time of flight must be an integer\n",
    "print(\"Measured TOF:\", tof_measured)\n",
    "print(\"Rounded TOF: \", tof)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4ecf58a",
   "metadata": {},
   "source": [
    "## Measure qubit histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4e6aa279",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:52.291366Z",
     "iopub.status.busy": "2023-11-08T11:49:52.290362Z",
     "iopub.status.idle": "2023-11-08T11:49:52.302861Z",
     "shell.execute_reply": "2023-11-08T11:49:52.301338Z"
    }
   },
   "outputs": [],
   "source": [
    "prog = f\"\"\"\n",
    "    move 0, R0\n",
    "    start: set_mrk 1\n",
    "    play 2, 3, {tof}\n",
    "    acquire 0, R0, 120\n",
    "    play 4, 5, {tof}\n",
    "    acquire 1, R0, 120\n",
    "    add R0, 1, R0\n",
    "    wait 4000\n",
    "    jlt R0, 1024, @start\n",
    "    set_mrk 0\n",
    "    upd_param 4\n",
    "    stop\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f49fbc63",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:52.307660Z",
     "iopub.status.busy": "2023-11-08T11:49:52.306653Z",
     "iopub.status.idle": "2023-11-08T11:49:52.455794Z",
     "shell.execute_reply": "2023-11-08T11:49:52.454768Z"
    }
   },
   "outputs": [],
   "source": [
    "readout_module.sequencer0.sequence(\n",
    "    {\"waveforms\": waveforms, \"program\": prog, \"acquisitions\": acquisitions, \"weights\": {}}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "82d8be81",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:52.460319Z",
     "iopub.status.busy": "2023-11-08T11:49:52.459301Z",
     "iopub.status.idle": "2023-11-08T11:49:52.517477Z",
     "shell.execute_reply": "2023-11-08T11:49:52.516497Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: STOPPED, Flags: ACQ_SCOPE_DONE_PATH_0, ACQ_SCOPE_DONE_PATH_1, ACQ_BINNING_DONE\n"
     ]
    }
   ],
   "source": [
    "readout_module.sequencer0.arm_sequencer()\n",
    "readout_module.sequencer0.start_sequencer()\n",
    "print(readout_module.sequencer0.get_sequencer_state(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "318254fa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:52.522579Z",
     "iopub.status.busy": "2023-11-08T11:49:52.521550Z",
     "iopub.status.idle": "2023-11-08T11:49:52.565318Z",
     "shell.execute_reply": "2023-11-08T11:49:52.563180Z"
    }
   },
   "outputs": [],
   "source": [
    "readout_module.sequencer0.get_acquisition_state(1)\n",
    "data = readout_module.sequencer0.get_acquisitions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "de8aec15",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:52.569463Z",
     "iopub.status.busy": "2023-11-08T11:49:52.569463Z",
     "iopub.status.idle": "2023-11-08T11:49:52.580048Z",
     "shell.execute_reply": "2023-11-08T11:49:52.578015Z"
    }
   },
   "outputs": [],
   "source": [
    "state0 = np.array(data[\"state0\"][\"acquisition\"][\"bins\"][\"integration\"][\"path0\"]) + 1j * np.array(\n",
    "    data[\"state0\"][\"acquisition\"][\"bins\"][\"integration\"][\"path1\"]\n",
    ")\n",
    "state1 = np.array(data[\"state1\"][\"acquisition\"][\"bins\"][\"integration\"][\"path0\"]) + 1j * np.array(\n",
    "    data[\"state1\"][\"acquisition\"][\"bins\"][\"integration\"][\"path1\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94ca513c",
   "metadata": {},
   "source": [
    "Given this data, we can now determine the rotation and thresholding values to discriminate the $\\left|0\\right\\rangle$ state from the $\\left|1\\right\\rangle$ state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9f4320f0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:52.586600Z",
     "iopub.status.busy": "2023-11-08T11:49:52.585162Z",
     "iopub.status.idle": "2023-11-08T11:49:52.609496Z",
     "shell.execute_reply": "2023-11-08T11:49:52.608466Z"
    }
   },
   "outputs": [],
   "source": [
    "rotation = np.mod(-np.angle(np.mean(state1) - np.mean(state0)), 2 * np.pi)\n",
    "threshold = (np.exp(1j * rotation) * (np.mean(state1) + np.mean(state0))).real / 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "20b9f9eb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:52.614530Z",
     "iopub.status.busy": "2023-11-08T11:49:52.613527Z",
     "iopub.status.idle": "2023-11-08T11:49:52.734326Z",
     "shell.execute_reply": "2023-11-08T11:49:52.733329Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scaling = 1000 / integration_length\n",
    "maxr = max(np.max(np.abs(state0)), np.max(np.abs(state1)))\n",
    "hist0, xedges, yedges = np.histogram2d(\n",
    "    state0.real, state0.imag, range=((-maxr, maxr), (-maxr, maxr)), bins=100\n",
    ")\n",
    "hist1, xedges, yedges = np.histogram2d(\n",
    "    state1.real, state1.imag, range=((-maxr, maxr), (-maxr, maxr)), bins=100\n",
    ")\n",
    "\n",
    "plt.imshow(\n",
    "    1 - np.array((hist0, hist0 + hist1, hist1)).transpose(2, 1, 0) / np.max(hist0 + hist1),\n",
    "    extent=(-maxr * scaling, maxr * scaling, -maxr * scaling, maxr * scaling),\n",
    "    origin=\"lower\",\n",
    ")\n",
    "plt.plot(\n",
    "    ((1j * xedges + threshold) * np.exp(-1j * rotation)).real * scaling,\n",
    "    ((1j * xedges + threshold) * np.exp(-1j * rotation)).imag * scaling,\n",
    "    \"k\",\n",
    ")\n",
    "plt.xlabel(\"I (mV)\")\n",
    "plt.ylabel(\"Q (mV)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "96a1b8de",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:52.738737Z",
     "iopub.status.busy": "2023-11-08T11:49:52.738737Z",
     "iopub.status.idle": "2023-11-08T11:49:52.782057Z",
     "shell.execute_reply": "2023-11-08T11:49:52.780970Z"
    }
   },
   "outputs": [],
   "source": [
    "readout_module.sequencer0.thresholded_acq_threshold(threshold)\n",
    "readout_module.sequencer0.thresholded_acq_rotation(rotation * 360 / (2 * np.pi))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ddceefe",
   "metadata": {},
   "source": [
    "Now that the rotation and thresholding values are programmed into the QRM module, the module will automatically assign a qubit state to every acquisition. By running the same sequence as before, we can check the the module is assigning the correct state to the qubit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3d47c594",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:52.787103Z",
     "iopub.status.busy": "2023-11-08T11:49:52.786113Z",
     "iopub.status.idle": "2023-11-08T11:49:52.844684Z",
     "shell.execute_reply": "2023-11-08T11:49:52.844181Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: STOPPED, Flags: ACQ_SCOPE_DONE_PATH_0, ACQ_SCOPE_DONE_PATH_1, ACQ_BINNING_DONE\n"
     ]
    }
   ],
   "source": [
    "readout_module.sequencer0.delete_acquisition_data(all=True)\n",
    "readout_module.sequencer0.arm_sequencer()\n",
    "readout_module.sequencer0.start_sequencer()\n",
    "print(readout_module.sequencer0.get_sequencer_state(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "adbde608",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:52.849474Z",
     "iopub.status.busy": "2023-11-08T11:49:52.849474Z",
     "iopub.status.idle": "2023-11-08T11:49:52.892871Z",
     "shell.execute_reply": "2023-11-08T11:49:52.890508Z"
    }
   },
   "outputs": [],
   "source": [
    "readout_module.sequencer0.get_acquisition_state(1)\n",
    "data = readout_module.sequencer0.get_acquisitions()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d13e94b2",
   "metadata": {},
   "source": [
    "To check that the assignments are made correctly, we calculate the confusion matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9678c999",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:52.896944Z",
     "iopub.status.busy": "2023-11-08T11:49:52.896944Z",
     "iopub.status.idle": "2023-11-08T11:49:52.905361Z",
     "shell.execute_reply": "2023-11-08T11:49:52.905361Z"
    }
   },
   "outputs": [],
   "source": [
    "confusion_matrix = [\n",
    "    [\n",
    "        np.sum(np.array(data[\"state0\"][\"acquisition\"][\"bins\"][\"threshold\"]) == 0),\n",
    "        np.sum(np.array(data[\"state0\"][\"acquisition\"][\"bins\"][\"threshold\"]) == 1),\n",
    "    ],\n",
    "    [\n",
    "        np.sum(np.array(data[\"state1\"][\"acquisition\"][\"bins\"][\"threshold\"]) == 0),\n",
    "        np.sum(np.array(data[\"state1\"][\"acquisition\"][\"bins\"][\"threshold\"]) == 1),\n",
    "    ],\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dd385c9f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:52.910871Z",
     "iopub.status.busy": "2023-11-08T11:49:52.909803Z",
     "iopub.status.idle": "2023-11-08T11:49:53.000584Z",
     "shell.execute_reply": "2023-11-08T11:49:52.999578Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = plt.matshow(confusion_matrix, cmap=\"Blues\", vmin=0, vmax=1024)\n",
    "ax = plt.gca()\n",
    "for i in [0, 1]:\n",
    "    for j in [0, 1]:\n",
    "        ax.annotate(\n",
    "            confusion_matrix[i][j],\n",
    "            xy=(j, i),\n",
    "            horizontalalignment=\"center\",\n",
    "            verticalalignment=\"center\",\n",
    "            color=p.cmap(1024 - confusion_matrix[i][j]),\n",
    "        )\n",
    "plt.xlabel(\"Assigned state\")\n",
    "plt.ylabel(\"Qubit state\")\n",
    "plt.xticks([0, 1], [\"|0>\", \"|1>\"])\n",
    "plt.yticks([0, 1], [\"|0>\", \"|1>\"])\n",
    "ax.xaxis.set_ticks_position(\"bottom\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05102792",
   "metadata": {},
   "source": [
    "## Sending results on the trigger network"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b237670",
   "metadata": {},
   "source": [
    "Now that the module can correctly identify the qubit state, we want to send this result to all other modules in the Cluster. Hence we must enable this functionality and specify an address used for sending these triggers. In this tutorial, we will only use address 1. Note that only one trigger can be sent simultaneously, even if the address is different."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "23bdd029",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:53.004802Z",
     "iopub.status.busy": "2023-11-08T11:49:53.004281Z",
     "iopub.status.idle": "2023-11-08T11:49:53.046464Z",
     "shell.execute_reply": "2023-11-08T11:49:53.045201Z"
    }
   },
   "outputs": [],
   "source": [
    "readout_module.sequencer0.thresholded_acq_trigger_address(1)\n",
    "readout_module.sequencer0.thresholded_acq_trigger_en(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04f7369d",
   "metadata": {},
   "source": [
    "To verify that the correct triggers are being sent, we can use the trigger monitor. This monitor can be reset with the following command:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "160d4fbf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:53.050128Z",
     "iopub.status.busy": "2023-11-08T11:49:53.050128Z",
     "iopub.status.idle": "2023-11-08T11:49:53.061955Z",
     "shell.execute_reply": "2023-11-08T11:49:53.060835Z"
    }
   },
   "outputs": [],
   "source": [
    "cluster.reset_trigger_monitor_count(address=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83d27ec1",
   "metadata": {},
   "source": [
    "Such that the monitor reports both a total trigger count of 0 and no latest trigger (also represented by the value 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e811d759",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:53.066290Z",
     "iopub.status.busy": "2023-11-08T11:49:53.065026Z",
     "iopub.status.idle": "2023-11-08T11:49:53.092377Z",
     "shell.execute_reply": "2023-11-08T11:49:53.091211Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster.trigger1_monitor_count(), cluster.trigger_monitor_latest()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "668f2ede",
   "metadata": {},
   "source": [
    "Now, to verify the Cluster is configured correctly, let us run the above program one more time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "977aa119",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:53.096447Z",
     "iopub.status.busy": "2023-11-08T11:49:53.096447Z",
     "iopub.status.idle": "2023-11-08T11:49:53.167240Z",
     "shell.execute_reply": "2023-11-08T11:49:53.166735Z"
    }
   },
   "outputs": [],
   "source": [
    "readout_module.sequencer0.delete_acquisition_data(all=True)\n",
    "readout_module.sequencer0.arm_sequencer()\n",
    "readout_module.sequencer0.start_sequencer()\n",
    "readout_module.sequencer0.get_sequencer_state(1)\n",
    "readout_module.sequencer0.get_acquisition_state(1)\n",
    "data = readout_module.sequencer0.get_acquisitions()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ebb04f7",
   "metadata": {},
   "source": [
    "The number of triggers received by the monitor should be equal to the number of measurements that have been classified as the $\\left|1\\right\\rangle$ state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5802cffe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:53.171400Z",
     "iopub.status.busy": "2023-11-08T11:49:53.171400Z",
     "iopub.status.idle": "2023-11-08T11:49:53.198580Z",
     "shell.execute_reply": "2023-11-08T11:49:53.197473Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acquisition: 1024\n",
      "Monitor:     1024\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    \"Acquisition:\",\n",
    "    np.sum(np.array(data[\"state0\"][\"acquisition\"][\"bins\"][\"threshold\"]) == 1)\n",
    "    + np.sum(np.array(data[\"state1\"][\"acquisition\"][\"bins\"][\"threshold\"]) == 1),\n",
    ")\n",
    "print(\"Monitor:    \", cluster.trigger1_monitor_count())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e90734f",
   "metadata": {},
   "source": [
    "The most recent trigger that has been received should now be equal to 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "884bf46a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:53.202818Z",
     "iopub.status.busy": "2023-11-08T11:49:53.202818Z",
     "iopub.status.idle": "2023-11-08T11:49:53.213426Z",
     "shell.execute_reply": "2023-11-08T11:49:53.212922Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster.trigger_monitor_latest()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45ff52e7",
   "metadata": {},
   "source": [
    "Besides investigating the sent triggers using this monitor, it can be convenient to visualize the triggers on an oscilloscope as they are being sent. In order the enable sending the trigger on marker 4, use the following commands. Enabling triggers on the marker outputs can also be used to control external equipment based on the measurement results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0822d408",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:53.218038Z",
     "iopub.status.busy": "2023-11-08T11:49:53.216967Z",
     "iopub.status.idle": "2023-11-08T11:49:53.261673Z",
     "shell.execute_reply": "2023-11-08T11:49:53.260490Z"
    }
   },
   "outputs": [],
   "source": [
    "readout_module.sequencer0.thresholded_acq_marker_en(True)\n",
    "readout_module.sequencer0.thresholded_acq_marker_address(8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0048796b",
   "metadata": {},
   "source": [
    "Now connect your marker output 4 to the oscilloscope and run the following cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "31e7b723",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:53.266098Z",
     "iopub.status.busy": "2023-11-08T11:49:53.266098Z",
     "iopub.status.idle": "2023-11-08T11:49:53.292512Z",
     "shell.execute_reply": "2023-11-08T11:49:53.291508Z"
    }
   },
   "outputs": [],
   "source": [
    "readout_module.sequencer0.arm_sequencer()\n",
    "readout_module.sequencer0.start_sequencer()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7838630",
   "metadata": {},
   "source": [
    "The following image can be obtained on the oscilloscope (C1 (yellow) is the marker 1 output whereas C3(blue) is marker 4 output:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b58087ca",
   "metadata": {},
   "source": [
    "![SDS6204A_PNG_5 (2).png](9d786b40-430b-47ab-9721-94f3e25fd196.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f19450b9",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Active reset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c99e2501",
   "metadata": {},
   "source": [
    "As the final part of this tutorial, we will show how the triggers we have been generating above, can be used to affect the behavior of other sequencers in the Cluster. We will show this capability using a active reset protocol as an example. Basically, in order to perform active reset on a qubit, we must first measure the qubit state, and if the state equals $\\left|1\\right\\rangle$, we must play a $\\pi$-pulse to flip the qubit back to the $\\left|0\\right\\rangle$ state."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "646ac541",
   "metadata": {},
   "source": [
    "In a real qubit setup, the QRM would play identical readout pulses and the actual qubit state would influence the acquisition result. For this tutorial, in lieu of a qubit, we determine the qubit state results in advance for the QRM, and play a different waveform depending on this pre-determined state. To still properly demonstrate the feedback capability, we ensure that the QCM is not aware of the pre-determined choices in advance, as it can figure out from the acquisitions results whether it should play a $\\pi$-pulse."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "449d6cfd",
   "metadata": {},
   "source": [
    "Generate 100 random qubit states:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e831f830",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:53.297187Z",
     "iopub.status.busy": "2023-11-08T11:49:53.296183Z",
     "iopub.status.idle": "2023-11-08T11:49:53.307596Z",
     "shell.execute_reply": "2023-11-08T11:49:53.306399Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0,\n",
       "       0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1,\n",
       "       0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1,\n",
       "       1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0,\n",
       "       1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "length = 100\n",
    "states = np.random.choice(2, length)\n",
    "states"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dedd6b31",
   "metadata": {},
   "source": [
    "For the QRM program, this means playing waveforms `'state0_I'` and `'state0_Q'` if the qubit state has to appear as $\\left|0\\right\\rangle$ while playing waveforms `'state1_I'` and `'state1_Q'` if the qubit state has to appear as $\\left|1\\right\\rangle$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6e6e069a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:53.312462Z",
     "iopub.status.busy": "2023-11-08T11:49:53.312462Z",
     "iopub.status.idle": "2023-11-08T11:49:53.322842Z",
     "shell.execute_reply": "2023-11-08T11:49:53.321767Z"
    }
   },
   "outputs": [],
   "source": [
    "qrm_prog = \"\"\"\n",
    "wait_sync 4\n",
    "set_mrk 1\n",
    "upd_param 1000\n",
    "move 0, R0\n",
    "\"\"\"\n",
    "for s in states:\n",
    "    if s == 1:\n",
    "        qrm_prog += f\"\"\"set_mrk 3\n",
    "        play 4, 5, {tof}\n",
    "        set_mrk 1\"\"\"\n",
    "    else:\n",
    "        qrm_prog += f\"play 2, 3, {tof}\"\n",
    "    qrm_prog += \"\"\"\n",
    "    acquire 0, R0, 120\n",
    "    wait 1000\n",
    "    \"\"\"\n",
    "qrm_prog += \"\"\"set_mrk 0\n",
    "upd_param 1000\n",
    "stop\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b10a031",
   "metadata": {},
   "source": [
    "So, in order to achieve feedback from the QRM module, we only had to set the thresholding and trigger parameters without any changes to the Q1ASM program. On the receiving side, for the QCM, the feedback is controlled via Q1ASM, however. Let's build up the program step by step."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27049fe7",
   "metadata": {},
   "source": [
    "First, we need to tell the module to listen for triggers on address 1 with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f9962feb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:53.326443Z",
     "iopub.status.busy": "2023-11-08T11:49:53.326443Z",
     "iopub.status.idle": "2023-11-08T11:49:53.338320Z",
     "shell.execute_reply": "2023-11-08T11:49:53.338320Z"
    }
   },
   "outputs": [],
   "source": [
    "qcm_prog = \"set_latch_en 1, 4     # Enabling listening to triggers on address 1\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8ece432",
   "metadata": {},
   "source": [
    "We can then sync with the other module and program our loop:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6cd23aaf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:53.343143Z",
     "iopub.status.busy": "2023-11-08T11:49:53.341753Z",
     "iopub.status.idle": "2023-11-08T11:49:53.354753Z",
     "shell.execute_reply": "2023-11-08T11:49:53.353649Z"
    }
   },
   "outputs": [],
   "source": [
    "qcm_prog += f\"\"\"\n",
    "wait_sync 4\n",
    "wait 1000\n",
    "move {len(states)}, R0\n",
    "s:\n",
    "\"\"\"\n",
    "# Here we need to write the code to receive and react to triggers\n",
    "# Therefore, we store then end of the program in a separate variable for now\n",
    "qcm_prog_tail = \"\"\"\n",
    "loop R0, @s\n",
    "stop\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cef0ff5",
   "metadata": {},
   "source": [
    "For every iteration of our loop, we first need to clear any received triggers and then wait for any new trigger to come in:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2282dad1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:53.359814Z",
     "iopub.status.busy": "2023-11-08T11:49:53.358776Z",
     "iopub.status.idle": "2023-11-08T11:49:53.368904Z",
     "shell.execute_reply": "2023-11-08T11:49:53.368904Z"
    }
   },
   "outputs": [],
   "source": [
    "qcm_prog += f\"\"\"\n",
    "latch_rst {tof+120}   # Deletes all previously received triggers\n",
    "wait      300         # gives the acquisition result time to be received by the QCM\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "443afb03",
   "metadata": {},
   "source": [
    "Next, we program a conditional play instruction. In order to mark a `play`, `acquire`, `acquire_weighed` or `wait` instruction as conditional, we open a conditionality block using the `set_cond <enable> <address_mask> <operator> <else_wait>` instruction.\n",
    "\n",
    "The `<enable>` argument sets whether subsequent real-time pipeline instructions are conditional or not. In case a conditional instruction is skipped, the `<else_wait>` argument determines how long the real-time pipeline will stall (minimum is 4 ns).\n",
    "\n",
    "The `<address_mask>` and `<operator>` determine the conditionality, if the condition evaluates to `True` the instruction es executed, otherwise the instruction will be skipped (equivalent to a `wait <else_wait>` instruction).\n",
    "\n",
    "When only listening to a single trigger address, the `<address mask>` should be made equal to `2**(address-1)` and the `<operator>` should be `0` for `OR` or `2` for `AND` (both having the same effect for a single input, feedback based on multiple trigger addresses is not covered in this tutorial). To invert the conditional, `<operator>` `1` for `NOR` or `3` for `NAND` can be used.\n",
    "\n",
    "For active reset, we can enable the conditional with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "cbc9eb54",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:53.373185Z",
     "iopub.status.busy": "2023-11-08T11:49:53.373185Z",
     "iopub.status.idle": "2023-11-08T11:49:53.385556Z",
     "shell.execute_reply": "2023-11-08T11:49:53.384483Z"
    }
   },
   "outputs": [],
   "source": [
    "qcm_prog += \"\"\"\n",
    "set_cond 1, 1, 0, 700 # Enable conditional commands\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca9175a6",
   "metadata": {},
   "source": [
    "Next we can invoke our play command as usual:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d00177ed",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:53.389958Z",
     "iopub.status.busy": "2023-11-08T11:49:53.388928Z",
     "iopub.status.idle": "2023-11-08T11:49:53.401825Z",
     "shell.execute_reply": "2023-11-08T11:49:53.400517Z"
    }
   },
   "outputs": [],
   "source": [
    "# print(qrm_prog)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e7df854e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:53.404375Z",
     "iopub.status.busy": "2023-11-08T11:49:53.404375Z",
     "iopub.status.idle": "2023-11-08T11:49:53.417384Z",
     "shell.execute_reply": "2023-11-08T11:49:53.416163Z"
    }
   },
   "outputs": [],
   "source": [
    "qcm_prog += \"play 6, 7, 700\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e903ead",
   "metadata": {},
   "source": [
    "Finally, do not forget to disable the conditional commands again (after the first `0`, the parameters do not matter):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4306e992",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:53.421137Z",
     "iopub.status.busy": "2023-11-08T11:49:53.419966Z",
     "iopub.status.idle": "2023-11-08T11:49:53.432998Z",
     "shell.execute_reply": "2023-11-08T11:49:53.431438Z"
    }
   },
   "outputs": [],
   "source": [
    "qcm_prog += (\n",
    "    \"\"\"\n",
    "set_cond 0, 1, 0, 700 # Disable conditional commands again\n",
    "\"\"\"\n",
    "    + qcm_prog_tail\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0af3c859",
   "metadata": {},
   "source": [
    "In summary, the complete QCM program is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "656b052f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:53.437104Z",
     "iopub.status.busy": "2023-11-08T11:49:53.437104Z",
     "iopub.status.idle": "2023-11-08T11:49:53.448298Z",
     "shell.execute_reply": "2023-11-08T11:49:53.447297Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "set_latch_en 1, 4     # Enabling listening to triggers on address 1\n",
      "wait_sync 4\n",
      "wait 1000\n",
      "move 100, R0\n",
      "s:\n",
      "\n",
      "latch_rst 264   # Deletes all previously received triggers\n",
      "wait      300         # gives the acquisition result time to be received by the QCM\n",
      "\n",
      "set_cond 1, 1, 0, 700 # Enable conditional commands\n",
      "play 6, 7, 700\n",
      "set_cond 0, 1, 0, 700 # Disable conditional commands again\n",
      "\n",
      "loop R0, @s\n",
      "stop\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(qcm_prog)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "271929a7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:53.451335Z",
     "iopub.status.busy": "2023-11-08T11:49:53.451335Z",
     "iopub.status.idle": "2023-11-08T11:49:53.728367Z",
     "shell.execute_reply": "2023-11-08T11:49:53.727363Z"
    }
   },
   "outputs": [],
   "source": [
    "control_module.sequencer1.sync_en(True)  # We did not enable the sync on the QCM sequencer yet\n",
    "\n",
    "readout_module.sequencer0.sequence(\n",
    "    {\"waveforms\": waveforms, \"program\": qrm_prog, \"acquisitions\": acquisitions, \"weights\": {}}\n",
    ")\n",
    "readout_module.sequencer0.arm_sequencer()\n",
    "\n",
    "control_module.sequencer1.sequence(\n",
    "    {\"waveforms\": waveforms, \"program\": qcm_prog, \"acquisitions\": acquisitions, \"weights\": {}}\n",
    ")\n",
    "control_module.sequencer1.arm_sequencer()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b93679e0",
   "metadata": {},
   "source": [
    "We are all set to witness the conditional playback! Please make sure that you have connected the QCM output 0 to the oscilloscope along with the marker 1 of QRM as trigger and marker 4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a0210957",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:53.732365Z",
     "iopub.status.busy": "2023-11-08T11:49:53.731364Z",
     "iopub.status.idle": "2023-11-08T11:49:53.760793Z",
     "shell.execute_reply": "2023-11-08T11:49:53.759151Z"
    }
   },
   "outputs": [],
   "source": [
    "readout_module.start_sequencer()\n",
    "control_module.start_sequencer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "1be90a04",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:53.764811Z",
     "iopub.status.busy": "2023-11-08T11:49:53.763812Z",
     "iopub.status.idle": "2023-11-08T11:49:53.792259Z",
     "shell.execute_reply": "2023-11-08T11:49:53.790822Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QRM:\n",
      "Status: STOPPED, Flags: ACQ_SCOPE_DONE_PATH_0, ACQ_SCOPE_DONE_PATH_1, ACQ_BINNING_DONE\n",
      "QCM:\n",
      "Status: STOPPED, Flags: NONE\n"
     ]
    }
   ],
   "source": [
    "print(\"QRM:\")\n",
    "print(readout_module.get_sequencer_state(0, 1))\n",
    "print(\"QCM:\")\n",
    "print(control_module.get_sequencer_state(0, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c68b969a",
   "metadata": {},
   "source": [
    "On the oscilloscope, you should see an image as below (CH1 (yellow) : Marker 1, CH2 (pink): Marker 2, CH3(blue) : QCM Output 0, CH4(green) : Marker 4). Marker 2 activation corresponds to a state 1 signal by QRM which is confirmed by the Marker 4\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22fb4de7",
   "metadata": {},
   "source": [
    "![image.png](5a08cf99-6cfa-49a0-94c7-accea41f8c21.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17ccdb5a",
   "metadata": {},
   "source": [
    "## Stop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "af13c9d2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:49:53.796589Z",
     "iopub.status.busy": "2023-11-08T11:49:53.795588Z",
     "iopub.status.idle": "2023-11-08T11:49:57.099150Z",
     "shell.execute_reply": "2023-11-08T11:49:57.098091Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QCM :\n",
      "Status: STOPPED, Flags: NONE\n",
      "\n",
      "QRM :\n",
      "Status: STOPPED, Flags: FORCED_STOP, ACQ_SCOPE_DONE_PATH_0, ACQ_SCOPE_DONE_PATH_1, ACQ_BINNING_DONE\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: OKAY, Flags: NONE, Slot flags: NONE\n"
     ]
    }
   ],
   "source": [
    "# Stop sequencers.\n",
    "control_module.stop_sequencer()\n",
    "readout_module.stop_sequencer()\n",
    "\n",
    "# Print status of sequencers.\n",
    "print(\"QCM :\")\n",
    "print(control_module.get_sequencer_state(0))\n",
    "print()\n",
    "\n",
    "print(\"QRM :\")\n",
    "print(readout_module.get_sequencer_state(0))\n",
    "print()\n",
    "\n",
    "# Reset the cluster\n",
    "cluster.reset()\n",
    "print(cluster.get_system_state())"
   ]
  }
 ],
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