{
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   "cell_type": "markdown",
   "id": "f7993409",
   "metadata": {},
   "source": [
    "Photon count based feedback using a QTM\n",
    "=========================================\n",
    "The QTM enables the experiment to carry out complex operations that rely on photon count based feedback.\n",
    "The Truth table operation inherent in the QTM enables the user to carry out logical operation using the acquisition\n",
    "results from up to 4 input channels of the **QTM**. Based on these logical operations, one can forward a trigger to\n",
    "the trigger network thus enabling count based feedback.\n",
    "\n",
    "For this tutorial, we need a QCM and a QTM module. We will output digital pulses from channels 1 and 3 of the QTM.\n",
    "These pulses will be acquired by the channels 5 and 7 of the same QTM.\n",
    "\n",
    ".. note::\n",
    "    Each channel of the QTM can both work in output or input mode. Their indexing in the `qblox_instruments` API goes\n",
    "    from :meth:`Module.io_channel0` to :meth:`Module.io_channel7`, whereas the indexing labeled on the front panel of\n",
    "    the module (from which we were referring to in the text above) goes from 1 to 8.\n",
    "\n",
    "The QCM will play a series of pulses depending on the acquisition measurements carried out by the QTM. We will\n",
    "require an oscilloscope to display the outcomes from the acquisition measurement carried out by the QTM.\n",
    "\n",
    "To display the behavior of the QCM, its marker channel M1 should be used to trigger an oscilloscope. We will then\n",
    "observe the outcome of the acquisition experiment by monitoring $\\text{O}^{1}$ of the QCM on the oscilloscope."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15cfb3ba",
   "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": "9137506a",
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2025-04-01T12:10:12.880194Z",
     "shell.execute_reply": "2025-04-01T12:10:12.879440Z"
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   "source": [
    "from __future__ import annotations\n",
    "\n",
    "from typing import TYPE_CHECKING, Literal\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy\n",
    "from qcodes.instrument import find_or_create_instrument\n",
    "\n",
    "from qblox_instruments import Cluster, ClusterType, Module\n",
    "from quantify_scheduler.waveforms import skewed_hermite\n",
    "\n",
    "if TYPE_CHECKING:\n",
    "    from qblox_instruments.qcodes_drivers.io_channel import IOChannel\n",
    "    from qblox_instruments.qcodes_drivers.sequencer import Sequencer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b16c206",
   "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\n",
    "package (see [Plug & Play](https://docs.qblox.com/en/main/api_reference/tools.html#api-pnp) for more info)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83476731",
   "metadata": {},
   "source": [
    "`!qblox-pnp list`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4b0bfe14",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-01T12:10:12.883934Z",
     "iopub.status.busy": "2025-04-01T12:10:12.883354Z",
     "iopub.status.idle": "2025-04-01T12:10:12.886430Z",
     "shell.execute_reply": "2025-04-01T12:10:12.886028Z"
    }
   },
   "outputs": [],
   "source": [
    "cluster_ip = \"10.10.200.42\"\n",
    "cluster_name: str = \"cluster0\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4667a628",
   "metadata": {},
   "source": [
    "### Connect to th Qblox cluster\n",
    "\n",
    "We now make a connection with the Cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3e10e52e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-01T12:10:12.888449Z",
     "iopub.status.busy": "2025-04-01T12:10:12.888304Z",
     "iopub.status.idle": "2025-04-01T12:10:13.698837Z",
     "shell.execute_reply": "2025-04-01T12:10:13.698141Z"
    }
   },
   "outputs": [],
   "source": [
    "cluster: Cluster = find_or_create_instrument(\n",
    "    Cluster,\n",
    "    recreate=True,\n",
    "    name=cluster_name,\n",
    "    identifier=cluster_ip,\n",
    "    dummy_cfg=(\n",
    "        {\n",
    "            2: ClusterType.CLUSTER_QCM,\n",
    "            4: ClusterType.CLUSTER_QRM,\n",
    "            6: ClusterType.CLUSTER_QCM_RF,\n",
    "            8: ClusterType.CLUSTER_QRM_RF,\n",
    "            10: ClusterType.CLUSTER_QTM,\n",
    "        }\n",
    "        if cluster_ip is None\n",
    "        else None\n",
    "    ),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c24ea97",
   "metadata": {},
   "source": [
    "#### Get connected modules\n",
    "Connect to the QTM and QCM modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d2ae2db4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-01T12:10:13.701780Z",
     "iopub.status.busy": "2025-04-01T12:10:13.701590Z",
     "iopub.status.idle": "2025-04-01T12:10:13.919519Z",
     "shell.execute_reply": "2025-04-01T12:10:13.918313Z"
    }
   },
   "outputs": [],
   "source": [
    "# QRM baseband modules\n",
    "timetag_modules: dict[int, Module] = cluster.get_connected_modules(lambda mod: mod.is_qtm_type)\n",
    "qtm: Module = timetag_modules[next(iter(timetag_modules))]\n",
    "\n",
    "# QCM baseband modules\n",
    "control_modules: dict[int, Module] = cluster.get_connected_modules(\n",
    "    lambda mod: mod.is_qcm_type and not mod.is_rf_type\n",
    ")\n",
    "qcm: Module = control_modules[next(iter(control_modules))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4afac2f5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-01T12:10:13.923434Z",
     "iopub.status.busy": "2025-04-01T12:10:13.923027Z",
     "iopub.status.idle": "2025-04-01T12:10:13.931666Z",
     "shell.execute_reply": "2025-04-01T12:10:13.930682Z"
    },
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# Define helper function to prepare sequencer by passing required arguments for the sequence dictionary.\n",
    "def prepare_sequence_object(\n",
    "    sequencer: Sequencer,\n",
    "    program: str | None = None,\n",
    "    waveforms: dict | None = None,\n",
    "    acquisitions: dict | None = None,\n",
    "    weights: dict | None = None,\n",
    ") -> None:\n",
    "    if acquisitions is None:\n",
    "        acquisitions = {}\n",
    "    if weights is None:\n",
    "        weights = {}\n",
    "    if waveforms is None:\n",
    "        waveforms = {}\n",
    "    if program is None:\n",
    "        program = \"stop\"\n",
    "\n",
    "    sequencer.sequence(\n",
    "        {\n",
    "            \"waveforms\": waveforms,\n",
    "            \"weights\": weights,\n",
    "            \"acquisitions\": acquisitions,\n",
    "            \"program\": program,\n",
    "        }\n",
    "    )\n",
    "    sequencer.arm_sequencer()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48cfcaa4",
   "metadata": {},
   "source": [
    "### Coincidence detection based feedback on two channels\n",
    "In this example, we will demonstrate how to carry out low latency feedback based upon a coincident detection of two\n",
    "photons. Coincident detection of detection events is a cornerstone of several experiments involving quantum computation\n",
    "and networking. It is indeed at the very core of correlation measurements, where various quantum effects are expected\n",
    "to occur (non-classical correlations is a common entanglement witness). It is also desirable in the context of heralding\n",
    "driven entanglement generation, which nowadays is a standard technique in photonics.\n",
    "\n",
    "Here we will use the truth-table feature of the QTM to generate a feedback signal based on the condition that two\n",
    "photons are detected within a certain time window."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "788cd5e8",
   "metadata": {},
   "source": [
    "#### Protocol for the experiment\n",
    "Photon incidence in this demonstration is simulated by digital pulses generated by the QTM itself. We will configure\n",
    "two channels to output digital pulses, with the possibility to creating a delay on one channel. The digital pulses\n",
    "will be acquired on two other channels within an acquisition window. These detections in the acquisition window are\n",
    "threshold-based on predefined count threshold, to determine the outcome of the acquisition. We will use this count\n",
    "threshold to define photon detection success. A detection event that passes this success condition on both channels\n",
    "defines a coincidence detection.\n",
    "\n",
    "We will configure the LUT to send triggers to different trigger addresses depending on:\n",
    "1. Whether a coincidence detection occurred.\n",
    "2. Or a single detection on either channel occurred.\n",
    "3. No detections occurred.\n",
    "\n",
    "Based on which trigger address is updated, we will use the QCM to play different sets of pulses which will be\n",
    "displayed on an oscilloscope, thereby demonstrating feedback."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "426e4088",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "##### Configure QTM and QCM channels\n",
    "\n",
    "First we configure the QTM channels which will detect the photons and provide the data for the look up table.\n",
    "\n",
    "The QTM currently only supports one to one channel to sequencer mapping: it is a good idea to define a helper functions\n",
    "meant to simplify the configuration of multiple sequencers which need to be initialized in a similar way. We will\n",
    "define such function in both cases of input and output channels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "129f6af0",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2025-04-01T12:10:13.935181Z",
     "iopub.status.idle": "2025-04-01T12:10:13.941958Z",
     "shell.execute_reply": "2025-04-01T12:10:13.940953Z"
    }
   },
   "outputs": [],
   "source": [
    "# Define a helper function that will configure a QTM channel and sequencer to be able to carry out an acquisition.\n",
    "def config_qtm_input_channel(io: IOChannel, seq: Sequencer, analog_thresh: float) -> None:\n",
    "    seq.sync_en(True)\n",
    "\n",
    "    io.out_mode(\"disabled\")\n",
    "    io.in_threshold_primary(analog_thresh)\n",
    "    io.binned_acq_threshold_source(\"thresh0\")\n",
    "\n",
    "    io.binned_acq_time_ref(\"start\")  # for timetag\n",
    "    io.binned_acq_time_source(\"first\")\n",
    "    io.binned_acq_on_invalid_time_delta(\"record_0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9f017d83",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-01T12:10:13.945580Z",
     "iopub.status.busy": "2025-04-01T12:10:13.945167Z",
     "iopub.status.idle": "2025-04-01T12:10:13.950560Z",
     "shell.execute_reply": "2025-04-01T12:10:13.949595Z"
    }
   },
   "outputs": [],
   "source": [
    "# Define helper function to allow QTM to output TTL signals.\n",
    "def config_qtm_output_channel(io: IOChannel, seq: Sequencer) -> None:\n",
    "    seq.sync_en(True)\n",
    "    io.out_mode(\"sequencer\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cc0c226",
   "metadata": {},
   "source": [
    "Configure the QCM channel and sequencer, and update trigger count thresholds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a5cf039e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-01T12:10:13.954126Z",
     "iopub.status.busy": "2025-04-01T12:10:13.953742Z",
     "iopub.status.idle": "2025-04-01T12:10:14.051634Z",
     "shell.execute_reply": "2025-04-01T12:10:14.050628Z"
    }
   },
   "outputs": [],
   "source": [
    "# Configure the QCM channels to output waveforms, and set the trigger address thresholds.\n",
    "qcm.disconnect_outputs()\n",
    "qcm.sequencer0.connect_sequencer(\"out0\")\n",
    "qcm.sequencer0.mod_en_awg(True)\n",
    "qcm.sequencer0.sync_en(True)\n",
    "qcm.sequencer0.nco_freq(100e6)\n",
    "\n",
    "# Update trigger count thresholds.\n",
    "qcm.sequencer0.trigger1_count_threshold(1)\n",
    "qcm.sequencer0.trigger1_threshold_invert(False)\n",
    "\n",
    "qcm.sequencer0.trigger2_count_threshold(1)\n",
    "qcm.sequencer0.trigger2_threshold_invert(False)\n",
    "\n",
    "qcm.sequencer0.trigger3_count_threshold(1)\n",
    "qcm.sequencer0.trigger3_threshold_invert(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "859c0e85",
   "metadata": {},
   "source": [
    "##### QCM Waveforms\n",
    "Here we create and plot the different waveforms that will be played when the QCM receives the trigger from the LUT."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "820f8e77",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-01T12:10:14.055260Z",
     "iopub.status.busy": "2025-04-01T12:10:14.054871Z",
     "iopub.status.idle": "2025-04-01T12:10:14.064039Z",
     "shell.execute_reply": "2025-04-01T12:10:14.063044Z"
    }
   },
   "outputs": [],
   "source": [
    "# Waveform parameters\n",
    "WAVEFORM_LENGTH: int = 200  # time in nanoseconds\n",
    "TIMES: np.ndarray = np.arange(WAVEFORM_LENGTH) * 1e-9  # goes from 0 to (WAVEFORM_LENGTH-1)\n",
    "GAUSSIAN: np.ndarray = scipy.signal.windows.gaussian(WAVEFORM_LENGTH, std=0.12 * WAVEFORM_LENGTH)\n",
    "HERMITE: np.ndarray = np.real(\n",
    "    skewed_hermite(t=TIMES, duration=WAVEFORM_LENGTH * 1e-9, amplitude=0.6, skewness=0, phase=0)\n",
    ")\n",
    "\n",
    "# Waveform dictionary (data will hold the samples and index will be used to select the waveforms in the instrument).\n",
    "WAVEFORMS = {\n",
    "    \"single_detection\": {\"data\": GAUSSIAN.tolist(), \"index\": 0},\n",
    "    \"coincident_detection\": {\"data\": HERMITE.tolist(), \"index\": 1},\n",
    "    \"no_detection\": {\"data\": [0.0] * WAVEFORM_LENGTH, \"index\": 2},\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d2e2be8",
   "metadata": {},
   "source": [
    "Let's plot the waveforms to see what we have created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "623a398d",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2025-04-01T12:10:14.067092Z",
     "iopub.status.idle": "2025-04-01T12:10:14.285009Z",
     "shell.execute_reply": "2025-04-01T12:10:14.284587Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x621 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "\n",
    "for wf, d in WAVEFORMS.items():\n",
    "    ax.plot(TIMES, d[\"data\"], label=wf)\n",
    "\n",
    "ax.legend()\n",
    "ax.grid(alpha=1 / 10)\n",
    "ax.set_ylabel(\"Waveform primitive amplitude\")\n",
    "ax.set_xlabel(\"Time (ns)\")\n",
    "\n",
    "plt.draw()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27628daf",
   "metadata": {},
   "source": [
    "#### Configure the truth-table.\n",
    "Here we will configure and enable the truth-table on the QTM.\n",
    "The truth-table data (specified in the manner of a dictionary) sets the conditions under which a trigger will be sent\n",
    "on the network and on which address."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8458081c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-01T12:10:14.287835Z",
     "iopub.status.busy": "2025-04-01T12:10:14.287646Z",
     "iopub.status.idle": "2025-04-01T12:10:14.309505Z",
     "shell.execute_reply": "2025-04-01T12:10:14.309128Z"
    }
   },
   "outputs": [],
   "source": [
    "# Enable the look up table.\n",
    "qtm.io_channel4.thresholded_acq_truth_table_en(True)\n",
    "# Configure the data in the truth-table.\n",
    "qtm.io_channel4.thresholded_acq_trigger_en(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "84b4ceef",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-01T12:10:14.311160Z",
     "iopub.status.busy": "2025-04-01T12:10:14.311016Z",
     "iopub.status.idle": "2025-04-01T12:10:14.324655Z",
     "shell.execute_reply": "2025-04-01T12:10:14.324239Z"
    }
   },
   "outputs": [],
   "source": [
    "# Here we define the truth-table (tt) config , that encodes the conditions under which a trigger will be sent.\n",
    "tt_config: list[dict[Literal[\"conditions\", \"trigger\"], list[dict[str, int | str]] | int]] = [\n",
    "    {\n",
    "        \"conditions\": [\n",
    "            {\"channel\": 4, \"measurement\": \"count\", \"level\": \"high\"},\n",
    "            {\"channel\": 6, \"measurement\": \"count\", \"level\": \"high\"},\n",
    "        ],\n",
    "        \"trigger\": 1,\n",
    "    },\n",
    "    {\n",
    "        \"conditions\": [\n",
    "            {\"channel\": 4, \"measurement\": \"count\", \"level\": \"mid\"},\n",
    "            {\"channel\": 6, \"measurement\": \"count\", \"level\": \"high\"},\n",
    "        ],\n",
    "        \"trigger\": 2,\n",
    "    },\n",
    "    {\n",
    "        \"conditions\": [\n",
    "            {\"channel\": 4, \"measurement\": \"count\", \"level\": \"high\"},\n",
    "            {\"channel\": 6, \"measurement\": \"count\", \"level\": \"mid\"},\n",
    "        ],\n",
    "        \"trigger\": 3,\n",
    "    },\n",
    "]\n",
    "# Upload truth table config to the QTM channel.\n",
    "qtm.io_channel4.thresholded_acq_truth_table_data(tt_config)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cd4792a",
   "metadata": {},
   "source": [
    "Below we define parameters for photon acquisition and thresholding.\n",
    "We define the two-photon count thresholds that will determine the success of a photon detection.\n",
    "The QTM supports dual-count based thresholds: these thresholds control how the number of detection events within an\n",
    "acquisition window map to the experiment outcome.\n",
    "\n",
    "- Counts less than first threshold (`THRESH_0`) returns the outcome `low`.\n",
    "- Counts equal or greater than first threshold (`THRESH_0`) returns the outcome `mid`.\n",
    "- Counts greater than second threshold (`THRESH_1`) returns the outcome `high`.\n",
    "\n",
    "These counts will then be used by the truth-table to perform conditional logic and send trigger pulses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3ac16802",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-01T12:10:14.325993Z",
     "iopub.status.busy": "2025-04-01T12:10:14.325849Z",
     "iopub.status.idle": "2025-04-01T12:10:14.328107Z",
     "shell.execute_reply": "2025-04-01T12:10:14.327743Z"
    }
   },
   "outputs": [],
   "source": [
    "THRESH_0: int = 0  # Acquisition will pass `mid` to LUT for counts that are equal to 0.\n",
    "THRESH_1: int = 1  # Acquisition will pass `high` to LUT for counts that are equal to 1.\n",
    "# Define acquisition parameters.\n",
    "ACQUISITION_WINDOW: int = 2000  # nanoseconds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ae07dde1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-01T12:10:14.329341Z",
     "iopub.status.busy": "2025-04-01T12:10:14.329200Z",
     "iopub.status.idle": "2025-04-01T12:10:14.331491Z",
     "shell.execute_reply": "2025-04-01T12:10:14.331125Z"
    }
   },
   "outputs": [],
   "source": [
    "# Define acquisition dictionary.\n",
    "ACQUISITION_MODE: dict[str, dict[str, int]] = {\n",
    "    \"timetag_single\": {\"num_bins\": 1, \"index\": 0},\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd07c2ec",
   "metadata": {},
   "source": [
    "Here we define the Q1ASM program responsible for opening and closing the acquisition window on the QTM channels that\n",
    "will detect the photon. In the program, we also update the two thresholds for count based thresholding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "38e58c84",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-01T12:10:14.332768Z",
     "iopub.status.busy": "2025-04-01T12:10:14.332625Z",
     "iopub.status.idle": "2025-04-01T12:10:14.335219Z",
     "shell.execute_reply": "2025-04-01T12:10:14.334821Z"
    }
   },
   "outputs": [],
   "source": [
    "# Define program to detect incoming photons within a certain time window.\n",
    "DETECTION_SCRIPT: str = f\"\"\"\n",
    "    upd_thres           0, {THRESH_0}, 4\n",
    "    upd_thres           1, {THRESH_1}, 4\n",
    "\n",
    "    wait_sync           4\n",
    "\n",
    "    acquire_timetags    0, 0, 1, 0, 4            # acq_idx, bin_idx, enable, fine_delay, duration\n",
    "    wait                {ACQUISITION_WINDOW - 4}\n",
    "    acquire_timetags    0, 0, 0, 0, 4\n",
    "\n",
    "    stop\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f43dd4c",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "To simulate the arrival of photons, we send a digital pulse from two channels of QTM using two sequencers.\n",
    "In order to deterministically fulfill or fail the truth-table conditions, we create helper function that allows us to\n",
    "add or remove a delay between the two sequencers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "93dad424",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-01T12:10:14.336638Z",
     "iopub.status.busy": "2025-04-01T12:10:14.336368Z",
     "iopub.status.idle": "2025-04-01T12:10:14.338899Z",
     "shell.execute_reply": "2025-04-01T12:10:14.338501Z"
    }
   },
   "outputs": [],
   "source": [
    "# Define function that returns a program for QTM channel to output digital pulse.\n",
    "def digital_out(delay: int = 200) -> str:\n",
    "    prog = f\"\"\"\n",
    "        wait_sync   4\n",
    "        wait        {delay}\n",
    "\n",
    "        set_digital 1,1,0\n",
    "        upd_param   4\n",
    "        wait        100\n",
    "        set_digital 0,1,0\n",
    "        upd_param   4\n",
    "\n",
    "        stop\n",
    "    \"\"\"\n",
    "\n",
    "    return prog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5a08fa11",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-01T12:10:14.340280Z",
     "iopub.status.busy": "2025-04-01T12:10:14.340028Z",
     "iopub.status.idle": "2025-04-01T12:10:14.342674Z",
     "shell.execute_reply": "2025-04-01T12:10:14.342269Z"
    }
   },
   "outputs": [],
   "source": [
    "# Define program to use conditional logic to play different waveforms depending on the result of an acquisition.\n",
    "QCM_FEEDBACK_PROG: str = f\"\"\"\n",
    "    set_latch_en    1, 4                       # Enable all trigger network address counters.\n",
    "    latch_rst       4                          # Reset all trigger network address counters back to 0.\n",
    "\n",
    "    wait_sync       4\n",
    "\n",
    "    wait            {ACQUISITION_WINDOW + 1200} # Add extra wait time to give trigger network time to react.\n",
    "\n",
    "    set_mrk         1\n",
    "    set_cond        1, 1, 0, 8                  # 1 = Enable, 1 = trig add, 0 = OR, 8 = wait_time.\n",
    "    play            1, 1, 1000\n",
    "    play            1, 1, 1000\n",
    "    set_cond        1, 2, 0, 8                  # 1 = Enable, 2 = trig add, 0 = OR, 8 = wait_time.\n",
    "    play            2, 2, 1000\n",
    "    play            0, 0, 1000\n",
    "    set_cond        1, 4, 0, 8                  # 1 = Enable, 3 = trig add, 0 = OR, 8 = wait_time.\n",
    "    play            0, 0, 1000\n",
    "    play            2, 2, 1000\n",
    "    set_cond        0, 0, 0, 8                  # Disable conditional.\n",
    "    set_mrk         0\n",
    "    upd_param       4\n",
    "\n",
    "    stop                                        # Stop the sequence after the last iteration.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2e4581a1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-01T12:10:14.343939Z",
     "iopub.status.busy": "2025-04-01T12:10:14.343797Z",
     "iopub.status.idle": "2025-04-01T12:10:14.479756Z",
     "shell.execute_reply": "2025-04-01T12:10:14.478284Z"
    }
   },
   "outputs": [],
   "source": [
    "# Configure channels 5 and 7 to carry out analog thresholded TTL acquisition and timetagging.\n",
    "config_qtm_input_channel(qtm.io_channel4, qtm.sequencer4, 0.5)\n",
    "config_qtm_input_channel(qtm.io_channel6, qtm.sequencer6, 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b4ad4429",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-01T12:10:14.483166Z",
     "iopub.status.busy": "2025-04-01T12:10:14.482924Z",
     "iopub.status.idle": "2025-04-01T12:10:14.518756Z",
     "shell.execute_reply": "2025-04-01T12:10:14.517725Z"
    }
   },
   "outputs": [],
   "source": [
    "# Configure output of two QTM channels.\n",
    "config_qtm_output_channel(qtm.io_channel0, qtm.sequencer0)\n",
    "config_qtm_output_channel(qtm.io_channel2, qtm.sequencer2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6b8e0422",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-01T12:10:14.521847Z",
     "iopub.status.busy": "2025-04-01T12:10:14.521653Z",
     "iopub.status.idle": "2025-04-01T12:10:14.579844Z",
     "shell.execute_reply": "2025-04-01T12:10:14.578858Z"
    }
   },
   "outputs": [],
   "source": [
    "# Upload and arm sequencers used for the detection.\n",
    "prepare_sequence_object(qtm.sequencer4, acquisitions=ACQUISITION_MODE, program=DETECTION_SCRIPT)\n",
    "prepare_sequence_object(qtm.sequencer6, acquisitions=ACQUISITION_MODE, program=DETECTION_SCRIPT)\n",
    "\n",
    "# Upload and arm sequencers used for the pulse generation, with the choice to include a delay on both channels.\n",
    "prepare_sequence_object(qtm.sequencer0, program=digital_out())\n",
    "prepare_sequence_object(qtm.sequencer2, program=digital_out())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bd1e0cba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-01T12:10:14.582977Z",
     "iopub.status.busy": "2025-04-01T12:10:14.582782Z",
     "iopub.status.idle": "2025-04-01T12:10:14.617634Z",
     "shell.execute_reply": "2025-04-01T12:10:14.616825Z"
    }
   },
   "outputs": [],
   "source": [
    "# Prepare sequencer of QCM to play series of pulses depending on outcome of detection.\n",
    "prepare_sequence_object(qcm.sequencer0, waveforms=WAVEFORMS, program=QCM_FEEDBACK_PROG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5ff73f3f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-01T12:10:14.620214Z",
     "iopub.status.busy": "2025-04-01T12:10:14.620020Z",
     "iopub.status.idle": "2025-04-01T12:10:14.627905Z",
     "shell.execute_reply": "2025-04-01T12:10:14.626934Z"
    }
   },
   "outputs": [],
   "source": [
    "qtm.start_sequencer()\n",
    "qcm.start_sequencer()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da471e1a",
   "metadata": {},
   "source": [
    "#### Outcome of the experiment.\n",
    "The outcome of the experiments we run should display the following results depending on the initial pulse conditions\n",
    "that we set."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f227002",
   "metadata": {},
   "source": [
    "Since we sent both digital pulses at the same time, the oscilloscope should display two pulses as shown in the figure\n",
    "below.\n",
    "\n",
    "![image.png](coincidence.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89244887",
   "metadata": {},
   "source": [
    "We can add a delay to channel 1, such that the pulse arrives after the acquisition window has closed. In such a case,\n",
    "the oscilloscope display will look like this.\n",
    "\n",
    "![image.png](success_0.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4500ce1e",
   "metadata": {},
   "source": [
    "Adding a delay to channel 3 results in an output that looks like this.\n",
    "\n",
    "![image.png](success_0.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e6633983",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-01T12:10:14.631328Z",
     "iopub.status.busy": "2025-04-01T12:10:14.631140Z",
     "iopub.status.idle": "2025-04-01T12:10:14.638678Z",
     "shell.execute_reply": "2025-04-01T12:10:14.637822Z"
    }
   },
   "outputs": [],
   "source": [
    "qtm.stop_sequencer()\n",
    "qcm.stop_sequencer()"
   ]
  }
 ],
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