{
 "cells": [
  {
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     "header_banner"
    ]
   },
   "source": [
    "<a href=\"figures/Banner.jpeg\">\n",
    "      <img src=\"figures/Banner.jpeg\" alt=\"image0\" width=\"100%\">\n",
    "</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "393bfbaa",
   "metadata": {
    "execution": {
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    "tags": [
     "imports",
     "header_0"
    ]
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import rich  # noqa:F401\n",
    "from qcodes.parameters import ManualParameter\n",
    "from simulated_data import get_simulated_tof_data\n",
    "\n",
    "import quantify_core.data.handling as dh\n",
    "from quantify_core.analysis.time_of_flight_analysis import TimeOfFlightAnalysis\n",
    "from quantify_scheduler import BasicTransmonElement, QuantumDevice, Schedule, ScheduleGettable\n",
    "from quantify_scheduler.backends.qblox import constants\n",
    "from quantify_scheduler.math import closest_number_ceil\n",
    "from quantify_scheduler.operations import Measure\n",
    "\n",
    "from utils import (  # noqa:F401  # noqa:F401\n",
    "    display_dict,\n",
    "    initialize_hardware,\n",
    "    run_schedule,\n",
    "    show_connectivity,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "599c2b8e",
   "metadata": {
    "execution": {
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    },
    "tags": [
     "config",
     "header_1"
    ]
   },
   "outputs": [],
   "source": [
    "hw_config_path = \"configs/tuning_transmon_coupled_pair_hardware_config.json\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "81a0cbbc",
   "metadata": {
    "execution": {
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    },
    "tags": [
     "header_2"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data will be saved in:\n",
      "/root/quantify-data\n"
     ]
    }
   ],
   "source": [
    "# Enter your own dataset directory here!\n",
    "dh.set_datadir(dh.default_datadir())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e1c904f4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-23T09:10:00.298084Z",
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    },
    "lines_to_next_cell": 2,
    "tags": [
     "header_3"
    ]
   },
   "outputs": [],
   "source": [
    "quantum_device = QuantumDevice(\"transmon_device\")\n",
    "quantum_device.hardware_config.load_from_json_file(hw_config_path)\n",
    "qubit = BasicTransmonElement(\"q0\")\n",
    "qubit.measure.acq_channel(0)\n",
    "quantum_device.add_element(qubit)\n",
    "cluster_ip = None\n",
    "meas_ctrl, inst_coord, cluster = initialize_hardware(quantum_device, ip=cluster_ip)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c47d85ef",
   "metadata": {},
   "source": [
    "# Time of flight measurement\n",
    "Here show how to measure time of flight for your system.\n",
    "This is useful to calibrate the acquisition delay for subsequent experiments."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46177ec5",
   "metadata": {
    "tags": [
     "no_demo"
    ]
   },
   "source": [
    "## Setup\n",
    "In this section we configure the hardware configuration which specifies the connectivity of our system.\n",
    "\n",
    "The experiments of this tutorial are meant to be executed with a Qblox Cluster controlling a transmon system.\n",
    "The experiments can also be executed using a dummy Qblox device that is created via an instance of the `Cluster` class, and is initialized with a dummy configuration.\n",
    "When using a dummy device, the analysis will not work because the experiments will return `np.nan` values.\n",
    "\n",
    "### Configuration file\n",
    "\n",
    "This is a template hardware configuration file for a 2-qubit system with a flux-control line which can be used to tune the qubit frequency. We will only work with qubit 0.\n",
    "\n",
    "The hardware connectivity is as follows, by cluster slot:\n",
    "\n",
    "- **QCM** (Slot 2)\n",
    "  - $\\text{O}^{1}$: Flux line for `q0`.\n",
    "  - $\\text{O}^{2}$: Flux line for `q1`.\n",
    "\n",
    "- **QCM-RF** (Slot 6)\n",
    "  - $\\text{O}^{1}$: Drive line for `q0` using fixed 80 MHz IF.\n",
    "  - $\\text{O}^{2}$: Drive line for `q1` using fixed 80 MHz IF.\n",
    "\n",
    "- **QRM-RF** (Slot 8)\n",
    "  - $\\text{O}^{1}$ and $\\text{I}^{1}$: Shared readout line for `q0`/`q1` using a fixed LO set at 7.5 GHz.\n",
    "\n",
    "Note that in the hardware configuration below the mixers are uncorrected, but for high fidelity experiments this should also be done for all the modules."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ece44fa9",
   "metadata": {
    "lines_to_next_cell": 2,
    "tags": [
     "no_demo"
    ]
   },
   "source": [
    "### Quantum device settings\n",
    "Here we initialize our `QuantumDevice` and our qubit parameters,  checkout this [tutorial](https://quantify-os.org/docs/quantify-scheduler/tutorials/Operations%20and%20Qubits.html) for further details.\n",
    "\n",
    "In short, a `QuantumDevice` contains device elements where we save our found parameters. Here we are loading a template for 2 qubits, but we will only use qubit 0."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b3f60f1",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "## Schedule definition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "39658ac6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-23T09:10:00.903798Z",
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   "source": [
    "def tof_trace_schedule(\n",
    "    qubit_name: str,\n",
    "    repetitions: int = 1,\n",
    ") -> Schedule:\n",
    "    schedule = Schedule(\"Trace measurement schedule\", repetitions=repetitions)\n",
    "    schedule.add(Measure(qubit_name, acq_protocol=\"Trace\"))\n",
    "    return schedule"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd604e98",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "## Measuring time of flight with trace acquisition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "65e15a5d",
   "metadata": {
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   "source": [
    "def set_readout_attenuation_hardware_config(attenuation_dB: int) -> None:\n",
    "    hwcfg = quantum_device.hardware_config()\n",
    "    output_att = hwcfg[\"hardware_options\"][\"output_att\"]\n",
    "    output_att[f\"{qubit.ports.readout()}-{qubit.name}.ro\"] = attenuation_dB\n",
    "    quantum_device.hardware_config(hwcfg)\n",
    "\n",
    "\n",
    "set_readout_attenuation_hardware_config(0)\n",
    "qubit.measure.pulse_duration(300e-9)\n",
    "qubit.measure.integration_time(1e-6)\n",
    "qubit.measure.pulse_amp(0.1)\n",
    "qubit.measure.acq_delay(4e-9)\n",
    "qubit.clock_freqs.readout(7.2e9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b9c3bbc5",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "tof_t = ManualParameter(name=\"tof_t\", unit=\"ns\", label=\"Trace acquisition sample\")\n",
    "tof_t.batched = True\n",
    "tof_t.batch_size = round(qubit.measure.integration_time() * constants.SAMPLING_RATE)\n",
    "\n",
    "tof_sched_kwargs = dict(\n",
    "    qubit_name=qubit.name,\n",
    ")\n",
    "\n",
    "# set gettable\n",
    "gettable = ScheduleGettable(\n",
    "    quantum_device,\n",
    "    schedule_function=tof_trace_schedule,\n",
    "    schedule_kwargs=tof_sched_kwargs,\n",
    "    real_imag=False,\n",
    "    batched=True,\n",
    ")\n",
    "\n",
    "# set measurement control\n",
    "meas_ctrl.gettables(gettable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ad3a1c55",
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    "execution": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting batched measurement...\n",
      "Iterative settable(s) [outer loop(s)]:\n",
      "\t --- (None) --- \n",
      "Batched settable(s):\n",
      "\t tof_t \n",
      "Batch size limit: 1000\n",
      "\n"
     ]
    },
    {
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       "model_id": "28cbd8a58d704b6e89c6e59dea6dfc8b",
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      },
      "text/plain": [
       "Completed:   0%|           [ elapsed time: 00:00 | time left: ? ] it"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tof_t_setpoints = np.arange(tof_t.batch_size)\n",
    "\n",
    "meas_ctrl.settables(tof_t)\n",
    "meas_ctrl.setpoints(tof_t_setpoints)\n",
    "\n",
    "if cluster_ip is None:\n",
    "\n",
    "    def dummy_gettable():\n",
    "        gettable.initialize()\n",
    "        return get_simulated_tof_data()\n",
    "\n",
    "    gettable.get = dummy_gettable\n",
    "\n",
    "tof_ds = dh.to_gridded_dataset(meas_ctrl.run(\"Time of flight measurement \" + qubit.name))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f516cc3",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "50d26330",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use fake data in case the cluster is a dummy\n",
    "tof_analysis = TimeOfFlightAnalysis(tuid=dh.get_latest_tuid())\n",
    "tof_analysis.run(playback_delay=149e-9).display_figs_mpl()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "eba7e865",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-23T09:10:02.047754Z",
     "iopub.status.busy": "2025-06-23T09:10:02.047560Z",
     "iopub.status.idle": "2025-06-23T09:10:02.051185Z",
     "shell.execute_reply": "2025-06-23T09:10:02.050578Z"
    }
   },
   "outputs": [],
   "source": [
    "fit_results = tof_analysis.quantities_of_interest\n",
    "nco_prop_delay = fit_results[\"nco_prop_delay\"]\n",
    "measured_tof = fit_results[\"tof\"]\n",
    "\n",
    "qubit.measure.acq_delay(\n",
    "    closest_number_ceil(\n",
    "        measured_tof * constants.SAMPLING_RATE, constants.MIN_TIME_BETWEEN_OPERATIONS\n",
    "    )\n",
    "    / constants.SAMPLING_RATE\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "95ef6be2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-23T09:10:02.053020Z",
     "iopub.status.busy": "2025-06-23T09:10:02.052853Z",
     "iopub.status.idle": "2025-06-23T09:10:02.297375Z",
     "shell.execute_reply": "2025-06-23T09:10:02.296680Z"
    },
    "tags": [
     "no_demo"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "188dc17a1ba3493188d8010073216c27",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Tab(children=(Tab(children=(Tab(children=(Output(),), selected_index=0, titles=('other values',)), Tab(childre…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "inst_coord.last_schedule.compiled_instructions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "87a19be0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-23T09:10:02.315886Z",
     "iopub.status.busy": "2025-06-23T09:10:02.315734Z",
     "iopub.status.idle": "2025-06-23T09:10:02.449296Z",
     "shell.execute_reply": "2025-06-23T09:10:02.448675Z"
    },
    "tags": [
     "no_demo"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fac782c996bb440383b5e930e8062cb0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Tab(children=(Tab(children=(Tab(children=(Tab(children=(Tab(children=(Output(),), selected_index=0, titles=('o…"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display_dict(quantum_device.hardware_config())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e7eb6949",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-23T09:10:02.470546Z",
     "iopub.status.busy": "2025-06-23T09:10:02.470368Z",
     "iopub.status.idle": "2025-06-23T09:10:02.476214Z",
     "shell.execute_reply": "2025-06-23T09:10:02.475775Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'devices/transmon_device_2025-06-23_09-10-02_UTC.json'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quantum_device.to_json_file(\"devices/\")"
   ]
  }
 ],
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",
          "text/plain": "<Figure size 1000x621 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "9c032309ed2f413aa330271caec577d9": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_537ca554672642b39203e4e1e56a8edb",
       "msg_id": "",
       "outputs": [],
       "tabbable": null,
       "tooltip": null
      }
     },
     "9c17f350778e4d13976d59c0e1d3c7c1": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_6e01674b069a47848607efb923db1229",
       "msg_id": "",
       "outputs": [],
       "tabbable": null,
       "tooltip": null
      }
     },
     "9c2a0b325bb34b579410478fa6172450": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_a4cbf0639aff46958b49ce2bcb6bc9f6",
       "msg_id": "",
       "outputs": [
        {
         "data": {
          "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>setting</th>\n      <th>value</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>offset_ch0_path_I</th>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>offset_ch0_path_Q</th>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>offset_ch1_path_I</th>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>offset_ch1_path_Q</th>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>in0_gain</th>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>in1_gain</th>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>distortion_corrections</th>\n      <td>[{'bt': {'coeffs': None, 'config': 'QbloxFilte...</td>\n    </tr>\n    <tr>\n      <th>out0_lo_freq_cal_type_default</th>\n      <td>LoCalEnum.ON_LO_INTERM_FREQ_CHANGE</td>\n    </tr>\n    <tr>\n      <th>out1_lo_freq_cal_type_default</th>\n      <td>LoCalEnum.OFF</td>\n    </tr>\n    <tr>\n      <th>lo0_freq</th>\n      <td>7500000000.0</td>\n    </tr>\n    <tr>\n      <th>lo1_freq</th>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>lo2_freq</th>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>lo3_freq</th>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>lo4_freq</th>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>lo5_freq</th>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>in0_freq</th>\n      <td>7500000000.0</td>\n    </tr>\n    <tr>\n      <th>in1_freq</th>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>out0_att</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>out1_att</th>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>out2_att</th>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>out3_att</th>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>out4_att</th>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>out5_att</th>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>in0_att</th>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>in1_att</th>\n      <td>None</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
          "text/plain": "setting                                                                    value\noffset_ch0_path_I                                                           None\noffset_ch0_path_Q                                                           None\noffset_ch1_path_I                                                           None\noffset_ch1_path_Q                                                           None\nin0_gain                                                                    None\nin1_gain                                                                    None\ndistortion_corrections         [{'bt': {'coeffs': None, 'config': 'QbloxFilte...\nout0_lo_freq_cal_type_default                 LoCalEnum.ON_LO_INTERM_FREQ_CHANGE\nout1_lo_freq_cal_type_default                                      LoCalEnum.OFF\nlo0_freq                                                            7500000000.0\nlo1_freq                                                                    None\nlo2_freq                                                                    None\nlo3_freq                                                                    None\nlo4_freq                                                                    None\nlo5_freq                                                                    None\nin0_freq                                                            7500000000.0\nin1_freq                                                                    None\nout0_att                                                                       0\nout1_att                                                                    None\nout2_att                                                                    None\nout3_att                                                                    None\nout4_att                                                                    None\nout5_att                                                                    None\nin0_att                                                                     None\nin1_att                                                                     None"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
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