{ "cells": [ { "cell_type": "markdown", "id": "16547dae", "metadata": {}, "source": [ "# Time of flight measurement\n", "The notebook will show how to measure time of flight for your system." ] }, { "cell_type": "code", "execution_count": 1, "id": "9df88460", "metadata": { "execution": { "iopub.execute_input": "2024-10-17T13:11:03.729239Z", "iopub.status.busy": "2024-10-17T13:11:03.727946Z", "iopub.status.idle": "2024-10-17T13:11:05.368272Z", "shell.execute_reply": "2024-10-17T13:11:05.367402Z" } }, "outputs": [], "source": [ "import numpy as np\n", "import rich # noqa:F401\n", "from qcodes.parameters import ManualParameter\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 Schedule\n", "from quantify_scheduler.backends.qblox import constants\n", "from quantify_scheduler.gettables import ScheduleGettable\n", "from quantify_scheduler.math import closest_number_ceil\n", "from quantify_scheduler.operations.gate_library import Measure" ] }, { "cell_type": "code", "execution_count": 2, "id": "316760d2", "metadata": { "execution": { "iopub.execute_input": "2024-10-17T13:11:05.371980Z", "iopub.status.busy": "2024-10-17T13:11:05.371244Z", "iopub.status.idle": "2024-10-17T13:11:05.378682Z", "shell.execute_reply": "2024-10-17T13:11:05.378165Z" } }, "outputs": [], "source": [ "import json\n", "\n", "import rich # noqa:F401\n", "\n", "import quantify_core.data.handling as dh\n", "from quantify_scheduler.device_under_test.quantum_device import QuantumDevice\n", "\n", "from utils import initialize_hardware, run # noqa:F401" ] }, { "cell_type": "markdown", "id": "dfc10e96", "metadata": {}, "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", "- **QCM** (Slot 2)\n", " - $\\text{O}^{1}$: Flux line for `q0`.\n", " - $\\text{O}^{2}$: Flux line for `q1`.\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", "- **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": "code", "execution_count": 3, "id": "bd1c6ffa", "metadata": { "execution": { "iopub.execute_input": "2024-10-17T13:11:05.381354Z", "iopub.status.busy": "2024-10-17T13:11:05.381185Z", "iopub.status.idle": "2024-10-17T13:11:05.385697Z", "shell.execute_reply": "2024-10-17T13:11:05.385123Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data will be saved in:\n", "/root/quantify-data\n" ] } ], "source": [ "with open(\"configs/tuning_transmon_coupled_pair_hardware_config.json\") as hw_cfg_json_file:\n", " hardware_cfg = json.load(hw_cfg_json_file)\n", "\n", "# Enter your own dataset directory here!\n", "dh.set_datadir(dh.default_datadir())" ] }, { "cell_type": "markdown", "id": "95f575e5", "metadata": {}, "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": "code", "execution_count": 4, "id": "b47e8488", "metadata": { "execution": { "iopub.execute_input": "2024-10-17T13:11:05.388258Z", "iopub.status.busy": "2024-10-17T13:11:05.388091Z", "iopub.status.idle": "2024-10-17T13:11:06.034129Z", "shell.execute_reply": "2024-10-17T13:11:06.033432Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.9/site-packages/quantify_scheduler/backends/types/qblox.py:1220: ValidationWarning: Setting `auto_lo_cal=on_lo_interm_freq_change` will overwrite settings `dc_offset_i=0.0` and `dc_offset_q=0.0`. To suppress this warning, do not set either `dc_offset_i` or `dc_offset_q` for this port-clock.\n", " warnings.warn(\n", "/usr/local/lib/python3.9/site-packages/quantify_scheduler/backends/types/qblox.py:1235: ValidationWarning: Setting `auto_sideband_cal=on_interm_freq_change` will overwrite settings `amp_ratio=1.0` and `phase_error=0.0`. To suppress this warning, do not set either `amp_ratio` or `phase_error` for this port-clock.\n", " warnings.warn(\n" ] } ], "source": [ "quantum_device = QuantumDevice.from_json_file(\"devices/transmon_device_2q.json\")\n", "qubit = quantum_device.get_element(\"q0\")\n", "quantum_device.hardware_config(hardware_cfg)\n", "meas_ctrl, _, cluster = initialize_hardware(quantum_device, ip=None)" ] }, { "cell_type": "markdown", "id": "70151abc", "metadata": { "lines_to_next_cell": 0 }, "source": [ "## Schedule definition" ] }, { "cell_type": "code", "execution_count": 5, "id": "03d62ad7", "metadata": { "execution": { "iopub.execute_input": "2024-10-17T13:11:06.037321Z", "iopub.status.busy": "2024-10-17T13:11:06.037117Z", "iopub.status.idle": "2024-10-17T13:11:06.040763Z", "shell.execute_reply": "2024-10-17T13:11:06.040077Z" } }, "outputs": [], "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": "110b5948", "metadata": { "lines_to_next_cell": 0 }, "source": [ "## Measuring time of flight with trace acquisition" ] }, { "cell_type": "code", "execution_count": 6, "id": "95d0fea8", "metadata": { "execution": { "iopub.execute_input": "2024-10-17T13:11:06.043497Z", "iopub.status.busy": "2024-10-17T13:11:06.043325Z", "iopub.status.idle": "2024-10-17T13:11:06.047451Z", "shell.execute_reply": "2024-10-17T13:11:06.046726Z" }, "lines_to_next_cell": 0 }, "outputs": [], "source": [ "def set_readout_attenuation_hardware_config(attenuation_dB: int):\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)" ] }, { "cell_type": "code", "execution_count": 7, "id": "b3e97262", "metadata": { "execution": { "iopub.execute_input": "2024-10-17T13:11:06.049385Z", "iopub.status.busy": "2024-10-17T13:11:06.049207Z", "iopub.status.idle": "2024-10-17T13:11:06.053573Z", "shell.execute_reply": "2024-10-17T13:11:06.052915Z" }, "lines_to_next_cell": 0 }, "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": "cfb620ed", "metadata": { "execution": { "iopub.execute_input": "2024-10-17T13:11:06.056218Z", "iopub.status.busy": "2024-10-17T13:11:06.056027Z", "iopub.status.idle": "2024-10-17T13:11:06.216366Z", "shell.execute_reply": "2024-10-17T13:11:06.215576Z" } }, "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" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "dac617ee454a4e5cb21598186d32e87a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Completed: 0%| [ elapsed time: 00:00 | time left: ? ] it" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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       "Dimensions:  (x0: 1000)\n",
       "Coordinates:\n",
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       "Attributes:\n",
       "    tuid:                             20241017-131106-059-645ea3\n",
       "    name:                             Time of flight measurement q0\n",
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" ], "text/plain": [ " Size: 24kB\n", "Dimensions: (x0: 1000)\n", "Coordinates:\n", " * x0 (x0) int64 8kB 0 1 2 3 4 5 6 7 ... 992 993 994 995 996 997 998 999\n", "Data variables:\n", " y0 (x0) float64 8kB -0.001583 -0.001463 ... 0.0008711 0.0005261\n", " y1 (x0) float64 8kB 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0\n", "Attributes:\n", " tuid: 20241017-131106-059-645ea3\n", " name: Time of flight measurement q0\n", " grid_2d: False\n", " grid_2d_uniformly_spaced: False\n", " 1d_2_settables_uniformly_spaced: False" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "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", "# replace the get method for the gettable in case the cluster is a dummy\n", "if \"dummy\" in str(cluster._transport):\n", "\n", " def get_fake_tof_data():\n", " \"\"\"Generate mock data for a time of flight measurement.\"\"\"\n", " y = (\n", " np.heaviside(tof_t_setpoints - 200, 0.5)\n", " - np.heaviside(tof_t_setpoints - tof_t_setpoints.size * 0.7, 0.5)\n", " ) * 30e-3\n", " y += np.random.normal(loc=0.0, scale=1e-3, size=y.size)\n", " return [y, np.zeros_like(y)]\n", "\n", " gettable.get = get_fake_tof_data\n", "\n", "tof_ds = dh.to_gridded_dataset(meas_ctrl.run(\"Time of flight measurement \" + qubit.name))\n", "tof_ds" ] }, { "cell_type": "markdown", "id": "45270c04", "metadata": { "lines_to_next_cell": 0 }, "source": [ "## Analysis" ] }, { "cell_type": "code", "execution_count": 9, "id": "9790bba8", "metadata": { "execution": { "iopub.execute_input": "2024-10-17T13:11:06.219342Z", "iopub.status.busy": "2024-10-17T13:11:06.219045Z", "iopub.status.idle": "2024-10-17T13:11:07.041217Z", "shell.execute_reply": "2024-10-17T13:11:07.040359Z" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "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": "2b394c18", "metadata": { "execution": { "iopub.execute_input": "2024-10-17T13:11:07.043309Z", "iopub.status.busy": "2024-10-17T13:11:07.043096Z", "iopub.status.idle": "2024-10-17T13:11:07.046884Z", "shell.execute_reply": "2024-10-17T13:11:07.046162Z" } }, "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": "a113c018", "metadata": { "execution": { "iopub.execute_input": "2024-10-17T13:11:07.048867Z", "iopub.status.busy": "2024-10-17T13:11:07.048677Z", "iopub.status.idle": "2024-10-17T13:11:07.083436Z", "shell.execute_reply": "2024-10-17T13:11:07.082711Z" } }, "outputs": [ { "data": { "text/html": [ "
{\n",
       "    'config_type': 'quantify_scheduler.backends.qblox_backend.QbloxHardwareCompilationConfig',\n",
       "    'hardware_description': {\n",
       "        'cluster0': {\n",
       "            'instrument_type': 'Cluster',\n",
       "            'modules': {\n",
       "                '6': {'instrument_type': 'QCM_RF'},\n",
       "                '2': {'instrument_type': 'QCM'},\n",
       "                '8': {'instrument_type': 'QRM_RF'}\n",
       "            },\n",
       "            'sequence_to_file': False,\n",
       "            'ref': 'internal'\n",
       "        }\n",
       "    },\n",
       "    'hardware_options': {\n",
       "        'output_att': {'q0:mw-q0.01': 10, 'q1:mw-q1.01': 10, 'q0:res-q0.ro': 0, 'q1:res-q1.ro': 60},\n",
       "        'mixer_corrections': {\n",
       "            'q0:mw-q0.01': {\n",
       "                'auto_lo_cal': 'on_lo_interm_freq_change',\n",
       "                'auto_sideband_cal': 'on_interm_freq_change',\n",
       "                'dc_offset_i': None,\n",
       "                'dc_offset_q': None,\n",
       "                'amp_ratio': 1.0,\n",
       "                'phase_error': 0.0\n",
       "            },\n",
       "            'q1:mw-q1.01': {\n",
       "                'auto_lo_cal': 'on_lo_interm_freq_change',\n",
       "                'auto_sideband_cal': 'on_interm_freq_change',\n",
       "                'dc_offset_i': None,\n",
       "                'dc_offset_q': None,\n",
       "                'amp_ratio': 1.0,\n",
       "                'phase_error': 0.0\n",
       "            },\n",
       "            'q0:res-q0.ro': {\n",
       "                'auto_lo_cal': 'on_lo_interm_freq_change',\n",
       "                'auto_sideband_cal': 'on_interm_freq_change',\n",
       "                'dc_offset_i': None,\n",
       "                'dc_offset_q': None,\n",
       "                'amp_ratio': 1.0,\n",
       "                'phase_error': 0.0\n",
       "            },\n",
       "            'q1:res-q1.ro': {\n",
       "                'auto_lo_cal': 'on_lo_interm_freq_change',\n",
       "                'auto_sideband_cal': 'on_interm_freq_change',\n",
       "                'dc_offset_i': None,\n",
       "                'dc_offset_q': None,\n",
       "                'amp_ratio': 1.0,\n",
       "                'phase_error': 0.0\n",
       "            }\n",
       "        },\n",
       "        'modulation_frequencies': {\n",
       "            'q0:mw-q0.01': {'interm_freq': 80000000.0},\n",
       "            'q1:mw-q1.01': {'interm_freq': 80000000.0},\n",
       "            'q0:res-q0.ro': {'lo_freq': 7500000000.0},\n",
       "            'q1:res-q1.ro': {'lo_freq': 7500000000.0}\n",
       "        }\n",
       "    },\n",
       "    'connectivity': {\n",
       "        'graph': [\n",
       "            ['cluster0.module6.complex_output_0', 'q0:mw'],\n",
       "            ['cluster0.module6.complex_output_1', 'q1:mw'],\n",
       "            ['cluster0.module2.real_output_0', 'q0:fl'],\n",
       "            ['cluster0.module2.real_output_1', 'q1:fl'],\n",
       "            ['cluster0.module8.complex_output_0', 'q0:res'],\n",
       "            ['cluster0.module8.complex_output_0', 'q1:res']\n",
       "        ]\n",
       "    }\n",
       "}\n",
       "
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