{ "cells": [ { "cell_type": "markdown", "id": "894ae875", "metadata": {}, "source": [ "(sec-timetagging)=\n", "\n", "# Tutorial: Timetagging\n", "\n", "```{seealso}\n", "The complete source code of this tutorial can be found in\n", "\n", "{nb-download}`Timetagging.ipynb`\n", "```\n", "\n", "## Introduction\n", "\n", "```{admonition} Note\n", "The Timetag and TimetagTrace protocols are currently only implemented for the Qblox backend, and they are available on the **QTM**. Please also see {ref}`sec-qblox-acquisition-details` for more information on Qblox module-specific behavior of these operations.\n", "```\n", "\n", "The timetag acquisitions return the time at which the input voltage crossed a set threshold. This tutorial explores various ways to perform timetag acquisitions, using a Qblox cluster with a QCM and a QTM module. Note that only a subset of the features of these acquisitions are shown here, see the {ref}`reference guide ` for more info.\n", "\n", "## Initial setup\n", "\n", "First, we import the required classes and set up the data directory." ] }, { "cell_type": "code", "execution_count": 1, "id": "5a1e4dba", "metadata": { "mystnb": { "remove_code_outputs": true } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data will be saved in:\n", "/root/quantify-data\n" ] } ], "source": [ "from qblox_instruments import Cluster, ClusterType\n", "\n", "from quantify_core.data import handling as dh\n", "from quantify_scheduler import InstrumentCoordinator, QuantumDevice, Schedule, SerialCompiler\n", "from quantify_scheduler.enums import BinMode, TimeRef, TimeSource\n", "from quantify_scheduler.qblox import ClusterComponent\n", "from quantify_scheduler.operations import SquarePulse, Timetag, TimetagTrace, Trace\n", "\n", "dh.set_datadir(dh.default_datadir())" ] }, { "cell_type": "markdown", "id": "25b1fa03", "metadata": {}, "source": [ "Next, we write the {ref}`hardware configuration `: \n", "\n", "- In the `\"hardware_description\"` we define a QCM and a QTM module (when using physical hardware, we also connect port 1 of the QCM with port 1 of the QTM).\n", "\n", "- In the `\"connectivity\"` we assign the QCM port 1 (`\"cluster0.module{QCM_SLOT}.real_output_0\"`) to a mock device port `\"qcm:out\"`, and assign the QTM port 1 (`\"cluster0.module{QTM_SLOT}.digital_output_0\"`) to the mock device port `\"qtm:in\"`.\n", "\n", "- In the {ref}`sec-qblox-digitization-thresholds` of `\"hardware_options\"` we set the value of `in_threshold_primary` field, which is the value of the voltage threshold that needs to be crossed to register a timetag in QTM modules. Note the `\"qtm:in-digital\"` key that is used here; `digital` is the default clock assigned to digital channels." ] }, { "cell_type": "code", "execution_count": 2, "id": "eff96101", "metadata": { "mystnb": { "remove_code_outputs": true } }, "outputs": [], "source": [ "QCM_SLOT = 7\n", "QTM_SLOT = 10\n", "\n", "hw_cfg = {\n", " \"config_type\": \"quantify_scheduler.backends.qblox_backend.QbloxHardwareCompilationConfig\",\n", " \"hardware_description\": {\n", " \"cluster0\": {\n", " \"instrument_type\": \"Cluster\",\n", " \"ref\": \"internal\",\n", " \"modules\": {\n", " f\"{QCM_SLOT}\": {\"instrument_type\": \"QCM\"},\n", " f\"{QTM_SLOT}\": {\"instrument_type\": \"QTM\"},\n", " },\n", " },\n", " },\n", " \"hardware_options\": {\n", " \"digitization_thresholds\": {\"qtm:in-digital\": {\"in_threshold_primary\": 0.5}},\n", " },\n", " \"connectivity\": {\n", " \"graph\": [\n", " [f\"cluster0.module{QCM_SLOT}.real_output_0\", \"qcm:out\"],\n", " [f\"cluster0.module{QTM_SLOT}.digital_input_0\", \"qtm:in\"],\n", " ]\n", " },\n", "}" ] }, { "cell_type": "markdown", "id": "deed893b", "metadata": {}, "source": [ "We create an {class}`~quantify_scheduler.instrument_coordinator.instrument_coordinator.InstrumentCoordinator` instance with a cluster, where we also provide a `dummy_cfg` dummy configuration so that this notebook can run without physical hardware (when using a real cluster, provide `identifier` instead of `dummy_cfg`)." ] }, { "cell_type": "code", "execution_count": 3, "id": "86404a05", "metadata": { "mystnb": { "remove_code_outputs": true } }, "outputs": [], "source": [ "instrument_coordinator = InstrumentCoordinator(name=\"ic\")\n", "cluster = Cluster(\n", " name=\"cluster0\",\n", " # identifier=\"10.10.10.10\",\n", " dummy_cfg={\n", " QCM_SLOT: ClusterType.CLUSTER_QCM,\n", " QTM_SLOT: ClusterType.CLUSTER_QTM,\n", " },\n", ")\n", "\n", "cluster_component = ClusterComponent(cluster)\n", "instrument_coordinator.add_component(cluster_component)" ] }, { "cell_type": "markdown", "id": "b72dce0b", "metadata": {}, "source": [ "Finally, we define a {class}`~quantify_scheduler.device_under_test.quantum_device.QuantumDevice` (since we do not have a \"real\" quantum device, this object serves simply to generate the compilation configuration later on)." ] }, { "cell_type": "code", "execution_count": 4, "id": "11c8bd71", "metadata": { "mystnb": { "remove_code_outputs": true } }, "outputs": [], "source": [ "quantum_device = QuantumDevice(name=\"quantum_device\")\n", "quantum_device.hardware_config(hw_cfg)" ] }, { "cell_type": "markdown", "id": "1eb4351d", "metadata": {}, "source": [ "## Timetag acquisition\n", "\n", "In all examples in this tutorial, except the gate-level example, the QCM will send four pulses with increasing time between the pulses.\n", "\n", "In the first example, we'll record a single timetag using the {class}`~quantify_scheduler.operations.acquisition_library.Timetag` acquisition protocol. The event that triggers this timetag is defined by the {class}`time_source ` parameter, which we set to the first detected rising edge (`TimeSource.FIRST`). The timetag represents the time elapsed since the moment specified by the {class}`time_ref ` parameter, which we set to the start of the acquisition (`TimeRef.START`). We will simply do one repetition of the schedule.\n", "\n", "```{note}\n", "With the `bin_mode` parameter it is possible to change the behaviour of the acquisition for multiple repetitions. Please see the {ref}`reference guide ` for more information.\n", "```" ] }, { "cell_type": "code", "execution_count": 5, "id": "5c2b3006", "metadata": { "mystnb": { "remove_code_outputs": true } }, "outputs": [], "source": [ "sched = Schedule(\"Timetag\")\n", "\n", "sched.add(\n", " Timetag(\n", " duration=10e-6,\n", " port=\"qtm:in\",\n", " clock=\"digital\",\n", " time_source=TimeSource.FIRST,\n", " time_ref=TimeRef.START,\n", " )\n", ")\n", "\n", "square_pulse = SquarePulse(amp=1.0, duration=200e-9, port=\"qcm:out\")\n", "sched.add(square_pulse, rel_time=100e-9, ref_pt=\"start\")\n", "for rel_time in (1e-6, 2e-6, 3e-6):\n", " sched.add(square_pulse, rel_time=rel_time, ref_pt=\"start\")" ] }, { "cell_type": "markdown", "id": "897a7085", "metadata": {}, "source": [ "Let's compile the schedule." ] }, { "cell_type": "code", "execution_count": 6, "id": "ffad29e8", "metadata": { "mystnb": { "remove_code_outputs": true } }, "outputs": [], "source": [ "compiler = SerialCompiler(name=\"compiler\")\n", "compiled_schedule = compiler.compile(\n", " schedule=sched,\n", " config=quantum_device.generate_compilation_config(),\n", ")" ] }, { "cell_type": "markdown", "id": "c2da473e", "metadata": {}, "source": [ "And send it to the hardware, and execute it." ] }, { "cell_type": "code", "execution_count": null, "id": "621c1a46", "metadata": { "tags": [ "skip-execution" ] }, "outputs": [], "source": [ "instrument_coordinator.prepare(compiled_schedule)\n", "instrument_coordinator.start()\n", "instrument_coordinator.wait_done(timeout_sec=10)\n", "acquisition = instrument_coordinator.retrieve_acquisition()" ] }, { "cell_type": "markdown", "id": "37f1559c", "metadata": {}, "source": [ "The acquisition data shows one timetag, in this case around 73 ns. This value can differ depending on cable length." ] }, { "cell_type": "code", "execution_count": 7, "id": "31356a9c", "metadata": { "tags": [ "remove-cell" ] }, "outputs": [], "source": [ "import numpy as np\n", "import xarray as xr\n", "\n", "data_array = xr.DataArray(\n", " np.array([72.97265625]).reshape((1, 1)),\n", " dims=[\"repetition\", \"acq_index_0\"],\n", " coords={\"acq_index_0\": [0]},\n", " attrs={\"acq_protocol\": \"Timetag\"},\n", " )\n", "acquisition = xr.Dataset({0: data_array})" ] }, { "cell_type": "code", "execution_count": 8, "id": "a61850d9", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset> Size: 16B\n",
       "Dimensions:      (acq_index_0: 1)\n",
       "Coordinates:\n",
       "  * acq_index_0  (acq_index_0) int64 8B 0\n",
       "Data variables:\n",
       "    0            (acq_index_0) float64 8B 72.97
" ], "text/plain": [ " Size: 16B\n", "Dimensions: (acq_index_0: 1)\n", "Coordinates:\n", " * acq_index_0 (acq_index_0) int64 8B 0\n", "Data variables:\n", " 0 (acq_index_0) float64 8B 72.97" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "acquisition.isel(repetition=0)" ] }, { "cell_type": "markdown", "id": "f831c850", "metadata": {}, "source": [ "Next up, we will show you how to record multiple timetags in an acquisition window.\n", "\n", "## TimetagTrace acquisition\n", "\n", "The {class}`~quantify_scheduler.operations.acquisition_library.TimetagTrace` acquisition can record a stream of timetags. Every time that a rising edge is detected, a timetag is recorded. Therefore, this time we expect to see 4 timetags from the 4 pulses sent by the QCM.\n", "\n", "The timetag values are equal to the time difference between the recorded rising edges and the `time_ref` ({class}`~quantify_scheduler.enums.TimeRef`), which we set to the start of the acquisition (`TimeRef.START`)." ] }, { "cell_type": "code", "execution_count": 9, "id": "fe06c83f", "metadata": { "mystnb": { "remove_code_outputs": true } }, "outputs": [], "source": [ "sched = Schedule(\"TimetagTrace\")\n", "\n", "sched.add(\n", " TimetagTrace(duration=10e-6, port=\"qtm:in\", clock=\"digital\", time_ref=TimeRef.START)\n", ")\n", "\n", "square_pulse = SquarePulse(amp=1.0, duration=200e-9, port=\"qcm:out\")\n", "sched.add(square_pulse, rel_time=100e-9, ref_pt=\"start\")\n", "for rel_time in (1e-6, 2e-6, 3e-6):\n", " sched.add(square_pulse, rel_time=rel_time, ref_pt=\"start\")" ] }, { "cell_type": "markdown", "id": "66e507e4", "metadata": {}, "source": [ "We compile the schedule." ] }, { "cell_type": "code", "execution_count": 10, "id": "ec4cc595", "metadata": {}, "outputs": [], "source": [ "compiler = SerialCompiler(name=\"compiler\")\n", "compiled_schedule = compiler.compile(\n", " schedule=sched,\n", " config=quantum_device.generate_compilation_config(),\n", ")" ] }, { "cell_type": "markdown", "id": "6d68d5ce", "metadata": {}, "source": [ "And we execute it on the hardware." ] }, { "cell_type": "code", "execution_count": null, "id": "1625913a", "metadata": { "tags": [ "skip-execution" ] }, "outputs": [], "source": [ "instrument_coordinator.prepare(compiled_schedule)\n", "instrument_coordinator.start()\n", "instrument_coordinator.wait_done(timeout_sec=10)\n", "\n", "acquisition = instrument_coordinator.retrieve_acquisition()" ] }, { "cell_type": "markdown", "id": "3edfe9d8", "metadata": {}, "source": [ "As expected, we record 4 timetags. The first one is roughly around the same value as the single timetag recorded above, and the other ones are 1000, 3000, and 6000 ns later, respectively." ] }, { "cell_type": "code", "execution_count": 11, "id": "e270e3b3", "metadata": { "tags": [ "remove-cell" ] }, "outputs": [], "source": [ "import xarray as xr\n", "\n", "data_array = xr.DataArray(\n", " np.array([72.99365234, 1072.96435547, 3072.94970703, 6072.97314453]).reshape((1, 1, 4)),\n", " dims=[\"repetition\", \"acq_index_0\", \"trace_index_0\"],\n", " coords={\"acq_index_0\": [0], \"trace_index_0\": list(range(4))},\n", " attrs={\"acq_protocol\": \"Timetag\"},\n", " )\n", "acquisition = xr.Dataset({0: data_array})" ] }, { "cell_type": "code", "execution_count": 12, "id": "43ed2cbc", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset> Size: 72B\n",
       "Dimensions:        (trace_index_0: 4)\n",
       "Coordinates:\n",
       "    acq_index_0    int64 8B 0\n",
       "  * trace_index_0  (trace_index_0) int64 32B 0 1 2 3\n",
       "Data variables:\n",
       "    0              (trace_index_0) float64 32B 72.99 1.073e+03 ... 6.073e+03
" ], "text/plain": [ " Size: 72B\n", "Dimensions: (trace_index_0: 4)\n", "Coordinates:\n", " acq_index_0 int64 8B 0\n", " * trace_index_0 (trace_index_0) int64 32B 0 1 2 3\n", "Data variables:\n", " 0 (trace_index_0) float64 32B 72.99 1.073e+03 ... 6.073e+03" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "acquisition.isel(acq_index_0=0, repetition=0)" ] }, { "cell_type": "markdown", "id": "1a504cb8", "metadata": {}, "source": [ "## Trace acquisition\n", "\n", "Finally, we can measure a trace of the input signal. The QTM digitizes this signal and will return `0` whenever the voltage is below the digitization threshold, and `1` when the voltage is above. The trace has a 1 ns time resolution.\n", "\n", "We use the same schedule again, but with the {class}`~quantify_scheduler.operations.acquisition_library.Trace` protocol. Note the `bin_mode`, which must be set to `BinMode.FIRST` when using `Trace` acquisition on a QTM module." ] }, { "cell_type": "code", "execution_count": 13, "id": "53528ec8", "metadata": { "mystnb": { "remove_code_outputs": true } }, "outputs": [], "source": [ "sched = Schedule(\"Trace\")\n", "\n", "sched.add(Trace(duration=10e-6, port=\"qtm:in\", clock=\"digital\", bin_mode=BinMode.FIRST))\n", "\n", "square_pulse = SquarePulse(amp=1.0, duration=200e-9, port=\"qcm:out\")\n", "sched.add(square_pulse, rel_time=100e-9, ref_pt=\"start\")\n", "for rel_time in (1e-6, 2e-6, 3e-6):\n", " sched.add(square_pulse, rel_time=rel_time, ref_pt=\"start\")" ] }, { "cell_type": "markdown", "id": "89e9a751", "metadata": {}, "source": [ "We compile the schedule." ] }, { "cell_type": "code", "execution_count": 14, "id": "5c68b530", "metadata": {}, "outputs": [], "source": [ "compiler = SerialCompiler(name=\"compiler\")\n", "compiled_schedule = compiler.compile(\n", " schedule=sched,\n", " config=quantum_device.generate_compilation_config(),\n", ")" ] }, { "cell_type": "markdown", "id": "487e823e", "metadata": {}, "source": [ "And execute it on the hardware again." ] }, { "cell_type": "code", "execution_count": null, "id": "3c543bfb", "metadata": { "tags": [ "skip-execution" ] }, "outputs": [], "source": [ "instrument_coordinator.prepare(compiled_schedule)\n", "instrument_coordinator.start()\n", "instrument_coordinator.wait_done(timeout_sec=10)\n", "\n", "acquisition = instrument_coordinator.retrieve_acquisition()" ] }, { "cell_type": "markdown", "id": "6cb2abcc", "metadata": {}, "source": [ "The result is a trace of the pulses sent by the QCM, digitized by the QTM." ] }, { "cell_type": "code", "execution_count": 15, "id": "f06bf687", "metadata": { "tags": [ "remove-cell" ] }, "outputs": [], "source": [ "import xarray as xr\n", "\n", "dummy_data = np.zeros((10000,))\n", "\n", "dummy_data[140:340] = 1\n", "dummy_data[1140:1340] = 1\n", "dummy_data[3140:3340] = 1\n", "dummy_data[6140:6340] = 1\n", "\n", "data_array = xr.DataArray(\n", " np.array(dummy_data, dtype=int).reshape(1, -1),\n", " dims=[\"acq_index_0\", \"trace_index_0\"],\n", " coords={\"acq_index_0\": [0], \"trace_index_0\": list(range(10000))},\n", " attrs={\"acq_protocol\": \"Timetag\"},\n", " )\n", "acquisition = xr.Dataset({0: data_array})" ] }, { "cell_type": "code", "execution_count": 16, "id": "f1ca7466", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "acquisition.to_dataarray().plot()" ] }, { "cell_type": "markdown", "id": "4400f375", "metadata": {}, "source": [ "## Gate-level: NV center example\n", "\n", "To finish the tutorial, we show how to use the timetag acquisition protocol with an NV center defined in the quantum device.\n", "\n", "To keep the tutorial focused, we only include the readout laser in the hardware configuration (and leave re-ionization and spin-pump lasers out). Again, we use port 1 of the QCM, this time connected to an optical modulator, and port 1 of the QTM, connected to a single-photon detector:" ] }, { "cell_type": "code", "execution_count": 17, "id": "41d1dca5", "metadata": { "mystnb": { "remove_code_outputs": true } }, "outputs": [], "source": [ "QCM_SLOT = 7\n", "QTM_SLOT = 10\n", "\n", "hw_cfg = {\n", " \"config_type\": \"quantify_scheduler.backends.qblox_backend.QbloxHardwareCompilationConfig\",\n", " \"hardware_description\": {\n", " \"cluster0\": {\n", " \"instrument_type\": \"Cluster\",\n", " \"ref\": \"internal\",\n", " \"modules\": {\n", " f\"{QCM_SLOT}\": {\"instrument_type\": \"QCM\"},\n", " f\"{QTM_SLOT}\": {\"instrument_type\": \"QTM\"},\n", " },\n", " },\n", " \"red_laser\": {\"instrument_type\": \"LocalOscillator\", \"power\": 1},\n", " \"optical_mod_red_laser\": {\"instrument_type\": \"OpticalModulator\"},\n", " },\n", " \"hardware_options\": {\n", " \"modulation_frequencies\": {\n", " \"qe0:optical_control-qe0.ge0\": {\"interm_freq\": 0.0, \"lo_freq\": None},\n", " \"qe0:optical_readout-qe0.ge0\": {\"interm_freq\": 0.0, \"lo_freq\": None},\n", " },\n", " \"digitization_thresholds\": {\n", " \"qe0:optical_readout-qe0.ge0\": {\"in_threshold_primary\": 0.5}\n", " },\n", " },\n", " \"connectivity\": {\n", " \"graph\": [\n", " [f\"cluster0.module{QCM_SLOT}.real_output_0\", \"optical_mod_red_laser.if\"],\n", " [\"red_laser.output\", \"optical_mod_red_laser.lo\"],\n", " [\"optical_mod_red_laser.out\", \"qe0:optical_control\"],\n", " [f\"cluster0.module{QTM_SLOT}.digital_input_0\", \"qe0:optical_readout\"],\n", " ]\n", " },\n", "}" ] }, { "cell_type": "markdown", "id": "61eec398", "metadata": {}, "source": [ "We now add a mock laser to our instrument coordinator so that the example compiles." ] }, { "cell_type": "code", "execution_count": 18, "id": "51b850de", "metadata": { "mystnb": { "remove_code_outputs": true } }, "outputs": [], "source": [ "from quantify_scheduler import GenericInstrumentCoordinatorComponent, MockLocalOscillator\n", "\n", "red_laser = MockLocalOscillator(name=\"red_laser\")\n", "instrument_coordinator.add_component(GenericInstrumentCoordinatorComponent(red_laser))" ] }, { "cell_type": "markdown", "id": "03a14dce", "metadata": {}, "source": [ "Then we add a {class}`~quantify_scheduler.device_under_test.nv_element.BasicElectronicNVElement` (our NV center) to the quantum device. Note that measurement parameters such as `time_source` and `time_ref` are now defined on the quantum device element." ] }, { "cell_type": "code", "execution_count": 19, "id": "d9b5871d", "metadata": { "mystnb": { "remove_code_outputs": true } }, "outputs": [], "source": [ "from quantify_scheduler import BasicElectronicNVElement\n", "\n", "quantum_device.hardware_config(hw_cfg)\n", "\n", "qe0 = BasicElectronicNVElement(\"qe0\")\n", "qe0.measure.pulse_amplitude(1.0)\n", "qe0.measure.time_source(TimeSource.FIRST)\n", "qe0.measure.time_ref(TimeRef.START)\n", "qe0.clock_freqs.ge0.set(470.4e12)\n", "quantum_device.add_element(qe0)" ] }, { "cell_type": "markdown", "id": "8bc9cee6", "metadata": {}, "source": [ "The schedule consists simply of a `Measure` operation, which includes a readout pulse (sent from the QCM to the optical modulator) and a timetag acquisition." ] }, { "cell_type": "code", "execution_count": 20, "id": "ab57242a", "metadata": { "mystnb": { "remove_code_outputs": true } }, "outputs": [ { "data": { "text/plain": [ "{'name': '404974e3-b32f-4077-812c-03c4de418385', 'operation_id': '6135460136591039693', 'timing_constraints': [{'rel_time': 0, 'ref_schedulable': None, 'ref_pt_new': None, 'ref_pt': None}], 'label': '404974e3-b32f-4077-812c-03c4de418385'}" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from quantify_scheduler.operations import Measure\n", "\n", "sched = Schedule(\"NV Timetag\")\n", "\n", "sched.add(Measure(\"qe0\", acq_protocol=\"Timetag\", bin_mode=BinMode.APPEND))" ] }, { "cell_type": "markdown", "id": "4ebbfb4e", "metadata": {}, "source": [ "Finally, we compile the schedule and show the pulse and the acquisition operation in a pulse diagram." ] }, { "cell_type": "code", "execution_count": 21, "id": "09696ffa", "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "fill": "tozeroy", "hoverinfo": "x+y+name", "hoverlabel": { "namelength": -1 }, "legendgroup": "0", "line": { "color": "#636EFA" }, "mode": "lines", "name": "VoltageOffset, clock qe0.ge0", "showlegend": true, "type": "scatter", "x": [ 0.0, 1.9995990000000002e-05, 1.9996000000000003e-05, 5e-05 ], "xaxis": "x", "y": [ 1.0, 1.0, 0.0, 0.0 ], "yaxis": "y" }, { "fill": "tozeroy", "hoverinfo": "x+y+name", "hoverlabel": { "namelength": -1 }, "legendgroup": "1", "line": { "color": "#EF553B" }, "mode": "lines", "name": "Measure qe0, clock qe0.ge0", "showlegend": true, "type": "scatter", "x": [ 1.9995990000000002e-05, 1.9996000000000003e-05, 1.9997000000000002e-05, 1.9998e-05, 1.9999e-05, 1.999999e-05, 2e-05 ], "xaxis": "x", "y": [ 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0 ], "yaxis": "y" }, { "marker": { "color": "rgba(0,0,0,.25)", "size": 15, "symbol": [ "arrow-bar-left", "arrow-bar-right" ] }, "mode": "markers", "name": "Measure qe0", "type": "scatter", "x": [ 0, 5e-05 ], "xaxis": "x2", "y": [ 0, 0 ], "yaxis": "y2" } ], "layout": { "height": 600, "shapes": [ { "fillcolor": "rgba(255,0,0,0.1)", "layer": "below", "line": { "color": "rgba(0,0,0,0)", "width": 3 }, "name": "Measure qe0", "type": "rect", "x0": 0, "x1": 5e-05, "xref": "x", "y0": 0, "y1": 1, "yref": "y2 domain" } ], "showlegend": false, "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "baxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "type": "carpet" } ], "choropleth": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "choropleth" } ], "contour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0.0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1.0, "#f0f921" ] ], "type": "contour" } ], "contourcarpet": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "contourcarpet" } ], "heatmap": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0.0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1.0, "#f0f921" ] ], "type": "heatmap" } ], "heatmapgl": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0.0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1.0, "#f0f921" ] ], "type": "heatmapgl" } ], "histogram": [ { "marker": { "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "histogram" } ], "histogram2d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0.0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1.0, "#f0f921" ] ], "type": "histogram2d" } ], "histogram2dcontour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0.0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1.0, "#f0f921" ] ], "type": "histogram2dcontour" } ], "mesh3d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "mesh3d" } ], "parcoords": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "parcoords" } ], "pie": [ { "automargin": true, "type": "pie" } ], "scatter": [ { "fillpattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 }, "type": "scatter" } ], "scatter3d": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter3d" } ], "scattercarpet": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattercarpet" } ], "scattergeo": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergeo" } ], "scattergl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergl" } ], "scattermapbox": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattermapbox" } ], "scatterpolar": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolar" } ], "scatterpolargl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolargl" } ], "scatterternary": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterternary" } ], "surface": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0.0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1.0, "#f0f921" ] ], "type": "surface" } ], "table": [ { "cells": { "fill": { "color": "#EBF0F8" }, "line": { "color": "white" } }, "header": { "fill": { "color": "#C8D4E3" }, "line": { "color": "white" } }, "type": "table" } ] }, "layout": { "annotationdefaults": { "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, "autotypenumbers": "strict", "coloraxis": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "colorscale": { "diverging": [ [ 0, "#8e0152" ], [ 0.1, "#c51b7d" ], [ 0.2, "#de77ae" ], [ 0.3, "#f1b6da" ], [ 0.4, "#fde0ef" ], [ 0.5, "#f7f7f7" ], [ 0.6, "#e6f5d0" ], [ 0.7, "#b8e186" ], [ 0.8, "#7fbc41" ], [ 0.9, "#4d9221" ], [ 1, "#276419" ] ], "sequential": [ [ 0.0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1.0, "#f0f921" ] ], "sequentialminus": [ [ 0.0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1.0, "#f0f921" ] ] }, "colorway": [ "#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52" ], "font": { "color": "#2a3f5f" }, "geo": { "bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "NV Timetag" }, "width": 1000, "xaxis": { "anchor": "y", "domain": [ 0.0, 1.0 ], "hoverformat": ".3s", "matches": "x2", "showgrid": true, "showticklabels": false, "tickformat": ".2s", "ticksuffix": "s" }, "xaxis2": { "anchor": "y2", "domain": [ 0.0, 1.0 ], "hoverformat": ".3s", "showgrid": true, "tickformat": ".2s", "tickformatstops": [ { "dtickrange": [ null, 1e-09 ], "value": ".10s" }, { "dtickrange": [ 1e-09, 1e-06 ], "value": ".7s" }, { "dtickrange": [ 1e-06, 0.001 ], "value": ".4s" } ], "ticksuffix": "s", "title": { "text": "Time" } }, "yaxis": { "anchor": "x", "autorange": true, "domain": [ 0.51, 1.0 ], "hoverformat": ".3s", "tickformat": ".2s", "ticksuffix": "V", "title": { "text": "qe0:optical_control" } }, "yaxis2": { "anchor": "x2", "autorange": true, "domain": [ 0.0, 0.49 ], "hoverformat": ".3s", "tickformat": ".2s", "ticksuffix": "V", "title": { "text": "qe0:optical_readout" } } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "compiler = SerialCompiler(name=\"compiler\")\n", "compiled_schedule = compiler.compile(\n", " schedule=sched,\n", " config=quantum_device.generate_compilation_config(),\n", ")\n", "\n", "compiled_schedule.plot_pulse_diagram(plot_backend=\"plotly\")" ] } ], "metadata": { "file_format": "mystnb", "kernelspec": { "display_name": "python3", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.20" }, "source_map": [ 6, 30, 44, 54, 84, 88, 105, 110, 117, 130, 151, 155, 165, 169, 175, 179, 193, 195, 205, 220, 224, 230, 234, 241, 245, 258, 260, 268, 281, 285, 291, 295, 302, 306, 326, 328, 336, 376, 380, 389, 393, 408, 412, 422, 426 ] }, "nbformat": 4, "nbformat_minor": 5 }