See also

A Jupyter notebook version of this tutorial can be downloaded here.

Time of flight measurement#

The notebook will show how to measure time of flight for your system.

[1]:

from __future__ import annotations

import json

import numpy as np
import rich  # noqa:F401
from qcodes.instrument import find_or_create_instrument
from qcodes.parameters import ManualParameter

import quantify_core.data.handling as dh
from qblox_instruments import Cluster, ClusterType
from quantify_core.analysis.time_of_flight_analysis import TimeOfFlightAnalysis
from quantify_core.measurement.control import MeasurementControl
from quantify_core.visualization.pyqt_plotmon import PlotMonitor_pyqt as PlotMonitor
from quantify_scheduler import Schedule
from quantify_scheduler.backends.qblox import constants
from quantify_scheduler.device_under_test.quantum_device import QuantumDevice
from quantify_scheduler.gettables import ScheduleGettable
from quantify_scheduler.instrument_coordinator import InstrumentCoordinator
from quantify_scheduler.instrument_coordinator.components.qblox import (
    ClusterComponent,
)
from quantify_scheduler.math import closest_number_ceil
from quantify_scheduler.operations.gate_library import Measure

Setup#

In this section we configure the hardware configuration which specifies the connectivity of our system.

The experiments of this tutorial are meant to be executed with a Qblox Cluster controlling a transmon system. 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. When using a dummy device, the analysis will not work because the experiments will return np.nan values.

Configuration file#

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.

The hardware setup is as follows, by cluster slot: - QCM (Slot 2) - Flux line for q0. - QCM-RF (Slot 6) - Drive line for q0 using fixed 80 MHz IF. - QRM-RF (Slot 8) - Readout line for q0 using a fixed LO set at 7.5 GHz.

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.

[2]:

with open("configs/tuning_transmon_coupled_pair_hardware_config.json") as hw_cfg_json_file:
    hardware_cfg = json.load(hw_cfg_json_file)

# Enter your own dataset directory here!
dh.set_datadir(dh.default_datadir())

Data will be saved in:
/root/quantify-data

Scan For Clusters#

We scan for the available devices connected via ethernet using the Plug & Play functionality of the Qblox Instruments package (see Plug & Play for more info).

[3]:

!qblox-pnp list

No devices found

[4]:

cluster_ip = None  # To run this tutorial on hardware, fill in the IP address of the cluster here
cluster_name = "cluster0"

Connect to Cluster#

We now make a connection with the Cluster.

[5]:

cluster = find_or_create_instrument(
    Cluster,
    recreate=True,
    name=cluster_name,
    identifier=cluster_ip,
    dummy_cfg=(
        {
            2: ClusterType.CLUSTER_QCM,
            4: ClusterType.CLUSTER_QRM,
            6: ClusterType.CLUSTER_QCM_RF,
            8: ClusterType.CLUSTER_QRM_RF,
        }
        if cluster_ip is None
        else None
    ),
)

Quantum device settings#

Here we initialize our QuantumDevice and our qubit parameters, checkout this tutorial for further details.

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.

[6]:

quantum_device = QuantumDevice.from_json_file("devices/transmon_device_2q.json")
qubit = quantum_device.get_element("q0")
quantum_device.hardware_config(hardware_cfg)

Configure measurement control loop#

We will use a MeasurementControl object for data acquisition as well as an InstrumentCoordinator for controlling the instruments in our setup.

The PlotMonitor is used for live plotting.

All of these are then associated with the QuantumDevice.

[7]:

def configure_measurement_control_loop(
    device: QuantumDevice, cluster: Cluster, live_plotting: bool = False
) -> tuple[MeasurementControl, InstrumentCoordinator]:
    meas_ctrl = find_or_create_instrument(MeasurementControl, recreate=True, name="meas_ctrl")
    ic = find_or_create_instrument(InstrumentCoordinator, recreate=True, name="ic")

    # Add cluster to instrument coordinator
    ic_cluster = ClusterComponent(cluster)
    ic.add_component(ic_cluster)

    if live_plotting:
        # Associate plot monitor with measurement controller
        plotmon = find_or_create_instrument(PlotMonitor, recreate=False, name="PlotMonitor")
        meas_ctrl.instr_plotmon(plotmon.name)

    # Associate measurement controller and instrument coordinator with the quantum device
    device.instr_measurement_control(meas_ctrl.name)
    device.instr_instrument_coordinator(ic.name)

    return (meas_ctrl, ic)


meas_ctrl, instrument_coordinator = configure_measurement_control_loop(quantum_device, cluster)

Schedule definition#

[8]:

def tof_trace_schedule(
    qubit_name: str,
    repetitions: int = 1,
) -> Schedule:
    schedule = Schedule("Trace measurement schedule", repetitions=repetitions)
    schedule.add(Measure(qubit_name, acq_protocol="Trace"))
    return schedule

Measuring time of flight with trace acquisition#

[9]:

def set_readout_attenuation_hardware_config(attenuation_dB: int):
    hwcfg = quantum_device.hardware_config()
    output_att = hwcfg["hardware_options"]["output_att"]
    output_att[f"{qubit.ports.readout()}-{qubit.name}.ro"] = attenuation_dB
    quantum_device.hardware_config(hwcfg)


set_readout_attenuation_hardware_config(0)
qubit.measure.pulse_duration(300e-9)
qubit.measure.integration_time(1e-6)
qubit.measure.pulse_amp(0.1)
qubit.measure.acq_delay(4e-9)

[10]:

tof_t = ManualParameter(name="tof_t", unit="ns", label="Trace acquisition sample")
tof_t.batched = True
tof_t.batch_size = round(qubit.measure.integration_time() * constants.SAMPLING_RATE)

tof_sched_kwargs = dict(
    qubit_name=qubit.name,
)

# set gettable
gettable = ScheduleGettable(
    quantum_device,
    schedule_function=tof_trace_schedule,
    schedule_kwargs=tof_sched_kwargs,
    real_imag=False,
    batched=True,
)

# set measurement control
meas_ctrl.gettables(gettable)

[11]:

tof_t_setpoints = np.arange(tof_t.batch_size)

meas_ctrl.settables(tof_t)
meas_ctrl.setpoints(tof_t_setpoints)

# replace the get method for the gettable in case the cluster is a dummy
if "dummy" in str(cluster._transport):

    def get_fake_tof_data():
        """Generate mock data for a time of flight measurement."""
        y = (
            np.heaviside(tof_t_setpoints - 200, 0.5)
            - np.heaviside(tof_t_setpoints - tof_t_setpoints.size * 0.7, 0.5)
        ) * 30e-3
        y += np.random.normal(loc=0.0, scale=1e-3, size=y.size)
        return [y, np.zeros_like(y)]

    gettable.get = get_fake_tof_data

tof_ds = dh.to_gridded_dataset(meas_ctrl.run("Time of flight measurement " + qubit.name))
tof_ds

Starting batched measurement...
Iterative settable(s) [outer loop(s)]:
         --- (None) ---
Batched settable(s):
         tof_t
Batch size limit: 1000


[11]:

<xarray.Dataset> Size: 24kB
Dimensions:  (x0: 1000)
Coordinates:
  * x0       (x0) int64 8kB 0 1 2 3 4 5 6 7 ... 992 993 994 995 996 997 998 999
Data variables:
    y0       (x0) float64 8kB 0.0004166 -0.002275 ... -0.0004985 -0.0005552
    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
Attributes:
    tuid:                             20240902-014549-194-f0e128
    name:                             Time of flight measurement q0
    grid_2d:                          False
    grid_2d_uniformly_spaced:         False
    1d_2_settables_uniformly_spaced:  False

Analysis#

[12]:

tof_analysis = TimeOfFlightAnalysis(tuid=dh.get_latest_tuid())
tof_analysis.run(playback_delay=149e-9).display_figs_mpl()
../../../_images/applications_quantify_transmon_time_of_flight_20_0.png 
[13]:

fit_results = tof_analysis.quantities_of_interest
nco_prop_delay = fit_results["nco_prop_delay"]
measured_tof = fit_results["tof"]

qubit.measure.acq_delay(
    closest_number_ceil(
        measured_tof * constants.SAMPLING_RATE, constants.MIN_TIME_BETWEEN_OPERATIONS
    )
    / constants.SAMPLING_RATE
)

[14]:



rich.print(quantum_device.hardware_config())

{
    'config_type': 'quantify_scheduler.backends.qblox_backend.QbloxHardwareCompilationConfig',
    'hardware_description': {
        'cluster0': {
            'instrument_type': 'Cluster',
            'modules': {
                '6': {'instrument_type': 'QCM_RF'},
                '2': {'instrument_type': 'QCM'},
                '8': {'instrument_type': 'QRM_RF'}
            },
            'sequence_to_file': False,
            'ref': 'internal'
        }
    },
    'hardware_options': {
        'output_att': {'q0:mw-q0.01': 10, 'q1:mw-q1.01': 10, 'q0:res-q0.ro': 0, 'q1:res-q1.ro': 60},
        'mixer_corrections': {
            'q0:mw-q0.01': {
                'auto_lo_cal': 'on_lo_interm_freq_change',
                'auto_sideband_cal': 'on_interm_freq_change',
                'dc_offset_i': 0.0,
                'dc_offset_q': 0.0,
                'amp_ratio': 1.0,
                'phase_error': 0.0
            },
            'q1:mw-q1.01': {
                'auto_lo_cal': 'on_lo_interm_freq_change',
                'auto_sideband_cal': 'on_interm_freq_change',
                'dc_offset_i': 0.0,
                'dc_offset_q': 0.0,
                'amp_ratio': 1.0,
                'phase_error': 0.0
            },
            'q0:res-q0.ro': {
                'auto_lo_cal': 'on_lo_interm_freq_change',
                'auto_sideband_cal': 'on_interm_freq_change',
                'dc_offset_i': 0.0,
                'dc_offset_q': 0.0,
                'amp_ratio': 1.0,
                'phase_error': 0.0
            },
            'q1:res-q1.ro': {
                'auto_lo_cal': 'on_lo_interm_freq_change',
                'auto_sideband_cal': 'on_interm_freq_change',
                'dc_offset_i': 0.0,
                'dc_offset_q': 0.0,
                'amp_ratio': 1.0,
                'phase_error': 0.0
            }
        },
        'modulation_frequencies': {
            'q0:mw-q0.01': {'interm_freq': 80000000.0},
            'q1:mw-q1.01': {'interm_freq': 80000000.0},
            'q0:res-q0.ro': {'lo_freq': 7500000000.0},
            'q1:res-q1.ro': {'lo_freq': 7500000000.0}
        }
    },
    'connectivity': {
        'graph': [
            ['cluster0.module6.complex_output_0', 'q0:mw'],
            ['cluster0.module6.complex_output_1', 'q1:mw'],
            ['cluster0.module2.real_output_0', 'q0:fl'],
            ['cluster0.module2.real_output_1', 'q1:fl'],
            ['cluster0.module8.complex_output_0', 'q0:res'],
            ['cluster0.module8.complex_output_0', 'q1:res']
        ]
    }
}

[15]:

quantum_device.to_json_file("devices/")

[15]:

'devices/device_2q_2024-09-02_01-45-50_UTC.json'
On this page
Show Source