See also

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

Rabi Oscillations

Here we will carry out an experiment to measure the Rabi frequency that is required to excite the qubit to \(|1\rangle\).

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.measurement.control import MeasurementControl
from quantify_core.visualization.pyqt_plotmon import PlotMonitor_pyqt as PlotMonitor
from quantify_scheduler import Schedule
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.operations.gate_library import Measure, Reset, X

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.

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).

!qblox-pnp list

No devices found

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.

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.

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.

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)

Rabi Oscillations

def rabi_amp_sched(
    qubit,  # noqa: ANN001
    pulse_amps: np.array,
    repetitions: int = 1,
) -> Schedule:
    """
    Generate a batched schedule rabi amplitude experiement.

    Parameters
    ----------
    qubit
        qubit that should be used.
    pulse_amps
        Sample amplitude for the burst pulse.
    repetitions
        The amount of times the Schedule will be repeated.

    """
    sched = Schedule("rabi_amplitude", repetitions)

    for acq_idx, pulse_amp in enumerate(pulse_amps):
        sched.add(Reset(qubit.name))
        sched.add(X(qubit.name, amp180=pulse_amp))
        sched.add(
            Measure(qubit.name, acq_index=acq_idx), rel_time=200e-9
        )  # wait 200ns before measuring
    return sched


pulse_amps = ManualParameter(name="pulse_amplitude", unit="V", label="amplitude")
pulse_amps.batched = True

rabi_sched_kwargs = {
    "qubit": qubit,  # noqa: ANN001
    "pulse_amps": pulse_amps,
}

gettable = ScheduleGettable(
    quantum_device,
    schedule_function=rabi_amp_sched,
    schedule_kwargs=rabi_sched_kwargs,
    batched=True,
)

meas_ctrl.gettables(gettable)

quantum_device.cfg_sched_repetitions(400)

amplitude_setpoints = np.linspace(-0.14, 0.14, 200)

meas_ctrl.settables(pulse_amps)
meas_ctrl.setpoints(amplitude_setpoints)

rabi_ds = meas_ctrl.run("Rabi")
rabi_ds

Starting batched measurement...
Iterative settable(s) [outer loop(s)]:
         --- (None) ---
Batched settable(s):
         pulse_amplitude
Batch size limit: 200

/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.
  warnings.warn(
/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.
  warnings.warn(

<xarray.Dataset> Size: 5kB
Dimensions:  (dim_0: 200)
Coordinates:
    x0       (dim_0) float64 2kB -0.14 -0.1386 -0.1372 ... 0.1372 0.1386 0.14
Dimensions without coordinates: dim_0
Data variables:
    y0       (dim_0) float64 2kB nan nan nan nan nan nan ... nan nan nan nan nan
    y1       (dim_0) float64 2kB nan nan nan nan nan nan ... nan nan nan nan nan
Attributes:
    tuid:                             20240902-014509-285-7eb163
    name:                             Rabi
    grid_2d:                          False
    grid_2d_uniformly_spaced:         False
    1d_2_settables_uniformly_spaced:  False

xarray.Dataset

• Dimensions:
  • dim_0: 200
• Coordinates: (1)
  • x0
    (dim_0)
    float64
    -0.14 -0.1386 ... 0.1386 0.14

    name :

    pulse_amplitude

    long_name :

    amplitude

    units :

    V

    batched :

    True

    array([-0.14      , -0.13859296, -0.13718593, -0.13577889, -0.13437186,
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           -0.02743719, -0.02603015, -0.02462312, -0.02321608, -0.02180905,
           -0.02040201, -0.01899497, -0.01758794, -0.0161809 , -0.01477387,
           -0.01336683, -0.0119598 , -0.01055276, -0.00914573, -0.00773869,
           -0.00633166, -0.00492462, -0.00351759, -0.00211055, -0.00070352,
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            0.00773869,  0.00914573,  0.01055276,  0.0119598 ,  0.01336683,
            0.01477387,  0.0161809 ,  0.01758794,  0.01899497,  0.02040201,
            0.02180905,  0.02321608,  0.02462312,  0.02603015,  0.02743719,
            0.02884422,  0.03025126,  0.03165829,  0.03306533,  0.03447236,
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            0.04994975,  0.05135678,  0.05276382,  0.05417085,  0.05557789,
            0.05698492,  0.05839196,  0.05979899,  0.06120603,  0.06261307,
            0.0640201 ,  0.06542714,  0.06683417,  0.06824121,  0.06964824,
            0.07105528,  0.07246231,  0.07386935,  0.07527638,  0.07668342,
            0.07809045,  0.07949749,  0.08090452,  0.08231156,  0.08371859,
            0.08512563,  0.08653266,  0.0879397 ,  0.08934673,  0.09075377,
            0.0921608 ,  0.09356784,  0.09497487,  0.09638191,  0.09778894,
            0.09919598,  0.10060302,  0.10201005,  0.10341709,  0.10482412,
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            0.11326633,  0.11467337,  0.1160804 ,  0.11748744,  0.11889447,
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            0.13437186,  0.13577889,  0.13718593,  0.13859296,  0.14      ])

• Data variables: (2)
  • y0
    (dim_0)
    float64
    nan nan nan nan ... nan nan nan nan

    name :

    I

    long_name :

    Voltage I0

    units :

    V

    batched :

    True

    batch_size :

    1024

    array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
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  • y1
    (dim_0)
    float64
    nan nan nan nan ... nan nan nan nan

    name :

    Q

    long_name :

    Voltage Q0

    units :

    V

    batched :

    True

    batch_size :

    1024

    array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
           nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
           nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
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• Indexes: (0)
• Attributes: (5)

  tuid :

  20240902-014509-285-7eb163

  name :

  Rabi

  grid_2d :

  False

  grid_2d_uniformly_spaced :

  False

  1d_2_settables_uniformly_spaced :

  False

qubit.rxy.amp180(0.07)

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': 60, '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': None,
                'dc_offset_q': None,
                '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': None,
                'dc_offset_q': None,
                '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': None,
                'dc_offset_q': None,
                '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': None,
                'dc_offset_q': None,
                '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']
        ]
    }
}

quantum_device.to_json_file("devices/")

'devices/device_2q_2024-09-02_01-45-09_UTC.json'