Single transmon qubit characterization#

Note

This notebook uses some python helper functions and example data. You can find both in a zipfile accessible with this link: gitlab.

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.

However, when using a dummy device, the analysis will not work because the experiments will return np.nan values.

When using this notebook together with a dummy device, example data is loaded from the "./example_data/" directory.

Setup#

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

Configuration file#

This is a template hardware configuration file for a 1-qubit system with a flux-control line which can be used to tune the qubit frequency.

The hardware setup is as follows, by cluster slot: 1. QCM-RF - Drive line for qubit using fixed 80 MHz IF. 2. QCM - Flux line for qubit. 6. QRM-RF - Readout line for qubit 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.

[1]:

hardware_cfg = {
    "backend": "quantify_scheduler.backends.qblox_backend.hardware_compile",
    "cluster0": {
        "sequence_to_file": False,  # Boolean flag which dumps waveforms and program dict to JSON file
        "ref": "internal",  # Use shared clock reference of the cluster
        "instrument_type": "Cluster",
        # ============ DRIVE ============#
        "cluster0_module1": {
            "instrument_type": "QCM_RF",
            "complex_output_0": {
                "output_att": 0,
                "dc_mixer_offset_I": 0.0,
                "dc_mixer_offset_Q": 0.0,
                "portclock_configs": [
                    {
                        "port": "qubit:mw",
                        "clock": "qubit.01",
                        "interm_freq": 80e6,
                        "mixer_amp_ratio": 1.0,
                        "mixer_phase_error_deg": 0.0,
                    }
                ],
            },
        },
        # ============ FLUX ============#
        "cluster0_module2": {
            "instrument_type": "QCM",
            "real_output_0": {"portclock_configs": [{"port": "qubit:fl", "clock": "cl0.baseband"}]},
        },
        # ============ READOUT ============#
        "cluster0_module3": {
            "instrument_type": "QRM_RF",
            "complex_output_0": {
                "output_att": 0,
                "input_att": 0,
                "dc_mixer_offset_I": 0.0,
                "dc_mixer_offset_Q": 0.0,
                "lo_freq": 7.5e9,
                "portclock_configs": [
                    {
                        "port": "qubit:res",
                        "clock": "qubit.ro",
                        "mixer_amp_ratio": 1.0,
                        "mixer_phase_error_deg": 0.0,
                    }
                ],
            },
        },
    },
}

[2]:

import contextlib
import warnings
from pathlib import Path

import ipywidgets as widgets
import numpy as np
from IPython.display import display
from qcodes import Instrument
from qcodes.parameters import ManualParameter
from utils.tutorial_analysis_classes import (
    QubitFluxSpectroscopyAnalysis,
    ResonatorFluxSpectroscopyAnalysis,
)
from utils.tutorial_utils import (
    set_drive_attenuation,
    set_readout_attenuation,
    show_args,
    show_drive_args,
    show_readout_args,
)

import quantify_core.data.handling as dh
from qblox_instruments import Cluster, ClusterType, PlugAndPlay
from quantify_core.analysis.single_qubit_timedomain import (
    RabiAnalysis,
    RamseyAnalysis,
    T1Analysis,
)
from quantify_core.analysis.spectroscopy_analysis import QubitSpectroscopyAnalysis
from quantify_core.measurement.control import MeasurementControl
from quantify_core.visualization.pyqt_plotmon import PlotMonitor_pyqt as PlotMonitor
from quantify_scheduler.device_under_test.quantum_device import QuantumDevice
from quantify_scheduler.device_under_test.transmon_element import BasicTransmonElement
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
from quantify_scheduler.operations.pulse_library import SetClockFrequency, SquarePulse
from quantify_scheduler.resources import ClockResource
from quantify_scheduler.schedules import heterodyne_spec_sched_nco, rabi_sched, t1_sched
from quantify_scheduler.schedules.schedule import Schedule
from quantify_scheduler.schedules.timedomain_schedules import ramsey_sched

Scan For Clusters#

We scan for the available clusters on our network using the Plug & Play functionality of the Qblox Instruments package (see Plug & Play for more info).

[3]:

warnings.simplefilter("ignore")

# Scan for available devices and display
with PlugAndPlay() as p:
    # Get info of all devices
    device_list = p.list_devices()

names = {dev_id: dev_info["description"]["name"] for dev_id, dev_info in device_list.items()}
names["dummy_cluster"] = "dummy_cluster"
ip_addresses = {dev_id: dev_info["identity"]["ip"] for dev_id, dev_info in device_list.items()}


# Create widget for names and ip addresses
connect = widgets.Dropdown(
    options=[["Dummy-Cluster", "dummy_cluster"]]
    + [(f"{names[dev_id]} @{ip_addresses[dev_id]}", dev_id) for dev_id in device_list],
    description="Select Device",
)
display(connect)

Connect to Cluster#

We now make a connection with the Cluster selected in the dropdown widget. We also define a function to find the modules we’re interested in. We select the readout and control module we want to use.

[4]:

# Close all existing QCoDeS Instrument instances
Instrument.close_all()

# Select the device
dev_id = connect.value

# Here we have the option to use a dummy device so that you can run your tests without a physical cluster
dummy_cfg = (
    {
        1: ClusterType.CLUSTER_QCM_RF,
        2: ClusterType.CLUSTER_QCM,
        3: ClusterType.CLUSTER_QRM_RF,
    }
    if dev_id == "dummy_cluster"
    else None
)

cluster = Cluster(name="cluster0", identifier=ip_addresses.get(dev_id), dummy_cfg=dummy_cfg)

print(f"{connect.label} connected")

Dummy-Cluster connected

Reset the Cluster#

We reset the Cluster to enter a well-defined state. Note that resetting will clear all stored parameters and repeats startup calibration, so resetting between experiments is usually not desirable.

[5]:

cluster.reset()
print(cluster.get_system_state())

Status: CRITICAL, Flags: TEMPERATURE_OUT_OF_RANGE, Slot flags: SLOT1_TEMPERATURE_OUT_OF_RANGE, SLOT2_TEMPERATURE_OUT_OF_RANGE, SLOT3_TEMPERATURE_OUT_OF_RANGE

Note that a dummy cluster will raise error flags, this is expected behavior and can be ignored.

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.

[6]:

qubit = BasicTransmonElement("qubit")
qubit.measure.acq_channel(0)

quantum_device = QuantumDevice("device_1q")
quantum_device.hardware_config(hardware_cfg)

quantum_device.add_element(qubit)

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
) -> None:
    # Close QCoDeS instruments with conflicting names
    for name in [
        "PlotMonitor",
        "meas_ctrl",
        "ic",
        "ic_generic",
        f"ic_{cluster.name}",
    ] + [f"ic_{module.name}" for module in cluster.modules]:
        with contextlib.suppress(KeyError):
            Instrument.find_instrument(name).close()

    meas_ctrl = MeasurementControl("meas_ctrl")
    ic = InstrumentCoordinator("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 = PlotMonitor("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)

Set data directory#

This directory is where all of the experimental data as well as all of the post processing will go.

[8]:

# Enter your own dataset directory here!
dh.set_datadir(Path("example_data").resolve())

Configure external flux control#

In the case of flux-tunable transmon qubits, we need to have some way of controlling the external flux.

This can be done by setting an output bias on a module of the cluster which is then connected to the flux line.

# e.g. nullify external flux by setting current to 0 A
cluster.module2.out0_current(0.0)

If your system is not using flux-tunable transmons, then you can skip to the next section.

[9]:

flux_settable: callable = cluster.module2.out0_offset
flux_settable(0.0)

Activate NCO delay compensation#

Compensate for the digital propagation delay for each qubit (i.e each sequencer)

For more info, please see the API reference.

To avoid mismatches between modulation and demodulation, the delay between any readout frequency or phase changes and the next acquisition should be equal or greater than the total propagation delay (146ns + user defined value).

[10]:

for i in range(6):
    getattr(cluster.module3, f"sequencer{i}").nco_prop_delay_comp_en(True)
    getattr(cluster.module3, f"sequencer{i}").nco_prop_delay_comp(50)

Characterization experiments#

The sweep setpoints for all experiments (e.g. frequency_setpoints in the spectroscopy experiments) are only examples. The sweep setpoints should be changed to match your own system.

We show two sets of experiments: The first contains generic characterization experiments for transmon qubits. The second contains 2D experiments for finding the flux sweetspot, applicable for flux-tunable qubits.

Here we consider five standard characterization experiments for single qubit tuneup. The experiments are: 1. Resonator spectroscopy - Used to find the frequency response of the readout resonator when the qubit is in \(|0\rangle\). 2. Qubit spectroscopy (a.k.a two-tone) - Used to find the \(|0\rangle \rightarrow |1\rangle\) drive frequency. 3. Rabi oscillations - Used to find precise excitation pulse required to excite the qubit to \(|1\rangle\). 4. Ramsey oscillations - Used to tune the \(|0\rangle \rightarrow |1\rangle\) drive frequency more precisely. - Used to measure \(T_2^*\). 5. T1 - Used to find the time it takes for the qubit to decay from \(|1\rangle\) to \(|0\rangle\), the \(T_1\) time.