See also

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

Coupled qubits characterization

The experiments of this tutorial are meant to be executed with a Qblox Cluster controlling a flux-tunable 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. In this case, example data is loaded from the "./example_data/" directory.

Hardware 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 2-qubit system (we name the qubits q0 and q1), with dedicated flux-control lines.

The hardware setup is as follows, by cluster slot: - QCM-RF (Slot 6) - Drive line for q0 using fixed 80 MHz IF. - Drive line for q1 using fixed 80 MHz IF. - QCM (Slot 2) - Flux line for q0. - Flux line for q1. - QRM-RF (Slot 8) - Shared readout line for q0/q1 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.

from __future__ import annotations

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_module6": {
            "instrument_type": "QCM_RF",
            "complex_output_0": {
                "output_att": 10,
                "dc_mixer_offset_I": 0.0,
                "dc_mixer_offset_Q": 0.0,
                "portclock_configs": [
                    {
                        "port": "q0:mw",
                        "clock": "q0.01",
                        "interm_freq": 80e6,
                        "mixer_amp_ratio": 1.0,
                        "mixer_phase_error_deg": 0.0,
                    }
                ],
            },
            "complex_output_1": {
                "output_att": 10,
                "dc_mixer_offset_I": 0.0,
                "dc_mixer_offset_Q": 0.0,
                "portclock_configs": [
                    {
                        "port": "q1:mw",
                        "clock": "q1.01",
                        "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": "q0:fl", "clock": "cl0.baseband"}]},
            "real_output_1": {"portclock_configs": [{"port": "q1:fl", "clock": "cl0.baseband"}]},
        },
        # ============ READOUT ============#
        "cluster0_module8": {
            "instrument_type": "QRM_RF",
            "complex_output_0": {
                "output_att": 60,
                "input_att": 0,
                "dc_mixer_offset_I": 0.0,
                "dc_mixer_offset_Q": 0.0,
                "lo_freq": 7.5e9,
                "portclock_configs": [
                    {
                        "port": "q0:res",
                        "clock": "q0.ro",
                        "mixer_amp_ratio": 1.0,
                        "mixer_phase_error_deg": 0.0,
                    },
                    {
                        "port": "q1:res",
                        "clock": "q1.ro",
                        "mixer_amp_ratio": 1.0,
                        "mixer_phase_error_deg": 0.0,
                    },
                ],
            },
        },
    },
}

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

Devices:
 - 10.10.200.13: cluster_mm 0.6.2 with name "QSE_1" and serial number 00015_2321_005
 - 10.10.200.42: cluster_mm 0.7.0 with name "cluster-mm" and serial number 00015_2321_004
 - 10.10.200.43: cluster_mm 0.7.0 with name "QAE-2" and serial number 00015_2206_003
 - 10.10.200.50: cluster_mm 0.7.0 with name "cluster-mm" and serial number 00015_2219_003
 - 10.10.200.53: cluster_mm 0.7.0 with name "cluster-mm" and serial number 00015_2320_004
 - 10.10.200.70: cluster_mm 0.7.0 with name "cluster-mm" and serial number 123-456-789
 - 10.10.200.80: cluster_mm 0.6.1 with name "cluster-mm" and serial number not_valid

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.

from pathlib import Path

from qcodes.instrument import find_or_create_instrument

from qblox_instruments import Cluster, ClusterType

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
    ),
)

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.

cluster.reset()
print(cluster.get_system_status())

Status: RESOLVED, Flags: TEMPERATURE_OUT_OF_RANGE, Slot flags: SLOT2_TEMPERATURE_OUT_OF_RANGE, SLOT4_TEMPERATURE_OUT_OF_RANGE, SLOT6_TEMPERATURE_OUT_OF_RANGE, SLOT8_TEMPERATURE_OUT_OF_RANGE

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

Experiment setup

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.

from quantify_scheduler.device_under_test.quantum_device import QuantumDevice
from quantify_scheduler.device_under_test.transmon_element import BasicTransmonElement

q0 = find_or_create_instrument(BasicTransmonElement, recreate=True, name="q0")
q1 = find_or_create_instrument(BasicTransmonElement, recreate=True, name="q1")
q0.measure.acq_channel(0)
q1.measure.acq_channel(1)

quantum_device = find_or_create_instrument(QuantumDevice, recreate=True, name="device_2q")
quantum_device.hardware_config(hardware_cfg)
quantum_device.add_element(q0)
quantum_device.add_element(q1)

Set calibrations

This tutorial explicitly only deals with 2-qubit experiments. As such, we assume that both the qubits and their resonators have already been characterized. For information on how to do this, please see the single transmon qubit tutorial. In order to use this tutorial on your own system, you must first change the calibrated values to match your own system.

For the sake of this tutorial, we will use some template values for both qubits. If you have an old experiment with instrument settings that you want to load, checkout the helper function load_settings_onto_instrument.

# ============ READOUT ============ #
q0.reset.duration(100e-6)
q0.measure.acq_delay(100e-9)
q0.measure.pulse_amp(0.05)
q0.measure.pulse_duration(2e-6)
q0.measure.integration_time(1.9e-6)

q1.reset.duration(100e-6)
q1.measure.acq_delay(100e-9)
q1.measure.pulse_amp(0.05)
q1.measure.pulse_duration(2e-6)
q1.measure.integration_time(1.9e-6)

q0.clock_freqs.readout(7.6e9)
q1.clock_freqs.readout(7.7e9)

# ============ DRIVE ============ #
q0.rxy.amp180(0.1)
q0.rxy.motzoi(0.05)
q0.rxy.duration(40e-9)

q1.rxy.amp180(0.1)
q1.rxy.motzoi(0.05)
q1.rxy.duration(40e-9)

q0.clock_freqs.f01(5.1e9)
q1.clock_freqs.f01(5.2e9)

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.

from quantify_core.measurement.control import MeasurementControl
from quantify_core.visualization.pyqt_plotmon import PlotMonitor_pyqt as PlotMonitor
from quantify_scheduler.instrument_coordinator import InstrumentCoordinator
from quantify_scheduler.instrument_coordinator.components.qblox import ClusterComponent


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)

Set data directory

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

import quantify_core.data.handling as dh

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

Configure external flux control

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-control line.

Here we are nullifying the external flux on both qubits.

flux_settables = {"q0": cluster.module2.out0_offset, "q1": cluster.module2.out1_offset}

for flux_settable in flux_settables.values():
    flux_settable.inter_delay = 100e-9  # Delay time in seconds between consecutive set operations.
    flux_settable.step = 0.3e-3  # Stepsize in V that this Parameter uses during set operation.
    flux_settable(0.0)

flux_settables[q0.name](0.0)  # enter your own value here for qubit 0
flux_settables[q1.name](0.0)  # enter your own value here for qubit 1

Activate NCO delay compensation

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

For more info, please see: https://qblox-qblox-instruments.readthedocs-hosted.com/en/main/api_reference/sequencer.html#Sequencer.nco_prop_delay_comp

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).

for i in range(6):
    getattr(cluster.module8, f"sequencer{i}").nco_prop_delay_comp_en(True)
    getattr(cluster.module8, f"sequencer{i}").nco_prop_delay_comp(10)

Experiments

As in the single qubit tuneup tutorial, the sweep setpoints for all experiments in this section are only examples. The sweep setpoints should be changed to match your own system. In this section we assume that each individual qubit has already been characterized, and that they have been biased to their sweetspots.

We will examine the flux response of the 2-qubit system. By “flux response” we mean the measured response of the system when the parameterization of a flux pulse is varied.

We consider two separate experiments which have their own separate parameterizations, they are:

1. Controlled phase calibration (a.k.a. two-qubit Chevron experiment)

   • Used to measure coupling strength between the two qubits.

   • Used to find the location of the \(|11\rangle \leftrightarrow |02\rangle\) avoided crossing.

2. Conditional oscillations

   • Used to measure the conditional phase of the controlled phase gate.

   • Used to estimate leakage to \(|02\rangle\).

   • Can also be used to measure single-qubit phases on the individual qubits.

In both of these experiments, we apply the flux pulse on the flux-control line of q1 (on top of the sweetspot bias) which we take to be the qubit with the higher frequency, i.e. \(\omega_1\) > \(\omega_0\).

Controlled phase calibration

import numpy as np
from qcodes.parameters import ManualParameter

from quantify_scheduler.gettables import ScheduleGettable
from quantify_scheduler.operations.gate_library import Measure, Reset, X
from quantify_scheduler.schedules.schedule import Schedule
from quantify_scheduler.schedules.two_qubit_transmon_schedules import chevron_cz_sched

duration = ManualParameter(name="dur", unit="Hz", label="Duration of flux pulse")
amplitude = ManualParameter(name="amp", unit="", label="Amplitude of flux pulse")

amplitude.batched = True
duration.batched = False

chevron_cz_sched_kwargs = dict(
    lf_qubit=q0.name, hf_qubit=q1.name, amplitudes=amplitude, duration=duration
)

gettable = ScheduleGettable(
    quantum_device,
    schedule_function=chevron_cz_sched,
    schedule_kwargs=chevron_cz_sched_kwargs,
    real_imag=False,
    data_labels=[
        "Magnitude lf qubit",
        "Phase lf qubit",
        "Magnitude hf qubit",
        "Phase hf qubit",
    ],
    batched=True,
    num_channels=2,
)

meas_ctrl.gettables(gettable)

quantum_device.cfg_sched_repetitions(400)

duration_setpoints = np.arange(4e-9, 100e-9, 4e-9)
amplitude_setpoints = np.linspace(0.05, 0.2, 100)

meas_ctrl.settables([duration, amplitude])
meas_ctrl.setpoints_grid((duration_setpoints, amplitude_setpoints))

chevron_ds = meas_ctrl.run("chevron")
chevron_ds

Starting batched measurement...
Iterative settable(s) [outer loop(s)]:
         dur
Batched settable(s):
         amp
Batch size limit: 1024

<xarray.Dataset> Size: 115kB
Dimensions:  (dim_0: 2400)
Coordinates:
    x0       (dim_0) float64 19kB 4e-09 4e-09 4e-09 ... 9.6e-08 9.6e-08 9.6e-08
    x1       (dim_0) float64 19kB 0.05 0.05152 0.05303 ... 0.197 0.1985 0.2
Dimensions without coordinates: dim_0
Data variables:
    y0       (dim_0) float64 19kB nan nan nan nan nan ... nan nan nan nan nan
    y1       (dim_0) float64 19kB nan nan nan nan nan ... nan nan nan nan nan
    y2       (dim_0) float64 19kB nan nan nan nan nan ... nan nan nan nan nan
    y3       (dim_0) float64 19kB nan nan nan nan nan ... nan nan nan nan nan
Attributes:
    tuid:                             20240422-174415-275-feb0ba
    name:                             chevron
    grid_2d:                          True
    grid_2d_uniformly_spaced:         True
    1d_2_settables_uniformly_spaced:  False
    xlen:                             24
    ylen:                             100

xarray.Dataset

• Dimensions:
  • dim_0: 2400
• Coordinates: (2)
  • x0
    (dim_0)
    float64
    4e-09 4e-09 ... 9.6e-08 9.6e-08

    name :

    dur

    long_name :

    Duration of flux pulse

    units :

    Hz

    batched :

    False

    array([4.0e-09, 4.0e-09, 4.0e-09, ..., 9.6e-08, 9.6e-08, 9.6e-08])

  • x1
    (dim_0)
    float64
    0.05 0.05152 0.05303 ... 0.1985 0.2

    name :

    amp

    long_name :

    Amplitude of flux pulse

    units :

    batched :

    True

    array([0.05      , 0.05151515, 0.0530303 , ..., 0.1969697 , 0.19848485,
           0.2       ])

• Data variables: (4)
  • y0
    (dim_0)
    float64
    nan nan nan nan ... nan nan nan nan

    name :

    magn

    long_name :

    Magnitude lf qubit

    units :

    V

    batched :

    True

    batch_size :

    1024

    array([nan, nan, nan, ..., nan, nan, nan])

  • y1
    (dim_0)
    float64
    nan nan nan nan ... nan nan nan nan

    name :

    phase

    long_name :

    Phase lf qubit

    units :

    deg

    batched :

    True

    batch_size :

    1024

    array([nan, nan, nan, ..., nan, nan, nan])

  • y2
    (dim_0)
    float64
    nan nan nan nan ... nan nan nan nan

    name :

    magn

    long_name :

    Magnitude hf qubit

    units :

    V

    batched :

    True

    batch_size :

    1024

    array([nan, nan, nan, ..., nan, nan, nan])

  • y3
    (dim_0)
    float64
    nan nan nan nan ... nan nan nan nan

    name :

    phase

    long_name :

    Phase hf qubit

    units :

    deg

    batched :

    True

    batch_size :

    1024

    array([nan, nan, nan, ..., nan, nan, nan])

• Indexes: (0)
• Attributes: (7)

  tuid :

  20240422-174415-275-feb0ba

  name :

  chevron

  grid_2d :

  True

  grid_2d_uniformly_spaced :

  True

  1d_2_settables_uniformly_spaced :

  False

  xlen :

  24

  ylen :

  100

# If on dummy, override with old data for analysis
if cluster_ip is None:
    # NOTE: This dataset uses an old version of quantify which specified amplitude in volts.
    chevron_ds = dh.to_gridded_dataset(dh.load_dataset(tuid="20230509-120110-134-51b044"))

import matplotlib.pyplot as plt

fig, axs = plt.subplots(1, 2, figsize=plt.figaspect(1 / 2))

# plot only magnitude data of both channels for simplicity
chevron_ds.y0.plot(ax=axs[0])
chevron_ds.y2.plot(ax=axs[1])

axs[0].set_title("Low freq. qubit (q0)")
axs[1].set_title("High freq. qubit (q1)")

fig.suptitle("Controlled phase calibration")

fig.tight_layout()

Conditional oscillations

from typing import TYPE_CHECKING, Literal

from quantify_scheduler.operations.gate_library import X90, Rxy
from quantify_scheduler.operations.pulse_library import SuddenNetZeroPulse

if TYPE_CHECKING:
    from numpy.typing import NDArray


def conditional_oscillation_sched(
    target_qubit: str,
    control_qubit: str,
    phases: NDArray,
    variant: Literal["OFF", "ON"],
    snz_A: float,
    snz_B: float,
    snz_scale: float,
    snz_dur: float,
    snz_t_phi: float,
    snz_t_integral_correction: float,
    flux_port: str | None = None,
    repetitions: int = 1,
) -> Schedule:
    """
    Make a conditional oscillation schedule to measure conditional phase.

    Parameters
    ----------
    target_qubit
        The name of a qubit, e.g., "q0", the qubit with lower frequency.
    control_qubit
        The name of coupled qubit, the qubit with the higher frequency.
    phases
        An array (or scalar) of recovery phases in degrees.
    variant
        A string specifying whether to excite the control qubit.
    snz_A
        Unitless amplitude of the main square pulse.
    snz_B
        Unitless scaling correction for the final sample of the first
        square and first sample of the second square pulse.
    snz_scale
        Amplitude scaling correction factor of the negative arm of the net-zero pulse.
    snz_dur
        The total duration of the two half square pulses.
    snz_t_phi
        The idling duration between the two half pulses.
    snz_t_integral_correction
        The duration in which any non-zero pulse amplitude needs to be corrected.
    flux_port
        An optional string for a flux port. Default is hf_qubit flux port.
    repetitions
        The amount of times the Schedule will be repeated.

    Returns
    -------
    :
        An experiment schedule.

    """
    sched = Schedule(f"ConditionalOscillation({variant})", repetitions)

    # Ensure phases is an iterable when passing a float
    phases = np.asarray(phases)
    phases = phases.reshape(phases.shape or (1,))

    # Ensure that variant is uppercase for switch
    variant = str.upper(variant)
    if variant not in {"OFF", "ON"}:
        raise ValueError("Schedule variant should be 'OFF' or 'ON'.")

    # Set flux port
    flux_port = flux_port if flux_port is not None else f"{control_qubit}:fl"

    for acq_index, phi in enumerate(phases):
        # Reset to |00>
        sched.add(Reset(target_qubit, control_qubit))

        # Apply a pi/2 pulse on the target qubit
        targ_eq_ref = sched.add(X90(target_qubit))

        if variant == "ON":
            # Also apply a pi pulse on the control qubit
            sched.add(X(control_qubit), ref_op=targ_eq_ref, ref_pt="start")

        # Go to |11> <=> |02> avoided crossing on positive & negative sides
        # using the SuddenNetZeroPulse
        sched.add(
            SuddenNetZeroPulse(
                amp_A=snz_A,
                amp_B=snz_B,
                net_zero_A_scale=snz_scale,
                t_pulse=snz_dur,
                t_phi=snz_t_phi,
                t_integral_correction=snz_t_integral_correction,
                port=flux_port,
                clock="cl0.baseband",
            )
        )

        # Apply a pi/2 recovery pulse on the target qubit
        targ_rec_ref = sched.add(Rxy(theta=90.0, phi=phi, qubit=target_qubit))

        if variant == "ON":
            # Also apply a pi pulse on the control qubit
            sched.add(X(control_qubit), ref_op=targ_rec_ref, ref_pt="start")

        # Measure system
        sched.add(Measure(target_qubit, control_qubit, acq_index=acq_index))

    return sched

phase = ManualParameter(name="ph", unit="deg", label="Recovery phase")
flux_pulse_amplitude = ManualParameter(name="A", unit="", label="Flux pulse amplitude")
scaling_correction = ManualParameter(name="B", unit="", label="Scaling correction")

phase.batched = True
flux_pulse_amplitude.batched = False
scaling_correction.batched = False

conditional_oscillation_sched_kwargs = dict(
    target_qubit=q0.name,
    control_qubit=q1.name,
    phases=phase,
    snz_A=flux_pulse_amplitude,
    snz_B=scaling_correction,
    snz_scale=1.0,
    snz_dur=40e-9,
    snz_t_phi=4e-9,
    snz_t_integral_correction=0.0,
)

gettable_off = ScheduleGettable(
    quantum_device,
    conditional_oscillation_sched,
    schedule_kwargs={**conditional_oscillation_sched_kwargs, **{"variant": "OFF"}},
    real_imag=False,
    batched=True,
    num_channels=2,
)
gettable_on = ScheduleGettable(
    quantum_device,
    conditional_oscillation_sched,
    schedule_kwargs={**conditional_oscillation_sched_kwargs, **{"variant": "ON"}},
    real_imag=False,
    batched=True,
    num_channels=2,
)

meas_ctrl.gettables((gettable_off, gettable_on))

quantum_device.cfg_sched_repetitions(400)

meas_ctrl.settables([phase, flux_pulse_amplitude, scaling_correction])
meas_ctrl.setpoints_grid((np.linspace(0.0, 360.0, 60), [0.8], [0.5]))

cond_osc_ds = meas_ctrl.run("conditional oscillation")
cond_osc_ds

Starting batched measurement...
Iterative settable(s) [outer loop(s)]:
         A, B
Batched settable(s):
         ph
Batch size limit: 60

c:\Users\Axel\miniconda3\envs\tt_v2\lib\site-packages\quantify_scheduler\waveforms.py:317: RuntimeWarning: divide by zero encountered in scalar divide
  integral_value = -sum(waveform) / num_corr_samples
c:\Users\Axel\miniconda3\envs\tt_v2\lib\site-packages\quantify_scheduler\waveforms.py:317: RuntimeWarning: divide by zero encountered in scalar divide
  integral_value = -sum(waveform) / num_corr_samples

<xarray.Dataset> Size: 5kB
Dimensions:  (dim_0: 60)
Coordinates:
    x0       (dim_0) float64 480B 0.0 6.102 12.2 18.31 ... 347.8 353.9 360.0
    x1       (dim_0) float64 480B 0.8 0.8 0.8 0.8 0.8 ... 0.8 0.8 0.8 0.8 0.8
    x2       (dim_0) float64 480B 0.5 0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5 0.5
Dimensions without coordinates: dim_0
Data variables:
    y0       (dim_0) float64 480B nan nan nan nan nan ... nan nan nan nan nan
    y1       (dim_0) float64 480B nan nan nan nan nan ... nan nan nan nan nan
    y2       (dim_0) float64 480B nan nan nan nan nan ... nan nan nan nan nan
    y3       (dim_0) float64 480B nan nan nan nan nan ... nan nan nan nan nan
    y4       (dim_0) float64 480B nan nan nan nan nan ... nan nan nan nan nan
    y5       (dim_0) float64 480B nan nan nan nan nan ... nan nan nan nan nan
    y6       (dim_0) float64 480B nan nan nan nan nan ... nan nan nan nan nan
    y7       (dim_0) float64 480B nan nan nan nan nan ... nan nan nan nan nan
Attributes:
    tuid:                             20240422-174425-935-9d4fd3
    name:                             conditional oscillation
    grid_2d:                          False
    grid_2d_uniformly_spaced:         False
    1d_2_settables_uniformly_spaced:  False

xarray.Dataset

• Dimensions:
  • dim_0: 60
• Coordinates: (3)
  • x0
    (dim_0)
    float64
    0.0 6.102 12.2 ... 353.9 360.0

    name :

    ph

    long_name :

    Recovery phase

    units :

    deg

    batched :

    True

    array([  0.        ,   6.10169492,  12.20338983,  18.30508475,
            24.40677966,  30.50847458,  36.61016949,  42.71186441,
            48.81355932,  54.91525424,  61.01694915,  67.11864407,
            73.22033898,  79.3220339 ,  85.42372881,  91.52542373,
            97.62711864, 103.72881356, 109.83050847, 115.93220339,
           122.03389831, 128.13559322, 134.23728814, 140.33898305,
           146.44067797, 152.54237288, 158.6440678 , 164.74576271,
           170.84745763, 176.94915254, 183.05084746, 189.15254237,
           195.25423729, 201.3559322 , 207.45762712, 213.55932203,
           219.66101695, 225.76271186, 231.86440678, 237.96610169,
           244.06779661, 250.16949153, 256.27118644, 262.37288136,
           268.47457627, 274.57627119, 280.6779661 , 286.77966102,
           292.88135593, 298.98305085, 305.08474576, 311.18644068,
           317.28813559, 323.38983051, 329.49152542, 335.59322034,
           341.69491525, 347.79661017, 353.89830508, 360.        ])

  • x1
    (dim_0)
    float64
    0.8 0.8 0.8 0.8 ... 0.8 0.8 0.8 0.8

    name :

    A

    long_name :

    Flux pulse amplitude

    units :

    batched :

    False

    array([0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8,
           0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8,
           0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8,
           0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8,
           0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8])

  • x2
    (dim_0)
    float64
    0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5

    name :

    B

    long_name :

    Scaling correction

    units :

    batched :

    False

    array([0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,
           0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,
           0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,
           0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,
           0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5])

• Data variables: (8)
  • y0
    (dim_0)
    float64
    nan nan nan nan ... nan nan nan nan

    name :

    magn

    long_name :

    Magnitude0

    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,
           nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
           nan, nan, nan, nan, nan, nan, nan, nan])

  • y1
    (dim_0)
    float64
    nan nan nan nan ... nan nan nan nan

    name :

    phase

    long_name :

    Phase0

    units :

    deg

    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,
           nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
           nan, nan, nan, nan, nan, nan, nan, nan])

  • y2
    (dim_0)
    float64
    nan nan nan nan ... nan nan nan nan

    name :

    magn

    long_name :

    Magnitude1

    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,
           nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
           nan, nan, nan, nan, nan, nan, nan, nan])

  • y3
    (dim_0)
    float64
    nan nan nan nan ... nan nan nan nan

    name :

    phase

    long_name :

    Phase1

    units :

    deg

    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,
           nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
           nan, nan, nan, nan, nan, nan, nan, nan])

  • y4
    (dim_0)
    float64
    nan nan nan nan ... nan nan nan nan

    name :

    magn

    long_name :

    Magnitude0

    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,
           nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
           nan, nan, nan, nan, nan, nan, nan, nan])

  • y5
    (dim_0)
    float64
    nan nan nan nan ... nan nan nan nan

    name :

    phase

    long_name :

    Phase0

    units :

    deg

    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,
           nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
           nan, nan, nan, nan, nan, nan, nan, nan])

  • y6
    (dim_0)
    float64
    nan nan nan nan ... nan nan nan nan

    name :

    magn

    long_name :

    Magnitude1

    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,
           nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
           nan, nan, nan, nan, nan, nan, nan, nan])

  • y7
    (dim_0)
    float64
    nan nan nan nan ... nan nan nan nan

    name :

    phase

    long_name :

    Phase1

    units :

    deg

    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,
           nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
           nan, nan, nan, nan, nan, nan, nan, nan])

• Indexes: (0)
• Attributes: (5)

  tuid :

  20240422-174425-935-9d4fd3

  name :

  conditional oscillation

  grid_2d :

  False

  grid_2d_uniformly_spaced :

  False

  1d_2_settables_uniformly_spaced :

  False

# If on dummy, override with old data for analysis
if cluster_ip is None:
    cond_osc_ds = dh.load_dataset(tuid="20230509-164908-713-788626")

from quantify_core.analysis.conditional_oscillation_analysis import ConditionalOscillationAnalysis

cond_osc_analysis = ConditionalOscillationAnalysis(dataset=cond_osc_ds)
cond_osc_analysis.run().display_figs_mpl()

import rich

rich.print(quantum_device.hardware_config())

{
    'backend': 'quantify_scheduler.backends.qblox_backend.hardware_compile',
    'cluster0': {
        'sequence_to_file': False,
        'ref': 'internal',
        'instrument_type': 'Cluster',
        'cluster0_module6': {
            'instrument_type': 'QCM_RF',
            'complex_output_0': {
                'output_att': 10,
                'dc_mixer_offset_I': 0.0,
                'dc_mixer_offset_Q': 0.0,
                'portclock_configs': [
                    {
                        'port': 'q0:mw',
                        'clock': 'q0.01',
                        'interm_freq': 80000000.0,
                        'mixer_amp_ratio': 1.0,
                        'mixer_phase_error_deg': 0.0
                    }
                ]
            },
            'complex_output_1': {
                'output_att': 10,
                'dc_mixer_offset_I': 0.0,
                'dc_mixer_offset_Q': 0.0,
                'portclock_configs': [
                    {
                        'port': 'q1:mw',
                        'clock': 'q1.01',
                        'interm_freq': 80000000.0,
                        'mixer_amp_ratio': 1.0,
                        'mixer_phase_error_deg': 0.0
                    }
                ]
            }
        },
        'cluster0_module2': {
            'instrument_type': 'QCM',
            'real_output_0': {'portclock_configs': [{'port': 'q0:fl', 'clock': 'cl0.baseband'}]},
            'real_output_1': {'portclock_configs': [{'port': 'q1:fl', 'clock': 'cl0.baseband'}]}
        },
        'cluster0_module8': {
            'instrument_type': 'QRM_RF',
            'complex_output_0': {
                'output_att': 60,
                'input_att': 0,
                'dc_mixer_offset_I': 0.0,
                'dc_mixer_offset_Q': 0.0,
                'lo_freq': 7500000000.0,
                'portclock_configs': [
                    {'port': 'q0:res', 'clock': 'q0.ro', 'mixer_amp_ratio': 1.0, 'mixer_phase_error_deg': 0.0},
                    {'port': 'q1:res', 'clock': 'q1.ro', 'mixer_amp_ratio': 1.0, 'mixer_phase_error_deg': 0.0}
                ]
            }
        }
    }
}