See also

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

Discriminated Single Shot Readout#

Here we show how to run a readout calibration experiment and fit a discriminator with a linear discriminant analysis. This experiment is sometimes called multi-state discrimination.

[1]:
import json
from functools import partial
from typing import Literal

import matplotlib.pyplot as plt
import numpy as np
import rich  # noqa:F401
from qcodes.parameters import ManualParameter
from sklearn.metrics import ConfusionMatrixDisplay

import quantify_core.data.handling as dh
from quantify_core.analysis.readout_calibration_analysis import ReadoutCalibrationAnalysis
from quantify_scheduler import Schedule
from quantify_scheduler.device_under_test.quantum_device import QuantumDevice
from quantify_scheduler.enums import BinMode
from quantify_scheduler.gettables import ScheduleGettable
from quantify_scheduler.operations.gate_library import Measure, Reset, Rxy

from utils import initialize_hardware, run  # noqa:F401
[2]:
hw_config_path = "configs/tuning_transmon_coupled_pair_hardware_config.json"
device_path = "devices/transmon_device_2q.json"
[3]:
with open(hw_config_path) 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
[4]:
quantum_device = QuantumDevice.from_json_file(device_path)
qubit = quantum_device.get_element("q0")
quantum_device.hardware_config(hardware_cfg)
meas_ctrl, _, cluster = initialize_hardware(quantum_device, ip=None)
/builds/qblox/packages/software/qblox_instruments_docs/.venv/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(
/builds/qblox/packages/software/qblox_instruments_docs/.venv/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(

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 connectivity is as follows, by cluster slot:

  • QCM (Slot 2)

    • \(\text{O}^{1}\): Flux line for q0.

    • \(\text{O}^{2}\): Flux line for q1.

  • QCM-RF (Slot 6)

    • \(\text{O}^{1}\): Drive line for q0 using fixed 80 MHz IF.

    • \(\text{O}^{2}\): Drive line for q1 using fixed 80 MHz IF.

  • QRM-RF (Slot 8)

    • \(\text{O}^{1}\) and \(\text{I}^{1}\): 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.

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.

Schedule definition#

[5]:
def readout_calibration_sched(
    qubit: str,
    prepared_states: list[int],
    repetitions: int = 1,
    acq_protocol: Literal[
        "SSBIntegrationComplex", "ThresholdedAcquisition"
    ] = "SSBIntegrationComplex",
) -> Schedule:
    """
    Make a schedule for readout calibration.

    Parameters
    ----------
    qubit
        The name of the qubit e.g., :code:`"q0"` to perform the experiment on.
    prepared_states
        A list of integers indicating which state to prepare the qubit in before measuring.
        The ground state corresponds to 0 and the first-excited state to 1.
    repetitions
        The number of times the schedule will be repeated. Fixed to 1 for this schedule.
    acq_protocol
        The acquisition protocol used for the readout calibration. By default
        "SSBIntegrationComplex", but "ThresholdedAcquisition" can be
        used for verifying thresholded acquisition parameters.

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

    Raises
    ------
    NotImplementedError
        If the prepared state is > 1.

    """
    schedule = Schedule(f"Readout calibration {qubit}", repetitions=1)

    for i, prep_state in enumerate(prepared_states):
        schedule.add(Reset(qubit), label=f"Reset {i}")
        if prep_state == 0:
            pass
        elif prep_state == 1:
            schedule.add(Rxy(qubit=qubit, theta=180, phi=0))
        else:
            raise NotImplementedError(
                "Preparing the qubit in the higher excited states is not supported yet."
            )
        schedule.add(
            Measure(qubit, acq_index=i, bin_mode=BinMode.APPEND, acq_protocol=acq_protocol),
            label=f"Measurement {i}",
        )
    return schedule

SSRO with single side band (SSB) integration#

[6]:
states = ManualParameter(name="states", unit="", label="Prepared state")
states.batch_size = 400
states.batched = True

readout_calibration_sched_kwargs = dict(
    qubit=qubit.name, prepared_states=states, acq_protocol="SSBIntegrationComplex"
)

# set gettable
ssro_gettable = ScheduleGettable(
    quantum_device,
    schedule_function=readout_calibration_sched,
    schedule_kwargs=readout_calibration_sched_kwargs,
    real_imag=True,
    batched=True,
)

# set measurement control
meas_ctrl.gettables(ssro_gettable)
[7]:
num_shots = 1000
state_setpoints = np.asarray([0, 1] * num_shots)

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

    ssro_gettable.get = partial(get_fake_ssro_data, num_shots=num_shots)

meas_ctrl.settables(states)
meas_ctrl.setpoints(state_setpoints)

ssro_ds = dh.to_gridded_dataset(meas_ctrl.run("Single shot readout experiment"))
ssro_ds
Starting batched measurement...
Iterative settable(s) [outer loop(s)]:
         --- (None) ---
Batched settable(s):
         states
Batch size limit: 400

[7]:
<xarray.Dataset> Size: 48kB
Dimensions:  (x0: 2000)
Coordinates:
  * x0       (x0) int64 16kB 0 1 0 1 0 1 0 1 0 1 0 1 ... 0 1 0 1 0 1 0 1 0 1 0 1
Data variables:
    y0       (x0) float64 16kB 0.01945 0.04584 0.02582 ... -0.003546 0.02104
    y1       (x0) float64 16kB 0.01745 0.02013 0.01338 ... -0.006011 0.03088
Attributes:
    tuid:                             20241113-023625-786-935aa9
    name:                             Single shot readout experiment
    grid_2d:                          False
    grid_2d_uniformly_spaced:         False
    1d_2_settables_uniformly_spaced:  False

Fit line discriminator with linear discriminant analysis (LDA)#

[8]:
ssro_analysis = ReadoutCalibrationAnalysis(tuid=dh.get_latest_tuid())
ssro_analysis.run().display_figs_mpl()
../../_images/applications_quantify_discriminated_ssro_13_0.png
[9]:
fit_results = ssro_analysis.fit_results["linear_discriminator"].params
acq_threshold = fit_results["acq_threshold"].value
acq_rotation = (np.rad2deg(fit_results["acq_rotation_rad"].value)) % 360

qubit.measure.acq_threshold(acq_threshold)
qubit.measure.acq_rotation(acq_rotation)

SSRO with thresholded acquisition#

[10]:
disc_ssro_gettable_kwargs = dict(
    qubit=qubit.name, prepared_states=states, acq_protocol="ThresholdedAcquisition"
)

# set gettable
disc_ssro_gettable = ScheduleGettable(
    quantum_device,
    schedule_function=readout_calibration_sched,
    schedule_kwargs=disc_ssro_gettable_kwargs,
    real_imag=True,
    batched=True,
)

# set measurement control
meas_ctrl.gettables(disc_ssro_gettable)
[11]:
num_shots = 10_000
state_setpoints = np.asarray([0, 1] * num_shots)

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

    disc_ssro_gettable.get = partial(get_fake_binary_ssro_data, num_shots=num_shots)

meas_ctrl.settables(states)
meas_ctrl.setpoints(state_setpoints)

disc_ssro_ds = dh.to_gridded_dataset(meas_ctrl.run("Discriminated single shot readout experiment"))
disc_ssro_ds
Starting batched measurement...
Iterative settable(s) [outer loop(s)]:
         --- (None) ---
Batched settable(s):
         states
Batch size limit: 400

[11]:
<xarray.Dataset> Size: 480kB
Dimensions:  (x0: 20000)
Coordinates:
  * x0       (x0) int64 160kB 0 1 0 1 0 1 0 1 0 1 0 1 ... 1 0 1 0 1 0 1 0 1 0 1
Data variables:
    y0       (x0) float64 160kB 0.0 1.0 0.0 1.0 0.0 1.0 ... 1.0 0.0 1.0 0.0 1.0
    y1       (x0) float64 160kB nan nan nan nan nan nan ... nan nan nan nan nan
Attributes:
    tuid:                             20241113-023627-114-f18e8c
    name:                             Discriminated single shot readout exper...
    grid_2d:                          False
    grid_2d_uniformly_spaced:         False
    1d_2_settables_uniformly_spaced:  False
[12]:
ConfusionMatrixDisplay.from_predictions(disc_ssro_ds.x0.data, disc_ssro_ds.y0.data)
plt.title("Confusion Matrix")
plt.xlabel("Measured State")
plt.ylabel("Prepared State")
[12]:
Text(0, 0.5, 'Prepared State')
../../_images/applications_quantify_discriminated_ssro_18_1.png
[13]:
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']
        ]
    }
}
[14]:
quantum_device.to_json_file("devices/")
[14]:
'devices/device_2q_2024-11-13_02-36-27_UTC.json'