Discriminated Single Shot Readout#

The notebook will 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]:

from __future__ import annotations

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.instrument import find_or_create_instrument
from qcodes.parameters import ManualParameter
from sklearn.metrics import ConfusionMatrixDisplay

import quantify_core.data.handling as dh
from qblox_instruments import Cluster, ClusterType
from quantify_core.analysis.readout_calibration_analysis import ReadoutCalibrationAnalysis
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.enums import BinMode
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, Rxy

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 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

Fit line discriminator with linear discriminant analysis (LDA)#

[11]:

ssro_analysis = ReadoutCalibrationAnalysis(tuid=dh.get_latest_tuid())
ssro_analysis.run().display_figs_mpl()

../../../_images/applications_quantify_transmon_discriminated_ssro_19_0.png

[12]:

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)