See also

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

T1#

The notebook will measure the \(T_1\) decoherence time of the qubit.

[1]:
import numpy as np
from qcodes.parameters import ManualParameter

from quantify_scheduler.gettables import ScheduleGettable
from quantify_scheduler.schedules.timedomain_schedules import t1_sched
[2]:
import json

import rich  # noqa:F401

import quantify_core.data.handling as dh
from quantify_scheduler.device_under_test.quantum_device import QuantumDevice

from utils import initialize_hardware, run  # noqa:F401

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.

[3]:
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

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.

[4]:
quantum_device = QuantumDevice.from_json_file("devices/transmon_device_2q.json")
qubit = quantum_device.get_element("q0")
quantum_device.hardware_config(hardware_cfg)
meas_ctrl, _, cluster = initialize_hardware(quantum_device, ip=None)
/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(

T1#

[5]:
tau = ManualParameter(name="tau_delay", unit="s", label="Delay")
tau.batched = True

t1_sched_kwargs = {"qubit": qubit.name, "times": tau}

gettable = ScheduleGettable(
    quantum_device,
    schedule_function=t1_sched,
    schedule_kwargs=t1_sched_kwargs,
    real_imag=False,
    batched=True,
)
meas_ctrl.gettables(gettable)
[6]:
delay_setpoints = np.arange(40e-9, 200e-6, 500e-9)

meas_ctrl.settables(tau)
meas_ctrl.setpoints(delay_setpoints)

quantum_device.cfg_sched_repetitions(300)
t1_ds = meas_ctrl.run("T1 experiment")
t1_ds
Starting batched measurement...
Iterative settable(s) [outer loop(s)]:
         --- (None) ---
Batched settable(s):
         tau_delay
Batch size limit: 400

[6]:
<xarray.Dataset> Size: 10kB
Dimensions:  (dim_0: 400)
Coordinates:
    x0       (dim_0) float64 3kB 4e-08 5.4e-07 1.04e-06 ... 0.000199 0.0001995
Dimensions without coordinates: dim_0
Data variables:
    y0       (dim_0) float64 3kB nan nan nan nan nan nan ... nan nan nan nan nan
    y1       (dim_0) float64 3kB nan nan nan nan nan nan ... nan nan nan nan nan
Attributes:
    tuid:                             20241017-131100-574-6e89e6
    name:                             T1 experiment
    grid_2d:                          False
    grid_2d_uniformly_spaced:         False
    1d_2_settables_uniformly_spaced:  False
[7]:
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']
        ]
    }
}
[8]:
quantum_device.to_json_file("devices/")
[8]:
'devices/device_2q_2024-10-17_13-11-01_UTC.json'