Qualib provides automatic calibrations for experiments on superconducting quantum circuits, based on Exopy.

Installation

1. git clone https://github.com/bastienvoirin/qualib.git
2. pip install . (or pip install <path to local Qualib repository>)

Usage

calibration_scheme.py defines the calibration sequence file to run.

CLI/module usage

python -m qualib.main calibration_scheme.py

Package usage

from qualib.main import Qualib

qualib = Qualib()
qualib.run_all('calibration_scheme.py')

Calibration sequence

assumptions.py

assumptions.py should be a Python dictionary of depth 1 or 2.

calibration_scheme.py

calibration_scheme.py should be a Python list. Each element of this list specifies a calibration to run.

Format:

[
    {"name": "a_calibration"},
    
    {"name": "another_calibration",
     "substitutions": {"NAME":                "variant_in_camel_case",
                       "A_SUBSTITUTION":      "a_value",
                       "ANOTHER_SUBTITUTION": "another_value"}}
]

• name: the calibration name
• substitutions: an optional dictionary of substitutions; if present, NAME must be defined.

Defining a calibration in qualib/calibrations/

See https://qualib.readthedocs.io/en/latest/user_guide.html for further explanations.

{calib_name}_template.meas.ini

• Placeholders format: $parameter or $section/parameter, where section and parameter are alphanumeric strings (regex: [a-zA-Z0-9_]) and support substitutions
• Examples: $averaging, $twpa/freq, $qubit/PULSE_LENGTH
• See Calibration(DefaultCalibration).__init__ in {calib_name}_utils.py

{calib_name}_utils.py

template_{calib_name}.ipynb

Typical Jupyter Notebook template:

# Calibration name ({SUBSTITUTION})
## Result: {POST_PLACEHOLDER} unit

# load experimental data
file = h5py.File('HDF5_PATH', 'r', swmr=True)
xdata = file['data']['x'][()]
ydata = file['data']['y'][()];

# fit experimental data
popt, pcov = opt.curve_fit(function, xdata, ydata, guesses=(0, 1, 2))
f'a, b, c = {popt}'

# plot experimental data and curve fit(s)
fig, ax = plt.subplots()
ax.plot(xdata, ydata, '.-', label='Data')
ax.plot(xdata, function(xdata, *popt), label=f'Fit: a = {popt[0]:f}')
ax.legend();

# optional interfaces: raise an exception when any(np.sqrt(np.diag(_cov)) >= 0.05*_opt)
# where np.sqrt(np.diag(_cov)) gives the standard deviation on the optimized parameters
_opt = popt
_cov = pcov

# optional interface: raises an exception when a specific condition is met
_err = {'Custom error', _opt[0] < 0}

# mandatory interface
_results = {'a': popt[0]}
_results

This Jupyter notebook should be saved as calib_name_template.ipynb under qualib/calibrations/calib_name.