v3.0.0: Full-feature Canvas, New Export and Initialization Methods

QFit version 3.0.0 is released! The new version brings full-feature canvases with clearer transition labeling, improved zoom controls, and uniform colormaps for consistent visualization. New export tools simplify saving calibration results, circuit parameters, and extracted points, while YAML-based initialization enables seamless batch processing. Key visualization and slider bugs are also fixed, making this a cleaner, more robust release for routine workflows.

Enhancements

• Full Feature Canvases
  • A button for easy zoom out is included in the main canvas
  • The standalone canvases now include a native matplotlib canvas control panel.
  • Both main and standalone canvases now try to label all the theoretical transitions that are visible in the canvas.
  • The newly computed theoretical transitions will span the full range of the x-axis of canvas (in the past, the spectrum spanned over the x range of measurement data).
  • Measurement colormap for each figure now auto-centers at the median of the figure’s z data (e.g. transmission amplitude) for a uniform presentation.
  • The visibility of theoretical transitions can be toggled.
• A set of more versatile export tools (available as methods of the Fit class). The user can now call
  • export_calibration_result to export calibration results, which returns an instance of PartialCalibrationResult or FullCalibrationResult classes that gathers data and functionalities related to calibrations, such as the map from dc biases to control parameters.
  • export_circuit_parameters to export circuit’s parameters (such as EJ, EC, EL, interaction strength) as a dictionary.
  • export_extracted_points to export all the extracted transition point information, including coordinates, tag types, labels, photon numbers.
• Initialize QFit with a YAML file. This automates the workflow from data import to fitting parameter configuration, which is particularly useful for batch-processing, such as when characterizing multiple, similar qubits. To use this functionality, fill in a template from generate_yaml_template and launch by Fit.new_by_yaml().

Bug Fixes

• Backend Models
  • Silence the scqubits warning when there is a standalone canvas.
• Views
  • If the user changes in min/max ranges of prefit sliders, the displayed parameter values would be incorrectly updated. This is fixed.
  • If the provided x axis was in descending order, the transition labels were missing in the canvas. This is fixed.
  • When there were multiple Y candidates in the import view and one of them was a constant, there was a chance that the figure was not correctly visualized. Now this is fixed.
  • When the canvas was zoomed / panned, the calibrated axis would be wrongly presented in the canvas. Now this is fixed.