qdesignoptimizer.sim_plot_progress module

Visualization utilities for tracking optimization progress of quantum circuit designs.

class qdesignoptimizer.sim_plot_progress.UnitEnum(value)

Bases: str, Enum

Supported frequency units for plotting.

HZ = 'Hz'

KHZ = 'kHz'

MHZ = 'MHz'

GHZ = 'GHz'

FF = 'fF'

S = 's'

MS = 'ms'

US = 'us'

NS = 'ns'

class qdesignoptimizer.sim_plot_progress.OptPltSet(x: str, y: str | List[str], x_label: str | None = None, y_label: str | None = None, x_scale: Literal['linear', 'log'] = 'linear', y_scale: Literal['linear', 'log'] = 'linear', unit: UnitEnum = UnitEnum.HZ)

Bases: object

Configuration for optimization progress plots.

x: str

X-axis parameter name.

y: str | List[str]

Y-axis parameter name(s).

x_label: str | None = None

Custom X-axis label (uses x if None).

y_label: str | None = None

Custom Y-axis label (uses y if None).

x_scale: Literal['linear', 'log'] = 'linear'

X-axis scale (linear or logarithmic).

y_scale: Literal['linear', 'log'] = 'linear'

Y-axis scale (linear or logarithmic).

unit: UnitEnum = 'Hz'

Display unit for y-axis values.

property normalization: float

Get the normalization factor for the selected unit.

get_x_label() → str

Get the x-axis label, using the default if none specified.

get_y_label() → str

Get the y-axis label, using the default if none specified.

class qdesignoptimizer.sim_plot_progress.DataExtractor(opt_results: List[List[Dict[str, Any]]], system_target_params: Dict[str, Any], opt_target_list: List[OptTarget] | None = None)

Bases: object

Extracts data from optimization results for plotting.

get_parameter_value(param_name: str, result: Dict[str, Any], iteration: int) → float | None

Extract parameter value from a result entry.

Parameters:

• param_name – Name of the parameter to extract

• result – Optimization result entry

• iteration – Iteration number (for ITERATION parameter)

Returns:

Parameter value or None if not found

get_design_var_name_for_param(target_parameter: str) → str

Find the design variable name associated with a target parameter.

Parameters:

target_parameter – Target parameter name

Returns:

Name of the associated design variable

Raises:

AssertionError – If the parameter is not found in optimization targets

get_design_var_for_param(target_parameter: str, result: Dict[str, Any]) → Tuple[float, str]

Get design variable value and unit for a target parameter.

Parameters:

• target_parameter – Target parameter name

• result – Optimization result entry

Returns:

Tuple of (value, unit)

extract_xy_data(x_param: str, y_param: str, run_index: int, use_design_var_as_x: bool = False, sort_by_x: bool = False) → Tuple[List[float], List[float]]

Extract x and y data series for plotting.

Parameters:

• x_param – X-axis parameter name

• y_param – Y-axis parameter name

• run_index – Index of the optimization run to use

• use_design_var_as_x – If True, use design variable for x instead of parameter

• sort_by_x – If True, sort the data by x values

Returns:

Tuple of (x_values, y_values)

get_y_data_with_statistics(x_param: str, y_param: str, use_design_var_as_x: bool = False, sort_by_x: bool = False) → Tuple[List[float], ndarray, ndarray]

Extract y data with mean and standard deviation across runs.

Parameters:

• x_param – X-axis parameter name

• y_param – Y-axis parameter name

• use_design_var_as_x – If True, use design variable for x instead of parameter

• sort_by_x – If True, sort the data by x values

Returns:

Tuple of (x_values, y_mean, y_std)

class qdesignoptimizer.sim_plot_progress.OptimizationPlotter(data_extractor: DataExtractor, plot_variance: bool = False, save_figures: bool = False)

Bases: object

Handles plotting of optimization progress data.

plot_standard(fig: Figure, axes: Axes | List[Axes], plot_settings: List[OptPltSet], plot_name: str) → None

Create standard parameter vs. iteration plots.

Parameters:

• fig – Matplotlib figure

• axes – Single axis or list of axes

• plot_settings – List of plot configurations

• plot_name – Name for the plot (used for saving)

plot_params_vs_design_vars(fig: Figure, axes: Axes | List[Axes], plot_settings: List[OptPltSet], plot_name: str, sort_by_x: bool = True) → None

Create plots of parameters vs. their associated design variables.

Parameters:

• fig – Matplotlib figure

• axes – Single axis or list of axes

• plot_settings – List of plot configurations

• plot_name – Name for the plot (used for saving)

• sort_by_x – If True, sort data points by x value

plot_design_vars_vs_iteration(fig: Figure, axes: Axes | List[Axes], plot_settings: List[OptPltSet], plot_name: str) → None

Create plots of design variables vs. iteration.

Parameters:

• fig – Matplotlib figure

• axes – Single axis or list of axes

• plot_settings – List of plot configurations

• plot_name – Name for the plot (used for saving)

qdesignoptimizer.sim_plot_progress.plot_progress(opt_results: List[List[Dict[str, Any]]], system_target_params: Dict[str, Any], plot_settings: Dict[str, List[OptPltSet]], block_plots: bool = False, save_figures: bool = False, plot_variance: bool = False, plot_design_variables: Literal['chronological', 'sorted'] | None = None, opt_target_list: List[OptTarget] | None = None) → None

Plot the progress of optimization iterations.

Parameters:

• opt_results – List of optimization result sequences (one per optimization run)

• system_target_params – Target system parameters

• plot_settings – Plot configurations by plot name

• block_plots – If True, block execution until plots are closed

• save_figures – If True, save figures to disk

• plot_variance – If True, plot mean and variance across runs

• plot_design_variables – How to plot design variables (“chronological”, “sorted”, or None to disable)

• opt_target_list – Optional list of optimization targets