Post Processor

A module of post-processing tools for POVM results.

Post-processing of the sampled POVM results (see also the sampler module) is required for the computation of expectation values of the observables of interest. In particular, one can optimize the Dual frame of a POVM to improve the obtained expectation values. For more details, refer to this how-to guide.

The PostProcessor

The main entry-point to the post-processing of POVM results is provided by the POVMPostProcessor class and its sub-classes.

POVMPostProcessor	The canonical POVM result post-processor.
MedianOfMeans	A POVM result post-processor which uses a 'median of means' estimator.

Various Dual Frames

Additionally, this module provides a number of functions to easily construct specific Dual frames. The functions dual_from_state() and dual_from_marginal_probabilities() require a reference state to be available, while dual_from_empirical_frequencies() does not.

dual_from_state	Return the Dual frame of povm based on the outcome distribution of a supplied state.
dual_from_marginal_probabilities	Return the Dual frame of povm based on the marginal distribution of a supplied state.
dual_from_empirical_frequencies	Return the Dual frame of povm based on the frequencies of the sampled outcomes.