pygsti.report.get_logl_confidence_region

pygsti.report.get_logl_confidence_region(gateset, dataset, confidenceLevel, gatestring_list=None, probClipInterval=(-1000000.0, 1000000.0), minProbClip=0.0001, radius=0.0001, hessianProjection='std', regionType='std', comm=None, memLimit=None)

Constructs a ConfidenceRegion given a gateset and dataset using the log-likelihood Hessian. (Internally, this evaluates the log-likelihood Hessian.)

Parameters:
  • gateset (GateSet) – the gate set point estimate that maximizes the logl or minimizes the chi2, and marks the point in gateset-space where the Hessian has been evaluated.
  • dataset (DataSet) – Probability data
  • confidenceLevel (float) – If not None, then the confidence level (between 0 and 100) used in the computation of confidence regions/intervals. If None, no confidence regions or intervals are computed.
  • gatestring_list (list of (tuples or GateStrings), optional) – Each element specifies a gate string to include in the log-likelihood sum. Default value of None implies all the gate strings in dataset should be used.
  • probClipInterval (2-tuple or None, optional) – (min,max) values used to clip the probabilities predicted by gateset. Defaults to no clipping.
  • minProbClip (float, optional) – The minimum probability treated normally in the evaluation of the log-likelihood. A penalty function replaces the true log-likelihood for probabilities that lie below this threshold so that the log-likelihood never becomes undefined.
  • radius (float, optional) – Specifies the severity of rounding used to “patch” the zero-frequency terms of the log-likelihood.
  • hessianProjection (string, optional) –

    Specifies how (and whether) to project the given hessian matrix onto a non-gauge space. Allowed values are:

    • ‘std’ – standard projection onto the space perpendicular to the gauge space.
    • ‘none’ – no projection is performed. Useful if the supplied hessian has already been projected.
    • ‘optimal gate CIs’ – a lengthier projection process in which a numerical optimization is performed to find the non-gauge space which minimizes the (average) size of the confidence intervals corresponding to gate (as opposed to SPAM vector) parameters.
  • regionType ({‘std’, ‘non-markovian’}, optional) – The type of confidence region to create. ‘std’ creates a standard confidence region, while ‘non-markovian’ creates a region which attempts to account for the non-markovian-ness of the data.
  • comm (mpi4py.MPI.Comm, optional) – When not None, an MPI communicator for distributing the computation across multiple processors.
  • memLimit (int, optional) – A rough memory limit in bytes which restricts the amount of intermediate values that are computed and stored.
Returns:

Return type:

ConfidenceRegion