pygsti.tools.chi2fns.chi2_jacobian

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pygsti.tools.chi2fns.chi2_jacobian#

chi2_jacobian(model, dataset, circuits=None, min_prob_clip_for_weighting=0.0001, prob_clip_interval=(-10000, 10000), op_label_aliases=None, mdc_store=None, comm=None, mem_limit=None)#

Compute the gradient of the chi^2 function computed by chi2().

The returned value holds the derivatives of the chi^2 function with respect to model’s parameters.

Parameters:

  • model (Model) – The model used to specify the probabilities and SPAM labels

  • dataset (DataSet) – The data used to specify frequencies and counts

  • circuits (list of Circuits or tuples, optional) – List of circuits whose terms will be included in chi^2 sum. Default value (None) means “all strings in dataset”.

  • min_prob_clip_for_weighting (float, optional) – defines the clipping interval for the statistical weight.

  • prob_clip_interval (tuple, optional) – A (min, max) tuple that specifies the minimum (possibly negative) and maximum values allowed for probabilities generated by the model. If the model gives probabilities outside this range they are clipped to min or max. These values can be quite generous, as the optimizers are quite tolerant of badly behaved probabilities.

  • op_label_aliases (dictionary, optional) – Dictionary whose keys are operation label “aliases” and whose values are tuples corresponding to what that operation label should be expanded into before querying the dataset. Defaults to the empty dictionary (no aliases defined) e.g. op_label_aliases[‘Gx^3’] = (‘Gx’,’Gx’,’Gx’)

  • mdc_store (ModelDatasetCircuitsStore, optional) – An object that bundles cached quantities along with a given model, dataset, and circuit list. If given, model and dataset and circuits should be set to None.

  • comm (mpi4py.MPI.Comm, optional) – When not None, an MPI communicator for distributing the computation across multiple processors.

  • mem_limit (int, optional) – A rough memory limit in bytes which restricts the amount of intermediate values that are computed and stored.

Returns:

The gradient vector of length model.num_params, the number of model parameters.

Return type:

numpy array

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