pygsti.tools.rbtheory.predicted_rb_decay_parameter

pygsti.tools.rbtheory.predicted_rb_decay_parameter#

predicted_rb_decay_parameter(model, target_model, weights=None)#

Computes the second largest eigenvalue of the ‘L matrix’ (see the L_matrix function).

For standard Clifford RB and direct RB, this corresponds to the RB decay parameter p in Pm = A + Bp^m for “reasonably low error” trace preserving and completely positive gates. See also the predicted_rb_number function.

Parameters:

  • model (Model) – The model to calculate the RB decay parameter of. This model is the model randomly sampled over, so this is not necessarily the set of physical primitives. In Clifford RB this is a set of Clifford gates; in “direct RB” this normally would be the physical primitives.

  • target_model (Model) – The target model corresponding to model. This function is not invariant under swapping model and target_model: this Model must be the target model, and should consistent of perfect gates.

  • weights (dict, optional) – If not None, a dictionary of floats, whereby the keys are the gates in model and the values are the unnormalized probabilities to apply each gate at each stage of the RB protocol. If not None, the values in weights must all be non-negative, and they must not all be zero. Because, when divided by their sum, they must be a valid probability distribution. If None, the weighting defaults to an equal weighting on all gates, as this is used in many RB protocols (e.g., Clifford RB). But, this weighting is flexible in the “direct RB” protocol.

Returns:

p – The second largest eigenvalue of L. This is the RB decay parameter for various types of RB.

Return type:

float.