Note

This is the documentation for the current state of the development branch of Qiskit Experiments. The documentation or APIs here can change prior to being released.

DiscriminatorNode

class DiscriminatorNode(discriminators, validate=True)

A class to discriminate kerneled data, e.g., IQ data, to produce counts.

This node integrates into the data processing chain a serializable BaseDiscriminator subclass instance which must have a predict() method that takes as input a list of lists and returns a list of labels. Crucially, this node can be initialized with a single discriminator which applies to each memory slot or it can be initialized with a list of discriminators, i.e., one for each slot.

Note

Future versions may see this class become a sub-class of TrainableDataAction.

Note

This node will drop uncertainty from unclassified nodes. Returned labels don’t have uncertainty.

Initialize the node with an object that can discriminate.

Parameters:

• discriminators (BaseDiscriminator | List[BaseDiscriminator]) – The entity that will perform the discrimination. This needs to be a BaseDiscriminator or a list thereof that takes as input a list of lists and returns a list of labels. If a list of discriminators is given then there should be as many discriminators as there will be slots in the memory. The discriminator at the i-th index will be applied to the i-th memory slot.

• validate (bool) – If set to False the DataAction will not validate its input.

Methods

__call__(data)

Call the data action of this node on the data.

Parameters:

data (ndarray) – A numpy array with arbitrary dtype. If the elements are ufloat objects consisting of a nominal value and a standard error, then the error propagation is done automatically.

Returns:

The processed data.

Return type:

ndarray