  Injecting gaussian noise into training features is well known to have
regularization properties. This paper considers noise injections to numeric or
categoric tabular features as passed to inference, which translates inference
to a non-deterministic outcome and may have relevance to fairness
considerations, adversarial example protection, or other use cases benefiting
from non-determinism. We offer the Automunge library for tabular preprocessing
as a resource for the practice, which includes options to integrate random
sampling or entropy seeding with the support of quantum circuits, representing
a new way to channel quantum algorithms into classical learning.
