  We propose a novel paradigm of integration of Grover's algorithm in a machine
learning framework: the inductive Grover oracular quantum neural network
(IGO-QNN). The model defines a variational quantum circuit with hidden layers
of parameterized quantum neurons densely connected via entangle synapses to
encode a dynamic Grover's search oracle that can be trained from a set of
database-hit training examples. This widens the range of problem applications
of Grover's unstructured search algorithm to include the vast majority of
problems lacking analytic descriptions of solution verifiers, allowing for
quadratic speed-up in unstructured search for the set of search problems with
relationships between input and output spaces that are tractably underivable
deductively. This generalization of Grover's oracularization may prove
particularly effective in deep reinforcement learning, computer vision, and,
more generally, as a feature vector classifier at the top of an existing model.

Quantum Physics;Machine Learning