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

IGO-QNN: Quantum Neural Network Architecture for Inductive Grover Oracularization